From b18cfac0fe8cbab927d1ebe3566a9b29da8c1443 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Tue, 10 Feb 2026 20:49:37 +0000 Subject: [PATCH 01/70] feat: Initialize rissk_kedro project structure with pipelines and documentation - Added initial documentation structure in index.rst - Created .gitkeep in notebooks directory to track empty folder - Set up pyproject.toml for project dependencies and configuration - Added requirements.txt for additional dependencies - Implemented __init__.py and __main__.py for package execution - Registered pipelines for data ingestion, feature engineering, and risk scoring - Developed nodes for data ingestion, feature processing, and risk scoring calculations - Established settings.py for project configuration --- environment_kedro.yml | 45 +++ rissk_kedro/.gitignore | 161 +++++++++ rissk_kedro/README.md | 100 ++++++ rissk_kedro/conf/README.md | 20 ++ rissk_kedro/conf/base/catalog.yml | 68 ++++ rissk_kedro/conf/base/parameters.yml | 80 +++++ rissk_kedro/conf/local/.gitkeep | 0 rissk_kedro/conf/logging.yml | 43 +++ rissk_kedro/docs/source/conf.py | 221 ++++++++++++ rissk_kedro/docs/source/index.rst | 19 ++ rissk_kedro/notebooks/.gitkeep | 0 rissk_kedro/pyproject.toml | 90 +++++ rissk_kedro/requirements.txt | 24 ++ rissk_kedro/src/rissk_kedro/__init__.py | 4 + rissk_kedro/src/rissk_kedro/__main__.py | 24 ++ .../src/rissk_kedro/pipeline_registry.py | 15 + .../src/rissk_kedro/pipelines/__init__.py | 0 .../pipelines/data_ingestion/__init__.py | 5 + .../pipelines/data_ingestion/nodes.py | 103 ++++++ .../pipelines/data_ingestion/pipeline.py | 25 ++ .../pipelines/feature_engineering/__init__.py | 5 + .../pipelines/feature_engineering/nodes.py | 172 ++++++++++ .../pipelines/feature_engineering/pipeline.py | 42 +++ .../pipelines/risk_scoring/__init__.py | 5 + .../pipelines/risk_scoring/nodes.py | 316 ++++++++++++++++++ .../pipelines/risk_scoring/pipeline.py | 25 ++ rissk_kedro/src/rissk_kedro/settings.py | 40 +++ rissk_kedro/tests/__init__.py | 0 rissk_kedro/tests/pipelines/__init__.py | 0 .../pipelines/data_science/test_pipeline.py | 63 ++++ rissk_kedro/tests/test_run.py | 20 ++ 31 files changed, 1735 insertions(+) create mode 100644 environment_kedro.yml create mode 100644 rissk_kedro/.gitignore create mode 100644 rissk_kedro/README.md create mode 100644 rissk_kedro/conf/README.md create mode 100644 rissk_kedro/conf/base/catalog.yml create mode 100644 rissk_kedro/conf/base/parameters.yml create mode 100644 rissk_kedro/conf/local/.gitkeep create mode 100644 rissk_kedro/conf/logging.yml create mode 100644 rissk_kedro/docs/source/conf.py create mode 100644 rissk_kedro/docs/source/index.rst create mode 100644 rissk_kedro/notebooks/.gitkeep create mode 100644 rissk_kedro/pyproject.toml create mode 100644 rissk_kedro/requirements.txt create mode 100644 rissk_kedro/src/rissk_kedro/__init__.py create mode 100644 rissk_kedro/src/rissk_kedro/__main__.py create mode 100644 rissk_kedro/src/rissk_kedro/pipeline_registry.py create mode 100644 rissk_kedro/src/rissk_kedro/pipelines/__init__.py create mode 100644 rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/__init__.py create mode 100644 rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py create mode 100644 rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py create mode 100644 rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/__init__.py create mode 100644 rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py create mode 100644 rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py create mode 100644 rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/__init__.py create mode 100644 rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/nodes.py create mode 100644 rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/pipeline.py create mode 100644 rissk_kedro/src/rissk_kedro/settings.py create mode 100644 rissk_kedro/tests/__init__.py create mode 100644 rissk_kedro/tests/pipelines/__init__.py create mode 100644 rissk_kedro/tests/pipelines/data_science/test_pipeline.py create mode 100644 rissk_kedro/tests/test_run.py diff --git a/environment_kedro.yml b/environment_kedro.yml new file mode 100644 index 0000000..5fd92e2 --- /dev/null +++ b/environment_kedro.yml @@ -0,0 +1,45 @@ +name: rissk_rs_01 +channels: + - conda-forge + - defaults +dependencies: + - python=3.9 + # R and its dependencies + - r-base + - r-ggplot2 + - r-dplyr + - r-tidyr + - r-shiny + - r-readr + - r-irkernel # For running R in Jupyter Notebooks + - r-stringr + # Interoperability + - rpy2 # For using R within Python + # Graphing libs + - graphviz + - pygraphviz + # Other tools + - pip + - pip: + - jupyter_contrib_nbextensions + - awscli + - botocore + - loguru==0.7.3 + - tqdm==4.67.1 + - pandas==2.2.2 + - seaborn==0.13.2 + - docutils==0.16 + - openpyxl==3.1.2 + - pyarrow==15.0.2 + - pyod==1.1.3 + - python-dotenv==1.0.1 + - pythresh==0.3.6 + - ploomber==0.23.3 + - ipywidgets==8.1.5 + - typer==0.15.1 + - boto3==1.35.88 + - botocore==1.35.88 + - kedro==1.0.0 + - kedro-viz==12.1.0 + - kedro-datasets[pandas-statadataset]==4.1.0 + - -e . \ No newline at end of file diff --git a/rissk_kedro/.gitignore b/rissk_kedro/.gitignore new file mode 100644 index 0000000..c10c9ca --- /dev/null +++ b/rissk_kedro/.gitignore @@ -0,0 +1,161 @@ +########################## +# KEDRO PROJECT + +# ignore all local configuration +conf/local/** +!conf/local/.gitkeep + +# ignore potentially sensitive credentials files +conf/**/*credentials* + +# ignore everything in the following folders +data/** + +# except their sub-folders +!data/**/ + +# also keep all .gitkeep files +!.gitkeep + +# also keep the example dataset +!data/01_raw/companies.csv +!data/01_raw/reviews.csv +!data/01_raw/shuttles.xlsx + +# ignore kedro-viz metadata +.viz + +# ignore file based logs +*.log + +########################## +# Common files + +# IntelliJ +.idea/ +*.iml +out/ +.idea_modules/ + +### macOS +*.DS_Store +.AppleDouble +.LSOverride +.Trashes + +# Vim +*~ +.*.swo +.*.swp + +# emacs +*~ +\#*\# +/.emacs.desktop +/.emacs.desktop.lock +*.elc + +# JIRA plugin +atlassian-ide-plugin.xml + +# C extensions +*.so + +### Python template +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +.static_storage/ +.media/ +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + +# mlflow local runs +mlruns/* diff --git a/rissk_kedro/README.md b/rissk_kedro/README.md new file mode 100644 index 0000000..433099e --- /dev/null +++ b/rissk_kedro/README.md @@ -0,0 +1,100 @@ +# rissk_kedro + +[![Powered by Kedro](https://img.shields.io/badge/powered_by-kedro-ffc900?logo=kedro)](https://kedro.org) + +## Overview + +This is your new Kedro project with PySpark setup, which was generated using `kedro 1.2.0`. + +Take a look at the [Kedro documentation](https://docs.kedro.org) to get started. + +## Rules and guidelines + +In order to get the best out of the template: + +* Don't remove any lines from the `.gitignore` file we provide +* Make sure your results can be reproduced by following a [data engineering convention](https://docs.kedro.org/en/stable/faq/faq.html#what-is-data-engineering-convention) +* Don't commit data to your repository +* Don't commit any credentials or your local configuration to your repository. Keep all your credentials and local configuration in `conf/local/` + +## How to install dependencies + +Declare any dependencies in `requirements.txt` for `pip` installation. + +To install them, run: + +``` +pip install -r requirements.txt +``` + +## How to run your Kedro pipeline + +You can run your Kedro project with: + +``` +kedro run +``` + +## How to test your Kedro project + +Have a look at the files `tests/test_run.py` and `tests/pipelines/data_science/test_pipeline.py` for instructions on how to write your tests. Run the tests as follows: + +``` +pytest +``` + +You can configure the coverage threshold in your project's `pyproject.toml` file under the `[tool.coverage.report]` section. + +## Project dependencies + +To see and update the dependency requirements for your project use `requirements.txt`. Install the project requirements with `pip install -r requirements.txt`. + +[Further information about project dependencies](https://docs.kedro.org/en/stable/kedro_project_setup/dependencies.html#project-specific-dependencies) + +## How to work with Kedro and notebooks + +> Note: Using `kedro jupyter` or `kedro ipython` to run your notebook provides these variables in scope: `catalog`, `context`, `pipelines` and `session`. +> +> Jupyter, JupyterLab, and IPython are already included in the project requirements by default, so once you have run `pip install -r requirements.txt` you will not need to take any extra steps before you use them. + +### Jupyter +To use Jupyter notebooks in your Kedro project, you need to install Jupyter: + +``` +pip install jupyter +``` + +After installing Jupyter, you can start a local notebook server: + +``` +kedro jupyter notebook +``` + +### JupyterLab +To use JupyterLab, you need to install it: + +``` +pip install jupyterlab +``` + +You can also start JupyterLab: + +``` +kedro jupyter lab +``` + +### IPython +And if you want to run an IPython session: + +``` +kedro ipython +``` + +### How to ignore notebook output cells in `git` +To automatically strip out all output cell contents before committing to `git`, you can use tools like [`nbstripout`](https://github.com/kynan/nbstripout). For example, you can add a hook in `.git/config` with `nbstripout --install`. This will run `nbstripout` before anything is committed to `git`. + +> *Note:* Your output cells will be retained locally. + +## Package your Kedro project + +[Further information about building project documentation and packaging your project](https://docs.kedro.org/en/stable/tutorial/package_a_project.html) diff --git a/rissk_kedro/conf/README.md b/rissk_kedro/conf/README.md new file mode 100644 index 0000000..b135e80 --- /dev/null +++ b/rissk_kedro/conf/README.md @@ -0,0 +1,20 @@ +# What is this for? + +This folder should be used to store configuration files used by Kedro or by separate tools. + +This file can be used to provide users with instructions for how to reproduce local configuration with their own credentials. You can edit the file however you like, but you may wish to retain the information below and add your own section in the section titled **Instructions**. + +## Local configuration + +The `local` folder should be used for configuration that is either user-specific (e.g. IDE configuration) or protected (e.g. security keys). + +> *Note:* Please do not check in any local configuration to version control. + +## Base configuration + +The `base` folder is for shared configuration, such as non-sensitive and project-related configuration that may be shared across team members. + +WARNING: Please do not put access credentials in the base configuration folder. + +## Find out more +You can find out more about configuration from the [user guide documentation](https://docs.kedro.org/en/stable/configuration/configuration_basics.html). diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml new file mode 100644 index 0000000..7726e5c --- /dev/null +++ b/rissk_kedro/conf/base/catalog.yml @@ -0,0 +1,68 @@ +# Here you can define all your data sets by using simple YAML syntax. +# +# === RAW DATA (Zipped Survey Solutions Exports) === +raw_zip_files: + type: PartitionedDataset + path: data/10_RAW + dataset: + type: kedro.extras.datasets.text.TextDataset # Placeholder, actual extraction in node + filename_suffix: ".zip" + +# === INTERMEDIATE (Extracted Files) === +extracted_survey_data: + type: PartitionedDataset + path: data/10_RAW + dataset: + type: kedro.extras.datasets.text.TextDataset + filename_suffix: "" + +# === PRIMARY (Ingested DataFrames) === +paradata_raw: + type: pandas.ParquetDataset + filepath: data/20_INTERIM/paradata_raw.parquet + +questionnaire_raw: + type: pandas.ParquetDataset + filepath: data/20_INTERIM/questionnaire_raw.parquet + +microdata_raw: + type: pandas.ParquetDataset + filepath: data/20_INTERIM/microdata_raw.parquet + +# === FEATURE PROCESSED === +paradata_processed: + type: pandas.ParquetDataset + filepath: data/30_PROCESSED/paradata_processed.parquet + +paradata_active: + type: pandas.ParquetDataset + filepath: data/30_PROCESSED/paradata_active.parquet + +item_features: + type: pandas.ParquetDataset + filepath: data/30_PROCESSED/item_features.parquet + +unit_features: + type: pandas.ParquetDataset + filepath: data/30_PROCESSED/unit_features.parquet + +unit_risk_scores_raw: + type: pandas.ParquetDataset + filepath: data/30_PROCESSED/unit_risk_scores_raw.parquet + +responsible_features: + type: pandas.ParquetDataset + filepath: data/30_PROCESSED/responsible_features.parquet + +# === MODEL OUTPUT === +unit_risk_scores: + type: pandas.CSVDataset + filepath: data/40_OUTPUTS/unit_risk_scores.csv + save_args: + index: false + +unit_feature_scores: + type: pandas.CSVDataset + filepath: data/40_OUTPUTS/unit_feature_scores.csv + save_args: + index: false diff --git a/rissk_kedro/conf/base/parameters.yml b/rissk_kedro/conf/base/parameters.yml new file mode 100644 index 0000000..3813191 --- /dev/null +++ b/rissk_kedro/conf/base/parameters.yml @@ -0,0 +1,80 @@ +# Survey Configuration (from env.yaml) +survey: + name: "hies2024" + questionnaires: + - name: "snb_hies_hh" + versions: [9, 10, 11] + - name: "slbhies_listing" + versions: [6, 7] + +# Processing Parameters +processing: + limit_unit: null # Set to filter by consent if needed + automatic_contamination: false + +# Feature Engineering Configuration (from configuration/main.yaml) +features: + answer_hour_set: + use: true + contamination: 0.11 + answer_changed: + use: true + contamination: 0.1 + answer_removed: + use: true + contamination: 0.1 + answer_selected: + use: true + contamination: 0.1 + answer_duration: + use: true + contamination: 0.1 + first_decimal: + use: true + contamination: 0.11 + frequency: 100 + first_digit: + use: true + last_digit: + use: false + numeric_response: + use: true + sequence_jump: + use: true + contamination: 0.1 + time_changed: + use: true + gps: + use: true + sub_features: [gps_latitude, gps_longitude, gps_accuracy] + contamination: 0.11 + pause_count: + use: true + contamination: 0.11 + pause_duration: + use: true + contamination: 0.11 + pause_list: + use: false + number_unanswered: + use: false + number_answered: + use: true + contamination: 0.11 + total_duration: + use: true + contamination: 0.11 + total_elapse: + use: true + contamination: 0.11 + single_question: + use: true + multi_option_question: + use: true + days_from_start: + use: false + +# Output Configuration +output: + feature_score: false + unit_risk_score_path: "results/unit_risk_score.csv" \ No newline at end of file diff --git a/rissk_kedro/conf/local/.gitkeep b/rissk_kedro/conf/local/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/rissk_kedro/conf/logging.yml b/rissk_kedro/conf/logging.yml new file mode 100644 index 0000000..6fb6607 --- /dev/null +++ b/rissk_kedro/conf/logging.yml @@ -0,0 +1,43 @@ +# To enable this custom logging configuration, set KEDRO_LOGGING_CONFIG to the path of this file. +# More information available at https://docs.kedro.org/en/stable/logging/logging.html +version: 1 + +disable_existing_loggers: False + +formatters: + simple: + format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s" + +handlers: + console: + class: logging.StreamHandler + level: INFO + formatter: simple + stream: ext://sys.stdout + + info_file_handler: + class: logging.handlers.RotatingFileHandler + level: INFO + formatter: simple + filename: info.log + maxBytes: 10485760 # 10MB + backupCount: 20 + encoding: utf8 + delay: True + + rich: + class: kedro.logging.RichHandler + rich_tracebacks: True + # Advance options for customisation. + # See https://docs.kedro.org/en/stable/logging/logging.html#project-side-logging-configuration + # tracebacks_show_locals: False + +loggers: + kedro: + level: INFO + + pyspark_spaceflights: + level: INFO + +root: + handlers: [rich, info_file_handler] diff --git a/rissk_kedro/docs/source/conf.py b/rissk_kedro/docs/source/conf.py new file mode 100644 index 0000000..592c48d --- /dev/null +++ b/rissk_kedro/docs/source/conf.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python3 + + +# rissk_kedro documentation build +# configuration file, created by sphinx-quickstart. +# +# This file is execfile()d with the current directory set to its +# containing dir. +# +# Note that not all possible configuration values are present in this +# autogenerated file. +# +# All configuration values have a default; values that are commented out +# serve to show the default. + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import re + +from rissk_kedro import __version__ as release + +# -- Project information ----------------------------------------------------- + +project = "rissk_kedro" +author = "Kedro" + +# The short X.Y version. +version = re.match(r"^([0-9]+\.[0-9]+).*", release).group(1) + +# -- General configuration --------------------------------------------------- + +# If your documentation needs a minimal Sphinx version, state it here. +# +# needs_sphinx = '1.0' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + "sphinx.ext.autodoc", + "sphinx.ext.napoleon", + "sphinx_autodoc_typehints", + "sphinx.ext.doctest", + "sphinx.ext.todo", + "sphinx.ext.coverage", + "sphinx.ext.ifconfig", + "sphinx.ext.viewcode", + "sphinx.ext.mathjax", + "nbsphinx", + "sphinx_copybutton", + "myst_parser", +] + +# enable autosummary plugin (table of contents for modules/classes/class +# methods) +autosummary_generate = True + +# Add any paths that contain templates here, relative to this directory. +templates_path = ["_templates"] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# +source_suffix = {".rst": "restructuredtext", ".md": "markdown"} + +# The master toctree document. +master_doc = "index" + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = "en" + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path . +exclude_patterns = ["_build", "**.ipynb_checkpoints"] + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = "sphinx" + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = "sphinx_rtd_theme" + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +# +html_theme_options = {"collapse_navigation": False, "style_external_links": True} + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ["_static"] + +# Custom sidebar templates, must be a dictionary that maps document names +# to template names. +# +# The default sidebars (for documents that don't match any pattern) are +# defined by theme itself. Builtin themes are using these templates by +# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', +# 'searchbox.html']``. +# +# html_sidebars = {} + +html_show_sourcelink = False + +# Removes, from all docs, the copyright footer. +html_show_copyright = False + +# -- Options for HTMLHelp output --------------------------------------------- + +# Output file base name for HTML help builder. +htmlhelp_basename = "rissk_kedrodoc" + +# -- Options for LaTeX output ------------------------------------------------ + +latex_elements = { + # The paper size ('letterpaper' or 'a4paper'). + # + # 'papersize': 'letterpaper', + # + # The font size ('10pt', '11pt' or '12pt'). + # + # 'pointsize': '10pt', + # + # Additional stuff for the LaTeX preamble. + # + # 'preamble': '', + # + # Latex figure (float) alignment + # + # 'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + ( + master_doc, + "rissk_kedro.tex", + "rissk_kedro Documentation", + "Kedro", + "manual", + ) +] + +# -- Options for manual page output ------------------------------------------ + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [ + ( + master_doc, + "rissk_kedro", + "rissk_kedro Documentation", + [author], + 1, + ) +] + +# -- Options for Texinfo output ---------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + ( + master_doc, + "rissk_kedro", + "rissk_kedro Documentation", + author, + "rissk_kedro", + "Project rissk_kedro codebase.", + "Data-Science", + ) +] + +# -- Options for todo extension ---------------------------------------------- + +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = False + +# -- Extension configuration ------------------------------------------------- + +# nbsphinx_prolog = """ +# see here for prolog/epilog details: +# https://nbsphinx.readthedocs.io/en/0.3.1/prolog-and-epilog.html +# """ + +# -- NBconvert kernel config ------------------------------------------------- +nbsphinx_kernel_name = "python3" + + +def remove_arrows_in_examples(lines): + for i, line in enumerate(lines): + lines[i] = line.replace(">>>", "") + + +def autodoc_process_docstring(app, what, name, obj, options, lines): + remove_arrows_in_examples(lines) + + +def skip(app, what, name, obj, skip, options): + if name == "__init__": + return False + return skip + + +def setup(app): + app.connect("autodoc-process-docstring", autodoc_process_docstring) + app.connect("autodoc-skip-member", skip) diff --git a/rissk_kedro/docs/source/index.rst b/rissk_kedro/docs/source/index.rst new file mode 100644 index 0000000..3df98ca --- /dev/null +++ b/rissk_kedro/docs/source/index.rst @@ -0,0 +1,19 @@ +.. rissk_kedro documentation master file, created by sphinx-quickstart. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to project rissk_kedro's API docs! +============================================= + +.. toctree:: + :maxdepth: 4 + + modules + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/rissk_kedro/notebooks/.gitkeep b/rissk_kedro/notebooks/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/rissk_kedro/pyproject.toml b/rissk_kedro/pyproject.toml new file mode 100644 index 0000000..cd67f67 --- /dev/null +++ b/rissk_kedro/pyproject.toml @@ -0,0 +1,90 @@ +[build-system] +requires = ["setuptools"] +build-backend = "setuptools.build_meta" + +[project] +requires-python = ">=3.10" +name = "rissk_kedro" +readme = "README.md" +dynamic = ["version"] +dependencies = [ + "ipython>=8.10", + "jupyterlab>=3.0", + "notebook", + "kedro[jupyter]~=1.0.0", + "kedro-datasets[pandas-csvdataset, pandas-exceldataset, pandas-parquetdataset, plotly-plotlydataset, plotly-jsondataset, matplotlib-matplotlibdataset, spark-sparkdataset]>=9.1", + "kedro-viz>=6.7.0", + "scikit-learn~=1.5.1", + "seaborn~=0.12.1", +] + +[project.scripts] +"rissk-kedro" = "rissk_kedro.__main__:main" + +[project.entry-points."kedro.hooks"] + +[project.optional-dependencies] +docs = [ + "docutils<0.21", + "sphinx>=5.3,<7.3", + "sphinx_rtd_theme==2.0.0", + "nbsphinx==0.8.1", + "sphinx-autodoc-typehints==1.20.2", + "sphinx_copybutton==0.5.2", + "ipykernel>=5.3, <7.0", + "Jinja2<3.2.0", + "myst-parser>=1.0,<2.1" +] +dev = [ + "pytest-cov>=3,<7", + "pytest-mock>=1.7.1, <2.0", + "pytest~=7.2", + "ruff~=0.12.0" +] + +[tool.setuptools.dynamic] +version = {attr = "rissk_kedro.__version__"} + +[tool.setuptools.packages.find] +where = ["src"] +namespaces = false + +[tool.kedro] +package_name = "rissk_kedro" +project_name = "rissk_kedro" +kedro_init_version = "1.0.0" +tools = "['Linting', 'Testing', 'Custom Logging', 'Documentation', 'Data Structure', 'PySpark']" +example_pipeline = "True" +source_dir = "src" + +[tool.pytest.ini_options] +addopts = """ +--cov-report term-missing \ +--cov src/rissk_kedro -ra""" + +[tool.coverage.report] +fail_under = 0 +show_missing = true +exclude_lines = ["pragma: no cover", "raise NotImplementedError"] + +[tool.ruff.format] +docstring-code-format = true + +[tool.ruff] +line-length = 88 +show-fixes = true + +[tool.ruff.lint] +select = [ + "F", # Pyflakes + "W", # pycodestyle + "E", # pycodestyle + "I", # isort + "UP", # pyupgrade + "PL", # Pylint + "T201", # Print Statement +] +ignore = ["E501"] # Ruff format takes care of line-too-long + +[tool.kedro_telemetry] +project_id = "d433b943a53d4dca931bf2a5084f67cb" diff --git a/rissk_kedro/requirements.txt b/rissk_kedro/requirements.txt new file mode 100644 index 0000000..4eab0d7 --- /dev/null +++ b/rissk_kedro/requirements.txt @@ -0,0 +1,24 @@ +# This file lists the dependencies for the Rissk Kedro project. + +# Kedro template dependencies +# python~=3.11 +ipython>=8.10 +jupyterlab>=3.0 +notebook +kedro[jupyter]~=1.2.0 +kedro-datasets[pandas-csvdataset, pandas-exceldataset, pandas-parquetdataset, pandas-statadataset, plotly-plotlydataset, plotly-jsondataset, matplotlib-matplotlibdataset, spark-sparkdataset]>=9.1 +kedro-viz>=6.7.0 +scikit-learn~=1.5.1 +seaborn~=0.12.1 + +# Additional dependencies for the Rissk project +hydra-core>=1.3.2 +numpy>=1.24.4 +pandas>=2.0.3 +openpyxl>=3.1.2 +# scikit-learn>=1.3.0 +scipy>=1.10.1 +# seaborn>=0.12.2 +pyod>=1.1.0 +pythresh>=0.3.3 + diff --git a/rissk_kedro/src/rissk_kedro/__init__.py b/rissk_kedro/src/rissk_kedro/__init__.py new file mode 100644 index 0000000..cc22393 --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/__init__.py @@ -0,0 +1,4 @@ +"""rissk_kedro +""" + +__version__ = "0.1" diff --git a/rissk_kedro/src/rissk_kedro/__main__.py b/rissk_kedro/src/rissk_kedro/__main__.py new file mode 100644 index 0000000..07e898c --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/__main__.py @@ -0,0 +1,24 @@ +"""rissk_kedro file for ensuring the package is executable +as `rissk-kedro` and `python -m rissk_kedro` +""" +import sys +from pathlib import Path +from typing import Any + +from kedro.framework.cli.utils import find_run_command +from kedro.framework.project import configure_project + + +def main(*args, **kwargs) -> Any: + package_name = Path(__file__).parent.name + configure_project(package_name) + + interactive = hasattr(sys, 'ps1') + kwargs["standalone_mode"] = not interactive + + run = find_run_command(package_name) + return run(*args, **kwargs) + + +if __name__ == "__main__": + main() diff --git a/rissk_kedro/src/rissk_kedro/pipeline_registry.py b/rissk_kedro/src/rissk_kedro/pipeline_registry.py new file mode 100644 index 0000000..0a10ab5 --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/pipeline_registry.py @@ -0,0 +1,15 @@ +"""Project pipelines.""" + +from kedro.framework.project import find_pipelines +from kedro.pipeline import Pipeline + + +def register_pipelines() -> dict[str, Pipeline]: + """Register the project's pipelines. + + Returns: + A mapping from pipeline names to ``Pipeline`` objects. + """ + pipelines = find_pipelines(raise_errors=True) + pipelines["__default__"] = sum(pipelines.values()) + return pipelines diff --git a/rissk_kedro/src/rissk_kedro/pipelines/__init__.py b/rissk_kedro/src/rissk_kedro/pipelines/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/__init__.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/__init__.py new file mode 100644 index 0000000..4cfe25a --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/__init__.py @@ -0,0 +1,5 @@ +"""Data ingestion pipeline.""" + +from .pipeline import create_pipeline + +__all__ = ["create_pipeline"] diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py new file mode 100644 index 0000000..5f98aaf --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py @@ -0,0 +1,103 @@ +"""Nodes for ingesting Survey Solutions export data.""" +import os +from pathlib import Path +from typing import Dict, List, Tuple +import pandas as pd +from loguru import logger + +# Import your existing utilities +from rissk.utils.import_utils import ( + extract_zip, + get_survey_info, + get_dataframes +) + + +def unzip_raw_surveys( + raw_zip_files: Dict[str, callable], + parameters: Dict +) -> None: + """ + Extract zipped Survey Solutions exports. + + Handles: + - Recursive unzipping (nested ZIPs) + - Password-protected ZIPs (from credentials) + - Mixed formats (.dta, .tab) + + Args: + raw_zip_files: Dictionary of ZIP files from catalog (PartitionedDataset) + parameters: Survey configuration from parameters.yml + + Side Effect: + Extracts files to same directory as ZIP (removes .zip extension) + """ + survey_name = parameters["name"] + questionnaires = parameters["questionnaires"] + + # Filter ZIP files based on survey configuration + questionnaire_names = [q["name"] for q in questionnaires] + + matching_files = [ + filename for filename in raw_zip_files.keys() + if any(qname in filename for qname in questionnaire_names) + ] + + logger.info(f"Found {len(matching_files)} ZIP files to extract: {matching_files}") + + for filename in matching_files: + # Get the full path to the ZIP file + zip_path = Path("data/10_RAW") / filename + dest_path = zip_path.with_suffix('') # Remove .zip extension + + logger.info(f"Extracting {filename} to {dest_path}") + extract_zip(zip_path, dest_path) + + logger.success(f"Extraction complete. Extracted {len(matching_files)} surveys.") + + +def load_survey_dataframes( + parameters: Dict +) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: + """ + Load paradata, questionnaire, and microdata from extracted files. + + Handles: + - Mixed file formats (.dta for Stata, .tab for tabular) + - Variable name parsing from Survey Solutions structure + - Multi-option/GPS/List question transformations + + Args: + parameters: Survey configuration + + Returns: + tuple: (paradata_df, questionnaire_df, microdata_df) + """ + # Use the data path from catalog structure + raw_data_dir = Path("data/10_RAW") + + # Scan extracted directories for survey info + survey_paths = [] + if raw_data_dir.exists(): + for item in raw_data_dir.iterdir(): + if item.is_dir(): + survey_paths.append(item) + + if not survey_paths: + raise FileNotFoundError( + f"No extracted survey data found in {raw_data_dir}. " + "Make sure to run the unzip_surveys_node first." + ) + + survey_info = get_survey_info(survey_paths) + + logger.info(f"Loading dataframes for surveys: {list(survey_info.keys())}") + + # Use your existing get_dataframes logic + paradata_df, questionnaire_df, microdata_df = get_dataframes(survey_info) + + logger.info(f"Loaded - Paradata: {paradata_df.shape}, " + f"Questionnaire: {questionnaire_df.shape}, " + f"Microdata: {microdata_df.shape}") + + return paradata_df, questionnaire_df, microdata_df diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py new file mode 100644 index 0000000..25cd7bf --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py @@ -0,0 +1,25 @@ +"""Data ingestion pipeline definition.""" +from kedro.pipeline import Pipeline, node, pipeline +from .nodes import unzip_raw_surveys, load_survey_dataframes + + +def create_pipeline(**kwargs) -> Pipeline: + """Create the data ingestion pipeline. + + Returns: + A pipeline that extracts and loads Survey Solutions data. + """ + return pipeline([ + node( + func=unzip_raw_surveys, + inputs=["raw_zip_files", "params:survey"], + outputs=None, # Side effect: extracts to same directory + name="unzip_surveys_node", + ), + node( + func=load_survey_dataframes, + inputs="params:survey", + outputs=["paradata_raw", "questionnaire_raw", "microdata_raw"], + name="load_dataframes_node", + ), + ]) diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/__init__.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/__init__.py new file mode 100644 index 0000000..9fd7813 --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/__init__.py @@ -0,0 +1,5 @@ +"""Feature engineering pipeline.""" + +from .pipeline import create_pipeline + +__all__ = ["create_pipeline"] diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py new file mode 100644 index 0000000..91466d2 --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py @@ -0,0 +1,172 @@ +"""Nodes for processing paradata and building features.""" +import pandas as pd +import numpy as np +from typing import Dict +from loguru import logger + + +def process_paradata_timestamps( + paradata_raw: pd.DataFrame +) -> pd.DataFrame: + """ + Process paradata timestamps and add hour features. + + This replicates logic from pipelines/feature_engineering/10_process_paradata.py + + Args: + paradata_raw: Raw paradata DataFrame + + Returns: + Processed paradata with timestamp features + """ + paradata = paradata_raw.copy() + + # Add answer hour feature (from 10_process_paradata.py line 29) + paradata['f__answer_hour_set'] = ( + paradata['timestamp_local'].dt.hour + + paradata['timestamp_local'].dt.round('30min').dt.minute / 60 + ) + + # Mark interviewing events (before Supervisor/HQ interaction) + events_split = ['RejectedBySupervisor', 'OpenedBySupervisor', 'OpenedByHQ', 'RejectedByHQ'] + paradata['flag'] = paradata['event'].isin(events_split) + + # Count flagged events for each interview + paradata['cumulative_flag'] = paradata.groupby('interview__id')['flag'].cumsum() + paradata['interviewing'] = np.where(paradata['cumulative_flag'] > 0, False, True) + + logger.info(f"Processed {len(paradata)} paradata records with timestamp features") + + return paradata + + +def filter_active_events( + paradata_processed: pd.DataFrame, + parameters: Dict +) -> pd.DataFrame: + """ + Filter paradata to active interviewer events. + + Replicates logic from pipelines/feature_engineering/11_process_paradata_active.py + + Args: + paradata_processed: Processed paradata + parameters: Config parameters (for limit_unit) + + Returns: + DataFrame with only active interviewer events + """ + active_events = [ + 'InterviewCreated', 'AnswerSet', 'Resumed', + 'AnswerRemoved', 'CommentSet', 'Restarted' + ] + + # Filter to active events + active_mask = ( + paradata_processed['event'].isin(active_events) & + paradata_processed['interviewing'] + ) + + # Apply limit_unit filter if specified + limit_unit = parameters.get('processing', {}).get('limit_unit') + if limit_unit is not None: + active_mask = active_mask & (paradata_processed['interview__id'].isin(limit_unit)) + + df_para_active = paradata_processed[active_mask].copy() + + logger.info(f"Filtered to {len(df_para_active)} active events") + + return df_para_active + + +def build_item_features( + microdata_raw: pd.DataFrame, + paradata_active: pd.DataFrame, + questionnaire_raw: pd.DataFrame, + parameters: Dict +) -> pd.DataFrame: + """ + Build item-level features from microdata and paradata. + + Uses logic from pipelines/feature_engineering/12_process_items.py + + Args: + microdata_raw: Raw microdata + paradata_active: Active paradata events + questionnaire_raw: Questionnaire structure + parameters: Feature configuration + + Returns: + DataFrame with item-level features + """ + logger.info("Building item-level features") + + # Create index column for joining + def make_index_col(df): + mask = (~df[['interview__id', 'variable_name', 'roster_level']].isnull()) & \ + (df[['interview__id', 'variable_name', 'roster_level']] != '') + filtered_df = df.where(mask, '') + df['index_col'] = ( + filtered_df['interview__id'].astype(str) + '__' + + filtered_df['variable_name'].astype(str) + '__' + + filtered_df['roster_level'].astype(str) + ) + return df + + microdata = make_index_col(microdata_raw.copy()) + + # Select relevant columns + item_level_columns = ['interview__id', 'variable_name', 'roster_level'] + df_item = microdata[['value', "qtype", 'is_integer', 'qnr_seq', + 'n_answers', 'answer_sequence', + 'cascade_from_question_id', 'is_filtered_combobox', + 'index_col'] + item_level_columns].copy() + + # Merge with active paradata + paradata_columns = ['responsible', 'f__answer_hour_set', 'interviewing', 'tz_offset'] + answer_set_mask = (paradata_active['event'] == 'AnswerSet') + data = paradata_active[answer_set_mask].drop_duplicates(subset='index_col', keep='last') + + df_item = df_item.merge( + data[paradata_columns + ['index_col']], + how='left', + on='index_col' + ) + + # Keep only interviewing events + df_item = df_item[df_item['interviewing'] == True] + + logger.info(f"Built {len(df_item)} item feature records") + + return df_item + + +def build_unit_features( + paradata_active: pd.DataFrame, + parameters: Dict +) -> pd.DataFrame: + """ + Build unit-level (interview-level) features. + + Uses logic from rissk/feature_processing.py make_df_unit method. + + Args: + paradata_active: Active paradata + parameters: Configuration + + Returns: + DataFrame with unit-level features + """ + df_unit = paradata_active[[ + 'interview__id', 'responsible', 'survey_name', 'survey_version' + ]].copy() + + df_unit.drop_duplicates(inplace=True) + df_unit = df_unit[ + (df_unit['responsible'] != '') & + (~pd.isnull(df_unit['responsible'])) + ] + + logger.info(f"Built {len(df_unit)} unit records") + + return df_unit diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py new file mode 100644 index 0000000..ea52425 --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py @@ -0,0 +1,42 @@ +"""Feature engineering pipeline definition.""" +from kedro.pipeline import Pipeline, node, pipeline +from .nodes import ( + process_paradata_timestamps, + filter_active_events, + build_item_features, + build_unit_features +) + + +def create_pipeline(**kwargs) -> Pipeline: + """Create the feature engineering pipeline. + + Returns: + A pipeline that processes paradata and builds features. + """ + return pipeline([ + node( + func=process_paradata_timestamps, + inputs="paradata_raw", + outputs="paradata_processed", + name="process_timestamps_node", + ), + node( + func=filter_active_events, + inputs=["paradata_processed", "parameters"], + outputs="paradata_active", + name="filter_active_events_node", + ), + node( + func=build_item_features, + inputs=["microdata_raw", "paradata_active", "questionnaire_raw", "parameters"], + outputs="item_features", + name="build_item_features_node", + ), + node( + func=build_unit_features, + inputs=["paradata_active", "parameters"], + outputs="unit_features", + name="build_unit_features_node", + ), + ]) diff --git a/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/__init__.py b/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/__init__.py new file mode 100644 index 0000000..0118779 --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/__init__.py @@ -0,0 +1,5 @@ +"""Risk scoring pipeline.""" + +from .pipeline import create_pipeline + +__all__ = ["create_pipeline"] diff --git a/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/nodes.py new file mode 100644 index 0000000..df8e2e1 --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/nodes.py @@ -0,0 +1,316 @@ +## Pipeline Nodes (pipelines) + +### 1 Ingestion Pipeline +import os +from pathlib import Path +from typing import Dict, List +import pandas as pd +from loguru import logger + +# Import your existing utilities +from rissk.utils.import_utils import ( + extract_zip, + get_survey_info, + get_dataframes +) + +"""Nodes for ingesting Survey Solutions export data.""" + +def unzip_raw_surveys( + parameters: Dict +) -> None: + """ + Extract zipped Survey Solutions exports. + + Handles: + - Recursive unzipping (nested ZIPs) + - Password-protected ZIPs (from credentials) + - Mixed formats (.dta, .tab) + + Args: + parameters: Survey configuration from parameters.yml + + Side Effect: + Extracts files to data/01_raw/{survey_name}/{version}/ + """ + from rissk.config import RAW_DATA_DIR + from rissk.utils.import_utils import get_zip_files + + survey_name = parameters["survey"]["name"] + questionnaires = parameters["survey"]["questionnaires"] + + # Get all ZIP files matching the survey config + zip_files = get_zip_files(RAW_DATA_DIR, survey_name, questionnaires) + + logger.info(f"Found {len(zip_files)} ZIP files to extract") + + for zip_file in zip_files: + dest_path = zip_file.with_suffix('') # Remove .zip extension + logger.info(f"Extracting {zip_file.name} to {dest_path}") + extract_zip(zip_file, dest_path) + + logger.success(f"Extraction complete. Files in {RAW_DATA_DIR}") + + +def load_survey_dataframes( + parameters: Dict +) -> tuple: + """ + Load paradata, questionnaire, and microdata from extracted files. + + Handles: + - Mixed file formats (.dta for Stata, .tab for tabular) + - Variable name parsing from Survey Solutions structure + - Multi-option/GPS/List question transformations + + Args: + parameters: Survey configuration + + Returns: + tuple: (paradata_df, questionnaire_df, microdata_df) + """ + from rissk.config import RAW_DATA_DIR + from rissk.utils.import_utils import get_survey_info, get_dataframes + + # Scan extracted directories for survey info + survey_paths = [] + for item in RAW_DATA_DIR.iterdir(): + if item.is_dir(): + survey_paths.append(item) + + survey_info = get_survey_info(survey_paths) + + logger.info(f"Loading dataframes for surveys: {list(survey_info.keys())}") + + # Use your existing get_dataframes logic + paradata_df, questionnaire_df, microdata_df = get_dataframes(survey_info) + + logger.info(f"Loaded - Paradata: {paradata_df.shape}, " + f"Questionnaire: {questionnaire_df.shape}, " + f"Microdata: {microdata_df.shape}") + + return paradata_df, questionnaire_df, microdata_df + + +### 2 Feature Engineering Pipeline + +"""Nodes for processing paradata and building features.""" +import pandas as pd +from typing import Dict +from loguru import logger + + +def process_paradata_timestamps( + paradata_raw: pd.DataFrame +) -> pd.DataFrame: + """ + Process paradata timestamps and add hour features. + + This replicates logic from pipelines/feature_engineering/10_process_paradata.py + + Args: + paradata_raw: Raw paradata DataFrame + + Returns: + Processed paradata with timestamp features + """ + paradata = paradata_raw.copy() + + # Add answer hour feature (from 10_process_paradata.py line 29) + paradata['f__answer_hour_set'] = ( + paradata['timestamp_local'].dt.hour + + paradata['timestamp_local'].dt.round('30min').dt.minute / 60 + ) + + # Add interviewing flag + paradata['interviewing'] = ~paradata['role'].isin([2, 3, 4]) + + logger.info(f"Processed {len(paradata)} paradata records") + + return paradata + + +def filter_active_events( + paradata_processed: pd.DataFrame, + parameters: Dict +) -> pd.DataFrame: + """ + Filter paradata to active interviewer events. + + Replicates logic from pipelines/feature_engineering/11_process_paradata_active.py + + Args: + paradata_processed: Processed paradata + parameters: Config parameters (for limit_unit) + + Returns: + DataFrame with only active interviewer events + """ + active_events = [ + 'InterviewCreated', 'AnswerSet', 'Resumed', + 'AnswerRemoved', 'CommentSet', 'Restarted' + ] + + # Filter logic from 11_process_paradata_active.py line 28 + active_mask = ( + paradata_processed['event'].isin(active_events) & + paradata_processed['question_scope'].isin([0, '']) & + (paradata_processed['role'] == 1) + ) + + vars_needed = [ + 'interview__id', 'order', 'event', 'responsible', 'role', 'tz_offset', + 'param', 'answer', 'roster_level', 'timestamp_local', 'variable_name', + 'question_sequence', 'question_scope', "qtype", 'question_type', + 'qnr', 'qnr_version', 'interviewing', 'yes_no_view', 'index_col', + 'f__answer_hour_set' + ] + + df_para_active = paradata_processed.loc[active_mask, vars_needed] + + logger.info(f"Filtered to {len(df_para_active)} active events") + + return df_para_active + + +def build_item_features( + microdata_raw: pd.DataFrame, + paradata_active: pd.DataFrame, + parameters: Dict +) -> pd.DataFrame: + """ + Build item-level features from microdata and paradata. + + Uses logic from rissk/feature_processing.py make_df_item method. + + Args: + microdata_raw: Raw microdata + paradata_active: Active paradata events + parameters: Feature configuration + + Returns: + DataFrame with item-level features + """ + from rissk.feature_processing import FeatureProcessing + + # Instantiate your existing class (or refactor to pure functions) + # For now, we'll use a wrapper approach + allowed_features = [ + f'f__{k}' for k, v in parameters['features'].items() + if v['use'] + ] + + logger.info(f"Building {len(allowed_features)} item features") + + # You would call methods like: + # df_item = feature_processor.make_df_item(microdata_raw) + # For brevity, returning placeholder + + df_item = microdata_raw.copy() # Replace with actual logic + + return df_item + + +def build_unit_features( + paradata_active: pd.DataFrame, + parameters: Dict +) -> pd.DataFrame: + """ + Build unit-level (interview-level) features. + + Uses logic from rissk/feature_processing.py make_df_unit method. + + Args: + paradata_active: Active paradata + parameters: Configuration + + Returns: + DataFrame with unit-level features + """ + df_unit = paradata_active[[ + 'interview__id', 'responsible', 'survey_name', 'survey_version' + ]].copy() + + df_unit.drop_duplicates(inplace=True) + df_unit = df_unit[ + (df_unit['responsible'] != '') & + (~pd.isnull(df_unit['responsible'])) + ] + + # Add pause features (from your add_pause_features method) + # Add time features (from add_unit_time_features) + + logger.info(f"Built {len(df_unit)} unit records") + + return df_unit + +### 3 Risk Scoring Pipeline + +# filepath: rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/nodes.py +"""Nodes for calculating risk scores.""" +import pandas as pd +from typing import Dict +from loguru import logger + + +def calculate_unit_risk_scores( + unit_features: pd.DataFrame, + item_features: pd.DataFrame, + parameters: Dict +) -> pd.DataFrame: + """ + Calculate global risk scores for each unit (interview). + + Uses logic from rissk/unit_proccessing.py make_global_score method. + + Args: + unit_features: Unit-level features + item_features: Item-level features + parameters: Feature configuration + + Returns: + DataFrame with unit_risk_score column + """ + from rissk.unit_proccessing import UnitDataProcessing + + # You would instantiate your class or refactor to pure functions + # For now, placeholder logic: + + unit_scores = unit_features.copy() + unit_scores['unit_risk_score'] = 0.0 # Replace with actual IForest scoring + + logger.info(f"Calculated risk scores for {len(unit_scores)} units") + + return unit_scores + + +def format_output_scores( + unit_risk_scores: pd.DataFrame, + parameters: Dict +) -> tuple: + """ + Format final output files. + + Args: + unit_risk_scores: Scores DataFrame + parameters: Output configuration + + Returns: + tuple: (unit_scores_df, feature_scores_df) if feature_score=True + """ + # Main output (from rissk/unit_proccessing.py save method line 104) + output_df = unit_risk_scores[[ + 'interview__id', 'responsible', 'unit_risk_score' + ]].copy() + + output_df['unit_risk_score'] = output_df['unit_risk_score'].round(2) + output_df.sort_values('unit_risk_score', inplace=True) + + logger.success(f"Formatted {len(output_df)} risk scores for output") + + if parameters['output']['feature_score']: + # Generate feature score breakdown + feature_scores_df = unit_risk_scores.copy() # Add all s__ columns + return output_df, feature_scores_df + + return output_df, None \ No newline at end of file diff --git a/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/pipeline.py new file mode 100644 index 0000000..1781e78 --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/pipeline.py @@ -0,0 +1,25 @@ +"""Risk scoring pipeline definition.""" +from kedro.pipeline import Pipeline, node, pipeline +from .nodes import calculate_unit_risk_scores, format_output_scores + + +def create_pipeline(**kwargs) -> Pipeline: + """Create the risk scoring pipeline. + + Returns: + A pipeline that calculates unit risk scores. + """ + return pipeline([ + node( + func=calculate_unit_risk_scores, + inputs=["unit_features", "item_features", "parameters"], + outputs="unit_risk_scores_raw", + name="calculate_scores_node", + ), + node( + func=format_output_scores, + inputs=["unit_risk_scores_raw", "parameters"], + outputs=["unit_risk_scores", "unit_feature_scores"], + name="format_outputs_node", + ), + ]) \ No newline at end of file diff --git a/rissk_kedro/src/rissk_kedro/settings.py b/rissk_kedro/src/rissk_kedro/settings.py new file mode 100644 index 0000000..d97a09e --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/settings.py @@ -0,0 +1,40 @@ +"""Project settings. There is no need to edit this file unless you want to change values +from the Kedro defaults. For further information, including these default values, see +https://docs.kedro.org/en/stable/kedro_project_setup/settings.html.""" + +# Instantiated project hooks. + +# Hooks are executed in a Last-In-First-Out (LIFO) order. +HOOKS = () + +# Installed plugins for which to disable hook auto-registration. +# DISABLE_HOOKS_FOR_PLUGINS = ("kedro-viz",) + +# Class that manages storing KedroSession data. +# from kedro.framework.session.store import BaseSessionStore +# SESSION_STORE_CLASS = BaseSessionStore +# Keyword arguments to pass to the `SESSION_STORE_CLASS` constructor. +# SESSION_STORE_ARGS = { +# "path": "./sessions" +# } + +# Directory that holds configuration. +# CONF_SOURCE = "conf" + +# Class that manages how configuration is loaded. +from kedro.config import OmegaConfigLoader # noqa: E402 + +CONFIG_LOADER_CLASS = OmegaConfigLoader +# Keyword arguments to pass to the `CONFIG_LOADER_CLASS` constructor. +CONFIG_LOADER_ARGS = { + "base_env": "base", + "default_run_env": "local", +} + +# Class that manages Kedro's library components. +# from kedro.framework.context import KedroContext +# CONTEXT_CLASS = KedroContext + +# Class that manages the Data Catalog. +# from kedro.io import DataCatalog +# DATA_CATALOG_CLASS = DataCatalog diff --git a/rissk_kedro/tests/__init__.py b/rissk_kedro/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rissk_kedro/tests/pipelines/__init__.py b/rissk_kedro/tests/pipelines/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rissk_kedro/tests/pipelines/data_science/test_pipeline.py b/rissk_kedro/tests/pipelines/data_science/test_pipeline.py new file mode 100644 index 0000000..294deaa --- /dev/null +++ b/rissk_kedro/tests/pipelines/data_science/test_pipeline.py @@ -0,0 +1,63 @@ +import logging +import pandas as pd +import pytest +from kedro.io import DataCatalog +from kedro.runner import SequentialRunner +from rissk_kedro.pipelines.data_science import create_pipeline as create_ds_pipeline +from rissk_kedro.pipelines.data_science.nodes import split_data + +@pytest.fixture +def dummy_data(): + return pd.DataFrame( + { + "engines": [1, 2, 3], + "crew": [4, 5, 6], + "passenger_capacity": [5, 6, 7], + "price": [120, 290, 30], + } + ) + +@pytest.fixture +def dummy_parameters(): + parameters = { + "model_options": { + "test_size": 0.2, + "random_state": 3, + "features": ["engines", "passenger_capacity", "crew"], + } + } + return parameters + + +def test_split_data(dummy_data, dummy_parameters): + X_train, X_test, y_train, y_test = split_data( + dummy_data, dummy_parameters["model_options"] + ) + assert len(X_train) == 2 + assert len(y_train) == 2 + assert len(X_test) == 1 + assert len(y_test) == 1 + +def test_split_data_missing_price(dummy_data, dummy_parameters): + dummy_data_missing_price = dummy_data.drop(columns="price") + with pytest.raises(KeyError) as e_info: + X_train, X_test, y_train, y_test = split_data(dummy_data_missing_price, dummy_parameters["model_options"]) + + assert "price" in str(e_info.value) + +def test_data_science_pipeline(caplog, dummy_data, dummy_parameters): + pipeline = ( + create_ds_pipeline() + .from_nodes("split_data_node") + .to_nodes("evaluate_model_node") + ) + catalog = DataCatalog() + catalog["model_input_table@pandas"] = dummy_data + catalog["params:model_options"] = dummy_parameters["model_options"] + + caplog.set_level(logging.DEBUG, logger="kedro") + successful_run_msg = "Pipeline execution completed successfully" + + SequentialRunner().run(pipeline, catalog) + + assert successful_run_msg in caplog.text diff --git a/rissk_kedro/tests/test_run.py b/rissk_kedro/tests/test_run.py new file mode 100644 index 0000000..addd1f9 --- /dev/null +++ b/rissk_kedro/tests/test_run.py @@ -0,0 +1,20 @@ +""" +This module contains example tests for a Kedro project. +Tests should be placed in ``src/tests``, in modules that mirror your +project's structure, and in files named test_*.py. +""" +from pathlib import Path + +from kedro.framework.session import KedroSession +from kedro.framework.startup import bootstrap_project + +# The tests below are here for the demonstration purpose +# and should be replaced with the ones testing the project +# functionality + +class TestKedroRun: + def test_kedro_run(self): + bootstrap_project(Path.cwd()) + + with KedroSession.create(project_path=Path.cwd()) as session: + assert session.run() is not None From ed6a615b0b9453bd0efd3152aff3707c14747a25 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Tue, 10 Feb 2026 22:27:21 +0000 Subject: [PATCH 02/70] change raw folder to parameter rather than in data catalogue, small refactor on rissk/utils extract_zip to clean up password handling --- rissk/utils/import_utils.py | 6 +- rissk_kedro/conf/base/catalog.yml | 88 ++++++------- rissk_kedro/conf/base/parameters.yml | 6 + .../pipelines/data_ingestion/nodes.py | 116 +++++------------- .../pipelines/data_ingestion/pipeline.py | 32 +++-- 5 files changed, 89 insertions(+), 159 deletions(-) diff --git a/rissk/utils/import_utils.py b/rissk/utils/import_utils.py index 74a2df6..641f7f6 100644 --- a/rissk/utils/import_utils.py +++ b/rissk/utils/import_utils.py @@ -51,7 +51,7 @@ def get_zip_files(data_dir: Path, survey: str, questionnaires: List[Dict[str, Li return matching_files -def extract_zip(file_source_path: Path, file_dest_path: Path): +def extract_zip(file_source_path: Path, file_dest_path: Path, password: Optional[str] = None): """ Extracts a zip file to the specified destination path. If nested zip files are encountered, they are extracted recursively. @@ -59,8 +59,10 @@ def extract_zip(file_source_path: Path, file_dest_path: Path): Parameters: - file_source_path (Path): Path to the source zip file. - file_dest_path (Path): Destination directory where files will be extracted. + - password (str, optional): Password for encrypted zip files. """ - password = os.getenv('PASSWORD', None) + if password is None: + password = os.getenv('PASSWORD', None) try: with file_source_path.open(mode='rb') as f: diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index 7726e5c..d0ca7df 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -1,68 +1,52 @@ # Here you can define all your data sets by using simple YAML syntax. # -# === RAW DATA (Zipped Survey Solutions Exports) === -raw_zip_files: - type: PartitionedDataset - path: data/10_RAW - dataset: - type: kedro.extras.datasets.text.TextDataset # Placeholder, actual extraction in node - filename_suffix: ".zip" - -# === INTERMEDIATE (Extracted Files) === -extracted_survey_data: - type: PartitionedDataset - path: data/10_RAW - dataset: - type: kedro.extras.datasets.text.TextDataset - filename_suffix: "" - # === PRIMARY (Ingested DataFrames) === -paradata_raw: +paradata_interim: type: pandas.ParquetDataset - filepath: data/20_INTERIM/paradata_raw.parquet + filepath: data/20_INTERIM/paradata.parquet -questionnaire_raw: +raw_questionnaire: type: pandas.ParquetDataset - filepath: data/20_INTERIM/questionnaire_raw.parquet + filepath: data/30_PROCESSED/questionnaire.parquet -microdata_raw: +raw_microdata: type: pandas.ParquetDataset - filepath: data/20_INTERIM/microdata_raw.parquet + filepath: data/30_PROCESSED/microdata.parquet -# === FEATURE PROCESSED === -paradata_processed: - type: pandas.ParquetDataset - filepath: data/30_PROCESSED/paradata_processed.parquet +# # === FEATURE PROCESSED === +# paradata_processed: +# type: pandas.ParquetDataset +# filepath: data/30_PROCESSED/paradata_processed.parquet -paradata_active: - type: pandas.ParquetDataset - filepath: data/30_PROCESSED/paradata_active.parquet +# paradata_active: +# type: pandas.ParquetDataset +# filepath: data/30_PROCESSED/paradata_active.parquet -item_features: - type: pandas.ParquetDataset - filepath: data/30_PROCESSED/item_features.parquet +# item_features: +# type: pandas.ParquetDataset +# filepath: data/30_PROCESSED/item_features.parquet -unit_features: - type: pandas.ParquetDataset - filepath: data/30_PROCESSED/unit_features.parquet +# unit_features: +# type: pandas.ParquetDataset +# filepath: data/30_PROCESSED/unit_features.parquet -unit_risk_scores_raw: - type: pandas.ParquetDataset - filepath: data/30_PROCESSED/unit_risk_scores_raw.parquet +# unit_risk_scores_raw: +# type: pandas.ParquetDataset +# filepath: data/30_PROCESSED/unit_risk_scores_raw.parquet -responsible_features: - type: pandas.ParquetDataset - filepath: data/30_PROCESSED/responsible_features.parquet +# responsible_features: +# type: pandas.ParquetDataset +# filepath: data/30_PROCESSED/responsible_features.parquet -# === MODEL OUTPUT === -unit_risk_scores: - type: pandas.CSVDataset - filepath: data/40_OUTPUTS/unit_risk_scores.csv - save_args: - index: false +# # === MODEL OUTPUT === +# unit_risk_scores: +# type: pandas.CSVDataset +# filepath: data/40_OUTPUTS/unit_risk_scores.csv +# save_args: +# index: false -unit_feature_scores: - type: pandas.CSVDataset - filepath: data/40_OUTPUTS/unit_feature_scores.csv - save_args: - index: false +# unit_feature_scores: +# type: pandas.CSVDataset +# filepath: data/40_OUTPUTS/unit_feature_scores.csv +# save_args: +# index: false diff --git a/rissk_kedro/conf/base/parameters.yml b/rissk_kedro/conf/base/parameters.yml index 3813191..404f1b9 100644 --- a/rissk_kedro/conf/base/parameters.yml +++ b/rissk_kedro/conf/base/parameters.yml @@ -7,6 +7,12 @@ survey: - name: "slbhies_listing" versions: [6, 7] +zip_password: ${oc.env:PASSWORD} + +# Ingestion Configuration +ingestion: + raw_data_path: "data/10_RAW" + # Processing Parameters processing: limit_unit: null # Set to filter by consent if needed diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py index 5f98aaf..fadb8aa 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py @@ -1,103 +1,43 @@ -"""Nodes for ingesting Survey Solutions export data.""" -import os -from pathlib import Path from typing import Dict, List, Tuple +from pathlib import Path +import os import pandas as pd from loguru import logger +from rissk.utils.import_utils import get_zip_files, extract_zip, get_survey_info, get_dataframes -# Import your existing utilities -from rissk.utils.import_utils import ( - extract_zip, - get_survey_info, - get_dataframes -) - - -def unzip_raw_surveys( - raw_zip_files: Dict[str, callable], - parameters: Dict -) -> None: +def unzip_survey_data_node( + survey_name: str, + raw_path_str: str, + questionnaires: List[Dict], + zip_password: str +) -> List[Path]: """ - Extract zipped Survey Solutions exports. - - Handles: - - Recursive unzipping (nested ZIPs) - - Password-protected ZIPs (from credentials) - - Mixed formats (.dta, .tab) - - Args: - raw_zip_files: Dictionary of ZIP files from catalog (PartitionedDataset) - parameters: Survey configuration from parameters.yml - - Side Effect: - Extracts files to same directory as ZIP (removes .zip extension) + Finds and extracts zips. Returns list of extracted project paths. + Wraps import_utils.extract_zip. """ - survey_name = parameters["name"] - questionnaires = parameters["questionnaires"] - - # Filter ZIP files based on survey configuration - questionnaire_names = [q["name"] for q in questionnaires] - - matching_files = [ - filename for filename in raw_zip_files.keys() - if any(qname in filename for qname in questionnaire_names) - ] + raw_path = Path(raw_path_str) - logger.info(f"Found {len(matching_files)} ZIP files to extract: {matching_files}") + logger.info(f"Looking for zips in {raw_path} for {survey_name}") + zip_files = get_zip_files(raw_path, survey_name, questionnaires) - for filename in matching_files: - # Get the full path to the ZIP file - zip_path = Path("data/10_RAW") / filename - dest_path = zip_path.with_suffix('') # Remove .zip extension + extracted_paths = [] + for zip_file in zip_files: + project_path = zip_file.with_suffix('') + extracted_paths.append(project_path) + # Extract using the password argument + extract_zip(zip_file, project_path, password=zip_password) - logger.info(f"Extracting {filename} to {dest_path}") - extract_zip(zip_path, dest_path) - - logger.success(f"Extraction complete. Extracted {len(matching_files)} surveys.") - + return extracted_paths -def load_survey_dataframes( - parameters: Dict -) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: +def load_survey_data_node(survey_paths: List[Path]) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: """ - Load paradata, questionnaire, and microdata from extracted files. - - Handles: - - Mixed file formats (.dta for Stata, .tab for tabular) - - Variable name parsing from Survey Solutions structure - - Multi-option/GPS/List question transformations - - Args: - parameters: Survey configuration - - Returns: - tuple: (paradata_df, questionnaire_df, microdata_df) + Loads dataframes from extracted folders. + Wraps import_utils.get_dataframes which handles .dta/.tab logic. """ - # Use the data path from catalog structure - raw_data_dir = Path("data/10_RAW") - - # Scan extracted directories for survey info - survey_paths = [] - if raw_data_dir.exists(): - for item in raw_data_dir.iterdir(): - if item.is_dir(): - survey_paths.append(item) - - if not survey_paths: - raise FileNotFoundError( - f"No extracted survey data found in {raw_data_dir}. " - "Make sure to run the unzip_surveys_node first." - ) - + logger.info(f"Processing survey info for {len(survey_paths)} paths") survey_info = get_survey_info(survey_paths) - logger.info(f"Loading dataframes for surveys: {list(survey_info.keys())}") - - # Use your existing get_dataframes logic - paradata_df, questionnaire_df, microdata_df = get_dataframes(survey_info) - - logger.info(f"Loaded - Paradata: {paradata_df.shape}, " - f"Questionnaire: {questionnaire_df.shape}, " - f"Microdata: {microdata_df.shape}") + # Returns: paradata, questionnaire, microdata + dfs_para, dfs_qnr, dfs_micro = get_dataframes(survey_info) - return paradata_df, questionnaire_df, microdata_df + return dfs_para, dfs_qnr, dfs_micro diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py index 25cd7bf..db1b78a 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py @@ -1,25 +1,23 @@ -"""Data ingestion pipeline definition.""" from kedro.pipeline import Pipeline, node, pipeline -from .nodes import unzip_raw_surveys, load_survey_dataframes - +from .nodes import unzip_survey_data_node, load_survey_data_node def create_pipeline(**kwargs) -> Pipeline: - """Create the data ingestion pipeline. - - Returns: - A pipeline that extracts and loads Survey Solutions data. - """ return pipeline([ node( - func=unzip_raw_surveys, - inputs=["raw_zip_files", "params:survey"], - outputs=None, # Side effect: extracts to same directory - name="unzip_surveys_node", + func=unzip_survey_data_node, + inputs=[ + "params:survey.name", + "params:ingestion.raw_data_path", + "params:survey.questionnaires", + "params:zip_password" + ], + outputs="extracted_survey_paths", + name="unzip_survey_data_node" ), node( - func=load_survey_dataframes, - inputs="params:survey", - outputs=["paradata_raw", "questionnaire_raw", "microdata_raw"], - name="load_dataframes_node", - ), + func=load_survey_data_node, + inputs="extracted_survey_paths", + outputs=["paradata_interim", "raw_questionnaire", "raw_microdata"], + name="load_survey_data_node" + ) ]) From 7e849a751ece194978b6a5f30bfaf93cf17237e2 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Wed, 11 Feb 2026 13:15:41 +0000 Subject: [PATCH 03/70] fix: Update questionnaire version keys and handle answer_sequence type in data ingestion --- rissk_kedro/conf/base/parameters.yml | 7 +- .../pipelines/data_ingestion/nodes.py | 9 + .../src/rissk_kedro/test_ingestion.ipynb | 415 ++++++++++++++++++ 3 files changed, 428 insertions(+), 3 deletions(-) create mode 100644 rissk_kedro/src/rissk_kedro/test_ingestion.ipynb diff --git a/rissk_kedro/conf/base/parameters.yml b/rissk_kedro/conf/base/parameters.yml index 404f1b9..e21ccee 100644 --- a/rissk_kedro/conf/base/parameters.yml +++ b/rissk_kedro/conf/base/parameters.yml @@ -3,11 +3,12 @@ survey: name: "hies2024" questionnaires: - name: "snb_hies_hh" - versions: [9, 10, 11] + VERSION: [9, 10, 11] - name: "slbhies_listing" - versions: [6, 7] + VERSION: [6, 7] -zip_password: ${oc.env:PASSWORD} +# If set to null, the system will look for the 'PASSWORD' environment variable +zip_password: null # Ingestion Configuration ingestion: diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py index fadb8aa..dfdcc83 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py @@ -39,5 +39,14 @@ def load_survey_data_node(survey_paths: List[Path]) -> Tuple[pd.DataFrame, pd.Da # Returns: paradata, questionnaire, microdata dfs_para, dfs_qnr, dfs_micro = get_dataframes(survey_info) + + if 'answer_sequence' in dfs_para.columns: + dfs_para['answer_sequence'] = dfs_para['answer_sequence'].apply(str) + + if 'answer_sequence' in dfs_qnr.columns: + dfs_qnr['answer_sequence'] = dfs_qnr['answer_sequence'].apply(str) + + if 'answer_sequence' in dfs_micro.columns: + dfs_micro['answer_sequence'] = dfs_micro['answer_sequence'].apply(str) return dfs_para, dfs_qnr, dfs_micro diff --git a/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb b/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb new file mode 100644 index 0000000..fe1b28e --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb @@ -0,0 +1,415 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7a72d996", + "metadata": {}, + "source": [ + "Tests data ingestion is the same as original code by comparing questionnaire, microdata and paradata output." + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "607ef013", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import pandas as pd\n", + "from rissk.config import DATA_DIR, RAW_DATA_DIR, PROCESSED_DATA_DIR, INTERIM_DATA_DIR, PROJ_ROOT" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "dc4569ea", + "metadata": {}, + "outputs": [], + "source": [ + "# original files\n", + "df_para = pd.read_parquet(INTERIM_DATA_DIR.joinpath(\"paradata.parquet\"))\n", + "df_questionnaire = pd.read_parquet(PROCESSED_DATA_DIR.joinpath(\"questionnaire.parquet\"))\n", + "df_microdata = pd.read_parquet(PROCESSED_DATA_DIR.joinpath(\"microdata.parquet\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "3d3ef86a", + "metadata": {}, + "outputs": [], + "source": [ + "# Kedro pipeline outputs\n", + "df_para_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", \"20_INTERIM\", \"paradata.parquet\"))\n", + "df_questionnaire_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", \"30_PROCESSED\", \"questionnaire.parquet\"))\n", + "df_microdata_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", \"30_PROCESSED\", \"microdata.parquet\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "72fdc596", + "metadata": {}, + "outputs": [], + "source": [ + "# Comparison utility inserted into notebook\n", + "from typing import Dict, Any, List, Optional, Tuple\n", + "import pandas as pd\n", + "import numpy as np\n", + "from collections import Counter\n", + "import math\n", + "from pandas.api import types as ptypes\n", + "\n", + "\n", + "def _dtype_map(dseries: pd.Series) -> Dict[str, str]:\n", + " return {col: str(dtype) for col, dtype in dseries.items()}\n", + "\n", + "\n", + "def _find_candidate_key(df_a: pd.DataFrame, df_b: pd.DataFrame, common_cols: List[str]) -> Optional[str]:\n", + " # Prefer obvious id-like columns\n", + " candidates = [c for c in common_cols if any(k in c.lower() for k in ['id', 'uuid', 'key', 'interview'])]\n", + " # fall back to all common cols\n", + " candidates = candidates + [c for c in common_cols if c not in candidates]\n", + " for c in candidates:\n", + " try:\n", + " if df_a[c].is_unique and df_b[c].is_unique:\n", + " return c\n", + " except Exception:\n", + " continue\n", + " return None\n", + "\n", + "\n", + "def _is_numeric_series(s: pd.Series) -> bool:\n", + " return ptypes.is_numeric_dtype(s.dtype)\n", + "\n", + "\n", + "def _compare_elementwise(a: pd.Series, b: pd.Series, atol: float, rtol: float) -> np.ndarray:\n", + " \"\"\"Return boolean mask where True indicates a != b (treating NaNs as equal).\n", + " Works for numeric (uses isclose) and non-numeric (stringified) series.\n", + " \"\"\"\n", + " # Align lengths assumed equal and indexes aligned\n", + " # Handle numeric\n", + " if _is_numeric_series(a) and _is_numeric_series(b):\n", + " # convert to float with NaN preserved\n", + " a_f = a.astype(float)\n", + " b_f = b.astype(float)\n", + " # both NaN -> equal\n", + " both_nan = a_f.isna() & b_f.isna()\n", + " # use isclose (NaNs produce False) then invert and mask NaNs\n", + " close = np.isclose(a_f.fillna(np.nan), b_f.fillna(np.nan), atol=atol, rtol=rtol, equal_nan=True)\n", + " neq = ~close\n", + " neq[both_nan.values] = False\n", + " return neq\n", + " else:\n", + " # compare as strings, treating NaN as a sentinel\n", + " a_s = a.fillna('__NA__').astype(str)\n", + " b_s = b.fillna('__NA__').astype(str)\n", + " return (a_s != b_s).to_numpy()\n", + "\n", + "\n", + "def compare_parquet_files(df_a: pd.DataFrame, df_b: pd.DataFrame, check: Optional[str] = None, atol: float = 1e-9, rtol: float = 1e-8) -> Tuple[bool, Dict[str, Any]]:\n", + " \"\"\"\n", + " Robust comparison of two DataFrames produced by different pipelines.\n", + "\n", + " - Basic checks (shape, columns, dtypes) always computed.\n", + " - `check` can be None, 'cells' or 'rows'.\n", + " - For 'cells': attempts label-aligned comparison using a detected unique key column\n", + " (preferred if present in both tables), otherwise aligns by index intersection when possible,\n", + " otherwise falls back to positional/overlap comparison. Reports columns that have any differing\n", + " cells and total number of differing cells compared.\n", + " - For 'rows': if a unique key column is detected the function compares rows by key (counts\n", + " keys only in A/B and per-key mismatches). Otherwise it falls back to an unordered multiset\n", + " (counts) comparison on the common columns, which handles duplicate rows.\n", + " - Missing entries are handled (NaNs compared as equal), numeric columns use tolerant comparison.\n", + " - The function never crashes on shape mismatch: it documents partial comparisons in `details`.\n", + "\n", + " Returns: (same: bool, details: dict)\n", + " \"\"\"\n", + " if not isinstance(df_a, pd.DataFrame) or not isinstance(df_b, pd.DataFrame):\n", + " raise TypeError('compare_parquet_files expects pandas DataFrame inputs')\n", + "\n", + " details: Dict[str, Any] = {}\n", + "\n", + " # Basic metadata\n", + " shape_equal = df_a.shape == df_b.shape\n", + " details['shape'] = {'equal': bool(shape_equal), 'shape_a': df_a.shape, 'shape_b': df_b.shape}\n", + "\n", + " cols_a = list(df_a.columns)\n", + " cols_b = list(df_b.columns)\n", + " only_a = [c for c in cols_a if c not in cols_b]\n", + " only_b = [c for c in cols_b if c not in cols_a]\n", + " different_columns = list(dict.fromkeys(only_a + only_b))\n", + " columns_equal = len(different_columns) == 0\n", + " details['columns'] = {'different_columns': different_columns, 'equal': bool(columns_equal), 'only_in_a': only_a, 'only_in_b': only_b}\n", + "\n", + " dtypes_a = _dtype_map(df_a.dtypes)\n", + " dtypes_b = _dtype_map(df_b.dtypes)\n", + " common = [c for c in cols_a if c in cols_b]\n", + " dtype_mismatch = [c for c in common if dtypes_a.get(c) != dtypes_b.get(c)]\n", + " dtypes_equal = len(dtype_mismatch) == 0\n", + " details['dtypes'] = {'mismatched_columns': dtype_mismatch, 'equal': bool(dtypes_equal)}\n", + "\n", + " same = bool(shape_equal and columns_equal and dtypes_equal)\n", + "\n", + " # Normalize alias\n", + " if check == 'cell':\n", + " check = 'cells'\n", + "\n", + " # Automatic key detection (if any)\n", + " candidate_key = _find_candidate_key(df_a, df_b, common)\n", + " details['auto_key'] = candidate_key\n", + "\n", + " # CELL-level comparison (position/label depending on alignment)\n", + " if check == 'cells':\n", + " cell_info: Dict[str, Any] = {'checked': True}\n", + " if len(common) == 0:\n", + " cell_info['note'] = 'no common columns to compare'\n", + " cell_info['columns_with_differences'] = []\n", + " cell_info['total_cell_differences'] = 0\n", + " else:\n", + " # Prefer key-based alignment\n", + " if candidate_key is not None:\n", + " note = f\"aligned by key='{candidate_key}'\"\n", + " # set index by key and intersect\n", + " a_k = df_a.set_index(candidate_key)\n", + " b_k = df_b.set_index(candidate_key)\n", + " common_idx = a_k.index.intersection(b_k.index)\n", + " a_al = a_k.loc[common_idx, common].fillna('__NA__')\n", + " b_al = b_k.loc[common_idx, common].fillna('__NA__')\n", + " rows_compared = len(common_idx)\n", + " else:\n", + " # try index-based alignment if helpful\n", + " inter_idx = df_a.index.intersection(df_b.index)\n", + " if df_a.index.is_unique and df_b.index.is_unique and len(inter_idx) > 0:\n", + " note = 'aligned by index intersection'\n", + " a_al = df_a.reindex(index=inter_idx, columns=common).fillna('__NA__')\n", + " b_al = df_b.reindex(index=inter_idx, columns=common).fillna('__NA__')\n", + " rows_compared = len(inter_idx)\n", + " else:\n", + " # fall back to positional comparison over overlap\n", + " rows_to_compare = min(len(df_a), len(df_b))\n", + " note = 'positional comparison over overlapping rows'\n", + " a_al = df_a.iloc[:rows_to_compare][common].fillna('__NA__').reset_index(drop=True)\n", + " b_al = df_b.iloc[:rows_to_compare][common].fillna('__NA__').reset_index(drop=True)\n", + " rows_compared = rows_to_compare\n", + "\n", + " # perform elementwise comparison with tolerance on numeric cols\n", + " neq_mask = np.zeros((rows_compared, len(common)), dtype=bool)\n", + " for j, col in enumerate(common):\n", + " a_col = a_al[col]\n", + " b_col = b_al[col]\n", + " col_neq = _compare_elementwise(a_col, b_col, atol=atol, rtol=rtol)\n", + " neq_mask[:, j] = col_neq\n", + "\n", + " neq_df = pd.DataFrame(neq_mask, columns=common)\n", + " cols_with_diff = neq_df.any(axis=0)\n", + " cols_with_diff_names = cols_with_diff[cols_with_diff].index.tolist()\n", + " total_cell_diffs = int(neq_df.values.sum())\n", + "\n", + " cell_info['columns_with_differences'] = cols_with_diff_names\n", + " cell_info['total_cell_differences'] = total_cell_diffs\n", + " cell_info['rows_compared'] = int(rows_compared)\n", + " cell_info['note'] = note\n", + "\n", + " if total_cell_diffs > 0:\n", + " same = False\n", + " details['cell_compare'] = cell_info\n", + "\n", + " # ROW-level comparison\n", + " if check == 'rows':\n", + " row_info: Dict[str, Any] = {'checked': True}\n", + " if len(common) == 0:\n", + " row_info['note'] = 'no common columns to compare; cannot perform row membership check'\n", + " row_info['rows_in_a_not_in_b'] = None\n", + " row_info['rows_in_b_not_in_a'] = None\n", + " row_info['num_rows_different'] = None\n", + " row_info['total_rows_a'] = len(df_a)\n", + " row_info['total_rows_b'] = len(df_b)\n", + " else:\n", + " if candidate_key is not None:\n", + " # compare by key: count keys only in A/B and mismatched rows for common keys\n", + " a_k = df_a.set_index(candidate_key)[common].fillna('__NA__')\n", + " b_k = df_b.set_index(candidate_key)[common].fillna('__NA__')\n", + " keys_a = set(a_k.index)\n", + " keys_b = set(b_k.index)\n", + " keys_only_a = keys_a - keys_b\n", + " keys_only_b = keys_b - keys_a\n", + " common_keys = keys_a & keys_b\n", + "\n", + " # count per-key mismatches\n", + " mismatched_keys = 0\n", + " for k in common_keys:\n", + " a_row = a_k.loc[k]\n", + " b_row = b_k.loc[k]\n", + " # elementwise comparison across common cols\n", + " neq_any = False\n", + " for col in common:\n", + " if _compare_elementwise(pd.Series([a_row[col]]), pd.Series([b_row[col]]), atol=atol, rtol=rtol)[0]:\n", + " neq_any = True\n", + " break\n", + " if neq_any:\n", + " mismatched_keys += 1\n", + "\n", + " row_info['keys_only_in_a'] = int(len(keys_only_a))\n", + " row_info['keys_only_in_b'] = int(len(keys_only_b))\n", + " row_info['mismatched_common_keys'] = int(mismatched_keys)\n", + " row_info['num_rows_different'] = int(len(keys_only_a) + len(keys_only_b) + mismatched_keys)\n", + " row_info['total_rows_a'] = len(df_a)\n", + " row_info['total_rows_b'] = len(df_b)\n", + " row_info['note'] = f\"compared by key='{candidate_key}'\"\n", + "\n", + " if row_info['num_rows_different'] > 0:\n", + " same = False\n", + " else:\n", + " # multiset row comparison on common cols (stringified)\n", + " a_rows = df_a[common].fillna('__NA__').astype(str)\n", + " b_rows = df_b[common].fillna('__NA__').astype(str)\n", + " a_tuples = [tuple(r) for r in a_rows.values]\n", + " b_tuples = [tuple(r) for r in b_rows.values]\n", + " cnt_a = Counter(a_tuples)\n", + " cnt_b = Counter(b_tuples)\n", + " rows_a_not_b = sum(max(cnt_a[k] - cnt_b.get(k, 0), 0) for k in cnt_a)\n", + " rows_b_not_a = sum(max(cnt_b[k] - cnt_a.get(k, 0), 0) for k in cnt_b)\n", + " num_diff = int(rows_a_not_b + rows_b_not_a)\n", + "\n", + " row_info['rows_in_a_not_in_b'] = int(rows_a_not_b)\n", + " row_info['rows_in_b_not_in_a'] = int(rows_b_not_a)\n", + " row_info['num_rows_different'] = num_diff\n", + " row_info['total_rows_a'] = len(a_tuples)\n", + " row_info['total_rows_b'] = len(b_tuples)\n", + " row_info['note'] = 'multiset row comparison on common columns'\n", + "\n", + " if num_diff > 0:\n", + " same = False\n", + " details['row_compare'] = row_info\n", + "\n", + " details['same'] = bool(same)\n", + " return bool(same), details\n" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "63867817", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1203121, 43)" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rows, cells = df_microdata.shape\n", + "rows, cells" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "792a94d3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(False,\n", + " {'shape': {'equal': True, 'shape_a': (1203121, 43), 'shape_b': (1203121, 43)},\n", + " 'columns': {'different_columns': [],\n", + " 'equal': True,\n", + " 'only_in_a': [],\n", + " 'only_in_b': []},\n", + " 'dtypes': {'mismatched_columns': [], 'equal': True},\n", + " 'auto_key': None,\n", + " 'row_compare': {'checked': True,\n", + " 'rows_in_a_not_in_b': 1122462,\n", + " 'rows_in_b_not_in_a': 1122462,\n", + " 'num_rows_different': 2244924,\n", + " 'total_rows_a': 1203121,\n", + " 'total_rows_b': 1203121,\n", + " 'note': 'multiset row comparison on common columns'},\n", + " 'same': False})" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "compare_parquet_files(df_para_kedro, df_para, check='cells')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "de3363a0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(False,\n", + " {'shape': {'equal': True, 'shape_a': (1203121, 43), 'shape_b': (1203121, 43)},\n", + " 'columns': {'different_columns': [],\n", + " 'equal': True,\n", + " 'only_in_a': [],\n", + " 'only_in_b': []},\n", + " 'dtypes': {'mismatched_columns': [], 'equal': True},\n", + " 'auto_key': None,\n", + " 'cell_compare': {'checked': True,\n", + " 'columns_with_differences': ['answer_sequence'],\n", + " 'total_cell_differences': 1122462,\n", + " 'rows_compared': 1203121,\n", + " 'note': 'aligned by index intersection'},\n", + " 'same': False})" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "compare_parquet_files(df_microdata_kedro, df_microdata, check='cells')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81f6cde7", + "metadata": {}, + "outputs": [], + "source": [ + "compare_parquet_files(df_questionnaire_kedro, df_questionnaire, check='cells')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rissk_rs_01", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.23" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 20111672815d94e1de2ebbd13ef407f4f7b2fa85 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Wed, 11 Feb 2026 13:53:54 +0000 Subject: [PATCH 04/70] docs: Update README with data encryption instructions and modify test ingestion notebook execution counts --- rissk_kedro/README.md | 15 ++++ .../src/rissk_kedro/test_ingestion.ipynb | 74 ++++++++++++------- 2 files changed, 63 insertions(+), 26 deletions(-) diff --git a/rissk_kedro/README.md b/rissk_kedro/README.md index 433099e..ae43bbf 100644 --- a/rissk_kedro/README.md +++ b/rissk_kedro/README.md @@ -17,6 +17,21 @@ In order to get the best out of the template: * Don't commit data to your repository * Don't commit any credentials or your local configuration to your repository. Keep all your credentials and local configuration in `conf/local/` +## Configuration & Secrets + +### Data Encryption +To handle password-protected zip files in the ingestion pipeline, you must provide the password locally. +Do NOT commit this password to version control. + +1. Create or edit `conf/local/parameters.yml` (this file is git-ignored). +2. Add the following key: + +```yaml +zip_password: "your_actual_password_here" +``` + +If this value is left as `null` (or missing from local config), the system will attempt to read the `PASSWORD` environment variable. + ## How to install dependencies Declare any dependencies in `requirements.txt` for `pip` installation. diff --git a/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb b/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb index fe1b28e..0831c15 100644 --- a/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb +++ b/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 111, "id": "607ef013", "metadata": {}, "outputs": [], @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 112, "id": "dc4569ea", "metadata": {}, "outputs": [], @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 113, "id": "3d3ef86a", "metadata": {}, "outputs": [], @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 114, "id": "72fdc596", "metadata": {}, "outputs": [], @@ -290,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 115, "id": "63867817", "metadata": {}, "outputs": [ @@ -300,7 +300,7 @@ "(1203121, 43)" ] }, - "execution_count": 100, + "execution_count": 115, "metadata": {}, "output_type": "execute_result" } @@ -312,32 +312,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 116, "id": "792a94d3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(False,\n", - " {'shape': {'equal': True, 'shape_a': (1203121, 43), 'shape_b': (1203121, 43)},\n", + "(True,\n", + " {'shape': {'equal': True, 'shape_a': (8536570, 27), 'shape_b': (8536570, 27)},\n", " 'columns': {'different_columns': [],\n", " 'equal': True,\n", " 'only_in_a': [],\n", " 'only_in_b': []},\n", " 'dtypes': {'mismatched_columns': [], 'equal': True},\n", " 'auto_key': None,\n", - " 'row_compare': {'checked': True,\n", - " 'rows_in_a_not_in_b': 1122462,\n", - " 'rows_in_b_not_in_a': 1122462,\n", - " 'num_rows_different': 2244924,\n", - " 'total_rows_a': 1203121,\n", - " 'total_rows_b': 1203121,\n", - " 'note': 'multiset row comparison on common columns'},\n", - " 'same': False})" + " 'cell_compare': {'checked': True,\n", + " 'columns_with_differences': [],\n", + " 'total_cell_differences': 0,\n", + " 'rows_compared': 8536570,\n", + " 'note': 'aligned by index intersection'},\n", + " 'same': True})" ] }, - "execution_count": 101, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" } @@ -348,14 +346,14 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 117, "id": "de3363a0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(False,\n", + "(True,\n", " {'shape': {'equal': True, 'shape_a': (1203121, 43), 'shape_b': (1203121, 43)},\n", " 'columns': {'different_columns': [],\n", " 'equal': True,\n", @@ -364,14 +362,14 @@ " 'dtypes': {'mismatched_columns': [], 'equal': True},\n", " 'auto_key': None,\n", " 'cell_compare': {'checked': True,\n", - " 'columns_with_differences': ['answer_sequence'],\n", - " 'total_cell_differences': 1122462,\n", + " 'columns_with_differences': [],\n", + " 'total_cell_differences': 0,\n", " 'rows_compared': 1203121,\n", " 'note': 'aligned by index intersection'},\n", - " 'same': False})" + " 'same': True})" ] }, - "execution_count": 102, + "execution_count": 117, "metadata": {}, "output_type": "execute_result" } @@ -382,10 +380,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 118, "id": "81f6cde7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(True,\n", + " {'shape': {'equal': True, 'shape_a': (3487, 38), 'shape_b': (3487, 38)},\n", + " 'columns': {'different_columns': [],\n", + " 'equal': True,\n", + " 'only_in_a': [],\n", + " 'only_in_b': []},\n", + " 'dtypes': {'mismatched_columns': [], 'equal': True},\n", + " 'auto_key': None,\n", + " 'cell_compare': {'checked': True,\n", + " 'columns_with_differences': [],\n", + " 'total_cell_differences': 0,\n", + " 'rows_compared': 3487,\n", + " 'note': 'aligned by index intersection'},\n", + " 'same': True})" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "compare_parquet_files(df_questionnaire_kedro, df_questionnaire, check='cells')" ] From 60e368667e13fa29622b5943ee0f910ff87526cf Mon Sep 17 00:00:00 2001 From: VJausovec Date: Wed, 11 Feb 2026 14:51:24 +0000 Subject: [PATCH 05/70] feat: Refactor data ingestion pipeline to separate loading of paradata, questionnaires, and microdata; update catalog paths to include survey name --- rissk_kedro/conf/base/catalog.yml | 22 ++-- .../pipelines/data_ingestion/nodes.py | 122 ++++++++++++++++-- .../pipelines/data_ingestion/pipeline.py | 25 +++- rissk_kedro/src/rissk_kedro/settings.py | 1 + 4 files changed, 141 insertions(+), 29 deletions(-) diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index d0ca7df..033362f 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -3,50 +3,50 @@ # === PRIMARY (Ingested DataFrames) === paradata_interim: type: pandas.ParquetDataset - filepath: data/20_INTERIM/paradata.parquet + filepath: data/${survey.name}/latest/20_INTERIM/paradata.parquet raw_questionnaire: type: pandas.ParquetDataset - filepath: data/30_PROCESSED/questionnaire.parquet + filepath: data/${survey.name}/latest/30_PROCESSED/questionnaire.parquet raw_microdata: type: pandas.ParquetDataset - filepath: data/30_PROCESSED/microdata.parquet + filepath: data/${survey.name}/latest/30_PROCESSED/microdata.parquet # # === FEATURE PROCESSED === # paradata_processed: # type: pandas.ParquetDataset -# filepath: data/30_PROCESSED/paradata_processed.parquet +# filepath: data/${survey.name}/latest/30_PROCESSED/paradata_processed.parquet # paradata_active: # type: pandas.ParquetDataset -# filepath: data/30_PROCESSED/paradata_active.parquet +# filepath: data/${survey.name}/latest/30_PROCESSED/paradata_active.parquet # item_features: # type: pandas.ParquetDataset -# filepath: data/30_PROCESSED/item_features.parquet +# filepath: data/${survey.name}/latest/30_PROCESSED/item_features.parquet # unit_features: # type: pandas.ParquetDataset -# filepath: data/30_PROCESSED/unit_features.parquet +# filepath: data/${survey.name}/latest/30_PROCESSED/unit_features.parquet # unit_risk_scores_raw: # type: pandas.ParquetDataset -# filepath: data/30_PROCESSED/unit_risk_scores_raw.parquet +# filepath: data/${survey.name}/latest/30_PROCESSED/unit_risk_scores_raw.parquet # responsible_features: # type: pandas.ParquetDataset -# filepath: data/30_PROCESSED/responsible_features.parquet +# filepath: data/${survey.name}/latest/30_PROCESSED/responsible_features.parquet # # === MODEL OUTPUT === # unit_risk_scores: # type: pandas.CSVDataset -# filepath: data/40_OUTPUTS/unit_risk_scores.csv +# filepath: data/${survey.name}/latest/40_OUTPUTS/unit_risk_scores.csv # save_args: # index: false # unit_feature_scores: # type: pandas.CSVDataset -# filepath: data/40_OUTPUTS/unit_feature_scores.csv +# filepath: data/${survey.name}/latest/40_OUTPUTS/unit_feature_scores.csv # save_args: # index: false diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py index dfdcc83..e7c8949 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py @@ -3,7 +3,15 @@ import os import pandas as pd from loguru import logger -from rissk.utils.import_utils import get_zip_files, extract_zip, get_survey_info, get_dataframes +from rissk.utils.import_utils import ( + get_zip_files, + extract_zip, + get_survey_info, + get_questionnaire, + get_paradata, + get_microdata +) + def unzip_survey_data_node( survey_name: str, @@ -29,24 +37,110 @@ def unzip_survey_data_node( return extracted_paths -def load_survey_data_node(survey_paths: List[Path]) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: + +def load_paradata_node(survey_paths: List[Path]) -> pd.DataFrame: + """ + Loads paradata from extracted folders. + Independent node that generates its own questionnaire reference. + """ + logger.info(f"Processing paradata for {len(survey_paths)} paths") + survey_info = get_survey_info(survey_paths) + + dfs_paradata = [] + + for survey_questionnaire, questionnaires_details in survey_info.items(): + for questionnaires_version, file_paths in questionnaires_details.items(): + tabular_path = file_paths['Tabular'] + paradata_path = file_paths['Paradata'] + + try: + # We need the questionnaire map even for paradata processing + df_questionnaires = get_questionnaire(tabular_path) + df_paradata = get_paradata(paradata_path, df_questionnaires) + + dfs_paradata.append(df_paradata) + logger.info(f"Loaded paradata for {survey_questionnaire} v{questionnaires_version}") + except Exception as e: + logger.error(f"Failed to load paradata for {survey_questionnaire} v{questionnaires_version}. Skipping. Error: {str(e)}") + continue + + if not dfs_paradata: + return pd.DataFrame() + + combined_df = pd.concat(dfs_paradata) + combined_df.reset_index(drop=True, inplace=True) + + if 'answer_sequence' in combined_df.columns: + combined_df['answer_sequence'] = combined_df['answer_sequence'].apply(str) + + return combined_df + + +def load_questionnaire_node(survey_paths: List[Path]) -> pd.DataFrame: """ - Loads dataframes from extracted folders. - Wraps import_utils.get_dataframes which handles .dta/.tab logic. + Loads questionnaire metadata from extracted folders. """ - logger.info(f"Processing survey info for {len(survey_paths)} paths") + logger.info(f"Processing questionnaires for {len(survey_paths)} paths") survey_info = get_survey_info(survey_paths) - # Returns: paradata, questionnaire, microdata - dfs_para, dfs_qnr, dfs_micro = get_dataframes(survey_info) + dfs_questionnaires = [] + + for survey_questionnaire, questionnaires_details in survey_info.items(): + for questionnaires_version, file_paths in questionnaires_details.items(): + tabular_path = file_paths['Tabular'] + + try: + df_questionnaires = get_questionnaire(tabular_path) + dfs_questionnaires.append(df_questionnaires) + logger.info(f"Loaded questionnaire for {survey_questionnaire} v{questionnaires_version}") + except Exception as e: + logger.error(f"Failed to load questionnaire for {survey_questionnaire} v{questionnaires_version}. Skipping. Error: {str(e)}") + continue + + if not dfs_questionnaires: + return pd.DataFrame() + + combined_df = pd.concat(dfs_questionnaires) + combined_df.reset_index(drop=True, inplace=True) + + if 'answer_sequence' in combined_df.columns: + combined_df['answer_sequence'] = combined_df['answer_sequence'].apply(str) + + return combined_df + - if 'answer_sequence' in dfs_para.columns: - dfs_para['answer_sequence'] = dfs_para['answer_sequence'].apply(str) +def load_microdata_node(survey_paths: List[Path]) -> pd.DataFrame: + """ + Loads microdata (answers) from extracted folders. + Independent node that generates its own questionnaire reference. + """ + logger.info(f"Processing microdata for {len(survey_paths)} paths") + survey_info = get_survey_info(survey_paths) + + dfs_microdata = [] + + for survey_questionnaire, questionnaires_details in survey_info.items(): + for questionnaires_version, file_paths in questionnaires_details.items(): + tabular_path = file_paths['Tabular'] + + try: + # We need the questionnaire map for variable types and structure + df_questionnaires = get_questionnaire(tabular_path) + df_microdata = get_microdata(tabular_path, df_questionnaires) + + dfs_microdata.append(df_microdata) + logger.info(f"Loaded microdata for {survey_questionnaire} v{questionnaires_version}") + except Exception as e: + logger.error(f"Failed to load microdata for {survey_questionnaire} v{questionnaires_version}. Skipping. Error: {str(e)}") + continue - if 'answer_sequence' in dfs_qnr.columns: - dfs_qnr['answer_sequence'] = dfs_qnr['answer_sequence'].apply(str) + if not dfs_microdata: + return pd.DataFrame() - if 'answer_sequence' in dfs_micro.columns: - dfs_micro['answer_sequence'] = dfs_micro['answer_sequence'].apply(str) + combined_df = pd.concat(dfs_microdata) + combined_df.reset_index(drop=True, inplace=True) - return dfs_para, dfs_qnr, dfs_micro + if 'answer_sequence' in combined_df.columns: + combined_df['answer_sequence'] = combined_df['answer_sequence'].apply(str) + + return combined_df diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py index db1b78a..7a3588c 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py @@ -1,5 +1,10 @@ from kedro.pipeline import Pipeline, node, pipeline -from .nodes import unzip_survey_data_node, load_survey_data_node +from .nodes import ( + unzip_survey_data_node, + load_paradata_node, + load_questionnaire_node, + load_microdata_node +) def create_pipeline(**kwargs) -> Pipeline: return pipeline([ @@ -15,9 +20,21 @@ def create_pipeline(**kwargs) -> Pipeline: name="unzip_survey_data_node" ), node( - func=load_survey_data_node, + func=load_paradata_node, inputs="extracted_survey_paths", - outputs=["paradata_interim", "raw_questionnaire", "raw_microdata"], - name="load_survey_data_node" + outputs="paradata_interim", + name="load_paradata_node" + ), + node( + func=load_questionnaire_node, + inputs="extracted_survey_paths", + outputs="raw_questionnaire", + name="load_questionnaire_node" + ), + node( + func=load_microdata_node, + inputs="extracted_survey_paths", + outputs="raw_microdata", + name="load_microdata_node" ) ]) diff --git a/rissk_kedro/src/rissk_kedro/settings.py b/rissk_kedro/src/rissk_kedro/settings.py index d97a09e..8c64aab 100644 --- a/rissk_kedro/src/rissk_kedro/settings.py +++ b/rissk_kedro/src/rissk_kedro/settings.py @@ -29,6 +29,7 @@ CONFIG_LOADER_ARGS = { "base_env": "base", "default_run_env": "local", + "globals_pattern": "*parameters.yml", } # Class that manages Kedro's library components. From 3ab3b6d455393f744e9272309dfd4601433e16fc Mon Sep 17 00:00:00 2001 From: VJausovec Date: Wed, 11 Feb 2026 19:54:15 +0000 Subject: [PATCH 06/70] feat: Add data ingestion pipeline with extraction logic and update catalog paths for hies2024 survey --- data_read_tes.ipynb | 58 +++++++++++++++++++ rissk_kedro/conf/base/catalog.yml | 6 +- rissk_kedro/conf/base/parameters.yml | 8 +-- .../pipelines/data_ingestion/nodes.py | 57 ++++++++++++++++++ rissk_kedro/src/rissk_kedro/settings.py | 1 - .../src/rissk_kedro/test_ingestion.ipynb | 40 ++++++++----- 6 files changed, 147 insertions(+), 23 deletions(-) create mode 100644 data_read_tes.ipynb diff --git a/data_read_tes.ipynb b/data_read_tes.ipynb new file mode 100644 index 0000000..4deedb5 --- /dev/null +++ b/data_read_tes.ipynb @@ -0,0 +1,58 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "db65a927", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import os\n", + "from pathlib import Path" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "451a7f55", + "metadata": {}, + "outputs": [], + "source": [ + "root_path = os.getcwd()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c1e47644", + "metadata": {}, + "outputs": [], + "source": [ + "file_path = Path(root_path).joinpath('data', 'raw', 'slchbs_saintlucia_2025_6_STATA_All', 'slchbs_saintlucia_2025_6', 'slchbs_saintlucia_2025.dta')\n", + "df_test = pd.read_stata(file_path, convert_categoricals=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rissk_env_01", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index 033362f..9069c6a 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -3,15 +3,15 @@ # === PRIMARY (Ingested DataFrames) === paradata_interim: type: pandas.ParquetDataset - filepath: data/${survey.name}/latest/20_INTERIM/paradata.parquet + filepath: data/hies2024/latest/20_INTERIM/paradata.parquet raw_questionnaire: type: pandas.ParquetDataset - filepath: data/${survey.name}/latest/30_PROCESSED/questionnaire.parquet + filepath: data/hies2024/latest/30_PROCESSED/questionnaire.parquet raw_microdata: type: pandas.ParquetDataset - filepath: data/${survey.name}/latest/30_PROCESSED/microdata.parquet + filepath: data/hies2024/latest/30_PROCESSED/microdata.parquet # # === FEATURE PROCESSED === # paradata_processed: diff --git a/rissk_kedro/conf/base/parameters.yml b/rissk_kedro/conf/base/parameters.yml index e21ccee..a47c558 100644 --- a/rissk_kedro/conf/base/parameters.yml +++ b/rissk_kedro/conf/base/parameters.yml @@ -7,12 +7,12 @@ survey: - name: "slbhies_listing" VERSION: [6, 7] -# If set to null, the system will look for the 'PASSWORD' environment variable -zip_password: null - # Ingestion Configuration ingestion: - raw_data_path: "data/10_RAW" + raw_data_path: "data/hies2024/latest/10_RAW" + +# If set to null, the system will look for the 'PASSWORD' environment variable +zip_password: null # Processing Parameters processing: diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py index e7c8949..fb6b5e9 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py @@ -37,6 +37,63 @@ def unzip_survey_data_node( return extracted_paths +## If I want to add the fallback logic for existing folders, I can modify the above function like this: +# ...existing code... +def unzip_survey_data_node( + survey_name: str, + raw_path_str: str, + questionnaires: List[Dict], + zip_password: str +) -> List[Path]: + """ + Finds and extracts zips. Returns list of extracted project paths. + If zips are missing but folders exist, returns those folders. + Wraps import_utils.extract_zip. + """ + raw_path = Path(raw_path_str) + + logger.info(f"Looking for data in {raw_path} for {survey_name}") + + # 1. Try to find zips + zip_files = get_zip_files(raw_path, survey_name, questionnaires) + + extracted_paths = [] + + if zip_files: + logger.info(f"Found {len(zip_files)} zip files to extract.") + for zip_file in zip_files: + project_path = zip_file.with_suffix('') + extracted_paths.append(project_path) + # Extract using the password argument + extract_zip(zip_file, project_path, password=zip_password) + else: + # 2. If no zips, look for existing directories matching the naming convention + logger.info("No zip files found. Looking for existing unzipped folders.") + import re + + for questionnaire in questionnaires: + name = questionnaire.get('name') + versions = questionnaire.get('VERSION', []) + version_pattern = "|".join(map(str, versions)) + # Matches folder names like: questionnaire_version_... + # Note: The regex mimics get_zip_files but without .zip extension + pattern = re.compile(rf"{name}_({version_pattern})_.*") + + matching_dirs = [ + d for d in raw_path.iterdir() + if d.is_dir() and pattern.match(d.name) + ] + extracted_paths.extend(matching_dirs) + + if extracted_paths: + logger.info(f"Found {len(extracted_paths)} existing unzipped folders.") + else: + logger.warning(f"No zip files or matching folders found in {raw_path}") + + return extracted_paths +# ...existing code... + + def load_paradata_node(survey_paths: List[Path]) -> pd.DataFrame: """ diff --git a/rissk_kedro/src/rissk_kedro/settings.py b/rissk_kedro/src/rissk_kedro/settings.py index 8c64aab..d97a09e 100644 --- a/rissk_kedro/src/rissk_kedro/settings.py +++ b/rissk_kedro/src/rissk_kedro/settings.py @@ -29,7 +29,6 @@ CONFIG_LOADER_ARGS = { "base_env": "base", "default_run_env": "local", - "globals_pattern": "*parameters.yml", } # Class that manages Kedro's library components. diff --git a/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb b/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb index 0831c15..f370fbd 100644 --- a/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb +++ b/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 9, "id": "607ef013", "metadata": {}, "outputs": [], @@ -22,7 +22,17 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 10, + "id": "a9c7966e", + "metadata": {}, + "outputs": [], + "source": [ + "SURVEY = \"hies2024\"" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "id": "dc4569ea", "metadata": {}, "outputs": [], @@ -35,20 +45,20 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 12, "id": "3d3ef86a", "metadata": {}, "outputs": [], "source": [ "# Kedro pipeline outputs\n", - "df_para_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", \"20_INTERIM\", \"paradata.parquet\"))\n", - "df_questionnaire_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", \"30_PROCESSED\", \"questionnaire.parquet\"))\n", - "df_microdata_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", \"30_PROCESSED\", \"microdata.parquet\"))" + "df_para_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"20_INTERIM\", \"paradata.parquet\"))\n", + "df_questionnaire_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"questionnaire.parquet\"))\n", + "df_microdata_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"microdata.parquet\"))" ] }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 13, "id": "72fdc596", "metadata": {}, "outputs": [], @@ -290,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 14, "id": "63867817", "metadata": {}, "outputs": [ @@ -300,7 +310,7 @@ "(1203121, 43)" ] }, - "execution_count": 115, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -312,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 15, "id": "792a94d3", "metadata": {}, "outputs": [ @@ -335,7 +345,7 @@ " 'same': True})" ] }, - "execution_count": 116, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -346,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 16, "id": "de3363a0", "metadata": {}, "outputs": [ @@ -369,7 +379,7 @@ " 'same': True})" ] }, - "execution_count": 117, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -380,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 17, "id": "81f6cde7", "metadata": {}, "outputs": [ @@ -403,7 +413,7 @@ " 'same': True})" ] }, - "execution_count": 118, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } From fed91057142a76ba984f5547d8320ed77ee50e9c Mon Sep 17 00:00:00 2001 From: VJausovec Date: Wed, 11 Feb 2026 20:39:43 +0000 Subject: [PATCH 07/70] refactor: Simplify FeatureProcessing constructor by removing survey_info parameter --- rissk/feature_processing.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/rissk/feature_processing.py b/rissk/feature_processing.py index 64c350c..5f5536a 100644 --- a/rissk/feature_processing.py +++ b/rissk/feature_processing.py @@ -4,9 +4,8 @@ class FeatureProcessing(object): - def __init__(self, survey_info, config): - - #self.config = config = {k:v for k,v in config.items()} + def __init__(self, config): + self.config = config self._reload = self.config['environment']['reload'] self._save_to_disk = self.config['environment']['save_to_disk'] From 8ca3de3835bf228aaa9839754af8bdb5a0a71517 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Thu, 12 Feb 2026 13:30:26 +0000 Subject: [PATCH 08/70] return initial folders to catalogue --- configuration/main.yaml | 4 ++-- rissk_kedro/conf/base/catalog.yml | 8 ++++++++ .../src/rissk_kedro/pipelines/data_ingestion/pipeline.py | 2 +- 3 files changed, 11 insertions(+), 3 deletions(-) diff --git a/configuration/main.yaml b/configuration/main.yaml index c8851ac..5e0729c 100644 --- a/configuration/main.yaml +++ b/configuration/main.yaml @@ -6,7 +6,7 @@ defaults: export_path: . output_file: results/unit_risk_score.csv -feature_score: false +feature_score: true surveys: [ifad_tunesia] survey_version: 'EndlineFINALV106_1' @@ -80,7 +80,7 @@ features: parameters: contamination: 0.11 total_elapse: - use: true + use: false parameters: contamination: 0.11 single_question: diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index 9069c6a..e98a334 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -1,5 +1,13 @@ # Here you can define all your data sets by using simple YAML syntax. # +# === INTERMEDIATE (Extracted Files) === +extracted_survey_data: + type: PartitionedDataset + path: data/hies2024/latest/10_RAW + dataset: + type: kedro.extras.datasets.text.TextDataset + filename_suffix: "" + # === PRIMARY (Ingested DataFrames) === paradata_interim: type: pandas.ParquetDataset diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py index 7a3588c..a18f413 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py @@ -5,7 +5,7 @@ load_questionnaire_node, load_microdata_node ) - +# catalog for path def create_pipeline(**kwargs) -> Pipeline: return pipeline([ node( From 7dda8cc83a642ba435ffb19e281c1bb93428cae3 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Thu, 12 Feb 2026 21:11:30 +0000 Subject: [PATCH 09/70] refactoring to unzip before finding file_paths --- rissk/import_utils_kedro.py | 65 ++++++++ rissk_kedro/conf/base/catalog.yml | 12 +- .../pipelines/data_ingestion/nodes.py | 154 ++++++------------ .../pipelines/data_ingestion/pipeline.py | 26 ++- rissk_kedro/src/rissk_kedro/settings.py | 13 +- 5 files changed, 153 insertions(+), 117 deletions(-) create mode 100644 rissk/import_utils_kedro.py diff --git a/rissk/import_utils_kedro.py b/rissk/import_utils_kedro.py new file mode 100644 index 0000000..baf42c9 --- /dev/null +++ b/rissk/import_utils_kedro.py @@ -0,0 +1,65 @@ +from pathlib import Path +from typing import List +import re + +from loguru import logger + +from rissk.utils.import_utils import extract_zip + + +def extract_all_zip_files(raw_path: Path, zip_password: str = None) -> None: + """ + Extract all zip files found at the top level of ``raw_path``. + + - Keeps naming convention: folder name = zip filename without ``.zip``. + - Delegates nested-zip handling to ``extract_zip``. + - Procedural utility; returns no value. + """ + if not raw_path.exists(): + logger.warning(f"Raw path does not exist: {raw_path}") + return + + zip_files = [ + file_path + for file_path in raw_path.iterdir() + if file_path.is_file() and file_path.suffix.lower() == ".zip" + ] + + logger.info(f"Found {len(zip_files)} zip files in {raw_path}") + + for zip_file in zip_files: + destination = zip_file.with_suffix("") + extract_zip(zip_file, destination, password=zip_password) + + +def filter_matching_folders(raw_path: Path, questionnaires: List[dict]) -> List[Path]: + # This is a folder only version of the filter_matching_zip_files function, which is used + # to find matching folders after extraction. The logic is the same, but it targets folders + # instead of zip files. + """ + Return folder paths in ``raw_path`` that match questionnaire/version patterns. + + Matching logic mirrors legacy ``get_zip_files`` naming rules, but targets + extracted folders to support scenarios where zip files are unavailable. + """ + if not raw_path.exists(): + logger.warning(f"Raw path does not exist: {raw_path}") + return [] + + matching_folders: List[Path] = [] + + for questionnaire in questionnaires: + name = questionnaire.get("name") + versions = questionnaire.get("VERSION", []) + + version_pattern = "|".join(map(str, versions)) + pattern = re.compile(rf"{name}_({version_pattern})_.*") + + matching_folders.extend( + path + for path in raw_path.iterdir() + if path.is_dir() and pattern.match(path.name) + ) + + logger.info(f"Filtered {len(matching_folders)} matching folders from {raw_path}") + return matching_folders \ No newline at end of file diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index e98a334..e7ce4e1 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -1,12 +1,12 @@ # Here you can define all your data sets by using simple YAML syntax. # # === INTERMEDIATE (Extracted Files) === -extracted_survey_data: - type: PartitionedDataset - path: data/hies2024/latest/10_RAW - dataset: - type: kedro.extras.datasets.text.TextDataset - filename_suffix: "" +# extracted_survey_data: +# type: PartitionedDataset +# path: data/hies2024/latest/10_RAW +# dataset: +# type: kedro.extras.datasets.text.TextDataset +# filename_suffix: "" # === PRIMARY (Ingested DataFrames) === paradata_interim: diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py index fb6b5e9..a3b1d7b 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py @@ -1,114 +1,52 @@ -from typing import Dict, List, Tuple +from typing import Dict, List from pathlib import Path -import os import pandas as pd from loguru import logger -from rissk.utils.import_utils import ( - get_zip_files, - extract_zip, - get_survey_info, - get_questionnaire, - get_paradata, - get_microdata -) - - -def unzip_survey_data_node( - survey_name: str, - raw_path_str: str, - questionnaires: List[Dict], - zip_password: str -) -> List[Path]: +from rissk.import_utils_kedro import extract_all_zip_files, filter_matching_folders +from rissk.utils.import_utils import get_survey_info, get_questionnaire, get_paradata, get_microdata + + +def extract_zip_files_node(raw_path_str: str, zip_password: str) -> None: """ - Finds and extracts zips. Returns list of extracted project paths. - Wraps import_utils.extract_zip. + Extract all top-level zip files in the raw data path. + Procedural node: extraction side-effect only. """ raw_path = Path(raw_path_str) - - logger.info(f"Looking for zips in {raw_path} for {survey_name}") - zip_files = get_zip_files(raw_path, survey_name, questionnaires) - - extracted_paths = [] - for zip_file in zip_files: - project_path = zip_file.with_suffix('') - extracted_paths.append(project_path) - # Extract using the password argument - extract_zip(zip_file, project_path, password=zip_password) - - return extracted_paths - -## If I want to add the fallback logic for existing folders, I can modify the above function like this: -# ...existing code... -def unzip_survey_data_node( - survey_name: str, - raw_path_str: str, - questionnaires: List[Dict], - zip_password: str -) -> List[Path]: + logger.info(f"Extracting zip files from {raw_path}") + extract_all_zip_files(raw_path, zip_password=zip_password) + + +def filter_extracted_survey_paths_node(raw_path_str: str, questionnaires: List[Dict]) -> List[Path]: """ - Finds and extracts zips. Returns list of extracted project paths. - If zips are missing but folders exist, returns those folders. - Wraps import_utils.extract_zip. + Return extracted folder paths matching questionnaire/version patterns. + This node does not perform extraction. """ raw_path = Path(raw_path_str) - - logger.info(f"Looking for data in {raw_path} for {survey_name}") - - # 1. Try to find zips - zip_files = get_zip_files(raw_path, survey_name, questionnaires) - - extracted_paths = [] - - if zip_files: - logger.info(f"Found {len(zip_files)} zip files to extract.") - for zip_file in zip_files: - project_path = zip_file.with_suffix('') - extracted_paths.append(project_path) - # Extract using the password argument - extract_zip(zip_file, project_path, password=zip_password) - else: - # 2. If no zips, look for existing directories matching the naming convention - logger.info("No zip files found. Looking for existing unzipped folders.") - import re - - for questionnaire in questionnaires: - name = questionnaire.get('name') - versions = questionnaire.get('VERSION', []) - version_pattern = "|".join(map(str, versions)) - # Matches folder names like: questionnaire_version_... - # Note: The regex mimics get_zip_files but without .zip extension - pattern = re.compile(rf"{name}_({version_pattern})_.*") - - matching_dirs = [ - d for d in raw_path.iterdir() - if d.is_dir() and pattern.match(d.name) - ] - extracted_paths.extend(matching_dirs) - - if extracted_paths: - logger.info(f"Found {len(extracted_paths)} existing unzipped folders.") - else: - logger.warning(f"No zip files or matching folders found in {raw_path}") - - return extracted_paths -# ...existing code... - - - -def load_paradata_node(survey_paths: List[Path]) -> pd.DataFrame: + logger.info(f"Collecting matching survey folders from {raw_path}") + return filter_matching_folders(raw_path, questionnaires) + + +def load_paradata_node(file_paths: List[Path]) -> pd.DataFrame: """ Loads paradata from extracted folders. Independent node that generates its own questionnaire reference. """ - logger.info(f"Processing paradata for {len(survey_paths)} paths") - survey_info = get_survey_info(survey_paths) + logger.info(f"Processing paradata for {len(file_paths)} paths") + survey_info = get_survey_info(file_paths) dfs_paradata = [] for survey_questionnaire, questionnaires_details in survey_info.items(): for questionnaires_version, file_paths in questionnaires_details.items(): - tabular_path = file_paths['Tabular'] - paradata_path = file_paths['Paradata'] + tabular_path = file_paths.get('Tabular') + paradata_path = file_paths.get('Paradata') + + if not tabular_path or not paradata_path: + logger.warning( + f"Skipping paradata load for {survey_questionnaire} v{questionnaires_version}: " + f"missing required exports (Tabular={bool(tabular_path)}, Paradata={bool(paradata_path)})" + ) + continue try: # We need the questionnaire map even for paradata processing @@ -133,18 +71,25 @@ def load_paradata_node(survey_paths: List[Path]) -> pd.DataFrame: return combined_df -def load_questionnaire_node(survey_paths: List[Path]) -> pd.DataFrame: +def load_questionnaire_node(file_paths: List[Path]) -> pd.DataFrame: """ Loads questionnaire metadata from extracted folders. """ - logger.info(f"Processing questionnaires for {len(survey_paths)} paths") - survey_info = get_survey_info(survey_paths) + logger.info(f"Processing questionnaires for {len(file_paths)} paths") + survey_info = get_survey_info(file_paths) dfs_questionnaires = [] for survey_questionnaire, questionnaires_details in survey_info.items(): for questionnaires_version, file_paths in questionnaires_details.items(): - tabular_path = file_paths['Tabular'] + tabular_path = file_paths.get('Tabular') + + if not tabular_path: + logger.warning( + f"Skipping questionnaire load for {survey_questionnaire} v{questionnaires_version}: " + "missing Tabular export" + ) + continue try: df_questionnaires = get_questionnaire(tabular_path) @@ -166,19 +111,26 @@ def load_questionnaire_node(survey_paths: List[Path]) -> pd.DataFrame: return combined_df -def load_microdata_node(survey_paths: List[Path]) -> pd.DataFrame: +def load_microdata_node(file_paths: List[Path]) -> pd.DataFrame: """ Loads microdata (answers) from extracted folders. Independent node that generates its own questionnaire reference. """ - logger.info(f"Processing microdata for {len(survey_paths)} paths") - survey_info = get_survey_info(survey_paths) + logger.info(f"Processing microdata for {len(file_paths)} paths") + survey_info = get_survey_info(file_paths) dfs_microdata = [] for survey_questionnaire, questionnaires_details in survey_info.items(): for questionnaires_version, file_paths in questionnaires_details.items(): - tabular_path = file_paths['Tabular'] + tabular_path = file_paths.get('Tabular') + + if not tabular_path: + logger.warning( + f"Skipping microdata load for {survey_questionnaire} v{questionnaires_version}: " + "missing Tabular export" + ) + continue try: # We need the questionnaire map for variable types and structure diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py index a18f413..852441d 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py @@ -1,6 +1,7 @@ from kedro.pipeline import Pipeline, node, pipeline from .nodes import ( - unzip_survey_data_node, + extract_zip_files_node, + filter_extracted_survey_paths_node, load_paradata_node, load_questionnaire_node, load_microdata_node @@ -9,31 +10,38 @@ def create_pipeline(**kwargs) -> Pipeline: return pipeline([ node( - func=unzip_survey_data_node, + func=extract_zip_files_node, inputs=[ - "params:survey.name", "params:ingestion.raw_data_path", - "params:survey.questionnaires", "params:zip_password" ], - outputs="extracted_survey_paths", - name="unzip_survey_data_node" + outputs=None, + name="extract_zip_files_node" + ), + node( + func=filter_extracted_survey_paths_node, + inputs=[ + "params:ingestion.raw_data_path", + "params:survey.questionnaires", + ], + outputs="file_paths", + name="filter_extracted_survey_paths_node" ), node( func=load_paradata_node, - inputs="extracted_survey_paths", + inputs="file_paths", outputs="paradata_interim", name="load_paradata_node" ), node( func=load_questionnaire_node, - inputs="extracted_survey_paths", + inputs="file_paths", outputs="raw_questionnaire", name="load_questionnaire_node" ), node( func=load_microdata_node, - inputs="extracted_survey_paths", + inputs="file_paths", outputs="raw_microdata", name="load_microdata_node" ) diff --git a/rissk_kedro/src/rissk_kedro/settings.py b/rissk_kedro/src/rissk_kedro/settings.py index d97a09e..ed10cf4 100644 --- a/rissk_kedro/src/rissk_kedro/settings.py +++ b/rissk_kedro/src/rissk_kedro/settings.py @@ -8,7 +8,7 @@ HOOKS = () # Installed plugins for which to disable hook auto-registration. -# DISABLE_HOOKS_FOR_PLUGINS = ("kedro-viz",) +DISABLE_HOOKS_FOR_PLUGINS = ("kedro-viz",) # Class that manages storing KedroSession data. # from kedro.framework.session.store import BaseSessionStore @@ -24,6 +24,17 @@ # Class that manages how configuration is loaded. from kedro.config import OmegaConfigLoader # noqa: E402 +try: # noqa: E402 + from kedro_viz.integrations.kedro import hooks as kedro_viz_hooks + + if ( + hasattr(kedro_viz_hooks, "dataset_stats_hook") + and not hasattr(kedro_viz_hooks.dataset_stats_hook, "datasets") + ): + kedro_viz_hooks.dataset_stats_hook.datasets = {} +except Exception: + pass + CONFIG_LOADER_CLASS = OmegaConfigLoader # Keyword arguments to pass to the `CONFIG_LOADER_CLASS` constructor. CONFIG_LOADER_ARGS = { From 9fc9e33d96af41925ec58fc60f0be9638cc7baa3 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Fri, 13 Feb 2026 10:22:15 +0000 Subject: [PATCH 10/70] Refactor feature engineering pipeline and update test ingestion notebook - Renamed nodes in the feature engineering pipeline for clarity: - `process_paradata_timestamps` to `process_paradata_node` - `filter_active_events` to `filter_active_paradata_node` - Updated inputs and outputs in the pipeline nodes to reflect changes in data structure. - Modified test ingestion notebook to adjust execution counts and outputs for consistency. - Enhanced output comparison in the notebook to reflect changes in data structure and ensure accurate testing. --- rissk_kedro/conf/base/catalog.yml | 28 +- .../conf/base/ingestion_data_sources.md | 76 ++ .../pipelines/feature_engineering/nodes.py | 156 +++- .../pipelines/feature_engineering/pipeline.py | 16 +- .../src/rissk_kedro/test_ingestion.ipynb | 847 +++++++++++++++++- 5 files changed, 1029 insertions(+), 94 deletions(-) create mode 100644 rissk_kedro/conf/base/ingestion_data_sources.md diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index e7ce4e1..772544e 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -1,6 +1,7 @@ # Here you can define all your data sets by using simple YAML syntax. # # === INTERMEDIATE (Extracted Files) === +# This is no better than parametrized raw path. We need to think of a better way if we want catalogue # extracted_survey_data: # type: PartitionedDataset # path: data/hies2024/latest/10_RAW @@ -21,40 +22,41 @@ raw_microdata: type: pandas.ParquetDataset filepath: data/hies2024/latest/30_PROCESSED/microdata.parquet -# # === FEATURE PROCESSED === -# paradata_processed: -# type: pandas.ParquetDataset -# filepath: data/${survey.name}/latest/30_PROCESSED/paradata_processed.parquet +paradata_processed: + type: pandas.ParquetDataset + filepath: data/hies2024/latest//30_PROCESSED/paradata_processed.parquet -# paradata_active: -# type: pandas.ParquetDataset -# filepath: data/${survey.name}/latest/30_PROCESSED/paradata_active.parquet +paradata_active: + type: pandas.ParquetDataset + filepath: data/hies2024/latest/30_PROCESSED/paradata_active.parquet + +# === FEATURE PROCESSED === # item_features: # type: pandas.ParquetDataset -# filepath: data/${survey.name}/latest/30_PROCESSED/item_features.parquet +# filepath: data/hies2024/latest/30_PROCESSED/item_features.parquet # unit_features: # type: pandas.ParquetDataset -# filepath: data/${survey.name}/latest/30_PROCESSED/unit_features.parquet +# filepath: data/hies2024/latest/30_PROCESSED/unit_features.parquet # unit_risk_scores_raw: # type: pandas.ParquetDataset -# filepath: data/${survey.name}/latest/30_PROCESSED/unit_risk_scores_raw.parquet +# filepath: data/hies2024/latest/30_PROCESSED/unit_risk_scores_raw.parquet # responsible_features: # type: pandas.ParquetDataset -# filepath: data/${survey.name}/latest/30_PROCESSED/responsible_features.parquet +# filepath: data/hies2024/latest/30_PROCESSED/responsible_features.parquet # # === MODEL OUTPUT === # unit_risk_scores: # type: pandas.CSVDataset -# filepath: data/${survey.name}/latest/40_OUTPUTS/unit_risk_scores.csv +# filepath: data/hies2024/latest/40_OUTPUTS/unit_risk_scores.csv # save_args: # index: false # unit_feature_scores: # type: pandas.CSVDataset -# filepath: data/${survey.name}/latest/40_OUTPUTS/unit_feature_scores.csv +# filepath: data/hies2024/latest/40_OUTPUTS/unit_feature_scores.csv # save_args: # index: false diff --git a/rissk_kedro/conf/base/ingestion_data_sources.md b/rissk_kedro/conf/base/ingestion_data_sources.md new file mode 100644 index 0000000..3b88446 --- /dev/null +++ b/rissk_kedro/conf/base/ingestion_data_sources.md @@ -0,0 +1,76 @@ +## PROMPT: DATA FLOW FOR INGESTION +Please analyze the attached files—specifically the catalog.yml and pipeline logic—to trace the lineage of paradata_interim, raw_questionnaire, and raw_microdata. Provide a precise list of the source file paths and file types if they can be inferred from catalogue or functions that process data. If a dataset is an output of a node, please identify the 'upstream' raw files that feed into that node. + + +Based on the provided files, here is the lineage trace for the three datasets. + +### 1. `paradata_interim` +* **Pipeline Node**: `load_paradata_node` (in nodes.py) +* **Catalog Entry**: paradata.parquet +* **Upstream Inputs**: + * **Source**: Extracted folders located in 10_RAW (defined in parameters.yml). + * **Specific Files Read**: + 1. `paradata.tab` (Parsed by `read_paradata` in import_utils.py) inside a folder ending in `_Paradata_All`. + 2. `Questionnaire/content/document.json` (Parsed by `read_json_questionnaire` for metadata) inside a folder ending in `_Tabular_All` (as a dependency). + +### 2. `raw_questionnaire` +* **Pipeline Node**: `load_questionnaire_node` (in nodes.py) +* **Catalog Entry**: questionnaire.parquet +* **Upstream Inputs**: + * **Source**: Extracted folders located in 10_RAW. + * **Specific Files Read**: + 1. `Questionnaire/content/document.json` (Main structure). + 2. `Questionnaire/content/Categories/*.xlsx` (Category metadata). + * Both are found inside folders ending in `_Tabular_All` . + +### 3. `raw_microdata` +* **Pipeline Node**: `load_microdata_node` (in nodes.py) +* **Catalog Entry**: microdata.parquet +* **Upstream Inputs**: + * **Source**: Extracted folders located in 10_RAW. + * **Specific Files Read**: + 1. **Data files**: All `.dta` (Stata) or `.tab` (TSV) files in the folder, excluding specific prefixes like `interview__` or `assignment__`. + 2. **Metadata**: `Questionnaire/content/document.json` (used to map variable types/questions). + * All found inside folders ending in `_Tabular_All`. + +### Summary of Dependencies +All three datasets originate from the **raw extracted folders** generated by `extract_zip_files_node` into 10_RAW. + +| Dataset | Primary Source File | Helper Source File | Folder Type | +| :--- | :--- | :--- | :--- | +| `paradata_interim` | `paradata.tab` | `document.json` | `_Paradata_All` (primary), `_Tabular_All` (helper) | +| `raw_questionnaire` | `document.json` | `Categories/*.xlsx` | `_Tabular_All` | +| `raw_microdata` | `*.dta` / `*.tab` | `document.json` | `_Tabular_All` | + + +### PROMPT: DEPENDENCE OF PARADATA AND MICRODATA ON `document.json` +Having looked at the data ingestion in detail can you ascertain if any information could be extracted for paradata_interim and raw_microdata if `Questionnaire/content/document.json` was missing? + + +Based on the code analysis, the short answer is **No, not with the current implementation.** + +Both `paradata_interim` and `raw_microdata` are strictly dependent on `Questionnaire/content/document.json`. If that file is missing, the ingestion logic explicitly fails or skips processing. + +Here is the detailed breakdown of why: + +### 1. `paradata_interim` (Paradata) +* **Dependency:** in `load_paradata_node`, the code does this: + ```python + df_questionnaires = get_questionnaire(tabular_path) + df_paradata = get_paradata(paradata_path, df_questionnaires) + ``` +* **Why it fails:** + * `get_questionnaire` loads the `document.json`. + * `get_paradata` requires `df_questionnaires` as a second argument. + * Inside `get_paradata`, the code uses the questionnaire dataframe to map variable names (e.g., `variable_name` matching). + * Without `document.json`, there is no `df_questionnaires`, and thus `get_paradata` cannot execute. + +### 2. `raw_microdata` (Microdata) +* **Dependency:** In `load_microdata_node` (and underlying `get_microdata`), the logic relies on the questionnaire structure to know *which* columns are valid and how to interpret them. +* **Why it fails:** + * The system uses `document.json` to build a schema of expected variables (the "questionnaire map"). + * It uses this map to normalize column names and types across the `.tab` or `.dta` files. + * If `document.json` is missing, the function that builds this map (`get_questionnaire`) fails, stopping the entire microdata loading process. + +### Conclusion +The `document.json` file is acting as the **schema definition** for the entire pipeline. Without it, the code treats the raw data (`.tab` / `.dta`) as uninterpretable blobs and will not permit ingestion. \ No newline at end of file diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py index 91466d2..4c2cfd3 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py @@ -5,29 +5,29 @@ from loguru import logger -def process_paradata_timestamps( - paradata_raw: pd.DataFrame +def process_paradata_node( + paradata_interim: pd.DataFrame, + parameters: Dict ) -> pd.DataFrame: """ - Process paradata timestamps and add hour features. - - This replicates logic from pipelines/feature_engineering/10_process_paradata.py + Process paradata timestamps, flags, and index creation. Args: - paradata_raw: Raw paradata DataFrame + paradata_interim: Interim paradata DataFrame + parameters: Pipeline parameters Returns: - Processed paradata with timestamp features + Processed paradata DataFrame """ - paradata = paradata_raw.copy() + paradata = paradata_interim.copy() - # Add answer hour feature (from 10_process_paradata.py line 29) + # Calculate f__answer_hour_set paradata['f__answer_hour_set'] = ( paradata['timestamp_local'].dt.hour + paradata['timestamp_local'].dt.round('30min').dt.minute / 60 ) - # Mark interviewing events (before Supervisor/HQ interaction) + # Calculate interviewing flag events_split = ['RejectedBySupervisor', 'OpenedBySupervisor', 'OpenedByHQ', 'RejectedByHQ'] paradata['flag'] = paradata['event'].isin(events_split) @@ -35,46 +35,81 @@ def process_paradata_timestamps( paradata['cumulative_flag'] = paradata.groupby('interview__id')['flag'].cumsum() paradata['interviewing'] = np.where(paradata['cumulative_flag'] > 0, False, True) - logger.info(f"Processed {len(paradata)} paradata records with timestamp features") + # Filter interviewing == True AND role == 1 + paradata.drop(['flag', 'cumulative_flag'], axis=1, inplace=True) + paradata = paradata[(paradata['interviewing'] == True) & (paradata['role'] == 1)].copy() + + # Implement make_index_col logic (concat ID parts) + # Using '_' separator to match previous notebook logic + def make_index_col(df): + mask = (~df[['interview__id', 'variable_name', 'roster_level']].isnull()) & \ + (df[['interview__id', 'variable_name', 'roster_level']] != '') + filtered_df = df.where(mask, '') + df['index_col'] = ( + filtered_df['interview__id'].astype(str) + "_" + + filtered_df['variable_name'].astype(str) + "_" + + filtered_df['roster_level'].astype(str) + ) + df['index_col'] = df['index_col'].str.strip('_') + return df + + paradata = make_index_col(paradata) + + # Sort by interview__id, order + paradata.sort_values(['interview__id', 'order'], inplace=True) + + # Limit Unit Logic + limit_unit = parameters.get('processing', {}).get('limit_unit') + if limit_unit is not None: + consent_variable = next(iter(limit_unit)) + consent_value = str(limit_unit[consent_variable]) + + cond1 = (paradata['variable_name'] == consent_variable) + cond2 = (paradata['answer'] == consent_value) + + filtered_interview_id = paradata[cond1 & cond2]['interview__id'].unique() + paradata = paradata[paradata['interview__id'].isin(filtered_interview_id)].copy() return paradata -def filter_active_events( +def filter_active_paradata_node( paradata_processed: pd.DataFrame, parameters: Dict ) -> pd.DataFrame: """ - Filter paradata to active interviewer events. - - Replicates logic from pipelines/feature_engineering/11_process_paradata_active.py + Filter paradata to active events. Args: - paradata_processed: Processed paradata - parameters: Config parameters (for limit_unit) + paradata_processed: Processed paradata DataFrame + parameters: Pipeline parameters Returns: - DataFrame with only active interviewer events + Active paradata DataFrame """ active_events = [ 'InterviewCreated', 'AnswerSet', 'Resumed', 'AnswerRemoved', 'CommentSet', 'Restarted' ] - # Filter to active events + # Filter conditions active_mask = ( - paradata_processed['event'].isin(active_events) & - paradata_processed['interviewing'] + (paradata_processed['event'].isin(active_events)) & + (paradata_processed['question_scope'].isin([0, ''])) & + (paradata_processed['role'] == 1) ) - # Apply limit_unit filter if specified - limit_unit = parameters.get('processing', {}).get('limit_unit') - if limit_unit is not None: - active_mask = active_mask & (paradata_processed['interview__id'].isin(limit_unit)) + vars_needed = [ + 'interview__id', 'order', 'event', 'responsible', 'role', 'tz_offset', + 'param', 'answer', 'roster_level', 'timestamp_local', 'variable_name', + 'question_sequence', 'question_scope', "qtype", 'question_type', + 'qnr', 'qnr_version', 'interviewing', 'yes_no_view', 'index_col', 'f__answer_hour_set' + ] - df_para_active = paradata_processed[active_mask].copy() + # Only keep columns present in the dataframe + vars_needed = [col for col in vars_needed if col in paradata_processed.columns] - logger.info(f"Filtered to {len(df_para_active)} active events") + df_para_active = paradata_processed.loc[active_mask, vars_needed].copy() return df_para_active @@ -88,8 +123,6 @@ def build_item_features( """ Build item-level features from microdata and paradata. - Uses logic from pipelines/feature_engineering/12_process_items.py - Args: microdata_raw: Raw microdata paradata_active: Active paradata events @@ -102,39 +135,55 @@ def build_item_features( logger.info("Building item-level features") # Create index column for joining + # Updated separator to '_' to match process_paradata_node def make_index_col(df): mask = (~df[['interview__id', 'variable_name', 'roster_level']].isnull()) & \ (df[['interview__id', 'variable_name', 'roster_level']] != '') filtered_df = df.where(mask, '') df['index_col'] = ( - filtered_df['interview__id'].astype(str) + '__' + - filtered_df['variable_name'].astype(str) + '__' + + filtered_df['interview__id'].astype(str) + '_' + + filtered_df['variable_name'].astype(str) + '_' + filtered_df['roster_level'].astype(str) ) + df['index_col'] = df['index_col'].str.strip('_') return df + if microdata_raw.empty: + logger.warning("Microdata is empty") + return pd.DataFrame() + microdata = make_index_col(microdata_raw.copy()) # Select relevant columns item_level_columns = ['interview__id', 'variable_name', 'roster_level'] - df_item = microdata[['value', "qtype", 'is_integer', 'qnr_seq', - 'n_answers', 'answer_sequence', - 'cascade_from_question_id', 'is_filtered_combobox', - 'index_col'] + item_level_columns].copy() + + # Identify available columns from the desired list + desired_cols = ['value', "qtype", 'is_integer', 'qnr_seq', + 'n_answers', 'answer_sequence', + 'cascade_from_question_id', 'is_filtered_combobox', + 'index_col'] + item_level_columns + + available_cols = [c for c in desired_cols if c in microdata.columns] + + df_item = microdata[available_cols].copy() # Merge with active paradata paradata_columns = ['responsible', 'f__answer_hour_set', 'interviewing', 'tz_offset'] answer_set_mask = (paradata_active['event'] == 'AnswerSet') data = paradata_active[answer_set_mask].drop_duplicates(subset='index_col', keep='last') + # Filter paradata columns to those present in data + available_para_cols = [col for col in paradata_columns if col in data.columns] + df_item = df_item.merge( - data[paradata_columns + ['index_col']], + data[available_para_cols + ['index_col']], how='left', on='index_col' ) - # Keep only interviewing events - df_item = df_item[df_item['interviewing'] == True] + # Keep only interviewing events if column exists + if 'interviewing' in df_item.columns: + df_item = df_item[df_item['interviewing'] == True] logger.info(f"Built {len(df_item)} item feature records") @@ -148,8 +197,6 @@ def build_unit_features( """ Build unit-level (interview-level) features. - Uses logic from rissk/feature_processing.py make_df_unit method. - Args: paradata_active: Active paradata parameters: Configuration @@ -157,16 +204,31 @@ def build_unit_features( Returns: DataFrame with unit-level features """ - df_unit = paradata_active[[ - 'interview__id', 'responsible', 'survey_name', 'survey_version' - ]].copy() + # Use qnr/qnr_version as survey_name/survey_version + cols_map = { + 'interview__id': 'interview__id', + 'responsible': 'responsible', + 'qnr': 'survey_name', + 'qnr_version': 'survey_version' + } + + # Only select columns that exist + available_cols = [c for c in cols_map.keys() if c in paradata_active.columns] + + df_unit = paradata_active[available_cols].copy() + + # Rename columns to match expected output + df_unit.rename(columns=cols_map, inplace=True) df_unit.drop_duplicates(inplace=True) - df_unit = df_unit[ - (df_unit['responsible'] != '') & - (~pd.isnull(df_unit['responsible'])) - ] + + if 'responsible' in df_unit.columns: + df_unit = df_unit[ + (df_unit['responsible'] != '') & + (~pd.isnull(df_unit['responsible'])) + ] logger.info(f"Built {len(df_unit)} unit records") return df_unit + diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py index ea52425..01f9e60 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py @@ -1,8 +1,8 @@ """Feature engineering pipeline definition.""" from kedro.pipeline import Pipeline, node, pipeline from .nodes import ( - process_paradata_timestamps, - filter_active_events, + process_paradata_node, + filter_active_paradata_node, build_item_features, build_unit_features ) @@ -16,20 +16,20 @@ def create_pipeline(**kwargs) -> Pipeline: """ return pipeline([ node( - func=process_paradata_timestamps, - inputs="paradata_raw", + func=process_paradata_node, + inputs=["paradata_interim", "parameters"], outputs="paradata_processed", - name="process_timestamps_node", + name="process_paradata_node", ), node( - func=filter_active_events, + func=filter_active_paradata_node, inputs=["paradata_processed", "parameters"], outputs="paradata_active", - name="filter_active_events_node", + name="filter_active_paradata_node", ), node( func=build_item_features, - inputs=["microdata_raw", "paradata_active", "questionnaire_raw", "parameters"], + inputs=["raw_microdata", "paradata_active", "raw_questionnaire", "parameters"], outputs="item_features", name="build_item_features_node", ), diff --git a/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb b/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb index f370fbd..c5a4972 100644 --- a/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb +++ b/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb @@ -10,10 +10,21 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "id": "607ef013", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2026-02-12 22:47:14.128\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m12\u001b[0m - \u001b[1mPROJ_ROOT path is: /Users/vanessa/Work/Rowsquared/RISSK/rissk\u001b[0m\n", + "\u001b[32m2026-02-12 22:47:14.129\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m39\u001b[0m - \u001b[1mAvaliable Questionnaires\u001b[0m\n", + "\u001b[32m2026-02-12 22:47:14.129\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mQuestionnaire: snb_hies_hh - Versions: [9, 10, 11]\u001b[0m\n", + "\u001b[32m2026-02-12 22:47:14.130\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mQuestionnaire: slbhies_listing - Versions: [6, 7]\u001b[0m\n" + ] + } + ], "source": [ "from pathlib import Path\n", "import pandas as pd\n", @@ -22,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "id": "a9c7966e", "metadata": {}, "outputs": [], @@ -32,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "id": "dc4569ea", "metadata": {}, "outputs": [], @@ -45,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "id": "3d3ef86a", "metadata": {}, "outputs": [], @@ -58,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "id": "72fdc596", "metadata": {}, "outputs": [], @@ -300,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "id": "63867817", "metadata": {}, "outputs": [ @@ -310,7 +321,7 @@ "(1203121, 43)" ] }, - "execution_count": 14, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -322,14 +333,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "id": "792a94d3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(True,\n", + "(False,\n", " {'shape': {'equal': True, 'shape_a': (8536570, 27), 'shape_b': (8536570, 27)},\n", " 'columns': {'different_columns': [],\n", " 'equal': True,\n", @@ -338,14 +349,39 @@ " 'dtypes': {'mismatched_columns': [], 'equal': True},\n", " 'auto_key': None,\n", " 'cell_compare': {'checked': True,\n", - " 'columns_with_differences': [],\n", - " 'total_cell_differences': 0,\n", + " 'columns_with_differences': ['interview__id',\n", + " 'order',\n", + " 'event',\n", + " 'responsible',\n", + " 'role',\n", + " 'timestamp_utc',\n", + " 'tz_offset',\n", + " 'parameters',\n", + " 'param',\n", + " 'answer',\n", + " 'roster_level',\n", + " 'timestamp_local',\n", + " 'qnr_version',\n", + " 'qnr_seq',\n", + " 'variable_name',\n", + " 'qtype',\n", + " 'question_type',\n", + " 'answers',\n", + " 'question_scope',\n", + " 'yes_no_view',\n", + " 'is_filtered_combobox',\n", + " 'is_integer',\n", + " 'cascade_from_question_id',\n", + " 'answer_sequence',\n", + " 'n_answers',\n", + " 'question_sequence'],\n", + " 'total_cell_differences': 135297839,\n", " 'rows_compared': 8536570,\n", " 'note': 'aligned by index intersection'},\n", - " 'same': True})" + " 'same': False})" ] }, - "execution_count": 15, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -356,14 +392,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, "id": "de3363a0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(True,\n", + "(False,\n", " {'shape': {'equal': True, 'shape_a': (1203121, 43), 'shape_b': (1203121, 43)},\n", " 'columns': {'different_columns': [],\n", " 'equal': True,\n", @@ -372,14 +408,52 @@ " 'dtypes': {'mismatched_columns': [], 'equal': True},\n", " 'auto_key': None,\n", " 'cell_compare': {'checked': True,\n", - " 'columns_with_differences': [],\n", - " 'total_cell_differences': 0,\n", + " 'columns_with_differences': ['interview__id',\n", + " 'roster_level',\n", + " 'variable',\n", + " 'value',\n", + " 'filename',\n", + " 'qnr_version',\n", + " 'qnr_seq',\n", + " 'variable_name',\n", + " 'qtype',\n", + " 'question_type',\n", + " 'answers',\n", + " 'condition_expression',\n", + " 'hide_if_disabled',\n", + " 'featured',\n", + " 'instructions',\n", + " 'properties',\n", + " 'public_key',\n", + " 'question_scope',\n", + " 'question_text',\n", + " 'stata_export_caption',\n", + " 'variable_label',\n", + " 'is_timestamp',\n", + " 'validation_conditions',\n", + " 'yes_no_view',\n", + " 'is_filtered_combobox',\n", + " 'is_integer',\n", + " 'categories_id',\n", + " 'linked_to_roster_id',\n", + " 'linked_to_question_id',\n", + " 'cascade_from_question_id',\n", + " 'parents',\n", + " 'answer_sequence',\n", + " 'n_answers',\n", + " 'is_linked',\n", + " 'parent_1',\n", + " 'parent_2',\n", + " 'parent_3',\n", + " 'parent_4',\n", + " 'question_sequence'],\n", + " 'total_cell_differences': 26398123,\n", " 'rows_compared': 1203121,\n", " 'note': 'aligned by index intersection'},\n", - " 'same': True})" + " 'same': False})" ] }, - "execution_count": 16, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -390,14 +464,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 9, "id": "81f6cde7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(True,\n", + "(False,\n", " {'shape': {'equal': True, 'shape_a': (3487, 38), 'shape_b': (3487, 38)},\n", " 'columns': {'different_columns': [],\n", " 'equal': True,\n", @@ -406,14 +480,50 @@ " 'dtypes': {'mismatched_columns': [], 'equal': True},\n", " 'auto_key': None,\n", " 'cell_compare': {'checked': True,\n", - " 'columns_with_differences': [],\n", - " 'total_cell_differences': 0,\n", + " 'columns_with_differences': ['qnr_seq',\n", + " 'variable_name',\n", + " 'qtype',\n", + " 'question_type',\n", + " 'answers',\n", + " 'children',\n", + " 'condition_expression',\n", + " 'hide_if_disabled',\n", + " 'featured',\n", + " 'instructions',\n", + " 'properties',\n", + " 'public_key',\n", + " 'question_scope',\n", + " 'question_text',\n", + " 'stata_export_caption',\n", + " 'variable_label',\n", + " 'is_timestamp',\n", + " 'validation_conditions',\n", + " 'yes_no_view',\n", + " 'is_filtered_combobox',\n", + " 'is_integer',\n", + " 'categories_id',\n", + " 'title',\n", + " 'is_roster',\n", + " 'linked_to_roster_id',\n", + " 'linked_to_question_id',\n", + " 'cascade_from_question_id',\n", + " 'parents',\n", + " 'answer_sequence',\n", + " 'n_answers',\n", + " 'is_linked',\n", + " 'parent_1',\n", + " 'parent_2',\n", + " 'parent_3',\n", + " 'parent_4',\n", + " 'question_sequence',\n", + " 'qnr_version'],\n", + " 'total_cell_differences': 20059,\n", " 'rows_compared': 3487,\n", " 'note': 'aligned by index intersection'},\n", - " 'same': True})" + " 'same': False})" ] }, - "execution_count": 17, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -421,6 +531,691 @@ "source": [ "compare_parquet_files(df_questionnaire_kedro, df_questionnaire, check='cells')" ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c2867586", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "interview__id", + "rawType": "object", + "type": "string" + }, + { + "name": "roster_level", + "rawType": "object", + "type": "string" + }, + { + "name": "variable", + "rawType": "object", + "type": "string" + }, + { + "name": "value", + "rawType": "object", + "type": "string" + }, + { + "name": "filename", + "rawType": "object", + "type": "string" + }, + { + "name": "qnr", + "rawType": "object", + "type": "string" + }, + { + "name": "qnr_version", + "rawType": "object", + "type": "string" + }, + { + "name": "qnr_seq", + "rawType": "int64", + "type": "integer" + }, + { + "name": "variable_name", + "rawType": "object", + "type": "string" + }, + { + "name": "qtype", + "rawType": "object", + "type": "string" + }, + { + "name": "question_type", + "rawType": "float64", + "type": "float" + }, + { + "name": "answers", + "rawType": "object", + "type": "unknown" + }, + { + "name": "children", + "rawType": "object", + "type": "unknown" + }, + { + "name": "condition_expression", + "rawType": "object", + "type": "unknown" + }, + { + "name": "hide_if_disabled", + "rawType": "object", + "type": "unknown" + }, + { + "name": "featured", + "rawType": "object", + "type": "unknown" + }, + { + "name": "instructions", + "rawType": "object", + "type": "unknown" + }, + { + "name": "properties", + "rawType": "object", + "type": "unknown" + }, + { + "name": "public_key", + "rawType": "object", + "type": "string" + }, + { + "name": "question_scope", + "rawType": "float64", + "type": "float" + }, + { + "name": "question_text", + "rawType": "object", + "type": "unknown" + }, + { + "name": "stata_export_caption", + "rawType": "object", + "type": "unknown" + }, + { + "name": "variable_label", + "rawType": "object", + "type": "unknown" + }, + { + "name": "is_timestamp", + "rawType": "object", + "type": "unknown" + }, + { + "name": "validation_conditions", + "rawType": "object", + "type": "unknown" + }, + { + "name": "yes_no_view", + "rawType": "object", + "type": "unknown" + }, + { + "name": "is_filtered_combobox", + "rawType": "object", + "type": "unknown" + }, + { + "name": "is_integer", + "rawType": "object", + "type": "unknown" + }, + { + "name": "categories_id", + "rawType": "object", + "type": "unknown" + }, + { + "name": "title", + "rawType": "object", + "type": "unknown" + }, + { + "name": "is_roster", + "rawType": "object", + "type": "unknown" + }, + { + "name": "linked_to_roster_id", + "rawType": "object", + "type": "unknown" + }, + { + "name": "linked_to_question_id", + "rawType": "object", + "type": "unknown" + }, + { + "name": "cascade_from_question_id", + "rawType": "object", + "type": "unknown" + }, + { + "name": "parents", + "rawType": "object", + "type": "string" + }, + { + "name": "answer_sequence", + "rawType": "object", + "type": "string" + }, + { + "name": "n_answers", + "rawType": "float64", + "type": "float" + }, + { + "name": "is_linked", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "parent_1", + "rawType": "object", + "type": "string" + }, + { + "name": "parent_2", + "rawType": "object", + "type": "unknown" + }, + { + "name": "parent_3", + "rawType": "object", + "type": "unknown" + }, + { + "name": "parent_4", + "rawType": "object", + "type": "unknown" + }, + { + "name": "question_sequence", + "rawType": "float64", + "type": "float" + } + ], + "ref": "87ff61f3-2ad0-45c2-a404-a181f758b4a9", + "rows": [ + [ + "0", + "0093cc0b63c24abd96eeed5cbc25600f", + "", + "sampling_hh", + "Solomon Roni", + "snb_hies_hh.dta", + "snb_hies_hh", + "9", + "1", + "sampling_hh", + "TextQuestion", + "7.0", + "[]", + "[]", + "", + "False", + "True", + "ENTER THE NAME/DESCRIPTION EXACTLY AS IT IS ON THE SAMPLING SHEET (IF THERE IS ANY UPDATE, IT CAN BE MADE IN THE HOUSEHOLD ROSTER)", + "{'GeometryInputMode': 0.0, 'GeometryType': 0.0, 'HideInstructions': False, 'IsCritical': False, 'OptionsFilterExpression': None, 'UseFormatting': False}", + "2f0be987-8953-2a02-d32e-b89e2eed4107", + "0.0", + "NAME OF SAMPLED UNIT", + "sampling_hh", + "SAMPLING NAME", + "False", + "[]", + null, + null, + null, + null, + null, + null, + null, + null, + null, + "Cover", + "nan", + null, + "False", + "Cover", + null, + null, + null, + "1.0" + ], + [ + "1", + "0093cc0b63c24abd96eeed5cbc25600f", + "", + "sampling_id", + "142", + "snb_hies_hh.dta", + "snb_hies_hh", + "9", + "2", + "sampling_id", + "TextQuestion", + "7.0", + "[]", + "[]", + "", + "False", + "True", + "ENTER THE ID FROM SAMPLING SHEET.
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" + ], + "text/plain": [ + " interview__id roster_level variable value \\\n", + "0 0093cc0b63c24abd96eeed5cbc25600f sampling_hh Solomon Roni \n", + "1 0093cc0b63c24abd96eeed5cbc25600f sampling_id 142 \n", + "2 0093cc0b63c24abd96eeed5cbc25600f RESULT Consent given \n", + "3 0093cc0b63c24abd96eeed5cbc25600f ward 211 \n", + "4 0093cc0b63c24abd96eeed5cbc25600f ea 211060200 \n", + "\n", + " filename qnr qnr_version qnr_seq variable_name \\\n", + "0 snb_hies_hh.dta snb_hies_hh 9 1 sampling_hh \n", + "1 snb_hies_hh.dta snb_hies_hh 9 2 sampling_id \n", + "2 snb_hies_hh.dta snb_hies_hh 9 3 RESULT \n", + "3 snb_hies_hh.dta snb_hies_hh 9 4 ward \n", + "4 snb_hies_hh.dta snb_hies_hh 9 5 ea \n", + "\n", + " qtype ... cascade_from_question_id parents \\\n", + "0 TextQuestion ... None Cover \n", + "1 TextQuestion ... None Cover \n", + "2 Variable ... None Cover \n", + "3 SingleQuestion ... None Cover \n", + "4 SingleQuestion ... 08380cbb-b78a-e407-903b-800aa1ed95da Cover \n", + "\n", + " answer_sequence n_answers is_linked parent_1 parent_2 parent_3 parent_4 \\\n", + "0 nan NaN False Cover None None None \n", + "1 nan NaN False Cover None None None \n", + "2 nan NaN False Cover None None None \n", + "3 nan NaN False Cover None None None \n", + "4 nan NaN False Cover None None None \n", + "\n", + " question_sequence \n", + "0 1.0 \n", + "1 2.0 \n", + "2 NaN \n", + "3 3.0 \n", + "4 4.0 \n", + "\n", + "[5 rows x 43 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_microdata_kedro.head(5)" + ] } ], "metadata": { From b1298dd435ec1d0c98d57c2b029d3db85c101a14 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Fri, 13 Feb 2026 15:28:28 +0000 Subject: [PATCH 11/70] - kedro ingestion: the initial data is passed as partitioned data in catalog not a raw path parameter - file cleanup --- configuration/main.yaml | 4 +- environment_kedro.yml | 2 +- rissk/import_utils_kedro.py | 65 --------- rissk/utils/import_utils_kedro.py | 138 ++++++++++++++++++ rissk_kedro/conf/base/catalog.yml | 15 +- .../conf/base/ingestion_data_sources.md | 76 ---------- .../pipelines/data_ingestion/nodes.py | 23 ++- .../pipelines/data_ingestion/pipeline.py | 5 +- 8 files changed, 162 insertions(+), 166 deletions(-) delete mode 100644 rissk/import_utils_kedro.py create mode 100644 rissk/utils/import_utils_kedro.py delete mode 100644 rissk_kedro/conf/base/ingestion_data_sources.md diff --git a/configuration/main.yaml b/configuration/main.yaml index 5e0729c..6d1e739 100644 --- a/configuration/main.yaml +++ b/configuration/main.yaml @@ -8,8 +8,8 @@ export_path: . output_file: results/unit_risk_score.csv feature_score: true -surveys: [ifad_tunesia] -survey_version: 'EndlineFINALV106_1' +surveys: ["hies2024"] # [ifad_tunesia] +survey_version: [9, 10, 11] #'EndlineFINALV106_1' password: null limit_unit: null diff --git a/environment_kedro.yml b/environment_kedro.yml index 5fd92e2..91d1629 100644 --- a/environment_kedro.yml +++ b/environment_kedro.yml @@ -41,5 +41,5 @@ dependencies: - botocore==1.35.88 - kedro==1.0.0 - kedro-viz==12.1.0 - - kedro-datasets[pandas-statadataset]==4.1.0 + - kedro-datasets==4.1.0 - -e . \ No newline at end of file diff --git a/rissk/import_utils_kedro.py b/rissk/import_utils_kedro.py deleted file mode 100644 index baf42c9..0000000 --- a/rissk/import_utils_kedro.py +++ /dev/null @@ -1,65 +0,0 @@ -from pathlib import Path -from typing import List -import re - -from loguru import logger - -from rissk.utils.import_utils import extract_zip - - -def extract_all_zip_files(raw_path: Path, zip_password: str = None) -> None: - """ - Extract all zip files found at the top level of ``raw_path``. - - - Keeps naming convention: folder name = zip filename without ``.zip``. - - Delegates nested-zip handling to ``extract_zip``. - - Procedural utility; returns no value. - """ - if not raw_path.exists(): - logger.warning(f"Raw path does not exist: {raw_path}") - return - - zip_files = [ - file_path - for file_path in raw_path.iterdir() - if file_path.is_file() and file_path.suffix.lower() == ".zip" - ] - - logger.info(f"Found {len(zip_files)} zip files in {raw_path}") - - for zip_file in zip_files: - destination = zip_file.with_suffix("") - extract_zip(zip_file, destination, password=zip_password) - - -def filter_matching_folders(raw_path: Path, questionnaires: List[dict]) -> List[Path]: - # This is a folder only version of the filter_matching_zip_files function, which is used - # to find matching folders after extraction. The logic is the same, but it targets folders - # instead of zip files. - """ - Return folder paths in ``raw_path`` that match questionnaire/version patterns. - - Matching logic mirrors legacy ``get_zip_files`` naming rules, but targets - extracted folders to support scenarios where zip files are unavailable. - """ - if not raw_path.exists(): - logger.warning(f"Raw path does not exist: {raw_path}") - return [] - - matching_folders: List[Path] = [] - - for questionnaire in questionnaires: - name = questionnaire.get("name") - versions = questionnaire.get("VERSION", []) - - version_pattern = "|".join(map(str, versions)) - pattern = re.compile(rf"{name}_({version_pattern})_.*") - - matching_folders.extend( - path - for path in raw_path.iterdir() - if path.is_dir() and pattern.match(path.name) - ) - - logger.info(f"Filtered {len(matching_folders)} matching folders from {raw_path}") - return matching_folders \ No newline at end of file diff --git a/rissk/utils/import_utils_kedro.py b/rissk/utils/import_utils_kedro.py new file mode 100644 index 0000000..56b40b1 --- /dev/null +++ b/rissk/utils/import_utils_kedro.py @@ -0,0 +1,138 @@ +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional +import re + +from loguru import logger + +from rissk.utils.import_utils import extract_zip + + +def _extract_path_candidates(partition_id: str, partition_loader: Any = None) -> List[Path]: + partition_path = Path(partition_id) + candidates = [partition_path, Path.cwd() / partition_path] + + loader = partition_loader + if callable(loader): + bound_loader = getattr(loader, "__self__", None) + if bound_loader is not None: + loader = bound_loader + + potential_sources = [loader] + + closure = getattr(partition_loader, "__closure__", None) + if closure: + for cell in closure: + potential_sources.append(cell.cell_contents) + + for source in potential_sources: + if source is None: + continue + + for attr in ("filepath", "_filepath", "path", "_path"): + value = getattr(source, attr, None) + if value: + source_path = Path(value) + candidates.extend([source_path, source_path.parent]) + + unique_candidates: List[Path] = [] + seen = set() + for candidate in candidates: + candidate_str = str(candidate) + if candidate_str in seen: + continue + seen.add(candidate_str) + unique_candidates.append(candidate) + + return unique_candidates + + +def _resolve_existing_path(partition_id: str, partition_loader: Any = None) -> Optional[Path]: + for candidate in _extract_path_candidates(partition_id, partition_loader): + if candidate.exists(): + return candidate + return None + + +def extract_all_zip_files(partitions: Dict[str, Callable[[], Any]], zip_password: str = None) -> None: + """ + Extract all zip files referenced by Kedro partition IDs. + + - Keeps naming convention: folder name = zip filename without ``.zip``. + - Delegates nested-zip handling to ``extract_zip``. + - Procedural utility; returns no value. + """ + if not partitions: + logger.warning("No partitions found for zip extraction") + return + + zip_files: List[Path] = [] + + for partition_id, loader in partitions.items(): + if not str(partition_id).lower().endswith(".zip"): + continue + + existing_path = _resolve_existing_path(partition_id, loader) + if existing_path and existing_path.is_file() and existing_path.suffix.lower() == ".zip": + zip_files.append(existing_path) + else: + logger.warning(f"Partition zip path not found on disk: {partition_id}") + + logger.info(f"Found {len(zip_files)} zip files from partition entries") + + for zip_file in zip_files: + destination = zip_file.with_suffix("") + extract_zip(zip_file, destination, password=zip_password) + + +def filter_matching_folders(partitions: Dict[str, Callable[[], Any]], questionnaires: List[dict]) -> List[Path]: + # This is a folder only version of the filter_matching_zip_files function, which is used + # to find matching folders after extraction. The logic is the same, but it targets folders + # instead of zip files. + """ + Return extracted folder paths matching questionnaire/version patterns. + + Matching logic mirrors legacy ``get_zip_files`` naming rules, but starts from + partition IDs instead of a raw path string. + """ + if not partitions: + logger.warning("No partitions found while filtering extracted folders") + return [] + + matching_folders: List[Path] = [] + seen = set() + + for questionnaire in questionnaires: + name = questionnaire.get("name") + versions = questionnaire.get("VERSION", []) + + version_pattern = "|".join(map(str, versions)) + pattern = re.compile(rf"{name}_({version_pattern})_.*") + + for partition_id, loader in partitions.items(): + partition_path = Path(partition_id) + candidate_name = partition_path.stem if partition_path.suffix.lower() == ".zip" else partition_path.name + + if not pattern.match(candidate_name): + continue + + existing_partition_path = _resolve_existing_path(partition_id, loader) + + folder_candidates: List[Path] = [] + if existing_partition_path and existing_partition_path.suffix.lower() == ".zip": + folder_candidates.append(existing_partition_path.with_suffix("")) + elif existing_partition_path: + folder_candidates.append(existing_partition_path) + + if not existing_partition_path and partition_path.suffix.lower() == ".zip": + for candidate in _extract_path_candidates(partition_id, loader): + folder_candidates.append(candidate.with_suffix("")) + + for folder_path in folder_candidates: + if folder_path.is_dir(): + folder_str = str(folder_path) + if folder_str not in seen: + seen.add(folder_str) + matching_folders.append(folder_path) + + logger.info(f"Filtered {len(matching_folders)} matching folders from partition entries") + return matching_folders diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index 772544e..8471c98 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -1,13 +1,12 @@ # Here you can define all your data sets by using simple YAML syntax. # -# === INTERMEDIATE (Extracted Files) === -# This is no better than parametrized raw path. We need to think of a better way if we want catalogue -# extracted_survey_data: -# type: PartitionedDataset -# path: data/hies2024/latest/10_RAW -# dataset: -# type: kedro.extras.datasets.text.TextDataset -# filename_suffix: "" +# === INTERMEDIATE (Raw Data Files) === +survey_partitions: + type: partitions.PartitionedDataset + path: data/hies2024/latest/10_RAW + dataset: + type: binary.BinaryDataset + filename_suffix: "" # === PRIMARY (Ingested DataFrames) === paradata_interim: diff --git a/rissk_kedro/conf/base/ingestion_data_sources.md b/rissk_kedro/conf/base/ingestion_data_sources.md deleted file mode 100644 index 3b88446..0000000 --- a/rissk_kedro/conf/base/ingestion_data_sources.md +++ /dev/null @@ -1,76 +0,0 @@ -## PROMPT: DATA FLOW FOR INGESTION -Please analyze the attached files—specifically the catalog.yml and pipeline logic—to trace the lineage of paradata_interim, raw_questionnaire, and raw_microdata. Provide a precise list of the source file paths and file types if they can be inferred from catalogue or functions that process data. If a dataset is an output of a node, please identify the 'upstream' raw files that feed into that node. - - -Based on the provided files, here is the lineage trace for the three datasets. - -### 1. `paradata_interim` -* **Pipeline Node**: `load_paradata_node` (in nodes.py) -* **Catalog Entry**: paradata.parquet -* **Upstream Inputs**: - * **Source**: Extracted folders located in 10_RAW (defined in parameters.yml). - * **Specific Files Read**: - 1. `paradata.tab` (Parsed by `read_paradata` in import_utils.py) inside a folder ending in `_Paradata_All`. - 2. `Questionnaire/content/document.json` (Parsed by `read_json_questionnaire` for metadata) inside a folder ending in `_Tabular_All` (as a dependency). - -### 2. `raw_questionnaire` -* **Pipeline Node**: `load_questionnaire_node` (in nodes.py) -* **Catalog Entry**: questionnaire.parquet -* **Upstream Inputs**: - * **Source**: Extracted folders located in 10_RAW. - * **Specific Files Read**: - 1. `Questionnaire/content/document.json` (Main structure). - 2. `Questionnaire/content/Categories/*.xlsx` (Category metadata). - * Both are found inside folders ending in `_Tabular_All` . - -### 3. `raw_microdata` -* **Pipeline Node**: `load_microdata_node` (in nodes.py) -* **Catalog Entry**: microdata.parquet -* **Upstream Inputs**: - * **Source**: Extracted folders located in 10_RAW. - * **Specific Files Read**: - 1. **Data files**: All `.dta` (Stata) or `.tab` (TSV) files in the folder, excluding specific prefixes like `interview__` or `assignment__`. - 2. **Metadata**: `Questionnaire/content/document.json` (used to map variable types/questions). - * All found inside folders ending in `_Tabular_All`. - -### Summary of Dependencies -All three datasets originate from the **raw extracted folders** generated by `extract_zip_files_node` into 10_RAW. - -| Dataset | Primary Source File | Helper Source File | Folder Type | -| :--- | :--- | :--- | :--- | -| `paradata_interim` | `paradata.tab` | `document.json` | `_Paradata_All` (primary), `_Tabular_All` (helper) | -| `raw_questionnaire` | `document.json` | `Categories/*.xlsx` | `_Tabular_All` | -| `raw_microdata` | `*.dta` / `*.tab` | `document.json` | `_Tabular_All` | - - -### PROMPT: DEPENDENCE OF PARADATA AND MICRODATA ON `document.json` -Having looked at the data ingestion in detail can you ascertain if any information could be extracted for paradata_interim and raw_microdata if `Questionnaire/content/document.json` was missing? - - -Based on the code analysis, the short answer is **No, not with the current implementation.** - -Both `paradata_interim` and `raw_microdata` are strictly dependent on `Questionnaire/content/document.json`. If that file is missing, the ingestion logic explicitly fails or skips processing. - -Here is the detailed breakdown of why: - -### 1. `paradata_interim` (Paradata) -* **Dependency:** in `load_paradata_node`, the code does this: - ```python - df_questionnaires = get_questionnaire(tabular_path) - df_paradata = get_paradata(paradata_path, df_questionnaires) - ``` -* **Why it fails:** - * `get_questionnaire` loads the `document.json`. - * `get_paradata` requires `df_questionnaires` as a second argument. - * Inside `get_paradata`, the code uses the questionnaire dataframe to map variable names (e.g., `variable_name` matching). - * Without `document.json`, there is no `df_questionnaires`, and thus `get_paradata` cannot execute. - -### 2. `raw_microdata` (Microdata) -* **Dependency:** In `load_microdata_node` (and underlying `get_microdata`), the logic relies on the questionnaire structure to know *which* columns are valid and how to interpret them. -* **Why it fails:** - * The system uses `document.json` to build a schema of expected variables (the "questionnaire map"). - * It uses this map to normalize column names and types across the `.tab` or `.dta` files. - * If `document.json` is missing, the function that builds this map (`get_questionnaire`) fails, stopping the entire microdata loading process. - -### Conclusion -The `document.json` file is acting as the **schema definition** for the entire pipeline. Without it, the code treats the raw data (`.tab` / `.dta`) as uninterpretable blobs and will not permit ingestion. \ No newline at end of file diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py index a3b1d7b..0aece82 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py @@ -1,29 +1,28 @@ -from typing import Dict, List from pathlib import Path +from typing import Any, Callable, Dict, List import pandas as pd from loguru import logger -from rissk.import_utils_kedro import extract_all_zip_files, filter_matching_folders +from rissk.utils.import_utils_kedro import extract_all_zip_files, filter_matching_folders from rissk.utils.import_utils import get_survey_info, get_questionnaire, get_paradata, get_microdata -def extract_zip_files_node(raw_path_str: str, zip_password: str) -> None: +def extract_zip_files_node(survey_partitions: Dict[str, Callable[[], Any]], zip_password: str) -> None: """ - Extract all top-level zip files in the raw data path. + Extract zip files referenced by the survey partition dataset. Procedural node: extraction side-effect only. """ - raw_path = Path(raw_path_str) - logger.info(f"Extracting zip files from {raw_path}") - extract_all_zip_files(raw_path, zip_password=zip_password) + logger.info(f"Extracting zip files from {len(survey_partitions)} partition entries") + extract_all_zip_files(survey_partitions, zip_password=zip_password) -def filter_extracted_survey_paths_node(raw_path_str: str, questionnaires: List[Dict]) -> List[Path]: +def filter_extracted_survey_paths_node(survey_partitions: Dict[str, Callable[[], Any]], questionnaires: List[Dict]) -> List[Path]: """ - Return extracted folder paths matching questionnaire/version patterns. + Return extracted folder paths matching questionnaire/version patterns + using survey partition entries. This node does not perform extraction. """ - raw_path = Path(raw_path_str) - logger.info(f"Collecting matching survey folders from {raw_path}") - return filter_matching_folders(raw_path, questionnaires) + logger.info(f"Collecting matching survey folders from {len(survey_partitions)} partition entries") + return filter_matching_folders(survey_partitions, questionnaires) def load_paradata_node(file_paths: List[Path]) -> pd.DataFrame: diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py index 852441d..43eca91 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py @@ -12,11 +12,12 @@ def create_pipeline(**kwargs) -> Pipeline: node( func=extract_zip_files_node, inputs=[ - "params:ingestion.raw_data_path", + "survey_partitions", "params:zip_password" ], outputs=None, - name="extract_zip_files_node" + name="extract_zip_files_node", + tags=["unzip_files"] ), node( func=filter_extracted_survey_paths_node, From 706265cbbec835c43fb42058a5d1d8aef8353377 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Sun, 15 Feb 2026 22:20:05 +0000 Subject: [PATCH 12/70] Refactor data ingestion pipeline: update partition names, enhance zip extraction, change README and upgrade to python 3.13 --- .gitignore | 5 + configuration/main.yaml | 2 +- environment_kedro.yml | 58 +- rissk/utils/import_utils_kedro.py | 590 +++++++++++++++--- rissk_kedro/README.md | 14 + rissk_kedro/conf/base/catalog.yml | 17 +- rissk_kedro/pyproject.toml | 106 +--- rissk_kedro/src/rissk_kedro/__init__.py | 5 +- .../pipelines/data_ingestion/nodes.py | 16 +- .../pipelines/data_ingestion/pipeline.py | 4 +- rissk_kedro/src/rissk_kedro/settings.py | 3 + 11 files changed, 597 insertions(+), 223 deletions(-) diff --git a/.gitignore b/.gitignore index 6bf31c0..4cd17a8 100644 --- a/.gitignore +++ b/.gitignore @@ -260,3 +260,8 @@ cython_debug/ #.idea/ embedded-assets/tmpo_c6_8gw.html +ingestion_nstructions.md +.gitignore +ingestion_refactor_context.md +rissk_kedro/stats.json +rissk_kedro/feature_process_ploomber_pipeline_integration.md diff --git a/configuration/main.yaml b/configuration/main.yaml index 6d1e739..cd7ee7c 100644 --- a/configuration/main.yaml +++ b/configuration/main.yaml @@ -8,7 +8,7 @@ export_path: . output_file: results/unit_risk_score.csv feature_score: true -surveys: ["hies2024"] # [ifad_tunesia] +surveys: [snb_hies_hh] # [ifad_tunesia] survey_version: [9, 10, 11] #'EndlineFINALV106_1' password: null limit_unit: null diff --git a/environment_kedro.yml b/environment_kedro.yml index 91d1629..27ad7f0 100644 --- a/environment_kedro.yml +++ b/environment_kedro.yml @@ -1,45 +1,37 @@ -name: rissk_rs_01 +name: rissk_py3_13_macos channels: - conda-forge - - defaults dependencies: - - python=3.9 - # R and its dependencies - - r-base + - python=3.13 + # R Core - 3.13 compatible binaries + - r-base>=4.4 - r-ggplot2 - r-dplyr - r-tidyr - - r-shiny - r-readr - - r-irkernel # For running R in Jupyter Notebooks - r-stringr - # Interoperability - - rpy2 # For using R within Python - # Graphing libs + # Graphviz System Deps (Crucial for macOS) - graphviz - - pygraphviz - # Other tools + - python-graphviz + - pydot # Highly recommended for macOS compatibility - pip - pip: - - jupyter_contrib_nbextensions - - awscli - - botocore - - loguru==0.7.3 - - tqdm==4.67.1 - - pandas==2.2.2 - - seaborn==0.13.2 - - docutils==0.16 - - openpyxl==3.1.2 - - pyarrow==15.0.2 - - pyod==1.1.3 - - python-dotenv==1.0.1 - - pythresh==0.3.6 - - ploomber==0.23.3 - - ipywidgets==8.1.5 - - typer==0.15.1 - - boto3==1.35.88 - - botocore==1.35.88 - - kedro==1.0.0 - - kedro-viz==12.1.0 - - kedro-datasets==4.1.0 + # Framework + - kedro==1.2.0 + - kedro-viz>=12.3.0 + - kedro-datasets[pandas,s3fs,excel,files]>=9.1.0 + + # Core Stack (Optimized for Apple Silicon) + - rpy2>=3.6.4 + - pandas>=2.2.3 + - numpy>=2.1.0 + - pyarrow>=18.0.0 + + # Analysis Tools + - pyod>=1.1.5 + - pythresh>=1.0.3 + - loguru>=0.7.3 + - tqdm>=4.67.0 + - boto3>=1.35.0 + - python-dotenv>=1.0.1 - -e . \ No newline at end of file diff --git a/rissk/utils/import_utils_kedro.py b/rissk/utils/import_utils_kedro.py index 56b40b1..3010e9b 100644 --- a/rissk/utils/import_utils_kedro.py +++ b/rissk/utils/import_utils_kedro.py @@ -1,138 +1,530 @@ +from __future__ import annotations + from pathlib import Path -from typing import Any, Callable, Dict, List, Optional +from typing import Any, Callable, Optional import re +import os +import zipfile +import json # Added json import +import pandas as pd # Added pandas import +import numpy as np # Added numpy import from loguru import logger -from rissk.utils.import_utils import extract_zip - - -def _extract_path_candidates(partition_id: str, partition_loader: Any = None) -> List[Path]: - partition_path = Path(partition_id) - candidates = [partition_path, Path.cwd() / partition_path] - - loader = partition_loader - if callable(loader): - bound_loader = getattr(loader, "__self__", None) - if bound_loader is not None: - loader = bound_loader - - potential_sources = [loader] - - closure = getattr(partition_loader, "__closure__", None) - if closure: - for cell in closure: - potential_sources.append(cell.cell_contents) - - for source in potential_sources: - if source is None: - continue - - for attr in ("filepath", "_filepath", "path", "_path"): - value = getattr(source, attr, None) - if value: - source_path = Path(value) - candidates.extend([source_path, source_path.parent]) - - unique_candidates: List[Path] = [] - seen = set() - for candidate in candidates: - candidate_str = str(candidate) - if candidate_str in seen: - continue - seen.add(candidate_str) - unique_candidates.append(candidate) - - return unique_candidates - - -def _resolve_existing_path(partition_id: str, partition_loader: Any = None) -> Optional[Path]: - for candidate in _extract_path_candidates(partition_id, partition_loader): - if candidate.exists(): - return candidate +from rissk.utils.file_process_utils import ( + get_file_parts, + transform_multi, + set_qnr_version, + normalize_column_name, + process_json_structure, + get_categories, + update_df_categories, + parse_filename +) + + +def extract_zip(file_source_path: Path, file_dest_path: Path, password: Optional[str] = None): + """Memory-efficient recursive extraction for Python 3.13.""" + current_pwd = password or os.getenv('PASSWORD') + pwd_bytes = current_pwd.encode() if current_pwd else None + + # Ensure destination exists + file_dest_path.mkdir(parents=True, exist_ok=True) + + try: + with zipfile.ZipFile(file_source_path, 'r') as zip_ref: + for file_info in zip_ref.infolist(): + target_path = file_dest_path / file_info.filename + + # Prevent directory traversal vulnerability + if not str(target_path.resolve()).startswith(str(file_dest_path.resolve())): + logger.warning(f"Skipping extraction of {file_info.filename}: path traversal attempt") + continue + + if file_info.is_dir(): + target_path.mkdir(parents=True, exist_ok=True) + continue + + target_path.parent.mkdir(parents=True, exist_ok=True) + + if target_path.exists(): + # Optional: Skip already extracted files or overwrite + pass + + with zip_ref.open(file_info, pwd=pwd_bytes) as source, \ + open(target_path, "wb") as target: + target.write(source.read()) + + if target_path.suffix.lower() == '.zip': + nested_dest = target_path.with_suffix('') + extract_zip(target_path, nested_dest, password=current_pwd) + + logger.info(f"Extracted: {file_source_path.name}") + except Exception as e: + logger.error(f"Failed {file_source_path}: {e}") + + +def _get_partition_path(partition_id: str, loader: Any) -> Optional[Path]: + """ + Robustly resolve partition path from a Kedro partition loader. + Compatible with Kedro 0.18+ and standard partition loaders. + """ + # 1. Try to get path from loader if it's a bound method (most datasets) + dataset = getattr(loader, "__self__", None) + if dataset: + for attr in ("_filepath", "filepath", "path", "_path"): + path = getattr(dataset, attr, None) + if path: + return Path(path) + + # 2. Try inspection for closures (legacy fallback) + try: + closure = getattr(loader, "__closure__", None) + if closure: + for cell in closure: + content = cell.cell_contents + for attr in ("_filepath", "filepath", "path", "_path"): + path = getattr(content, attr, None) + if path: + return Path(path) + except Exception: + pass + + # 3. Last resort: Assume partition_id is relative to current working directory + # (Unlikely in Kedro context but safe fallback structure wise if ID is path-like) + candidate = Path(partition_id) + if candidate.exists(): + return candidate + return None -def extract_all_zip_files(partitions: Dict[str, Callable[[], Any]], zip_password: str = None) -> None: +def extract_all_zip_files(partitions: dict[str, Any], zip_password: str = None) -> None: """ Extract all zip files referenced by Kedro partition IDs. - - - Keeps naming convention: folder name = zip filename without ``.zip``. - - Delegates nested-zip handling to ``extract_zip``. - - Procedural utility; returns no value. + Recursively extracts nested zips. """ if not partitions: logger.warning("No partitions found for zip extraction") return - zip_files: List[Path] = [] - + # Collect source zips + zip_paths: list[Path] = [] + for partition_id, loader in partitions.items(): + # Partition keys are typically relative paths + # We need the absolute path to the zip file + + # Only process items that look like zips if not str(partition_id).lower().endswith(".zip"): continue - - existing_path = _resolve_existing_path(partition_id, loader) - if existing_path and existing_path.is_file() and existing_path.suffix.lower() == ".zip": - zip_files.append(existing_path) + + zip_path = _get_partition_path(partition_id, loader) + + if zip_path and zip_path.exists(): + zip_paths.append(zip_path) else: - logger.warning(f"Partition zip path not found on disk: {partition_id}") + logger.warning(f"Could not resolve path for partition: {partition_id}") - logger.info(f"Found {len(zip_files)} zip files from partition entries") + logger.info(f"Found {len(zip_paths)} top-level zip files to extract") - for zip_file in zip_files: - destination = zip_file.with_suffix("") - extract_zip(zip_file, destination, password=zip_password) + for zip_path in zip_paths: + destination = zip_path.with_suffix("") + extract_zip(zip_path, destination, password=zip_password) -def filter_matching_folders(partitions: Dict[str, Callable[[], Any]], questionnaires: List[dict]) -> List[Path]: - # This is a folder only version of the filter_matching_zip_files function, which is used - # to find matching folders after extraction. The logic is the same, but it targets folders - # instead of zip files. +def filter_matching_folders(partitions: dict[str, Any], questionnaires: list[dict]) -> list[Path]: """ Return extracted folder paths matching questionnaire/version patterns. - - Matching logic mirrors legacy ``get_zip_files`` naming rules, but starts from - partition IDs instead of a raw path string. + Iterates over extracted folders (datasets) to find matches. """ if not partitions: logger.warning("No partitions found while filtering extracted folders") return [] - matching_folders: List[Path] = [] - seen = set() - - for questionnaire in questionnaires: - name = questionnaire.get("name") - versions = questionnaire.get("VERSION", []) + matching_folders: list[Path] = [] + seen_paths = set() + # Pre-compile patterns + patterns = [] + for q in questionnaires: + name = q.get("name") + versions = q.get("VERSION", []) version_pattern = "|".join(map(str, versions)) - pattern = re.compile(rf"{name}_({version_pattern})_.*") + # Matches: NAME_VERSION_... (e.g. slbhies_listing_6_Paradata_All) + patterns.append(re.compile(rf"^{name}_({version_pattern})_.*")) + + logger.info(f"Scanning {len(partitions)} folder partitions against {len(patterns)} patterns") + + for partition_id, loader in partitions.items(): + partition_path_obj = Path(partition_id) + folder_name = partition_path_obj.name + + # Check against patterns + is_match = False + for pattern in patterns: + if pattern.match(folder_name): + is_match = True + break + + if not is_match: + continue + + # Resolved path + folder_path = _get_partition_path(partition_id, loader) + + if folder_path and folder_path.is_dir(): + folder_str = str(folder_path.resolve()) + if folder_str not in seen_paths: + seen_paths.add(folder_str) + matching_folders.append(folder_path) + + logger.info(f"Found {len(matching_folders)} matching folders") + return matching_folders - for partition_id, loader in partitions.items(): - partition_path = Path(partition_id) - candidate_name = partition_path.stem if partition_path.suffix.lower() == ".zip" else partition_path.name - if not pattern.match(candidate_name): - continue +# --- Legacy Functions Migrated from import_utils.py --- - existing_partition_path = _resolve_existing_path(partition_id, loader) +def get_survey_info(survey_files: list[Path]) -> dict[str, dict[str, dict[str, Path]]]: + """ + Organizes survey files into a structured dictionary. + + Structure: + { + 'questionnaire_name': { + 'qnr_version_string': { + 'file_format': Path(/path/to/folder) + } + } + } + """ + survey_info = {} + + for survey_path in survey_files: + filename = survey_path.name + try: + questionnaire, version, file_format, interview_status = get_file_parts(filename) + except ValueError as e: + logger.warning(f"Skipping {filename}: {e}") + continue + + qnr_version = f"{questionnaire}_{str(version)}" + + survey_info.setdefault(questionnaire, {}) + survey_info[questionnaire].setdefault(qnr_version, {}) + survey_info[questionnaire][qnr_version][file_format] = survey_path + + return survey_info + + +def read_json_questionnaire(survey_path: Path, questionnaire_path: Optional[Path] = None) -> dict: + """Reads the questionnaire JSON definition.""" + if questionnaire_path is None: + file_path = survey_path / 'Questionnaire' / 'content' / 'document.json' + else: + # If explicit questionnaire_path is given (rare case in current pipeline usage) + # We need check if it points to a specific file or directory + # This part assumes structure compatible with get_questionnaire_map from legacy code + # simplified here for clarity/robustness: + if questionnaire_path.is_file(): + file_path = questionnaire_path + else: + # Fallback logic mirroring legacy get_questionnaire_id/map behavior if needed + # For now, simplistic implementation assuming standard export structure + file_path = survey_path / 'Questionnaire' / 'content' / 'document.json' - folder_candidates: List[Path] = [] - if existing_partition_path and existing_partition_path.suffix.lower() == ".zip": - folder_candidates.append(existing_partition_path.with_suffix("")) - elif existing_partition_path: - folder_candidates.append(existing_partition_path) + if not file_path.exists(): + logger.warning(f"Questionnaire document not found at {file_path}") + return None - if not existing_partition_path and partition_path.suffix.lower() == ".zip": - for candidate in _extract_path_candidates(partition_id, loader): - folder_candidates.append(candidate.with_suffix("")) + with file_path.open('r', encoding='utf-8') as f: + return json.load(f) - for folder_path in folder_candidates: - if folder_path.is_dir(): - folder_str = str(folder_path) - if folder_str not in seen: - seen.add(folder_str) - matching_folders.append(folder_path) - logger.info(f"Filtered {len(matching_folders)} matching folders from partition entries") - return matching_folders +def get_questionnaire(data_path: Path, questionnaire_path: Optional[Path] = None) -> pd.DataFrame: + """ + Loads and processes a questionnaire from a JSON file located at the specified path. + Also handles categorization of data. + """ + q_data = read_json_questionnaire(data_path, questionnaire_path=questionnaire_path) + + qnr_df = pd.DataFrame() + + if q_data is not None: + question_data = [] + question_counter = 0 + + # process_json_structure modifies question_data list in-place + process_json_structure(q_data.get("Children", []), "", question_counter, question_data) + + if question_data: + qnr_df = pd.DataFrame(question_data) + + # Type-safe transformations + qnr_df['answer_sequence'] = qnr_df['Answers'].apply( + lambda x: [int(item['AnswerValue']) for item in x] if x else np.nan + ) + qnr_df['n_answers'] = qnr_df['Answers'].apply(lambda x: len(x) if x else np.nan) + qnr_df['is_linked'] = (qnr_df['LinkedToRosterId'].notna()) | (qnr_df['LinkedToQuestionId'].notna()) + + if 'parents' in qnr_df.columns: + qnr_df['parents'] = qnr_df['parents'].str.lstrip(' > ') + split_columns = qnr_df['parents'].str.split(' > ', expand=True) + split_columns.columns = [f"parent_{i + 1}" for i in range(split_columns.shape[1])] + qnr_df = pd.concat([qnr_df, split_columns], axis=1) + + if 'QuestionScope' in qnr_df.columns: + qmask = qnr_df['QuestionScope'] == 0 + qnr_df['question_sequence'] = qmask.cumsum() + qnr_df.loc[~qmask, 'question_sequence'] = None + + categories_path = data_path / 'Questionnaire' / 'content' / 'Categories' + + if categories_path.exists(): + categories = get_categories(categories_path) + if not qnr_df.empty: + qnr_df = qnr_df.apply(lambda row: update_df_categories(row, categories), axis=1) + + if not qnr_df.empty: + qnr_df.reset_index(drop=True, inplace=True) + # Normalize columns + qnr_df.columns = [normalize_column_name(c) for c in qnr_df.columns] + + try: + parts = parse_filename(data_path.name) + # parts is a list: [questionnaire, version, format, status] + # set_qnr_version expects (df, questionaire_name, qnr_version) + qnr_df = set_qnr_version(qnr_df, parts[0], parts[1]) # parts[0]=name, parts[1]=version + except ValueError: + logger.warning(f"Could not parse filename '{data_path.name}' for version info") + + return qnr_df + + +def read_paradata(survey_path: Path, delimiter='\t') -> pd.DataFrame: + file_path = survey_path / 'paradata.tab' + if not file_path.exists(): + raise FileNotFoundError(f"Paradata file not found: {file_path}") + + with file_path.open('r', encoding='utf-8') as f: + # low_memory=False to avoid DtypeWarnings on large files, standard pandas practice + df = pd.read_csv(f, delimiter=delimiter, low_memory=False) + return df + + +def get_paradata(data_path: Path, df_questionnaires: pd.DataFrame) -> pd.DataFrame: + """ + Loads and processes a paradata file from the provided path and merges it with the questionnaire dataframe. + """ + try: + df_para = read_paradata(data_path, delimiter='\t') + except Exception as e: + logger.error(f"Error reading paradata from {data_path}: {e}") + return pd.DataFrame() + + if 'parameters' in df_para.columns: + # split the parameter column + # Using n=1 to limit splits is correct + # Check if expand=True returns intended shape + split_param = df_para['parameters'].str.split(r'\|\|', n=1, expand=True) + if split_param.shape[1] == 2: + df_para['param'] = split_param[0] + df_para['answer'] = split_param[1] + else: + df_para['param'] = df_para['parameters'] + df_para['answer'] = None + + if 'answer' in df_para.columns and df_para['answer'].notna().any(): + split_answer = df_para['answer'].str.rsplit(r'||', n=1, expand=True) + if split_answer.shape[1] == 2: + df_para['answer'] = split_answer[0] + df_para['roster_level'] = split_answer[1] + else: + df_para['roster_level'] = None # Or empty string + + return df_para + + +def get_microdata_file_list(data_path: Path) -> list[str]: + """ + Get a list of microdata files in the specified directory. + """ + excluded_prefixes = ('interview__', 'assignment__') + excluded_files = {'paradata.tab'} + valid_extensions = {'.dta', '.tab'} + + file_names = [] + if data_path.exists(): + for file in data_path.iterdir(): + if file.is_file() and file.suffix in valid_extensions: + if file.name not in excluded_files and not file.name.startswith(excluded_prefixes): + file_names.append(file.name) + return file_names + + +def read_microdata_file(data_path: Path, file_name: str) -> pd.DataFrame: + file_path = data_path / file_name + + if file_path.suffix == '.dta': + try: + # Using 'with' open ensures file handle closure + with file_path.open('rb') as f: + # convert_categoricals=False matches legacy beahvior + df = pd.read_stata(f, convert_categoricals=False, convert_missing=True) + + # Vectorized replacement is faster + # Replace '.a' Stata missing value with -999999999 + # Replace '.' Stata missing value with NaN + # Use strict type checking or conversion to string if mixed + + # Safety: ensure we don't fail if column is all numeric types (no '.a') + # convert to object if needed? usually .dta loads with correct types or object if strings exist + + # Legacy logic: df.astype(str) != '.a' -> expensive full copy? + # Better: replace specific values + df.replace({'.a': -999999999, '.': np.nan}, inplace=True) + + except Exception as e: + logger.error(f"Error reading {file_path}: {e}") + return pd.DataFrame() + else: # .tab file + try: + with file_path.open('r', encoding='utf-8') as f: + df = pd.read_csv(f, delimiter='\t', low_memory=False) + except Exception as e: + logger.error(f"Error reading csv {file_path}: {e}") + return pd.DataFrame() + + return df + + +def get_microdata(data_path: Path, df_questionnaires: pd.DataFrame) -> pd.DataFrame: + drop_list = {'interview__key', 'sssys_irnd', 'has__errors', 'interview__status', 'assignment__id'} + + file_names = get_microdata_file_list(data_path) + + # Pre-calculate masks outside loop + multi_unlinked_vars = [] + multi_linked_vars = [] + list_vars = [] + gps_vars = [] + + if not df_questionnaires.empty: + # Use boolean indexing + unlinked_mask = (df_questionnaires["qtype"] == 'MultyOptionsQuestion') & (df_questionnaires['is_linked'] == False) + linked_mask = (df_questionnaires["qtype"] == 'MultyOptionsQuestion') & (df_questionnaires['is_linked'] == True) + list_mask = (df_questionnaires["qtype"] == 'TextListQuestion') + gps_mask = (df_questionnaires["qtype"] == 'GpsCoordinateQuestion') + + multi_unlinked_vars = df_questionnaires.loc[unlinked_mask, 'variable_name'].tolist() + multi_linked_vars = df_questionnaires.loc[linked_mask, 'variable_name'].tolist() + list_vars = df_questionnaires.loc[list_mask, 'variable_name'].tolist() + gps_vars = df_questionnaires.loc[gps_mask, 'variable_name'].tolist() + + all_dfs = [] + for file_name in file_names: + df = read_microdata_file(data_path, file_name) + if df.empty: + continue + + #Efficient drop + cols_to_drop = [col for col in drop_list if col in df.columns] + if cols_to_drop: + df.drop(columns=cols_to_drop, inplace=True) + + if not df_questionnaires.empty: + df = transform_multi(df, multi_unlinked_vars, 'unlinked') + df = transform_multi(df, multi_linked_vars, 'linked') + df = transform_multi(df, list_vars, 'list') + df = transform_multi(df, gps_vars, 'gps') + + # Handle roster IDs + roster_ids = [col for col in df.columns if col.endswith("__id") and col != "interview__id"] + if roster_ids: + # Vectorized string join is harder in pandas, apply is okay here + df['roster_level'] = df[roster_ids].astype(str).agg(','.join, axis=1) + df.drop(columns=roster_ids, inplace=True) + else: + df['roster_level'] = '' + + id_vars = ['interview__id', 'roster_level'] + value_vars = [col for col in df.columns if col not in id_vars] + + if not value_vars: + continue + + df_long = df.melt(id_vars=id_vars, value_vars=value_vars, var_name='variable', value_name='value') + df_long['filename'] = file_name + all_dfs.append(df_long) + + if all_dfs: + combined_df = pd.concat(all_dfs, ignore_index=True) + else: + return pd.DataFrame() + + # Filter invalid values + # Optimized filter: + # Check for empty string or NaN. Note: 'value' column is mixed type probably. + # Convert 'value' to string could simplify emptiness check but be careful with NaN + + # Vectorized check is faster than apply + # combined_df['value'] is likely object type + + # is_valid logic from legacy: + # if list: return True + # if string/other: value != '' and notna(value) + + # Since we can't easily vectorize types check mixed with lists in pandas, use apply only if needed + # But usually transform_multi returns lists for some columns. + + def is_valid_fast(val): + if val is None: return False + if isinstance(val, (list, tuple)): return True # Not empty list check? Legacy said 'return True' commented 'bool(value)' + if val == '': return False + try: + if pd.isna(val): return False + except: + pass # list not hashable for isna sometimes? + return True + + combined_df = combined_df[combined_df['value'].apply(is_valid_fast)] + + try: + parts = parse_filename(data_path.name) + questionaire_name = parts[0] + qnr_version = parts[1] + combined_df = set_qnr_version(combined_df, questionaire_name, qnr_version) + except ValueError: + logger.warning(f"Could not set version for {data_path.name}") + + if not df_questionnaires.empty: + # Merge setup + roster_columns = [c for c in combined_df.columns if '__id' in c and c != 'interview__id'] + + # Ensure join keys have matching types + # variable, qnr, qnr_version are strings/objects + + merge_on_left = ['variable', 'qnr', 'qnr_version'] + merge_on_right = ['variable_name', 'qnr', 'qnr_version'] + + combined_df = combined_df.merge( + df_questionnaires, + how='left', + left_on=merge_on_left, + right_on=merge_on_right + ) + + sort_cols = ['interview__id'] + if 'qnr_seq' in combined_df.columns: + sort_cols.append('qnr_seq') + sort_cols.extend(roster_columns) + + # Safe sort (ignore missing cols) + actual_sort_cols = [c for c in sort_cols if c in combined_df.columns] + combined_df.sort_values(actual_sort_cols, inplace=True) + + combined_df.reset_index(drop=True, inplace=True) + combined_df.columns = [normalize_column_name(c) for c in combined_df.columns] + combined_df['value'] = combined_df['value'].astype(str) + + return combined_df diff --git a/rissk_kedro/README.md b/rissk_kedro/README.md index ae43bbf..ccac267 100644 --- a/rissk_kedro/README.md +++ b/rissk_kedro/README.md @@ -1,3 +1,17 @@ +### Download Data from storage system +Please Note that you need [aws cli](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html#getting-started-install-instructions) installed and set up with credentials to download and uplaod the data. If you do not need the sync from S#, you can simply comment with `#` the next line. + + +# How to Install RISSK +1. go to terminal inside the rissk folder and run +```bash +$ make create_environment +``` +2. run +```bash +$ conda activate rissk +``` + # rissk_kedro [![Powered by Kedro](https://img.shields.io/badge/powered_by-kedro-ffc900?logo=kedro)](https://kedro.org) diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index 8471c98..f98e17b 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -1,14 +1,23 @@ # Here you can define all your data sets by using simple YAML syntax. # -# === INTERMEDIATE (Raw Data Files) === -survey_partitions: +# === RAW DATA === +# The source partitions (Zips) +survey_zip_partitions: type: partitions.PartitionedDataset path: data/hies2024/latest/10_RAW dataset: type: binary.BinaryDataset - filename_suffix: "" + filename_suffix: ".zip" -# === PRIMARY (Ingested DataFrames) === +# The extracted result (Folders) +# Used by downstream nodes to find the directories +extracted_survey_folders: + type: partitions.PartitionedDataset + path: data/hies2024/latest/10_RAW + dataset: + type: folder.FolderDataset + +# === INGESTED DataFrames === paradata_interim: type: pandas.ParquetDataset filepath: data/hies2024/latest/20_INTERIM/paradata.parquet diff --git a/rissk_kedro/pyproject.toml b/rissk_kedro/pyproject.toml index cd67f67..362a3c0 100644 --- a/rissk_kedro/pyproject.toml +++ b/rissk_kedro/pyproject.toml @@ -1,90 +1,44 @@ [build-system] -requires = ["setuptools"] -build-backend = "setuptools.build_meta" +requires = ["flit_core >=3.2,<4"] +build-backend = "flit_core.buildapi" [project] -requires-python = ">=3.10" -name = "rissk_kedro" +name = "rissk" +version = "0.1.2" +description = "Automatically identify at-risk interviews from your Survey Solutions export files." +authors = [{ name = "rowsquared" }] +license = { file = "LICENSE" } readme = "README.md" -dynamic = ["version"] -dependencies = [ - "ipython>=8.10", - "jupyterlab>=3.0", - "notebook", - "kedro[jupyter]~=1.0.0", - "kedro-datasets[pandas-csvdataset, pandas-exceldataset, pandas-parquetdataset, plotly-plotlydataset, plotly-jsondataset, matplotlib-matplotlibdataset, spark-sparkdataset]>=9.1", - "kedro-viz>=6.7.0", - "scikit-learn~=1.5.1", - "seaborn~=0.12.1", +classifiers = [ + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.13", + "License :: OSI Approved :: MIT License", + "Operating System :: MacOS", ] -[project.scripts] -"rissk-kedro" = "rissk_kedro.__main__:main" - -[project.entry-points."kedro.hooks"] +# Standard Dependencies (formerly requirements.txt) +dependencies = [ + "kedro==1.2.0", + "kedro-datasets[pandas,s3fs,excel,files]>=9.1.0", + "rpy2>=3.6.4", + "pandas>=2.2.3", + "numpy>=2.1.0", + "pyod>=1.1.5", + "loguru>=0.7.3", +] +# Replaces tests_require from setup.py [project.optional-dependencies] -docs = [ - "docutils<0.21", - "sphinx>=5.3,<7.3", - "sphinx_rtd_theme==2.0.0", - "nbsphinx==0.8.1", - "sphinx-autodoc-typehints==1.20.2", - "sphinx_copybutton==0.5.2", - "ipykernel>=5.3, <7.0", - "Jinja2<3.2.0", - "myst-parser>=1.0,<2.1" +test = [ + "pytest>=8.0", + "pytest-cov", ] -dev = [ - "pytest-cov>=3,<7", - "pytest-mock>=1.7.1, <2.0", - "pytest~=7.2", - "ruff~=0.12.0" -] - -[tool.setuptools.dynamic] -version = {attr = "rissk_kedro.__version__"} - -[tool.setuptools.packages.find] -where = ["src"] -namespaces = false [tool.kedro] -package_name = "rissk_kedro" -project_name = "rissk_kedro" -kedro_init_version = "1.0.0" -tools = "['Linting', 'Testing', 'Custom Logging', 'Documentation', 'Data Structure', 'PySpark']" -example_pipeline = "True" +package_name = "rissk" +project_name = "rissk" +kedro_init_version = "1.2.0" source_dir = "src" [tool.pytest.ini_options] -addopts = """ ---cov-report term-missing \ ---cov src/rissk_kedro -ra""" - -[tool.coverage.report] -fail_under = 0 -show_missing = true -exclude_lines = ["pragma: no cover", "raise NotImplementedError"] - -[tool.ruff.format] -docstring-code-format = true - -[tool.ruff] -line-length = 88 -show-fixes = true - -[tool.ruff.lint] -select = [ - "F", # Pyflakes - "W", # pycodestyle - "E", # pycodestyle - "I", # isort - "UP", # pyupgrade - "PL", # Pylint - "T201", # Print Statement -] -ignore = ["E501"] # Ruff format takes care of line-too-long - -[tool.kedro_telemetry] -project_id = "d433b943a53d4dca931bf2a5084f67cb" +testpaths = ["tests"] \ No newline at end of file diff --git a/rissk_kedro/src/rissk_kedro/__init__.py b/rissk_kedro/src/rissk_kedro/__init__.py index cc22393..375f686 100644 --- a/rissk_kedro/src/rissk_kedro/__init__.py +++ b/rissk_kedro/src/rissk_kedro/__init__.py @@ -1,4 +1,3 @@ -"""rissk_kedro -""" +"""RISSK: Automatically identify at-risk interviews.""" -__version__ = "0.1" +__version__ = "0.1.2" \ No newline at end of file diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py index 0aece82..e89d6e1 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py @@ -2,17 +2,23 @@ from typing import Any, Callable, Dict, List import pandas as pd from loguru import logger -from rissk.utils.import_utils_kedro import extract_all_zip_files, filter_matching_folders -from rissk.utils.import_utils import get_survey_info, get_questionnaire, get_paradata, get_microdata +from rissk.utils.import_utils_kedro import ( + extract_all_zip_files, + filter_matching_folders, + get_survey_info, + get_questionnaire, + get_paradata, + get_microdata +) -def extract_zip_files_node(survey_partitions: Dict[str, Callable[[], Any]], zip_password: str) -> None: +def extract_zip_files_node(survey_zip_partitions: Dict[str, Callable[[], Any]], zip_password: str) -> None: """ Extract zip files referenced by the survey partition dataset. Procedural node: extraction side-effect only. """ - logger.info(f"Extracting zip files from {len(survey_partitions)} partition entries") - extract_all_zip_files(survey_partitions, zip_password=zip_password) + logger.info(f"Extracting zip files from {len(survey_zip_partitions)} partition entries") + extract_all_zip_files(survey_zip_partitions, zip_password=zip_password) def filter_extracted_survey_paths_node(survey_partitions: Dict[str, Callable[[], Any]], questionnaires: List[Dict]) -> List[Path]: diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py index 43eca91..45a092d 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py @@ -12,7 +12,7 @@ def create_pipeline(**kwargs) -> Pipeline: node( func=extract_zip_files_node, inputs=[ - "survey_partitions", + "survey_zip_partitions", "params:zip_password" ], outputs=None, @@ -22,7 +22,7 @@ def create_pipeline(**kwargs) -> Pipeline: node( func=filter_extracted_survey_paths_node, inputs=[ - "params:ingestion.raw_data_path", + "extracted_survey_folders", # This is where the extracted folders are passed. "params:survey.questionnaires", ], outputs="file_paths", diff --git a/rissk_kedro/src/rissk_kedro/settings.py b/rissk_kedro/src/rissk_kedro/settings.py index ed10cf4..8b31499 100644 --- a/rissk_kedro/src/rissk_kedro/settings.py +++ b/rissk_kedro/src/rissk_kedro/settings.py @@ -42,6 +42,9 @@ "default_run_env": "local", } +# # The folder where the source code is located +# CONF_SOURCE = "conf" + # Class that manages Kedro's library components. # from kedro.framework.context import KedroContext # CONTEXT_CLASS = KedroContext From ef24c0e4469fc6dec5f6f62a6b5f1b1939497d22 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Mon, 16 Feb 2026 16:59:46 +0000 Subject: [PATCH 13/70] Refactor data ingestion pipeline: - implement FolderDataset - enhance zip extraction - update catalog configuration --- .gitignore | 1 + rissk/utils/import_utils_kedro.py | 173 ++++++------------ rissk_kedro/conf/base/catalog.yml | 6 +- rissk_kedro/pyproject.toml | 6 +- .../src/rissk_kedro/datasets/__init__.py | 1 + .../src/rissk_kedro/datasets/folder.py | 29 +++ .../pipelines/data_ingestion/nodes.py | 27 ++- 7 files changed, 110 insertions(+), 133 deletions(-) create mode 100644 rissk_kedro/src/rissk_kedro/datasets/__init__.py create mode 100644 rissk_kedro/src/rissk_kedro/datasets/folder.py diff --git a/.gitignore b/.gitignore index 4cd17a8..0074df5 100644 --- a/.gitignore +++ b/.gitignore @@ -265,3 +265,4 @@ ingestion_nstructions.md ingestion_refactor_context.md rissk_kedro/stats.json rissk_kedro/feature_process_ploomber_pipeline_integration.md +data_ingestion_function_changes.md diff --git a/rissk/utils/import_utils_kedro.py b/rissk/utils/import_utils_kedro.py index 3010e9b..69d559d 100644 --- a/rissk/utils/import_utils_kedro.py +++ b/rissk/utils/import_utils_kedro.py @@ -5,6 +5,7 @@ import re import os import zipfile +import shutil import json # Added json import import pandas as pd # Added pandas import import numpy as np # Added numpy import @@ -22,23 +23,18 @@ parse_filename ) - def extract_zip(file_source_path: Path, file_dest_path: Path, password: Optional[str] = None): - """Memory-efficient recursive extraction for Python 3.13.""" - current_pwd = password or os.getenv('PASSWORD') - pwd_bytes = current_pwd.encode() if current_pwd else None - - # Ensure destination exists + """Memory-efficient recursive extraction.""" + pwd_bytes = password.encode() if password else None file_dest_path.mkdir(parents=True, exist_ok=True) try: with zipfile.ZipFile(file_source_path, 'r') as zip_ref: for file_info in zip_ref.infolist(): - target_path = file_dest_path / file_info.filename + target_path = (file_dest_path / file_info.filename).resolve() - # Prevent directory traversal vulnerability - if not str(target_path.resolve()).startswith(str(file_dest_path.resolve())): - logger.warning(f"Skipping extraction of {file_info.filename}: path traversal attempt") + # Security: Prevent ZipSlip/Path Traversal + if not str(target_path).startswith(str(file_dest_path.resolve())): continue if file_info.is_dir(): @@ -47,139 +43,74 @@ def extract_zip(file_source_path: Path, file_dest_path: Path, password: Optional target_path.parent.mkdir(parents=True, exist_ok=True) - if target_path.exists(): - # Optional: Skip already extracted files or overwrite - pass - + # Stream content to file to keep memory usage low with zip_ref.open(file_info, pwd=pwd_bytes) as source, \ open(target_path, "wb") as target: - target.write(source.read()) + shutil.copyfileobj(source, target) + # Recursive call for nested zips if target_path.suffix.lower() == '.zip': - nested_dest = target_path.with_suffix('') - extract_zip(target_path, nested_dest, password=current_pwd) - - logger.info(f"Extracted: {file_source_path.name}") + extract_zip(target_path, target_path.with_suffix(''), password=password) + except Exception as e: - logger.error(f"Failed {file_source_path}: {e}") - - -def _get_partition_path(partition_id: str, loader: Any) -> Optional[Path]: - """ - Robustly resolve partition path from a Kedro partition loader. - Compatible with Kedro 0.18+ and standard partition loaders. - """ - # 1. Try to get path from loader if it's a bound method (most datasets) - dataset = getattr(loader, "__self__", None) - if dataset: - for attr in ("_filepath", "filepath", "path", "_path"): - path = getattr(dataset, attr, None) - if path: - return Path(path) - - # 2. Try inspection for closures (legacy fallback) - try: - closure = getattr(loader, "__closure__", None) - if closure: - for cell in closure: - content = cell.cell_contents - for attr in ("_filepath", "filepath", "path", "_path"): - path = getattr(content, attr, None) - if path: - return Path(path) - except Exception: - pass - - # 3. Last resort: Assume partition_id is relative to current working directory - # (Unlikely in Kedro context but safe fallback structure wise if ID is path-like) - candidate = Path(partition_id) - if candidate.exists(): - return candidate - - return None + logger.error(f"Failed to extract {file_source_path.name}: {e}") -def extract_all_zip_files(partitions: dict[str, Any], zip_password: str = None) -> None: +def filter_matching_folders( + partitions: Dict[str, Callable[[], Path]], + questionnaires: List[Dict] +) -> List[Path]: """ - Extract all zip files referenced by Kedro partition IDs. - Recursively extracts nested zips. + Filters partition paths to return only directories that match + specific questionnaire name and version patterns. """ if not partitions: - logger.warning("No partitions found for zip extraction") - return - - # Collect source zips - zip_paths: list[Path] = [] - - for partition_id, loader in partitions.items(): - # Partition keys are typically relative paths - # We need the absolute path to the zip file - - # Only process items that look like zips - if not str(partition_id).lower().endswith(".zip"): - continue - - zip_path = _get_partition_path(partition_id, loader) - - if zip_path and zip_path.exists(): - zip_paths.append(zip_path) - else: - logger.warning(f"Could not resolve path for partition: {partition_id}") - - logger.info(f"Found {len(zip_paths)} top-level zip files to extract") - - for zip_path in zip_paths: - destination = zip_path.with_suffix("") - extract_zip(zip_path, destination, password=zip_password) - - -def filter_matching_folders(partitions: dict[str, Any], questionnaires: list[dict]) -> list[Path]: - """ - Return extracted folder paths matching questionnaire/version patterns. - Iterates over extracted folders (datasets) to find matches. - """ - if not partitions: - logger.warning("No partitions found while filtering extracted folders") + logger.warning("No partitions found while filtering extracted folders.") return [] - matching_folders: list[Path] = [] - seen_paths = set() - - # Pre-compile patterns + # 1. Pre-compile patterns for efficiency + # We use \b or strict string termination to ensure version 1 doesn't match 10 patterns = [] for q in questionnaires: name = q.get("name") versions = q.get("VERSION", []) + if not name or not versions: + continue + version_pattern = "|".join(map(str, versions)) - # Matches: NAME_VERSION_... (e.g. slbhies_listing_6_Paradata_All) - patterns.append(re.compile(rf"^{name}_({version_pattern})_.*")) + # Pattern: Matches start of string, the name, an underscore, + # one of the versions, and then an underscore or end of string. + # Example: ^slbhies_listing_(1|2|6)_.* + regex = re.compile(rf"^{re.escape(name)}_({version_pattern})_.*") + patterns.append(regex) - logger.info(f"Scanning {len(partitions)} folder partitions against {len(patterns)} patterns") + matching_folders: List[Path] = [] + seen_paths = set() + # 2. Iterate and validate for partition_id, loader in partitions.items(): - partition_path_obj = Path(partition_id) - folder_name = partition_path_obj.name - - # Check against patterns - is_match = False - for pattern in patterns: - if pattern.match(folder_name): - is_match = True - break - - if not is_match: - continue + try: + # Get the path from our FolderDataset + folder_path = loader() + + # CRITICAL CHECK: Ignore if it's a file (like the original .zip) + if not folder_path.is_dir(): + continue - # Resolved path - folder_path = _get_partition_path(partition_id, loader) - - if folder_path and folder_path.is_dir(): - folder_str = str(folder_path.resolve()) - if folder_str not in seen_paths: - seen_paths.add(folder_str) - matching_folders.append(folder_path) + folder_name = folder_path.name + + # Check against patterns + if any(pattern.match(folder_name) for pattern in patterns): + # Use resolve() to ensure uniqueness (avoids symlink duplicates) + resolved_path = folder_path.resolve() + if resolved_path not in seen_paths: + seen_paths.add(resolved_path) + matching_folders.append(folder_path) + + except Exception as e: + logger.error(f"Error processing partition {partition_id}: {e}") - logger.info(f"Found {len(matching_folders)} matching folders") + logger.info(f"Successfully matched {len(matching_folders)} survey directories.") return matching_folders diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index f98e17b..c704302 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -6,8 +6,8 @@ survey_zip_partitions: type: partitions.PartitionedDataset path: data/hies2024/latest/10_RAW dataset: - type: binary.BinaryDataset - filename_suffix: ".zip" + type: rissk_kedro.datasets.FolderDataset + filename_suffix: ".zip" # The extracted result (Folders) # Used by downstream nodes to find the directories @@ -15,7 +15,7 @@ extracted_survey_folders: type: partitions.PartitionedDataset path: data/hies2024/latest/10_RAW dataset: - type: folder.FolderDataset + type: rissk_kedro.datasets.FolderDataset # === INGESTED DataFrames === paradata_interim: diff --git a/rissk_kedro/pyproject.toml b/rissk_kedro/pyproject.toml index 362a3c0..a31a821 100644 --- a/rissk_kedro/pyproject.toml +++ b/rissk_kedro/pyproject.toml @@ -35,10 +35,12 @@ test = [ ] [tool.kedro] -package_name = "rissk" +package_name = "rissk_kedro" project_name = "rissk" kedro_init_version = "1.2.0" source_dir = "src" [tool.pytest.ini_options] -testpaths = ["tests"] \ No newline at end of file +testpaths = ["tests"] +[tool.kedro_telemetry] +project_id = "63718528078e46d1bd5fa456d349edb2" diff --git a/rissk_kedro/src/rissk_kedro/datasets/__init__.py b/rissk_kedro/src/rissk_kedro/datasets/__init__.py new file mode 100644 index 0000000..b23e013 --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/datasets/__init__.py @@ -0,0 +1 @@ +from .folder import FolderDataset \ No newline at end of file diff --git a/rissk_kedro/src/rissk_kedro/datasets/folder.py b/rissk_kedro/src/rissk_kedro/datasets/folder.py new file mode 100644 index 0000000..6f98be1 --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/datasets/folder.py @@ -0,0 +1,29 @@ +from __future__ import annotations +from pathlib import Path +from typing import Any +from kedro.io import AbstractDataset + +class FolderDataset(AbstractDataset[Path, Path]): + """ + A Kedro dataset that returns the Path to a file or directory. + Perfect for PartitionedDatasets where the node needs the file path + to perform custom operations (like unzipping). + """ + def __init__(self, filepath: str, **kwargs: Any): + self._filepath = Path(filepath) + # Store metadata (like suffix) for the _describe method + self._metadata = kwargs + + def _exists(self) -> bool: + return self._filepath.exists() + + def _load(self) -> Path: + # Simply return the path object to the node + return self._filepath + + def _save(self, data: Any = None) -> None: + # Ensure the directory exists; ignore 'data' if passed + self._filepath.mkdir(parents=True, exist_ok=True) + + def _describe(self) -> dict[str, Any]: + return dict(filepath=str(self._filepath), **self._metadata) diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py index e89d6e1..70bfaac 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py @@ -1,9 +1,9 @@ from pathlib import Path -from typing import Any, Callable, Dict, List +from typing import Any, Callable, Dict, List, Optional import pandas as pd from loguru import logger from rissk.utils.import_utils_kedro import ( - extract_all_zip_files, + extract_zip, filter_matching_folders, get_survey_info, get_questionnaire, @@ -12,13 +12,26 @@ ) -def extract_zip_files_node(survey_zip_partitions: Dict[str, Callable[[], Any]], zip_password: str) -> None: +def extract_zip_files_node(survey_zip_partitions: Dict[str, Callable[[], Path]], zip_password: str) -> None: """ - Extract zip files referenced by the survey partition dataset. - Procedural node: extraction side-effect only. + Node that iterates through partitions and triggers extraction. + Note: The type hint shows the loader returns a Path. """ - logger.info(f"Extracting zip files from {len(survey_zip_partitions)} partition entries") - extract_all_zip_files(survey_zip_partitions, zip_password=zip_password) + if not survey_zip_partitions: + logger.warning("No zip partitions found to extract.") + return + + for partition_id, loader in survey_zip_partitions.items(): + # 1. LOAD THE PATH (This calls FolderDataset._load) + zip_path = loader() + + # 2. VALIDATE & EXTRACT + if zip_path.suffix.lower() == ".zip" and zip_path.exists(): + destination = zip_path.with_suffix("") + logger.info(f"Extracting partition [{partition_id}] from {zip_path}") + extract_zip(zip_path, destination, password=zip_password) + else: + logger.debug(f"Skipping non-zip partition: {partition_id}") def filter_extracted_survey_paths_node(survey_partitions: Dict[str, Callable[[], Any]], questionnaires: List[Dict]) -> List[Path]: From 95ca0ce04bb9dfadb5a4d1a3857ac65503630f32 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Tue, 17 Feb 2026 16:17:20 +0000 Subject: [PATCH 14/70] Refactor import_utils_kedro.py to improve folder filtering logic and enhance error handling; update nodes.py to remove unnecessary debug logging; modify test_ingestion.ipynb to reflect changes in data structure and update expected outputs. --- rissk/utils/import_utils_kedro.py | 63 +- .../pipelines/data_ingestion/nodes.py | 4 +- .../src/rissk_kedro/test_ingestion.ipynb | 785 +++--------------- 3 files changed, 162 insertions(+), 690 deletions(-) diff --git a/rissk/utils/import_utils_kedro.py b/rissk/utils/import_utils_kedro.py index 69d559d..d0a2337 100644 --- a/rissk/utils/import_utils_kedro.py +++ b/rissk/utils/import_utils_kedro.py @@ -1,7 +1,7 @@ from __future__ import annotations from pathlib import Path -from typing import Any, Callable, Optional +from typing import Any, Callable, Optional, Dict, List import re import os import zipfile @@ -56,10 +56,7 @@ def extract_zip(file_source_path: Path, file_dest_path: Path, password: Optional logger.error(f"Failed to extract {file_source_path.name}: {e}") -def filter_matching_folders( - partitions: Dict[str, Callable[[], Path]], - questionnaires: List[Dict] -) -> List[Path]: +def filter_matching_folders(partitions: Dict[str, Callable[[], Path]], questionnaires: List[Dict]) -> List[Path]: """ Filters partition paths to return only directories that match specific questionnaire name and version patterns. @@ -88,25 +85,57 @@ def filter_matching_folders( seen_paths = set() # 2. Iterate and validate + # FIX: Use partition keys to strictly identify the top-level folder relative to the root. + # Keys in PartitionedDataset are relative paths like "SurveyFolder/Sub/File.ext". + # We only check the first component ("SurveyFolder") against the regex. + for partition_id, loader in partitions.items(): try: - # Get the path from our FolderDataset - folder_path = loader() + # partition_id is the relative path (e.g. "folder/sub/file.txt") + # We normalize it to a Path object to handle OS separators safely + relative_path = Path(partition_id) - # CRITICAL CHECK: Ignore if it's a file (like the original .zip) - if not folder_path.is_dir(): + # We expect at least a folder and a file (parts > 1) + # If the zip extracted to flat files at root, this checks prevents errors. + if len(relative_path.parts) < 2: continue - folder_name = folder_path.name + # The top-level folder name is the first part of the relative path + top_level_name = relative_path.parts[0] + + # Check if this top-level folder matches our patterns + is_match = False + for pattern in patterns: + if pattern.match(top_level_name): + is_match = True + break - # Check against patterns - if any(pattern.match(folder_name) for pattern in patterns): - # Use resolve() to ensure uniqueness (avoids symlink duplicates) - resolved_path = folder_path.resolve() - if resolved_path not in seen_paths: - seen_paths.add(resolved_path) - matching_folders.append(folder_path) + if is_match: + # Calculate the absolute path of the top-level folder + # We do this by taking the file's full path and stripping the + # sub-directories indicated by the relative path key. + file_path = loader() + + # We need to go up N levels where N = number of parts in relative path - 1 + # Example: Key="A/B/file" (3 parts). Path=".../A/B/file". + # We want ".../A". We need to go up 2 levels (file->B, B->A). + levels_up = len(relative_path.parts) - 1 + + # parents[0] is the directory containing the file. + # parents[levels_up-1] is the directory we want. + # Path.parents sequence: [parent, parent.parent, ...] + # Index 0 is the immediate parent. + + if levels_up > 0 and len(file_path.parents) >= levels_up: + # -1 because parents is 0-indexed (0 is 1 level up) + survey_folder = file_path.parents[levels_up - 1] + # Double check name consistency (sanity check) + if survey_folder.name == top_level_name: + resolved_path = survey_folder.resolve() + if resolved_path not in seen_paths: + seen_paths.add(resolved_path) + matching_folders.append(survey_folder) except Exception as e: logger.error(f"Error processing partition {partition_id}: {e}") diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py index 70bfaac..1cf85fd 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py @@ -30,8 +30,8 @@ def extract_zip_files_node(survey_zip_partitions: Dict[str, Callable[[], Path]], destination = zip_path.with_suffix("") logger.info(f"Extracting partition [{partition_id}] from {zip_path}") extract_zip(zip_path, destination, password=zip_password) - else: - logger.debug(f"Skipping non-zip partition: {partition_id}") + # else: + # logger.debug(f"Skipping non-zip partition: {partition_id}") def filter_extracted_survey_paths_node(survey_partitions: Dict[str, Callable[[], Any]], questionnaires: List[Dict]) -> List[Path]: diff --git a/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb b/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb index c5a4972..3db59e6 100644 --- a/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb +++ b/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb @@ -18,10 +18,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2026-02-12 22:47:14.128\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m12\u001b[0m - \u001b[1mPROJ_ROOT path is: /Users/vanessa/Work/Rowsquared/RISSK/rissk\u001b[0m\n", - "\u001b[32m2026-02-12 22:47:14.129\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m39\u001b[0m - \u001b[1mAvaliable Questionnaires\u001b[0m\n", - "\u001b[32m2026-02-12 22:47:14.129\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mQuestionnaire: snb_hies_hh - Versions: [9, 10, 11]\u001b[0m\n", - "\u001b[32m2026-02-12 22:47:14.130\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mQuestionnaire: slbhies_listing - Versions: [6, 7]\u001b[0m\n" + "\u001b[32m2026-02-16 23:26:50.695\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m12\u001b[0m - \u001b[1mPROJ_ROOT path is: /Users/vanessa/Work/Rowsquared/RISSK/rissk\u001b[0m\n", + "\u001b[32m2026-02-16 23:26:50.696\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m39\u001b[0m - \u001b[1mAvaliable Questionnaires\u001b[0m\n", + "\u001b[32m2026-02-16 23:26:50.696\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mQuestionnaire: snb_hies_hh - Versions: [9, 10, 11]\u001b[0m\n", + "\u001b[32m2026-02-16 23:26:50.697\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mQuestionnaire: slbhies_listing - Versions: [6, 7]\u001b[0m\n" ] } ], @@ -341,26 +341,11 @@ "data": { "text/plain": [ "(False,\n", - " {'shape': {'equal': True, 'shape_a': (8536570, 27), 'shape_b': (8536570, 27)},\n", - " 'columns': {'different_columns': [],\n", - " 'equal': True,\n", - " 'only_in_a': [],\n", - " 'only_in_b': []},\n", - " 'dtypes': {'mismatched_columns': [], 'equal': True},\n", - " 'auto_key': None,\n", - " 'cell_compare': {'checked': True,\n", - " 'columns_with_differences': ['interview__id',\n", - " 'order',\n", - " 'event',\n", - " 'responsible',\n", - " 'role',\n", - " 'timestamp_utc',\n", - " 'tz_offset',\n", - " 'parameters',\n", - " 'param',\n", - " 'answer',\n", - " 'roster_level',\n", - " 'timestamp_local',\n", + " {'shape': {'equal': False,\n", + " 'shape_a': (8536570, 11),\n", + " 'shape_b': (8536570, 27)},\n", + " 'columns': {'different_columns': ['timestamp_local',\n", + " 'qnr',\n", " 'qnr_version',\n", " 'qnr_seq',\n", " 'variable_name',\n", @@ -375,7 +360,40 @@ " 'answer_sequence',\n", " 'n_answers',\n", " 'question_sequence'],\n", - " 'total_cell_differences': 135297839,\n", + " 'equal': False,\n", + " 'only_in_a': [],\n", + " 'only_in_b': ['timestamp_local',\n", + " 'qnr',\n", + " 'qnr_version',\n", + " 'qnr_seq',\n", + " 'variable_name',\n", + " 'qtype',\n", + " 'question_type',\n", + " 'answers',\n", + " 'question_scope',\n", + " 'yes_no_view',\n", + " 'is_filtered_combobox',\n", + " 'is_integer',\n", + " 'cascade_from_question_id',\n", + " 'answer_sequence',\n", + " 'n_answers',\n", + " 'question_sequence']},\n", + " 'dtypes': {'mismatched_columns': ['timestamp_utc', 'tz_offset'],\n", + " 'equal': False},\n", + " 'auto_key': None,\n", + " 'cell_compare': {'checked': True,\n", + " 'columns_with_differences': ['interview__id',\n", + " 'order',\n", + " 'event',\n", + " 'responsible',\n", + " 'role',\n", + " 'timestamp_utc',\n", + " 'tz_offset',\n", + " 'parameters',\n", + " 'param',\n", + " 'answer',\n", + " 'roster_level'],\n", + " 'total_cell_differences': 72638817,\n", " 'rows_compared': 8536570,\n", " 'note': 'aligned by index intersection'},\n", " 'same': False})" @@ -400,25 +418,20 @@ "data": { "text/plain": [ "(False,\n", - " {'shape': {'equal': True, 'shape_a': (1203121, 43), 'shape_b': (1203121, 43)},\n", - " 'columns': {'different_columns': [],\n", - " 'equal': True,\n", - " 'only_in_a': [],\n", - " 'only_in_b': []},\n", - " 'dtypes': {'mismatched_columns': [], 'equal': True},\n", - " 'auto_key': None,\n", - " 'cell_compare': {'checked': True,\n", - " 'columns_with_differences': ['interview__id',\n", + " {'shape': {'equal': False, 'shape_a': (0, 0), 'shape_b': (1203121, 43)},\n", + " 'columns': {'different_columns': ['interview__id',\n", " 'roster_level',\n", " 'variable',\n", " 'value',\n", " 'filename',\n", + " 'qnr',\n", " 'qnr_version',\n", " 'qnr_seq',\n", " 'variable_name',\n", " 'qtype',\n", " 'question_type',\n", " 'answers',\n", + " 'children',\n", " 'condition_expression',\n", " 'hide_if_disabled',\n", " 'featured',\n", @@ -435,6 +448,8 @@ " 'is_filtered_combobox',\n", " 'is_integer',\n", " 'categories_id',\n", + " 'title',\n", + " 'is_roster',\n", " 'linked_to_roster_id',\n", " 'linked_to_question_id',\n", " 'cascade_from_question_id',\n", @@ -447,9 +462,57 @@ " 'parent_3',\n", " 'parent_4',\n", " 'question_sequence'],\n", - " 'total_cell_differences': 26398123,\n", - " 'rows_compared': 1203121,\n", - " 'note': 'aligned by index intersection'},\n", + " 'equal': False,\n", + " 'only_in_a': [],\n", + " 'only_in_b': ['interview__id',\n", + " 'roster_level',\n", + " 'variable',\n", + " 'value',\n", + " 'filename',\n", + " 'qnr',\n", + " 'qnr_version',\n", + " 'qnr_seq',\n", + " 'variable_name',\n", + " 'qtype',\n", + " 'question_type',\n", + " 'answers',\n", + " 'children',\n", + " 'condition_expression',\n", + " 'hide_if_disabled',\n", + " 'featured',\n", + " 'instructions',\n", + " 'properties',\n", + " 'public_key',\n", + " 'question_scope',\n", + " 'question_text',\n", + " 'stata_export_caption',\n", + " 'variable_label',\n", + " 'is_timestamp',\n", + " 'validation_conditions',\n", + " 'yes_no_view',\n", + " 'is_filtered_combobox',\n", + " 'is_integer',\n", + " 'categories_id',\n", + " 'title',\n", + " 'is_roster',\n", + " 'linked_to_roster_id',\n", + " 'linked_to_question_id',\n", + " 'cascade_from_question_id',\n", + " 'parents',\n", + " 'answer_sequence',\n", + " 'n_answers',\n", + " 'is_linked',\n", + " 'parent_1',\n", + " 'parent_2',\n", + " 'parent_3',\n", + " 'parent_4',\n", + " 'question_sequence']},\n", + " 'dtypes': {'mismatched_columns': [], 'equal': True},\n", + " 'auto_key': None,\n", + " 'cell_compare': {'checked': True,\n", + " 'note': 'no common columns to compare',\n", + " 'columns_with_differences': [],\n", + " 'total_cell_differences': 0},\n", " 'same': False})" ] }, @@ -513,11 +576,12 @@ " 'is_linked',\n", " 'parent_1',\n", " 'parent_2',\n", - " 'parent_3',\n", - " 'parent_4',\n", " 'question_sequence',\n", - " 'qnr_version'],\n", - " 'total_cell_differences': 20059,\n", + " 'qnr',\n", + " 'qnr_version',\n", + " 'parent_3',\n", + " 'parent_4'],\n", + " 'total_cell_differences': 68650,\n", " 'rows_compared': 3487,\n", " 'note': 'aligned by index intersection'},\n", " 'same': False})" @@ -546,459 +610,13 @@ "name": "index", "rawType": "int64", "type": "integer" - }, - { - "name": "interview__id", - "rawType": "object", - "type": "string" - }, - { - "name": "roster_level", - "rawType": "object", - "type": "string" - }, - { - "name": "variable", - "rawType": "object", - "type": "string" - }, - { - "name": "value", - "rawType": "object", - "type": "string" - }, - { - "name": "filename", - "rawType": "object", - "type": "string" - }, - { - "name": "qnr", - "rawType": "object", - "type": "string" - }, - { - "name": "qnr_version", - "rawType": "object", - "type": "string" - }, - { - "name": "qnr_seq", - "rawType": "int64", - "type": "integer" - }, - { - "name": "variable_name", - "rawType": "object", - "type": "string" - }, - { - "name": "qtype", - "rawType": "object", - "type": "string" - }, - { - "name": "question_type", - "rawType": "float64", - "type": "float" - }, - { - "name": "answers", - "rawType": "object", - "type": "unknown" - }, - { - "name": "children", - "rawType": "object", - "type": "unknown" - }, - { - "name": "condition_expression", - "rawType": "object", - "type": "unknown" - }, - { - "name": "hide_if_disabled", - "rawType": "object", - "type": "unknown" - }, - { - "name": "featured", - "rawType": "object", - "type": "unknown" - }, - { - "name": "instructions", - "rawType": "object", - "type": "unknown" - }, - { - "name": "properties", - "rawType": "object", - "type": "unknown" - }, - { - "name": "public_key", - "rawType": "object", - "type": "string" - }, - { - "name": "question_scope", - "rawType": "float64", - "type": "float" - }, - { - "name": "question_text", - "rawType": "object", - "type": "unknown" - }, - { - "name": "stata_export_caption", - "rawType": "object", - "type": "unknown" - }, - { - "name": "variable_label", - "rawType": "object", - "type": "unknown" - }, - { - "name": "is_timestamp", - "rawType": "object", - "type": "unknown" - }, - { - "name": "validation_conditions", - "rawType": "object", - "type": "unknown" - }, - { - "name": "yes_no_view", - "rawType": "object", - "type": "unknown" - }, - { - "name": "is_filtered_combobox", - "rawType": "object", - "type": "unknown" - }, - { - "name": "is_integer", - "rawType": "object", - "type": "unknown" - }, - { - "name": "categories_id", - "rawType": "object", - "type": "unknown" - }, - { - "name": "title", - "rawType": "object", - "type": "unknown" - }, - { - "name": "is_roster", - "rawType": "object", - "type": "unknown" - }, - { - "name": "linked_to_roster_id", - "rawType": "object", - "type": "unknown" - }, - { - "name": "linked_to_question_id", - "rawType": "object", - "type": "unknown" - }, - { - "name": "cascade_from_question_id", - "rawType": "object", - "type": "unknown" - }, - { - "name": "parents", - "rawType": "object", - "type": "string" - }, - { - "name": "answer_sequence", - "rawType": "object", - "type": "string" - }, - { - "name": "n_answers", - "rawType": "float64", - "type": "float" - }, - { - "name": "is_linked", - "rawType": "bool", - "type": "boolean" - }, - { - "name": "parent_1", - "rawType": "object", - "type": "string" - }, - { - "name": "parent_2", - "rawType": "object", - "type": "unknown" - }, - { - "name": "parent_3", - "rawType": "object", - "type": "unknown" - }, - { - "name": "parent_4", - "rawType": "object", - "type": "unknown" - }, - { - "name": "question_sequence", - "rawType": "float64", - "type": "float" } ], - "ref": "87ff61f3-2ad0-45c2-a404-a181f758b4a9", - "rows": [ - [ - "0", - "0093cc0b63c24abd96eeed5cbc25600f", - "", - "sampling_hh", - "Solomon Roni", - "snb_hies_hh.dta", - "snb_hies_hh", - "9", - "1", - "sampling_hh", - "TextQuestion", - "7.0", - "[]", - "[]", - "", - "False", - "True", - "ENTER THE NAME/DESCRIPTION EXACTLY AS IT IS ON THE SAMPLING SHEET (IF THERE IS ANY UPDATE, IT CAN BE MADE IN THE HOUSEHOLD ROSTER)", - "{'GeometryInputMode': 0.0, 'GeometryType': 0.0, 'HideInstructions': False, 'IsCritical': False, 'OptionsFilterExpression': None, 'UseFormatting': False}", - "2f0be987-8953-2a02-d32e-b89e2eed4107", - "0.0", - "NAME OF SAMPLED UNIT", - "sampling_hh", - "SAMPLING NAME", - "False", - "[]", - null, - null, - null, - null, - null, - null, - null, - null, - null, - "Cover", - "nan", - null, - "False", - "Cover", - null, - null, - null, - "1.0" - ], - [ - "1", - "0093cc0b63c24abd96eeed5cbc25600f", - "", - "sampling_id", - "142", - "snb_hies_hh.dta", - "snb_hies_hh", - "9", - "2", - "sampling_id", - "TextQuestion", - "7.0", - "[]", - "[]", - "", - "False", - "True", - "ENTER THE ID FROM SAMPLING SHEET.
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\n", "" ], "text/plain": [ - " interview__id roster_level variable value \\\n", - "0 0093cc0b63c24abd96eeed5cbc25600f sampling_hh Solomon Roni \n", - "1 0093cc0b63c24abd96eeed5cbc25600f sampling_id 142 \n", - "2 0093cc0b63c24abd96eeed5cbc25600f RESULT Consent given \n", - "3 0093cc0b63c24abd96eeed5cbc25600f ward 211 \n", - "4 0093cc0b63c24abd96eeed5cbc25600f ea 211060200 \n", - "\n", - " filename qnr qnr_version qnr_seq variable_name \\\n", - "0 snb_hies_hh.dta snb_hies_hh 9 1 sampling_hh \n", - "1 snb_hies_hh.dta snb_hies_hh 9 2 sampling_id \n", - "2 snb_hies_hh.dta snb_hies_hh 9 3 RESULT \n", - "3 snb_hies_hh.dta snb_hies_hh 9 4 ward \n", - "4 snb_hies_hh.dta snb_hies_hh 9 5 ea \n", - "\n", - " qtype ... cascade_from_question_id parents \\\n", - "0 TextQuestion ... None Cover \n", - "1 TextQuestion ... None Cover \n", - "2 Variable ... None Cover \n", - "3 SingleQuestion ... None Cover \n", - "4 SingleQuestion ... 08380cbb-b78a-e407-903b-800aa1ed95da Cover \n", - "\n", - " answer_sequence n_answers is_linked parent_1 parent_2 parent_3 parent_4 \\\n", - "0 nan NaN False Cover None None None \n", - "1 nan NaN False Cover None None None \n", - "2 nan NaN False Cover None None None \n", - "3 nan NaN False Cover None None None \n", - "4 nan NaN False Cover None None None \n", - "\n", - " question_sequence \n", - "0 1.0 \n", - "1 2.0 \n", - "2 NaN \n", - "3 3.0 \n", - "4 4.0 \n", - "\n", - "[5 rows x 43 columns]" + "Empty DataFrame\n", + "Columns: []\n", + "Index: []" ] }, "execution_count": 10, @@ -1220,7 +663,7 @@ ], "metadata": { "kernelspec": { - "display_name": "rissk_rs_01", + "display_name": "rissk_py3_13_macos", "language": "python", "name": "python3" }, @@ -1234,7 +677,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.23" + "version": "3.13.12" } }, "nbformat": 4, From 9b335f64646e524487d3408cf7ec4754d4a10596 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Tue, 17 Feb 2026 17:18:28 +0000 Subject: [PATCH 15/70] Refactor code structure for improved readability and maintainability --- rissk_kedro/conf/base/catalog.yml | 4 +- .../src/rissk_kedro/datasets/__init__.py | 2 +- .../datasets/{folder.py => path.py} | 10 +- .../pipelines/data_ingestion/nodes.py | 2 +- .../src/rissk_kedro/test_ingestion.ipynb | 2128 ++++++++++++++++- 5 files changed, 2047 insertions(+), 99 deletions(-) rename rissk_kedro/src/rissk_kedro/datasets/{folder.py => path.py} (71%) diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index c704302..3a9cfe4 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -6,7 +6,7 @@ survey_zip_partitions: type: partitions.PartitionedDataset path: data/hies2024/latest/10_RAW dataset: - type: rissk_kedro.datasets.FolderDataset + type: rissk_kedro.datasets.PathDataset filename_suffix: ".zip" # The extracted result (Folders) @@ -15,7 +15,7 @@ extracted_survey_folders: type: partitions.PartitionedDataset path: data/hies2024/latest/10_RAW dataset: - type: rissk_kedro.datasets.FolderDataset + type: rissk_kedro.datasets.PathDataset # === INGESTED DataFrames === paradata_interim: diff --git a/rissk_kedro/src/rissk_kedro/datasets/__init__.py b/rissk_kedro/src/rissk_kedro/datasets/__init__.py index b23e013..dd98a7a 100644 --- a/rissk_kedro/src/rissk_kedro/datasets/__init__.py +++ b/rissk_kedro/src/rissk_kedro/datasets/__init__.py @@ -1 +1 @@ -from .folder import FolderDataset \ No newline at end of file +from .path import PathDataset \ No newline at end of file diff --git a/rissk_kedro/src/rissk_kedro/datasets/folder.py b/rissk_kedro/src/rissk_kedro/datasets/path.py similarity index 71% rename from rissk_kedro/src/rissk_kedro/datasets/folder.py rename to rissk_kedro/src/rissk_kedro/datasets/path.py index 6f98be1..711f398 100644 --- a/rissk_kedro/src/rissk_kedro/datasets/folder.py +++ b/rissk_kedro/src/rissk_kedro/datasets/path.py @@ -3,11 +3,17 @@ from typing import Any from kedro.io import AbstractDataset -class FolderDataset(AbstractDataset[Path, Path]): +class PathDataset(AbstractDataset[Path, Path]): """ A Kedro dataset that returns the Path to a file or directory. - Perfect for PartitionedDatasets where the node needs the file path + Perfect for PartitionedDatasets where the node needs the file path to perform custom operations (like unzipping). + + Note: the Kedro implementation that uses this dataset performs a + "walk" over the target path and therefore returns all file paths + underneath the directory (i.e., a recursive listing). This + dataset exposes the `Path` object; downstream nodes should handle + whether the path is a file or directory and act accordingly. """ def __init__(self, filepath: str, **kwargs: Any): self._filepath = Path(filepath) diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py index 1cf85fd..ba9acb4 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py @@ -22,7 +22,7 @@ def extract_zip_files_node(survey_zip_partitions: Dict[str, Callable[[], Path]], return for partition_id, loader in survey_zip_partitions.items(): - # 1. LOAD THE PATH (This calls FolderDataset._load) + # 1. LOAD THE PATH (This calls PathDataset._load) zip_path = loader() # 2. VALIDATE & EXTRACT diff --git a/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb b/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb index 3db59e6..ca0fd6f 100644 --- a/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb +++ b/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb @@ -18,10 +18,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2026-02-16 23:26:50.695\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m12\u001b[0m - \u001b[1mPROJ_ROOT path is: /Users/vanessa/Work/Rowsquared/RISSK/rissk\u001b[0m\n", - "\u001b[32m2026-02-16 23:26:50.696\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m39\u001b[0m - \u001b[1mAvaliable Questionnaires\u001b[0m\n", - "\u001b[32m2026-02-16 23:26:50.696\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mQuestionnaire: snb_hies_hh - Versions: [9, 10, 11]\u001b[0m\n", - "\u001b[32m2026-02-16 23:26:50.697\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mQuestionnaire: slbhies_listing - Versions: [6, 7]\u001b[0m\n" + "\u001b[32m2026-02-17 16:12:26.489\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m12\u001b[0m - \u001b[1mPROJ_ROOT path is: /Users/vanessa/Work/Rowsquared/RISSK/rissk\u001b[0m\n", + "\u001b[32m2026-02-17 16:12:26.490\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m39\u001b[0m - \u001b[1mAvaliable Questionnaires\u001b[0m\n", + "\u001b[32m2026-02-17 16:12:26.490\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mQuestionnaire: snb_hies_hh - Versions: [9, 10, 11]\u001b[0m\n", + "\u001b[32m2026-02-17 16:12:26.490\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mQuestionnaire: slbhies_listing - Versions: [6, 7]\u001b[0m\n" ] } ], @@ -318,7 +318,7 @@ { "data": { "text/plain": [ - "(1203121, 43)" + "(23127, 41)" ] }, "execution_count": 6, @@ -334,6 +334,27 @@ { "cell_type": "code", "execution_count": 7, + "id": "2bc15ac6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(78413, 11)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_para_kedro.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 8, "id": "792a94d3", "metadata": {}, "outputs": [ @@ -341,9 +362,7 @@ "data": { "text/plain": [ "(False,\n", - " {'shape': {'equal': False,\n", - " 'shape_a': (8536570, 11),\n", - " 'shape_b': (8536570, 27)},\n", + " {'shape': {'equal': False, 'shape_a': (78413, 11), 'shape_b': (78413, 27)},\n", " 'columns': {'different_columns': ['timestamp_local',\n", " 'qnr',\n", " 'qnr_version',\n", @@ -382,24 +401,14 @@ " 'equal': False},\n", " 'auto_key': None,\n", " 'cell_compare': {'checked': True,\n", - " 'columns_with_differences': ['interview__id',\n", - " 'order',\n", - " 'event',\n", - " 'responsible',\n", - " 'role',\n", - " 'timestamp_utc',\n", - " 'tz_offset',\n", - " 'parameters',\n", - " 'param',\n", - " 'answer',\n", - " 'roster_level'],\n", - " 'total_cell_differences': 72638817,\n", - " 'rows_compared': 8536570,\n", + " 'columns_with_differences': ['timestamp_utc', 'tz_offset'],\n", + " 'total_cell_differences': 156826,\n", + " 'rows_compared': 78413,\n", " 'note': 'aligned by index intersection'},\n", " 'same': False})" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -410,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "de3363a0", "metadata": {}, "outputs": [ @@ -418,7 +427,7 @@ "data": { "text/plain": [ "(False,\n", - " {'shape': {'equal': False, 'shape_a': (0, 0), 'shape_b': (1203121, 43)},\n", + " {'shape': {'equal': False, 'shape_a': (0, 0), 'shape_b': (23127, 41)},\n", " 'columns': {'different_columns': ['interview__id',\n", " 'roster_level',\n", " 'variable',\n", @@ -459,8 +468,6 @@ " 'is_linked',\n", " 'parent_1',\n", " 'parent_2',\n", - " 'parent_3',\n", - " 'parent_4',\n", " 'question_sequence'],\n", " 'equal': False,\n", " 'only_in_a': [],\n", @@ -504,8 +511,6 @@ " 'is_linked',\n", " 'parent_1',\n", " 'parent_2',\n", - " 'parent_3',\n", - " 'parent_4',\n", " 'question_sequence']},\n", " 'dtypes': {'mismatched_columns': [], 'equal': True},\n", " 'auto_key': None,\n", @@ -516,7 +521,7 @@ " 'same': False})" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -527,78 +532,1266 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "81f6cde7", "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "\"['public_key'] not in index\"", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mcompare_parquet_files\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_questionnaire_kedro\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_questionnaire\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mcells\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 123\u001b[39m, in \u001b[36mcompare_parquet_files\u001b[39m\u001b[34m(df_a, df_b, check, atol, rtol)\u001b[39m\n\u001b[32m 121\u001b[39m b_k = df_b.set_index(candidate_key)\n\u001b[32m 122\u001b[39m common_idx = a_k.index.intersection(b_k.index)\n\u001b[32m--> \u001b[39m\u001b[32m123\u001b[39m a_al = \u001b[43ma_k\u001b[49m\u001b[43m.\u001b[49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcommon_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcommon\u001b[49m\u001b[43m]\u001b[49m.fillna(\u001b[33m'\u001b[39m\u001b[33m__NA__\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 124\u001b[39m b_al = b_k.loc[common_idx, common].fillna(\u001b[33m'\u001b[39m\u001b[33m__NA__\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 125\u001b[39m rows_compared = \u001b[38;5;28mlen\u001b[39m(common_idx)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexing.py:1185\u001b[39m, in \u001b[36m_LocationIndexer.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 1183\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._is_scalar_access(key):\n\u001b[32m 1184\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.obj._get_value(*key, takeable=\u001b[38;5;28mself\u001b[39m._takeable)\n\u001b[32m-> \u001b[39m\u001b[32m1185\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_getitem_tuple\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1186\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1187\u001b[39m \u001b[38;5;66;03m# we by definition only have the 0th axis\u001b[39;00m\n\u001b[32m 1188\u001b[39m axis = \u001b[38;5;28mself\u001b[39m.axis \u001b[38;5;129;01mor\u001b[39;00m \u001b[32m0\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexing.py:1376\u001b[39m, in \u001b[36m_LocIndexer._getitem_tuple\u001b[39m\u001b[34m(self, tup)\u001b[39m\n\u001b[32m 1374\u001b[39m \u001b[38;5;66;03m# ugly hack for GH #836\u001b[39;00m\n\u001b[32m 1375\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._multi_take_opportunity(tup):\n\u001b[32m-> \u001b[39m\u001b[32m1376\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_multi_take\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1378\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._getitem_tuple_same_dim(tup)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexing.py:1328\u001b[39m, in \u001b[36m_LocIndexer._multi_take\u001b[39m\u001b[34m(self, tup)\u001b[39m\n\u001b[32m 1311\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 1312\u001b[39m \u001b[33;03mCreate the indexers for the passed tuple of keys, and\u001b[39;00m\n\u001b[32m 1313\u001b[39m \u001b[33;03mexecutes the take operation. This allows the take operation to be\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 1324\u001b[39m \u001b[33;03mvalues: same type as the object being indexed\u001b[39;00m\n\u001b[32m 1325\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 1326\u001b[39m \u001b[38;5;66;03m# GH 836\u001b[39;00m\n\u001b[32m 1327\u001b[39m d = {\n\u001b[32m-> \u001b[39m\u001b[32m1328\u001b[39m axis: \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_get_listlike_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1329\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m (key, axis) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(tup, \u001b[38;5;28mself\u001b[39m.obj._AXIS_ORDERS)\n\u001b[32m 1330\u001b[39m }\n\u001b[32m 1331\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.obj._reindex_with_indexers(d, copy=\u001b[38;5;28;01mTrue\u001b[39;00m, allow_dups=\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexing.py:1559\u001b[39m, in \u001b[36m_LocIndexer._get_listlike_indexer\u001b[39m\u001b[34m(self, key, axis)\u001b[39m\n\u001b[32m 1556\u001b[39m ax = \u001b[38;5;28mself\u001b[39m.obj._get_axis(axis)\n\u001b[32m 1557\u001b[39m axis_name = \u001b[38;5;28mself\u001b[39m.obj._get_axis_name(axis)\n\u001b[32m-> \u001b[39m\u001b[32m1559\u001b[39m keyarr, indexer = \u001b[43max\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1561\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m keyarr, indexer\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexes/base.py:6212\u001b[39m, in \u001b[36mIndex._get_indexer_strict\u001b[39m\u001b[34m(self, key, axis_name)\u001b[39m\n\u001b[32m 6209\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 6210\u001b[39m keyarr, indexer, new_indexer = \u001b[38;5;28mself\u001b[39m._reindex_non_unique(keyarr)\n\u001b[32m-> \u001b[39m\u001b[32m6212\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 6214\u001b[39m keyarr = \u001b[38;5;28mself\u001b[39m.take(indexer)\n\u001b[32m 6215\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[32m 6216\u001b[39m \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexes/base.py:6264\u001b[39m, in \u001b[36mIndex._raise_if_missing\u001b[39m\u001b[34m(self, key, indexer, axis_name)\u001b[39m\n\u001b[32m 6261\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m]\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 6263\u001b[39m not_found = \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask.nonzero()[\u001b[32m0\u001b[39m]].unique())\n\u001b[32m-> \u001b[39m\u001b[32m6264\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m not in index\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[31mKeyError\u001b[39m: \"['public_key'] not in index\"" + ] + } + ], + "source": [ + "compare_parquet_files(df_questionnaire_kedro, df_questionnaire, check='cells')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "143d6b7f", + "metadata": {}, "outputs": [ { "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "qnr_seq", + "rawType": "int64", + "type": "integer" + }, + { + "name": "variable_name", + "rawType": "object", + "type": "string" + }, + { + "name": "qtype", + "rawType": "object", + "type": "string" + }, + { + "name": "question_type", + "rawType": "float64", + "type": "float" + }, + { + "name": "answers", + "rawType": "object", + "type": "unknown" + }, + { + "name": "children", + "rawType": "object", + "type": "unknown" + }, + { + "name": "condition_expression", + "rawType": "object", + "type": "unknown" + }, + { + "name": "hide_if_disabled", + "rawType": "object", + "type": "unknown" + }, + { + "name": "featured", + "rawType": "object", + "type": "unknown" + }, + { + "name": "instructions", + "rawType": "object", + "type": "unknown" + }, + { + "name": "properties", + "rawType": "object", + "type": "unknown" + }, + { + "name": "public_key", + "rawType": "object", + "type": "string" + }, + { + "name": "question_scope", + "rawType": "float64", + "type": "float" + }, + { + "name": "question_text", + "rawType": "object", + "type": "unknown" + }, + { + "name": "stata_export_caption", + "rawType": "object", + "type": "unknown" + }, + { + "name": "variable_label", + "rawType": "object", + "type": "unknown" + }, + { + "name": "is_timestamp", + "rawType": "object", + "type": "unknown" + }, + { + "name": "validation_conditions", + "rawType": "object", + "type": "unknown" + }, + { + "name": "yes_no_view", + "rawType": "object", + "type": "unknown" + }, + { + "name": "is_filtered_combobox", + "rawType": "object", + "type": "unknown" + }, + { + "name": "is_integer", + "rawType": "object", + "type": "unknown" + }, + { + "name": "categories_id", + "rawType": "object", + "type": "unknown" + }, + { + "name": "title", + "rawType": "object", + "type": "unknown" + }, + { + "name": "is_roster", + "rawType": "object", + "type": "unknown" + }, + { + "name": "linked_to_roster_id", + "rawType": "object", + "type": "unknown" + }, + { + "name": "linked_to_question_id", + "rawType": "object", + "type": "unknown" + }, + { + "name": "cascade_from_question_id", + "rawType": "object", + "type": "unknown" + }, + { + "name": "parents", + "rawType": "object", + "type": "string" + }, + { + "name": "answer_sequence", + "rawType": "object", + "type": "string" + }, + { + "name": "n_answers", + "rawType": "float64", + "type": "float" + }, + { + "name": "is_linked", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "parent_1", + "rawType": "object", + "type": "string" + }, + { + "name": "parent_2", + "rawType": "object", + "type": "unknown" + }, + { + "name": "question_sequence", + "rawType": "float64", + "type": "float" + }, + { + "name": "qnr", + "rawType": "object", + "type": "string" + }, + { + "name": "qnr_version", + "rawType": "object", + "type": "string" + } + ], + "ref": "271671f3-501a-4566-a15f-60b705b0f50e", + "rows": [ + [ + "0", + "0", + "", + "Group", + null, + null, + "[{'$type': 'SingleQuestion', 'Answers': array([], dtype=object), 'AttachmentName': None, 'CascadeFromQuestionId': None, 'CategoriesId': '351e8a12-c335-9e8e-a196-7a4191f33880', 'Children': array([], dtype=object), 'ConditionExpression': '', 'CustomRosterTitle': None, 'Description': None, 'DisplayMode': None, 'DoNotExport': None, 'Enabled': None, 'Expression': None, 'Featured': True, 'FixedRosterTitles': None, 'HideIfDisabled': False, 'Instructions': '', 'IsFilteredCombobox': True, 'IsFlatMode': None, 'IsInteger': None, 'IsPlainMode': None, 'IsRoster': None, 'IsSignature': None, 'IsTimestamp': False, 'Label': None, 'MaxAnswerCount': None, 'Name': None, 'Properties': {'GeometryInputMode': None, 'GeometryOverlapDetection': None, 'GeometryType': None, 'HideInstructions': False, 'IsCritical': False, 'UseFormatting': False}, 'PublicKey': '330266f5-d168-b402-a4d3-24921597cd86', 'QuestionScope': 0.0, 'QuestionText': 'WARD', 'QuestionType': 0.0, 'RosterSizeQuestionId': None, 'RosterSizeSource': None, 'ShowAsList': False, 'ShowAsListThreshold': None, 'StataExportCaption': 'ward', 'Text': None, 'Title': None, 'Type': None, 'UseFormatting': None, 'ValidationConditions': array([], dtype=object), 'VariableLabel': 'WARD', 'VariableName': 'ward'}\n {'$type': 'SingleQuestion', 'Answers': array([], dtype=object), 'AttachmentName': None, 'CascadeFromQuestionId': 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NaN None NaN None \n", + "1 None ... NaN None NaN None \n", + "2 ... NaN None NaN None \n", + "3 None ... NaN None NaN None \n", + "4 None ... NaN None NaN None \n", + "\n", + " is_filtered_combobox is_integer cascade_from_question_id answer_sequence \\\n", + "0 None None None nan \n", + "1 None None None nan \n", + "2 None None None nan \n", + "3 None None None nan \n", + "4 None None None nan \n", + "\n", + " n_answers question_sequence \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + "[5 rows x 27 columns]" ] }, - "execution_count": 10, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_microdata_kedro.head(5)" + "df_para.head(5)" ] } ], From f2054ceea5113a3e5b1dcad1333db413a2b533c9 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Tue, 17 Feb 2026 23:00:27 +0000 Subject: [PATCH 16/70] Refactor code structure for improved readability and maintainability --- ingestion_discrepancies.md | 27 + rissk/utils/file_process_utils_kedro.py | 287 ++ rissk/utils/import_utils_kedro.py | 63 +- .../src/rissk_kedro/test_ingestion.ipynb | 2873 +++++++++-------- 4 files changed, 1886 insertions(+), 1364 deletions(-) create mode 100644 ingestion_discrepancies.md create mode 100644 rissk/utils/file_process_utils_kedro.py diff --git a/ingestion_discrepancies.md b/ingestion_discrepancies.md new file mode 100644 index 0000000..8601a26 --- /dev/null +++ b/ingestion_discrepancies.md @@ -0,0 +1,27 @@ +# Data Ingestion Discrepancies: Ploomber (Legacy) vs Kedro (New) + +## Overview +This document tracks intentional data discrepancies between the legacy Ploomber pipeline and the new Kedro pipeline. These differences are accepted improve data quality or cleanliness. + +## 1. Microdata `value` Column Normalization + +### The Discrepancy +- **Legacy (Ploomber):** The `value` column in `microdata.parquet` contains a mix of format styles for integer-like values. + - Example: `1` (integer-like string) and `1.0` (float-like string) appear inconsistently for the same logical value. +- **New (Kedro):** The pipeline now explicitly normalizes values before conversion to string. + - Logic: If a float value `x` is equivalent to an integer (`x.is_integer() is True`), it is converted to an integer before stringification. + - Result: `1.0` becomes `"1"`. `1.5` remains `"1.5"`. + - Lists: This normalization is also applied to values inside list-strings (e.g., `"[1.0, 2.0]"` becomes `"[1, 2]"`). + +### Decision +**Status:** ACCEPTED (Intentional Deviation) + +We have chosen to keep the cleaner, normalized integer format in Kedro. +- **Reasoning:** + 1. The values are typically categorical codes (IDs, boolean flags like 0/1), where `1` is semantically more accurate than `1.0`. + 2. Mixed formatting in the legacy pipeline appears to be an artifact of how Pandas handles `NaN`s (forcing floats) rather than intentional data design. + 3. Uniform formatting simplifies downstream processing. + +### Downstream Implications +Any downstream code (feature engineering, analysis) that performs **exact string matching** against float-strings (e.g., `val == "1.0"`) may fail or return empty results. +- **Action Required:** Ensure downstream filtering uses type-safe comparisons (convert to float/int before comparing) or checks for the normalized string `"1"`. diff --git a/rissk/utils/file_process_utils_kedro.py b/rissk/utils/file_process_utils_kedro.py new file mode 100644 index 0000000..fc2d871 --- /dev/null +++ b/rissk/utils/file_process_utils_kedro.py @@ -0,0 +1,287 @@ +import os +from pathlib import Path +import pandas as pd +from typing import Dict +import re + + + +def set_qnr_version(df, survey_project, project_version): + df['qnr'] = survey_project + df['qnr_version'] = project_version + return df + + +def normalize_column_name(s): + """ + This function converts any string with capital letters to a string all lowercase with a "_" before any previously capital letter. + + Parameters: + s (str): The string to convert. + + Returns: + new_s (str): The converted string. + """ + new_s = "" + for i, char in enumerate(s): + if char.isupper(): + # Add underscore only if it's not the first or last character + if i != 0 and i != len(s) - 1: + new_s += "_" + new_s += char.lower() + else: + new_s += char + return new_s + + +def transform_multi(df, variable_list, transformation_type): + """ + This function takes a DataFrame and a list of variable names and applies a transformation depending on + transformation_type to the variables in the DataFrame that start with the given variable names. + + The transformation can be either 'unlinked,' 'linked,' 'list,' or 'gps.' + + Parameters: + df (DataFrame): The DataFrame to be transformed. + variable_list (list): The list of variable names to be transformed. + transformation_type (str): The type of transformation to apply. Must be 'unlinked,' 'linked,' 'list,' or 'gps.' + + Returns: + DataFrame: The transformed DataFrame. + + Raises: + ValueError: If transformation_type is not 'unlinked,' 'linked,' 'list,' or 'gps.' + """ + if transformation_type not in ['unlinked', 'linked', 'list', 'gps']: + raise ValueError("transformation_type must be either 'unlinked', 'linked', 'list', or 'gps'") + + transformed_df = pd.DataFrame(index=df.index) # DataFrame for storing transformations + + for var in variable_list: + if var in df.columns: + # Drop the target column, should it exist (only text list question on a linked roster) + df = df.drop(var, axis=1) + + related_cols = [col for col in df.columns if col.startswith(f"{var}__")] + + if related_cols: + transformation = [[] for _ in range(len(df))] \ + if transformation_type != 'gps' \ + else ['' for _ in range(len(df))] + + for col in related_cols: + + if transformation_type == 'unlinked': + suffix = int(col.split('__')[1].replace('n', '-')) + mask = df[col] > 0 + transformation = [x + [suffix] if mask.iloc[i] else x for i, x in enumerate(transformation)] + elif transformation_type == 'linked': + # !NOTE! if you add the (df[col] != -999999999) filter it removes also list that not only + # contains -999... + mask = (df[col].notna()) # & (df[col] != -999999999) + transformation = [x + [df.at[i, col]] if mask.iloc[i] else x for i, x in enumerate(transformation)] + elif transformation_type == 'list': + mask = (df[col] != '##N/A##') & (df[col] != '') + transformation = [x + [df.at[i, col]] if mask.iloc[i] else x for i, x in enumerate(transformation)] + elif transformation_type == 'gps': + transformation = [x + (',' if x else '') + (str(df.at[i, col]) + if pd.notna(df.at[i, col]) + and df.at[i, col] not in ['##N/A##', -999999999] + else '') for i, x in enumerate(transformation)] + + def remove_unset_value(sub_list): + # Normalize numeric types (float -> int if integer) inside the list construction + # This ensures lists like [1.0, 2.0] become [1, 2], and np.float64(nan) -> np.nan (float) + def normalize(v): + if isinstance(v, float) and v.is_integer(): + return int(v) + # Convert np.float64 or other numpy scalars to native python types, especially NaN + if isinstance(v, (np.floating, np.integer)): + if np.isnan(v): + return float('nan') + return v.item() + return v + + sub = list(filter(lambda v: v not in [-999999999, '##N/A##'], sub_list)) + sub = [normalize(ele) for ele in sub] + + # Check for empty list safely, avoiding numpy array ambiguity + sub = [ele if (not isinstance(ele, list) or len(ele) > 0) else '##N/A##' for ele in sub] + + # Check if sub is not empty list AND contains only '##N/A##' + # list(set(sub)) might fail if elements are unhashable (like lists), which sub might contain? + # If sub contains lists, set(sub) will fail. + # Assuming elements are hashable for now as they come from microdata values (scalars usually). + # But if we have nested lists? transform_multi is for multi-select questions. + # The values are usually roster indices (ints) or values (scalars). + + is_only_na = False + if len(sub) > 0: + try: + if list(set(sub)) == ['##N/A##']: + is_only_na = True + except TypeError: + # Fallback if unhashable elements (unlikely for scalars, but possible if details got messed up) + is_only_na = all(s == '##N/A##' for s in sub) + + sub = sub if (len(sub) > 0 and not is_only_na) else '##N/A##' + return sub + + transformation = [remove_unset_value(x) + if x else float('nan') for x in transformation] if transformation_type != 'gps' else [ + x if x else '' for x in transformation] + transformed_df[var] = transformation # Add the transformation to the transformed DataFrame + df = df.drop(related_cols, axis=1) # Drop the original columns + + df = pd.concat([df, transformed_df], axis=1) # Concatenate the original DataFrame with the transformations + + return df.copy() + + +def process_json_structure(children, parent_group_title, counter, question_data): + """ + This function processes the JSON structure of a questionnaire, collecting information about the questions. + + Parameters: + children (list): The children nodes in the current JSON structure. + parent_group_title (str): The title of the parent group for the current child nodes. + counter (int): A counter to keep track of the sequence of questions. + question_data (list): A list where data about each question is appended as a dictionary. + + Returns: + counter (int): The updated counter value after processing all children nodes. + + """ + for child in children: + if "$type" in child: + question_data.append({ + "qnr_seq": counter, + "VariableName": child.get("VariableName"), + "qtype": child["$type"], + "QuestionType": child.get("QuestionType"), + "Answers": child.get("Answers"), + "Children": child.get("Children"), + "ConditionExpression": child.get("ConditionExpression"), + "HideIfDisabled": child.get("HideIfDisabled"), + "Featured": child.get("Featured"), + "Instructions": child.get("Instructions"), + "Properties": child.get("Properties"), + "PublicKey": child.get("PublicKey"), + "QuestionScope": child.get("QuestionScope"), + "QuestionText": child.get("QuestionText"), + "StataExportCaption": child.get("StataExportCaption"), + "VariableLabel": child.get("VariableLabel"), + "IsTimestamp": child.get("IsTimestamp"), + "ValidationConditions": child.get("ValidationConditions"), + "YesNoView": child.get("YesNoView"), + "IsFilteredCombobox": child.get("IsFilteredCombobox"), + "IsInteger": child.get("IsInteger"), + "CategoriesId": child.get("CategoriesId"), + "Title": child.get("Title"), + "IsRoster": child.get("IsRoster"), + "LinkedToRosterId": child.get("LinkedToRosterId"), + "LinkedToQuestionId": child.get("LinkedToQuestionId"), + "CascadeFromQuestionId": child.get("CascadeFromQuestionId"), + "parents": parent_group_title + }) + counter += 1 + + if "Children" in child: + child_group_title = child.get("Title", "") + counter = process_json_structure(child["Children"], parent_group_title + " > " + child_group_title, counter, + question_data) + + return counter + + +def get_categories(directory: Path) -> Dict[str, Dict[str, list]]: + """ + This function retrieves categories from Excel files within a directory. + + Parameters: + directory (Path): The directory where the category Excel files are stored. + + Returns: + Dict[str, Dict[str, list]]: A dictionary containing category data. Each key represents a filename, and each value is + another dictionary containing 'n_answers' and 'answer_sequence' which represents the number of answers and the + sequence of the answer IDs respectively. + """ + categories = {} + + # List all Excel files in the directory + files = directory.glob('*.xlsx') # Finds .xlsx files + files = list(files) + list(directory.glob('*.xls')) # Adds .xls files + + for file in files: + df = pd.read_excel(file) + n_answers = df.shape[0] + answer_sequence = df['id'].tolist() + categories[file.name] = {'n_answers': n_answers, 'answer_sequence': answer_sequence} + + return categories + + +def update_df_categories(row, categories): + """ + This function updates a DataFrame row with category information if applicable. + + Parameters: + row (Series): The Questioner DataFrame row to be updated. + categories (dict): A dictionary containing category data, keys are 'CategoriesId'. + + Returns: + Series: The updated DataFrame row. + + """ + if row['CategoriesId'] in categories: + row['n_answers'] = categories[row['CategoriesId']]['n_answers'] + row['answer_sequence'] = categories[row['CategoriesId']]['answer_sequence'] + return row + +def parse_filename(filename: str): + """ + Parses a filename based on the pattern ___. + + Parameters: + filename (str): The filename to parse. + + Returns: + dict: A dictionary containing 'questionnaire', 'version', 'format', and 'status'. + """ + # Regex pattern to match the filename structure + pattern = r"^(?P.+)_(?P[0-9]+)_(?P.+)_(?P.+)$" + + match = re.match(pattern, filename) + if not match: + raise ValueError(f"Filename '{filename}' does not match the expected pattern.") + + components = match.groupdict() + + # Extract components as a list + components = [ + match.group('questionnaire'), + match.group('version'), + match.group('format'), + match.group('status') + ] + return components + + +def get_file_parts(filename): + + questionnaire, version, file_format, interview_status = parse_filename(filename) + try: + version = int(version) + except ValueError: + raise ValueError(f"ERROR: {filename} Not a valid Survey Solutions export file. Version not found.") + + # Test input file has the correct name + if file_format not in ["Tabular", "STATA", "SPSS", "Paradata"]: + raise ValueError(f"ERROR: {filename} Not a valid Survey Solutions export file. Export type not found") + + if interview_status not in ["Approved", "InterviewerAssigned", "ApprovedBySupervisor", "ApprovedByHQ", "All", + 'ApprovedByHeadquarters']: + raise ValueError(f"ERROR: {filename} Not a valid Survey Solutions export file. Interview status not found.") + + file_format = file_format if file_format == 'Paradata' else 'Tabular' + return questionnaire, version, file_format, interview_status diff --git a/rissk/utils/import_utils_kedro.py b/rissk/utils/import_utils_kedro.py index d0a2337..c85729a 100644 --- a/rissk/utils/import_utils_kedro.py +++ b/rissk/utils/import_utils_kedro.py @@ -12,7 +12,7 @@ from loguru import logger -from rissk.utils.file_process_utils import ( +from rissk.utils.file_process_utils_kedro import ( get_file_parts, transform_multi, set_qnr_version, @@ -332,17 +332,23 @@ def read_microdata_file(data_path: Path, file_name: str) -> pd.DataFrame: # convert_categoricals=False matches legacy beahvior df = pd.read_stata(f, convert_categoricals=False, convert_missing=True) - # Vectorized replacement is faster - # Replace '.a' Stata missing value with -999999999 - # Replace '.' Stata missing value with NaN - # Use strict type checking or conversion to string if mixed - - # Safety: ensure we don't fail if column is all numeric types (no '.a') - # convert to object if needed? usually .dta loads with correct types or object if strings exist - - # Legacy logic: df.astype(str) != '.a' -> expensive full copy? - # Better: replace specific values - df.replace({'.a': -999999999, '.': np.nan}, inplace=True) + # Handle StataMissingValue objects which are unhashable + # Replace '.a' with -999999999 and '.' with NaN + from pandas.io.stata import StataMissingValue + + def replace_stata_missing(val): + if isinstance(val, StataMissingValue): + s_val = str(val) + if s_val == '.a': + return -999999999 + elif s_val == '.': + return np.nan + return np.nan # defaulting other missing values to NaN + return val + + # Apply only to object columns where StataMissingValue might exist + for col in df.select_dtypes(include=['object']).columns: + df[col] = df[col].apply(replace_stata_missing) except Exception as e: logger.error(f"Error reading {file_path}: {e}") @@ -439,12 +445,21 @@ def get_microdata(data_path: Path, df_questionnaires: pd.DataFrame) -> pd.DataFr def is_valid_fast(val): if val is None: return False - if isinstance(val, (list, tuple)): return True # Not empty list check? Legacy said 'return True' commented 'bool(value)' - if val == '': return False + if isinstance(val, (list, tuple)): return len(val) > 0 # Empty list should be invalid? Legacy: 'return True' + if isinstance(val, (np.ndarray,)): return val.size > 0 + if isinstance(val, str) and val == '': return False + # Fallback for other types where equality might be array-like (though unlikely for scalars) + if hasattr(val, 'size') and hasattr(val, 'shape'): # duck typing for arrays + return val.size > 0 + try: if pd.isna(val): return False except: - pass # list not hashable for isna sometimes? + pass + + # Check for empty string equality safely + if str(val) == '': return False + return True combined_df = combined_df[combined_df['value'].apply(is_valid_fast)] @@ -485,6 +500,22 @@ def is_valid_fast(val): combined_df.reset_index(drop=True, inplace=True) combined_df.columns = [normalize_column_name(c) for c in combined_df.columns] - combined_df['value'] = combined_df['value'].astype(str) + # Normalize float values that are actually integers (e.g. 1.0 -> 1) before string conversion + # This ensures "107080102.0" becomes "107080102" matching legacy output + def normalize_and_stringify(val): + if isinstance(val, float) and val.is_integer(): + return str(int(val)) + if isinstance(val, (list, tuple, np.ndarray)): + # If it's a list (from transform_multi), we might need to normalize internal floats tool? + # Legacy code just did astype(str), which calls str(val). + # str([1.0, 2.0]) -> "[1.0, 2.0]" + # str([1, 2]) -> "[1, 2]" + # So we might need to clean up lists too if we want exact match. + # However, let's stick to scalar normalization first as that's the primary complaint. + return str(val) + return str(val) + + # Use apply for robust conversion + combined_df['value'] = combined_df['value'].apply(normalize_and_stringify) return combined_df diff --git a/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb b/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb index ca0fd6f..53eb542 100644 --- a/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb +++ b/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb @@ -10,21 +10,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 54, "id": "607ef013", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2026-02-17 16:12:26.489\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m12\u001b[0m - \u001b[1mPROJ_ROOT path is: /Users/vanessa/Work/Rowsquared/RISSK/rissk\u001b[0m\n", - "\u001b[32m2026-02-17 16:12:26.490\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m39\u001b[0m - \u001b[1mAvaliable Questionnaires\u001b[0m\n", - "\u001b[32m2026-02-17 16:12:26.490\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mQuestionnaire: snb_hies_hh - Versions: [9, 10, 11]\u001b[0m\n", - "\u001b[32m2026-02-17 16:12:26.490\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mrissk.config\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m43\u001b[0m - \u001b[1mQuestionnaire: slbhies_listing - Versions: [6, 7]\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "from pathlib import Path\n", "import pandas as pd\n", @@ -33,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 55, "id": "a9c7966e", "metadata": {}, "outputs": [], @@ -43,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 56, "id": "dc4569ea", "metadata": {}, "outputs": [], @@ -56,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 57, "id": "3d3ef86a", "metadata": {}, "outputs": [], @@ -69,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 58, "id": "72fdc596", "metadata": {}, "outputs": [], @@ -105,12 +94,49 @@ " return ptypes.is_numeric_dtype(s.dtype)\n", "\n", "\n", + "def _try_convert_numeric(val):\n", + " try:\n", + " if isinstance(val, str):\n", + " # Check if it looks like a list\n", + " if val.startswith('[') and val.endswith(']'):\n", + " # It's a list string. Compare as list?\n", + " # For now, just return as is (string) comparison\n", + " return val\n", + " return float(val)\n", + " except (ValueError, TypeError):\n", + " return val\n", + "\n", + "\n", "def _compare_elementwise(a: pd.Series, b: pd.Series, atol: float, rtol: float) -> np.ndarray:\n", " \"\"\"Return boolean mask where True indicates a != b (treating NaNs as equal).\n", " Works for numeric (uses isclose) and non-numeric (stringified) series.\n", + " Has special handling for numeric-string mismatch (e.g. '1' vs '1.0').\n", " \"\"\"\n", " # Align lengths assumed equal and indexes aligned\n", - " # Handle numeric\n", + " \n", + " # helper for mixed types\n", + " def smart_compare(val_a, val_b):\n", + " if val_a == val_b:\n", + " return True\n", + " # checks for nan\n", + " try:\n", + " if np.isnan(val_a) and np.isnan(val_b):\n", + " return True\n", + " except:\n", + " pass\n", + " \n", + " # Try numeric conversion\n", + " try:\n", + " fa = float(val_a)\n", + " fb = float(val_b)\n", + " if np.isnan(fa) and np.isnan(fb):\n", + " return True\n", + " return np.isclose(fa, fb, atol=atol, rtol=rtol)\n", + " except (ValueError, TypeError):\n", + " # If conversion fails, strict string comparison was already done at start\n", + " return str(val_a) == str(val_b)\n", + "\n", + " # 1. If numeric series, use vectorized numeric comparison\n", " if _is_numeric_series(a) and _is_numeric_series(b):\n", " # convert to float with NaN preserved\n", " a_f = a.astype(float)\n", @@ -122,11 +148,39 @@ " neq = ~close\n", " neq[both_nan.values] = False\n", " return neq\n", - " else:\n", - " # compare as strings, treating NaN as a sentinel\n", - " a_s = a.fillna('__NA__').astype(str)\n", - " b_s = b.fillna('__NA__').astype(str)\n", - " return (a_s != b_s).to_numpy()\n", + " \n", + " # 2. For object/mixed series, use element-wise smart comparison\n", + " # This is slower but necessary for '1' vs '1.0' in object columns\n", + " # We can optimize by first checking string equality\n", + " a_s = a.fillna('__NA__').astype(str)\n", + " b_s = b.fillna('__NA__').astype(str)\n", + " \n", + " # Boolean mask of string mismatches\n", + " neq_mask = (a_s != b_s).to_numpy()\n", + " \n", + " # If no string mismatches, we are done\n", + " if not neq_mask.any():\n", + " return neq_mask\n", + " \n", + " # For the mismatches, try numeric comparison\n", + " # Get indices of mismatches\n", + " mismatch_indices = np.where(neq_mask)[0]\n", + " \n", + " # Use list comprehension for the mismatched subset\n", + " a_vals = a.iloc[mismatch_indices].values\n", + " b_vals = b.iloc[mismatch_indices].values\n", + " \n", + " resolved_mask = []\n", + " for va, vb in zip(a_vals, b_vals):\n", + " # We need to check if they are \"close enough\"\n", + " is_equal = smart_compare(va, vb)\n", + " # If is_equal is True, then we flag it as FALSE (no difference)\n", + " resolved_mask.append(not is_equal)\n", + " \n", + " # Update the neq_mask\n", + " neq_mask[mismatch_indices] = resolved_mask\n", + " \n", + " return neq_mask\n", "\n", "\n", "def compare_parquet_files(df_a: pd.DataFrame, df_b: pd.DataFrame, check: Optional[str] = None, atol: float = 1e-9, rtol: float = 1e-8) -> Tuple[bool, Dict[str, Any]]:\n", @@ -139,11 +193,6 @@ " (preferred if present in both tables), otherwise aligns by index intersection when possible,\n", " otherwise falls back to positional/overlap comparison. Reports columns that have any differing\n", " cells and total number of differing cells compared.\n", - " - For 'rows': if a unique key column is detected the function compares rows by key (counts\n", - " keys only in A/B and per-key mismatches). Otherwise it falls back to an unordered multiset\n", - " (counts) comparison on the common columns, which handles duplicate rows.\n", - " - Missing entries are handled (NaNs compared as equal), numeric columns use tolerant comparison.\n", - " - The function never crashes on shape mismatch: it documents partial comparisons in `details`.\n", "\n", " Returns: (same: bool, details: dict)\n", " \"\"\"\n", @@ -184,6 +233,7 @@ " # CELL-level comparison (position/label depending on alignment)\n", " if check == 'cells':\n", " cell_info: Dict[str, Any] = {'checked': True}\n", + " diff_df = None\n", " if len(common) == 0:\n", " cell_info['note'] = 'no common columns to compare'\n", " cell_info['columns_with_differences'] = []\n", @@ -232,21 +282,38 @@ " cell_info['total_cell_differences'] = total_cell_diffs\n", " cell_info['rows_compared'] = int(rows_compared)\n", " cell_info['note'] = note\n", + " \n", + " # Generate difference dataframe if needed\n", + " if total_cell_diffs > 0:\n", + " diff_list = []\n", + " # Finding indices (row, col) of differences\n", + " rows, cols = np.where(neq_mask)\n", + " for r, c in zip(rows, cols):\n", + " col_name = common[c]\n", + " # Get index label if available\n", + " idx_label = a_al.index[r]\n", + " val_a = a_al.iloc[r, c]\n", + " val_b = b_al.iloc[r, c]\n", + " diff_list.append({\n", + " 'index': idx_label,\n", + " 'column': col_name,\n", + " 'value_a': val_a,\n", + " 'value_b': val_b\n", + " })\n", + " diff_df = pd.DataFrame(diff_list)\n", "\n", " if total_cell_diffs > 0:\n", " same = False\n", " details['cell_compare'] = cell_info\n", + " if diff_df is not None:\n", + " details['diff_df'] = diff_df\n", "\n", " # ROW-level comparison\n", " if check == 'rows':\n", " row_info: Dict[str, Any] = {'checked': True}\n", " if len(common) == 0:\n", " row_info['note'] = 'no common columns to compare; cannot perform row membership check'\n", - " row_info['rows_in_a_not_in_b'] = None\n", - " row_info['rows_in_b_not_in_a'] = None\n", - " row_info['num_rows_different'] = None\n", - " row_info['total_rows_a'] = len(df_a)\n", - " row_info['total_rows_b'] = len(df_b)\n", + " \n", " else:\n", " if candidate_key is not None:\n", " # compare by key: count keys only in A/B and mismatched rows for common keys\n", @@ -311,258 +378,40 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "63867817", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(23127, 41)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rows, cells = df_microdata.shape\n", - "rows, cells" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "2bc15ac6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(78413, 11)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_para_kedro.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 8, + "execution_count": 59, "id": "792a94d3", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(False,\n", - " {'shape': {'equal': False, 'shape_a': (78413, 11), 'shape_b': (78413, 27)},\n", - " 'columns': {'different_columns': ['timestamp_local',\n", - " 'qnr',\n", - " 'qnr_version',\n", - " 'qnr_seq',\n", - " 'variable_name',\n", - " 'qtype',\n", - " 'question_type',\n", - " 'answers',\n", - " 'question_scope',\n", - " 'yes_no_view',\n", - " 'is_filtered_combobox',\n", - " 'is_integer',\n", - " 'cascade_from_question_id',\n", - " 'answer_sequence',\n", - " 'n_answers',\n", - " 'question_sequence'],\n", - " 'equal': False,\n", - " 'only_in_a': [],\n", - " 'only_in_b': ['timestamp_local',\n", - " 'qnr',\n", - " 'qnr_version',\n", - " 'qnr_seq',\n", - " 'variable_name',\n", - " 'qtype',\n", - " 'question_type',\n", - " 'answers',\n", - " 'question_scope',\n", - " 'yes_no_view',\n", - " 'is_filtered_combobox',\n", - " 'is_integer',\n", - " 'cascade_from_question_id',\n", - " 'answer_sequence',\n", - " 'n_answers',\n", - " 'question_sequence']},\n", - " 'dtypes': {'mismatched_columns': ['timestamp_utc', 'tz_offset'],\n", - " 'equal': False},\n", - " 'auto_key': None,\n", - " 'cell_compare': {'checked': True,\n", - " 'columns_with_differences': ['timestamp_utc', 'tz_offset'],\n", - " 'total_cell_differences': 156826,\n", - " 'rows_compared': 78413,\n", - " 'note': 'aligned by index intersection'},\n", - " 'same': False})" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "compare_parquet_files(df_para_kedro, df_para, check='cells')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "de3363a0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(False,\n", - " {'shape': {'equal': False, 'shape_a': (0, 0), 'shape_b': (23127, 41)},\n", - " 'columns': {'different_columns': ['interview__id',\n", - " 'roster_level',\n", - " 'variable',\n", - " 'value',\n", - " 'filename',\n", - " 'qnr',\n", - " 'qnr_version',\n", - " 'qnr_seq',\n", - " 'variable_name',\n", - " 'qtype',\n", - " 'question_type',\n", - " 'answers',\n", - " 'children',\n", - " 'condition_expression',\n", - " 'hide_if_disabled',\n", - " 'featured',\n", - " 'instructions',\n", - " 'properties',\n", - " 'public_key',\n", - " 'question_scope',\n", - " 'question_text',\n", - " 'stata_export_caption',\n", - " 'variable_label',\n", - " 'is_timestamp',\n", - " 'validation_conditions',\n", - " 'yes_no_view',\n", - " 'is_filtered_combobox',\n", - " 'is_integer',\n", - " 'categories_id',\n", - " 'title',\n", - " 'is_roster',\n", - " 'linked_to_roster_id',\n", - " 'linked_to_question_id',\n", - " 'cascade_from_question_id',\n", - " 'parents',\n", - " 'answer_sequence',\n", - " 'n_answers',\n", - " 'is_linked',\n", - " 'parent_1',\n", - " 'parent_2',\n", - " 'question_sequence'],\n", - " 'equal': False,\n", - " 'only_in_a': [],\n", - " 'only_in_b': ['interview__id',\n", - " 'roster_level',\n", - " 'variable',\n", - " 'value',\n", - " 'filename',\n", - " 'qnr',\n", - " 'qnr_version',\n", - " 'qnr_seq',\n", - " 'variable_name',\n", - " 'qtype',\n", - " 'question_type',\n", - " 'answers',\n", - " 'children',\n", - " 'condition_expression',\n", - " 'hide_if_disabled',\n", - " 'featured',\n", - " 'instructions',\n", - " 'properties',\n", - " 'public_key',\n", - " 'question_scope',\n", - " 'question_text',\n", - " 'stata_export_caption',\n", - " 'variable_label',\n", - " 'is_timestamp',\n", - " 'validation_conditions',\n", - " 'yes_no_view',\n", - " 'is_filtered_combobox',\n", - " 'is_integer',\n", - " 'categories_id',\n", - " 'title',\n", - " 'is_roster',\n", - " 'linked_to_roster_id',\n", - " 'linked_to_question_id',\n", - " 'cascade_from_question_id',\n", - " 'parents',\n", - " 'answer_sequence',\n", - " 'n_answers',\n", - " 'is_linked',\n", - " 'parent_1',\n", - " 'parent_2',\n", - " 'question_sequence']},\n", - " 'dtypes': {'mismatched_columns': [], 'equal': True},\n", - " 'auto_key': None,\n", - " 'cell_compare': {'checked': True,\n", - " 'note': 'no common columns to compare',\n", - " 'columns_with_differences': [],\n", - " 'total_cell_differences': 0},\n", - " 'same': False})" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "compare_parquet_files(df_microdata_kedro, df_microdata, check='cells')" + "_, details = compare_parquet_files(df_para_kedro, df_para, check='cells')\n" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "81f6cde7", + "execution_count": 60, + "id": "0a094beb", "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "\"['public_key'] not in index\"", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mcompare_parquet_files\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_questionnaire_kedro\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_questionnaire\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mcells\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 123\u001b[39m, in \u001b[36mcompare_parquet_files\u001b[39m\u001b[34m(df_a, df_b, check, atol, rtol)\u001b[39m\n\u001b[32m 121\u001b[39m b_k = df_b.set_index(candidate_key)\n\u001b[32m 122\u001b[39m common_idx = a_k.index.intersection(b_k.index)\n\u001b[32m--> \u001b[39m\u001b[32m123\u001b[39m a_al = \u001b[43ma_k\u001b[49m\u001b[43m.\u001b[49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcommon_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcommon\u001b[49m\u001b[43m]\u001b[49m.fillna(\u001b[33m'\u001b[39m\u001b[33m__NA__\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 124\u001b[39m b_al = b_k.loc[common_idx, common].fillna(\u001b[33m'\u001b[39m\u001b[33m__NA__\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 125\u001b[39m rows_compared = \u001b[38;5;28mlen\u001b[39m(common_idx)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexing.py:1185\u001b[39m, in \u001b[36m_LocationIndexer.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 1183\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._is_scalar_access(key):\n\u001b[32m 1184\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.obj._get_value(*key, takeable=\u001b[38;5;28mself\u001b[39m._takeable)\n\u001b[32m-> \u001b[39m\u001b[32m1185\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_getitem_tuple\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1186\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1187\u001b[39m \u001b[38;5;66;03m# we by definition only have the 0th axis\u001b[39;00m\n\u001b[32m 1188\u001b[39m axis = \u001b[38;5;28mself\u001b[39m.axis \u001b[38;5;129;01mor\u001b[39;00m \u001b[32m0\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexing.py:1376\u001b[39m, in \u001b[36m_LocIndexer._getitem_tuple\u001b[39m\u001b[34m(self, tup)\u001b[39m\n\u001b[32m 1374\u001b[39m \u001b[38;5;66;03m# ugly hack for GH #836\u001b[39;00m\n\u001b[32m 1375\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._multi_take_opportunity(tup):\n\u001b[32m-> \u001b[39m\u001b[32m1376\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_multi_take\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1378\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._getitem_tuple_same_dim(tup)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexing.py:1328\u001b[39m, in \u001b[36m_LocIndexer._multi_take\u001b[39m\u001b[34m(self, tup)\u001b[39m\n\u001b[32m 1311\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 1312\u001b[39m \u001b[33;03mCreate the indexers for the passed tuple of keys, and\u001b[39;00m\n\u001b[32m 1313\u001b[39m \u001b[33;03mexecutes the take operation. This allows the take operation to be\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 1324\u001b[39m \u001b[33;03mvalues: same type as the object being indexed\u001b[39;00m\n\u001b[32m 1325\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 1326\u001b[39m \u001b[38;5;66;03m# GH 836\u001b[39;00m\n\u001b[32m 1327\u001b[39m d = {\n\u001b[32m-> \u001b[39m\u001b[32m1328\u001b[39m axis: \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_get_listlike_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1329\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m (key, axis) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(tup, \u001b[38;5;28mself\u001b[39m.obj._AXIS_ORDERS)\n\u001b[32m 1330\u001b[39m }\n\u001b[32m 1331\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.obj._reindex_with_indexers(d, copy=\u001b[38;5;28;01mTrue\u001b[39;00m, allow_dups=\u001b[38;5;28;01mTrue\u001b[39;00m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexing.py:1559\u001b[39m, in \u001b[36m_LocIndexer._get_listlike_indexer\u001b[39m\u001b[34m(self, key, axis)\u001b[39m\n\u001b[32m 1556\u001b[39m ax = \u001b[38;5;28mself\u001b[39m.obj._get_axis(axis)\n\u001b[32m 1557\u001b[39m axis_name = \u001b[38;5;28mself\u001b[39m.obj._get_axis_name(axis)\n\u001b[32m-> \u001b[39m\u001b[32m1559\u001b[39m keyarr, indexer = \u001b[43max\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1561\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m keyarr, indexer\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexes/base.py:6212\u001b[39m, in \u001b[36mIndex._get_indexer_strict\u001b[39m\u001b[34m(self, key, axis_name)\u001b[39m\n\u001b[32m 6209\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 6210\u001b[39m keyarr, indexer, new_indexer = \u001b[38;5;28mself\u001b[39m._reindex_non_unique(keyarr)\n\u001b[32m-> \u001b[39m\u001b[32m6212\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 6214\u001b[39m keyarr = \u001b[38;5;28mself\u001b[39m.take(indexer)\n\u001b[32m 6215\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[32m 6216\u001b[39m \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexes/base.py:6264\u001b[39m, in \u001b[36mIndex._raise_if_missing\u001b[39m\u001b[34m(self, key, indexer, axis_name)\u001b[39m\n\u001b[32m 6261\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m]\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 6263\u001b[39m not_found = \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask.nonzero()[\u001b[32m0\u001b[39m]].unique())\n\u001b[32m-> \u001b[39m\u001b[32m6264\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m not in index\u001b[39m\u001b[33m\"\u001b[39m)\n", - "\u001b[31mKeyError\u001b[39m: \"['public_key'] not in index\"" + "name": "stdout", + "output_type": "stream", + "text": [ + "{'equal': False, 'shape_a': (78413, 11), 'shape_b': (78413, 27)}\n", + "{'different_columns': ['timestamp_local', 'qnr', 'qnr_version', 'qnr_seq', 'variable_name', 'qtype', 'question_type', 'answers', 'question_scope', 'yes_no_view', 'is_filtered_combobox', 'is_integer', 'cascade_from_question_id', 'answer_sequence', 'n_answers', 'question_sequence'], 'equal': False, 'only_in_a': [], 'only_in_b': ['timestamp_local', 'qnr', 'qnr_version', 'qnr_seq', 'variable_name', 'qtype', 'question_type', 'answers', 'question_scope', 'yes_no_view', 'is_filtered_combobox', 'is_integer', 'cascade_from_question_id', 'answer_sequence', 'n_answers', 'question_sequence']}\n", + "{'mismatched_columns': ['timestamp_utc', 'tz_offset'], 'equal': False}\n" ] } ], "source": [ - "compare_parquet_files(df_questionnaire_kedro, df_questionnaire, check='cells')" + "print(details['shape'])\n", + "print(details['columns'])\n", + "print(details['dtypes'])" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "143d6b7f", + "execution_count": 61, + "id": "5a2e9402", "metadata": {}, "outputs": [ { @@ -575,387 +424,823 @@ "type": "integer" }, { - "name": "qnr_seq", + "name": "index", "rawType": "int64", "type": "integer" }, { - "name": "variable_name", - "rawType": "object", - "type": "string" - }, - { - "name": "qtype", - "rawType": "object", - "type": "string" - }, - { - "name": "question_type", - "rawType": "float64", - "type": "float" - }, - { - "name": "answers", - "rawType": "object", - "type": "unknown" - }, - { - "name": "children", - "rawType": "object", - "type": "unknown" - }, - { - "name": "condition_expression", - "rawType": "object", - "type": "unknown" - }, - { - "name": "hide_if_disabled", - "rawType": "object", - "type": "unknown" - }, - { - "name": "featured", - "rawType": "object", - "type": "unknown" - }, - { - "name": "instructions", - "rawType": "object", - "type": "unknown" - }, - { - "name": "properties", - "rawType": "object", - "type": "unknown" - }, - { - "name": "public_key", - "rawType": "object", - "type": "string" - }, - { - "name": "question_scope", - "rawType": "float64", - "type": "float" - }, - { - "name": "question_text", - "rawType": "object", - "type": "unknown" - }, - { - "name": "stata_export_caption", - "rawType": "object", - "type": "unknown" - }, - { - "name": "variable_label", - "rawType": "object", - "type": "unknown" - }, - { - "name": "is_timestamp", - "rawType": "object", - "type": "unknown" - }, - { - "name": "validation_conditions", - "rawType": "object", - "type": "unknown" - }, - { - "name": "yes_no_view", - "rawType": "object", - "type": "unknown" - }, - { - "name": "is_filtered_combobox", - "rawType": "object", - "type": "unknown" - }, - { - "name": "is_integer", - "rawType": "object", - "type": "unknown" - }, - { - "name": "categories_id", - "rawType": "object", - "type": "unknown" - }, - { - "name": "title", - "rawType": "object", - "type": "unknown" - }, - { - "name": "is_roster", - "rawType": "object", - "type": "unknown" - }, - { - "name": "linked_to_roster_id", - "rawType": "object", - "type": "unknown" - }, - { - "name": "linked_to_question_id", - "rawType": "object", - "type": "unknown" - }, - { - "name": "cascade_from_question_id", - "rawType": "object", - "type": "unknown" - }, - { - "name": "parents", - "rawType": "object", - "type": "string" - }, - { - "name": "answer_sequence", + "name": "column", "rawType": "object", "type": "string" }, { - "name": "n_answers", - "rawType": "float64", - "type": "float" - }, - { - "name": "is_linked", - "rawType": "bool", - "type": "boolean" - }, - { - "name": "parent_1", + "name": "value_a", "rawType": "object", "type": "string" }, { - "name": "parent_2", + "name": "value_b", "rawType": "object", "type": "unknown" - }, - { - "name": "question_sequence", - "rawType": "float64", - "type": "float" - }, - { - "name": "qnr", - "rawType": "object", - "type": "string" - }, - { - "name": "qnr_version", - "rawType": "object", - "type": "string" } ], - 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{}, + "output_type": "execute_result" + } + ], + "source": [ + "details['diff_df']" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "de3363a0", + "metadata": {}, + "outputs": [], + "source": [ + "_, details = compare_parquet_files(df_microdata_kedro, df_microdata, check='cells')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4850564f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'equal': False, 'shape_a': (0, 0), 'shape_b': (23127, 41)}\n", + "{'different_columns': ['interview__id', 'roster_level', 'variable', 'value', 'filename', 'qnr', 'qnr_version', 'qnr_seq', 'variable_name', 'qtype', 'question_type', 'answers', 'children', 'condition_expression', 'hide_if_disabled', 'featured', 'instructions', 'properties', 'public_key', 'question_scope', 'question_text', 'stata_export_caption', 'variable_label', 'is_timestamp', 'validation_conditions', 'yes_no_view', 'is_filtered_combobox', 'is_integer', 'categories_id', 'title', 'is_roster', 'linked_to_roster_id', 'linked_to_question_id', 'cascade_from_question_id', 'parents', 'answer_sequence', 'n_answers', 'is_linked', 'parent_1', 'parent_2', 'question_sequence'], 'equal': False, 'only_in_a': [], 'only_in_b': ['interview__id', 'roster_level', 'variable', 'value', 'filename', 'qnr', 'qnr_version', 'qnr_seq', 'variable_name', 'qtype', 'question_type', 'answers', 'children', 'condition_expression', 'hide_if_disabled', 'featured', 'instructions', 'properties', 'public_key', 'question_scope', 'question_text', 'stata_export_caption', 'variable_label', 'is_timestamp', 'validation_conditions', 'yes_no_view', 'is_filtered_combobox', 'is_integer', 'categories_id', 'title', 'is_roster', 'linked_to_roster_id', 'linked_to_question_id', 'cascade_from_question_id', 'parents', 'answer_sequence', 'n_answers', 'is_linked', 'parent_1', 'parent_2', 'question_sequence']}\n", + "{'mismatched_columns': [], 'equal': True}\n", + "{'checked': True, 'note': 'no common columns to compare', 'columns_with_differences': [], 'total_cell_differences': 0}\n" + ] + } + ], + "source": [ + "print(details['shape'])\n", + "print(details['columns'])\n", + "print(details['dtypes'])\n", + "print(details['cell_compare'])\n", + "try:\n", + " display(details['df_diff'])\n", + "except:\n", + " print('Cells are the same')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "beeb1d8e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(52, 36) (52, 36)\n" + ] + } + ], + "source": [ + "print(df_questionnaire.shape, df_questionnaire_kedro.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d466e2a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, True, True, True, True, True, True, True, True,\n", + " True, True, True, True, True, True, True, True, True,\n", + " True, True, True, True, True, True, True, True, True,\n", + " True, True, True, True, True, True, True, True, True])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_questionnaire.columns == df_questionnaire_kedro.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1fddd0a2", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "object", + "type": "string" + }, + { + "name": "df_questionnaire", + "rawType": "object", + "type": "unknown" + }, + { + "name": "df_questionnaire_kedro", + "rawType": "object", + "type": "unknown" + } + ], + "ref": "6f43feec-7cae-4a4f-a4a0-08df6164b6c0", + "rows": [ + [ + "qnr_seq", + "int64", + "int64" + ], + [ + "variable_name", + "object", + "object" + ], + [ + "qtype", + "object", + "object" + ], + [ + "question_type", + "float64", + "float64" + ], + [ + "answers", + "object", + "object" + ], + [ + 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" Cover\n", - " nan\n", - " NaN\n", - " False\n", - " Cover\n", - " None\n", - " 2.0\n", - " slbhies_listing\n", - " 6\n", + " question_sequence\n", + " float64\n", + " float64\n", " \n", " \n", - " 3\n", - " 3\n", - " UNITS\n", - " Variable\n", - " NaN\n", - " None\n", - " []\n", - " None\n", - " None\n", - " None\n", - " None\n", - " ...\n", - " None\n", - " Cover\n", - " nan\n", - " NaN\n", - " False\n", - " Cover\n", - " None\n", - " NaN\n", - " slbhies_listing\n", - " 6\n", + " qnr\n", + " object\n", + " object\n", " \n", " \n", - " 4\n", - " 4\n", - " ELIGIBLE\n", - " Variable\n", - " NaN\n", - " None\n", - " []\n", - " None\n", - " None\n", - " None\n", - " None\n", - " ...\n", - " None\n", - " Cover\n", - " nan\n", - " NaN\n", - " False\n", - " Cover\n", - " None\n", - " NaN\n", - " slbhies_listing\n", - " 6\n", + " qnr_version\n", + " object\n", + " object\n", " \n", " \n", "\n", - "

5 rows × 36 columns

\n", "" ], "text/plain": [ - " qnr_seq variable_name qtype question_type answers \\\n", - "0 0 Group NaN None \n", - "1 1 ward SingleQuestion 0.0 [] \n", - "2 2 ea SingleQuestion 0.0 [] \n", - "3 3 UNITS Variable NaN None \n", - "4 4 ELIGIBLE Variable NaN None \n", - "\n", - " children condition_expression \\\n", - "0 [{'$type': 'SingleQuestion', 'Answers': [], 'A... \n", - "1 [] \n", - "2 [] \n", - "3 [] None \n", - "4 [] None \n", - "\n", - " hide_if_disabled featured instructions ... \\\n", - "0 False None None ... \n", - "1 False True ... \n", - "2 False True ... \n", - "3 None None None ... \n", - "4 None None None ... \n", - "\n", - " cascade_from_question_id parents answer_sequence n_answers \\\n", - "0 None nan NaN \n", - "1 None Cover nan NaN \n", - "2 330266f5-d168-b402-a4d3-24921597cd86 Cover nan NaN \n", - "3 None Cover nan NaN \n", - "4 None Cover nan NaN \n", - "\n", - " is_linked parent_1 parent_2 question_sequence qnr qnr_version \n", - "0 False None NaN slbhies_listing 6 \n", - "1 False Cover None 1.0 slbhies_listing 6 \n", - "2 False Cover None 2.0 slbhies_listing 6 \n", - "3 False Cover None NaN slbhies_listing 6 \n", - "4 False Cover None NaN slbhies_listing 6 \n", - "\n", - "[5 rows x 36 columns]" + " df_questionnaire df_questionnaire_kedro\n", + "qnr_seq int64 int64\n", + "variable_name object object\n", + "qtype object object\n", + "question_type float64 float64\n", + "answers object object\n", + "children object object\n", + "condition_expression object object\n", + "hide_if_disabled object object\n", + "featured object object\n", + "instructions object object\n", + "properties object object\n", + "public_key object object\n", + "question_scope float64 float64\n", + "question_text object object\n", + "stata_export_caption object object\n", + "variable_label object object\n", + "is_timestamp object object\n", + "validation_conditions object object\n", + "yes_no_view object object\n", + "is_filtered_combobox object object\n", + "is_integer object object\n", + "categories_id object object\n", + "title object object\n", + "is_roster object object\n", + "linked_to_roster_id object object\n", + "linked_to_question_id object object\n", + "cascade_from_question_id object object\n", + "parents object object\n", + "answer_sequence object object\n", + "n_answers float64 float64\n", + "is_linked bool bool\n", + "parent_1 object object\n", + "parent_2 object object\n", + "question_sequence float64 float64\n", + "qnr object object\n", + "qnr_version object object" ] }, - "execution_count": 11, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_questionnaire.head(5)" + "pd.DataFrame({'df_questionnaire': df_questionnaire.dtypes, 'df_questionnaire_kedro': df_questionnaire_kedro.dtypes})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81f6cde7", + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "\"['public_key'] not in index\"", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[43]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mcompare_parquet_files\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_questionnaire_kedro\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_questionnaire\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mcells\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[37]\u001b[39m\u001b[32m, line 184\u001b[39m, in \u001b[36mcompare_parquet_files\u001b[39m\u001b[34m(df_a, df_b, check, atol, rtol)\u001b[39m\n\u001b[32m 182\u001b[39m b_k = df_b.set_index(candidate_key)\n\u001b[32m 183\u001b[39m common_idx = a_k.index.intersection(b_k.index)\n\u001b[32m--> \u001b[39m\u001b[32m184\u001b[39m a_al = \u001b[43ma_k\u001b[49m\u001b[43m.\u001b[49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcommon_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcommon\u001b[49m\u001b[43m]\u001b[49m.fillna(\u001b[33m'\u001b[39m\u001b[33m__NA__\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 185\u001b[39m b_al = b_k.loc[common_idx, common].fillna(\u001b[33m'\u001b[39m\u001b[33m__NA__\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 186\u001b[39m rows_compared = \u001b[38;5;28mlen\u001b[39m(common_idx)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexing.py:1185\u001b[39m, in \u001b[36m_LocationIndexer.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 1183\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._is_scalar_access(key):\n\u001b[32m 1184\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.obj._get_value(*key, takeable=\u001b[38;5;28mself\u001b[39m._takeable)\n\u001b[32m-> \u001b[39m\u001b[32m1185\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_getitem_tuple\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1186\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1187\u001b[39m \u001b[38;5;66;03m# we by definition only have the 0th axis\u001b[39;00m\n\u001b[32m 1188\u001b[39m axis = \u001b[38;5;28mself\u001b[39m.axis \u001b[38;5;129;01mor\u001b[39;00m \u001b[32m0\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexing.py:1376\u001b[39m, in \u001b[36m_LocIndexer._getitem_tuple\u001b[39m\u001b[34m(self, tup)\u001b[39m\n\u001b[32m 1374\u001b[39m \u001b[38;5;66;03m# ugly hack for GH #836\u001b[39;00m\n\u001b[32m 1375\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._multi_take_opportunity(tup):\n\u001b[32m-> \u001b[39m\u001b[32m1376\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_multi_take\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1378\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._getitem_tuple_same_dim(tup)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexing.py:1328\u001b[39m, in \u001b[36m_LocIndexer._multi_take\u001b[39m\u001b[34m(self, tup)\u001b[39m\n\u001b[32m 1311\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 1312\u001b[39m \u001b[33;03mCreate the indexers for the passed tuple of keys, and\u001b[39;00m\n\u001b[32m 1313\u001b[39m \u001b[33;03mexecutes the take operation. This allows the take operation to be\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 1324\u001b[39m \u001b[33;03mvalues: same type as the object being indexed\u001b[39;00m\n\u001b[32m 1325\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 1326\u001b[39m \u001b[38;5;66;03m# GH 836\u001b[39;00m\n\u001b[32m 1327\u001b[39m d = {\n\u001b[32m-> \u001b[39m\u001b[32m1328\u001b[39m axis: \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_get_listlike_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1329\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m (key, axis) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(tup, \u001b[38;5;28mself\u001b[39m.obj._AXIS_ORDERS)\n\u001b[32m 1330\u001b[39m }\n\u001b[32m 1331\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.obj._reindex_with_indexers(d, copy=\u001b[38;5;28;01mTrue\u001b[39;00m, allow_dups=\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexing.py:1559\u001b[39m, in \u001b[36m_LocIndexer._get_listlike_indexer\u001b[39m\u001b[34m(self, key, axis)\u001b[39m\n\u001b[32m 1556\u001b[39m ax = \u001b[38;5;28mself\u001b[39m.obj._get_axis(axis)\n\u001b[32m 1557\u001b[39m axis_name = \u001b[38;5;28mself\u001b[39m.obj._get_axis_name(axis)\n\u001b[32m-> \u001b[39m\u001b[32m1559\u001b[39m keyarr, indexer = \u001b[43max\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1561\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m keyarr, indexer\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexes/base.py:6212\u001b[39m, in \u001b[36mIndex._get_indexer_strict\u001b[39m\u001b[34m(self, key, axis_name)\u001b[39m\n\u001b[32m 6209\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 6210\u001b[39m keyarr, indexer, new_indexer = \u001b[38;5;28mself\u001b[39m._reindex_non_unique(keyarr)\n\u001b[32m-> \u001b[39m\u001b[32m6212\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 6214\u001b[39m keyarr = \u001b[38;5;28mself\u001b[39m.take(indexer)\n\u001b[32m 6215\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[32m 6216\u001b[39m \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexes/base.py:6264\u001b[39m, in \u001b[36mIndex._raise_if_missing\u001b[39m\u001b[34m(self, key, indexer, axis_name)\u001b[39m\n\u001b[32m 6261\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m]\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 6263\u001b[39m not_found = \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask.nonzero()[\u001b[32m0\u001b[39m]].unique())\n\u001b[32m-> \u001b[39m\u001b[32m6264\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m not in index\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[31mKeyError\u001b[39m: \"['public_key'] not in index\"" + ] + } + ], + "source": [ + "compare_parquet_files(df_questionnaire_kedro, df_questionnaire, check='cells')" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "f2112454", + "execution_count": null, + "id": "143d6b7f", "metadata": {}, "outputs": [ { @@ -1370,7 +1725,7 @@ "type": "string" } ], - "ref": "63f6ecf0-ba02-46e6-8fa8-ed25edc0b6ce", + "ref": "149a90ca-a99a-4b66-a642-b5e13a8d2137", "rows": [ [ "0", @@ -1708,379 +2063,91 @@ " False\n", " Cover\n", " None\n", - " NaN\n", - " slbhies_listing\n", - " 6\n", - " \n", - " \n", - " 4\n", - " 4\n", - " ELIGIBLE\n", - " Variable\n", - " NaN\n", - " None\n", - " []\n", - " None\n", - " None\n", - " None\n", - " None\n", - " ...\n", - " None\n", - " Cover\n", - " nan\n", - " NaN\n", - " False\n", - " Cover\n", - " None\n", - " NaN\n", - " slbhies_listing\n", - " 6\n", - " \n", - " \n", - "\n", - "

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\n", "\n", - " roster_level \n", - "0 None \n", - "1 None \n", - "2 None \n", - "3 None \n", - "4 None " + "[5 rows x 36 columns]" ] }, - "execution_count": 13, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_para_kedro.head(5)" + "df_questionnaire.head(5)" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "313dc912", + "execution_count": null, + "id": "f2112454", "metadata": {}, "outputs": [ { @@ -2093,104 +2160,94 @@ "type": "integer" }, { - "name": "interview__id", - "rawType": "object", - "type": "string" - }, - { - "name": "order", + "name": "qnr_seq", "rawType": "int64", "type": "integer" }, { - "name": "event", + "name": "variable_name", "rawType": "object", "type": "string" }, { - "name": "responsible", + "name": "qtype", "rawType": "object", - "type": "unknown" - }, - { - "name": "role", - "rawType": "int64", - "type": "integer" + "type": "string" }, { - "name": "timestamp_utc", - "rawType": "datetime64[ns]", - "type": "datetime" + "name": "question_type", + "rawType": "float64", + "type": "float" }, { - "name": "tz_offset", - "rawType": "timedelta64[ns]", + "name": "answers", + "rawType": "object", "type": "unknown" }, { - "name": "parameters", + "name": "children", "rawType": "object", "type": "unknown" }, { - "name": "param", + "name": "condition_expression", "rawType": "object", "type": "unknown" }, { - "name": "answer", + "name": "hide_if_disabled", "rawType": "object", "type": "unknown" }, { - "name": "roster_level", + "name": "featured", "rawType": "object", "type": "unknown" }, { - "name": "timestamp_local", - "rawType": "datetime64[ns]", - "type": "datetime" + "name": "instructions", + "rawType": "object", + "type": "unknown" }, { - "name": "qnr", + "name": "properties", "rawType": "object", - "type": "string" + "type": "unknown" }, { - "name": "qnr_version", + "name": "public_key", "rawType": "object", "type": "string" }, { - "name": "qnr_seq", + "name": "question_scope", "rawType": "float64", "type": "float" }, { - "name": "variable_name", + "name": "question_text", "rawType": "object", "type": "unknown" }, { - "name": "qtype", + "name": "stata_export_caption", "rawType": "object", "type": "unknown" }, { - "name": "question_type", - "rawType": "float64", - "type": "float" + "name": "variable_label", + "rawType": "object", + "type": "unknown" }, { - "name": "answers", + "name": "is_timestamp", "rawType": "object", "type": "unknown" }, { - "name": "question_scope", - "rawType": "float64", - "type": "float" + "name": "validation_conditions", + "rawType": "object", + "type": "unknown" }, { "name": "yes_no_view", @@ -2207,11 +2264,41 @@ "rawType": "object", "type": "unknown" }, + { + "name": "categories_id", + "rawType": "object", + "type": "unknown" + }, + { + "name": "title", + "rawType": "object", + "type": "unknown" + }, + { + "name": "is_roster", + "rawType": "object", + "type": "unknown" + }, + { + "name": "linked_to_roster_id", + "rawType": "object", + "type": "unknown" + }, + { + "name": "linked_to_question_id", + "rawType": "object", + "type": "unknown" + }, { "name": "cascade_from_question_id", "rawType": "object", "type": "unknown" }, + { + "name": "parents", + "rawType": "object", + "type": "string" + }, { "name": "answer_sequence", "rawType": "object", @@ -2222,90 +2309,177 @@ "rawType": "float64", "type": "float" }, + { + "name": "is_linked", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "parent_1", + "rawType": "object", + "type": "string" + }, + { + "name": "parent_2", + "rawType": "object", + "type": "unknown" + }, { "name": "question_sequence", "rawType": "float64", "type": "float" + }, + { + "name": "qnr", + "rawType": "object", + "type": "string" + }, + { + "name": "qnr_version", + "rawType": "object", + "type": "string" } ], - "ref": "012b8cf8-00a7-42d5-93d4-2eb21c7dfbee", + "ref": "04b74b80-21ec-4587-9490-d0c74b4d7da5", "rows": [ [ "0", - "468fc58b1d4b4196af97bcbfbc5464bb", + "0", + "", + "Group", + null, + null, 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0.0, 'GeometryOverlapDetection': None, 'GeometryType': 0.0, 'HideInstructions': False, 'IsCritical': False, 'UseFormatting': False}, 'PublicKey': '6dae3a13-ed96-0dd9-e705-0c1c0503da1a', 'QuestionScope': 0.0, 'QuestionText': 'EA', 'QuestionType': 0.0, 'RosterSizeQuestionId': None, 'RosterSizeSource': None, 'ShowAsList': True, 'ShowAsListThreshold': 3.0, 'StataExportCaption': 'ea', 'Text': None, 'Title': None, 'Type': None, 'UseFormatting': None, 'ValidationConditions': array([], dtype=object), 'VariableLabel': 'EA', 'VariableName': 'ea'}\n {'$type': 'Variable', 'Answers': None, 'AttachmentName': None, 'CascadeFromQuestionId': None, 'CategoriesId': None, 'Children': array([], dtype=object), 'ConditionExpression': None, 'CustomRosterTitle': None, 'Description': None, 'DisplayMode': None, 'DoNotExport': True, 'Enabled': None, 'Expression': 'list_hh.Length', 'Featured': None, 'FixedRosterTitles': None, 'HideIfDisabled': None, 'Instructions': None, 'IsFilteredCombobox': None, 'IsFlatMode': None, 'IsInteger': None, 'IsPlainMode': None, 'IsRoster': None, 'IsSignature': None, 'IsTimestamp': None, 'Label': '# UNITS LISTED', 'MaxAnswerCount': None, 'Name': 'UNITS', 'Properties': None, 'PublicKey': '18d1eac1-5a6c-6a9d-6946-13c636d8def4', 'QuestionScope': None, 'QuestionText': None, 'QuestionType': None, 'RosterSizeQuestionId': None, 'RosterSizeSource': None, 'ShowAsList': None, 'ShowAsListThreshold': None, 'StataExportCaption': None, 'Text': None, 'Title': None, 'Type': 1.0, 'UseFormatting': None, 'ValidationConditions': None, 'VariableLabel': None, 'VariableName': 'UNITS'}\n {'$type': 'Variable', 'Answers': None, 'AttachmentName': None, 'CascadeFromQuestionId': None, 'CategoriesId': None, 'Children': array([], dtype=object), 'ConditionExpression': None, 'CustomRosterTitle': None, 'Description': None, 'DisplayMode': None, 'DoNotExport': False, 'Enabled': None, 'Expression': 'n_eligible', 'Featured': None, 'FixedRosterTitles': None, 'HideIfDisabled': None, 'Instructions': None, 'IsFilteredCombobox': None, 'IsFlatMode': None, 'IsInteger': None, 'IsPlainMode': None, 'IsRoster': None, 'IsSignature': None, 'IsTimestamp': None, 'Label': '# UNITS ELIGIBLE', 'MaxAnswerCount': None, 'Name': 'ELIGIBLE', 'Properties': None, 'PublicKey': 'fdd6775c-edbf-60f9-99f8-be76fa4462f8', 'QuestionScope': None, 'QuestionText': None, 'QuestionType': None, 'RosterSizeQuestionId': None, 'RosterSizeSource': None, 'ShowAsList': None, 'ShowAsListThreshold': None, 'StataExportCaption': None, 'Text': None, 'Title': None, 'Type': 1.0, 'UseFormatting': None, 'ValidationConditions': None, 'VariableLabel': None, 'VariableName': 'ELIGIBLE'}]", + "", + "False", + null, + null, + null, + "3c05a450-f5a1-42dc-aa56-427d4277ded6", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + "Cover", + "False", + null, + null, + null, + "", + "nan", + null, + "False", + "", + null, + null, + "slbhies_listing", + "6" + ], + [ "1", - "InterviewCreated", - "WEST_Sup200", "1", - "2024-10-29 01:17:15.712000", - "0 days 11:00:00", + "ward", + "SingleQuestion", + "0.0", + "[]", + "[]", + "", + "False", + "True", + "", + "{'GeometryInputMode': None, 'GeometryOverlapDetection': None, 'GeometryType': None, 'HideInstructions': False, 'IsCritical': False, 'UseFormatting': False}", + "330266f5-d168-b402-a4d3-24921597cd86", + "0.0", + "WARD", + "ward", + "WARD", + "False", + "[]", null, + "True", + null, + "351e8a12-c335-9e8e-a196-7a4191f33880", null, null, null, - "2024-10-29 12:17:15.712000", - "slbhies_listing", - "6", null, null, + "Cover", + "nan", null, + "False", + "Cover", null, + "1.0", + "slbhies_listing", + "6" + ], + [ + "2", + "2", + "ea", + "SingleQuestion", + "0.0", + "[]", + "[]", + "", + "False", + "True", + "", + "{'GeometryInputMode': 0.0, 'GeometryOverlapDetection': None, 'GeometryType': 0.0, 'HideInstructions': False, 'IsCritical': False, 'UseFormatting': False}", + "6dae3a13-ed96-0dd9-e705-0c1c0503da1a", + "0.0", + "EA", + "ea", + "EA", + "False", + "[]", null, + "False", null, + "6a2693d0-2335-f234-7cbf-f86484e035fe", null, null, null, null, + "330266f5-d168-b402-a4d3-24921597cd86", + "Cover", "nan", null, - null + "False", + "Cover", + null, + "2.0", + "slbhies_listing", + "6" ], [ - "1", - "468fc58b1d4b4196af97bcbfbc5464bb", - "2", - "SupervisorAssigned", - "WEST_Sup200", - "1", - "2024-10-29 01:17:15.712000", - "0 days 11:00:00", - null, - null, + "3", + "3", + "UNITS", + "Variable", null, null, - "2024-10-29 12:17:15.712000", - "slbhies_listing", - "6", + "[]", null, null, null, null, null, + "18d1eac1-5a6c-6a9d-6946-13c636d8def4", null, null, null, null, null, - "nan", null, - null - ], - [ - "2", - "468fc58b1d4b4196af97bcbfbc5464bb", - "3", - "InterviewModeChanged", - "WEST_Sup200", - "1", - "2024-10-29 01:17:15.712000", - "0 days 11:00:00", - "CAPI||", - "CAPI", - "", null, - "2024-10-29 12:17:15.712000", - "slbhies_listing", - "6", null, null, null, @@ -2314,58 +2488,37 @@ null, null, null, + "Cover", + "nan", null, + "False", + "Cover", null, - "nan", null, - null + "slbhies_listing", + "6" ], [ - "3", - "468fc58b1d4b4196af97bcbfbc5464bb", "4", - "InterviewerAssigned", - "WEST_Sup200", - "1", - "2024-10-29 01:17:15.712000", - "0 days 11:00:00", - "WEST_Sup200", - "WEST_Sup200", - null, - null, - "2024-10-29 12:17:15.712000", - "slbhies_listing", - "6", + "4", + "ELIGIBLE", + "Variable", null, null, + "[]", null, null, null, null, null, + "fdd6775c-edbf-60f9-99f8-be76fa4462f8", null, null, null, - "nan", null, - null - ], - [ - "4", - "468fc58b1d4b4196af97bcbfbc5464bb", - "5", - "KeyAssigned", null, - "0", - "2024-10-29 01:17:15.712000", - "0 days 11:00:00", - "66-54-06-24", - "66-54-06-24", null, null, - "2024-10-29 12:17:15.712000", - "slbhies_listing", - "6", null, null, null, @@ -2374,15 +2527,19 @@ null, null, null, + "Cover", + "nan", null, + "False", + "Cover", null, - "nan", null, - null + "slbhies_listing", + "6" ] ], "shape": { - "columns": 27, + "columns": 36, "rows": 5 } }, @@ -2405,199 +2562,219 @@ " \n", " \n", " \n", - " interview__id\n", - " order\n", - " event\n", - " responsible\n", - " role\n", - " timestamp_utc\n", - " tz_offset\n", - " parameters\n", - " param\n", - " answer\n", - " ...\n", + " qnr_seq\n", + " variable_name\n", + " qtype\n", " question_type\n", " answers\n", - " question_scope\n", - " yes_no_view\n", - " is_filtered_combobox\n", - " is_integer\n", + " children\n", + " condition_expression\n", + " hide_if_disabled\n", + " featured\n", + " instructions\n", + " ...\n", " cascade_from_question_id\n", + " parents\n", " answer_sequence\n", " n_answers\n", + " is_linked\n", + " parent_1\n", + " parent_2\n", " question_sequence\n", + " qnr\n", + " qnr_version\n", " \n", " \n", " \n", " \n", " 0\n", - " 468fc58b1d4b4196af97bcbfbc5464bb\n", - " 1\n", - " InterviewCreated\n", - " WEST_Sup200\n", - " 1\n", - " 2024-10-29 01:17:15.712\n", - " 0 days 11:00:00\n", - " None\n", - " None\n", - " None\n", - " ...\n", - " NaN\n", - " None\n", + " 0\n", + " \n", + " Group\n", " NaN\n", " None\n", + " [{'$type': 'SingleQuestion', 'Answers': [], 'A...\n", + " \n", + " False\n", " None\n", " None\n", + " ...\n", " None\n", + " \n", " nan\n", " NaN\n", + " False\n", + " \n", + " None\n", " NaN\n", + " slbhies_listing\n", + " 6\n", " \n", " \n", " 1\n", - " 468fc58b1d4b4196af97bcbfbc5464bb\n", - " 2\n", - " SupervisorAssigned\n", - " WEST_Sup200\n", " 1\n", - " 2024-10-29 01:17:15.712\n", - " 0 days 11:00:00\n", - " None\n", - " None\n", - " None\n", + " ward\n", + " SingleQuestion\n", + " 0.0\n", + " []\n", + " []\n", + " \n", + " False\n", + " True\n", + " \n", " ...\n", - " NaN\n", - " None\n", - " NaN\n", - " None\n", - " None\n", - " None\n", " None\n", + " Cover\n", " nan\n", " NaN\n", - " NaN\n", + " False\n", + " Cover\n", + " None\n", + " 1.0\n", + " slbhies_listing\n", + " 6\n", " \n", " \n", " 2\n", - " 468fc58b1d4b4196af97bcbfbc5464bb\n", - " 3\n", - " InterviewModeChanged\n", - " WEST_Sup200\n", - " 1\n", - " 2024-10-29 01:17:15.712\n", - " 0 days 11:00:00\n", - " CAPI||\n", - " CAPI\n", + " 2\n", + " ea\n", + " SingleQuestion\n", + " 0.0\n", + " []\n", + " []\n", + " \n", + " False\n", + " True\n", " \n", " ...\n", - " NaN\n", - " None\n", - " NaN\n", - " None\n", - " None\n", - " None\n", - " None\n", + " 330266f5-d168-b402-a4d3-24921597cd86\n", + " Cover\n", " nan\n", " NaN\n", - " NaN\n", + " False\n", + " Cover\n", + " None\n", + " 2.0\n", + " slbhies_listing\n", + " 6\n", " \n", " \n", " 3\n", - " 468fc58b1d4b4196af97bcbfbc5464bb\n", - " 4\n", - " InterviewerAssigned\n", - " WEST_Sup200\n", - " 1\n", - " 2024-10-29 01:17:15.712\n", - " 0 days 11:00:00\n", - " WEST_Sup200\n", - " WEST_Sup200\n", - " None\n", - " ...\n", + " 3\n", + " UNITS\n", + " Variable\n", " NaN\n", " None\n", - " NaN\n", + " []\n", " None\n", " None\n", " None\n", " None\n", + " ...\n", + " None\n", + " Cover\n", " nan\n", " NaN\n", + " False\n", + " Cover\n", + " None\n", " NaN\n", + " slbhies_listing\n", + " 6\n", " \n", " \n", " 4\n", - " 468fc58b1d4b4196af97bcbfbc5464bb\n", - " 5\n", - " KeyAssigned\n", - " None\n", - " 0\n", - " 2024-10-29 01:17:15.712\n", - " 0 days 11:00:00\n", - " 66-54-06-24\n", - " 66-54-06-24\n", - " None\n", - " ...\n", + " 4\n", + " ELIGIBLE\n", + " Variable\n", " NaN\n", " None\n", - " NaN\n", + " []\n", " None\n", " None\n", " None\n", " None\n", + " ...\n", + " None\n", + " Cover\n", " nan\n", " NaN\n", + " False\n", + " Cover\n", + " None\n", " NaN\n", + " slbhies_listing\n", + " 6\n", " \n", " \n", "\n", - "

5 rows × 27 columns

\n", + "

5 rows × 36 columns

\n", "" ], "text/plain": [ - " interview__id order event responsible \\\n", - "0 468fc58b1d4b4196af97bcbfbc5464bb 1 InterviewCreated WEST_Sup200 \n", - "1 468fc58b1d4b4196af97bcbfbc5464bb 2 SupervisorAssigned WEST_Sup200 \n", - "2 468fc58b1d4b4196af97bcbfbc5464bb 3 InterviewModeChanged WEST_Sup200 \n", - "3 468fc58b1d4b4196af97bcbfbc5464bb 4 InterviewerAssigned WEST_Sup200 \n", - "4 468fc58b1d4b4196af97bcbfbc5464bb 5 KeyAssigned None \n", + " qnr_seq variable_name qtype question_type answers \\\n", + "0 0 Group NaN None \n", + "1 1 ward SingleQuestion 0.0 [] \n", + "2 2 ea SingleQuestion 0.0 [] \n", + "3 3 UNITS Variable NaN None \n", + "4 4 ELIGIBLE Variable NaN None \n", "\n", - " role timestamp_utc tz_offset parameters param \\\n", - "0 1 2024-10-29 01:17:15.712 0 days 11:00:00 None None \n", - "1 1 2024-10-29 01:17:15.712 0 days 11:00:00 None None \n", - "2 1 2024-10-29 01:17:15.712 0 days 11:00:00 CAPI|| CAPI \n", - "3 1 2024-10-29 01:17:15.712 0 days 11:00:00 WEST_Sup200 WEST_Sup200 \n", - "4 0 2024-10-29 01:17:15.712 0 days 11:00:00 66-54-06-24 66-54-06-24 \n", + " children condition_expression \\\n", + "0 [{'$type': 'SingleQuestion', 'Answers': [], 'A... \n", + "1 [] \n", + "2 [] \n", + "3 [] None \n", + "4 [] None \n", "\n", - " answer ... question_type answers question_scope yes_no_view \\\n", - "0 None ... NaN None NaN None \n", - "1 None ... NaN None NaN None \n", - "2 ... NaN None NaN None \n", - "3 None ... NaN None NaN None \n", - "4 None ... NaN None NaN None \n", + " hide_if_disabled featured instructions ... \\\n", + "0 False None None ... \n", + "1 False True ... \n", + "2 False True ... \n", + "3 None None None ... \n", + "4 None None None ... \n", "\n", - " is_filtered_combobox is_integer cascade_from_question_id answer_sequence \\\n", - "0 None None None nan \n", - "1 None None None nan \n", - "2 None None None nan \n", - "3 None None None nan \n", - "4 None None None nan \n", + " cascade_from_question_id parents answer_sequence n_answers \\\n", + "0 None nan NaN \n", + "1 None Cover nan NaN \n", + "2 330266f5-d168-b402-a4d3-24921597cd86 Cover nan NaN \n", + "3 None Cover nan NaN \n", + "4 None Cover nan NaN \n", "\n", - " n_answers question_sequence \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", + " is_linked parent_1 parent_2 question_sequence qnr qnr_version \n", + "0 False None NaN slbhies_listing 6 \n", + "1 False Cover None 1.0 slbhies_listing 6 \n", + "2 False Cover None 2.0 slbhies_listing 6 \n", + "3 False Cover None NaN slbhies_listing 6 \n", + "4 False Cover None NaN slbhies_listing 6 \n", "\n", - "[5 rows x 27 columns]" + "[5 rows x 36 columns]" ] }, - "execution_count": 14, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], + "source": [ + "df_questionnaire_kedro.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2867586", + "metadata": {}, + "outputs": [], + "source": [ + "df_para_kedro.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "313dc912", + "metadata": {}, + "outputs": [], "source": [ "df_para.head(5)" ] From 918355006784c5d228642d2d4dd356ca9df5599a Mon Sep 17 00:00:00 2001 From: VJausovec Date: Wed, 18 Feb 2026 11:54:18 +0000 Subject: [PATCH 17/70] Refactor code structure for improved readability and maintainability --- rissk/utils/file_process_utils_kedro.py | 1 + .../src/rissk_kedro/test_ingestion.ipynb | 973 ++++++++++++++++-- 2 files changed, 907 insertions(+), 67 deletions(-) diff --git a/rissk/utils/file_process_utils_kedro.py b/rissk/utils/file_process_utils_kedro.py index fc2d871..1494e9e 100644 --- a/rissk/utils/file_process_utils_kedro.py +++ b/rissk/utils/file_process_utils_kedro.py @@ -3,6 +3,7 @@ import pandas as pd from typing import Dict import re +import numpy as np diff --git a/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb b/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb index 53eb542..24212d2 100644 --- a/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb +++ b/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb @@ -10,19 +10,26 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 21, "id": "607ef013", "metadata": {}, "outputs": [], "source": [ "from pathlib import Path\n", "import pandas as pd\n", + "from typing import Dict, Any, List, Optional, Tuple\n", + "import numpy as np\n", + "from collections import Counter\n", + "import math\n", + "from pandas.api import types as ptypes\n", + "import pyarrow.parquet as pq\n", + "\n", "from rissk.config import DATA_DIR, RAW_DATA_DIR, PROCESSED_DATA_DIR, INTERIM_DATA_DIR, PROJ_ROOT" ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 2, "id": "a9c7966e", "metadata": {}, "outputs": [], @@ -32,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 3, "id": "dc4569ea", "metadata": {}, "outputs": [], @@ -45,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 4, "id": "3d3ef86a", "metadata": {}, "outputs": [], @@ -58,20 +65,12 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 5, "id": "72fdc596", "metadata": {}, "outputs": [], "source": [ "# Comparison utility inserted into notebook\n", - "from typing import Dict, Any, List, Optional, Tuple\n", - "import pandas as pd\n", - "import numpy as np\n", - "from collections import Counter\n", - "import math\n", - "from pandas.api import types as ptypes\n", - "\n", - "\n", "def _dtype_map(dseries: pd.Series) -> Dict[str, str]:\n", " return {col: str(dtype) for col, dtype in dseries.items()}\n", "\n", @@ -229,6 +228,19 @@ " # Automatic key detection (if any)\n", " candidate_key = _find_candidate_key(df_a, df_b, common)\n", " details['auto_key'] = candidate_key\n", + " # Determine columns to compare when using a key-based alignment.\n", + " # Exclude the key column(s) from the per-column comparison because\n", + " # setting the key as index will remove it from the DataFrame columns.\n", + " if candidate_key is None:\n", + " cols_to_compare = common\n", + " else:\n", + " if isinstance(candidate_key, (list, tuple)):\n", + " key_list = list(candidate_key)\n", + " else:\n", + " key_list = [candidate_key]\n", + " cols_to_compare = [c for c in common if c not in key_list]\n", + " # choose active columns for comparison depending on whether we will align by key\n", + " cols = cols_to_compare if candidate_key is not None else common\n", "\n", " # CELL-level comparison (position/label depending on alignment)\n", " if check == 'cells':\n", @@ -246,8 +258,8 @@ " a_k = df_a.set_index(candidate_key)\n", " b_k = df_b.set_index(candidate_key)\n", " common_idx = a_k.index.intersection(b_k.index)\n", - " a_al = a_k.loc[common_idx, common].fillna('__NA__')\n", - " b_al = b_k.loc[common_idx, common].fillna('__NA__')\n", + " a_al = a_k.loc[common_idx, cols].fillna('__NA__')\n", + " b_al = b_k.loc[common_idx, cols].fillna('__NA__')\n", " rows_compared = len(common_idx)\n", " else:\n", " # try index-based alignment if helpful\n", @@ -266,14 +278,14 @@ " rows_compared = rows_to_compare\n", "\n", " # perform elementwise comparison with tolerance on numeric cols\n", - " neq_mask = np.zeros((rows_compared, len(common)), dtype=bool)\n", - " for j, col in enumerate(common):\n", + " neq_mask = np.zeros((rows_compared, len(cols)), dtype=bool)\n", + " for j, col in enumerate(cols):\n", " a_col = a_al[col]\n", " b_col = b_al[col]\n", " col_neq = _compare_elementwise(a_col, b_col, atol=atol, rtol=rtol)\n", " neq_mask[:, j] = col_neq\n", "\n", - " neq_df = pd.DataFrame(neq_mask, columns=common)\n", + " neq_df = pd.DataFrame(neq_mask, columns=cols)\n", " cols_with_diff = neq_df.any(axis=0)\n", " cols_with_diff_names = cols_with_diff[cols_with_diff].index.tolist()\n", " total_cell_diffs = int(neq_df.values.sum())\n", @@ -287,9 +299,9 @@ " if total_cell_diffs > 0:\n", " diff_list = []\n", " # Finding indices (row, col) of differences\n", - " rows, cols = np.where(neq_mask)\n", - " for r, c in zip(rows, cols):\n", - " col_name = common[c]\n", + " rows, cols_idx = np.where(neq_mask)\n", + " for r, c in zip(rows, cols_idx):\n", + " col_name = cols[c]\n", " # Get index label if available\n", " idx_label = a_al.index[r]\n", " val_a = a_al.iloc[r, c]\n", @@ -317,8 +329,8 @@ " else:\n", " if candidate_key is not None:\n", " # compare by key: count keys only in A/B and mismatched rows for common keys\n", - " a_k = df_a.set_index(candidate_key)[common].fillna('__NA__')\n", - " b_k = df_b.set_index(candidate_key)[common].fillna('__NA__')\n", + " a_k = df_a.set_index(candidate_key)[cols].fillna('__NA__')\n", + " b_k = df_b.set_index(candidate_key)[cols].fillna('__NA__')\n", " keys_a = set(a_k.index)\n", " keys_b = set(b_k.index)\n", " keys_only_a = keys_a - keys_b\n", @@ -332,7 +344,7 @@ " b_row = b_k.loc[k]\n", " # elementwise comparison across common cols\n", " neq_any = False\n", - " for col in common:\n", + " for col in cols:\n", " if _compare_elementwise(pd.Series([a_row[col]]), pd.Series([b_row[col]]), atol=atol, rtol=rtol)[0]:\n", " neq_any = True\n", " break\n", @@ -378,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 6, "id": "792a94d3", "metadata": {}, "outputs": [], @@ -388,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 7, "id": "0a094beb", "metadata": {}, "outputs": [ @@ -410,7 +422,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 8, "id": "5a2e9402", "metadata": {}, "outputs": [ @@ -444,7 +456,7 @@ "type": "unknown" } ], - "ref": "9db9f18a-beea-4712-9041-f5ca9d603dc8", + "ref": "df32ed59-ae08-4a84-9798-997b419e5a62", "rows": [ [ "0", @@ -940,7 +952,7 @@ "[156826 rows x 4 columns]" ] }, - "execution_count": 61, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -951,7 +963,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 9, "id": "de3363a0", "metadata": {}, "outputs": [], @@ -961,7 +973,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "4850564f", "metadata": {}, "outputs": [ @@ -972,7 +984,8 @@ "{'equal': False, 'shape_a': (0, 0), 'shape_b': (23127, 41)}\n", "{'different_columns': ['interview__id', 'roster_level', 'variable', 'value', 'filename', 'qnr', 'qnr_version', 'qnr_seq', 'variable_name', 'qtype', 'question_type', 'answers', 'children', 'condition_expression', 'hide_if_disabled', 'featured', 'instructions', 'properties', 'public_key', 'question_scope', 'question_text', 'stata_export_caption', 'variable_label', 'is_timestamp', 'validation_conditions', 'yes_no_view', 'is_filtered_combobox', 'is_integer', 'categories_id', 'title', 'is_roster', 'linked_to_roster_id', 'linked_to_question_id', 'cascade_from_question_id', 'parents', 'answer_sequence', 'n_answers', 'is_linked', 'parent_1', 'parent_2', 'question_sequence'], 'equal': False, 'only_in_a': [], 'only_in_b': ['interview__id', 'roster_level', 'variable', 'value', 'filename', 'qnr', 'qnr_version', 'qnr_seq', 'variable_name', 'qtype', 'question_type', 'answers', 'children', 'condition_expression', 'hide_if_disabled', 'featured', 'instructions', 'properties', 'public_key', 'question_scope', 'question_text', 'stata_export_caption', 'variable_label', 'is_timestamp', 'validation_conditions', 'yes_no_view', 'is_filtered_combobox', 'is_integer', 'categories_id', 'title', 'is_roster', 'linked_to_roster_id', 'linked_to_question_id', 'cascade_from_question_id', 'parents', 'answer_sequence', 'n_answers', 'is_linked', 'parent_1', 'parent_2', 'question_sequence']}\n", "{'mismatched_columns': [], 'equal': True}\n", - "{'checked': True, 'note': 'no common columns to compare', 'columns_with_differences': [], 'total_cell_differences': 0}\n" + "{'checked': True, 'note': 'no common columns to compare', 'columns_with_differences': [], 'total_cell_differences': 0}\n", + "Cells are the same\n" ] } ], @@ -989,7 +1002,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "beeb1d8e", "metadata": {}, "outputs": [ @@ -1007,7 +1020,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "2d466e2a", "metadata": {}, "outputs": [ @@ -1020,7 +1033,7 @@ " True, True, True, True, True, True, True, True, True])" ] }, - "execution_count": 41, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1031,7 +1044,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "1fddd0a2", "metadata": {}, "outputs": [ @@ -1055,7 +1068,7 @@ "type": "unknown" } ], - "ref": "6f43feec-7cae-4a4f-a4a0-08df6164b6c0", + "ref": "8010cabe-74d9-434f-a442-f68abb06dd8a", "rows": [ [ "qnr_seq", @@ -1491,7 +1504,7 @@ "qnr_version object object" ] }, - "execution_count": 42, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1502,27 +1515,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "81f6cde7", "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "\"['public_key'] not in index\"", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[43]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mcompare_parquet_files\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_questionnaire_kedro\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_questionnaire\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mcells\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[37]\u001b[39m\u001b[32m, line 184\u001b[39m, in \u001b[36mcompare_parquet_files\u001b[39m\u001b[34m(df_a, df_b, check, atol, rtol)\u001b[39m\n\u001b[32m 182\u001b[39m b_k = df_b.set_index(candidate_key)\n\u001b[32m 183\u001b[39m common_idx = a_k.index.intersection(b_k.index)\n\u001b[32m--> \u001b[39m\u001b[32m184\u001b[39m a_al = \u001b[43ma_k\u001b[49m\u001b[43m.\u001b[49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcommon_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcommon\u001b[49m\u001b[43m]\u001b[49m.fillna(\u001b[33m'\u001b[39m\u001b[33m__NA__\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 185\u001b[39m b_al = b_k.loc[common_idx, common].fillna(\u001b[33m'\u001b[39m\u001b[33m__NA__\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 186\u001b[39m rows_compared = \u001b[38;5;28mlen\u001b[39m(common_idx)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexing.py:1185\u001b[39m, in \u001b[36m_LocationIndexer.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 1183\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._is_scalar_access(key):\n\u001b[32m 1184\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.obj._get_value(*key, takeable=\u001b[38;5;28mself\u001b[39m._takeable)\n\u001b[32m-> \u001b[39m\u001b[32m1185\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_getitem_tuple\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1186\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1187\u001b[39m \u001b[38;5;66;03m# we by definition only have the 0th axis\u001b[39;00m\n\u001b[32m 1188\u001b[39m axis = \u001b[38;5;28mself\u001b[39m.axis \u001b[38;5;129;01mor\u001b[39;00m \u001b[32m0\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexing.py:1376\u001b[39m, in \u001b[36m_LocIndexer._getitem_tuple\u001b[39m\u001b[34m(self, tup)\u001b[39m\n\u001b[32m 1374\u001b[39m \u001b[38;5;66;03m# ugly hack for GH #836\u001b[39;00m\n\u001b[32m 1375\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._multi_take_opportunity(tup):\n\u001b[32m-> \u001b[39m\u001b[32m1376\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_multi_take\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1378\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._getitem_tuple_same_dim(tup)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexing.py:1328\u001b[39m, in \u001b[36m_LocIndexer._multi_take\u001b[39m\u001b[34m(self, tup)\u001b[39m\n\u001b[32m 1311\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 1312\u001b[39m \u001b[33;03mCreate the indexers for the passed tuple of keys, and\u001b[39;00m\n\u001b[32m 1313\u001b[39m \u001b[33;03mexecutes the take operation. This allows the take operation to be\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 1324\u001b[39m \u001b[33;03mvalues: same type as the object being indexed\u001b[39;00m\n\u001b[32m 1325\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 1326\u001b[39m \u001b[38;5;66;03m# GH 836\u001b[39;00m\n\u001b[32m 1327\u001b[39m d = {\n\u001b[32m-> \u001b[39m\u001b[32m1328\u001b[39m axis: \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_get_listlike_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1329\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m (key, axis) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(tup, \u001b[38;5;28mself\u001b[39m.obj._AXIS_ORDERS)\n\u001b[32m 1330\u001b[39m }\n\u001b[32m 1331\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.obj._reindex_with_indexers(d, copy=\u001b[38;5;28;01mTrue\u001b[39;00m, allow_dups=\u001b[38;5;28;01mTrue\u001b[39;00m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexing.py:1559\u001b[39m, in \u001b[36m_LocIndexer._get_listlike_indexer\u001b[39m\u001b[34m(self, key, axis)\u001b[39m\n\u001b[32m 1556\u001b[39m ax = \u001b[38;5;28mself\u001b[39m.obj._get_axis(axis)\n\u001b[32m 1557\u001b[39m axis_name = \u001b[38;5;28mself\u001b[39m.obj._get_axis_name(axis)\n\u001b[32m-> \u001b[39m\u001b[32m1559\u001b[39m keyarr, indexer = \u001b[43max\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1561\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m keyarr, indexer\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexes/base.py:6212\u001b[39m, in \u001b[36mIndex._get_indexer_strict\u001b[39m\u001b[34m(self, key, axis_name)\u001b[39m\n\u001b[32m 6209\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 6210\u001b[39m keyarr, indexer, new_indexer = \u001b[38;5;28mself\u001b[39m._reindex_non_unique(keyarr)\n\u001b[32m-> \u001b[39m\u001b[32m6212\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 6214\u001b[39m keyarr = \u001b[38;5;28mself\u001b[39m.take(indexer)\n\u001b[32m 6215\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[32m 6216\u001b[39m \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/indexes/base.py:6264\u001b[39m, in \u001b[36mIndex._raise_if_missing\u001b[39m\u001b[34m(self, key, indexer, axis_name)\u001b[39m\n\u001b[32m 6261\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m]\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 6263\u001b[39m not_found = \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask.nonzero()[\u001b[32m0\u001b[39m]].unique())\n\u001b[32m-> \u001b[39m\u001b[32m6264\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m not in index\u001b[39m\u001b[33m\"\u001b[39m)\n", - "\u001b[31mKeyError\u001b[39m: \"['public_key'] not in index\"" - ] + "data": { + "text/plain": [ + "(True,\n", + " {'shape': {'equal': True, 'shape_a': (52, 36), 'shape_b': (52, 36)},\n", + " 'columns': {'different_columns': [],\n", + " 'equal': True,\n", + " 'only_in_a': [],\n", + " 'only_in_b': []},\n", + " 'dtypes': {'mismatched_columns': [], 'equal': True},\n", + " 'auto_key': 'public_key',\n", + " 'cell_compare': {'checked': True,\n", + " 'columns_with_differences': [],\n", + " 'total_cell_differences': 0,\n", + " 'rows_compared': 52,\n", + " 'note': \"aligned by key='public_key'\"},\n", + " 'same': True})" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -1531,7 +1549,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "143d6b7f", "metadata": {}, "outputs": [ @@ -1725,7 +1743,7 @@ "type": "string" } ], - "ref": "149a90ca-a99a-4b66-a642-b5e13a8d2137", + "ref": "0ee8d433-a459-4294-be15-ddcf2b5208ff", "rows": [ [ "0", @@ -2135,7 +2153,7 @@ "[5 rows x 36 columns]" ] }, - "execution_count": 30, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -2146,7 +2164,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "f2112454", "metadata": {}, "outputs": [ @@ -2340,7 +2358,7 @@ "type": "string" } ], - "ref": "04b74b80-21ec-4587-9490-d0c74b4d7da5", + "ref": "728371fd-c6af-4a2f-9875-c7c9eeb22e83", "rows": [ [ "0", @@ -2750,7 +2768,7 @@ "[5 rows x 36 columns]" ] }, - "execution_count": 31, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -2761,22 +2779,843 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "c2867586", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "interview__id", + "rawType": "object", + "type": "string" + }, + { + "name": "order", + "rawType": "int64", + "type": "integer" + }, + { + "name": "event", + "rawType": "object", + "type": "string" + }, + { + "name": "responsible", + "rawType": "object", + "type": "unknown" + }, + { + "name": "role", + "rawType": "int64", + "type": "integer" + }, + { + "name": "timestamp_utc", + "rawType": "object", + "type": "string" + }, + { + "name": "tz_offset", + "rawType": "object", + "type": "string" + }, + { + "name": "parameters", + "rawType": "object", + "type": "unknown" + }, + { + "name": "param", + "rawType": "object", + "type": "unknown" + }, + { + "name": "answer", + "rawType": "object", + "type": "unknown" + }, + { + "name": "roster_level", + "rawType": "object", + "type": "unknown" + } + ], + "ref": "41afd7e0-e12d-4cd9-b72f-57042f2fef74", + "rows": [ + [ + "0", + "468fc58b1d4b4196af97bcbfbc5464bb", + "1", + "InterviewCreated", + "WEST_Sup200", + "1", + "2024-10-29T01:17:15.712", + "11:00:00", + null, + null, + null, + null + ], + [ + "1", + "468fc58b1d4b4196af97bcbfbc5464bb", + "2", + "SupervisorAssigned", + "WEST_Sup200", + "1", + "2024-10-29T01:17:15.712", + "11:00:00", + null, + null, + null, + null + ], + [ + "2", + "468fc58b1d4b4196af97bcbfbc5464bb", + "3", + "InterviewModeChanged", + "WEST_Sup200", + "1", + "2024-10-29T01:17:15.712", + "11:00:00", + "CAPI||", + "CAPI", + "", + null + ], + [ + "3", + "468fc58b1d4b4196af97bcbfbc5464bb", + "4", + "InterviewerAssigned", + "WEST_Sup200", + "1", + "2024-10-29T01:17:15.712", + "11:00:00", + "WEST_Sup200", + "WEST_Sup200", + null, + null + ], + [ + "4", + "468fc58b1d4b4196af97bcbfbc5464bb", + "5", + "KeyAssigned", + null, + "0", + "2024-10-29T01:17:15.712", + "11:00:00", + "66-54-06-24", + "66-54-06-24", + null, + null + ] + ], + "shape": { + "columns": 11, + "rows": 5 + } + }, + "text/html": [ + "
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interview__idordereventresponsibleroletimestamp_utctz_offsetparametersparamanswerroster_level
0468fc58b1d4b4196af97bcbfbc5464bb1InterviewCreatedWEST_Sup20012024-10-29T01:17:15.71211:00:00NoneNoneNoneNone
1468fc58b1d4b4196af97bcbfbc5464bb2SupervisorAssignedWEST_Sup20012024-10-29T01:17:15.71211:00:00NoneNoneNoneNone
2468fc58b1d4b4196af97bcbfbc5464bb3InterviewModeChangedWEST_Sup20012024-10-29T01:17:15.71211:00:00CAPI||CAPINone
3468fc58b1d4b4196af97bcbfbc5464bb4InterviewerAssignedWEST_Sup20012024-10-29T01:17:15.71211:00:00WEST_Sup200WEST_Sup200NoneNone
4468fc58b1d4b4196af97bcbfbc5464bb5KeyAssignedNone02024-10-29T01:17:15.71211:00:0066-54-06-2466-54-06-24NoneNone
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interview__idordereventresponsibleroletimestamp_utctz_offsetparametersparamanswer...question_typeanswersquestion_scopeyes_no_viewis_filtered_comboboxis_integercascade_from_question_idanswer_sequencen_answersquestion_sequence
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" + ], + "text/plain": [ + " interview__id order event responsible \\\n", + "0 468fc58b1d4b4196af97bcbfbc5464bb 1 InterviewCreated WEST_Sup200 \n", + "1 468fc58b1d4b4196af97bcbfbc5464bb 2 SupervisorAssigned WEST_Sup200 \n", + "2 468fc58b1d4b4196af97bcbfbc5464bb 3 InterviewModeChanged WEST_Sup200 \n", + "3 468fc58b1d4b4196af97bcbfbc5464bb 4 InterviewerAssigned WEST_Sup200 \n", + "4 468fc58b1d4b4196af97bcbfbc5464bb 5 KeyAssigned None \n", + "\n", + " role timestamp_utc tz_offset parameters param \\\n", + "0 1 2024-10-29 01:17:15.712 0 days 11:00:00 None None \n", + "1 1 2024-10-29 01:17:15.712 0 days 11:00:00 None None \n", + "2 1 2024-10-29 01:17:15.712 0 days 11:00:00 CAPI|| CAPI \n", + "3 1 2024-10-29 01:17:15.712 0 days 11:00:00 WEST_Sup200 WEST_Sup200 \n", + "4 0 2024-10-29 01:17:15.712 0 days 11:00:00 66-54-06-24 66-54-06-24 \n", + "\n", + " answer ... question_type answers question_scope yes_no_view \\\n", + "0 None ... NaN None NaN None \n", + "1 None ... NaN None NaN None \n", + "2 ... NaN None NaN None \n", + "3 None ... NaN None NaN None \n", + "4 None ... NaN None NaN None \n", + "\n", + " is_filtered_combobox is_integer cascade_from_question_id answer_sequence \\\n", + "0 None None None nan \n", + "1 None None None nan \n", + "2 None None None nan \n", + "3 None None None nan \n", + "4 None None None nan \n", + "\n", + " n_answers question_sequence \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + "[5 rows x 27 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_para.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e10c6d7d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Old Compression: SNAPPY\n", + "New Compression: SNAPPY\n", + "5387\n", + "5405\n" + ] + } + ], + "source": [ + "# meta_old = pq.read_metadata(PROCESSED_DATA_DIR.joinpath(\"microdata.parquet\"))\n", + "# meta_new = pq.read_metadata(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"microdata.parquet\"))\n", + "\n", + "# print(f\"Old Compression: {meta_old.row_group(0).column(0).compression}\")\n", + "# print(f\"New Compression: {meta_new.row_group(0).column(0).compression}\")\n", + "\n", + "# print(len(meta_old.metadata[b'pandas']))\n", + "# print(len(meta_new.metadata[b'pandas']))" ] } ], From f2a642db85cdefef5cc67427b87134617231e2cb Mon Sep 17 00:00:00 2001 From: VJausovec Date: Mon, 23 Feb 2026 13:59:16 +0000 Subject: [PATCH 18/70] Refactor import_utils_kedro.py for timestamp processing and error handling; update catalog.yml and globals.yml for dynamic data paths; modify parameters.yml to pull questionnaire config from globals; clean up nodes.py and pipeline.py; remove unused test files. --- rissk/utils/import_utils_kedro.py | 68 ++++- rissk_kedro/conf/base/catalog.yml | 26 +- rissk_kedro/conf/base/globals.yml | 23 ++ rissk_kedro/conf/base/parameters.yml | 21 +- .../pipelines/feature_engineering/nodes.py | 10 +- .../pipelines/feature_engineering/pipeline.py | 24 +- .../pipelines/risk_scoring/nodes.py | 252 +++++++++--------- rissk_kedro/tests/__init__.py | 0 rissk_kedro/tests/pipelines/__init__.py | 0 .../pipelines/data_science/test_pipeline.py | 63 ----- rissk_kedro/tests/test_run.py | 20 -- 11 files changed, 245 insertions(+), 262 deletions(-) create mode 100644 rissk_kedro/conf/base/globals.yml delete mode 100644 rissk_kedro/tests/__init__.py delete mode 100644 rissk_kedro/tests/pipelines/__init__.py delete mode 100644 rissk_kedro/tests/pipelines/data_science/test_pipeline.py delete mode 100644 rissk_kedro/tests/test_run.py diff --git a/rissk/utils/import_utils_kedro.py b/rissk/utils/import_utils_kedro.py index c85729a..75a20ab 100644 --- a/rissk/utils/import_utils_kedro.py +++ b/rissk/utils/import_utils_kedro.py @@ -302,6 +302,40 @@ def get_paradata(data_path: Path, df_questionnaires: pd.DataFrame) -> pd.DataFra else: df_para['roster_level'] = None # Or empty string + if 'timestamp_utc' in df_para.columns and 'tz_offset' in df_para.columns: + df_para['timestamp_utc'] = pd.to_datetime(df_para['timestamp_utc']) + # Only apply if tz_offset is string + if pd.api.types.is_string_dtype(df_para['tz_offset']): + df_para['tz_offset'] = pd.to_timedelta(df_para['tz_offset'].str.replace(':', ' hours ') + ' minutes') + df_para['timestamp_local'] = df_para['timestamp_utc'] + df_para['tz_offset'] + + try: + parts = parse_filename(data_path.name) + qnr_name = parts[0] + qnr_version = parts[1] + df_para = set_qnr_version(df_para, qnr_name, qnr_version) + except ValueError: + logger.warning(f"Could not parse filename '{data_path.name}' for version info") + + if not df_questionnaires.empty: + q_columns = ['qnr_seq', 'variable_name', "qtype", 'question_type', + 'answers', 'question_scope', + 'yes_no_view', 'is_filtered_combobox', + 'is_integer', 'cascade_from_question_id', + 'answer_sequence', 'n_answers', 'question_sequence', + 'qnr', 'qnr_version'] + + # Ensure columns exist in questionnaire df before selecting + q_columns = [c for c in q_columns if c in df_questionnaires.columns] + + # Merge + df_para = df_para.merge(df_questionnaires[q_columns], how='left', + left_on=['param', 'qnr', 'qnr_version'], + right_on=['variable_name', 'qnr', 'qnr_version']) + + # Normalize column names + df_para.columns = [normalize_column_name(c) for c in df_para.columns] + return df_para @@ -365,28 +399,35 @@ def replace_stata_missing(val): def get_microdata(data_path: Path, df_questionnaires: pd.DataFrame) -> pd.DataFrame: - drop_list = {'interview__key', 'sssys_irnd', 'has__errors', 'interview__status', 'assignment__id'} + drop_list = ['interview__key', 'sssys_irnd', 'has__errors', 'interview__status', 'assignment__id'] file_names = get_microdata_file_list(data_path) - # Pre-calculate masks outside loop - multi_unlinked_vars = [] - multi_linked_vars = [] - list_vars = [] - gps_vars = [] - + # # Pre-calculate masks outside loop + # # Pre-initialize these variable lists once so they exist when the questionnaire DF is empty + # # (avoids NameError and avoids recalculating per-file). + # multi_unlinked_vars = [] + # multi_linked_vars = [] + # list_vars = [] + # gps_vars = [] + + # define multi/list question conditions if not df_questionnaires.empty: # Use boolean indexing - unlinked_mask = (df_questionnaires["qtype"] == 'MultyOptionsQuestion') & (df_questionnaires['is_linked'] == False) - linked_mask = (df_questionnaires["qtype"] == 'MultyOptionsQuestion') & (df_questionnaires['is_linked'] == True) + unlinked_mask = (df_questionnaires["qtype"] == 'MultyOptionsQuestion') & ( + df_questionnaires['is_linked'] == False) + linked_mask = (df_questionnaires["qtype"] == 'MultyOptionsQuestion') & ( + df_questionnaires['is_linked'] == True) list_mask = (df_questionnaires["qtype"] == 'TextListQuestion') gps_mask = (df_questionnaires["qtype"] == 'GpsCoordinateQuestion') - + + # extract multi/list question lists from conditions multi_unlinked_vars = df_questionnaires.loc[unlinked_mask, 'variable_name'].tolist() multi_linked_vars = df_questionnaires.loc[linked_mask, 'variable_name'].tolist() list_vars = df_questionnaires.loc[list_mask, 'variable_name'].tolist() gps_vars = df_questionnaires.loc[gps_mask, 'variable_name'].tolist() - + + # Iterate over each file all_dfs = [] for file_name in file_names: df = read_microdata_file(data_path, file_name) @@ -404,11 +445,10 @@ def get_microdata(data_path: Path, df_questionnaires: pd.DataFrame) -> pd.DataFr df = transform_multi(df, list_vars, 'list') df = transform_multi(df, gps_vars, 'gps') - # Handle roster IDs + # create roster_level from __id columns if on roster level, else '' if main questionnaire file roster_ids = [col for col in df.columns if col.endswith("__id") and col != "interview__id"] if roster_ids: - # Vectorized string join is harder in pandas, apply is okay here - df['roster_level'] = df[roster_ids].astype(str).agg(','.join, axis=1) + df['roster_level'] = df[roster_ids].apply(lambda row: ",".join(map(str, row)), axis=1) df.drop(columns=roster_ids, inplace=True) else: df['roster_level'] = '' diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index 3a9cfe4..7560adc 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -4,7 +4,7 @@ # The source partitions (Zips) survey_zip_partitions: type: partitions.PartitionedDataset - path: data/hies2024/latest/10_RAW + path: data/${globals:survey.name}/latest/10_RAW dataset: type: rissk_kedro.datasets.PathDataset filename_suffix: ".zip" @@ -13,58 +13,58 @@ survey_zip_partitions: # Used by downstream nodes to find the directories extracted_survey_folders: type: partitions.PartitionedDataset - path: data/hies2024/latest/10_RAW + path: data/${globals:survey.name}/latest/10_RAW dataset: type: rissk_kedro.datasets.PathDataset # === INGESTED DataFrames === paradata_interim: type: pandas.ParquetDataset - filepath: data/hies2024/latest/20_INTERIM/paradata.parquet + filepath: data/${globals:survey.name}/latest/20_INTERIM/paradata.parquet raw_questionnaire: type: pandas.ParquetDataset - filepath: data/hies2024/latest/30_PROCESSED/questionnaire.parquet + filepath: data/${globals:survey.name}/latest/30_PROCESSED/questionnaire.parquet raw_microdata: type: pandas.ParquetDataset - filepath: data/hies2024/latest/30_PROCESSED/microdata.parquet + filepath: data/${globals:survey.name}/latest/30_PROCESSED/microdata.parquet paradata_processed: type: pandas.ParquetDataset - filepath: data/hies2024/latest//30_PROCESSED/paradata_processed.parquet + filepath: data/${globals:survey.name}/latest/30_PROCESSED/paradata_processed.parquet paradata_active: type: pandas.ParquetDataset - filepath: data/hies2024/latest/30_PROCESSED/paradata_active.parquet + filepath: data/${globals:survey.name}/latest/30_PROCESSED/paradata_active.parquet # === FEATURE PROCESSED === # item_features: # type: pandas.ParquetDataset -# filepath: data/hies2024/latest/30_PROCESSED/item_features.parquet +# filepath: data/${globals:survey.name}/latest/30_PROCESSED/item_features.parquet # unit_features: # type: pandas.ParquetDataset -# filepath: data/hies2024/latest/30_PROCESSED/unit_features.parquet +# filepath: data/${globals:survey.name}/latest/30_PROCESSED/unit_features.parquet # unit_risk_scores_raw: # type: pandas.ParquetDataset -# filepath: data/hies2024/latest/30_PROCESSED/unit_risk_scores_raw.parquet +# filepath: data/${globals:survey.name}/latest/30_PROCESSED/unit_risk_scores_raw.parquet # responsible_features: # type: pandas.ParquetDataset -# filepath: data/hies2024/latest/30_PROCESSED/responsible_features.parquet +# filepath: data/${globals:survey.name}/latest/30_PROCESSED/responsible_features.parquet # # === MODEL OUTPUT === # unit_risk_scores: # type: pandas.CSVDataset -# filepath: data/hies2024/latest/40_OUTPUTS/unit_risk_scores.csv +# filepath: data/${globals:survey.name}/latest/40_OUTPUTS/unit_risk_scores.csv # save_args: # index: false # unit_feature_scores: # type: pandas.CSVDataset -# filepath: data/hies2024/latest/40_OUTPUTS/unit_feature_scores.csv +# filepath: data/${globals:survey.name}/latest/40_OUTPUTS/unit_feature_scores.csv # save_args: # index: false diff --git a/rissk_kedro/conf/base/globals.yml b/rissk_kedro/conf/base/globals.yml new file mode 100644 index 0000000..c9171c8 --- /dev/null +++ b/rissk_kedro/conf/base/globals.yml @@ -0,0 +1,23 @@ +# Survey Configuration (from env.yaml) + +# survey: +# name: "hies2024" +# questionnaires: +# - name: "snb_hies_hh" +# VERSION: [9, 10, 11] +# - name: "slbhies_listing" +# VERSION: [6, 7] + +# survey: +# name: "pmpmd" +# questionnaires: +# - name: "pmpmd_community" +# VERSION: [2, 3, 4, 5] +# - name: "pmpmd_household" +# VERSION: [4, 5, 6] + +survey: + name: "slchbs" + questionnaires: + - name: "slchbs_saintlucia_2025" + VERSION: [5, 6, 7] diff --git a/rissk_kedro/conf/base/parameters.yml b/rissk_kedro/conf/base/parameters.yml index a47c558..c54bd1d 100644 --- a/rissk_kedro/conf/base/parameters.yml +++ b/rissk_kedro/conf/base/parameters.yml @@ -1,17 +1,14 @@ -# Survey Configuration (from env.yaml) +# Survey Configuration has to be defined in globals.yml survey: - name: "hies2024" - questionnaires: - - name: "snb_hies_hh" - VERSION: [9, 10, 11] - - name: "slbhies_listing" - VERSION: [6, 7] + # This tells Kedro to pull the entire block from globals.yml + questionnaires: ${globals:survey.questionnaires} -# Ingestion Configuration -ingestion: - raw_data_path: "data/hies2024/latest/10_RAW" +# # Ingestion Configuration +# ingestion: +# raw_data_path: "data/hies2024/latest/10_RAW" -# If set to null, the system will look for the 'PASSWORD' environment variable +# Set the password for zip files in local/parameters.yml if needed. +# It will override the base/parameters.yml setting. zip_password: null # Processing Parameters @@ -83,5 +80,5 @@ features: # Output Configuration output: - feature_score: false + feature_score: true unit_risk_score_path: "results/unit_risk_score.csv" \ No newline at end of file diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py index 4c2cfd3..f42dc9f 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py @@ -45,6 +45,8 @@ def make_index_col(df): mask = (~df[['interview__id', 'variable_name', 'roster_level']].isnull()) & \ (df[['interview__id', 'variable_name', 'roster_level']] != '') filtered_df = df.where(mask, '') + + # Concatenate the columns with an underscore separator df['index_col'] = ( filtered_df['interview__id'].astype(str) + "_" + filtered_df['variable_name'].astype(str) + "_" + @@ -57,6 +59,7 @@ def make_index_col(df): # Sort by interview__id, order paradata.sort_values(['interview__id', 'order'], inplace=True) + paradata.reset_index(drop=True, inplace=True) # Limit Unit Logic limit_unit = parameters.get('processing', {}).get('limit_unit') @@ -85,13 +88,16 @@ def filter_active_paradata_node( parameters: Pipeline parameters Returns: - Active paradata DataFrame + Active paradata DataFrame: keep active events, prior rejection/review events, for questions with scope interviewer """ active_events = [ 'InterviewCreated', 'AnswerSet', 'Resumed', 'AnswerRemoved', 'CommentSet', 'Restarted' ] - + # only keep events done by interview (in most cases this should be all, after above filters, + # just in case supervisor or HQ answered something while interviewer answered on web mode) + # keep active events, prior rejection/review events, for questions with scope interviewer + # Filter conditions active_mask = ( (paradata_processed['event'].isin(active_events)) & diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py index 01f9e60..53f0e7b 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py @@ -27,16 +27,16 @@ def create_pipeline(**kwargs) -> Pipeline: outputs="paradata_active", name="filter_active_paradata_node", ), - node( - func=build_item_features, - inputs=["raw_microdata", "paradata_active", "raw_questionnaire", "parameters"], - outputs="item_features", - name="build_item_features_node", - ), - node( - func=build_unit_features, - inputs=["paradata_active", "parameters"], - outputs="unit_features", - name="build_unit_features_node", - ), + # node( + # func=build_item_features, + # inputs=["raw_microdata", "paradata_active", "raw_questionnaire", "parameters"], + # outputs="item_features", + # name="build_item_features_node", + # ), + # node( + # func=build_unit_features, + # inputs=["paradata_active", "parameters"], + # outputs="unit_features", + # name="build_unit_features_node", + # ), ]) diff --git a/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/nodes.py index df8e2e1..412c4f9 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/nodes.py @@ -14,163 +14,163 @@ get_dataframes ) -"""Nodes for ingesting Survey Solutions export data.""" +# """Nodes for ingesting Survey Solutions export data.""" -def unzip_raw_surveys( - parameters: Dict -) -> None: - """ - Extract zipped Survey Solutions exports. - - Handles: - - Recursive unzipping (nested ZIPs) - - Password-protected ZIPs (from credentials) - - Mixed formats (.dta, .tab) - - Args: - parameters: Survey configuration from parameters.yml +# def unzip_raw_surveys( +# parameters: Dict +# ) -> None: +# """ +# Extract zipped Survey Solutions exports. + +# Handles: +# - Recursive unzipping (nested ZIPs) +# - Password-protected ZIPs (from credentials) +# - Mixed formats (.dta, .tab) + +# Args: +# parameters: Survey configuration from parameters.yml - Side Effect: - Extracts files to data/01_raw/{survey_name}/{version}/ - """ - from rissk.config import RAW_DATA_DIR - from rissk.utils.import_utils import get_zip_files +# Side Effect: +# Extracts files to data/01_raw/{survey_name}/{version}/ +# """ +# from rissk.config import RAW_DATA_DIR +# from rissk.utils.import_utils import get_zip_files - survey_name = parameters["survey"]["name"] - questionnaires = parameters["survey"]["questionnaires"] +# survey_name = parameters["survey"]["name"] +# questionnaires = parameters["survey"]["questionnaires"] - # Get all ZIP files matching the survey config - zip_files = get_zip_files(RAW_DATA_DIR, survey_name, questionnaires) +# # Get all ZIP files matching the survey config +# zip_files = get_zip_files(RAW_DATA_DIR, survey_name, questionnaires) - logger.info(f"Found {len(zip_files)} ZIP files to extract") +# logger.info(f"Found {len(zip_files)} ZIP files to extract") - for zip_file in zip_files: - dest_path = zip_file.with_suffix('') # Remove .zip extension - logger.info(f"Extracting {zip_file.name} to {dest_path}") - extract_zip(zip_file, dest_path) +# for zip_file in zip_files: +# dest_path = zip_file.with_suffix('') # Remove .zip extension +# logger.info(f"Extracting {zip_file.name} to {dest_path}") +# extract_zip(zip_file, dest_path) - logger.success(f"Extraction complete. Files in {RAW_DATA_DIR}") +# logger.success(f"Extraction complete. Files in {RAW_DATA_DIR}") -def load_survey_dataframes( - parameters: Dict -) -> tuple: - """ - Load paradata, questionnaire, and microdata from extracted files. - - Handles: - - Mixed file formats (.dta for Stata, .tab for tabular) - - Variable name parsing from Survey Solutions structure - - Multi-option/GPS/List question transformations - - Args: - parameters: Survey configuration +# def load_survey_dataframes( +# parameters: Dict +# ) -> tuple: +# """ +# Load paradata, questionnaire, and microdata from extracted files. + +# Handles: +# - Mixed file formats (.dta for Stata, .tab for tabular) +# - Variable name parsing from Survey Solutions structure +# - Multi-option/GPS/List question transformations + +# Args: +# parameters: Survey configuration - Returns: - tuple: (paradata_df, questionnaire_df, microdata_df) - """ - from rissk.config import RAW_DATA_DIR - from rissk.utils.import_utils import get_survey_info, get_dataframes +# Returns: +# tuple: (paradata_df, questionnaire_df, microdata_df) +# """ +# from rissk.config import RAW_DATA_DIR +# from rissk.utils.import_utils import get_survey_info, get_dataframes - # Scan extracted directories for survey info - survey_paths = [] - for item in RAW_DATA_DIR.iterdir(): - if item.is_dir(): - survey_paths.append(item) +# # Scan extracted directories for survey info +# survey_paths = [] +# for item in RAW_DATA_DIR.iterdir(): +# if item.is_dir(): +# survey_paths.append(item) - survey_info = get_survey_info(survey_paths) +# survey_info = get_survey_info(survey_paths) - logger.info(f"Loading dataframes for surveys: {list(survey_info.keys())}") +# logger.info(f"Loading dataframes for surveys: {list(survey_info.keys())}") - # Use your existing get_dataframes logic - paradata_df, questionnaire_df, microdata_df = get_dataframes(survey_info) +# # Use your existing get_dataframes logic +# paradata_df, questionnaire_df, microdata_df = get_dataframes(survey_info) - logger.info(f"Loaded - Paradata: {paradata_df.shape}, " - f"Questionnaire: {questionnaire_df.shape}, " - f"Microdata: {microdata_df.shape}") +# logger.info(f"Loaded - Paradata: {paradata_df.shape}, " +# f"Questionnaire: {questionnaire_df.shape}, " +# f"Microdata: {microdata_df.shape}") - return paradata_df, questionnaire_df, microdata_df +# return paradata_df, questionnaire_df, microdata_df -### 2 Feature Engineering Pipeline +# ### 2 Feature Engineering Pipeline -"""Nodes for processing paradata and building features.""" -import pandas as pd -from typing import Dict -from loguru import logger +# """Nodes for processing paradata and building features.""" +# import pandas as pd +# from typing import Dict +# from loguru import logger -def process_paradata_timestamps( - paradata_raw: pd.DataFrame -) -> pd.DataFrame: - """ - Process paradata timestamps and add hour features. +# def process_paradata_timestamps( +# paradata_raw: pd.DataFrame +# ) -> pd.DataFrame: +# """ +# Process paradata timestamps and add hour features. - This replicates logic from pipelines/feature_engineering/10_process_paradata.py +# This replicates logic from pipelines/feature_engineering/10_process_paradata.py - Args: - paradata_raw: Raw paradata DataFrame +# Args: +# paradata_raw: Raw paradata DataFrame - Returns: - Processed paradata with timestamp features - """ - paradata = paradata_raw.copy() +# Returns: +# Processed paradata with timestamp features +# """ +# paradata = paradata_raw.copy() - # Add answer hour feature (from 10_process_paradata.py line 29) - paradata['f__answer_hour_set'] = ( - paradata['timestamp_local'].dt.hour + - paradata['timestamp_local'].dt.round('30min').dt.minute / 60 - ) +# # Add answer hour feature (from 10_process_paradata.py line 29) +# paradata['f__answer_hour_set'] = ( +# paradata['timestamp_local'].dt.hour + +# paradata['timestamp_local'].dt.round('30min').dt.minute / 60 +# ) - # Add interviewing flag - paradata['interviewing'] = ~paradata['role'].isin([2, 3, 4]) +# # Add interviewing flag +# paradata['interviewing'] = ~paradata['role'].isin([2, 3, 4]) - logger.info(f"Processed {len(paradata)} paradata records") +# logger.info(f"Processed {len(paradata)} paradata records") - return paradata +# return paradata -def filter_active_events( - paradata_processed: pd.DataFrame, - parameters: Dict -) -> pd.DataFrame: - """ - Filter paradata to active interviewer events. - - Replicates logic from pipelines/feature_engineering/11_process_paradata_active.py - - Args: - paradata_processed: Processed paradata - parameters: Config parameters (for limit_unit) +# def filter_active_events( +# paradata_processed: pd.DataFrame, +# parameters: Dict +# ) -> pd.DataFrame: +# """ +# Filter paradata to active interviewer events. + +# Replicates logic from pipelines/feature_engineering/11_process_paradata_active.py + +# Args: +# paradata_processed: Processed paradata +# parameters: Config parameters (for limit_unit) - Returns: - DataFrame with only active interviewer events - """ - active_events = [ - 'InterviewCreated', 'AnswerSet', 'Resumed', - 'AnswerRemoved', 'CommentSet', 'Restarted' - ] - - # Filter logic from 11_process_paradata_active.py line 28 - active_mask = ( - paradata_processed['event'].isin(active_events) & - paradata_processed['question_scope'].isin([0, '']) & - (paradata_processed['role'] == 1) - ) - - vars_needed = [ - 'interview__id', 'order', 'event', 'responsible', 'role', 'tz_offset', - 'param', 'answer', 'roster_level', 'timestamp_local', 'variable_name', - 'question_sequence', 'question_scope', "qtype", 'question_type', - 'qnr', 'qnr_version', 'interviewing', 'yes_no_view', 'index_col', - 'f__answer_hour_set' - ] - - df_para_active = paradata_processed.loc[active_mask, vars_needed] - - logger.info(f"Filtered to {len(df_para_active)} active events") - - return df_para_active +# Returns: +# DataFrame with only active interviewer events +# """ +# active_events = [ +# 'InterviewCreated', 'AnswerSet', 'Resumed', +# 'AnswerRemoved', 'CommentSet', 'Restarted' +# ] + +# # Filter logic from 11_process_paradata_active.py line 28 +# active_mask = ( +# paradata_processed['event'].isin(active_events) & +# paradata_processed['question_scope'].isin([0, '']) & +# (paradata_processed['role'] == 1) +# ) + +# vars_needed = [ +# 'interview__id', 'order', 'event', 'responsible', 'role', 'tz_offset', +# 'param', 'answer', 'roster_level', 'timestamp_local', 'variable_name', +# 'question_sequence', 'question_scope', "qtype", 'question_type', +# 'qnr', 'qnr_version', 'interviewing', 'yes_no_view', 'index_col', +# 'f__answer_hour_set' +# ] + +# df_para_active = paradata_processed.loc[active_mask, vars_needed] + +# logger.info(f"Filtered to {len(df_para_active)} active events") + +# return df_para_active def build_item_features( diff --git a/rissk_kedro/tests/__init__.py b/rissk_kedro/tests/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/rissk_kedro/tests/pipelines/__init__.py b/rissk_kedro/tests/pipelines/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/rissk_kedro/tests/pipelines/data_science/test_pipeline.py b/rissk_kedro/tests/pipelines/data_science/test_pipeline.py deleted file mode 100644 index 294deaa..0000000 --- a/rissk_kedro/tests/pipelines/data_science/test_pipeline.py +++ /dev/null @@ -1,63 +0,0 @@ -import logging -import pandas as pd -import pytest -from kedro.io import DataCatalog -from kedro.runner import SequentialRunner -from rissk_kedro.pipelines.data_science import create_pipeline as create_ds_pipeline -from rissk_kedro.pipelines.data_science.nodes import split_data - -@pytest.fixture -def dummy_data(): - return pd.DataFrame( - { - "engines": [1, 2, 3], - "crew": [4, 5, 6], - "passenger_capacity": [5, 6, 7], - "price": [120, 290, 30], - } - ) - -@pytest.fixture -def dummy_parameters(): - parameters = { - "model_options": { - "test_size": 0.2, - "random_state": 3, - "features": ["engines", "passenger_capacity", "crew"], - } - } - return parameters - - -def test_split_data(dummy_data, dummy_parameters): - X_train, X_test, y_train, y_test = split_data( - dummy_data, dummy_parameters["model_options"] - ) - assert len(X_train) == 2 - assert len(y_train) == 2 - assert len(X_test) == 1 - assert len(y_test) == 1 - -def test_split_data_missing_price(dummy_data, dummy_parameters): - dummy_data_missing_price = dummy_data.drop(columns="price") - with pytest.raises(KeyError) as e_info: - X_train, X_test, y_train, y_test = split_data(dummy_data_missing_price, dummy_parameters["model_options"]) - - assert "price" in str(e_info.value) - -def test_data_science_pipeline(caplog, dummy_data, dummy_parameters): - pipeline = ( - create_ds_pipeline() - .from_nodes("split_data_node") - .to_nodes("evaluate_model_node") - ) - catalog = DataCatalog() - catalog["model_input_table@pandas"] = dummy_data - catalog["params:model_options"] = dummy_parameters["model_options"] - - caplog.set_level(logging.DEBUG, logger="kedro") - successful_run_msg = "Pipeline execution completed successfully" - - SequentialRunner().run(pipeline, catalog) - - assert successful_run_msg in caplog.text diff --git a/rissk_kedro/tests/test_run.py b/rissk_kedro/tests/test_run.py deleted file mode 100644 index addd1f9..0000000 --- a/rissk_kedro/tests/test_run.py +++ /dev/null @@ -1,20 +0,0 @@ -""" -This module contains example tests for a Kedro project. -Tests should be placed in ``src/tests``, in modules that mirror your -project's structure, and in files named test_*.py. -""" -from pathlib import Path - -from kedro.framework.session import KedroSession -from kedro.framework.startup import bootstrap_project - -# The tests below are here for the demonstration purpose -# and should be replaced with the ones testing the project -# functionality - -class TestKedroRun: - def test_kedro_run(self): - bootstrap_project(Path.cwd()) - - with KedroSession.create(project_path=Path.cwd()) as session: - assert session.run() is not None From e8074e4428f913bdc9d65dbe16ad7d7ff168cbf3 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Thu, 26 Feb 2026 11:09:42 +0000 Subject: [PATCH 19/70] Refactor import_utils_kedro.py for categories Id match; update catalog.yml and globals.yml for dynamic data paths; modify parameters.yml to pull questionnaire config from globals; clean up nodes.py and pipeline.py --- rissk/feature_processing_kedro.py | 590 ++++++++++++++++++ rissk/utils/file_process_utils_kedro.py | 31 +- rissk_kedro/conf/base/catalog.yml | 46 +- rissk_kedro/conf/base/globals.yml | 24 +- rissk_kedro/src/rissk_kedro/__init__.py | 5 + .../pipelines/feature_creation/__init__.py | 5 + .../pipelines/feature_creation/nodes.py | 54 ++ .../pipelines/feature_creation/pipeline.py | 41 ++ .../pipelines/feature_engineering/nodes.py | 122 +--- .../pipelines/feature_engineering/pipeline.py | 14 - 10 files changed, 755 insertions(+), 177 deletions(-) create mode 100644 rissk/feature_processing_kedro.py create mode 100644 rissk_kedro/src/rissk_kedro/pipelines/feature_creation/__init__.py create mode 100644 rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py create mode 100644 rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py new file mode 100644 index 0000000..509521a --- /dev/null +++ b/rissk/feature_processing_kedro.py @@ -0,0 +1,590 @@ +import pandas as pd +import numpy as np +import logging + +logger = logging.getLogger(__name__) + +# --- Helper Functions --- + +def _make_index_col(df: pd.DataFrame) -> pd.DataFrame: + """Creates a unique index column based on interview_id, variable_name, and roster_level.""" + # Filter out columns with NaN and empty strings for the mask + # Using fillna('') to handle NaNs safely for string concatenation + + # Create mask for valid rows (not null and not empty string in key columns) + # Note: In Py3.13/Pandas 2.x, strict comparison rules apply. + + df_temp = df[['interview__id', 'variable_name', 'roster_level']].fillna('').astype(str) + + # Concatenate columns + df['index_col'] = df_temp['interview__id'] + "_" + df_temp['variable_name'] + "_" + df_temp['roster_level'] + + # Remove trailing and leading underscores + df['index_col'] = df['index_col'].str.strip('_') + return df + +def _get_df_time(df_active_paradata: pd.DataFrame) -> pd.DataFrame: + """Calculates time differences and durations from paradata.""" + df_time = df_active_paradata.copy() + + # Sort to ensure diff works correctly + df_time = df_time.sort_values(['interview__id', 'timestamp_local']) + + # calculate time difference in seconds + df_time['time_difference'] = df_time.groupby('interview__id')['timestamp_local'].diff() + df_time['time_difference'] = df_time['time_difference'].dt.total_seconds() + + # Logic for f__time_changed (negative time diffs < -180s) + df_time['f__time_changed'] = np.where(df_time['time_difference'] < -180, df_time['time_difference'], np.nan) + + # Mask negative time differences for duration calculations + # Using pd.NA for nullable integers/floats in pandas if column allows, or np.nan + df_time.loc[df_time['time_difference'] < 0, 'time_difference'] = np.nan + + # time for answers/comments + df_time['f__answer_duration'] = df_time.loc[ + df_time['event'].isin(['AnswerSet', 'AnswerRemoved']), 'time_difference'] + df_time['f__comment_duration'] = df_time.loc[df_time['event'] == 'CommentSet', 'time_difference'] + df_time['f__pause_duration'] = df_time.loc[df_time['event'].isin(['Resumed', 'Restarted']), 'time_difference'] + + # UNIT features helper logic + active_events = ['AnswerSet', 'AnswerRemoved', 'CommentSet', 'Resumed', 'Restarted'] + + # Calculate total duration (capped at 30 mins per event) + condition = (df_time['event'].isin(active_events)) & (df_time['time_difference'] < 30 * 60) + df_time['f__total_duration'] = df_time.loc[condition, 'time_difference'] + + # Starting timestamp per interview + # Use transform to broadcast min timestamp to all rows of the group + starting_timestamp = df_time[df_time['event'] == 'AnswerSet'].groupby('interview__id')['timestamp_local'].transform('min') + + # We need to map this back to the main df_time + # Since transform returns a series aligned with the filtered df, we need a safer merge/map strategy + # Or just calculate on the full group if efficient. + # The original code used map on a groupby result. + + start_time_map = df_time[df_time['event'] == 'AnswerSet'].groupby('interview__id')['timestamp_local'].min() + df_time['f__starting_timestamp'] = df_time['interview__id'].map(start_time_map) + + min_date = df_time['f__starting_timestamp'].min() + if pd.notna(min_date): + df_time['f__days_from_start'] = (df_time['timestamp_local'] - min_date).dt.days.abs() + else: + df_time['f__days_from_start'] = np.nan + + return df_time + +def _get_df_sequence(df_active_paradata: pd.DataFrame) -> pd.DataFrame: + """Calculates sequence-based features (jumps, previous answers).""" + # Filter for AnswerSet and get the last entry per index_col + mask = df_active_paradata['event'] == 'AnswerSet' + df_last = df_active_paradata[mask].groupby('index_col').last() + + # The groupby puts index_col in the index. + # We need to sort by interview_id and order to reconstruct the sequence flow. + # 'order' column is assumed to exist from ingestion. + df_last = df_last.sort_values(['interview__id', 'order']).reset_index() + + # f__previous_question, f__previous_answer, f__previous_roster + # Using shift on the group + df_last['f__previous_question'] = df_last.groupby('interview__id')['variable_name'].shift() + df_last['f__previous_answer'] = df_last.groupby('interview__id')['answer'].shift().fillna('') + df_last['f__previous_roster'] = df_last.groupby('interview__id')['roster_level'].shift().fillna('') + + # f__sequence_jump + # Calculate answer sequence (1, 2, 3...) based on actual occurrence + df_last['answer_sequence'] = df_last.groupby('interview__id').cumcount() + 1 + + # Diff between questionnaire sequence and answer sequence + # Ensure types are compatible + df_last['question_sequence'] = pd.to_numeric(df_last['question_sequence'], errors='coerce').fillna(0) + df_last['diff'] = df_last['question_sequence'] - df_last['answer_sequence'] + + # The 'jump' is the difference of the difference + df_last['f__sequence_jump'] = df_last.groupby('interview__id')['diff'].diff() + + return df_last + +def _add_sequence_features(df_item: pd.DataFrame, df_sequence: pd.DataFrame, allowed_features: list) -> pd.DataFrame: + sequence_features = ['f__previous_question', 'f__previous_answer', + 'f__previous_roster', 'f__sequence_jump'] + + # Filter to only allowed features + selected_features = [f for f in sequence_features if f in allowed_features] + + if selected_features: + # Select columns to merge + cols_to_use = ['index_col'] + selected_features + # Ensure columns exist in df_sequence + cols_to_use = [c for c in cols_to_use if c in df_sequence.columns] + + if len(cols_to_use) > 1: # at least index_col + 1 feature + df_item = df_item.merge(df_sequence[cols_to_use], how='left', on='index_col') + + return df_item + +def _add_item_time_features(df_item: pd.DataFrame, df_time: pd.DataFrame, allowed_features: list, item_level_columns: list) -> pd.DataFrame: + time_features = ['f__answer_duration', 'f__comment_duration'] + + selected_features = [f for f in time_features if f in allowed_features] + + if selected_features: + # Filter out empty variable_name (Pauses) + df_time_filtered = df_time[df_time['variable_name'] != ''].copy() + + # Summarize on item level + # Note: df_time might have multiple events per item (e.g. AnswerRemoved then AnswerSet) + # We sum the durations. + agg_dict = {} + if 'f__answer_duration' in selected_features: + agg_dict['f__answer_duration'] = 'sum' + if 'f__comment_duration' in selected_features: + agg_dict['f__comment_duration'] = 'sum' + + if agg_dict: + # Ensure grouping columns exist + group_cols = [c for c in item_level_columns + ['index_col'] if c in df_time_filtered.columns] + + df_agg = df_time_filtered.groupby(group_cols).agg(agg_dict).reset_index() + + # Merge + df_agg = df_agg[['index_col'] + list(agg_dict.keys())] + df_item = df_item.merge(df_agg, how='left', on='index_col') + + return df_item + +def _add_pause_features(df_unit: pd.DataFrame, df_time: pd.DataFrame, allowed_features: list) -> pd.DataFrame: + pause_features = ['f__pause_count', 'f__pause_duration', 'f__pause_list'] + selected_features = [f for f in pause_features if f in allowed_features] + + if selected_features: + # Calculate pause stats per interview + # f__pause_duration column in df_time contains the duration for Resumed/Restarted events + + # Custom aggregation for list + def to_list(x): + return x.tolist() + + agg_dict = {} + if 'f__pause_count' in selected_features: + # count all occurrences (size) where pause_duration is not null is implied by how df_time was built? + # Actually df_time['f__pause_duration'] is NaN for non-pause events. + # So we should count non-nulls. 'count' counts non-NA. 'size' counts matches. + agg_dict['f__pause_count'] = ('f__pause_duration', 'count') + if 'f__pause_duration' in selected_features: + agg_dict['f__pause_duration'] = ('f__pause_duration', 'sum') + if 'f__pause_list' in selected_features: + # This might be tricky in aggregation if all are NaN. + # We filter first. + pass + + if agg_dict: + df_pause = df_time.groupby('interview__id').agg(**agg_dict).reset_index() + + # Handle list separately if needed or include in agg above if simple + if 'f__pause_list' in selected_features: + # Only rows with valid pause duration + pause_rows = df_time.dropna(subset=['f__pause_duration']) + if not pause_rows.empty: + list_agg = pause_rows.groupby('interview__id')['f__pause_duration'].apply(list).reset_index(name='f__pause_list') + df_pause = df_pause.merge(list_agg, how='left', on='interview__id') + + df_unit = df_unit.merge(df_pause, how='left', on='interview__id') + + return df_unit + +def _add_unit_time_features(df_unit: pd.DataFrame, df_time: pd.DataFrame, allowed_features: list) -> pd.DataFrame: + time_features = ['f__total_duration', 'f__total_elapse', 'f__days_from_start', 'f__time_changed'] + selected_features = [f for f in time_features if f in allowed_features] + + if selected_features: + agg_dict = {} + if 'f__total_duration' in selected_features: + agg_dict['f__total_duration'] = ('f__total_duration', 'sum') + if 'f__total_elapse' in selected_features: + # Lambda in agg is slower, but compatible. + agg_dict['f__total_elapse'] = ('timestamp_local', lambda x: (x.max() - x.min()).total_seconds() if not x.empty else 0) + if 'f__time_changed' in selected_features: + agg_dict['f__time_changed'] = ('f__time_changed', 'sum') + if 'f__days_from_start' in selected_features: + agg_dict['f__days_from_start'] = ('f__days_from_start', 'min') + + if agg_dict: + df_dur = df_time.groupby('interview__id').agg(**agg_dict).reset_index() + df_unit = df_unit.merge(df_dur, how='left', on='interview__id') + + return df_unit + + +# --- Base Table Creation --- + +def create_base_item_table(microdata: pd.DataFrame, paradata_active: pd.DataFrame, parameters: dict) -> pd.DataFrame: + """ + Creates the base item table by merging microdata with paradata information. + Equivalent to FeatureProcessing.make_df_item. + """ + logger.info("Creating base item table...") + + item_level_columns = ['interview__id', 'variable_name', 'roster_level'] + allowed_features = ['f__' + k for k, v in parameters['features'].items() if v.get('use', False)] + + # 1. Create Index Column on Microdata + df_item = _make_index_col(microdata.copy()) + + # 2. Select initial columns + initial_cols = ['value', "qtype", 'is_integer', 'qnr_seq', + 'n_answers', 'answer_sequence', + 'cascade_from_question_id', 'is_filtered_combobox', + 'index_col'] + item_level_columns + + # Intersect with available columns to avoid KeyErrors + cols_to_keep = [c for c in initial_cols if c in df_item.columns] + df_item = df_item[cols_to_keep] + + # 3. Prepare Paradata for Merge + # We want the *last* AnswerSet for each item + paradata_columns = ['responsible', 'f__answer_hour_set', 'interviewing', 'tz_offset'] + available_para_cols = [c for c in paradata_columns if c in paradata_active.columns] + + answer_set_mask = (paradata_active['event'] == 'AnswerSet') + + # Ensure index_col exists in paradata. It should be there from ingestion/processing. + # If not, we might need to recreate it. Assuming it exists or we create it. + if 'index_col' not in paradata_active.columns: + paradata_active = _make_index_col(paradata_active.copy()) + + data_to_merge = paradata_active[answer_set_mask].drop_duplicates(subset='index_col', keep='last') + + # 4. Merge + df_item = df_item.merge(data_to_merge[available_para_cols + ['index_col']], how='left', on='index_col') + + # 5. Filter for 'interviewing' == True (Supervisor Logic) + if 'interviewing' in df_item.columns: + # Fill NaN with False or True? Original code assumed boolean column. + df_item = df_item[df_item['interviewing'] == True] + + # 6. Add Sequence Features + # Pre-calculate sequence df + df_sequence = _get_df_sequence(paradata_active) + df_item = _add_sequence_features(df_item, df_sequence, allowed_features) + + # 7. Add Time Features + # Pre-calculate time df + df_time = _get_df_time(paradata_active) + df_item = _add_item_time_features(df_item, df_time, allowed_features, item_level_columns) + + return df_item + +def create_base_unit_table(paradata_active: pd.DataFrame, parameters: dict) -> pd.DataFrame: + """ + Creates the base unit table (one row per interview). + Equivalent to FeatureProcessing.make_df_unit. + """ + logger.info("Creating base unit table...") + allowed_features = ['f__' + k for k, v in parameters['features'].items() if v.get('use', False)] + + # 1. Initialize from paradata + cols = ['interview__id', 'responsible', 'survey_name', 'survey_version'] + cols = [c for c in cols if c in paradata_active.columns] + + df_unit = paradata_active[cols].copy() + df_unit.drop_duplicates(inplace=True) + + # Filter valid responsible + df_unit = df_unit[(df_unit['responsible'] != '') & (df_unit['responsible'].notna())] + + # 2. Add Pause Features + df_time = _get_df_time(paradata_active) + df_unit = _add_pause_features(df_unit, df_time, allowed_features) + + # 3. Add Unit Time Features + df_unit = _add_unit_time_features(df_unit, df_time, allowed_features) + + return df_unit + + +# --- Feature Enrichment Functions (Item) --- + +def _feat_string_length(df_item, **kwargs): + feature_name = 'f__string_length' + mask = df_item["qtype"] == 'TextQuestion' + df_item[feature_name] = pd.NA + # Use str.len() + if mask.any(): + df_item.loc[mask, feature_name] = df_item.loc[mask, 'value'].astype(str).str.len() + return df_item + +def _feat_numeric_response(df_item, **kwargs): + feature_name = 'f__numeric_response' + numeric_mask = (df_item["qtype"] == 'NumericQuestion') & pd.to_numeric(df_item['value'], errors='coerce').notna() + df_item[feature_name] = np.nan + if numeric_mask.any(): + df_item.loc[numeric_mask, feature_name] = df_item.loc[numeric_mask, 'value'].astype(float) + return df_item + +def _feat_first_digit(df_item, **kwargs): + feature_name = 'f__first_digit' + # Logic: abs(value), str[0] + numeric_mask = (df_item["qtype"] == 'NumericQuestion') & pd.to_numeric(df_item['value'], errors='coerce').notna() + df_item[feature_name] = pd.NA + if numeric_mask.any(): + # Convert to float, absolute, string, take first char + vals = pd.to_numeric(df_item.loc[numeric_mask, 'value']).abs().astype(str).str[0] + # Check if digit + # vals = vals[vals.str.isdigit()] # Should be digit if from float + df_item.loc[numeric_mask, feature_name] = pd.to_numeric(vals, errors='coerce') + return df_item + +def _feat_last_digit(df_item, **kwargs): + feature_name = 'f__last_digit' + numeric_mask = (df_item["qtype"] == 'NumericQuestion') & pd.to_numeric(df_item['value'], errors='coerce').notna() + df_item[feature_name] = pd.NA + + if numeric_mask.any(): + # Only for integer-like values >= 1? Legacy used >= 1 check on value + vals = pd.to_numeric(df_item.loc[numeric_mask, 'value']) + + # Check conditions + # We can implement this vectorally + valid_vals = (vals.abs() >= 1) + + # Modulo 10 + # Be careful with floats. 12.0 % 10 = 2.0. + res = vals % 10 + + # Apply mask + res = res.where(valid_vals, pd.NA) + df_item.loc[numeric_mask, feature_name] = res + + return df_item + +def _feat_first_decimal(df_item, **kwargs): + feature_name = 'f__first_decimal' + # mask: not integer and not empty + mask = (df_item['is_integer'] == False) & (df_item['value'] != '') + df_item[feature_name] = pd.NA + + if mask.any(): + values = pd.to_numeric(df_item.loc[mask, 'value'], errors='coerce') + # floor(val * 100) % 100 ?? Legacy code: np.floor(values * 100) % 100 + # This actually gets the first two decimals? + # Example: 0.123 -> 12.3 -> 12. + # Wait, if I want first decimal digit (e.g. 1 in 0.123): floor(val * 10) % 10 + # Documentation says "first decimal digit". Code says *100 % 100. + # I will strictly follow legacy code logic. + res = np.floor(values * 100) % 100 + df_item.loc[mask, feature_name] = res + + return df_item + +def _feat_answer_position(df_item, **kwargs): + feature_name = 'f__answer_position' # in legacy it was f__rel_answer_position sometimes? code says f__answer_position + + # filters + mask = ((df_item["qtype"] == 'SingleQuestion') + & (df_item['n_answers'] > 2) + & (df_item['is_filtered_combobox'] == False) + & (df_item['cascade_from_question_id'].isna())) + + df_item[feature_name] = np.nan + + if mask.any(): + # logic: index of value in answer_sequence / (n_answers - 1) + # answer_sequence is typically a list or string representation of list + # We need to iterate or apply + + def calc_pos(row): + val = row['value'] + seq = row['answer_sequence'] + n = row['n_answers'] + if isinstance(seq, list) and val in seq: + try: + idx = seq.index(val) + return round(idx / (n - 1), 3) + except: + return None + return None + + # Apply is slow but robust for list operations in cells + df_item.loc[mask, feature_name] = df_item.loc[mask].apply(calc_pos, axis=1) + + return df_item + +def _feat_answer_changed(df_item, **kwargs): + feature_name = 'f__answer_changed' + paradata_active = kwargs.get('paradata_active') + + if paradata_active is None: + return df_item + + # Logic involves reconstructing history of AnswerSet + df_changed = paradata_active[paradata_active['event'] == 'AnswerSet'].copy() + + if 'index_col' not in df_changed.columns: + df_changed = _make_index_col(df_changed) + + df_changed[feature_name] = False + + # We need qtype. Merge it? Or is it in paradata? 'qtype' is in paradata. + + # Logic for lists (split by |) + list_mask = (df_changed["qtype"] == 'TextListQuestion') + multi_mask = (df_changed['yes_no_view'] == False) if 'yes_no_view' in df_changed.columns else pd.Series(False, index=df_changed.index) + + # This logic is quite complex to port perfectly without testing. + # Simplified approach: Group by index_col, count AnswerSet events? + # No, legacy checks if answer *content* changed relative to previous. + + # For refactor safety, I will implement a simplified count-based approach if logic is too brittle, + # OR try to replicate exact logic if possible. + + # Let's try replicating the "Single Answer" logic which is most common + df_changed['prev_answer'] = df_changed.groupby('interview__id')['answer'].shift() + # But wait, groupby interview_id mixes questions. Logic needs to account for question sequence. + # Legacy: df.groupby(item_level_cols + index_col)['answer'].shift() + # If grouped by index_col, we trace history of THAT question. + + df_changed['prev_answer'] = df_changed.groupby('index_col')['answer'].shift() + + # Detect change + # Note: first answer is not a change. + change_mask = (df_changed['prev_answer'].notna()) & (df_changed['answer'] != df_changed['prev_answer']) + df_changed.loc[change_mask, feature_name] = True + + # Set to features + # Sum of changes per item + changes_per_item = df_changed.groupby('index_col')[feature_name].sum() + + # Map back + df_item[feature_name] = df_item['index_col'].map(changes_per_item).fillna(0) + + return df_item + +def _feat_answer_selected(df_item, **kwargs): + feature_name = 'f__answer_selected' + mask = df_item["qtype"].isin(['MultyOptionsQuestion']) + + df_item[feature_name] = np.nan + + # Value is list? Or string? Usually lists in newer pandas if parquet preserved it, + # but legacy often had strings. + # Assuming value is list if parquet + + if mask.any(): + def count_els(x): + if isinstance(x, list): return len(x) + if isinstance(x, str): return len(x.split('|')) # simple heuristic for pipe-sep + return np.nan + + df_item.loc[mask, feature_name] = df_item.loc[mask, 'value'].apply(count_els) + # Ratio + df_item.loc[mask, feature_name] = df_item.loc[mask, feature_name] / df_item.loc[mask, 'n_answers'] + + return df_item + +def _feat_gps(df_item, **kwargs): + # Sets f__gps_latitude etc. + mask = df_item["qtype"] == 'GpsCoordinateQuestion' + if mask.any(): + # Split value "lat,lon,acc,alt,time" + gps_data = df_item.loc[mask, 'value'].str.split(',', expand=True) + # Expecting at least 3 cols + if gps_data.shape[1] >= 3: + df_item.loc[mask, 'f__gps_latitude'] = pd.to_numeric(gps_data[0], errors='coerce') + df_item.loc[mask, 'f__gps_longitude'] = pd.to_numeric(gps_data[1], errors='coerce') + df_item.loc[mask, 'f__gps_accuracy'] = pd.to_numeric(gps_data[2], errors='coerce') + + return df_item + + +# Dispatcher +ITEM_FEATURE_MAP = { + 'string_length': _feat_string_length, + 'numeric_response': _feat_numeric_response, + 'first_digit': _feat_first_digit, + 'last_digit': _feat_last_digit, + 'first_decimal': _feat_first_decimal, + 'answer_position': _feat_answer_position, + 'answer_changed': _feat_answer_changed, + 'answer_selected': _feat_answer_selected, + 'gps': _feat_gps, + # 'comment_length': Use pre-calculated f__comment_duration from base table? No, that's duration. + # comment_length is length of txt. + # 'comment_set': Count of comments. +} + + +def enrich_item_features(df_item: pd.DataFrame, paradata_active: pd.DataFrame, parameters: dict) -> pd.DataFrame: + """ + Applies feature engineering logic to the item table. + """ + logger.info("Enriching item features...") + allowed_features = parameters.get('features', {}) + + # Helper: Ensure index_col in paradata for lookups + if 'index_col' not in paradata_active.columns: + paradata_active = _make_index_col(paradata_active.copy()) + + for feat_key, feat_cfg in allowed_features.items(): + if feat_cfg.get('use', False): + # Map 'string_length' -> _feat_string_length + func = ITEM_FEATURE_MAP.get(feat_key) + if func: + logger.info(f"Calculating item feature: {feat_key}") + try: + df_item = func(df_item, paradata_active=paradata_active) + except Exception as e: + logger.warning(f"Failed to calculate {feat_key}: {e}") + + return df_item + + +# --- Feature Enrichment Functions (Unit) --- + +def _feat_unit_number_answered(df_unit, item_features, **kwargs): + feature_name = 'f__number_answered' + # Count valid answers in item table + # Valid = not null, not missing codes + mask = (item_features['value'].notna()) & (item_features['value'] != '') + + counts = item_features[mask].groupby('interview__id').size() + df_unit[feature_name] = df_unit['interview__id'].map(counts).fillna(0) + return df_unit + +def _feat_unit_number_unanswered(df_unit, item_features, **kwargs): + feature_name = 'f__number_unanswered' + # Check for missing codes like -999... or ##N/A## + # Simplified check + mask = (item_features['value'].astype(str).str.contains('##N/A##')) | (item_features['value'] == -999999999) + + counts = item_features[mask].groupby('interview__id').size() + df_unit[feature_name] = df_unit['interview__id'].map(counts).fillna(0) + return df_unit + +UNIT_FEATURE_MAP = { + 'number_answered': _feat_unit_number_answered, + 'number_unanswered': _feat_unit_number_unanswered, + # ... Add others as needed from feature_processing.py +} + +def enrich_unit_features(df_unit: pd.DataFrame, item_features: pd.DataFrame, parameters: dict) -> pd.DataFrame: + """ + Applies feature engineering logic to the unit table. + """ + logger.info("Enriching unit features...") + allowed_features = parameters.get('features', {}) + + for feat_key, feat_cfg in allowed_features.items(): + if feat_cfg.get('use', False): + # Prefix mapping check? "f__" is usually stripped in config keys? + # Config keys: 'string_length', 'number_answered' + + func = UNIT_FEATURE_MAP.get(feat_key) + if func: + logger.info(f"Calculating unit feature: {feat_key}") + try: + df_unit = func(df_unit, item_features=item_features) + except Exception as e: + logger.warning(f"Failed to calculate {feat_key}: {e}") + + return df_unit diff --git a/rissk/utils/file_process_utils_kedro.py b/rissk/utils/file_process_utils_kedro.py index 1494e9e..a7c0843 100644 --- a/rissk/utils/file_process_utils_kedro.py +++ b/rissk/utils/file_process_utils_kedro.py @@ -4,6 +4,7 @@ from typing import Dict import re import numpy as np +import unicodedata @@ -217,7 +218,7 @@ def get_categories(directory: Path) -> Dict[str, Dict[str, list]]: df = pd.read_excel(file) n_answers = df.shape[0] answer_sequence = df['id'].tolist() - categories[file.name] = {'n_answers': n_answers, 'answer_sequence': answer_sequence} + categories[file.stem] = {'n_answers': n_answers, 'answer_sequence': answer_sequence} return categories @@ -227,16 +228,36 @@ def update_df_categories(row, categories): This function updates a DataFrame row with category information if applicable. Parameters: - row (Series): The Questioner DataFrame row to be updated. + row (Series): The Questionnaire DataFrame row to be updated. categories (dict): A dictionary containing category data, keys are 'CategoriesId'. Returns: Series: The updated DataFrame row. """ - if row['CategoriesId'] in categories: - row['n_answers'] = categories[row['CategoriesId']]['n_answers'] - row['answer_sequence'] = categories[row['CategoriesId']]['answer_sequence'] + cid = row.get('CategoriesId') + if pd.isna(cid) or cid in (None, ''): + return row + + # Normalize ID: strip all unicode dash characters + cid_str = str(cid) + cid_clean = ''.join(c for c in cid_str if unicodedata.category(c) != 'Pd') + + # Try direct match with cleaned ID (assuming keys might be cleaned/filenames) + match = categories.get(cid_clean) + + if match is None: + # Fallback: Compare against cleaned keys from the dictionary + # This handles cases where filenames might still have dashes or other formatting + for key, val in categories.items(): + key_clean = ''.join(c for c in key if unicodedata.category(c) != 'Pd') + if key_clean == cid_clean: + match = val + break + + if match: + row['n_answers'] = match['n_answers'] + row['answer_sequence'] = match['answer_sequence'] return row def parse_filename(filename: str): diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index 7560adc..5ed76b9 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -38,33 +38,29 @@ paradata_active: type: pandas.ParquetDataset filepath: data/${globals:survey.name}/latest/30_PROCESSED/paradata_active.parquet -# === FEATURE PROCESSED === - -# item_features: -# type: pandas.ParquetDataset -# filepath: data/${globals:survey.name}/latest/30_PROCESSED/item_features.parquet +# === FEATURE CREATION DataFrames === +item_features_base: + type: pandas.ParquetDataset + filepath: data/${globals:survey.name}/latest/20_INTERIM/item_features_base.parquet -# unit_features: -# type: pandas.ParquetDataset -# filepath: data/${globals:survey.name}/latest/30_PROCESSED/unit_features.parquet +unit_features_base: + type: pandas.ParquetDataset + filepath: data/${globals:survey.name}/latest/20_INTERIM/unit_features_base.parquet -# unit_risk_scores_raw: -# type: pandas.ParquetDataset -# filepath: data/${globals:survey.name}/latest/30_PROCESSED/unit_risk_scores_raw.parquet +# Final Feature Tables (Input to Risk Scoring) +item_features: + type: pandas.ParquetDataset + filepath: data/${globals:survey.name}/latest/30_PROCESSED/item_features.parquet -# responsible_features: -# type: pandas.ParquetDataset -# filepath: data/${globals:survey.name}/latest/30_PROCESSED/responsible_features.parquet +unit_features: + type: pandas.ParquetDataset + filepath: data/${globals:survey.name}/latest/30_PROCESSED/unit_features.parquet -# # === MODEL OUTPUT === -# unit_risk_scores: -# type: pandas.CSVDataset -# filepath: data/${globals:survey.name}/latest/40_OUTPUTS/unit_risk_scores.csv -# save_args: -# index: false +# === LEGACY DATA FOR TESTING === +legacy_microdata: + type: pandas.ParquetDataset + filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/hies2024/latest/30_PROCESSED/microdata.parquet -# unit_feature_scores: -# type: pandas.CSVDataset -# filepath: data/${globals:survey.name}/latest/40_OUTPUTS/unit_feature_scores.csv -# save_args: -# index: false +legacy_paradata_active: + type: pandas.ParquetDataset + filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/hies2024/latest/30_PROCESSED/paradata_active.parquet \ No newline at end of file diff --git a/rissk_kedro/conf/base/globals.yml b/rissk_kedro/conf/base/globals.yml index c9171c8..8e977af 100644 --- a/rissk_kedro/conf/base/globals.yml +++ b/rissk_kedro/conf/base/globals.yml @@ -1,12 +1,12 @@ # Survey Configuration (from env.yaml) -# survey: -# name: "hies2024" -# questionnaires: -# - name: "snb_hies_hh" -# VERSION: [9, 10, 11] -# - name: "slbhies_listing" -# VERSION: [6, 7] +survey: + name: "hies2024" + questionnaires: + - name: "snb_hies_hh" + VERSION: [9, 10, 11] + - name: "slbhies_listing" + VERSION: [6, 7] # survey: # name: "pmpmd" @@ -16,8 +16,8 @@ # - name: "pmpmd_household" # VERSION: [4, 5, 6] -survey: - name: "slchbs" - questionnaires: - - name: "slchbs_saintlucia_2025" - VERSION: [5, 6, 7] +# survey: +# name: "slchbs" +# questionnaires: +# - name: "slchbs_saintlucia_2025" +# VERSION: [5, 6, 7] diff --git a/rissk_kedro/src/rissk_kedro/__init__.py b/rissk_kedro/src/rissk_kedro/__init__.py index 375f686..4d4e1f2 100644 --- a/rissk_kedro/src/rissk_kedro/__init__.py +++ b/rissk_kedro/src/rissk_kedro/__init__.py @@ -1,3 +1,8 @@ """RISSK: Automatically identify at-risk interviews.""" +import warnings + +# Filter out Kedro deprecation warning about pipeline_name +warnings.filterwarnings("ignore", message="`pipeline_name` is deprecated") + __version__ = "0.1.2" \ No newline at end of file diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/__init__.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/__init__.py new file mode 100644 index 0000000..beba04d --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/__init__.py @@ -0,0 +1,5 @@ +"""Feature Creation pipeline for generating item and unit tables.""" + +from .pipeline import create_pipeline + +__all__ = ["create_pipeline"] diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py new file mode 100644 index 0000000..dd7b75b --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py @@ -0,0 +1,54 @@ +"""Nodes for the Feature Creation pipeline.""" +import logging +import pandas as pd +from typing import Dict, Any + +# Assuming rissk is importable as a package +# If running kedro from rissk_kedro root, ensure PYTHONPATH includes ../rissk +from rissk.feature_processing_kedro import ( + create_base_item_table, + create_base_unit_table, + enrich_item_features, + enrich_unit_features +) + +logger = logging.getLogger(__name__) + +def create_base_item_table_node( + microdata: pd.DataFrame, + paradata_active: pd.DataFrame, + parameters: Dict[str, Any] +) -> pd.DataFrame: + """ + Node wrapper for create_base_item_table. + """ + return create_base_item_table(microdata, paradata_active, parameters) + +def create_base_unit_table_node( + paradata_active: pd.DataFrame, + parameters: Dict[str, Any] +) -> pd.DataFrame: + """ + Node wrapper for create_base_unit_table. + """ + return create_base_unit_table(paradata_active, parameters) + +def enrich_item_features_node( + item_features_base: pd.DataFrame, + paradata_active: pd.DataFrame, + parameters: Dict[str, Any] +) -> pd.DataFrame: + """ + Node wrapper for enrich_item_features. + """ + return enrich_item_features(item_features_base, paradata_active, parameters) + +def enrich_unit_features_node( + unit_features_base: pd.DataFrame, + item_features: pd.DataFrame, + parameters: Dict[str, Any] +) -> pd.DataFrame: + """ + Node wrapper for enrich_unit_features. + """ + return enrich_unit_features(unit_features_base, item_features, parameters) diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py new file mode 100644 index 0000000..1f69d8b --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py @@ -0,0 +1,41 @@ +"""Feature Creation pipeline definition.""" +from kedro.pipeline import Pipeline, node, pipeline +from .nodes import ( + create_base_item_table_node, + create_base_unit_table_node, + enrich_item_features_node, + enrich_unit_features_node +) + +def create_pipeline(**kwargs) -> Pipeline: + """Create the feature creation pipeline. + + Returns: + A pipeline that builds item and unit feature tables. + """ + return pipeline([ + node( + func=create_base_item_table_node, + inputs=["legacy_microdata", "legacy_paradata_active", "parameters"], + outputs="item_features_base", + name="create_base_item_table_node", + ), + node( + func=create_base_unit_table_node, + inputs=["legacy_paradata_active", "parameters"], + outputs="unit_features_base", + name="create_base_unit_table_node", + ), + node( + func=enrich_item_features_node, + inputs=["item_features_base", "legacy_paradata_active", "parameters"], + outputs="item_features", + name="enrich_item_features_node", + ), + node( + func=enrich_unit_features_node, + inputs=["unit_features_base", "item_features", "parameters"], + outputs="unit_features", + name="enrich_unit_features_node", + ), + ]) diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py index f42dc9f..e1cb256 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py @@ -117,124 +117,4 @@ def filter_active_paradata_node( df_para_active = paradata_processed.loc[active_mask, vars_needed].copy() - return df_para_active - - -def build_item_features( - microdata_raw: pd.DataFrame, - paradata_active: pd.DataFrame, - questionnaire_raw: pd.DataFrame, - parameters: Dict -) -> pd.DataFrame: - """ - Build item-level features from microdata and paradata. - - Args: - microdata_raw: Raw microdata - paradata_active: Active paradata events - questionnaire_raw: Questionnaire structure - parameters: Feature configuration - - Returns: - DataFrame with item-level features - """ - logger.info("Building item-level features") - - # Create index column for joining - # Updated separator to '_' to match process_paradata_node - def make_index_col(df): - mask = (~df[['interview__id', 'variable_name', 'roster_level']].isnull()) & \ - (df[['interview__id', 'variable_name', 'roster_level']] != '') - filtered_df = df.where(mask, '') - df['index_col'] = ( - filtered_df['interview__id'].astype(str) + '_' + - filtered_df['variable_name'].astype(str) + '_' + - filtered_df['roster_level'].astype(str) - ) - df['index_col'] = df['index_col'].str.strip('_') - return df - - if microdata_raw.empty: - logger.warning("Microdata is empty") - return pd.DataFrame() - - microdata = make_index_col(microdata_raw.copy()) - - # Select relevant columns - item_level_columns = ['interview__id', 'variable_name', 'roster_level'] - - # Identify available columns from the desired list - desired_cols = ['value', "qtype", 'is_integer', 'qnr_seq', - 'n_answers', 'answer_sequence', - 'cascade_from_question_id', 'is_filtered_combobox', - 'index_col'] + item_level_columns - - available_cols = [c for c in desired_cols if c in microdata.columns] - - df_item = microdata[available_cols].copy() - - # Merge with active paradata - paradata_columns = ['responsible', 'f__answer_hour_set', 'interviewing', 'tz_offset'] - answer_set_mask = (paradata_active['event'] == 'AnswerSet') - data = paradata_active[answer_set_mask].drop_duplicates(subset='index_col', keep='last') - - # Filter paradata columns to those present in data - available_para_cols = [col for col in paradata_columns if col in data.columns] - - df_item = df_item.merge( - data[available_para_cols + ['index_col']], - how='left', - on='index_col' - ) - - # Keep only interviewing events if column exists - if 'interviewing' in df_item.columns: - df_item = df_item[df_item['interviewing'] == True] - - logger.info(f"Built {len(df_item)} item feature records") - - return df_item - - -def build_unit_features( - paradata_active: pd.DataFrame, - parameters: Dict -) -> pd.DataFrame: - """ - Build unit-level (interview-level) features. - - Args: - paradata_active: Active paradata - parameters: Configuration - - Returns: - DataFrame with unit-level features - """ - # Use qnr/qnr_version as survey_name/survey_version - cols_map = { - 'interview__id': 'interview__id', - 'responsible': 'responsible', - 'qnr': 'survey_name', - 'qnr_version': 'survey_version' - } - - # Only select columns that exist - available_cols = [c for c in cols_map.keys() if c in paradata_active.columns] - - df_unit = paradata_active[available_cols].copy() - - # Rename columns to match expected output - df_unit.rename(columns=cols_map, inplace=True) - - df_unit.drop_duplicates(inplace=True) - - if 'responsible' in df_unit.columns: - df_unit = df_unit[ - (df_unit['responsible'] != '') & - (~pd.isnull(df_unit['responsible'])) - ] - - logger.info(f"Built {len(df_unit)} unit records") - - return df_unit - + return df_para_active \ No newline at end of file diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py index 53f0e7b..9967586 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py @@ -3,8 +3,6 @@ from .nodes import ( process_paradata_node, filter_active_paradata_node, - build_item_features, - build_unit_features ) @@ -27,16 +25,4 @@ def create_pipeline(**kwargs) -> Pipeline: outputs="paradata_active", name="filter_active_paradata_node", ), - # node( - # func=build_item_features, - # inputs=["raw_microdata", "paradata_active", "raw_questionnaire", "parameters"], - # outputs="item_features", - # name="build_item_features_node", - # ), - # node( - # func=build_unit_features, - # inputs=["paradata_active", "parameters"], - # outputs="unit_features", - # name="build_unit_features_node", - # ), ]) From 9437bf73f60be210529a268d9676721d5161c4a4 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Thu, 26 Feb 2026 11:28:44 +0000 Subject: [PATCH 20/70] Refactor read_json_questionnaire function for simplified logic --- rissk/utils/import_utils_kedro.py | 30 +++++++++--------------------- 1 file changed, 9 insertions(+), 21 deletions(-) diff --git a/rissk/utils/import_utils_kedro.py b/rissk/utils/import_utils_kedro.py index 75a20ab..7145170 100644 --- a/rissk/utils/import_utils_kedro.py +++ b/rissk/utils/import_utils_kedro.py @@ -177,36 +177,24 @@ def get_survey_info(survey_files: list[Path]) -> dict[str, dict[str, dict[str, P return survey_info -def read_json_questionnaire(survey_path: Path, questionnaire_path: Optional[Path] = None) -> dict: +def read_json_questionnaire(survey_path: Path) -> dict: """Reads the questionnaire JSON definition.""" - if questionnaire_path is None: - file_path = survey_path / 'Questionnaire' / 'content' / 'document.json' - else: - # If explicit questionnaire_path is given (rare case in current pipeline usage) - # We need check if it points to a specific file or directory - # This part assumes structure compatible with get_questionnaire_map from legacy code - # simplified here for clarity/robustness: - if questionnaire_path.is_file(): - file_path = questionnaire_path - else: - # Fallback logic mirroring legacy get_questionnaire_id/map behavior if needed - # For now, simplistic implementation assuming standard export structure - file_path = survey_path / 'Questionnaire' / 'content' / 'document.json' - - if not file_path.exists(): - logger.warning(f"Questionnaire document not found at {file_path}") + # Try to open the JSON file + file_path = survey_path / 'Questionnaire' / 'content' / 'document.json' + try: + with file_path.open('r', encoding='utf-8') as f: + return json.load(f) + except (Exception) as e: + logger.warning(f"Questionnaire document not found or invalid at {file_path}: {e}") return None - with file_path.open('r', encoding='utf-8') as f: - return json.load(f) - def get_questionnaire(data_path: Path, questionnaire_path: Optional[Path] = None) -> pd.DataFrame: """ Loads and processes a questionnaire from a JSON file located at the specified path. Also handles categorization of data. """ - q_data = read_json_questionnaire(data_path, questionnaire_path=questionnaire_path) + q_data = read_json_questionnaire(data_path) qnr_df = pd.DataFrame() From ddfa0512f414b98812ee7b3794b57c4f7c2c541a Mon Sep 17 00:00:00 2001 From: VJausovec Date: Sat, 28 Feb 2026 20:57:03 +0000 Subject: [PATCH 21/70] Refactor transform_multi function for improved value normalization; update read_microdata_file to avoid direct import dependency on StataMissingValue; adjust globals.yml for questionnaire version consistency --- rissk/utils/file_process_utils_kedro.py | 44 +++++++++++-------------- rissk/utils/import_utils_kedro.py | 4 +-- rissk_kedro/conf/base/globals.yml | 2 +- 3 files changed, 23 insertions(+), 27 deletions(-) diff --git a/rissk/utils/file_process_utils_kedro.py b/rissk/utils/file_process_utils_kedro.py index a7c0843..0fbec87 100644 --- a/rissk/utils/file_process_utils_kedro.py +++ b/rissk/utils/file_process_utils_kedro.py @@ -93,41 +93,37 @@ def transform_multi(df, variable_list, transformation_type): def remove_unset_value(sub_list): # Normalize numeric types (float -> int if integer) inside the list construction - # This ensures lists like [1.0, 2.0] become [1, 2], and np.float64(nan) -> np.nan (float) + # This ensures lists like [1.0, 2.0] become [1, 2], and np.float64(nan) -> float('nan') def normalize(v): - if isinstance(v, float) and v.is_integer(): - return int(v) - # Convert np.float64 or other numpy scalars to native python types, especially NaN if isinstance(v, (np.floating, np.integer)): if np.isnan(v): return float('nan') return v.item() + if isinstance(v, float) and v.is_integer(): + return int(v) return v - sub = list(filter(lambda v: v not in [-999999999, '##N/A##'], sub_list)) + # Filter explicit Survey Solutions structural missing values + # We do not filter 'true' NaNs yet, as they represent system-missing/skipped + sub = [x for x in sub_list if x not in [-999999999, '##N/A##']] sub = [normalize(ele) for ele in sub] # Check for empty list safely, avoiding numpy array ambiguity - sub = [ele if (not isinstance(ele, list) or len(ele) > 0) else '##N/A##' for ele in sub] - - # Check if sub is not empty list AND contains only '##N/A##' - # list(set(sub)) might fail if elements are unhashable (like lists), which sub might contain? - # If sub contains lists, set(sub) will fail. - # Assuming elements are hashable for now as they come from microdata values (scalars usually). - # But if we have nested lists? transform_multi is for multi-select questions. - # The values are usually roster indices (ints) or values (scalars). + # Do NOT filter out NaNs (system missing/skipped) as per requirements to distinguish them from explicit missing + clean_sub = [] + for ele in sub: + if isinstance(ele, list): + if len(ele) > 0: + clean_sub.append(ele) + else: + clean_sub.append(ele) - is_only_na = False - if len(sub) > 0: - try: - if list(set(sub)) == ['##N/A##']: - is_only_na = True - except TypeError: - # Fallback if unhashable elements (unlikely for scalars, but possible if details got messed up) - is_only_na = all(s == '##N/A##' for s in sub) - - sub = sub if (len(sub) > 0 and not is_only_na) else '##N/A##' - return sub + # If the cleaning results in an empty list, it implies all values were + # missing, skipped, or invalid. We return '##N/A##' to match legacy behavior. + if len(clean_sub) == 0: + return '##N/A##' + + return clean_sub transformation = [remove_unset_value(x) if x else float('nan') for x in transformation] if transformation_type != 'gps' else [ diff --git a/rissk/utils/import_utils_kedro.py b/rissk/utils/import_utils_kedro.py index 7145170..27e5860 100644 --- a/rissk/utils/import_utils_kedro.py +++ b/rissk/utils/import_utils_kedro.py @@ -356,10 +356,10 @@ def read_microdata_file(data_path: Path, file_name: str) -> pd.DataFrame: # Handle StataMissingValue objects which are unhashable # Replace '.a' with -999999999 and '.' with NaN - from pandas.io.stata import StataMissingValue def replace_stata_missing(val): - if isinstance(val, StataMissingValue): + # Duck typing check for StataMissingValue to avoid direct import dependency + if type(val).__name__ == 'StataMissingValue': s_val = str(val) if s_val == '.a': return -999999999 diff --git a/rissk_kedro/conf/base/globals.yml b/rissk_kedro/conf/base/globals.yml index 8e977af..5e88c34 100644 --- a/rissk_kedro/conf/base/globals.yml +++ b/rissk_kedro/conf/base/globals.yml @@ -6,7 +6,7 @@ survey: - name: "snb_hies_hh" VERSION: [9, 10, 11] - name: "slbhies_listing" - VERSION: [6, 7] + VERSION: [5, 6, 7] # survey: # name: "pmpmd" From a681d15bf01d7fc54d1d61caf26be8f19c3ae86c Mon Sep 17 00:00:00 2001 From: VJausovec Date: Sun, 1 Mar 2026 00:04:53 +0000 Subject: [PATCH 22/70] Refactor feature creation and pipeline configuration; add missing features for comment handling and translation positions; update input parameters for nodes; enhance data validation logic in microdata retrieval. --- rissk/feature_processing_kedro.py | 375 ++++++++++++------ rissk/utils/import_utils_kedro.py | 13 +- rissk_kedro/conf/base/catalog.yml | 29 +- rissk_kedro/conf/base/parameters.yml | 9 +- .../pipelines/feature_creation/nodes.py | 17 +- .../pipelines/feature_creation/pipeline.py | 12 +- 6 files changed, 311 insertions(+), 144 deletions(-) diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index 509521a..fcb669e 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -23,6 +23,15 @@ def _make_index_col(df: pd.DataFrame) -> pd.DataFrame: df['index_col'] = df['index_col'].str.strip('_') return df +def _get_numeric_mask(df_item: pd.DataFrame) -> pd.Series: + """Returns a boolean mask for valid numeric question rows, matching the legacy numeric_question_mask.""" + return ( + (df_item["qtype"] == 'NumericQuestion') & + (df_item['value'] != '') & + (~pd.isnull(df_item['value'])) & + (df_item['value'] != -999999999) + ) + def _get_df_time(df_active_paradata: pd.DataFrame) -> pd.DataFrame: """Calculates time differences and durations from paradata.""" df_time = df_active_paradata.copy() @@ -54,15 +63,8 @@ def _get_df_time(df_active_paradata: pd.DataFrame) -> pd.DataFrame: condition = (df_time['event'].isin(active_events)) & (df_time['time_difference'] < 30 * 60) df_time['f__total_duration'] = df_time.loc[condition, 'time_difference'] - # Starting timestamp per interview - # Use transform to broadcast min timestamp to all rows of the group - starting_timestamp = df_time[df_time['event'] == 'AnswerSet'].groupby('interview__id')['timestamp_local'].transform('min') - - # We need to map this back to the main df_time - # Since transform returns a series aligned with the filtered df, we need a safer merge/map strategy - # Or just calculate on the full group if efficient. - # The original code used map on a groupby result. - + # Starting timestamp per interview: min timestamp of the first AnswerSet event per interview. + # Using map on a pre-computed groupby result (matching the legacy approach). start_time_map = df_time[df_time['event'] == 'AnswerSet'].groupby('interview__id')['timestamp_local'].min() df_time['f__starting_timestamp'] = df_time['interview__id'].map(start_time_map) @@ -309,53 +311,42 @@ def _feat_string_length(df_item, **kwargs): feature_name = 'f__string_length' mask = df_item["qtype"] == 'TextQuestion' df_item[feature_name] = pd.NA - # Use str.len() if mask.any(): - df_item.loc[mask, feature_name] = df_item.loc[mask, 'value'].astype(str).str.len() + # Use .str.len() directly to preserve NA (astype(str) would convert NaN -> "nan", length 3) + df_item.loc[mask, feature_name] = df_item.loc[mask, 'value'].str.len().astype('Int64') return df_item def _feat_numeric_response(df_item, **kwargs): feature_name = 'f__numeric_response' - numeric_mask = (df_item["qtype"] == 'NumericQuestion') & pd.to_numeric(df_item['value'], errors='coerce').notna() + numeric_mask = _get_numeric_mask(df_item) df_item[feature_name] = np.nan if numeric_mask.any(): - df_item.loc[numeric_mask, feature_name] = df_item.loc[numeric_mask, 'value'].astype(float) + df_item.loc[numeric_mask, feature_name] = pd.to_numeric(df_item.loc[numeric_mask, 'value'], errors='coerce') return df_item def _feat_first_digit(df_item, **kwargs): feature_name = 'f__first_digit' - # Logic: abs(value), str[0] - numeric_mask = (df_item["qtype"] == 'NumericQuestion') & pd.to_numeric(df_item['value'], errors='coerce').notna() + numeric_mask = _get_numeric_mask(df_item) df_item[feature_name] = pd.NA if numeric_mask.any(): - # Convert to float, absolute, string, take first char + # Take absolute value, convert to string, extract first character vals = pd.to_numeric(df_item.loc[numeric_mask, 'value']).abs().astype(str).str[0] - # Check if digit - # vals = vals[vals.str.isdigit()] # Should be digit if from float - df_item.loc[numeric_mask, feature_name] = pd.to_numeric(vals, errors='coerce') + df_item.loc[numeric_mask, feature_name] = pd.to_numeric(vals, errors='coerce').astype('Int64') return df_item def _feat_last_digit(df_item, **kwargs): feature_name = 'f__last_digit' - numeric_mask = (df_item["qtype"] == 'NumericQuestion') & pd.to_numeric(df_item['value'], errors='coerce').notna() + # Use the same mask as legacy: excludes empty, null, and -999999999 + numeric_mask = _get_numeric_mask(df_item) df_item[feature_name] = pd.NA - + if numeric_mask.any(): - # Only for integer-like values >= 1? Legacy used >= 1 check on value - vals = pd.to_numeric(df_item.loc[numeric_mask, 'value']) - - # Check conditions - # We can implement this vectorally - valid_vals = (vals.abs() >= 1) - - # Modulo 10 - # Be careful with floats. 12.0 % 10 = 2.0. - res = vals % 10 - - # Apply mask - res = res.where(valid_vals, pd.NA) - df_item.loc[numeric_mask, feature_name] = res - + # Cast to Int64 (nullable) matching legacy astype('int64'), then apply x >= 1 check + # Note: legacy checks x >= 1 (not abs(x) >= 1), so negative values correctly yield NA + vals = pd.to_numeric(df_item.loc[numeric_mask, 'value']).astype('Int64') + # .where(condition) keeps values where True, sets False to NA + df_item.loc[numeric_mask, feature_name] = (vals % 10).where(vals >= 1) + return df_item def _feat_first_decimal(df_item, **kwargs): @@ -373,7 +364,7 @@ def _feat_first_decimal(df_item, **kwargs): # Documentation says "first decimal digit". Code says *100 % 100. # I will strictly follow legacy code logic. res = np.floor(values * 100) % 100 - df_item.loc[mask, feature_name] = res + df_item.loc[mask, feature_name] = res.astype('Int64') return df_item @@ -411,53 +402,76 @@ def calc_pos(row): return df_item def _feat_answer_changed(df_item, **kwargs): + """ + ⚠️ Legacy bug fixed: the legacy code applied the yes_list change + check and immediately overwrote it with the no_list check (two separate .loc assignments + on the same mask), so yes_list changes were always ignored. This implementation + combines both checks using a bitwise OR. + """ feature_name = 'f__answer_changed' paradata_active = kwargs.get('paradata_active') - + if paradata_active is None: return df_item - - # Logic involves reconstructing history of AnswerSet + + item_level_columns = ['interview__id', 'variable_name', 'roster_level'] df_changed = paradata_active[paradata_active['event'] == 'AnswerSet'].copy() - + if 'index_col' not in df_changed.columns: df_changed = _make_index_col(df_changed) - + df_changed[feature_name] = False - - # We need qtype. Merge it? Or is it in paradata? 'qtype' is in paradata. - - # Logic for lists (split by |) + group_cols = [c for c in item_level_columns + ['index_col'] if c in df_changed.columns] + has_yes_no = 'yes_no_view' in df_changed.columns + + # --- Case 1: TextListQuestion and MultyOptionsQuestion (without yes_no_view mode) --- list_mask = (df_changed["qtype"] == 'TextListQuestion') - multi_mask = (df_changed['yes_no_view'] == False) if 'yes_no_view' in df_changed.columns else pd.Series(False, index=df_changed.index) - - # This logic is quite complex to port perfectly without testing. - # Simplified approach: Group by index_col, count AnswerSet events? - # No, legacy checks if answer *content* changed relative to previous. - - # For refactor safety, I will implement a simplified count-based approach if logic is too brittle, - # OR try to replicate exact logic if possible. - - # Let's try replicating the "Single Answer" logic which is most common - df_changed['prev_answer'] = df_changed.groupby('interview__id')['answer'].shift() - # But wait, groupby interview_id mixes questions. Logic needs to account for question sequence. - # Legacy: df.groupby(item_level_cols + index_col)['answer'].shift() - # If grouped by index_col, we trace history of THAT question. - - df_changed['prev_answer'] = df_changed.groupby('index_col')['answer'].shift() - - # Detect change - # Note: first answer is not a change. - change_mask = (df_changed['prev_answer'].notna()) & (df_changed['answer'] != df_changed['prev_answer']) - df_changed.loc[change_mask, feature_name] = True - - # Set to features - # Sum of changes per item + multi_mask = (df_changed['yes_no_view'] == False) if has_yes_no else pd.Series(False, index=df_changed.index) + + df_changed['answer_list'] = pd.NA + df_changed.loc[list_mask, 'answer_list'] = df_changed.loc[list_mask, 'answer'].str.split('|') + df_changed.loc[multi_mask, 'answer_list'] = df_changed.loc[multi_mask, 'answer'].str.split(r', |\|') + + df_changed['prev_answer_list'] = df_changed.groupby(group_cols)['answer_list'].shift() + answers_mask = df_changed['prev_answer_list'].notna() + if answers_mask.any(): + df_changed.loc[answers_mask, feature_name] = df_changed.loc[answers_mask].apply( + lambda row: not set(row['prev_answer_list']).issubset(set(row['answer_list'])), axis=1 + ) + + # --- Case 2: Single-answer questions --- + df_changed['prev_answer'] = df_changed.groupby(group_cols)['answer'].shift() + single_answer_mask = ( + (~df_changed["qtype"].isin(['MultyOptionsQuestion', 'TextListQuestion'])) & + (df_changed['prev_answer'].notna()) & + (df_changed['answer'] != df_changed['prev_answer']) + ) + df_changed.loc[single_answer_mask, feature_name] = True + + # --- Case 3: Yes/No view questions --- + if has_yes_no: + yesno_mask = (df_changed['yes_no_view'] == True) + if yesno_mask.any(): + df_filtered = df_changed[yesno_mask].copy() + df_filtered[['yes_list', 'no_list']] = df_filtered['answer'].str.split('|', expand=True) + df_filtered['yes_list'] = df_filtered['yes_list'].str.split(', ').apply( + lambda x: [] if x == [''] or x is None else x) + df_filtered['no_list'] = df_filtered['no_list'].str.split(', ').apply( + lambda x: [] if x == [''] or x is None else x) + yesno_group_cols = [c for c in group_cols if c in df_filtered.columns] + df_filtered['prev_yes_list'] = df_filtered.groupby(yesno_group_cols)['yes_list'].shift(fill_value=[]) + df_filtered['prev_no_list'] = df_filtered.groupby(yesno_group_cols)['no_list'].shift(fill_value=[]) + # A change occurs if either yes or no selections changed + yes_changed = df_filtered.apply( + lambda row: not set(row['prev_yes_list']).issubset(set(row['yes_list'])), axis=1) + no_changed = df_filtered.apply( + lambda row: not set(row['prev_no_list']).issubset(set(row['no_list'])), axis=1) + df_changed.loc[yesno_mask, feature_name] = (yes_changed | no_changed).values + + # Sum changes per item and map back changes_per_item = df_changed.groupby('index_col')[feature_name].sum() - - # Map back df_item[feature_name] = df_item['index_col'].map(changes_per_item).fillna(0) - + return df_item def _feat_answer_selected(df_item, **kwargs): @@ -483,17 +497,104 @@ def count_els(x): return df_item def _feat_gps(df_item, **kwargs): - # Sets f__gps_latitude etc. + # Sets f__gps boolean flag plus f__gps_latitude, f__gps_longitude, f__gps_accuracy + feature_name = 'f__gps' mask = df_item["qtype"] == 'GpsCoordinateQuestion' + df_item[feature_name] = False if mask.any(): - # Split value "lat,lon,acc,alt,time" + df_item.loc[mask, feature_name] = True + # Split value "lat,lon,acc,alt,timestamp_utc" gps_data = df_item.loc[mask, 'value'].str.split(',', expand=True) - # Expecting at least 3 cols if gps_data.shape[1] >= 3: - df_item.loc[mask, 'f__gps_latitude'] = pd.to_numeric(gps_data[0], errors='coerce') - df_item.loc[mask, 'f__gps_longitude'] = pd.to_numeric(gps_data[1], errors='coerce') - df_item.loc[mask, 'f__gps_accuracy'] = pd.to_numeric(gps_data[2], errors='coerce') - + df_item.loc[mask, 'f__gps_latitude'] = pd.to_numeric(gps_data[0], errors='coerce') + df_item.loc[mask, 'f__gps_longitude'] = pd.to_numeric(gps_data[1], errors='coerce') + df_item.loc[mask, 'f__gps_accuracy'] = pd.to_numeric(gps_data[2], errors='coerce') + return df_item + + +def _feat_comment_length(df_item, **kwargs): + """Total character length of all comments left on each item. + Matches legacy make_feature_item__comment_length which uses self.df_paradata + (all events, role=1, interviewing=True — not limited to active events). + """ + feature_name = 'f__comment_length' + paradata_full = kwargs.get('paradata_full') + if paradata_full is None: + return df_item + + comment_mask = ( + (paradata_full['event'] == 'CommentSet') & + (paradata_full['role'] == 1) + ) + df_comment = paradata_full[comment_mask].copy() + if df_comment.empty: + return df_item + + if 'index_col' not in df_comment.columns: + df_comment = _make_index_col(df_comment) + + df_comment[feature_name] = df_comment['answer'].str.len() + df_agg = df_comment.groupby('index_col').agg(f__comment_length=(feature_name, 'sum')) + df_item[feature_name] = df_item['index_col'].map(df_agg['f__comment_length']) + return df_item + + +def _feat_comment_set(df_item, **kwargs): + """Count of CommentSet events per item. + Matches legacy make_feature_item__comment_set which uses self.df_paradata + (all events, role=1, interviewing=True — not limited to active events). + """ + feature_name = 'f__comment_set' + paradata_full = kwargs.get('paradata_full') + if paradata_full is None: + return df_item + + comment_mask = ( + (paradata_full['event'] == 'CommentSet') & + (paradata_full['role'] == 1) + ) + df_comment = paradata_full[comment_mask].copy() + if df_comment.empty: + return df_item + + if 'index_col' not in df_comment.columns: + df_comment = _make_index_col(df_comment) + + df_agg = df_comment.groupby('index_col').agg(f__comment_set=('order', 'count')) + df_item[feature_name] = df_item['index_col'].map(df_agg['f__comment_set']) + return df_item + + +def _feat_answer_removed(df_item, **kwargs): + """Count of AnswerRemoved events per item. + Matches legacy get_feature_item__answer_removed which uses self.df_paradata + (all events, role=1, interviewing=True — not limited to active events). + The legacy method notes this feature may include items no longer in microdata. + """ + feature_name = 'f__answer_removed' + paradata_full = kwargs.get('paradata_full') + if paradata_full is None: + return df_item + + removed_mask = ( + (paradata_full['event'] == 'AnswerRemoved') & + (paradata_full['role'] == 1) + ) + df_removed = paradata_full[removed_mask] + if df_removed.empty: + return df_item + + # Legacy groups on interview__id + responsible + variable_name + qnr_seq; + # we merge on interview__id + variable_name which is safe since qnr_seq is 1:1 with variable_name. + df_agg = df_removed.groupby( + ['interview__id', 'variable_name'] + ).agg(f__answer_removed=('order', 'count')).reset_index() + + df_item = df_item.merge( + df_agg[['interview__id', 'variable_name', feature_name]], + how='left', + on=['interview__id', 'variable_name'] + ) return df_item @@ -507,35 +608,38 @@ def _feat_gps(df_item, **kwargs): 'answer_position': _feat_answer_position, 'answer_changed': _feat_answer_changed, 'answer_selected': _feat_answer_selected, + 'answer_removed': _feat_answer_removed, + 'comment_length': _feat_comment_length, + 'comment_set': _feat_comment_set, 'gps': _feat_gps, - # 'comment_length': Use pre-calculated f__comment_duration from base table? No, that's duration. - # comment_length is length of txt. - # 'comment_set': Count of comments. } -def enrich_item_features(df_item: pd.DataFrame, paradata_active: pd.DataFrame, parameters: dict) -> pd.DataFrame: +def enrich_item_features(df_item: pd.DataFrame, paradata_active: pd.DataFrame, paradata_full: pd.DataFrame, parameters: dict) -> pd.DataFrame: """ Applies feature engineering logic to the item table. + paradata_active: active interviewer events (self.df_active_paradata equivalent). + paradata_full: all processed events, role=1, interviewing=True (self.df_paradata equivalent). """ logger.info("Enriching item features...") allowed_features = parameters.get('features', {}) - - # Helper: Ensure index_col in paradata for lookups + + # Ensure index_col in paradata for lookups if 'index_col' not in paradata_active.columns: - paradata_active = _make_index_col(paradata_active.copy()) + paradata_active = _make_index_col(paradata_active.copy()) + if 'index_col' not in paradata_full.columns: + paradata_full = _make_index_col(paradata_full.copy()) for feat_key, feat_cfg in allowed_features.items(): if feat_cfg.get('use', False): - # Map 'string_length' -> _feat_string_length func = ITEM_FEATURE_MAP.get(feat_key) if func: logger.info(f"Calculating item feature: {feat_key}") try: - df_item = func(df_item, paradata_active=paradata_active) + df_item = func(df_item, paradata_active=paradata_active, paradata_full=paradata_full) except Exception as e: logger.warning(f"Failed to calculate {feat_key}: {e}") - + return df_item @@ -543,48 +647,97 @@ def enrich_item_features(df_item: pd.DataFrame, paradata_active: pd.DataFrame, p def _feat_unit_number_answered(df_unit, item_features, **kwargs): feature_name = 'f__number_answered' - # Count valid answers in item table - # Valid = not null, not missing codes - mask = (item_features['value'].notna()) & (item_features['value'] != '') - - counts = item_features[mask].groupby('interview__id').size() - df_unit[feature_name] = df_unit['interview__id'].map(counts).fillna(0) + # Match legacy make_feature_unit__number_answered: exclude null, -999999999, '##N/A##', + # empty string, and Variable-type questions + mask = ( + (~pd.isnull(item_features['value'])) & + (item_features['value'] != -999999999) & + (item_features['value'] != '##N/A##') & + (item_features['value'] != '') & + (item_features['qtype'] != 'Variable') + ) + df_agg = item_features[mask].groupby('interview__id').agg( + f__number_answered=('value', 'count') + ) + df_unit[feature_name] = df_unit['interview__id'].map(df_agg['f__number_answered']).fillna(0) return df_unit def _feat_unit_number_unanswered(df_unit, item_features, **kwargs): feature_name = 'f__number_unanswered' - # Check for missing codes like -999... or ##N/A## - # Simplified check - mask = (item_features['value'].astype(str).str.contains('##N/A##')) | (item_features['value'] == -999999999) - - counts = item_features[mask].groupby('interview__id').size() - df_unit[feature_name] = df_unit['interview__id'].map(counts).fillna(0) + # Match legacy make_feature_unit__number_unanswered: -999999999 or '##N/A##', excluding Variable type + mask = ( + ( + (item_features['value'] == -999999999) | + (item_features['value'] == '##N/A##') + ) & + (item_features['qtype'] != 'Variable') + ) + df_agg = item_features[mask].groupby('interview__id').agg( + f__number_unanswered=('value', 'count') + ) + df_unit[feature_name] = df_unit['interview__id'].map(df_agg['f__number_unanswered']).fillna(0) return df_unit +def _feat_unit_translation_positions(df_unit, item_features, **kwargs): + """Relative positions of TranslationSwitched events within each interview. + Matches legacy make_feature_unit__translation_positions which uses self.df_paradata. + + Returns a list of relative positions (0..1) per interview. + """ + feature_name = 'f__translation_positions' + paradata_full = kwargs.get('paradata_full') + if paradata_full is None: + return df_unit + + trans_mask = paradata_full['event'].isin(['AnswerSet', 'TranslationSwitched']) + df_trans = paradata_full.loc[trans_mask, ['interview__id', 'order', 'event', 'param']].copy() + if df_trans.empty: + return df_unit + + df_trans = df_trans.sort_values(['interview__id', 'order']).reset_index(drop=True) + df_trans['seq'] = df_trans.groupby('interview__id').cumcount() + 1 + + def relative_translation_positions(group): + total_rows = len(group) + translation_positions = group.loc[group['event'] == 'TranslationSwitched', 'seq'] + return [pos / total_rows for pos in translation_positions] + + # include_groups=False: 'interview__id' is not needed inside the function; suppresses FutureWarning + # in pandas 2.2+ where including the grouping column in the passed group is deprecated. + result = df_trans.groupby('interview__id').apply( + relative_translation_positions, include_groups=False + ).reset_index() + result.columns = ['interview__id', feature_name] + + df_unit[feature_name] = df_unit['interview__id'].map( + result.set_index('interview__id')[feature_name] + ) + return df_unit + + UNIT_FEATURE_MAP = { 'number_answered': _feat_unit_number_answered, 'number_unanswered': _feat_unit_number_unanswered, - # ... Add others as needed from feature_processing.py + 'translation_positions': _feat_unit_translation_positions, } -def enrich_unit_features(df_unit: pd.DataFrame, item_features: pd.DataFrame, parameters: dict) -> pd.DataFrame: +def enrich_unit_features(df_unit: pd.DataFrame, item_features: pd.DataFrame, paradata_full: pd.DataFrame, parameters: dict) -> pd.DataFrame: """ Applies feature engineering logic to the unit table. + paradata_full: all processed events, role=1, interviewing=True (self.df_paradata equivalent). + Required for f__translation_positions. """ logger.info("Enriching unit features...") allowed_features = parameters.get('features', {}) - + for feat_key, feat_cfg in allowed_features.items(): - if feat_cfg.get('use', False): - # Prefix mapping check? "f__" is usually stripped in config keys? - # Config keys: 'string_length', 'number_answered' - + if feat_cfg.get('use', False): func = UNIT_FEATURE_MAP.get(feat_key) if func: logger.info(f"Calculating unit feature: {feat_key}") try: - df_unit = func(df_unit, item_features=item_features) + df_unit = func(df_unit, item_features=item_features, paradata_full=paradata_full) except Exception as e: logger.warning(f"Failed to calculate {feat_key}: {e}") - + return df_unit diff --git a/rissk/utils/import_utils_kedro.py b/rissk/utils/import_utils_kedro.py index 27e5860..7203d89 100644 --- a/rissk/utils/import_utils_kedro.py +++ b/rissk/utils/import_utils_kedro.py @@ -390,14 +390,6 @@ def get_microdata(data_path: Path, df_questionnaires: pd.DataFrame) -> pd.DataFr drop_list = ['interview__key', 'sssys_irnd', 'has__errors', 'interview__status', 'assignment__id'] file_names = get_microdata_file_list(data_path) - - # # Pre-calculate masks outside loop - # # Pre-initialize these variable lists once so they exist when the questionnaire DF is empty - # # (avoids NameError and avoids recalculating per-file). - # multi_unlinked_vars = [] - # multi_linked_vars = [] - # list_vars = [] - # gps_vars = [] # define multi/list question conditions if not df_questionnaires.empty: @@ -473,7 +465,10 @@ def get_microdata(data_path: Path, df_questionnaires: pd.DataFrame) -> pd.DataFr def is_valid_fast(val): if val is None: return False - if isinstance(val, (list, tuple)): return len(val) > 0 # Empty list should be invalid? Legacy: 'return True' + if isinstance(val, (list, tuple)): + if len(val) == 0: return False + # Filter out lists that contain only NaNs or empty strings + return any(pd.notna(x) and x != '' for x in val) if isinstance(val, (np.ndarray,)): return val.size > 0 if isinstance(val, str) and val == '': return False # Fallback for other types where equality might be array-like (though unlikely for scalars) diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index 5ed76b9..a8d0455 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -47,6 +47,24 @@ unit_features_base: type: pandas.ParquetDataset filepath: data/${globals:survey.name}/latest/20_INTERIM/unit_features_base.parquet +# === LEGACY DATA FOR PIPELINE TESTING === +# Uncomment these and update pipeline.py inputs to test against legacy-produced data. +# +# legacy_microdata: +# type: pandas.ParquetDataset +# filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/hies2024/latest/30_PROCESSED/microdata.parquet +# +# # Equivalent to paradata_processed (output of process_paradata_node). +# # The legacy pipeline saved this as paradata.parquet (not paradata_processed.parquet). +# legacy_paradata_processed: +# type: pandas.ParquetDataset +# filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/hies2024/latest/30_PROCESSED/paradata.parquet +# +# # Equivalent to paradata_active (output of filter_active_paradata_node). +# legacy_paradata_active: +# type: pandas.ParquetDataset +# filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/hies2024/latest/30_PROCESSED/paradata_active.parquet + # Final Feature Tables (Input to Risk Scoring) item_features: type: pandas.ParquetDataset @@ -54,13 +72,4 @@ item_features: unit_features: type: pandas.ParquetDataset - filepath: data/${globals:survey.name}/latest/30_PROCESSED/unit_features.parquet - -# === LEGACY DATA FOR TESTING === -legacy_microdata: - type: pandas.ParquetDataset - filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/hies2024/latest/30_PROCESSED/microdata.parquet - -legacy_paradata_active: - type: pandas.ParquetDataset - filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/hies2024/latest/30_PROCESSED/paradata_active.parquet \ No newline at end of file + filepath: data/${globals:survey.name}/latest/30_PROCESSED/unit_features.parquet \ No newline at end of file diff --git a/rissk_kedro/conf/base/parameters.yml b/rissk_kedro/conf/base/parameters.yml index c54bd1d..c5dcee6 100644 --- a/rissk_kedro/conf/base/parameters.yml +++ b/rissk_kedro/conf/base/parameters.yml @@ -40,7 +40,7 @@ features: first_digit: use: true last_digit: - use: false + use: true numeric_response: use: true sequence_jump: @@ -59,9 +59,10 @@ features: use: true contamination: 0.11 pause_list: - use: false + use: true + contamination: 0.11 number_unanswered: - use: false + use: true number_answered: use: true contamination: 0.11 @@ -76,7 +77,7 @@ features: multi_option_question: use: true days_from_start: - use: false + use: true # Output Configuration output: diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py index dd7b75b..b0b1834 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py @@ -34,21 +34,26 @@ def create_base_unit_table_node( return create_base_unit_table(paradata_active, parameters) def enrich_item_features_node( - item_features_base: pd.DataFrame, - paradata_active: pd.DataFrame, + item_features_base: pd.DataFrame, + paradata_active: pd.DataFrame, + paradata_full: pd.DataFrame, parameters: Dict[str, Any] ) -> pd.DataFrame: """ Node wrapper for enrich_item_features. + paradata_active: active interviewer events only (equivalent to self.df_active_paradata). + paradata_full: all processed events, role=1, interviewing=True (equivalent to self.df_paradata). """ - return enrich_item_features(item_features_base, paradata_active, parameters) + return enrich_item_features(item_features_base, paradata_active, paradata_full, parameters) def enrich_unit_features_node( - unit_features_base: pd.DataFrame, - item_features: pd.DataFrame, + unit_features_base: pd.DataFrame, + item_features: pd.DataFrame, + paradata_full: pd.DataFrame, parameters: Dict[str, Any] ) -> pd.DataFrame: """ Node wrapper for enrich_unit_features. + paradata_full: all processed events, role=1, interviewing=True (equivalent to self.df_paradata). """ - return enrich_unit_features(unit_features_base, item_features, parameters) + return enrich_unit_features(unit_features_base, item_features, paradata_full, parameters) diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py index 1f69d8b..d6a9f03 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py @@ -16,25 +16,29 @@ def create_pipeline(**kwargs) -> Pipeline: return pipeline([ node( func=create_base_item_table_node, - inputs=["legacy_microdata", "legacy_paradata_active", "parameters"], + inputs=["raw_microdata", "paradata_active", "parameters"], + # Legacy test data: inputs=["legacy_microdata", "legacy_paradata_active", "parameters"], outputs="item_features_base", name="create_base_item_table_node", ), node( func=create_base_unit_table_node, - inputs=["legacy_paradata_active", "parameters"], + inputs=["paradata_active", "parameters"], + # Legacy test data: inputs=["legacy_paradata_active", "parameters"], outputs="unit_features_base", name="create_base_unit_table_node", ), node( func=enrich_item_features_node, - inputs=["item_features_base", "legacy_paradata_active", "parameters"], + inputs=["item_features_base", "paradata_active", "paradata_processed", "parameters"], + # Legacy test data: inputs=["item_features_base", "legacy_paradata_active", "legacy_paradata_processed", "parameters"], outputs="item_features", name="enrich_item_features_node", ), node( func=enrich_unit_features_node, - inputs=["unit_features_base", "item_features", "parameters"], + inputs=["unit_features_base", "item_features", "paradata_processed", "parameters"], + # Legacy test data: inputs=["unit_features_base", "item_features", "legacy_paradata_processed", "parameters"], outputs="unit_features", name="enrich_unit_features_node", ), From 3ad4de222cb34a114e691704f30cdea7d1bcfdc9 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Wed, 4 Mar 2026 10:47:18 +0000 Subject: [PATCH 23/70] Refactor feature processing functions for consistency; rename private functions to public; update pipeline and configuration files for legacy data handling; enhance parameters for new features. --- rissk/feature_processing_kedro.py | 120 +++++++++--------- rissk_kedro/conf/base/catalog.yml | 20 +-- rissk_kedro/conf/base/parameters.yml | 10 ++ .../pipelines/feature_creation/pipeline.py | 20 +-- .../pipelines/feature_engineering/nodes.py | 19 +-- 5 files changed, 94 insertions(+), 95 deletions(-) diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index fcb669e..6e74b59 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -6,7 +6,7 @@ # --- Helper Functions --- -def _make_index_col(df: pd.DataFrame) -> pd.DataFrame: +def make_index_col(df: pd.DataFrame) -> pd.DataFrame: """Creates a unique index column based on interview_id, variable_name, and roster_level.""" # Filter out columns with NaN and empty strings for the mask # Using fillna('') to handle NaNs safely for string concatenation @@ -23,7 +23,7 @@ def _make_index_col(df: pd.DataFrame) -> pd.DataFrame: df['index_col'] = df['index_col'].str.strip('_') return df -def _get_numeric_mask(df_item: pd.DataFrame) -> pd.Series: +def get_numeric_mask(df_item: pd.DataFrame) -> pd.Series: """Returns a boolean mask for valid numeric question rows, matching the legacy numeric_question_mask.""" return ( (df_item["qtype"] == 'NumericQuestion') & @@ -32,7 +32,7 @@ def _get_numeric_mask(df_item: pd.DataFrame) -> pd.Series: (df_item['value'] != -999999999) ) -def _get_df_time(df_active_paradata: pd.DataFrame) -> pd.DataFrame: +def get_df_time(df_active_paradata: pd.DataFrame) -> pd.DataFrame: """Calculates time differences and durations from paradata.""" df_time = df_active_paradata.copy() @@ -76,7 +76,7 @@ def _get_df_time(df_active_paradata: pd.DataFrame) -> pd.DataFrame: return df_time -def _get_df_sequence(df_active_paradata: pd.DataFrame) -> pd.DataFrame: +def get_df_sequence(df_active_paradata: pd.DataFrame) -> pd.DataFrame: """Calculates sequence-based features (jumps, previous answers).""" # Filter for AnswerSet and get the last entry per index_col mask = df_active_paradata['event'] == 'AnswerSet' @@ -107,7 +107,7 @@ def _get_df_sequence(df_active_paradata: pd.DataFrame) -> pd.DataFrame: return df_last -def _add_sequence_features(df_item: pd.DataFrame, df_sequence: pd.DataFrame, allowed_features: list) -> pd.DataFrame: +def add_sequence_features(df_item: pd.DataFrame, df_sequence: pd.DataFrame, allowed_features: list) -> pd.DataFrame: sequence_features = ['f__previous_question', 'f__previous_answer', 'f__previous_roster', 'f__sequence_jump'] @@ -125,7 +125,7 @@ def _add_sequence_features(df_item: pd.DataFrame, df_sequence: pd.DataFrame, all return df_item -def _add_item_time_features(df_item: pd.DataFrame, df_time: pd.DataFrame, allowed_features: list, item_level_columns: list) -> pd.DataFrame: +def add_item_time_features(df_item: pd.DataFrame, df_time: pd.DataFrame, allowed_features: list, item_level_columns: list) -> pd.DataFrame: time_features = ['f__answer_duration', 'f__comment_duration'] selected_features = [f for f in time_features if f in allowed_features] @@ -155,7 +155,7 @@ def _add_item_time_features(df_item: pd.DataFrame, df_time: pd.DataFrame, allowe return df_item -def _add_pause_features(df_unit: pd.DataFrame, df_time: pd.DataFrame, allowed_features: list) -> pd.DataFrame: +def add_pause_features(df_unit: pd.DataFrame, df_time: pd.DataFrame, allowed_features: list) -> pd.DataFrame: pause_features = ['f__pause_count', 'f__pause_duration', 'f__pause_list'] selected_features = [f for f in pause_features if f in allowed_features] @@ -195,7 +195,7 @@ def to_list(x): return df_unit -def _add_unit_time_features(df_unit: pd.DataFrame, df_time: pd.DataFrame, allowed_features: list) -> pd.DataFrame: +def add_unit_time_features(df_unit: pd.DataFrame, df_time: pd.DataFrame, allowed_features: list) -> pd.DataFrame: time_features = ['f__total_duration', 'f__total_elapse', 'f__days_from_start', 'f__time_changed'] selected_features = [f for f in time_features if f in allowed_features] @@ -231,7 +231,7 @@ def create_base_item_table(microdata: pd.DataFrame, paradata_active: pd.DataFram allowed_features = ['f__' + k for k, v in parameters['features'].items() if v.get('use', False)] # 1. Create Index Column on Microdata - df_item = _make_index_col(microdata.copy()) + df_item = make_index_col(microdata.copy()) # 2. Select initial columns initial_cols = ['value', "qtype", 'is_integer', 'qnr_seq', @@ -250,10 +250,9 @@ def create_base_item_table(microdata: pd.DataFrame, paradata_active: pd.DataFram answer_set_mask = (paradata_active['event'] == 'AnswerSet') - # Ensure index_col exists in paradata. It should be there from ingestion/processing. - # If not, we might need to recreate it. Assuming it exists or we create it. - if 'index_col' not in paradata_active.columns: - paradata_active = _make_index_col(paradata_active.copy()) + # # Already present in paradata_active from ingestion, but ensure it's there for merging + # if 'index_col' not in paradata_active.columns: + # paradata_active = make_index_col(paradata_active.copy()) data_to_merge = paradata_active[answer_set_mask].drop_duplicates(subset='index_col', keep='last') @@ -261,19 +260,18 @@ def create_base_item_table(microdata: pd.DataFrame, paradata_active: pd.DataFram df_item = df_item.merge(data_to_merge[available_para_cols + ['index_col']], how='left', on='index_col') # 5. Filter for 'interviewing' == True (Supervisor Logic) - if 'interviewing' in df_item.columns: - # Fill NaN with False or True? Original code assumed boolean column. - df_item = df_item[df_item['interviewing'] == True] + # Remove items that are not in interviewing + df_item = df_item[df_item['interviewing'] == True].copy() # 6. Add Sequence Features # Pre-calculate sequence df - df_sequence = _get_df_sequence(paradata_active) - df_item = _add_sequence_features(df_item, df_sequence, allowed_features) + df_sequence = get_df_sequence(paradata_active) + df_item = add_sequence_features(df_item, df_sequence, allowed_features) # 7. Add Time Features # Pre-calculate time df - df_time = _get_df_time(paradata_active) - df_item = _add_item_time_features(df_item, df_time, allowed_features, item_level_columns) + df_time = get_df_time(paradata_active) + df_item = add_item_time_features(df_item, df_time, allowed_features, item_level_columns) return df_item @@ -296,18 +294,18 @@ def create_base_unit_table(paradata_active: pd.DataFrame, parameters: dict) -> p df_unit = df_unit[(df_unit['responsible'] != '') & (df_unit['responsible'].notna())] # 2. Add Pause Features - df_time = _get_df_time(paradata_active) - df_unit = _add_pause_features(df_unit, df_time, allowed_features) + df_time = get_df_time(paradata_active) + df_unit = add_pause_features(df_unit, df_time, allowed_features) # 3. Add Unit Time Features - df_unit = _add_unit_time_features(df_unit, df_time, allowed_features) + df_unit = add_unit_time_features(df_unit, df_time, allowed_features) return df_unit # --- Feature Enrichment Functions (Item) --- -def _feat_string_length(df_item, **kwargs): +def feat_string_length(df_item, **kwargs): feature_name = 'f__string_length' mask = df_item["qtype"] == 'TextQuestion' df_item[feature_name] = pd.NA @@ -316,17 +314,17 @@ def _feat_string_length(df_item, **kwargs): df_item.loc[mask, feature_name] = df_item.loc[mask, 'value'].str.len().astype('Int64') return df_item -def _feat_numeric_response(df_item, **kwargs): +def feat_numeric_response(df_item, **kwargs): feature_name = 'f__numeric_response' - numeric_mask = _get_numeric_mask(df_item) + numeric_mask = get_numeric_mask(df_item) df_item[feature_name] = np.nan if numeric_mask.any(): df_item.loc[numeric_mask, feature_name] = pd.to_numeric(df_item.loc[numeric_mask, 'value'], errors='coerce') return df_item -def _feat_first_digit(df_item, **kwargs): +def feat_first_digit(df_item, **kwargs): feature_name = 'f__first_digit' - numeric_mask = _get_numeric_mask(df_item) + numeric_mask = get_numeric_mask(df_item) df_item[feature_name] = pd.NA if numeric_mask.any(): # Take absolute value, convert to string, extract first character @@ -334,10 +332,10 @@ def _feat_first_digit(df_item, **kwargs): df_item.loc[numeric_mask, feature_name] = pd.to_numeric(vals, errors='coerce').astype('Int64') return df_item -def _feat_last_digit(df_item, **kwargs): +def feat_last_digit(df_item, **kwargs): feature_name = 'f__last_digit' # Use the same mask as legacy: excludes empty, null, and -999999999 - numeric_mask = _get_numeric_mask(df_item) + numeric_mask = get_numeric_mask(df_item) df_item[feature_name] = pd.NA if numeric_mask.any(): @@ -349,7 +347,7 @@ def _feat_last_digit(df_item, **kwargs): return df_item -def _feat_first_decimal(df_item, **kwargs): +def feat_first_decimal(df_item, **kwargs): feature_name = 'f__first_decimal' # mask: not integer and not empty mask = (df_item['is_integer'] == False) & (df_item['value'] != '') @@ -368,7 +366,7 @@ def _feat_first_decimal(df_item, **kwargs): return df_item -def _feat_answer_position(df_item, **kwargs): +def feat_answer_position(df_item, **kwargs): feature_name = 'f__answer_position' # in legacy it was f__rel_answer_position sometimes? code says f__answer_position # filters @@ -401,7 +399,7 @@ def calc_pos(row): return df_item -def _feat_answer_changed(df_item, **kwargs): +def feat_answer_changed(df_item, **kwargs): """ ⚠️ Legacy bug fixed: the legacy code applied the yes_list change check and immediately overwrote it with the no_list check (two separate .loc assignments @@ -418,7 +416,7 @@ def _feat_answer_changed(df_item, **kwargs): df_changed = paradata_active[paradata_active['event'] == 'AnswerSet'].copy() if 'index_col' not in df_changed.columns: - df_changed = _make_index_col(df_changed) + df_changed = make_index_col(df_changed) df_changed[feature_name] = False group_cols = [c for c in item_level_columns + ['index_col'] if c in df_changed.columns] @@ -474,7 +472,7 @@ def _feat_answer_changed(df_item, **kwargs): return df_item -def _feat_answer_selected(df_item, **kwargs): +def feat_answer_selected(df_item, **kwargs): feature_name = 'f__answer_selected' mask = df_item["qtype"].isin(['MultyOptionsQuestion']) @@ -496,7 +494,7 @@ def count_els(x): return df_item -def _feat_gps(df_item, **kwargs): +def feat_gps(df_item, **kwargs): # Sets f__gps boolean flag plus f__gps_latitude, f__gps_longitude, f__gps_accuracy feature_name = 'f__gps' mask = df_item["qtype"] == 'GpsCoordinateQuestion' @@ -512,7 +510,7 @@ def _feat_gps(df_item, **kwargs): return df_item -def _feat_comment_length(df_item, **kwargs): +def feat_comment_length(df_item, **kwargs): """Total character length of all comments left on each item. Matches legacy make_feature_item__comment_length which uses self.df_paradata (all events, role=1, interviewing=True — not limited to active events). @@ -531,7 +529,7 @@ def _feat_comment_length(df_item, **kwargs): return df_item if 'index_col' not in df_comment.columns: - df_comment = _make_index_col(df_comment) + df_comment = make_index_col(df_comment) df_comment[feature_name] = df_comment['answer'].str.len() df_agg = df_comment.groupby('index_col').agg(f__comment_length=(feature_name, 'sum')) @@ -539,7 +537,7 @@ def _feat_comment_length(df_item, **kwargs): return df_item -def _feat_comment_set(df_item, **kwargs): +def feat_comment_set(df_item, **kwargs): """Count of CommentSet events per item. Matches legacy make_feature_item__comment_set which uses self.df_paradata (all events, role=1, interviewing=True — not limited to active events). @@ -558,14 +556,14 @@ def _feat_comment_set(df_item, **kwargs): return df_item if 'index_col' not in df_comment.columns: - df_comment = _make_index_col(df_comment) + df_comment = make_index_col(df_comment) df_agg = df_comment.groupby('index_col').agg(f__comment_set=('order', 'count')) df_item[feature_name] = df_item['index_col'].map(df_agg['f__comment_set']) return df_item -def _feat_answer_removed(df_item, **kwargs): +def feat_answer_removed(df_item, **kwargs): """Count of AnswerRemoved events per item. Matches legacy get_feature_item__answer_removed which uses self.df_paradata (all events, role=1, interviewing=True — not limited to active events). @@ -600,18 +598,18 @@ def _feat_answer_removed(df_item, **kwargs): # Dispatcher ITEM_FEATURE_MAP = { - 'string_length': _feat_string_length, - 'numeric_response': _feat_numeric_response, - 'first_digit': _feat_first_digit, - 'last_digit': _feat_last_digit, - 'first_decimal': _feat_first_decimal, - 'answer_position': _feat_answer_position, - 'answer_changed': _feat_answer_changed, - 'answer_selected': _feat_answer_selected, - 'answer_removed': _feat_answer_removed, - 'comment_length': _feat_comment_length, - 'comment_set': _feat_comment_set, - 'gps': _feat_gps, + 'string_length': feat_string_length, + 'numeric_response': feat_numeric_response, + 'first_digit': feat_first_digit, + 'last_digit': feat_last_digit, + 'first_decimal': feat_first_decimal, + 'answer_position': feat_answer_position, + 'answer_changed': feat_answer_changed, + 'answer_selected': feat_answer_selected, + 'answer_removed': feat_answer_removed, + 'comment_length': feat_comment_length, + 'comment_set': feat_comment_set, + 'gps': feat_gps, } @@ -626,9 +624,9 @@ def enrich_item_features(df_item: pd.DataFrame, paradata_active: pd.DataFrame, p # Ensure index_col in paradata for lookups if 'index_col' not in paradata_active.columns: - paradata_active = _make_index_col(paradata_active.copy()) + paradata_active = make_index_col(paradata_active.copy()) if 'index_col' not in paradata_full.columns: - paradata_full = _make_index_col(paradata_full.copy()) + paradata_full = make_index_col(paradata_full.copy()) for feat_key, feat_cfg in allowed_features.items(): if feat_cfg.get('use', False): @@ -645,7 +643,7 @@ def enrich_item_features(df_item: pd.DataFrame, paradata_active: pd.DataFrame, p # --- Feature Enrichment Functions (Unit) --- -def _feat_unit_number_answered(df_unit, item_features, **kwargs): +def feat_unit_number_answered(df_unit, item_features, **kwargs): feature_name = 'f__number_answered' # Match legacy make_feature_unit__number_answered: exclude null, -999999999, '##N/A##', # empty string, and Variable-type questions @@ -662,7 +660,7 @@ def _feat_unit_number_answered(df_unit, item_features, **kwargs): df_unit[feature_name] = df_unit['interview__id'].map(df_agg['f__number_answered']).fillna(0) return df_unit -def _feat_unit_number_unanswered(df_unit, item_features, **kwargs): +def feat_unit_number_unanswered(df_unit, item_features, **kwargs): feature_name = 'f__number_unanswered' # Match legacy make_feature_unit__number_unanswered: -999999999 or '##N/A##', excluding Variable type mask = ( @@ -678,7 +676,7 @@ def _feat_unit_number_unanswered(df_unit, item_features, **kwargs): df_unit[feature_name] = df_unit['interview__id'].map(df_agg['f__number_unanswered']).fillna(0) return df_unit -def _feat_unit_translation_positions(df_unit, item_features, **kwargs): +def feat_unit_translation_positions(df_unit, item_features, **kwargs): """Relative positions of TranslationSwitched events within each interview. Matches legacy make_feature_unit__translation_positions which uses self.df_paradata. @@ -716,9 +714,9 @@ def relative_translation_positions(group): UNIT_FEATURE_MAP = { - 'number_answered': _feat_unit_number_answered, - 'number_unanswered': _feat_unit_number_unanswered, - 'translation_positions': _feat_unit_translation_positions, + 'number_answered': feat_unit_number_answered, + 'number_unanswered': feat_unit_number_unanswered, + 'translation_positions': feat_unit_translation_positions, } def enrich_unit_features(df_unit: pd.DataFrame, item_features: pd.DataFrame, paradata_full: pd.DataFrame, parameters: dict) -> pd.DataFrame: diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index a8d0455..fae4d0b 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -49,21 +49,21 @@ unit_features_base: # === LEGACY DATA FOR PIPELINE TESTING === # Uncomment these and update pipeline.py inputs to test against legacy-produced data. -# -# legacy_microdata: -# type: pandas.ParquetDataset -# filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/hies2024/latest/30_PROCESSED/microdata.parquet + +legacy_microdata: + type: pandas.ParquetDataset + filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/hies2024/latest/30_PROCESSED/microdata.parquet # # # Equivalent to paradata_processed (output of process_paradata_node). # # The legacy pipeline saved this as paradata.parquet (not paradata_processed.parquet). -# legacy_paradata_processed: -# type: pandas.ParquetDataset -# filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/hies2024/latest/30_PROCESSED/paradata.parquet +legacy_paradata_processed: + type: pandas.ParquetDataset + filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/hies2024/latest/30_PROCESSED/paradata.parquet # # # Equivalent to paradata_active (output of filter_active_paradata_node). -# legacy_paradata_active: -# type: pandas.ParquetDataset -# filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/hies2024/latest/30_PROCESSED/paradata_active.parquet +legacy_paradata_active: + type: pandas.ParquetDataset + filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/hies2024/latest/30_PROCESSED/paradata_active.parquet # Final Feature Tables (Input to Risk Scoring) item_features: diff --git a/rissk_kedro/conf/base/parameters.yml b/rissk_kedro/conf/base/parameters.yml index c5dcee6..8afd318 100644 --- a/rissk_kedro/conf/base/parameters.yml +++ b/rissk_kedro/conf/base/parameters.yml @@ -30,6 +30,8 @@ features: answer_selected: use: true contamination: 0.1 + answer_share_selected: + use: true answer_duration: use: true contamination: 0.1 @@ -61,6 +63,12 @@ features: pause_list: use: true contamination: 0.11 + comment_length: + use: true + comment_set: + use: true + comment_duration: + use: true number_unanswered: use: true number_answered: @@ -78,6 +86,8 @@ features: use: true days_from_start: use: true + string_length: + use: true # Output Configuration output: diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py index d6a9f03..d43ade8 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py @@ -16,29 +16,33 @@ def create_pipeline(**kwargs) -> Pipeline: return pipeline([ node( func=create_base_item_table_node, - inputs=["raw_microdata", "paradata_active", "parameters"], - # Legacy test data: inputs=["legacy_microdata", "legacy_paradata_active", "parameters"], + # inputs=["raw_microdata", "paradata_active", "parameters"], + # Legacy test data: + inputs=["legacy_microdata", "legacy_paradata_active", "parameters"], outputs="item_features_base", name="create_base_item_table_node", ), node( func=create_base_unit_table_node, - inputs=["paradata_active", "parameters"], - # Legacy test data: inputs=["legacy_paradata_active", "parameters"], + # inputs=["paradata_active", "parameters"], + # Legacy test data: + inputs=["legacy_paradata_active", "parameters"], outputs="unit_features_base", name="create_base_unit_table_node", ), node( func=enrich_item_features_node, - inputs=["item_features_base", "paradata_active", "paradata_processed", "parameters"], - # Legacy test data: inputs=["item_features_base", "legacy_paradata_active", "legacy_paradata_processed", "parameters"], + # inputs=["item_features_base", "paradata_active", "paradata_processed", "parameters"], + # Legacy test data: + inputs=["item_features_base", "legacy_paradata_active", "legacy_paradata_processed", "parameters"], outputs="item_features", name="enrich_item_features_node", ), node( func=enrich_unit_features_node, - inputs=["unit_features_base", "item_features", "paradata_processed", "parameters"], - # Legacy test data: inputs=["unit_features_base", "item_features", "legacy_paradata_processed", "parameters"], + # inputs=["unit_features_base", "item_features", "paradata_processed", "parameters"], + # Legacy test data: + inputs=["unit_features_base", "item_features", "legacy_paradata_processed", "parameters"], outputs="unit_features", name="enrich_unit_features_node", ), diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py index e1cb256..52dc338 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py @@ -4,6 +4,8 @@ from typing import Dict from loguru import logger +from rissk.feature_processing_kedro import make_index_col + def process_paradata_node( paradata_interim: pd.DataFrame, @@ -39,22 +41,7 @@ def process_paradata_node( paradata.drop(['flag', 'cumulative_flag'], axis=1, inplace=True) paradata = paradata[(paradata['interviewing'] == True) & (paradata['role'] == 1)].copy() - # Implement make_index_col logic (concat ID parts) - # Using '_' separator to match previous notebook logic - def make_index_col(df): - mask = (~df[['interview__id', 'variable_name', 'roster_level']].isnull()) & \ - (df[['interview__id', 'variable_name', 'roster_level']] != '') - filtered_df = df.where(mask, '') - - # Concatenate the columns with an underscore separator - df['index_col'] = ( - filtered_df['interview__id'].astype(str) + "_" + - filtered_df['variable_name'].astype(str) + "_" + - filtered_df['roster_level'].astype(str) - ) - df['index_col'] = df['index_col'].str.strip('_') - return df - + # Use shared helper to avoid drift with feature_processing_kedro paradata = make_index_col(paradata) # Sort by interview__id, order From 5092b9844abb3b30cdb6762bbda938b57838cdec Mon Sep 17 00:00:00 2001 From: VJausovec Date: Sat, 7 Mar 2026 14:52:32 +0000 Subject: [PATCH 24/70] Refactor time and pause feature calculations for improved clarity; for testing add legacy microdata and paradata to catalog --- rissk/feature_processing_kedro.py | 134 +++++++++++++-------------- rissk_kedro/conf/base/catalog.yml | 6 +- rissk_kedro/conf/base/parameters.yml | 46 ++++++--- 3 files changed, 99 insertions(+), 87 deletions(-) diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index 6e74b59..c1b7876 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -36,9 +36,6 @@ def get_df_time(df_active_paradata: pd.DataFrame) -> pd.DataFrame: """Calculates time differences and durations from paradata.""" df_time = df_active_paradata.copy() - # Sort to ensure diff works correctly - df_time = df_time.sort_values(['interview__id', 'timestamp_local']) - # calculate time difference in seconds df_time['time_difference'] = df_time.groupby('interview__id')['timestamp_local'].diff() df_time['time_difference'] = df_time['time_difference'].dt.total_seconds() @@ -48,7 +45,7 @@ def get_df_time(df_active_paradata: pd.DataFrame) -> pd.DataFrame: # Mask negative time differences for duration calculations # Using pd.NA for nullable integers/floats in pandas if column allows, or np.nan - df_time.loc[df_time['time_difference'] < 0, 'time_difference'] = np.nan + df_time.loc[df_time['time_difference'] < 0, 'time_difference'] = pd.NA # time for answers/comments df_time['f__answer_duration'] = df_time.loc[ @@ -159,61 +156,46 @@ def add_pause_features(df_unit: pd.DataFrame, df_time: pd.DataFrame, allowed_fea pause_features = ['f__pause_count', 'f__pause_duration', 'f__pause_list'] selected_features = [f for f in pause_features if f in allowed_features] - if selected_features: - # Calculate pause stats per interview - # f__pause_duration column in df_time contains the duration for Resumed/Restarted events - - # Custom aggregation for list - def to_list(x): - return x.tolist() + if not selected_features: + return df_unit - agg_dict = {} - if 'f__pause_count' in selected_features: - # count all occurrences (size) where pause_duration is not null is implied by how df_time was built? - # Actually df_time['f__pause_duration'] is NaN for non-pause events. - # So we should count non-nulls. 'count' counts non-NA. 'size' counts matches. - agg_dict['f__pause_count'] = ('f__pause_duration', 'count') - if 'f__pause_duration' in selected_features: - agg_dict['f__pause_duration'] = ('f__pause_duration', 'sum') - if 'f__pause_list' in selected_features: - # This might be tricky in aggregation if all are NaN. - # We filter first. - pass + # Legacy-like flow: compute all pause aggregations once, then keep selected columns. + # Keep the correction vs legacy: count only non-null pauses. + df_pause = df_time.groupby('interview__id').agg( + f__pause_count=('f__pause_duration', 'count'), + f__pause_duration=('f__pause_duration', 'sum'), + # Keep only real pause durations; all-NaN groups become an empty list. + f__pause_list=('f__pause_duration', lambda x: [v for v in x.tolist() if pd.notna(v)]), + ).reset_index() - if agg_dict: - df_pause = df_time.groupby('interview__id').agg(**agg_dict).reset_index() - - # Handle list separately if needed or include in agg above if simple - if 'f__pause_list' in selected_features: - # Only rows with valid pause duration - pause_rows = df_time.dropna(subset=['f__pause_duration']) - if not pause_rows.empty: - list_agg = pause_rows.groupby('interview__id')['f__pause_duration'].apply(list).reset_index(name='f__pause_list') - df_pause = df_pause.merge(list_agg, how='left', on='interview__id') - - df_unit = df_unit.merge(df_pause, how='left', on='interview__id') + df_pause = df_pause[['interview__id'] + selected_features] + df_unit = df_unit.merge(df_pause, how='left', on='interview__id') + + if 'f__pause_list' in selected_features: + # Ensure interviews absent in df_time also get an empty list after merge. + df_unit['f__pause_list'] = df_unit['f__pause_list'].apply( + lambda x: x if isinstance(x, list) else [] + ) return df_unit def add_unit_time_features(df_unit: pd.DataFrame, df_time: pd.DataFrame, allowed_features: list) -> pd.DataFrame: time_features = ['f__total_duration', 'f__total_elapse', 'f__days_from_start', 'f__time_changed'] selected_features = [f for f in time_features if f in allowed_features] - - if selected_features: - agg_dict = {} - if 'f__total_duration' in selected_features: - agg_dict['f__total_duration'] = ('f__total_duration', 'sum') - if 'f__total_elapse' in selected_features: - # Lambda in agg is slower, but compatible. - agg_dict['f__total_elapse'] = ('timestamp_local', lambda x: (x.max() - x.min()).total_seconds() if not x.empty else 0) - if 'f__time_changed' in selected_features: - agg_dict['f__time_changed'] = ('f__time_changed', 'sum') - if 'f__days_from_start' in selected_features: - agg_dict['f__days_from_start'] = ('f__days_from_start', 'min') - if agg_dict: - df_dur = df_time.groupby('interview__id').agg(**agg_dict).reset_index() - df_unit = df_unit.merge(df_dur, how='left', on='interview__id') + if not selected_features: + return df_unit + + # Legacy-like flow: compute all unit-time aggregations once, then keep selected columns. + df_dur = df_time.groupby('interview__id').agg( + f__total_duration=('f__total_duration', 'sum'), + f__total_elapse=('timestamp_local', lambda x: (x.max() - x.min()).total_seconds()), + f__time_changed=('f__time_changed', 'sum'), + f__days_from_start=('f__days_from_start', 'min'), + ).reset_index() + + df_dur = df_dur[['interview__id'] + selected_features] + df_unit = df_unit.merge(df_dur, how='left', on='interview__id') return df_unit @@ -230,18 +212,23 @@ def create_base_item_table(microdata: pd.DataFrame, paradata_active: pd.DataFram item_level_columns = ['interview__id', 'variable_name', 'roster_level'] allowed_features = ['f__' + k for k, v in parameters['features'].items() if v.get('use', False)] + sequence_features = ['f__previous_question', 'f__previous_answer', 'f__previous_roster', 'f__sequence_jump'] + time_features = ['f__answer_duration', 'f__comment_duration'] + calculate_sequence = any(f in allowed_features for f in sequence_features) + calculate_time = any(f in allowed_features for f in time_features) + # 1. Create Index Column on Microdata df_item = make_index_col(microdata.copy()) # 2. Select initial columns - initial_cols = ['value', "qtype", 'is_integer', 'qnr_seq', + columns = ['value', "qtype", 'is_integer', 'qnr_seq', 'n_answers', 'answer_sequence', 'cascade_from_question_id', 'is_filtered_combobox', 'index_col'] + item_level_columns # Intersect with available columns to avoid KeyErrors - cols_to_keep = [c for c in initial_cols if c in df_item.columns] - df_item = df_item[cols_to_keep] + # columns = [c for c in columns if c in df_item.columns] + df_item = df_item[columns].copy() # 3. Prepare Paradata for Merge # We want the *last* AnswerSet for each item @@ -254,7 +241,11 @@ def create_base_item_table(microdata: pd.DataFrame, paradata_active: pd.DataFram # if 'index_col' not in paradata_active.columns: # paradata_active = make_index_col(paradata_active.copy()) - data_to_merge = paradata_active[answer_set_mask].drop_duplicates(subset='index_col', keep='last') + data_to_merge = ( + paradata_active[answer_set_mask] + .dropna(subset=['index_col']) # drop rows without index_col + .drop_duplicates(subset='index_col', keep='last') + ) # 4. Merge df_item = df_item.merge(data_to_merge[available_para_cols + ['index_col']], how='left', on='index_col') @@ -264,14 +255,14 @@ def create_base_item_table(microdata: pd.DataFrame, paradata_active: pd.DataFram df_item = df_item[df_item['interviewing'] == True].copy() # 6. Add Sequence Features - # Pre-calculate sequence df - df_sequence = get_df_sequence(paradata_active) - df_item = add_sequence_features(df_item, df_sequence, allowed_features) + if calculate_sequence: + df_sequence = get_df_sequence(paradata_active) + df_item = add_sequence_features(df_item, df_sequence, allowed_features) # 7. Add Time Features - # Pre-calculate time df - df_time = get_df_time(paradata_active) - df_item = add_item_time_features(df_item, df_time, allowed_features, item_level_columns) + if calculate_time: + df_time = get_df_time(paradata_active) + df_item = add_item_time_features(df_item, df_time, allowed_features, item_level_columns) return df_item @@ -284,21 +275,26 @@ def create_base_unit_table(paradata_active: pd.DataFrame, parameters: dict) -> p allowed_features = ['f__' + k for k, v in parameters['features'].items() if v.get('use', False)] # 1. Initialize from paradata - cols = ['interview__id', 'responsible', 'survey_name', 'survey_version'] - cols = [c for c in cols if c in paradata_active.columns] + columns = ['interview__id', 'responsible', 'qnr', 'qnr_version'] + # columns = [c for c in columns if c in paradata_active.columns] - df_unit = paradata_active[cols].copy() + df_unit = paradata_active[columns].copy() df_unit.drop_duplicates(inplace=True) # Filter valid responsible df_unit = df_unit[(df_unit['responsible'] != '') & (df_unit['responsible'].notna())] - # 2. Add Pause Features - df_time = get_df_time(paradata_active) - df_unit = add_pause_features(df_unit, df_time, allowed_features) - - # 3. Add Unit Time Features - df_unit = add_unit_time_features(df_unit, df_time, allowed_features) + pause_features = ['f__pause_count', 'f__pause_duration', 'f__pause_list'] + unit_time_features = ['f__total_duration', 'f__total_elapse', 'f__days_from_start', 'f__time_changed'] + calculate_pause = any(f in allowed_features for f in pause_features) + calculate_unit_time = any(f in allowed_features for f in unit_time_features) + + if calculate_pause or calculate_unit_time: + df_time = get_df_time(paradata_active) + if calculate_pause: + df_unit = add_pause_features(df_unit, df_time, allowed_features) + if calculate_unit_time: + df_unit = add_unit_time_features(df_unit, df_time, allowed_features) return df_unit diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index fae4d0b..235a7cc 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -52,18 +52,18 @@ unit_features_base: legacy_microdata: type: pandas.ParquetDataset - filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/hies2024/latest/30_PROCESSED/microdata.parquet + filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/${globals:survey.name}/latest/30_PROCESSED/microdata.parquet # # # Equivalent to paradata_processed (output of process_paradata_node). # # The legacy pipeline saved this as paradata.parquet (not paradata_processed.parquet). legacy_paradata_processed: type: pandas.ParquetDataset - filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/hies2024/latest/30_PROCESSED/paradata.parquet + filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/${globals:survey.name}/latest/30_PROCESSED/paradata.parquet # # # Equivalent to paradata_active (output of filter_active_paradata_node). legacy_paradata_active: type: pandas.ParquetDataset - filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/hies2024/latest/30_PROCESSED/paradata_active.parquet + filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/${globals:survey.name}/latest/30_PROCESSED/paradata_active.parquet # Final Feature Tables (Input to Risk Scoring) item_features: diff --git a/rissk_kedro/conf/base/parameters.yml b/rissk_kedro/conf/base/parameters.yml index 8afd318..8bfc90b 100644 --- a/rissk_kedro/conf/base/parameters.yml +++ b/rissk_kedro/conf/base/parameters.yml @@ -20,25 +20,31 @@ processing: features: answer_hour_set: use: true - contamination: 0.11 + parameters: + contamination: 0.11 answer_changed: use: true - contamination: 0.1 + parameters: + contamination: 0.1 answer_removed: use: true - contamination: 0.1 + parameters: + contamination: 0.1 answer_selected: use: true - contamination: 0.1 + parameters: + contamination: 0.1 answer_share_selected: use: true answer_duration: use: true - contamination: 0.1 + parameters: + contamination: 0.1 first_decimal: use: true - contamination: 0.11 - frequency: 100 + parameters: + contamination: 0.11 + frequency: 100 first_digit: use: true last_digit: @@ -47,22 +53,27 @@ features: use: true sequence_jump: use: true - contamination: 0.1 + parameters: + contamination: 0.1 time_changed: use: true gps: use: true sub_features: [gps_latitude, gps_longitude, gps_accuracy] - contamination: 0.11 + parameters: + contamination: 0.11 pause_count: use: true - contamination: 0.11 + parameters: + contamination: 0.11 pause_duration: use: true - contamination: 0.11 + parameters: + contamination: 0.11 pause_list: use: true - contamination: 0.11 + parameters: + contamination: 0.11 comment_length: use: true comment_set: @@ -73,19 +84,24 @@ features: use: true number_answered: use: true - contamination: 0.11 + parameters: + contamination: 0.11 total_duration: use: true - contamination: 0.11 + parameters: + contamination: 0.11 total_elapse: use: true - contamination: 0.11 + parameters: + contamination: 0.11 single_question: use: true multi_option_question: use: true days_from_start: use: true + answer_position: + use: true string_length: use: true From 750c3f85d80ce1d8e41590ebd5f579785b8ca64e Mon Sep 17 00:00:00 2001 From: VJausovec Date: Mon, 9 Mar 2026 09:14:41 +0000 Subject: [PATCH 25/70] Refactor numeric feature handling for improved robustness; add missing answer removed feature; enhance GPS data extraction and validation logic. --- rissk/feature_processing_kedro.py | 292 ++++++++++++++++++------------ 1 file changed, 175 insertions(+), 117 deletions(-) diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index c1b7876..4177c86 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -1,5 +1,6 @@ import pandas as pd import numpy as np +import ast import logging logger = logging.getLogger(__name__) @@ -25,12 +26,37 @@ def make_index_col(df: pd.DataFrame) -> pd.DataFrame: def get_numeric_mask(df_item: pd.DataFrame) -> pd.Series: """Returns a boolean mask for valid numeric question rows, matching the legacy numeric_question_mask.""" - return ( + sentinel_mask = _is_missing_numeric_sentinel(df_item['value']) + mask = ( (df_item["qtype"] == 'NumericQuestion') & (df_item['value'] != '') & (~pd.isnull(df_item['value'])) & - (df_item['value'] != -999999999) + (~sentinel_mask) ) + return mask + + +def _is_missing_numeric_sentinel(values: pd.Series) -> pd.Series: + """Robustly detects the numeric missing-value sentinel across mixed object values.""" + return pd.to_numeric(values, errors='coerce').eq(-999999999) + +def _coerce_numeric_with_warning(df_item: pd.DataFrame, numeric_mask: pd.Series, feature_name: str) -> pd.Series: + """Coerce numeric values and warn about rows that cannot be parsed.""" + values = df_item.loc[numeric_mask, 'value'] + coerced = pd.to_numeric(values, errors='coerce') + + failed_mask = coerced.isna() & values.notna() & (values != '') + failed_count = int(failed_mask.sum()) + if failed_count > 0: + sample_bad_values = values[failed_mask].astype(str).drop_duplicates().head(10).tolist() + logger.warning( + "%s: failed to parse %d numeric value(s); coerced to NaN. Sample values: %s", + feature_name, + failed_count, + sample_bad_values, + ) + + return coerced def get_df_time(df_active_paradata: pd.DataFrame) -> pd.DataFrame: """Calculates time differences and durations from paradata.""" @@ -302,6 +328,7 @@ def create_base_unit_table(paradata_active: pd.DataFrame, parameters: dict) -> p # --- Feature Enrichment Functions (Item) --- def feat_string_length(df_item, **kwargs): + # f__string_length, length of string answer, if TextQuestions else empty pd.NA feature_name = 'f__string_length' mask = df_item["qtype"] == 'TextQuestion' df_item[feature_name] = pd.NA @@ -311,39 +338,46 @@ def feat_string_length(df_item, **kwargs): return df_item def feat_numeric_response(df_item, **kwargs): + # f__numeric_response, response, if NumericQuestions, else empty pd.NA feature_name = 'f__numeric_response' numeric_mask = get_numeric_mask(df_item) df_item[feature_name] = np.nan if numeric_mask.any(): - df_item.loc[numeric_mask, feature_name] = pd.to_numeric(df_item.loc[numeric_mask, 'value'], errors='coerce') + numeric_values = _coerce_numeric_with_warning(df_item, numeric_mask, feature_name) + df_item.loc[numeric_mask, feature_name] = numeric_values return df_item def feat_first_digit(df_item, **kwargs): + # f__first_digit, first digit of the response if numeric question else empty pd.NA feature_name = 'f__first_digit' numeric_mask = get_numeric_mask(df_item) df_item[feature_name] = pd.NA if numeric_mask.any(): + numeric_values = _coerce_numeric_with_warning(df_item, numeric_mask, feature_name) # Take absolute value, convert to string, extract first character - vals = pd.to_numeric(df_item.loc[numeric_mask, 'value']).abs().astype(str).str[0] + vals = numeric_values.abs().astype(str).str[0] df_item.loc[numeric_mask, feature_name] = pd.to_numeric(vals, errors='coerce').astype('Int64') return df_item def feat_last_digit(df_item, **kwargs): + # f__last_digit, modulus of 10 of the response if numeric question else empty pd.NA feature_name = 'f__last_digit' # Use the same mask as legacy: excludes empty, null, and -999999999 numeric_mask = get_numeric_mask(df_item) df_item[feature_name] = pd.NA if numeric_mask.any(): - # Cast to Int64 (nullable) matching legacy astype('int64'), then apply x >= 1 check - # Note: legacy checks x >= 1 (not abs(x) >= 1), so negative values correctly yield NA - vals = pd.to_numeric(df_item.loc[numeric_mask, 'value']).astype('Int64') + numeric_values = _coerce_numeric_with_warning(df_item, numeric_mask, feature_name) + # Legacy casts to int64 before extracting the last digit. + # Use truncation toward zero so decimals behave like integer casting. + vals = np.trunc(numeric_values).astype('Int64') # .where(condition) keeps values where True, sets False to NA df_item.loc[numeric_mask, feature_name] = (vals % 10).where(vals >= 1) - + return df_item def feat_first_decimal(df_item, **kwargs): + # f__first_decimal, first decimal digit if numeric question else empty pd.NA feature_name = 'f__first_decimal' # mask: not integer and not empty mask = (df_item['is_integer'] == False) & (df_item['value'] != '') @@ -351,53 +385,102 @@ def feat_first_decimal(df_item, **kwargs): if mask.any(): values = pd.to_numeric(df_item.loc[mask, 'value'], errors='coerce') - # floor(val * 100) % 100 ?? Legacy code: np.floor(values * 100) % 100 - # This actually gets the first two decimals? - # Example: 0.123 -> 12.3 -> 12. - # Wait, if I want first decimal digit (e.g. 1 in 0.123): floor(val * 10) % 10 - # Documentation says "first decimal digit". Code says *100 % 100. - # I will strictly follow legacy code logic. res = np.floor(values * 100) % 100 df_item.loc[mask, feature_name] = res.astype('Int64') + + # Match legacy: ensure the full feature column uses nullable integer dtype. + df_item[feature_name] = df_item[feature_name].astype('Int64') return df_item def feat_answer_position(df_item, **kwargs): - feature_name = 'f__answer_position' # in legacy it was f__rel_answer_position sometimes? code says f__answer_position - + # f__answer_position, relative position of the selected answer + # only questions with more than two answers + feature_name = 'f__answer_position' + # filters mask = ((df_item["qtype"] == 'SingleQuestion') & (df_item['n_answers'] > 2) & (df_item['is_filtered_combobox'] == False) - & (df_item['cascade_from_question_id'].isna())) - + & (df_item['cascade_from_question_id'].isna())) df_item[feature_name] = np.nan if mask.any(): # logic: index of value in answer_sequence / (n_answers - 1) - # answer_sequence is typically a list or string representation of list - # We need to iterate or apply - + # answer_sequence is a list-like or serialized as string. + def calc_pos(row): - val = row['value'] - seq = row['answer_sequence'] - n = row['n_answers'] - if isinstance(seq, list) and val in seq: - try: - idx = seq.index(val) - return round(idx / (n - 1), 3) - except: - return None - return None + try: + seq = ast.literal_eval(str(row['answer_sequence'])) + if not isinstance(seq, list) or len(seq) == 0: + return np.nan + + val = pd.to_numeric(row['value'], errors='coerce') + if pd.isna(val): + return np.nan + + # Align numeric types when seq is integer-coded. + if all(isinstance(x, (int, np.integer)) for x in seq) and float(val).is_integer(): + val = int(val) + + if val not in seq: + return np.nan + + n = row['n_answers'] + if pd.isna(n) or n <= 1: + return np.nan + + idx = seq.index(val) + return round(idx / (n - 1), 3) + except Exception: + return np.nan # Apply is slow but robust for list operations in cells df_item.loc[mask, feature_name] = df_item.loc[mask].apply(calc_pos, axis=1) return df_item +def feat_answer_removed(df_item, **kwargs): + # f__answer_removed, answers removed (by interviewer, or by system as a result of interviewer action). + # Matches legacy get_feature_item__answer_removed which uses self.df_paradata, but it appends the + # feature to the item table instead of returning a separate dataframe. + # (all events, role=1, interviewing=True — not limited to active events). + # The legacy method notes this feature may include items no longer in microdata. + feature_name = 'f__answer_removed' + paradata_full = kwargs.get('paradata_full') + if paradata_full is None: + return df_item + + removed_mask = ( + (paradata_full['event'] == 'AnswerRemoved') & + (paradata_full['role'] == 1) + ) + df_removed = paradata_full[removed_mask] + if df_removed.empty: + return df_item + + # Align grouping grain with legacy helper exactly. + group_cols = ['interview__id', 'responsible', 'variable_name', 'qnr_seq'] + if any(c not in df_removed.columns for c in group_cols) or any(c not in df_item.columns for c in group_cols): + logger.warning( + "%s: missing one or more legacy group columns (%s); skipping feature.", + feature_name, + group_cols, + ) + return df_item + + df_agg = df_removed.groupby(group_cols).agg( + f__answer_removed=('order', 'count') + ).reset_index() + + # Keep item table cardinality while assigning legacy-grain counts. + df_item = df_item.merge(df_agg[group_cols + [feature_name]], how='left', on=group_cols) + return df_item + + def feat_answer_changed(df_item, **kwargs): """ - ⚠️ Legacy bug fixed: the legacy code applied the yes_list change + Legacy bug fixed: the legacy code applied the yes_list change check and immediately overwrote it with the no_list check (two separate .loc assignments on the same mask), so yes_list changes were always ignored. This implementation combines both checks using a bitwise OR. @@ -411,16 +494,20 @@ def feat_answer_changed(df_item, **kwargs): item_level_columns = ['interview__id', 'variable_name', 'roster_level'] df_changed = paradata_active[paradata_active['event'] == 'AnswerSet'].copy() - if 'index_col' not in df_changed.columns: - df_changed = make_index_col(df_changed) - df_changed[feature_name] = False group_cols = [c for c in item_level_columns + ['index_col'] if c in df_changed.columns] has_yes_no = 'yes_no_view' in df_changed.columns # --- Case 1: TextListQuestion and MultyOptionsQuestion (without yes_no_view mode) --- - list_mask = (df_changed["qtype"] == 'TextListQuestion') - multi_mask = (df_changed['yes_no_view'] == False) if has_yes_no else pd.Series(False, index=df_changed.index) + # Keep flow aligned with legacy while scoping masks to their intended qtypes. + list_mask = ( + (df_changed["qtype"] == 'TextListQuestion') & + (df_changed['yes_no_view'] == False) + ) if has_yes_no else (df_changed["qtype"] == 'TextListQuestion') + multi_mask = ( + (df_changed["qtype"] == 'MultyOptionsQuestion') & + (df_changed['yes_no_view'] == False) + ) if has_yes_no else (df_changed["qtype"] == 'MultyOptionsQuestion') df_changed['answer_list'] = pd.NA df_changed.loc[list_mask, 'answer_list'] = df_changed.loc[list_mask, 'answer'].str.split('|') @@ -455,7 +542,7 @@ def feat_answer_changed(df_item, **kwargs): yesno_group_cols = [c for c in group_cols if c in df_filtered.columns] df_filtered['prev_yes_list'] = df_filtered.groupby(yesno_group_cols)['yes_list'].shift(fill_value=[]) df_filtered['prev_no_list'] = df_filtered.groupby(yesno_group_cols)['no_list'].shift(fill_value=[]) - # A change occurs if either yes or no selections changed + # A change occurs if either yes or no selections have been removed yes_changed = df_filtered.apply( lambda row: not set(row['prev_yes_list']).issubset(set(row['yes_list'])), axis=1) no_changed = df_filtered.apply( @@ -468,51 +555,47 @@ def feat_answer_changed(df_item, **kwargs): return df_item + def feat_answer_selected(df_item, **kwargs): + # f__answers_selected, number of answers selected in a multi-answer or list question, + # divided by n_answers to get share selected (only for unlinked questions). feature_name = 'f__answer_selected' - mask = df_item["qtype"].isin(['MultyOptionsQuestion']) - + # Select only MultyOptionsQuestion as legacy does. + multi_list_mask = df_item["qtype"].isin(['MultyOptionsQuestion']) + # Include only rows where n_answers can be parsed as a positive number to avoid division issues. + n_answers_num = pd.to_numeric(df_item.loc[multi_list_mask, 'n_answers'], errors='coerce') + valid_denominator_mask = n_answers_num > 0 + # Combine masks to ensure we only calculate for valid MultyOptionsQuestion rows with a positive n_answers. + mask = multi_list_mask & valid_denominator_mask + df_item[feature_name] = np.nan - # Value is list? Or string? Usually lists in newer pandas if parquet preserved it, - # but legacy often had strings. - # Assuming value is list if parquet - - if mask.any(): - def count_els(x): - if isinstance(x, list): return len(x) - if isinstance(x, str): return len(x.split('|')) # simple heuristic for pipe-sep + # Function to calculate the number of elements in a list or return nan + def count_elements_or_nan(val): + try: + val = ast.literal_eval(str(val)) + return len(val) + except (ValueError, SyntaxError, TypeError): return np.nan - - df_item.loc[mask, feature_name] = df_item.loc[mask, 'value'].apply(count_els) - # Ratio - df_item.loc[mask, feature_name] = df_item.loc[mask, feature_name] / df_item.loc[mask, 'n_answers'] - return df_item - -def feat_gps(df_item, **kwargs): - # Sets f__gps boolean flag plus f__gps_latitude, f__gps_longitude, f__gps_accuracy - feature_name = 'f__gps' - mask = df_item["qtype"] == 'GpsCoordinateQuestion' - df_item[feature_name] = False if mask.any(): - df_item.loc[mask, feature_name] = True - # Split value "lat,lon,acc,alt,timestamp_utc" - gps_data = df_item.loc[mask, 'value'].str.split(',', expand=True) - if gps_data.shape[1] >= 3: - df_item.loc[mask, 'f__gps_latitude'] = pd.to_numeric(gps_data[0], errors='coerce') - df_item.loc[mask, 'f__gps_longitude'] = pd.to_numeric(gps_data[1], errors='coerce') - df_item.loc[mask, 'f__gps_accuracy'] = pd.to_numeric(gps_data[2], errors='coerce') + df_item.loc[mask, feature_name] = df_item.loc[mask, 'value'].apply(count_elements_or_nan) + # f__share_selected, share between answers selected and available answers (only for unlinked questions). + # Linked questions will be implicitly excluded since they have nan n_answers after coercion. + df_item.loc[mask, feature_name] = ( + df_item.loc[mask, feature_name] / n_answers_num.loc[mask] + ) + return df_item def feat_comment_length(df_item, **kwargs): - """Total character length of all comments left on each item. - Matches legacy make_feature_item__comment_length which uses self.df_paradata - (all events, role=1, interviewing=True — not limited to active events). - """ + ## Total character length of all comments left on each item. feature_name = 'f__comment_length' paradata_full = kwargs.get('paradata_full') + + df_item[feature_name] = pd.NA + if paradata_full is None: return df_item @@ -524,22 +607,20 @@ def feat_comment_length(df_item, **kwargs): if df_comment.empty: return df_item - if 'index_col' not in df_comment.columns: - df_comment = make_index_col(df_comment) - df_comment[feature_name] = df_comment['answer'].str.len() df_agg = df_comment.groupby('index_col').agg(f__comment_length=(feature_name, 'sum')) df_item[feature_name] = df_item['index_col'].map(df_agg['f__comment_length']) + return df_item def feat_comment_set(df_item, **kwargs): - """Count of CommentSet events per item. - Matches legacy make_feature_item__comment_set which uses self.df_paradata - (all events, role=1, interviewing=True — not limited to active events). - """ + ## Count of CommentSet events per item. feature_name = 'f__comment_set' paradata_full = kwargs.get('paradata_full') + + df_item[feature_name] = pd.NA + if paradata_full is None: return df_item @@ -551,44 +632,25 @@ def feat_comment_set(df_item, **kwargs): if df_comment.empty: return df_item - if 'index_col' not in df_comment.columns: - df_comment = make_index_col(df_comment) - df_agg = df_comment.groupby('index_col').agg(f__comment_set=('order', 'count')) df_item[feature_name] = df_item['index_col'].map(df_agg['f__comment_set']) return df_item -def feat_answer_removed(df_item, **kwargs): - """Count of AnswerRemoved events per item. - Matches legacy get_feature_item__answer_removed which uses self.df_paradata - (all events, role=1, interviewing=True — not limited to active events). - The legacy method notes this feature may include items no longer in microdata. - """ - feature_name = 'f__answer_removed' - paradata_full = kwargs.get('paradata_full') - if paradata_full is None: - return df_item - - removed_mask = ( - (paradata_full['event'] == 'AnswerRemoved') & - (paradata_full['role'] == 1) - ) - df_removed = paradata_full[removed_mask] - if df_removed.empty: - return df_item - - # Legacy groups on interview__id + responsible + variable_name + qnr_seq; - # we merge on interview__id + variable_name which is safe since qnr_seq is 1:1 with variable_name. - df_agg = df_removed.groupby( - ['interview__id', 'variable_name'] - ).agg(f__answer_removed=('order', 'count')).reset_index() +def feat_gps(df_item, **kwargs): + # Sets f__gps boolean flag plus f__gps_latitude, f__gps_longitude, f__gps_accuracy + feature_name = 'f__gps' + mask = df_item["qtype"] == 'GpsCoordinateQuestion' + df_item[feature_name] = False + if mask.any(): + df_item.loc[mask, feature_name] = True + # Split value "lat,lon,acc,alt,timestamp_utc" + gps_data = df_item.loc[mask, 'value'].str.split(',', expand=True) + if gps_data.shape[1] >= 3: + df_item.loc[mask, 'f__gps_latitude'] = pd.to_numeric(gps_data[0], errors='coerce') + df_item.loc[mask, 'f__gps_longitude'] = pd.to_numeric(gps_data[1], errors='coerce') + df_item.loc[mask, 'f__gps_accuracy'] = pd.to_numeric(gps_data[2], errors='coerce') - df_item = df_item.merge( - df_agg[['interview__id', 'variable_name', feature_name]], - how='left', - on=['interview__id', 'variable_name'] - ) return df_item @@ -618,12 +680,6 @@ def enrich_item_features(df_item: pd.DataFrame, paradata_active: pd.DataFrame, p logger.info("Enriching item features...") allowed_features = parameters.get('features', {}) - # Ensure index_col in paradata for lookups - if 'index_col' not in paradata_active.columns: - paradata_active = make_index_col(paradata_active.copy()) - if 'index_col' not in paradata_full.columns: - paradata_full = make_index_col(paradata_full.copy()) - for feat_key, feat_cfg in allowed_features.items(): if feat_cfg.get('use', False): func = ITEM_FEATURE_MAP.get(feat_key) @@ -643,9 +699,10 @@ def feat_unit_number_answered(df_unit, item_features, **kwargs): feature_name = 'f__number_answered' # Match legacy make_feature_unit__number_answered: exclude null, -999999999, '##N/A##', # empty string, and Variable-type questions + sentinel_mask = _is_missing_numeric_sentinel(item_features['value']) mask = ( (~pd.isnull(item_features['value'])) & - (item_features['value'] != -999999999) & + (~sentinel_mask) & (item_features['value'] != '##N/A##') & (item_features['value'] != '') & (item_features['qtype'] != 'Variable') @@ -659,9 +716,10 @@ def feat_unit_number_answered(df_unit, item_features, **kwargs): def feat_unit_number_unanswered(df_unit, item_features, **kwargs): feature_name = 'f__number_unanswered' # Match legacy make_feature_unit__number_unanswered: -999999999 or '##N/A##', excluding Variable type + sentinel_mask = _is_missing_numeric_sentinel(item_features['value']) mask = ( ( - (item_features['value'] == -999999999) | + sentinel_mask | (item_features['value'] == '##N/A##') ) & (item_features['qtype'] != 'Variable') From 54e67caa1fae53e92a52a067ef0146208e195c3d Mon Sep 17 00:00:00 2001 From: VJausovec Date: Mon, 9 Mar 2026 13:56:09 +0000 Subject: [PATCH 26/70] Add Kedro pirpeline for scoring functions and detection algorithms; update catalog with new dataframes for scoring. --- rissk/detection_algorithms_kedro.py | 222 ++++++++++++ rissk/feature_processing_kedro.py | 7 +- rissk/item_processing_kedro.py | 534 ++++++++++++++++++++++++++++ rissk_kedro/conf/base/catalog.yml | 19 +- 4 files changed, 777 insertions(+), 5 deletions(-) create mode 100644 rissk/detection_algorithms_kedro.py create mode 100644 rissk/item_processing_kedro.py diff --git a/rissk/detection_algorithms_kedro.py b/rissk/detection_algorithms_kedro.py new file mode 100644 index 0000000..5707fef --- /dev/null +++ b/rissk/detection_algorithms_kedro.py @@ -0,0 +1,222 @@ +import pandas as pd +import numpy as np +from sklearn.preprocessing import LabelEncoder +from sklearn.ensemble import IsolationForest +from sklearn.preprocessing import OneHotEncoder +from sklearn.neighbors import NearestNeighbors +from scipy.spatial import distance_matrix +from scipy.stats import mstats +import math + + +def lat_lon_to_cartesian(lat, lon, R=6371): + """ + Convert lat, lon into 3D cartesian coordinates + + Parameters: + lat, lon: latitude and longitude in degrees + R: radius of the Earth (default is in kilometers) + + Returns: + x, y, z: 3D cartesian coordinates + """ + lat, lon = np.radians(lat), np.radians(lon) + x = R * np.cos(lat) * np.cos(lon) + y = R * np.cos(lat) * np.sin(lon) + z = R * np.sin(lat) + return x, y, z + + +def haversine(lat1, lon1, lat2, lon2): + """Calculate the great circle distance in kilometers between two points + on the earth (specified in decimal degrees)""" + # convert decimal degrees to radians + lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2]) + + # haversine formula + dlon = lon2 - lon1 + dlat = lat2 - lat1 + a = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2 + c = 2 * np.arcsin(np.sqrt(a)) + r = 6371 # Radius of earth in kilometers. Use 3956 for miles. Determines return value units. + return c * r + + +def check_distance(data, min_distance=20, lat='f__gps_latitude_GPS', lon='f___gps_longitude_GPS'): + df = data.copy() + df.reset_index(inplace=True) + df['is_too_close'] = False + + # Calculate the pairwise distances between all GPS coordinates + distances = distance_matrix(df[[lat, lon]].values, df[[lat, lon]].values) + distances = np.triu(distances) # Only keep the upper triangular part (excluding the diagonal) + + # Find pairs of coordinates that are closer than 20 meters + too_close_indices = np.argwhere(distances < min_distance) + + # Update 'is_too_close' column based on the pairs of coordinates that are too close + for i, j in too_close_indices: + if i != j: + df.at[i, 'is_too_close'] = True + df.at[j, 'is_too_close'] = True + return df + + +# Create a function to report the limits of the Z-Score +def z_score_limits(df, column_name): + """ returns the upper and lower limits of the Z-score """ + + # Compute the limits + upper_limit = df[column_name].mean() + 2.5 * df[column_name].std() + lower_limit = df[column_name].mean() - 2.5 * df[column_name].std() + + # Round and return the limits + upper_limit = round(upper_limit, 2) + lower_limit = round(lower_limit, 2) + + return lower_limit, upper_limit + + +def log_transformation_function(df, column_name): + """ Conduct a log transformation of a variable """ + # Replace the values with log-transformed values + df[[column_name]] = df[[column_name]].apply(np.log) + + +def fix_anomalies(data, col, threshold_percentage=0.3): + # If same column value is marked according to a distinct responsible both 1 and -1 than unset all anomalies + data['anomaly'] = data[col].replace(data.groupby(col)['anomaly'].max().to_dict()) + + # same if there is more than 30% of responsible that have that anomaly, set it to one + # Get all responsible that been marked anomalus for a specific value + grouped_df = data[data['anomaly'] == -1].groupby(col)['responsible'].nunique().reset_index(name='count') + # Compute the percentage + grouped_df['anomaly_percentage'] = grouped_df['count'] / data['responsible'].nunique() + update_anomalies_list = grouped_df[grouped_df['anomaly_percentage'] >= threshold_percentage][col].values + data.loc[data[col].isin(update_anomalies_list), 'anomaly'] = 1 + return data + + +def find_anomalies(df, index_col=['interview__id', 'roster_level', 'responsible'], overwrite_col=True, + contamination=0.1): + index_col = [col for col in index_col if col in df.columns] + df['index_col'] = df[index_col].apply(lambda row: '_'.join([str(row[col]) for col in index_col]), axis=1) + + for col in df.drop(columns=index_col + ['index_col']).columns: + # col = 'age_adult'#df_sequence_jump.columns[9] + data = df[~pd.isnull(df[col])].copy() + + onehot_encoder = OneHotEncoder() + responsible_encoded = onehot_encoder.fit_transform(data[['responsible']]).toarray() + # Extract the 'jump' and 'responsible_label' columns as features + # encoded_df = pd.DataFrame(responsible_encoded, columns=onehot_encoder.get_feature_names(['responsible'])) + encoded_df = pd.DataFrame(responsible_encoded, columns=onehot_encoder.get_feature_names_out(['responsible'])) + + # Combine the one-hot encoded DataFrame with the original DataFrame (excluding 'responsible') + encoded_df[col] = data[col].values + X = encoded_df.values.copy() # data[[col, 'responsible_label']].copy() + # Initialize and fit the Isolation Forest model + model = IsolationForest(contamination=contamination, + random_state=42) # Adjust contamination based on your anomaly threshold + # model = GaussianMixture(n_components=2, random_state=42) + # model = HBOS(n_bins=5) + # model = CBLOF(contamination=0.05, n_clusters=3) + model.fit(X) + # Predict the anomalies (1 for normal, -1 for anomalies) + anomaly_predictions = model.predict(X) + # anomaly_scores = model.decision_function(X) + # Add the anomaly predictions as a new column in the DataFrame + data['anomaly'] = anomaly_predictions + # data['anomaly_scores'] = anomaly_predictions + data = fix_anomalies(data, col, 0.6) + data['anomaly'] = data['anomaly'].replace({1: 0, -1: 1}) + if overwrite_col: + df[col] = df['index_col'].map(data.set_index('index_col')['anomaly']) + else: + df[col + '_anomaly'] = df['index_col'].map(data.set_index('index_col')['anomaly']) + df.drop(columns=['index_col'], inplace=True) + columns = df.drop(columns=index_col).columns + df = df.groupby('interview__id')[columns].sum() + df = df.reset_index() + return df + + +def find_outliers(df, index_col=['interview__id', 'roster_level', 'responsible']): + df['index_col'] = df[index_col].apply(lambda row: '_'.join([str(row[col]) for col in index_col]), axis=1) + + for col in df.drop(columns=index_col + ['index_col']).columns: + # col = 'age_adult'#df_sequence_jump.columns[9] + data = df[~pd.isnull(df[col])].copy() + + q_high = data[col].quantile(0.75) + q_low = data[col].quantile(0.25) + iqr = q_high - q_low + data['anomaly'] = 0 + data.loc[(data[col] < q_low - 1.5 * iqr) | (data[col] > q_high + 1.5 * iqr), 'anomaly'] = 1 + + df[col] = df['index_col'].map(data.set_index('index_col')['anomaly']) + + df.drop(columns=['index_col'], inplace=True) + return df + + +def find_consecutive_anomalies(df, index_col=['interview__id', 'roster_level', 'responsible']): + df['index_col'] = df[index_col].apply(lambda row: '_'.join([str(row[col]) for col in index_col]), axis=1) + + for col in df.drop(columns=index_col + ['index_col']).columns: + # col = 'age_adult'#df_sequence_jump.columns[9] + data = df[~pd.isnull(df[col])].copy() + + q_high = data[col].quantile(0.75) + q_low = data[col].quantile(0.25) + iqr = q_high - q_low + data['anomaly'] = 0 + data.loc[(data[col] < q_low - 1.5 * iqr) | (data[col] > q_high + 1.5 * iqr), 'anomaly'] = 1 + + q_high = data[data['anomaly'] == 0][col].quantile(0.75) + q_low = data[data['anomaly'] == 0][col].quantile(0.25) + iqr = q_high - q_low + data.loc[(data[col] < q_low - 1.5 * iqr), 'anomaly'] = 1 + + # data = fix_anomalies(data, col, 0.6) + + df[col] = df['index_col'].map(data.set_index('index_col')['anomaly']) + + df.drop(columns=['index_col'], inplace=True) + return df + + +def detect_duration_outliers_by_magnitude(data, column_name): + data = data[~pd.isnull(data[column_name])].copy() + # data = data.copy() + # data[column_name].fillna(0, inplace=True) + + # Create bins based on the order of magnitude + orders_of_magnitude = np.floor(np.log10(data[column_name] + 1)) + data['magnitude_order'] = orders_of_magnitude + + # Calculate the 10th last percentile range for the counts + # Let's assume that 1 order of magnitude is still not an outlier + upper_bound = max(1, data['magnitude_order'].quantile(0.9)) # Q3 + 1.5 * IQR + + # Mark the corresponding values as outliers + data['is_extreme_outlier'] = False + data.loc[(data['magnitude_order'] > upper_bound), 'is_extreme_outlier'] = True + + q1 = data[data['is_extreme_outlier'] == False][column_name].quantile(0.25) + q3 = data[data['is_extreme_outlier'] == False][column_name].quantile(0.75) + iqr = q3 - q1 + lower_bound = q1 - 1.5 * iqr + upper_bound = q3 + 1.5 * iqr + # Mark the corresponding values as outliers + data['is_outlier'] = False + # data.loc[(data[column_name] < lower_bound) | (data[column_name] > upper_bound), 'is_outlier'] = True + data.loc[(data[column_name] > upper_bound), 'is_outlier'] = True + # Remove the temporary magnitude_order column + data.drop(columns=['magnitude_order'], inplace=True) + max_non_outlier = data[data['is_outlier'] == False][column_name].max() + # winsorized_data = mstats.winsorize(data, limits=[0.0, max_non_outlier]) + data.loc[data['is_outlier'] == True, column_name] = max_non_outlier + return data + + diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index 4177c86..4b0bd82 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -731,13 +731,12 @@ def feat_unit_number_unanswered(df_unit, item_features, **kwargs): return df_unit def feat_unit_translation_positions(df_unit, item_features, **kwargs): - """Relative positions of TranslationSwitched events within each interview. - Matches legacy make_feature_unit__translation_positions which uses self.df_paradata. + # Relative positions of TranslationSwitched events within each interview. + # Returns a list of relative positions per interview. - Returns a list of relative positions (0..1) per interview. - """ feature_name = 'f__translation_positions' paradata_full = kwargs.get('paradata_full') + df_unit[feature_name] = np.nan if paradata_full is None: return df_unit diff --git a/rissk/item_processing_kedro.py b/rissk/item_processing_kedro.py new file mode 100644 index 0000000..2548b36 --- /dev/null +++ b/rissk/item_processing_kedro.py @@ -0,0 +1,534 @@ +import pandas as pd +import numpy as np +import logging +from typing import List, Dict, Any, Tuple + +logger = logging.getLogger(__name__) + +def rename_feature(feature_name: str, starting_string: str = 'f', new_string: str = 's') -> str: + """Rename feature correctly mapping to score (f__ -> s__).""" + starting_string = starting_string + '__' + new_string = new_string + '__' + if feature_name.startswith(starting_string): + return feature_name.replace(starting_string, new_string) + return feature_name + +def get_contamination_parameter(config_features: dict, feature_name: str, automatic_contamination: bool = False, method: str = 'medfilt', random_state: int = 42) -> float: + """Fetch contamination parameter from Kedro parameters/config features.""" + f_name = feature_name.replace('f__', '') + contamination = config_features.get(f_name, {}).get('parameters', {}).get('contamination') + if contamination is None or contamination == 'auto' or automatic_contamination is True: + # TODO: Add automatic detection from legacy ItemFeatureProcessing if required. + return 0.1 + else: + return float(contamination) + +def filter_variable_name_by_frequency(df: pd.DataFrame, feature_name: str, frequency: int = 100, min_unique_values: int = 3) -> List[str]: + """Filter variables by frequency and unique values.""" + if feature_name not in df.columns: + return [] + valid_data = df[~pd.isnull(df[feature_name])] + grouped_df = valid_data.groupby('variable_name')[feature_name].agg(['count', 'nunique']) + valid_variables = grouped_df[(grouped_df['count'] >= frequency) & (grouped_df['nunique'] >= min_unique_values)].index + return valid_variables.tolist() + +def filter_columns(data: pd.DataFrame, index_col: List[str], threshold: int = 100) -> Tuple[List[str], List[str]]: + """Determine columns to keep/drop based on threshold (placeholder refactor)""" + # Count non-null values for each column + non_null_counts = data.drop(columns=index_col, errors='ignore').count() + # Filter columns to keep + keep_columns = non_null_counts[non_null_counts >= threshold].index.tolist() + drop_columns = non_null_counts[non_null_counts < threshold].index.tolist() + return index_col + keep_columns, drop_columns + +def get_clean_pivot_table(df_item: pd.DataFrame, feature_name: str, remove_low_freq_col: bool = True, filter_conditions=None, threshold: int = 100) -> Tuple[pd.DataFrame, List[str]]: + """Create a pivot table handling columns and filtering.""" + index_col = ['interview__id', 'roster_level', 'responsible'] + data = df_item.copy() + + if filter_conditions is not None: # Not yet strictly typed since condition type unknown + pass # To fully mimic we'd apply filter + + data = pd.pivot_table(data=data, index=index_col, columns='variable_name', + values=feature_name, fill_value=np.NAN) + data = data.reset_index() + + if data.columns.nlevels > 1: + pass # In case of multi index columns flatten, handled differently? + + index_col = [col for col in index_col if col in data.columns] + keep_columns, drop_columns = filter_columns(data, index_col, threshold=threshold) + + if remove_low_freq_col: + data = data[keep_columns] + + return data, index_col + +# --- SCORING FUNCTIONS BEGIN --- +# (To be filled out next, mapping make_score__*) + +from pyod.models.ecod import ECOD +from rissk.detection_algorithms_kedro import find_anomalies + +def calculate_answer_hour_set_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + feature_name = 'f__answer_hour_set' + score_name = rename_feature(feature_name) + df = df_item[~pd.isnull(df_item[feature_name])].copy() + + if df.empty: + df_item[score_name] = np.nan + return df_item + + sorted_hours = df[feature_name].value_counts().index + hour_to_rank = {hour: rank for rank, hour in enumerate(sorted_hours)} + df['frequency'] = df[feature_name].map(hour_to_rank) + + contamination_param = parameters.get('features', {}) + contamination = get_contamination_parameter(contamination_param, feature_name) + + model = ECOD(contamination=contamination) + model.fit(df[[feature_name]]) + df[score_name] = model.predict(df[[feature_name]]) + + df.loc[df['frequency'] <= df[df[score_name] == 0]['frequency'].min(), score_name] = 0 + df.drop(columns=['frequency'], inplace=True) + + # Merge back to original dataframe + df_out = df_item.copy() + df_out[score_name] = df_out.index.map(df[score_name]) + return df_out + +def calculate_sequence_jump_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + feature_name = 'f__sequence_jump' + score_name = rename_feature(feature_name) + df = df_item[~pd.isnull(df_item[feature_name])].copy() + valid_variables = filter_variable_name_by_frequency(df, feature_name, frequency=100, min_unique_values=3) + df[score_name] = 0 + contamination = get_contamination_parameter(parameters.get('features', {}), feature_name) + + for var in valid_variables: + var_mask = df['variable_name'] == var + if df[var_mask].shape[0] > 0: + # Note: find_anomalies historically operates grouped by interview, so this requires + # matching the output to the df index + anomaly_df = find_anomalies(df[var_mask].copy(), contamination=contamination) + # In purely functional kedro, typically we map back via index_col or similar + if score_name in anomaly_df.columns: + pass # Mapping logic goes here based on specific algorithm outputs + + df_out = df_item.copy() + return df_out + +from pyod.models.cof import COF + +def calculate_first_decimal_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + feature_name = 'f__first_decimal' + score_name = rename_feature(feature_name) + df = df_item.copy() + + if feature_name not in df.columns or df[feature_name].dropna().empty: + df[score_name] = np.nan + return df + + valid_data = df[~pd.isnull(df[feature_name])] + valid_variables = filter_variable_name_by_frequency(valid_data, feature_name, frequency=100, min_unique_values=3) + df[score_name] = np.nan + df.loc[~pd.isnull(df[feature_name]), score_name] = 0 + contamination = get_contamination_parameter(parameters.get('features', {}), feature_name, method='medfilt', random_state=42) + + for var in valid_variables: + mask = (df['variable_name'] == var) & (~pd.isnull(df[feature_name])) + if mask.sum() > 0: + model = COF(contamination=contamination) + model.fit(df.loc[mask, [feature_name]]) + df.loc[mask, score_name] = model.predict(df.loc[mask, [feature_name]]) + + return df + +def calculate_answer_changed_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + feature_name = 'f__answer_changed' + score_name = rename_feature(feature_name) + df = df_item.copy() + + if feature_name not in df.columns or df[feature_name].dropna().empty: + df[score_name] = np.nan + return df + + valid_data = df[~pd.isnull(df[feature_name])] + valid_variables = filter_variable_name_by_frequency(valid_data, feature_name, frequency=100, min_unique_values=1) + df[score_name] = np.nan + df.loc[~pd.isnull(df[feature_name]), score_name] = 0 + contamination = get_contamination_parameter(parameters.get('features', {}), feature_name, method='medfilt', random_state=42) + + for var in valid_variables: + mask = (df['variable_name'] == var) & (~pd.isnull(df[feature_name])) + if mask.sum() > 0: + model = ECOD(contamination=contamination) + model.fit(df.loc[mask, [feature_name]]) + df.loc[mask, score_name] = model.predict(df.loc[mask, [feature_name]]) + + return df + +def calculate_answer_removed_score(df_item: pd.DataFrame, df_item_removed: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + feature_name = 'f__answer_removed' + score_name = rename_feature(feature_name) + + # In legacy, this feature comes from df_item_removed instead of direct df_item mapped. + # Therefore, we pass in df_item_removed (grouped paradata) as a distinct input + df = df_item_removed.copy() + + if df.empty or feature_name not in df.columns: + df_item_out = df_item.copy() + df_item_out[score_name] = np.nan + return df_item_out + + valid_variables = filter_variable_name_by_frequency(df, feature_name, frequency=100, min_unique_values=1) + df[score_name] = 0 + contamination = get_contamination_parameter(parameters.get('features', {}), feature_name, method='medfilt', random_state=42) + + for var in valid_variables: + mask = (df['variable_name'] == var) + if mask.sum() > 0: + model = ECOD(contamination=contamination) + model.fit(df.loc[mask, [feature_name]]) + df.loc[mask, score_name] = model.predict(df.loc[mask, [feature_name]]) + + # Typically this must be mapped back to df_item or remain as its own independent output. + # For now we'll integrate it by merging on interview__id or equivalent context, + # but functionally we should return either the updated df or merge it onto df_item. + return df + +from scipy.spatial import cKDTree +from pyod.models.lof import LOF + +# Attempting to import legacy stats_utils safely for the math functions +try: + from rissk.utils.stats_utils import ( + calculate_entropy, + calculate_list_entropy, + filter_variables_by_magnitude, + apply_benford_tests + ) +except ImportError: + pass + +try: + from rissk.detection_algorithms_kedro import lat_lon_to_cartesian +except ImportError: + pass + +def calculate_answer_position_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + feature_name = 'f__answer_position' + score_name = rename_feature(feature_name) + df = df_item.copy() + + if feature_name not in df.columns or df[feature_name].dropna().empty: + df[score_name] = np.nan + return df + + valid_data = df[~pd.isnull(df[feature_name])] + valid_variables = filter_variable_name_by_frequency(valid_data, feature_name, frequency=100, min_unique_values=3) + df[score_name] = 0 + + for var in valid_variables: + mask = (df['variable_name'] == var) + if mask.sum() > 0: + unique_values = df[mask][feature_name].nunique() + try: + entropy_df = df[mask].groupby('responsible')[feature_name].apply( + calculate_entropy, unique_values=unique_values, min_record_sample=10 + ).reset_index() + except NameError: + continue # if calculate_entropy not found + + entropy_df = entropy_df[~pd.isnull(entropy_df[feature_name])] + + if entropy_df.shape[0] > 0: + entropy_df.sort_values(feature_name, inplace=True, ascending=False) + median_value = entropy_df[feature_name].median() + entropy_df[score_name] = entropy_df[feature_name].apply( + lambda x: 1 if x < median_value - 0.5 * median_value else 0) + + # Apply map safely + responsible_map = entropy_df.set_index('responsible')[score_name].to_dict() + df.loc[mask, score_name] = df.loc[mask, 'responsible'].map(responsible_map).fillna(0) + return df + +def calculate_answer_selected_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + feature_name = 'f__answer_selected' + score_name = rename_feature(feature_name) + df = df_item.copy() + + if feature_name not in df.columns or df[feature_name].dropna().empty: + df[score_name + '_lower'] = np.nan + df[score_name + '_upper'] = np.nan + return df + + valid_data = df[~pd.isnull(df[feature_name])] + valid_variables = filter_variable_name_by_frequency(valid_data, feature_name, frequency=100, min_unique_values=3) + + score_name1 = score_name + '_lower' + score_name2 = score_name + '_upper' + df[score_name] = 0 + + contamination = get_contamination_parameter(parameters.get('features', {}), feature_name, method='medfilt', random_state=42) + + for var in valid_variables: + mask = (df['variable_name'] == var) & (~pd.isnull(df[feature_name])) + if mask.sum() > 0: + model = ECOD(contamination=contamination) + model.fit(df.loc[mask, [feature_name]]) + + df.loc[mask, score_name] = model.predict(df.loc[mask, [feature_name]]) + + non_anomalies = df.loc[mask & (df[score_name] == 0), feature_name] + if not non_anomalies.empty: + min_good_value = non_anomalies.min() + max_good_value = non_anomalies.max() + + df.loc[mask, score_name1] = 0 + df.loc[mask, score_name2] = 0 + + df.loc[mask & (df[feature_name] < min_good_value), score_name1] = 1 + df.loc[mask & (df[feature_name] > max_good_value), score_name2] = 1 + + df.drop(columns=[score_name], errors='ignore', inplace=True) + return df + +def calculate_answer_duration_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + feature_name = 'f__answer_duration' + score_name = rename_feature(feature_name) + df = df_item.copy() + + if feature_name not in df.columns or df[feature_name].dropna().empty: + df[score_name + '_lower'] = np.nan + df[score_name + '_upper'] = np.nan + return df + + valid_data = df[~pd.isnull(df[feature_name])] + valid_variables = filter_variable_name_by_frequency(valid_data, feature_name, frequency=100, min_unique_values=3) + + score_name1 = score_name + '_lower' + score_name2 = score_name + '_upper' + df[score_name1] = 0 + df[score_name2] = 0 + df[score_name] = 0 + + contamination = get_contamination_parameter(parameters.get('features', {}), feature_name, method='medfilt', random_state=42) + + for var in valid_variables: + mask = (df['variable_name'] == var) & (~pd.isnull(df[feature_name])) + if mask.sum() > 0: + model = ECOD(contamination=contamination) + model.fit(df.loc[mask, [feature_name]]) + df.loc[mask, score_name] = model.predict(df.loc[mask, [feature_name]]) + + non_anomalies = df.loc[mask & (df[score_name] == 0), feature_name] + if not non_anomalies.empty: + min_good_value = non_anomalies.min() + max_good_value = non_anomalies.max() + + df.loc[mask, score_name1] = 0 + df.loc[mask, score_name2] = 0 + + df.loc[mask & (df[feature_name] < min_good_value), score_name1] = 1 + df.loc[mask & (df[feature_name] > max_good_value), score_name2] = 1 + + df.drop(columns=[score_name], errors='ignore', inplace=True) + return df + +def calculate_single_question_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + feature_name = 'f__single_question' + score_name = rename_feature(feature_name) + df = df_item.copy() + + if 'qtype' not in df.columns or 'n_answers' not in df.columns or 'value' not in df.columns: + df[score_name] = np.nan + return df + + # Mask specific for single questions without filter rules bypassing cascades + single_question_mask = ( + (df["qtype"] == 'SingleQuestion') & + (df['n_answers'] > 1) & + (df.get('is_filtered_combobox', False) == False) & + (pd.isnull(df.get('cascade_from_question_id', np.nan))) + ) + + df[score_name] = 0 + valid_data = df[single_question_mask] + if valid_data.empty: return df + + variables = filter_variable_name_by_frequency(valid_data, 'value', frequency=100, min_unique_values=3) + + for var in variables: + mask = (df['variable_name'] == var) & single_question_mask + if mask.sum() > 0: + unique_values = df.loc[mask, 'value'].nunique() + try: + entropy_df = df[mask].groupby('responsible')['value'].apply( + calculate_entropy, unique_values=unique_values + ).reset_index() + except NameError: + continue + + entropy_df = entropy_df[~pd.isnull(entropy_df['value'])] + + if entropy_df.shape[0] > 0: + entropy_df.sort_values('value', inplace=True, ascending=False) + median_value = entropy_df['value'].median() + entropy_df[score_name] = entropy_df['value'].apply( + lambda x: 1 if x < median_value - 0.5 * median_value else 0) + + responsible_map = entropy_df.set_index('responsible')[score_name].to_dict() + df.loc[mask, score_name] = df.loc[mask, 'responsible'].map(responsible_map).fillna(0) + + return df + +def calculate_multi_option_question_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + feature_name = 'f__multi_option_question' + score_name = rename_feature(feature_name) + df = df_item.copy() + + if 'qtype' not in df.columns or 'value' not in df.columns: + df[score_name] = np.nan + return df + + multi_question_mask = (df["qtype"] == 'MultyOptionsQuestion') + valid_data = df[multi_question_mask] + + df[score_name] = 0 + if valid_data.empty: return df + + # Filter variables safely via counts + val_counts = valid_data['variable_name'].value_counts() + variables = val_counts[val_counts >= 100].index + + for var in variables: + mask = (df['variable_name'] == var) & multi_question_mask + if mask.sum() > 0: + # Need safely explode nested lists in values + exploded_vals = df.loc[mask, 'value'].explode() + unique_values = len([v for v in exploded_vals.unique() if v != '##N/A##']) + try: + entropy_df = df[mask].groupby('responsible')['value'].apply( + calculate_list_entropy, unique_values=unique_values, min_record_sample=5 + ).reset_index() + except NameError: + continue + + entropy_df = entropy_df[~pd.isnull(entropy_df['value'])] + + if entropy_df.shape[0] > 0: + entropy_df.sort_values('value', inplace=True, ascending=False) + median_value = entropy_df['value'].median() + entropy_df[score_name] = entropy_df['value'].apply( + lambda x: 1 if x < median_value - 0.5 * median_value else 0) + + responsible_map = entropy_df.set_index('responsible')[score_name].to_dict() + df.loc[mask, score_name] = df.loc[mask, 'responsible'].map(responsible_map).fillna(0) + + return df + +def calculate_first_digit_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + feature_name = 'f__numeric_response' + score_name = 's__first_digit' + df = df_item.copy() + + if feature_name not in df.columns or df[feature_name].dropna().empty: + df[score_name] = np.nan + return df + + valid_data = df[~pd.isnull(df[feature_name])] + valid_variables = filter_variable_name_by_frequency(valid_data, feature_name, frequency=100, min_unique_values=3) + df[score_name] = 0 + + try: + valid_variables = filter_variables_by_magnitude(valid_data, feature_name, valid_variables, min_order_of_magnitude=3) + benford_jensen_df = apply_benford_tests( + valid_data, valid_variables, 'responsible', feature_name, apply_first_digit=True, minimum_sample=50 + ) + except NameError: + return df # dependencies missing + + if not benford_jensen_df.empty: + variable_list = benford_jensen_df['variable_name'].unique() + for var in variable_list: + bj_mask = (benford_jensen_df['variable_name'] == var) & (~pd.isnull(benford_jensen_df[feature_name])) + bj_df = benford_jensen_df[bj_mask].copy() + if bj_df.shape[0] > 0: + bj_df.sort_values(feature_name, inplace=True, ascending=True) + median_value = bj_df[feature_name].median() + bj_df[score_name] = bj_df[feature_name].apply( + lambda x: 1 if x > median_value + 0.5 * median_value else 0) + + mask = (df['variable_name'] == var) + responsible_map = bj_df.set_index('responsible')[score_name].to_dict() + df.loc[mask, score_name] = df.loc[mask, 'responsible'].map(responsible_map).fillna(0) + + return df + +def calculate_gps_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + feature_name = 'f__gps' + score_name = rename_feature(feature_name) + df = df_item.copy() + + if feature_name not in df.columns or df[feature_name].dropna().empty: + df[score_name] = np.nan + return df + + # Prepare DataFrame + df[score_name] = 0 + df['latitude'] = np.nan + df['longitude'] = np.nan + df['valid_gps'] = False + + # Extract valid coordinates silently + def extract_coords(val): + try: + val_list = eval(val) + if isinstance(val_list, list) and len(val_list) >= 2: + lat, lon = float(val_list[0]), float(val_list[1]) + return lat, lon, True + except: + return np.nan, np.nan, False + return np.nan, np.nan, False + + mask_valid = ~pd.isnull(df[feature_name]) + if mask_valid.sum() == 0: + return df + + coords = df.loc[mask_valid, feature_name].apply(extract_coords) + df.loc[mask_valid, 'latitude'] = coords.apply(lambda x: x[0]) + df.loc[mask_valid, 'longitude'] = coords.apply(lambda x: x[1]) + df.loc[mask_valid, 'valid_gps'] = coords.apply(lambda x: x[2]) + + valid_data = df[df['valid_gps']] + if valid_data.empty: return df + + # We need to project to cartesian map + valid_variables = filter_variable_name_by_frequency(valid_data, feature_name, frequency=100, min_unique_values=3) + + for var in valid_variables: + mask = (df['variable_name'] == var) & df['valid_gps'] + if mask.sum() > 0: + latitudes = df.loc[mask, 'latitude'].values + longitudes = df.loc[mask, 'longitude'].values + + try: + # Need detection algorithms functions here + x, y, z = lat_lon_to_cartesian(latitudes, longitudes) + coordinates = np.column_stack((x, y, z)) + tree = cKDTree(coordinates) + k = 3 + if len(coordinates) > k: + distances, indices = tree.query(coordinates, k=k) + df.loc[mask, score_name] = distances[:, -1] + else: + df.loc[mask, score_name] = 0.0 + + except NameError: + # lat_lon_to_cartesian not found + continue + + return df + diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index 235a7cc..9bbfd9d 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -72,4 +72,21 @@ item_features: unit_features: type: pandas.ParquetDataset - filepath: data/${globals:survey.name}/latest/30_PROCESSED/unit_features.parquet \ No newline at end of file + filepath: data/${globals:survey.name}/latest/30_PROCESSED/unit_features.parquet +# === PRE-SCORING DataFrames === +item_features_removed: + type: pandas.ParquetDataset + filepath: data/${globals:survey.name}/latest/20_INTERIM/item_features_removed.parquet + +# === SCORING DataFrames === +item_scores: + type: pandas.ParquetDataset + filepath: data/${globals:survey.name}/latest/40_SCORED/item_scores.parquet + +unit_risk_scores: + type: pandas.ParquetDataset + filepath: data/${globals:survey.name}/latest/40_SCORED/unit_risk_scores.parquet + +responsible_scores: + type: pandas.ParquetDataset + filepath: data/${globals:survey.name}/latest/40_SCORED/responsible_scores.parquet From aa5a7cf79f66c4513df592e0a702857b40e5d80d Mon Sep 17 00:00:00 2001 From: VJausovec Date: Mon, 9 Mar 2026 13:57:30 +0000 Subject: [PATCH 27/70] Implement risk scoring pipeline with item and unit score calculations; add new nodes for scoring logic and restructure pipeline definition. --- rissk/unit_processing_kedro.py | 239 +++++++++++++ .../pipelines/risk_scoring/__init__.py | 5 - .../pipelines/risk_scoring/nodes.py | 316 ------------------ .../pipelines/risk_scoring/pipeline.py | 25 -- .../pipelines/rissk_scoring/__init__.py | 9 + .../pipelines/rissk_scoring/nodes.py | 85 +++++ .../pipelines/rissk_scoring/pipeline.py | 24 ++ 7 files changed, 357 insertions(+), 346 deletions(-) create mode 100644 rissk/unit_processing_kedro.py delete mode 100644 rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/__init__.py delete mode 100644 rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/nodes.py delete mode 100644 rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/pipeline.py create mode 100644 rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/__init__.py create mode 100644 rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py create mode 100644 rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py diff --git a/rissk/unit_processing_kedro.py b/rissk/unit_processing_kedro.py new file mode 100644 index 0000000..d478707 --- /dev/null +++ b/rissk/unit_processing_kedro.py @@ -0,0 +1,239 @@ +import pandas as pd +import numpy as np +import logging +from typing import List, Dict, Any, Tuple +from pyod.models.pca import PCA +from pyod.models.iforest import IForest +from sklearn.preprocessing import StandardScaler, MinMaxScaler, normalize + +logger = logging.getLogger(__name__) + +def windsorize_95_percentile(df: pd.DataFrame) -> pd.DataFrame: + """ + Windsorize values in all columns of the DataFrame that are above the 95th percentile. + + Args: + - df (pd.DataFrame): Input DataFrame + + Returns: + - pd.DataFrame: Windsorized DataFrame + """ + df_out = df.copy() + for column in df_out.columns: + if pd.api.types.is_numeric_dtype(df_out[column]): + # Calculate the 95th percentile for the column + percentile_95 = df_out[column].quantile(0.95) + + # Set values above the 95th percentile to the value at the 95th percentile + df_out[column] = df_out[column].apply(lambda x: min(x, percentile_95)) + + return df_out + +# -- Note: Aggregation methods map closely to make_score_unit__* in legacy -- + +def aggregate_unit_score_mean(df_item_scores: pd.DataFrame, df_unit: pd.DataFrame, score_source_name: str, score_target_name: str) -> pd.DataFrame: + """Helper purely taking interview__id mapped scores and doing mean aggregation into unit df.""" + if score_source_name not in df_item_scores.columns: + return df_unit + + data = df_item_scores.groupby(['interview__id']).agg({score_source_name: 'mean'}) + df_out = df_unit.copy() + df_out[score_target_name] = df_out['interview__id'].map(data[score_source_name]) + df_out[score_target_name] = df_out[score_target_name].fillna(0) + return df_out + +def calculate_global_score(df_unit_scores: pd.DataFrame, df_resp_scores: pd.DataFrame, score_columns: List[str], combine_resp_score: bool = True, restricted_columns: List[str] = None) -> pd.DataFrame: + """ + Calculate the global unit risk score. + Maps legacy `make_global_score` inside UnitDataProcessing. + """ + df_unit = df_unit_scores.copy() + df_unit['unit_risk_score'] = 0 + scaler = StandardScaler() + + # Select columns + columns = score_columns if score_columns else [] + if restricted_columns is not None: + columns = [col for col in columns if col not in restricted_columns] + + available_cols = [c for c in columns if c in df_unit.columns] + + if not available_cols: + logger.warning("No score columns available to compute global risk score.") + return df_unit + + df = df_unit[available_cols].copy() + df = pd.DataFrame(scaler.fit_transform(df), columns=available_cols) + + model = IForest(random_state=42) + model.fit(df.fillna(0)) + + scaler = MinMaxScaler(feature_range=(0, 100)) + df_unit['unit_risk_score'] = model.decision_scores_ + + # Windsorize + df_unit['unit_risk_score'] = windsorize_95_percentile(df_unit[['unit_risk_score']])['unit_risk_score'] + + # Scale to 0-100 + df_unit['unit_risk_score'] = scaler.fit_transform(df_unit[['unit_risk_score']]) + + # Merge unit score with responsible score + if combine_resp_score and 'responsible' in df_unit.columns and df_resp_scores is not None and 'responsible_score' in df_resp_scores.columns: + df_resp_map = df_resp_scores.set_index('responsible')['responsible_score'].to_dict() + df_unit['responsible_score'] = df_unit['responsible'].map(df_resp_map).fillna(0) + df_unit['unit_risk_score'] = df_unit['unit_risk_score'] + df_unit['responsible_score'] * 100 + + return df_unit + +def aggregate_item_to_unit_scores(df_unit: pd.DataFrame, df_item_scores: pd.DataFrame) -> pd.DataFrame: + """Aggregates item-level scores up to the unit (interview) level.""" + df_out = df_unit.copy() + + # 1. Simple mean aggregations + mean_scores = [ + 's__answer_hour_set', 's__answer_removed', 's__answer_changed', + 's__first_decimal', 's__sequence_jump' + ] + for score in mean_scores: + if score in df_item_scores.columns: + data = df_item_scores.groupby('interview__id')[score].mean() + df_out[score] = df_out['interview__id'].map(data).fillna(0) + + # 2. Lower/Upper mean aggregations + lower_upper_scores = [ + 's__answer_selected', 's__answer_duration' + ] + for score_base in lower_upper_scores: + for suffix in ['_lower', '_upper']: + score = score_base + suffix + if score in df_item_scores.columns: + data = df_item_scores.groupby('interview__id')[score].mean() + df_out[score] = df_out['interview__id'].map(data).fillna(0) + + # 3. GPS specifics (if gps scores exist) + gps_features = ['s__gps_proximity_counts', 's__gps_outlier', 's__gps_extreme_outlier', 's__gps'] + for score in gps_features: + if score in df_item_scores.columns: + data = df_item_scores.groupby('interview__id')[score].sum() + df_out[score] = df_out['interview__id'].map(data).fillna(0) + + return df_out + +def calculate_unit_level_scores(df_unit: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + """Calculate scores that are purely derived from unit-level features.""" + from pyod.models.ecod import ECOD + df = df_unit.copy() + + if 'f__time_changed' in df.columns: + df['s__time_changed'] = round(df['f__time_changed'].abs() / 600) + + if 'f__total_duration' in df.columns: + df['s__total_duration'] = round(df['f__total_duration'] / 300) + + if 'f__days_from_start' in df.columns: + df['s__days_from_start'] = (df['f__days_from_start'] / 7).astype(int) + + if 'f__total_elapse' in df.columns: + score_name = 's__total_elapse' + df['f__total_elapse_scaled'] = round(df['f__total_elapse'] / 300) + + # contamination from parameters or fallback + contamination = 0.05 + if 'features' in parameters and 'f__total_elapse' in parameters['features']: + contamination = parameters['features']['f__total_elapse'].get('contamination', 0.05) + + model = ECOD(contamination=contamination) + valid_mask = ~df['f__total_elapse_scaled'].isnull() + if valid_mask.sum() > 0: + model.fit(df.loc[valid_mask, ['f__total_elapse_scaled']]) + df.loc[valid_mask, score_name] = model.predict(df.loc[valid_mask, ['f__total_elapse_scaled']]) + + score_name1 = score_name + '_lower' + score_name2 = score_name + '_upper' + df[score_name1] = 0 + df[score_name2] = 0 + + non_anomalies = df.loc[(df[score_name] == 0) & valid_mask, 'f__total_elapse_scaled'] + if not non_anomalies.empty: + min_val = non_anomalies.min() + max_val = non_anomalies.max() + df.loc[valid_mask & (df['f__total_elapse_scaled'] < min_val), score_name1] = 1 + df.loc[valid_mask & (df['f__total_elapse_scaled'] > max_val), score_name2] = 1 + df.drop(columns=[score_name, 'f__total_elapse_scaled'], inplace=True, errors='ignore') + + if 'f__pause_duration' in df.columns and 'f__total_elapse' in df.columns: + df['s__pause_duration'] = np.where(df['f__total_elapse'] != 0, + df['f__pause_duration'] / df['f__total_elapse'], 0) + + if 'f__pause_count' in df.columns and 'f__number_answered' in df.columns: + df['s__pause_count'] = np.where(df['f__number_answered'] != 0, + df['f__pause_count'] / df['f__number_answered'], 0) + + if 'f__number_answered' in df.columns: + df['s__number_answered'] = df['f__number_answered'] + + if 'f__number_unanswered' in df.columns: + df['s__number_unanswered'] = df['f__number_unanswered'] + + return df + +def aggregate_item_to_responsible_scores(df_resp: pd.DataFrame, df_item_scores: pd.DataFrame) -> pd.DataFrame: + """Aggregates item-level scores to the responsible (enumerator) level.""" + df_out = df_resp.copy() + if df_out.empty and 'responsible' in df_item_scores.columns: + df_out = pd.DataFrame({'responsible': df_item_scores['responsible'].unique()}) + + if df_out.empty: + return df_out + + # Mean across responsible directly + scores_double_mean = ['s__single_question', 's__multi_option_question', 's__answer_position'] + for score in scores_double_mean: + if score in df_item_scores.columns: + data = df_item_scores.groupby(['responsible', 'variable_name'])[score].mean().reset_index() + data = data.groupby('responsible')[score].mean() + if 'responsible' in df_out.columns: + df_out[score] = df_out['responsible'].map(data).fillna(0) + + if 's__first_digit' in df_item_scores.columns: + data = df_item_scores.groupby('responsible')['s__first_digit'].mean() + if 'responsible' in df_out.columns: + df_out['s__first_digit'] = df_out['responsible'].map(data).fillna(0) + + return df_out + +def calculate_responsible_score(df_resp_features: pd.DataFrame, restricted_columns: List[str] = None) -> pd.DataFrame: + """ + Calculate the global responsible (enumerator) score using PCA. + Maps legacy `make_responsible_score`. + """ + df_resp = df_resp_features.copy() + if df_resp.empty or 'responsible' not in df_resp.columns: + return df_resp + + scaler = StandardScaler() + columns = [col for col in df_resp.columns if not col.startswith('responsible') and (not restricted_columns or col not in restricted_columns)] + + if not columns: + df_resp['responsible_score'] = 0.0 + return df_resp + + df_grouped = df_resp.groupby('responsible')[columns].mean().reset_index() + + df_pca_input = df_grouped[columns].fillna(0) + df_pca_input = df_pca_input.loc[:, df_pca_input.nunique() != 1] + + if df_pca_input.empty: + df_resp['responsible_score'] = 0.0 + return df_resp + + df_pca_scaled = pd.DataFrame(scaler.fit_transform(df_pca_input), columns=df_pca_input.columns) + + model = PCA(random_state=42) + model.fit(df_pca_scaled) + df_grouped['responsible_score'] = model.decision_scores_ + + df_grouped['responsible_score'] = normalize(df_grouped[['responsible_score']], norm='l1', axis=0) + + # Merge back to original resp mapping + return df_resp.merge(df_grouped[['responsible', 'responsible_score']], on='responsible', how='left') diff --git a/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/__init__.py b/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/__init__.py deleted file mode 100644 index 0118779..0000000 --- a/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -"""Risk scoring pipeline.""" - -from .pipeline import create_pipeline - -__all__ = ["create_pipeline"] diff --git a/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/nodes.py deleted file mode 100644 index 412c4f9..0000000 --- a/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/nodes.py +++ /dev/null @@ -1,316 +0,0 @@ -## Pipeline Nodes (pipelines) - -### 1 Ingestion Pipeline -import os -from pathlib import Path -from typing import Dict, List -import pandas as pd -from loguru import logger - -# Import your existing utilities -from rissk.utils.import_utils import ( - extract_zip, - get_survey_info, - get_dataframes -) - -# """Nodes for ingesting Survey Solutions export data.""" - -# def unzip_raw_surveys( -# parameters: Dict -# ) -> None: -# """ -# Extract zipped Survey Solutions exports. - -# Handles: -# - Recursive unzipping (nested ZIPs) -# - Password-protected ZIPs (from credentials) -# - Mixed formats (.dta, .tab) - -# Args: -# parameters: Survey configuration from parameters.yml - -# Side Effect: -# Extracts files to data/01_raw/{survey_name}/{version}/ -# """ -# from rissk.config import RAW_DATA_DIR -# from rissk.utils.import_utils import get_zip_files - -# survey_name = parameters["survey"]["name"] -# questionnaires = parameters["survey"]["questionnaires"] - -# # Get all ZIP files matching the survey config -# zip_files = get_zip_files(RAW_DATA_DIR, survey_name, questionnaires) - -# logger.info(f"Found {len(zip_files)} ZIP files to extract") - -# for zip_file in zip_files: -# dest_path = zip_file.with_suffix('') # Remove .zip extension -# logger.info(f"Extracting {zip_file.name} to {dest_path}") -# extract_zip(zip_file, dest_path) - -# logger.success(f"Extraction complete. Files in {RAW_DATA_DIR}") - - -# def load_survey_dataframes( -# parameters: Dict -# ) -> tuple: -# """ -# Load paradata, questionnaire, and microdata from extracted files. - -# Handles: -# - Mixed file formats (.dta for Stata, .tab for tabular) -# - Variable name parsing from Survey Solutions structure -# - Multi-option/GPS/List question transformations - -# Args: -# parameters: Survey configuration - -# Returns: -# tuple: (paradata_df, questionnaire_df, microdata_df) -# """ -# from rissk.config import RAW_DATA_DIR -# from rissk.utils.import_utils import get_survey_info, get_dataframes - -# # Scan extracted directories for survey info -# survey_paths = [] -# for item in RAW_DATA_DIR.iterdir(): -# if item.is_dir(): -# survey_paths.append(item) - -# survey_info = get_survey_info(survey_paths) - -# logger.info(f"Loading dataframes for surveys: {list(survey_info.keys())}") - -# # Use your existing get_dataframes logic -# paradata_df, questionnaire_df, microdata_df = get_dataframes(survey_info) - -# logger.info(f"Loaded - Paradata: {paradata_df.shape}, " -# f"Questionnaire: {questionnaire_df.shape}, " -# f"Microdata: {microdata_df.shape}") - -# return paradata_df, questionnaire_df, microdata_df - - -# ### 2 Feature Engineering Pipeline - -# """Nodes for processing paradata and building features.""" -# import pandas as pd -# from typing import Dict -# from loguru import logger - - -# def process_paradata_timestamps( -# paradata_raw: pd.DataFrame -# ) -> pd.DataFrame: -# """ -# Process paradata timestamps and add hour features. - -# This replicates logic from pipelines/feature_engineering/10_process_paradata.py - -# Args: -# paradata_raw: Raw paradata DataFrame - -# Returns: -# Processed paradata with timestamp features -# """ -# paradata = paradata_raw.copy() - -# # Add answer hour feature (from 10_process_paradata.py line 29) -# paradata['f__answer_hour_set'] = ( -# paradata['timestamp_local'].dt.hour + -# paradata['timestamp_local'].dt.round('30min').dt.minute / 60 -# ) - -# # Add interviewing flag -# paradata['interviewing'] = ~paradata['role'].isin([2, 3, 4]) - -# logger.info(f"Processed {len(paradata)} paradata records") - -# return paradata - - -# def filter_active_events( -# paradata_processed: pd.DataFrame, -# parameters: Dict -# ) -> pd.DataFrame: -# """ -# Filter paradata to active interviewer events. - -# Replicates logic from pipelines/feature_engineering/11_process_paradata_active.py - -# Args: -# paradata_processed: Processed paradata -# parameters: Config parameters (for limit_unit) - -# Returns: -# DataFrame with only active interviewer events -# """ -# active_events = [ -# 'InterviewCreated', 'AnswerSet', 'Resumed', -# 'AnswerRemoved', 'CommentSet', 'Restarted' -# ] - -# # Filter logic from 11_process_paradata_active.py line 28 -# active_mask = ( -# paradata_processed['event'].isin(active_events) & -# paradata_processed['question_scope'].isin([0, '']) & -# (paradata_processed['role'] == 1) -# ) - -# vars_needed = [ -# 'interview__id', 'order', 'event', 'responsible', 'role', 'tz_offset', -# 'param', 'answer', 'roster_level', 'timestamp_local', 'variable_name', -# 'question_sequence', 'question_scope', "qtype", 'question_type', -# 'qnr', 'qnr_version', 'interviewing', 'yes_no_view', 'index_col', -# 'f__answer_hour_set' -# ] - -# df_para_active = paradata_processed.loc[active_mask, vars_needed] - -# logger.info(f"Filtered to {len(df_para_active)} active events") - -# return df_para_active - - -def build_item_features( - microdata_raw: pd.DataFrame, - paradata_active: pd.DataFrame, - parameters: Dict -) -> pd.DataFrame: - """ - Build item-level features from microdata and paradata. - - Uses logic from rissk/feature_processing.py make_df_item method. - - Args: - microdata_raw: Raw microdata - paradata_active: Active paradata events - parameters: Feature configuration - - Returns: - DataFrame with item-level features - """ - from rissk.feature_processing import FeatureProcessing - - # Instantiate your existing class (or refactor to pure functions) - # For now, we'll use a wrapper approach - allowed_features = [ - f'f__{k}' for k, v in parameters['features'].items() - if v['use'] - ] - - logger.info(f"Building {len(allowed_features)} item features") - - # You would call methods like: - # df_item = feature_processor.make_df_item(microdata_raw) - # For brevity, returning placeholder - - df_item = microdata_raw.copy() # Replace with actual logic - - return df_item - - -def build_unit_features( - paradata_active: pd.DataFrame, - parameters: Dict -) -> pd.DataFrame: - """ - Build unit-level (interview-level) features. - - Uses logic from rissk/feature_processing.py make_df_unit method. - - Args: - paradata_active: Active paradata - parameters: Configuration - - Returns: - DataFrame with unit-level features - """ - df_unit = paradata_active[[ - 'interview__id', 'responsible', 'survey_name', 'survey_version' - ]].copy() - - df_unit.drop_duplicates(inplace=True) - df_unit = df_unit[ - (df_unit['responsible'] != '') & - (~pd.isnull(df_unit['responsible'])) - ] - - # Add pause features (from your add_pause_features method) - # Add time features (from add_unit_time_features) - - logger.info(f"Built {len(df_unit)} unit records") - - return df_unit - -### 3 Risk Scoring Pipeline - -# filepath: rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/nodes.py -"""Nodes for calculating risk scores.""" -import pandas as pd -from typing import Dict -from loguru import logger - - -def calculate_unit_risk_scores( - unit_features: pd.DataFrame, - item_features: pd.DataFrame, - parameters: Dict -) -> pd.DataFrame: - """ - Calculate global risk scores for each unit (interview). - - Uses logic from rissk/unit_proccessing.py make_global_score method. - - Args: - unit_features: Unit-level features - item_features: Item-level features - parameters: Feature configuration - - Returns: - DataFrame with unit_risk_score column - """ - from rissk.unit_proccessing import UnitDataProcessing - - # You would instantiate your class or refactor to pure functions - # For now, placeholder logic: - - unit_scores = unit_features.copy() - unit_scores['unit_risk_score'] = 0.0 # Replace with actual IForest scoring - - logger.info(f"Calculated risk scores for {len(unit_scores)} units") - - return unit_scores - - -def format_output_scores( - unit_risk_scores: pd.DataFrame, - parameters: Dict -) -> tuple: - """ - Format final output files. - - Args: - unit_risk_scores: Scores DataFrame - parameters: Output configuration - - Returns: - tuple: (unit_scores_df, feature_scores_df) if feature_score=True - """ - # Main output (from rissk/unit_proccessing.py save method line 104) - output_df = unit_risk_scores[[ - 'interview__id', 'responsible', 'unit_risk_score' - ]].copy() - - output_df['unit_risk_score'] = output_df['unit_risk_score'].round(2) - output_df.sort_values('unit_risk_score', inplace=True) - - logger.success(f"Formatted {len(output_df)} risk scores for output") - - if parameters['output']['feature_score']: - # Generate feature score breakdown - feature_scores_df = unit_risk_scores.copy() # Add all s__ columns - return output_df, feature_scores_df - - return output_df, None \ No newline at end of file diff --git a/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/pipeline.py deleted file mode 100644 index 1781e78..0000000 --- a/rissk_kedro/src/rissk_kedro/pipelines/risk_scoring/pipeline.py +++ /dev/null @@ -1,25 +0,0 @@ -"""Risk scoring pipeline definition.""" -from kedro.pipeline import Pipeline, node, pipeline -from .nodes import calculate_unit_risk_scores, format_output_scores - - -def create_pipeline(**kwargs) -> Pipeline: - """Create the risk scoring pipeline. - - Returns: - A pipeline that calculates unit risk scores. - """ - return pipeline([ - node( - func=calculate_unit_risk_scores, - inputs=["unit_features", "item_features", "parameters"], - outputs="unit_risk_scores_raw", - name="calculate_scores_node", - ), - node( - func=format_output_scores, - inputs=["unit_risk_scores_raw", "parameters"], - outputs=["unit_risk_scores", "unit_feature_scores"], - name="format_outputs_node", - ), - ]) \ No newline at end of file diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/__init__.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/__init__.py new file mode 100644 index 0000000..3b71ef3 --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/__init__.py @@ -0,0 +1,9 @@ +""" +This is a boilerplate pipeline 'rissk_scoring' +generate with Kedro 0.19.x or later. +""" + +from .pipeline import create_pipeline + +__all__ = ["create_pipeline"] +__version__ = "0.1" diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py new file mode 100644 index 0000000..3c2addf --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py @@ -0,0 +1,85 @@ +import pandas as pd +from typing import Dict, Any, Tuple +import logging + +from rissk.item_processing_kedro import ( + calculate_answer_hour_set_score, + calculate_sequence_jump_score, + calculate_first_decimal_score, + calculate_answer_changed_score, + calculate_answer_removed_score, + calculate_answer_position_score, + calculate_answer_selected_score, + calculate_answer_duration_score, + calculate_single_question_score, + calculate_multi_option_question_score, + calculate_first_digit_score, + calculate_gps_score +) +from rissk.unit_processing_kedro import ( + calculate_global_score, + aggregate_unit_score_mean, + aggregate_item_to_unit_scores, + calculate_unit_level_scores, + aggregate_item_to_responsible_scores, + calculate_responsible_score +) + +logger = logging.getLogger(__name__) + +def calculate_item_scores(df_item: pd.DataFrame, df_item_removed: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + """ + Run item level scoring applying various mathematical models. + """ + logger.info("Calculating Item Scores...") + df_scored = calculate_answer_hour_set_score(df_item, parameters) + df_scored = calculate_sequence_jump_score(df_scored, parameters) + df_scored = calculate_first_decimal_score(df_scored, parameters) + df_scored = calculate_answer_changed_score(df_scored, parameters) + df_scored = calculate_answer_position_score(df_scored, parameters) + df_scored = calculate_answer_selected_score(df_scored, parameters) + df_scored = calculate_answer_duration_score(df_scored, parameters) + df_scored = calculate_single_question_score(df_scored, parameters) + df_scored = calculate_multi_option_question_score(df_scored, parameters) + df_scored = calculate_first_digit_score(df_scored, parameters) + df_scored = calculate_gps_score(df_scored, parameters) + + # Needs to handle distinct output of removed scores mapping + df_removed_scored = calculate_answer_removed_score(df_scored, df_item_removed, parameters) + + # ... other item scores would be chained here ... + return df_scored + +def calculate_unit_scores(df_unit: pd.DataFrame, df_item_scores: pd.DataFrame, parameters: Dict[str, Any]) -> Tuple[pd.DataFrame, pd.DataFrame]: + """ + Aggregate item scores to unit, extract responsible scores, and calculate global risk. + """ + logger.info("Calculating Unit Scores and Global Risk...") + + # 1. Aggregate item-level scores up to unit level + df_unit_scored = aggregate_item_to_unit_scores(df_unit, df_item_scores) + + # 2. Add pure unit-level calculations + df_unit_scored = calculate_unit_level_scores(df_unit_scored, parameters) + + # 3. Aggregate item-level scores up to responsible level + df_resp_scored = pd.DataFrame() + df_resp_scored = aggregate_item_to_responsible_scores(df_resp_scored, df_item_scores) + + # 4. Calculate final responsible score via PCA + restricted_columns = parameters.get('scoring', {}).get('restricted_columns', []) + df_resp_scored = calculate_responsible_score(df_resp_scored, restricted_columns) + + # Determine all scored columns dynamically (s_*) + score_columns = [col for col in df_unit_scored.columns if col.startswith('s__')] + + # 5. Calculate final global unit risk score + df_final_unit = calculate_global_score( + df_unit_scores=df_unit_scored, + df_resp_scores=df_resp_scored, + score_columns=score_columns, + combine_resp_score=True, + restricted_columns=restricted_columns + ) + + return df_final_unit, df_resp_scored diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py new file mode 100644 index 0000000..2084ca4 --- /dev/null +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py @@ -0,0 +1,24 @@ +"""Rissk scoring pipeline definition.""" +from kedro.pipeline import Pipeline, node, pipeline +from .nodes import calculate_item_scores, calculate_unit_scores + +def create_pipeline(**kwargs) -> Pipeline: + """Create the scoring pipeline. + + Returns: + A pipeline that calculates item and unit risk scores. + """ + return pipeline([ + node( + func=calculate_item_scores, + inputs=["item_features", "item_features_removed", "parameters"], + outputs="item_scores", + name="calculate_item_scores_node", + ), + node( + func=calculate_unit_scores, + inputs=["unit_features", "item_scores", "parameters"], + outputs=["unit_risk_scores", "responsible_scores"], + name="calculate_unit_scores_node", + ), + ]) From 4f73c28925ceab04769c296479baec3652352427 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Mon, 9 Mar 2026 17:01:28 +0000 Subject: [PATCH 28/70] Refactor scoring functions and pipeline definitions; update contamination parameter handling and improve GPS score calculations. --- rissk/item_processing_kedro.py | 190 ++++++++++-------- rissk/unit_processing_kedro.py | 17 +- .../pipelines/feature_creation/nodes.py | 2 +- .../pipelines/feature_creation/pipeline.py | 2 +- .../pipelines/rissk_scoring/nodes.py | 11 +- .../pipelines/rissk_scoring/pipeline.py | 2 +- 6 files changed, 121 insertions(+), 103 deletions(-) diff --git a/rissk/item_processing_kedro.py b/rissk/item_processing_kedro.py index 2548b36..781395e 100644 --- a/rissk/item_processing_kedro.py +++ b/rissk/item_processing_kedro.py @@ -2,6 +2,7 @@ import numpy as np import logging from typing import List, Dict, Any, Tuple +from pyod.models.thresholds import FILTER logger = logging.getLogger(__name__) @@ -13,13 +14,16 @@ def rename_feature(feature_name: str, starting_string: str = 'f', new_string: st return feature_name.replace(starting_string, new_string) return feature_name -def get_contamination_parameter(config_features: dict, feature_name: str, automatic_contamination: bool = False, method: str = 'medfilt', random_state: int = 42) -> float: - """Fetch contamination parameter from Kedro parameters/config features.""" +def get_contamination_parameter(config_features: dict, feature_name: str, automatic_contamination: bool = False, method: str = 'medfilt', random_state: int = 42): + """Fetch contamination parameter from Kedro parameters/config features. + + Returns a FILTER object for automatic contamination detection (matching legacy behaviour), + or a fixed float when a contamination value is explicitly configured. + """ f_name = feature_name.replace('f__', '') contamination = config_features.get(f_name, {}).get('parameters', {}).get('contamination') if contamination is None or contamination == 'auto' or automatic_contamination is True: - # TODO: Add automatic detection from legacy ItemFeatureProcessing if required. - return 0.1 + return FILTER(method=method, random_state=random_state) else: return float(contamination) @@ -68,7 +72,6 @@ def get_clean_pivot_table(df_item: pd.DataFrame, feature_name: str, remove_low_f # (To be filled out next, mapping make_score__*) from pyod.models.ecod import ECOD -from rissk.detection_algorithms_kedro import find_anomalies def calculate_answer_hour_set_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: feature_name = 'f__answer_hour_set' @@ -101,23 +104,26 @@ def calculate_answer_hour_set_score(df_item: pd.DataFrame, parameters: Dict[str, def calculate_sequence_jump_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: feature_name = 'f__sequence_jump' score_name = rename_feature(feature_name) - df = df_item[~pd.isnull(df_item[feature_name])].copy() - valid_variables = filter_variable_name_by_frequency(df, feature_name, frequency=100, min_unique_values=3) + df = df_item.copy() + + if feature_name not in df.columns or df[feature_name].dropna().empty: + df[score_name] = np.nan + return df + + valid_data = df[~pd.isnull(df[feature_name])].copy() + valid_variables = filter_variable_name_by_frequency(valid_data, feature_name, frequency=100, min_unique_values=3) df[score_name] = 0 contamination = get_contamination_parameter(parameters.get('features', {}), feature_name) - + + from pyod.models.inne import INNE for var in valid_variables: - var_mask = df['variable_name'] == var - if df[var_mask].shape[0] > 0: - # Note: find_anomalies historically operates grouped by interview, so this requires - # matching the output to the df index - anomaly_df = find_anomalies(df[var_mask].copy(), contamination=contamination) - # In purely functional kedro, typically we map back via index_col or similar - if score_name in anomaly_df.columns: - pass # Mapping logic goes here based on specific algorithm outputs + mask = (df['variable_name'] == var) & (~pd.isnull(df[feature_name])) + if mask.sum() > 0: + model = INNE(contamination=contamination, random_state=42) + model.fit(df.loc[mask, [feature_name]]) + df.loc[mask, score_name] = model.predict(df.loc[mask, [feature_name]]) - df_out = df_item.copy() - return df_out + return df from pyod.models.cof import COF @@ -169,33 +175,27 @@ def calculate_answer_changed_score(df_item: pd.DataFrame, parameters: Dict[str, return df -def calculate_answer_removed_score(df_item: pd.DataFrame, df_item_removed: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: +def calculate_answer_removed_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: feature_name = 'f__answer_removed' score_name = rename_feature(feature_name) - - # In legacy, this feature comes from df_item_removed instead of direct df_item mapped. - # Therefore, we pass in df_item_removed (grouped paradata) as a distinct input - df = df_item_removed.copy() - - if df.empty or feature_name not in df.columns: - df_item_out = df_item.copy() - df_item_out[score_name] = np.nan - return df_item_out + df = df_item.copy() + + if feature_name not in df.columns or df[feature_name].dropna().empty: + df[score_name] = np.nan + return df valid_variables = filter_variable_name_by_frequency(df, feature_name, frequency=100, min_unique_values=1) - df[score_name] = 0 + df[score_name] = np.nan + df.loc[~pd.isnull(df[feature_name]), score_name] = 0 contamination = get_contamination_parameter(parameters.get('features', {}), feature_name, method='medfilt', random_state=42) - + for var in valid_variables: - mask = (df['variable_name'] == var) + mask = (df['variable_name'] == var) & (~pd.isnull(df[feature_name])) if mask.sum() > 0: model = ECOD(contamination=contamination) model.fit(df.loc[mask, [feature_name]]) df.loc[mask, score_name] = model.predict(df.loc[mask, [feature_name]]) - - # Typically this must be mapped back to df_item or remain as its own independent output. - # For now we'll integrate it by merging on interview__id or equivalent context, - # but functionally we should return either the updated df or merge it onto df_item. + return df from scipy.spatial import cKDTree @@ -468,67 +468,81 @@ def calculate_first_digit_score(df_item: pd.DataFrame, parameters: Dict[str, Any return df def calculate_gps_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: - feature_name = 'f__gps' - score_name = rename_feature(feature_name) df = df_item.copy() - - if feature_name not in df.columns or df[feature_name].dropna().empty: - df[score_name] = np.nan + + required_columns = ['f__gps_latitude', 'f__gps_longitude', 'f__gps_accuracy'] + if any(col not in df.columns for col in required_columns): + for col in ['s__gps_proximity_counts', 's__gps_outlier', 's__gps_extreme_outlier']: + df[col] = np.nan return df - # Prepare DataFrame - df[score_name] = 0 - df['latitude'] = np.nan - df['longitude'] = np.nan - df['valid_gps'] = False - - # Extract valid coordinates silently - def extract_coords(val): - try: - val_list = eval(val) - if isinstance(val_list, list) and len(val_list) >= 2: - lat, lon = float(val_list[0]), float(val_list[1]) - return lat, lon, True - except: - return np.nan, np.nan, False - return np.nan, np.nan, False - - mask_valid = ~pd.isnull(df[feature_name]) - if mask_valid.sum() == 0: + gps_mask = (~pd.isnull(df['f__gps_latitude'])) & (~pd.isnull(df['f__gps_longitude'])) + if gps_mask.sum() == 0: + for col in ['s__gps_proximity_counts', 's__gps_outlier', 's__gps_extreme_outlier']: + df[col] = np.nan return df - coords = df.loc[mask_valid, feature_name].apply(extract_coords) - df.loc[mask_valid, 'latitude'] = coords.apply(lambda x: x[0]) - df.loc[mask_valid, 'longitude'] = coords.apply(lambda x: x[1]) - df.loc[mask_valid, 'valid_gps'] = coords.apply(lambda x: x[2]) + data = df.loc[gps_mask].copy() + data['s__gps_extreme_outlier'] = 0 + data.loc[data['f__gps_latitude'] == 0.0, 's__gps_extreme_outlier'] = 1 + data.loc[data['f__gps_longitude'] == 0.0, 's__gps_extreme_outlier'] = 1 + + data['x'], data['y'], data['z'] = lat_lon_to_cartesian(data['f__gps_latitude'], data['f__gps_longitude']) + data['accuracy'] = data['f__gps_accuracy'].fillna(0) / 1e6 + + tree = cKDTree(data[['x', 'y', 'z']]) + radius = 10 / 1e6 + counts = [ + len(tree.query_ball_point(xyz, r=radius + acc)) - 1 + for xyz, acc in zip(data[['x', 'y', 'z']].values, data['accuracy']) + ] + data['s__gps_proximity_counts'] = counts + + mask = data['s__gps_extreme_outlier'] < 1 + data['distance_to_median'] = np.nan + if mask.sum() > 0: + median_x = data.loc[mask].drop_duplicates(subset='x')['x'].median() + median_y = data.loc[mask].drop_duplicates(subset='y')['y'].median() + median_z = data.loc[mask].drop_duplicates(subset='z')['z'].median() + + data.loc[mask, 'distance_to_median'] = np.sqrt( + (data.loc[mask, 'x'] - median_x) ** 2 + + (data.loc[mask, 'y'] - median_y) ** 2 + + (data.loc[mask, 'z'] - median_z) ** 2 + ) - valid_data = df[df['valid_gps']] - if valid_data.empty: return df + p75 = data.loc[mask, 'distance_to_median'].quantile(0.75) + median = data.loc[mask, 'distance_to_median'].median() + range_75 = p75 - median + threshold = p75 + 3.5 * range_75 + data.loc[mask, 's__gps_extreme_outlier'] = ( + data.loc[mask, 'distance_to_median'] > threshold + ).astype(int) + + contamination = get_contamination_parameter( + parameters.get('features', {}), + 'f__gps', + method='medfilt', + random_state=42, + ) + coords_columns = ['x', 'y'] + if data.loc[mask].shape[0] < 10000: + model = COF(contamination=contamination) + else: + model = LOF(contamination=contamination, n_neighbors=20) + model.fit(data.loc[mask, coords_columns]) + data.loc[mask, 's__gps_outlier'] = model.predict(data.loc[mask, coords_columns]) + else: + data['s__gps_outlier'] = 0 - # We need to project to cartesian map - valid_variables = filter_variable_name_by_frequency(valid_data, feature_name, frequency=100, min_unique_values=3) - - for var in valid_variables: - mask = (df['variable_name'] == var) & df['valid_gps'] - if mask.sum() > 0: - latitudes = df.loc[mask, 'latitude'].values - longitudes = df.loc[mask, 'longitude'].values - - try: - # Need detection algorithms functions here - x, y, z = lat_lon_to_cartesian(latitudes, longitudes) - coordinates = np.column_stack((x, y, z)) - tree = cKDTree(coordinates) - k = 3 - if len(coordinates) > k: - distances, indices = tree.query(coordinates, k=k) - df.loc[mask, score_name] = distances[:, -1] - else: - df.loc[mask, score_name] = 0.0 + data['s__gps_outlier'] = data['s__gps_outlier'].fillna(0) + df.loc[data.index, 's__gps_proximity_counts'] = data['s__gps_proximity_counts'] + df.loc[data.index, 's__gps_outlier'] = data['s__gps_outlier'] + df.loc[data.index, 's__gps_extreme_outlier'] = data['s__gps_extreme_outlier'] - except NameError: - # lat_lon_to_cartesian not found - continue + for col in ['s__gps_proximity_counts', 's__gps_outlier', 's__gps_extreme_outlier']: + if col in df.columns: + df[col] = df[col].fillna(0) return df diff --git a/rissk/unit_processing_kedro.py b/rissk/unit_processing_kedro.py index d478707..912e134 100644 --- a/rissk/unit_processing_kedro.py +++ b/rissk/unit_processing_kedro.py @@ -81,7 +81,8 @@ def calculate_global_score(df_unit_scores: pd.DataFrame, df_resp_scores: pd.Data if combine_resp_score and 'responsible' in df_unit.columns and df_resp_scores is not None and 'responsible_score' in df_resp_scores.columns: df_resp_map = df_resp_scores.set_index('responsible')['responsible_score'].to_dict() df_unit['responsible_score'] = df_unit['responsible'].map(df_resp_map).fillna(0) - df_unit['unit_risk_score'] = df_unit['unit_risk_score'] + df_unit['responsible_score'] * 100 + df_unit['unit_risk_score'] = df_unit['unit_risk_score'] * df_unit['responsible_score'] + df_unit['unit_risk_score'] = scaler.fit_transform(df_unit[['unit_risk_score']]) return df_unit @@ -111,17 +112,23 @@ def aggregate_item_to_unit_scores(df_unit: pd.DataFrame, df_item_scores: pd.Data df_out[score] = df_out['interview__id'].map(data).fillna(0) # 3. GPS specifics (if gps scores exist) - gps_features = ['s__gps_proximity_counts', 's__gps_outlier', 's__gps_extreme_outlier', 's__gps'] + gps_features = ['s__gps_proximity_counts', 's__gps_outlier', 's__gps_extreme_outlier'] for score in gps_features: if score in df_item_scores.columns: data = df_item_scores.groupby('interview__id')[score].sum() df_out[score] = df_out['interview__id'].map(data).fillna(0) + + # Legacy parity: s__gps is the sum of f__gps at interview level. + if 'f__gps' in df_item_scores.columns: + data = df_item_scores.groupby('interview__id')['f__gps'].sum() + df_out['s__gps'] = df_out['interview__id'].map(data).fillna(0) return df_out def calculate_unit_level_scores(df_unit: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: """Calculate scores that are purely derived from unit-level features.""" from pyod.models.ecod import ECOD + from rissk.item_processing_kedro import get_contamination_parameter df = df_unit.copy() if 'f__time_changed' in df.columns: @@ -138,9 +145,9 @@ def calculate_unit_level_scores(df_unit: pd.DataFrame, parameters: Dict[str, Any df['f__total_elapse_scaled'] = round(df['f__total_elapse'] / 300) # contamination from parameters or fallback - contamination = 0.05 - if 'features' in parameters and 'f__total_elapse' in parameters['features']: - contamination = parameters['features']['f__total_elapse'].get('contamination', 0.05) + contamination = get_contamination_parameter( + parameters.get('features', {}), 'f__total_elapse', method='medfilt', random_state=42 + ) model = ECOD(contamination=contamination) valid_mask = ~df['f__total_elapse_scaled'].isnull() diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py index b0b1834..a9143fc 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py @@ -9,7 +9,7 @@ create_base_item_table, create_base_unit_table, enrich_item_features, - enrich_unit_features + enrich_unit_features, ) logger = logging.getLogger(__name__) diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py index d43ade8..cd6a80f 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py @@ -4,7 +4,7 @@ create_base_item_table_node, create_base_unit_table_node, enrich_item_features_node, - enrich_unit_features_node + enrich_unit_features_node, ) def create_pipeline(**kwargs) -> Pipeline: diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py index 3c2addf..f12ac33 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py @@ -27,15 +27,17 @@ logger = logging.getLogger(__name__) -def calculate_item_scores(df_item: pd.DataFrame, df_item_removed: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: +def calculate_item_scores(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: """ Run item level scoring applying various mathematical models. + f__answer_removed is already present in df_item from the feature creation pipeline. """ logger.info("Calculating Item Scores...") df_scored = calculate_answer_hour_set_score(df_item, parameters) df_scored = calculate_sequence_jump_score(df_scored, parameters) df_scored = calculate_first_decimal_score(df_scored, parameters) df_scored = calculate_answer_changed_score(df_scored, parameters) + df_scored = calculate_answer_removed_score(df_scored, parameters) df_scored = calculate_answer_position_score(df_scored, parameters) df_scored = calculate_answer_selected_score(df_scored, parameters) df_scored = calculate_answer_duration_score(df_scored, parameters) @@ -43,11 +45,6 @@ def calculate_item_scores(df_item: pd.DataFrame, df_item_removed: pd.DataFrame, df_scored = calculate_multi_option_question_score(df_scored, parameters) df_scored = calculate_first_digit_score(df_scored, parameters) df_scored = calculate_gps_score(df_scored, parameters) - - # Needs to handle distinct output of removed scores mapping - df_removed_scored = calculate_answer_removed_score(df_scored, df_item_removed, parameters) - - # ... other item scores would be chained here ... return df_scored def calculate_unit_scores(df_unit: pd.DataFrame, df_item_scores: pd.DataFrame, parameters: Dict[str, Any]) -> Tuple[pd.DataFrame, pd.DataFrame]: @@ -67,7 +64,7 @@ def calculate_unit_scores(df_unit: pd.DataFrame, df_item_scores: pd.DataFrame, p df_resp_scored = aggregate_item_to_responsible_scores(df_resp_scored, df_item_scores) # 4. Calculate final responsible score via PCA - restricted_columns = parameters.get('scoring', {}).get('restricted_columns', []) + restricted_columns = parameters.get('unit_scoring', {}).get('restricted_columns', []) df_resp_scored = calculate_responsible_score(df_resp_scored, restricted_columns) # Determine all scored columns dynamically (s_*) diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py index 2084ca4..7957b54 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py @@ -11,7 +11,7 @@ def create_pipeline(**kwargs) -> Pipeline: return pipeline([ node( func=calculate_item_scores, - inputs=["item_features", "item_features_removed", "parameters"], + inputs=["item_features", "parameters"], outputs="item_scores", name="calculate_item_scores_node", ), From 7f1ddfa9b746360250cc7e1e73b9b05c8ea68642 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Wed, 11 Mar 2026 12:30:17 +0000 Subject: [PATCH 29/70] Refactor scoring functions for improved clarity and robustness; enhance contamination parameter handling and GPS score calculations. --- rissk/item_processing_kedro.py | 502 +++++++++++++++++++++------------ 1 file changed, 322 insertions(+), 180 deletions(-) diff --git a/rissk/item_processing_kedro.py b/rissk/item_processing_kedro.py index 781395e..7505825 100644 --- a/rissk/item_processing_kedro.py +++ b/rissk/item_processing_kedro.py @@ -3,6 +3,19 @@ import logging from typing import List, Dict, Any, Tuple from pyod.models.thresholds import FILTER +from pyod.models.ecod import ECOD +from pyod.models.cof import COF +from pyod.models.inne import INNE +from pyod.models.lof import LOF +from scipy.spatial import cKDTree + +from rissk.utils.stats_utils import ( + calculate_entropy, + calculate_list_entropy, + filter_variables_by_magnitude, + apply_benford_tests +) +from rissk.detection_algorithms_kedro import lat_lon_to_cartesian logger = logging.getLogger(__name__) @@ -14,9 +27,14 @@ def rename_feature(feature_name: str, starting_string: str = 'f', new_string: st return feature_name.replace(starting_string, new_string) return feature_name -def get_contamination_parameter(config_features: dict, feature_name: str, automatic_contamination: bool = False, method: str = 'medfilt', random_state: int = 42): +def get_contamination_parameter( + config_features: dict, + feature_name: str, + automatic_contamination: bool = False, + method: str = 'medfilt', + random_state: int = 42 + ): """Fetch contamination parameter from Kedro parameters/config features. - Returns a FILTER object for automatic contamination detection (matching legacy behaviour), or a fixed float when a contamination value is explicitly configured. """ @@ -27,95 +45,221 @@ def get_contamination_parameter(config_features: dict, feature_name: str, automa else: return float(contamination) -def filter_variable_name_by_frequency(df: pd.DataFrame, feature_name: str, frequency: int = 100, min_unique_values: int = 3) -> List[str]: +def filter_variable_name_by_frequency( + df: pd.DataFrame, + feature_name: str, + frequency: int = 100, + min_unique_values: int = 3 + ) -> List[str]: """Filter variables by frequency and unique values.""" if feature_name not in df.columns: return [] + # Count non-null frequency and unique values for each variable valid_data = df[~pd.isnull(df[feature_name])] grouped_df = valid_data.groupby('variable_name')[feature_name].agg(['count', 'nunique']) valid_variables = grouped_df[(grouped_df['count'] >= frequency) & (grouped_df['nunique'] >= min_unique_values)].index + # Return a list of unique variable names that meet the criteria return valid_variables.tolist() -def filter_columns(data: pd.DataFrame, index_col: List[str], threshold: int = 100) -> Tuple[List[str], List[str]]: - """Determine columns to keep/drop based on threshold (placeholder refactor)""" +def filter_columns( + data: pd.DataFrame, + index_col: List[str], + threshold: int = 100, + min_unique_values: int = 3, +) -> Tuple[List[str], List[str]]: + """Determine columns to keep/drop based on threshold and minimum unique values. + Keeps a column only if both the non-null count is >= `threshold` and the + number of unique (non-null) values is >= `min_unique_values`. + """ + # Prepare column set excluding index columns + data_cols = data.drop(columns=index_col, errors='ignore') + # Count non-null values for each column - non_null_counts = data.drop(columns=index_col, errors='ignore').count() - # Filter columns to keep - keep_columns = non_null_counts[non_null_counts >= threshold].index.tolist() - drop_columns = non_null_counts[non_null_counts < threshold].index.tolist() + non_null_counts = data_cols.count() + + # Count unique non-null values for each column + unique_counts = data_cols.nunique(dropna=True) + + # Keep columns that meet both thresholds + keep_mask = (non_null_counts >= threshold) & (unique_counts >= min_unique_values) + keep_columns = non_null_counts[keep_mask].index.tolist() + drop_columns = non_null_counts[~keep_mask].index.tolist() + return index_col + keep_columns, drop_columns -def get_clean_pivot_table(df_item: pd.DataFrame, feature_name: str, remove_low_freq_col: bool = True, filter_conditions=None, threshold: int = 100) -> Tuple[pd.DataFrame, List[str]]: +def get_clean_pivot_table( + df_item: pd.DataFrame, + feature_name: str, + remove_low_freq_col: bool = True, + filter_conditions=None, + threshold: int = 100, + min_unique_values: int = 3, +) -> Tuple[pd.DataFrame, List[str]]: """Create a pivot table handling columns and filtering.""" index_col = ['interview__id', 'roster_level', 'responsible'] data = df_item.copy() - if filter_conditions is not None: # Not yet strictly typed since condition type unknown - pass # To fully mimic we'd apply filter + if filter_conditions is not None: + data = data.loc[filter_conditions] data = pd.pivot_table(data=data, index=index_col, columns='variable_name', - values=feature_name, fill_value=np.NAN) + values=feature_name, fill_value=np.nan) data = data.reset_index() if data.columns.nlevels > 1: - pass # In case of multi index columns flatten, handled differently? + data.columns = [f'{col[0]}_{col[1]}'.rstrip('_') for col in data.columns] index_col = [col for col in index_col if col in data.columns] - keep_columns, drop_columns = filter_columns(data, index_col, threshold=threshold) + keep_columns, drop_columns = filter_columns( + data, index_col, threshold=threshold, min_unique_values=min_unique_values + ) if remove_low_freq_col: - data = data[keep_columns] + data = data[keep_columns].copy() return data, index_col + # --- SCORING FUNCTIONS BEGIN --- -# (To be filled out next, mapping make_score__*) -from pyod.models.ecod import ECOD +def calculate_gps_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + df = df_item.copy() + score_cols = ['s__gps_proximity_counts', 's__gps_outlier', 's__gps_extreme_outlier'] + required_columns = ['f__gps_latitude', 'f__gps_longitude', 'f__gps_accuracy'] + index_col = ['interview__id', 'roster_level', 'responsible'] -def calculate_answer_hour_set_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: - feature_name = 'f__answer_hour_set' - score_name = rename_feature(feature_name) - df = df_item[~pd.isnull(df_item[feature_name])].copy() + # If required GPS columns are missing, return original df + if any(col not in df.columns for col in required_columns): + return df - if df.empty: - df_item[score_name] = np.nan - return df_item + gps_mask = (~pd.isnull(df['f__gps_latitude'])) & (~pd.isnull(df['f__gps_longitude'])) + if gps_mask.sum() == 0: + for col in score_cols: + df[col] = np.nan + return df - sorted_hours = df[feature_name].value_counts().index - hour_to_rank = {hour: rank for rank, hour in enumerate(sorted_hours)} - df['frequency'] = df[feature_name].map(hour_to_rank) + # Aggregate to one row per interview by averaging GPS columns across all GPS + # variable_names, matching the legacy pivot_table(aggfunc='mean') behaviour. + # When a questionnaire has multiple GPS questions this produces a mean + # coordinate; for single-GPS questionnaires the result is identical to the + # raw value. (This mirrors legacy pivot semantics where duplicates are + # collapsed by mean so spatial comparisons are one point per interview.) + data = ( + df.loc[gps_mask, index_col + required_columns] + .groupby(index_col, as_index=False)[required_columns] + .mean() + ) - contamination_param = parameters.get('features', {}) - contamination = get_contamination_parameter(contamination_param, feature_name) - - model = ECOD(contamination=contamination) - model.fit(df[[feature_name]]) - df[score_name] = model.predict(df[[feature_name]]) + # Everything that has 0,0 as coordinates is considered an extreme outlier + # (devices sometimes report 0,0 when a fix failed); mark these explicitly + # so they can be excluded from median/distance calculations. + data['s__gps_extreme_outlier'] = 0 + data.loc[data['f__gps_latitude'] == 0.0, 's__gps_extreme_outlier'] = 1 + data.loc[data['f__gps_longitude'] == 0.0, 's__gps_extreme_outlier'] = 1 + + # Convert lat/lon into 3D Cartesian coordinates on a sphere (units = km). + # Using Cartesian coords lets KDTree operate in Euclidean space instead of + # running great-circle calculations for every pair. + data['x'], data['y'], data['z'] = lat_lon_to_cartesian(data['f__gps_latitude'], data['f__gps_longitude']) + # Accuracy is expected to accompany a GPS fix (Survey Solutions provides it). + # We convert `f__gps_accuracy` from metres → kilometres to match `lat_lon_to_cartesian` + # and use `fillna(0)` to avoid NaN radii. Revisit this behaviour because `query_ball_point` may + # return empty neighbor lists or raise when given NaN radii. + + data['accuracy'] = data['f__gps_accuracy'].fillna(0) / 1e3 + + # Build spatial index (KDTree) on 3D cartesian coords to count neighbours. + # Note: KDTree distances are Euclidean in the same units as x/y/z (km). + tree = cKDTree(data[['x', 'y', 'z']]) + # Radius (search distance) passed to `query_ball_point` — same units as x/y/z (kilometres) + # Legacy code converted 10 metres into the same units; keep that behaviour. + radius = 10 / 1e3 + counts = [ + len(tree.query_ball_point(xyz, r=radius + acc)) - 1 + for xyz, acc in zip(data[['x', 'y', 'z']].values, data['accuracy']) + ] + data['s__gps_proximity_counts'] = counts + + # Exclude explicitly-marked extreme outliers (e.g., 0,0 fixes) from + # median/distance computations so they don't skew the central location. + mask = data['s__gps_extreme_outlier'] < 1 + data['distance_to_median'] = np.nan + if mask.sum() > 0: + median_x = data.loc[mask].drop_duplicates(subset='x')['x'].median() + median_y = data.loc[mask].drop_duplicates(subset='y')['y'].median() + median_z = data.loc[mask].drop_duplicates(subset='z')['z'].median() + + data.loc[mask, 'distance_to_median'] = np.sqrt( + (data.loc[mask, 'x'] - median_x) ** 2 + + (data.loc[mask, 'y'] - median_y) ** 2 + + (data.loc[mask, 'z'] - median_z) ** 2 + ) + + # Set a threshold for extreme spatial outliers. Legacy code used a + # percentile + scaled IQR-like range; keep that heuristic here. + p75 = data.loc[mask, 'distance_to_median'].quantile(0.75) + median_dist = data.loc[mask, 'distance_to_median'].median() + range_75 = p75 - median_dist + threshold = p75 + 3.5 * range_75 + data.loc[mask, 's__gps_extreme_outlier'] = ( + data.loc[mask, 'distance_to_median'] > threshold + ).astype(int) + + contamination = get_contamination_parameter( + parameters.get('features', {}), + 'f__gps', + automatic_contamination=parameters.get('automatic_contamination', False), + method='medfilt', + random_state=42, + ) + # We use only ['x', 'y'] to match legacy 2D behaviour for the COF/LOF + # model (a planar approximation). For larger geographic extents consider + # switching to ['x','y','z'] or a geodesic distance measure. + coords_columns = ['x', 'y'] + + # USE COF if dataset has less than 10000 samples else use LOF + if data.loc[mask].shape[0] < 10000: + model = COF(contamination=contamination) + else: + model = LOF(contamination=contamination, n_neighbors=20) + model.fit(data.loc[mask, coords_columns]) + data.loc[mask, 's__gps_outlier'] = model.predict(data.loc[mask, coords_columns]) + else: + data['s__gps_outlier'] = 0 + + data['s__gps_outlier'] = data['s__gps_outlier'].fillna(0) + + # Merge interview-level scores back to every row in the full long-format df. + # Rows for interviews that had no GPS answers are left as NaN — they are not + # scored, matching legacy behaviour where those interviews simply had no entry + # in the returned pivot output. + score_data = data[index_col + score_cols] + df = df.merge(score_data, on=index_col, how='left') + + return df - df.loc[df['frequency'] <= df[df[score_name] == 0]['frequency'].min(), score_name] = 0 - df.drop(columns=['frequency'], inplace=True) - - # Merge back to original dataframe - df_out = df_item.copy() - df_out[score_name] = df_out.index.map(df[score_name]) - return df_out def calculate_sequence_jump_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: feature_name = 'f__sequence_jump' score_name = rename_feature(feature_name) df = df_item.copy() - if feature_name not in df.columns or df[feature_name].dropna().empty: + if feature_name not in df.columns: + return df + + if df[feature_name].dropna().empty: df[score_name] = np.nan return df valid_data = df[~pd.isnull(df[feature_name])].copy() valid_variables = filter_variable_name_by_frequency(valid_data, feature_name, frequency=100, min_unique_values=3) - df[score_name] = 0 - contamination = get_contamination_parameter(parameters.get('features', {}), feature_name) + df[score_name] = np.nan + contamination = get_contamination_parameter( + parameters.get('features', {}), + feature_name, + automatic_contamination=parameters.get('automatic_contamination', False), + ) - from pyod.models.inne import INNE for var in valid_variables: mask = (df['variable_name'] == var) & (~pd.isnull(df[feature_name])) if mask.sum() > 0: @@ -125,22 +269,29 @@ def calculate_sequence_jump_score(df_item: pd.DataFrame, parameters: Dict[str, A return df -from pyod.models.cof import COF def calculate_first_decimal_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: feature_name = 'f__first_decimal' score_name = rename_feature(feature_name) df = df_item.copy() - - if feature_name not in df.columns or df[feature_name].dropna().empty: + + if feature_name not in df.columns: + return df_item + if df[feature_name].dropna().empty: df[score_name] = np.nan return df - valid_data = df[~pd.isnull(df[feature_name])] + valid_data = df[~pd.isnull(df[feature_name])].copy() + # Select only those variables that have at least three distinct values and more than one hundred records valid_variables = filter_variable_name_by_frequency(valid_data, feature_name, frequency=100, min_unique_values=3) df[score_name] = np.nan - df.loc[~pd.isnull(df[feature_name]), score_name] = 0 - contamination = get_contamination_parameter(parameters.get('features', {}), feature_name, method='medfilt', random_state=42) + contamination = get_contamination_parameter( + parameters.get('features', {}), + feature_name, + automatic_contamination=parameters.get('automatic_contamination', False), + method='medfilt', + random_state=42, + ) for var in valid_variables: mask = (df['variable_name'] == var) & (~pd.isnull(df[feature_name])) @@ -151,20 +302,81 @@ def calculate_first_decimal_score(df_item: pd.DataFrame, parameters: Dict[str, A return df + +def calculate_answer_hour_set_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + # Detect time set anomalies using ECOD algorithm. + # ECOD is a parameter-free, highly interpretable outlier detection algorithm based on empirical CDF functions + feature_name = 'f__answer_hour_set' + score_name = rename_feature(feature_name) + df_out = df_item.copy() + + if feature_name not in df_out.columns: + return df_item + + df_out[score_name] = np.nan + df_out[feature_name] = pd.to_numeric(df_out[feature_name], errors='coerce') + + mask = ~pd.isnull(df_out[feature_name]) + df = df_out[mask].copy() + + if df.empty: + return df_out + # Sorting the DataFrame based on the 'frequency' answer_hour_set in descending order + sorted_hours = df[feature_name].value_counts().index + hour_to_rank = {hour: rank for rank, hour in enumerate(sorted_hours)} + # Create a frequency column + df['frequency'] = df[feature_name].map(hour_to_rank) + + # IDENTIFY Outliers by ECOD anomaly detection model + contamination = get_contamination_parameter( + parameters.get('features', {}), + feature_name, + automatic_contamination=parameters.get('automatic_contamination', False), + ) + + model = ECOD(contamination=contamination) + model.fit(df[[feature_name]]) + df[score_name] = model.predict(df[[feature_name]]) + # In case has detected "high frequencies anomalies", set them to 0 + df.loc[df['frequency'] <= df[df[score_name] == 0]['frequency'].min(), score_name] = 0 + + + # # In case ECOD has flagged high-frequency hours as anomalies, revert them to 0. + # # Guard against the degenerate case where every row is an outlier (no inliers), + # # which would make df[df[score_name] == 0]['frequency'].min() return NaN and + # # silently skip the correction via NaN comparison. + # inlier_mask = df[score_name] == 0 + # if inlier_mask.any(): + # min_inlier_rank = df.loc[inlier_mask, 'frequency'].min() + # df.loc[df['frequency'] <= min_inlier_rank, score_name] = 0 + + + # Assign scores back using index labels — safe regardless of index type or value + df_out.loc[df.index, score_name] = df[score_name].values + return df_out + + def calculate_answer_changed_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: feature_name = 'f__answer_changed' score_name = rename_feature(feature_name) df = df_item.copy() - - if feature_name not in df.columns or df[feature_name].dropna().empty: + + if feature_name not in df.columns: + return df_item + if df[feature_name].dropna().empty: df[score_name] = np.nan return df valid_data = df[~pd.isnull(df[feature_name])] valid_variables = filter_variable_name_by_frequency(valid_data, feature_name, frequency=100, min_unique_values=1) df[score_name] = np.nan - df.loc[~pd.isnull(df[feature_name]), score_name] = 0 - contamination = get_contamination_parameter(parameters.get('features', {}), feature_name, method='medfilt', random_state=42) + contamination = get_contamination_parameter( + parameters.get('features', {}), + feature_name, + automatic_contamination=parameters.get('automatic_contamination', False), + method='medfilt', + random_state=42, + ) for var in valid_variables: mask = (df['variable_name'] == var) & (~pd.isnull(df[feature_name])) @@ -180,14 +392,21 @@ def calculate_answer_removed_score(df_item: pd.DataFrame, parameters: Dict[str, score_name = rename_feature(feature_name) df = df_item.copy() - if feature_name not in df.columns or df[feature_name].dropna().empty: + if feature_name not in df.columns: + return df_item + if df[feature_name].dropna().empty: df[score_name] = np.nan return df valid_variables = filter_variable_name_by_frequency(df, feature_name, frequency=100, min_unique_values=1) df[score_name] = np.nan - df.loc[~pd.isnull(df[feature_name]), score_name] = 0 - contamination = get_contamination_parameter(parameters.get('features', {}), feature_name, method='medfilt', random_state=42) + contamination = get_contamination_parameter( + parameters.get('features', {}), + feature_name, + automatic_contamination=parameters.get('automatic_contamination', False), + method='medfilt', + random_state=42, + ) for var in valid_variables: mask = (df['variable_name'] == var) & (~pd.isnull(df[feature_name])) @@ -198,37 +417,21 @@ def calculate_answer_removed_score(df_item: pd.DataFrame, parameters: Dict[str, return df -from scipy.spatial import cKDTree -from pyod.models.lof import LOF - -# Attempting to import legacy stats_utils safely for the math functions -try: - from rissk.utils.stats_utils import ( - calculate_entropy, - calculate_list_entropy, - filter_variables_by_magnitude, - apply_benford_tests - ) -except ImportError: - pass - -try: - from rissk.detection_algorithms_kedro import lat_lon_to_cartesian -except ImportError: - pass def calculate_answer_position_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: feature_name = 'f__answer_position' score_name = rename_feature(feature_name) df = df_item.copy() - - if feature_name not in df.columns or df[feature_name].dropna().empty: + + if feature_name not in df.columns: + return df_item + if df[feature_name].dropna().empty: df[score_name] = np.nan return df valid_data = df[~pd.isnull(df[feature_name])] valid_variables = filter_variable_name_by_frequency(valid_data, feature_name, frequency=100, min_unique_values=3) - df[score_name] = 0 + df[score_name] = np.nan for var in valid_variables: mask = (df['variable_name'] == var) @@ -258,8 +461,10 @@ def calculate_answer_selected_score(df_item: pd.DataFrame, parameters: Dict[str, feature_name = 'f__answer_selected' score_name = rename_feature(feature_name) df = df_item.copy() - - if feature_name not in df.columns or df[feature_name].dropna().empty: + + if feature_name not in df.columns: + return df_item + if df[feature_name].dropna().empty: df[score_name + '_lower'] = np.nan df[score_name + '_upper'] = np.nan return df @@ -269,9 +474,15 @@ def calculate_answer_selected_score(df_item: pd.DataFrame, parameters: Dict[str, score_name1 = score_name + '_lower' score_name2 = score_name + '_upper' - df[score_name] = 0 + df[score_name] = np.nan - contamination = get_contamination_parameter(parameters.get('features', {}), feature_name, method='medfilt', random_state=42) + contamination = get_contamination_parameter( + parameters.get('features', {}), + feature_name, + automatic_contamination=parameters.get('automatic_contamination', False), + method='medfilt', + random_state=42, + ) for var in valid_variables: mask = (df['variable_name'] == var) & (~pd.isnull(df[feature_name])) @@ -299,8 +510,10 @@ def calculate_answer_duration_score(df_item: pd.DataFrame, parameters: Dict[str, feature_name = 'f__answer_duration' score_name = rename_feature(feature_name) df = df_item.copy() - - if feature_name not in df.columns or df[feature_name].dropna().empty: + + if feature_name not in df.columns: + return df_item + if df[feature_name].dropna().empty: df[score_name + '_lower'] = np.nan df[score_name + '_upper'] = np.nan return df @@ -310,11 +523,17 @@ def calculate_answer_duration_score(df_item: pd.DataFrame, parameters: Dict[str, score_name1 = score_name + '_lower' score_name2 = score_name + '_upper' - df[score_name1] = 0 - df[score_name2] = 0 - df[score_name] = 0 + df[score_name1] = np.nan + df[score_name2] = np.nan + df[score_name] = np.nan - contamination = get_contamination_parameter(parameters.get('features', {}), feature_name, method='medfilt', random_state=42) + contamination = get_contamination_parameter( + parameters.get('features', {}), + feature_name, + automatic_contamination=parameters.get('automatic_contamination', False), + method='medfilt', + random_state=42, + ) for var in valid_variables: mask = (df['variable_name'] == var) & (~pd.isnull(df[feature_name])) @@ -343,8 +562,7 @@ def calculate_single_question_score(df_item: pd.DataFrame, parameters: Dict[str, df = df_item.copy() if 'qtype' not in df.columns or 'n_answers' not in df.columns or 'value' not in df.columns: - df[score_name] = np.nan - return df + return df_item # Mask specific for single questions without filter rules bypassing cascades single_question_mask = ( @@ -354,9 +572,10 @@ def calculate_single_question_score(df_item: pd.DataFrame, parameters: Dict[str, (pd.isnull(df.get('cascade_from_question_id', np.nan))) ) - df[score_name] = 0 + df[score_name] = np.nan valid_data = df[single_question_mask] - if valid_data.empty: return df + if valid_data.empty: + return df variables = filter_variable_name_by_frequency(valid_data, 'value', frequency=100, min_unique_values=3) @@ -390,14 +609,14 @@ def calculate_multi_option_question_score(df_item: pd.DataFrame, parameters: Dic df = df_item.copy() if 'qtype' not in df.columns or 'value' not in df.columns: - df[score_name] = np.nan - return df + return df_item multi_question_mask = (df["qtype"] == 'MultyOptionsQuestion') valid_data = df[multi_question_mask] - - df[score_name] = 0 - if valid_data.empty: return df + + df[score_name] = np.nan + if valid_data.empty: + return df # Filter variables safely via counts val_counts = valid_data['variable_name'].value_counts() @@ -433,14 +652,16 @@ def calculate_first_digit_score(df_item: pd.DataFrame, parameters: Dict[str, Any feature_name = 'f__numeric_response' score_name = 's__first_digit' df = df_item.copy() - - if feature_name not in df.columns or df[feature_name].dropna().empty: + + if feature_name not in df.columns: + return df_item + if df[feature_name].dropna().empty: df[score_name] = np.nan return df valid_data = df[~pd.isnull(df[feature_name])] valid_variables = filter_variable_name_by_frequency(valid_data, feature_name, frequency=100, min_unique_values=3) - df[score_name] = 0 + df[score_name] = np.nan try: valid_variables = filter_variables_by_magnitude(valid_data, feature_name, valid_variables, min_order_of_magnitude=3) @@ -467,82 +688,3 @@ def calculate_first_digit_score(df_item: pd.DataFrame, parameters: Dict[str, Any return df -def calculate_gps_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: - df = df_item.copy() - - required_columns = ['f__gps_latitude', 'f__gps_longitude', 'f__gps_accuracy'] - if any(col not in df.columns for col in required_columns): - for col in ['s__gps_proximity_counts', 's__gps_outlier', 's__gps_extreme_outlier']: - df[col] = np.nan - return df - - gps_mask = (~pd.isnull(df['f__gps_latitude'])) & (~pd.isnull(df['f__gps_longitude'])) - if gps_mask.sum() == 0: - for col in ['s__gps_proximity_counts', 's__gps_outlier', 's__gps_extreme_outlier']: - df[col] = np.nan - return df - - data = df.loc[gps_mask].copy() - data['s__gps_extreme_outlier'] = 0 - data.loc[data['f__gps_latitude'] == 0.0, 's__gps_extreme_outlier'] = 1 - data.loc[data['f__gps_longitude'] == 0.0, 's__gps_extreme_outlier'] = 1 - - data['x'], data['y'], data['z'] = lat_lon_to_cartesian(data['f__gps_latitude'], data['f__gps_longitude']) - data['accuracy'] = data['f__gps_accuracy'].fillna(0) / 1e6 - - tree = cKDTree(data[['x', 'y', 'z']]) - radius = 10 / 1e6 - counts = [ - len(tree.query_ball_point(xyz, r=radius + acc)) - 1 - for xyz, acc in zip(data[['x', 'y', 'z']].values, data['accuracy']) - ] - data['s__gps_proximity_counts'] = counts - - mask = data['s__gps_extreme_outlier'] < 1 - data['distance_to_median'] = np.nan - if mask.sum() > 0: - median_x = data.loc[mask].drop_duplicates(subset='x')['x'].median() - median_y = data.loc[mask].drop_duplicates(subset='y')['y'].median() - median_z = data.loc[mask].drop_duplicates(subset='z')['z'].median() - - data.loc[mask, 'distance_to_median'] = np.sqrt( - (data.loc[mask, 'x'] - median_x) ** 2 - + (data.loc[mask, 'y'] - median_y) ** 2 - + (data.loc[mask, 'z'] - median_z) ** 2 - ) - - p75 = data.loc[mask, 'distance_to_median'].quantile(0.75) - median = data.loc[mask, 'distance_to_median'].median() - range_75 = p75 - median - threshold = p75 + 3.5 * range_75 - data.loc[mask, 's__gps_extreme_outlier'] = ( - data.loc[mask, 'distance_to_median'] > threshold - ).astype(int) - - contamination = get_contamination_parameter( - parameters.get('features', {}), - 'f__gps', - method='medfilt', - random_state=42, - ) - coords_columns = ['x', 'y'] - if data.loc[mask].shape[0] < 10000: - model = COF(contamination=contamination) - else: - model = LOF(contamination=contamination, n_neighbors=20) - model.fit(data.loc[mask, coords_columns]) - data.loc[mask, 's__gps_outlier'] = model.predict(data.loc[mask, coords_columns]) - else: - data['s__gps_outlier'] = 0 - - data['s__gps_outlier'] = data['s__gps_outlier'].fillna(0) - df.loc[data.index, 's__gps_proximity_counts'] = data['s__gps_proximity_counts'] - df.loc[data.index, 's__gps_outlier'] = data['s__gps_outlier'] - df.loc[data.index, 's__gps_extreme_outlier'] = data['s__gps_extreme_outlier'] - - for col in ['s__gps_proximity_counts', 's__gps_outlier', 's__gps_extreme_outlier']: - if col in df.columns: - df[col] = df[col].fillna(0) - - return df - From 5d45d8e58d469f14cf89d0e1e6fe93e196ea4e7e Mon Sep 17 00:00:00 2001 From: VJausovec Date: Wed, 11 Mar 2026 12:30:43 +0000 Subject: [PATCH 30/70] Refactor catalog.yml to reorganize feature engineering and creation DataFrames; update file types for risk scores to CSV format. --- rissk_kedro/conf/base/catalog.yml | 35 ++++++++++++++++--------------- 1 file changed, 18 insertions(+), 17 deletions(-) diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index 9bbfd9d..1687757 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -30,6 +30,8 @@ raw_microdata: type: pandas.ParquetDataset filepath: data/${globals:survey.name}/latest/30_PROCESSED/microdata.parquet +# === FEATURE ENGINEERING DataFrames === + paradata_processed: type: pandas.ParquetDataset filepath: data/${globals:survey.name}/latest/30_PROCESSED/paradata_processed.parquet @@ -38,14 +40,6 @@ paradata_active: type: pandas.ParquetDataset filepath: data/${globals:survey.name}/latest/30_PROCESSED/paradata_active.parquet -# === FEATURE CREATION DataFrames === -item_features_base: - type: pandas.ParquetDataset - filepath: data/${globals:survey.name}/latest/20_INTERIM/item_features_base.parquet - -unit_features_base: - type: pandas.ParquetDataset - filepath: data/${globals:survey.name}/latest/20_INTERIM/unit_features_base.parquet # === LEGACY DATA FOR PIPELINE TESTING === # Uncomment these and update pipeline.py inputs to test against legacy-produced data. @@ -65,7 +59,17 @@ legacy_paradata_active: type: pandas.ParquetDataset filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/${globals:survey.name}/latest/30_PROCESSED/paradata_active.parquet -# Final Feature Tables (Input to Risk Scoring) + +# === FEATURE CREATION DataFrames === +item_features_base: + type: pandas.ParquetDataset + filepath: data/${globals:survey.name}/latest/20_INTERIM/item_features_base.parquet + +unit_features_base: + type: pandas.ParquetDataset + filepath: data/${globals:survey.name}/latest/20_INTERIM/unit_features_base.parquet + + # Final Feature Tables (Input to Risk Scoring) item_features: type: pandas.ParquetDataset filepath: data/${globals:survey.name}/latest/30_PROCESSED/item_features.parquet @@ -73,10 +77,7 @@ item_features: unit_features: type: pandas.ParquetDataset filepath: data/${globals:survey.name}/latest/30_PROCESSED/unit_features.parquet -# === PRE-SCORING DataFrames === -item_features_removed: - type: pandas.ParquetDataset - filepath: data/${globals:survey.name}/latest/20_INTERIM/item_features_removed.parquet + # === SCORING DataFrames === item_scores: @@ -84,9 +85,9 @@ item_scores: filepath: data/${globals:survey.name}/latest/40_SCORED/item_scores.parquet unit_risk_scores: - type: pandas.ParquetDataset - filepath: data/${globals:survey.name}/latest/40_SCORED/unit_risk_scores.parquet + type: pandas.CSVDataset + filepath: data/${globals:survey.name}/latest/40_SCORED/unit_risk_scores.csv responsible_scores: - type: pandas.ParquetDataset - filepath: data/${globals:survey.name}/latest/40_SCORED/responsible_scores.parquet + type: pandas.CSVDataset + filepath: data/${globals:survey.name}/latest/40_SCORED/responsible_scores.csv From 0f21b9c45167c4cd936eb665c3fc8cbc7d41fd3e Mon Sep 17 00:00:00 2001 From: VJausovec Date: Thu, 12 Mar 2026 14:44:45 +0000 Subject: [PATCH 31/70] Refactor scoring pipeline and functions to improve handling of answer removal scores; introduce new stats utility functions and ensure legacy behavior is matched for deleted items. --- rissk/feature_processing_kedro.py | 2 +- rissk/item_processing_kedro.py | 109 ++++-- rissk/unit_processing_kedro.py | 5 +- rissk/utils/stats_utils_kedro.py | 309 ++++++++++++++++++ .../pipelines/rissk_scoring/nodes.py | 35 +- .../pipelines/rissk_scoring/pipeline.py | 6 +- 6 files changed, 424 insertions(+), 42 deletions(-) create mode 100644 rissk/utils/stats_utils_kedro.py diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index 4b0bd82..1297b42 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -444,7 +444,7 @@ def feat_answer_removed(df_item, **kwargs): # f__answer_removed, answers removed (by interviewer, or by system as a result of interviewer action). # Matches legacy get_feature_item__answer_removed which uses self.df_paradata, but it appends the # feature to the item table instead of returning a separate dataframe. - # (all events, role=1, interviewing=True — not limited to active events). + # (all events, role=1, interviewing=True). # The legacy method notes this feature may include items no longer in microdata. feature_name = 'f__answer_removed' paradata_full = kwargs.get('paradata_full') diff --git a/rissk/item_processing_kedro.py b/rissk/item_processing_kedro.py index 7505825..1ea44a8 100644 --- a/rissk/item_processing_kedro.py +++ b/rissk/item_processing_kedro.py @@ -9,10 +9,10 @@ from pyod.models.lof import LOF from scipy.spatial import cKDTree -from rissk.utils.stats_utils import ( - calculate_entropy, - calculate_list_entropy, - filter_variables_by_magnitude, +from rissk.utils.stats_utils_kedro import ( + calculate_entropy, + calculate_list_entropy, + filter_variables_by_magnitude, apply_benford_tests ) from rissk.detection_algorithms_kedro import lat_lon_to_cartesian @@ -276,7 +276,7 @@ def calculate_first_decimal_score(df_item: pd.DataFrame, parameters: Dict[str, A df = df_item.copy() if feature_name not in df.columns: - return df_item + return df if df[feature_name].dropna().empty: df[score_name] = np.nan return df @@ -311,7 +311,7 @@ def calculate_answer_hour_set_score(df_item: pd.DataFrame, parameters: Dict[str, df_out = df_item.copy() if feature_name not in df_out.columns: - return df_item + return df_out df_out[score_name] = np.nan df_out[feature_name] = pd.to_numeric(df_out[feature_name], errors='coerce') @@ -362,12 +362,13 @@ def calculate_answer_changed_score(df_item: pd.DataFrame, parameters: Dict[str, df = df_item.copy() if feature_name not in df.columns: - return df_item + return df if df[feature_name].dropna().empty: df[score_name] = np.nan return df valid_data = df[~pd.isnull(df[feature_name])] + # Select only those variables that have at least 1 distinct values and more than one hundred records valid_variables = filter_variable_name_by_frequency(valid_data, feature_name, frequency=100, min_unique_values=1) df[score_name] = np.nan contamination = get_contamination_parameter( @@ -387,19 +388,63 @@ def calculate_answer_changed_score(df_item: pd.DataFrame, parameters: Dict[str, return df -def calculate_answer_removed_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: +# NOTE: s__answer_removed is NOT computed at item level. +# +# Legacy `make_score__answer_removed` operated on a frame derived directly from +# df_paradata (via get_feature_item__answer_removed), which includes AnswerRemoved +# events for items that were subsequently deleted from microdata and are therefore +# absent from df_item. In the Kedro pipeline, df_item is built from microdata and +# f__answer_removed is merged back with how='left' — so those deleted items are +# silently dropped, making an item-level s__answer_removed on df_item structurally +# incomplete and potentially misleading. +# +# The authoritative score is computed at UNIT level from paradata_full directly by +# calculate_answer_removed_unit_score below, matching legacy coverage exactly. + + +def calculate_answer_removed_unit_score( + paradata_full: pd.DataFrame, + parameters: Dict[str, Any], +) -> pd.Series: + """Score answer-removal anomalies from paradata_full directly, matching legacy + make_score_unit__answer_removed which operated on self.df_paradata (NOT df_item). + + Items deleted from microdata — whose AnswerRemoved events are absent from df_item + because of the how='left' merge in feat_answer_removed — are included here, + eliminating the undercount introduced by the Kedro item-table path. + + Returns a Series indexed by interview__id → mean s__answer_removed score, + ready to be mapped directly into df_unit. + """ feature_name = 'f__answer_removed' score_name = rename_feature(feature_name) - df = df_item.copy() - if feature_name not in df.columns: - return df_item - if df[feature_name].dropna().empty: - df[score_name] = np.nan - return df + required_cols = ['event', 'role', 'order', 'interview__id', 'variable_name'] + if any(c not in paradata_full.columns for c in required_cols): + logger.warning( + "calculate_answer_removed_unit_score: paradata_full is missing one or more " + "required columns %s; returning empty Series.", required_cols + ) + return pd.Series(dtype=float) + + # Replicate legacy get_feature_item__answer_removed exactly. + removed_mask = (paradata_full['event'] == 'AnswerRemoved') & (paradata_full['role'] == 1) + df_removed = paradata_full[removed_mask] + + if df_removed.empty: + return pd.Series(dtype=float) + + # Match legacy groupby grain: (interview__id, responsible, variable_name, qnr_seq). + # qnr_seq may be absent in some paradata versions; fall back gracefully. + group_cols = [c for c in ['interview__id', 'responsible', 'variable_name', 'qnr_seq'] + if c in df_removed.columns] + df = df_removed.groupby(group_cols).agg( + f__answer_removed=('order', 'count') + ).reset_index() valid_variables = filter_variable_name_by_frequency(df, feature_name, frequency=100, min_unique_values=1) - df[score_name] = np.nan + + df[score_name] = 0 contamination = get_contamination_parameter( parameters.get('features', {}), feature_name, @@ -415,35 +460,31 @@ def calculate_answer_removed_score(df_item: pd.DataFrame, parameters: Dict[str, model.fit(df.loc[mask, [feature_name]]) df.loc[mask, score_name] = model.predict(df.loc[mask, [feature_name]]) - return df + # Aggregate to interview__id level matching legacy make_score_unit__answer_removed. + return df.groupby('interview__id')[score_name].mean() -def calculate_answer_position_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: +def calculate_answer_position_score(df_item: pd.DataFrame) -> pd.DataFrame: feature_name = 'f__answer_position' score_name = rename_feature(feature_name) df = df_item.copy() if feature_name not in df.columns: - return df_item + return df if df[feature_name].dropna().empty: df[score_name] = np.nan return df - valid_data = df[~pd.isnull(df[feature_name])] - valid_variables = filter_variable_name_by_frequency(valid_data, feature_name, frequency=100, min_unique_values=3) + valid_variables = filter_variable_name_by_frequency(df[~pd.isnull(df[feature_name])], feature_name, frequency=100, min_unique_values=3) df[score_name] = np.nan for var in valid_variables: - mask = (df['variable_name'] == var) + mask = (df['variable_name'] == var) & (~pd.isnull(df[feature_name])) if mask.sum() > 0: unique_values = df[mask][feature_name].nunique() - try: - entropy_df = df[mask].groupby('responsible')[feature_name].apply( - calculate_entropy, unique_values=unique_values, min_record_sample=10 - ).reset_index() - except NameError: - continue # if calculate_entropy not found - + entropy_df = df[mask].groupby('responsible')[feature_name].apply( + calculate_entropy, unique_values=unique_values, min_record_sample=10 + ).reset_index() entropy_df = entropy_df[~pd.isnull(entropy_df[feature_name])] if entropy_df.shape[0] > 0: @@ -454,7 +495,7 @@ def calculate_answer_position_score(df_item: pd.DataFrame, parameters: Dict[str, # Apply map safely responsible_map = entropy_df.set_index('responsible')[score_name].to_dict() - df.loc[mask, score_name] = df.loc[mask, 'responsible'].map(responsible_map).fillna(0) + df.loc[mask, score_name] = df.loc[mask, 'responsible'].map(responsible_map) return df def calculate_answer_selected_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: @@ -463,7 +504,7 @@ def calculate_answer_selected_score(df_item: pd.DataFrame, parameters: Dict[str, df = df_item.copy() if feature_name not in df.columns: - return df_item + return df if df[feature_name].dropna().empty: df[score_name + '_lower'] = np.nan df[score_name + '_upper'] = np.nan @@ -512,7 +553,7 @@ def calculate_answer_duration_score(df_item: pd.DataFrame, parameters: Dict[str, df = df_item.copy() if feature_name not in df.columns: - return df_item + return df if df[feature_name].dropna().empty: df[score_name + '_lower'] = np.nan df[score_name + '_upper'] = np.nan @@ -562,7 +603,7 @@ def calculate_single_question_score(df_item: pd.DataFrame, parameters: Dict[str, df = df_item.copy() if 'qtype' not in df.columns or 'n_answers' not in df.columns or 'value' not in df.columns: - return df_item + return df # Mask specific for single questions without filter rules bypassing cascades single_question_mask = ( @@ -609,7 +650,7 @@ def calculate_multi_option_question_score(df_item: pd.DataFrame, parameters: Dic df = df_item.copy() if 'qtype' not in df.columns or 'value' not in df.columns: - return df_item + return df multi_question_mask = (df["qtype"] == 'MultyOptionsQuestion') valid_data = df[multi_question_mask] @@ -654,7 +695,7 @@ def calculate_first_digit_score(df_item: pd.DataFrame, parameters: Dict[str, Any df = df_item.copy() if feature_name not in df.columns: - return df_item + return df if df[feature_name].dropna().empty: df[score_name] = np.nan return df diff --git a/rissk/unit_processing_kedro.py b/rissk/unit_processing_kedro.py index 912e134..de262cd 100644 --- a/rissk/unit_processing_kedro.py +++ b/rissk/unit_processing_kedro.py @@ -91,8 +91,11 @@ def aggregate_item_to_unit_scores(df_unit: pd.DataFrame, df_item_scores: pd.Data df_out = df_unit.copy() # 1. Simple mean aggregations + # Note: s__answer_removed is intentionally excluded here — it is scored + # at unit level directly from paradata_full by calculate_answer_removed_unit_score + # in calculate_unit_scores, so that items deleted from microdata are included. mean_scores = [ - 's__answer_hour_set', 's__answer_removed', 's__answer_changed', + 's__answer_hour_set', 's__answer_changed', 's__first_decimal', 's__sequence_jump' ] for score in mean_scores: diff --git a/rissk/utils/stats_utils_kedro.py b/rissk/utils/stats_utils_kedro.py new file mode 100644 index 0000000..9c811df --- /dev/null +++ b/rissk/utils/stats_utils_kedro.py @@ -0,0 +1,309 @@ +""" +stats_utils_kedro.py +==================== +Kedro-pipeline equivalent of stats_utils.py. + +Changelog vs stats_utils.py +---------------------------- +[FIX] calculate_entropy: normalisation divisor corrected from np.log2(unique_values) + to np.log(unique_values). scipy.stats.entropy uses the natural logarithm (nats) + by default, so the maximum entropy for n categories is ln(n) nats. Dividing by + log2(n) instead of ln(n) introduced a constant scale factor of ln(2) ≈ 0.693, + capping the output at ~0.693 instead of 1.0 and making the values uninterpretable + as a true [0, 1] normalised measure. Binary anomaly-flag outputs were unaffected + (the factor cancelled in relative comparisons), but the raw entropy values stored + or logged were misleading. + +[FIX] calculate_list_entropy: same normalisation correction as calculate_entropy. + +[REMOVED] Duplicate median_value computation that appeared twice on consecutive lines + in the caller functions (item_processing.py lines 259–261, 353–355, 389–391). + Those callers have been cleaned in item_processing_kedro.py; the note is kept + here for traceability. + +No algorithmic or interface changes; all function signatures are identical to legacy. +""" + +import math +import pandas as pd +import numpy as np +from scipy.stats import entropy +from scipy import stats +from sklearn.preprocessing import StandardScaler +from scipy.stats import chisquare, fisher_exact +from collections import Counter +from scipy.stats.mstats import winsorize + + +# --------------------------------------------------------------------------- +# Jensen-Shannon helpers (unchanged from legacy) +# --------------------------------------------------------------------------- + +def jensen_shannon_divergence(p, q): + m = 0.5 * (p + q) + return 0.5 * (entropy(p, m) + entropy(q, m)) + + +def jensen_shannon_distance(p, q): + return np.sqrt(jensen_shannon_divergence(p, q)) + + +# --------------------------------------------------------------------------- +# Digit helpers (unchanged from legacy) +# --------------------------------------------------------------------------- + +def get_digit_frequecies(df, feature_name, apply_first_digit, minimum_sample=50): + digit_mask = (df[feature_name] != 0) + if apply_first_digit: + total_digit_values = df[digit_mask][feature_name].apply(first_digit) + else: + total_digit_values = df[digit_mask][feature_name].apply(last_digit) + total_digit_count = Counter(total_digit_values) + total_digit_count = [total_digit_count.get(i, 0) for i in range(1, 10)] + if sum(total_digit_count) < minimum_sample: + total_digit_freq = None + else: + total_digit_freq = [v / sum(total_digit_count) for v in total_digit_count] + return total_digit_freq + + +def first_digit(val): + """Extract the first digit from a value.""" + val = abs(val) + return int(str(val)[0]) + + +def last_digit(val): + """Extract the last digit from a value.""" + return int(str(int(val))[-1]) + + +def apply_benford_tests(df, valid_variables, responsible_col, feature_name, + apply_first_digit=True, minimum_sample=50): + responsible_list = df[responsible_col].unique() + results = [] + for var in valid_variables: + variable_mask = df['variable_name'] == var + for resp in responsible_list: + score = None + resp_mask = (df[responsible_col] == resp) + total_digit_count = get_digit_frequecies( + df[variable_mask & (~resp_mask)], feature_name, apply_first_digit, + minimum_sample=minimum_sample, + ) + resp_digit_count = get_digit_frequecies( + df[variable_mask & resp_mask], feature_name, apply_first_digit, + minimum_sample=minimum_sample, + ) + if resp_digit_count is not None and total_digit_count is not None: + score = jensen_shannon_distance( + np.array(total_digit_count), np.array(resp_digit_count) + ) + results.append((resp, var, score)) + return pd.DataFrame(results, columns=[responsible_col, 'variable_name', feature_name]) + + +# --------------------------------------------------------------------------- +# Outlier helpers (unchanged from legacy) +# --------------------------------------------------------------------------- + +def get_outlier_by_magnitude(series, mode_deviation=3, threshold_freq=0.02): + """ + Detects values that are anomalies based on their order of magnitude. + + Args: + - series (pd.Series): Series of numeric values. + - mode_deviation (int): Maximum allowable deviation from the mode's order of magnitude. + - threshold_freq (float): Maximum relative frequency for an order of magnitude to be + considered anomalous. + + Returns: + - pd.Series: Boolean Series with True for anomalies and False for normal values. + """ + min_value = series.min() + if min_value <= 0: + order_of_magnitude = np.floor(np.log10(series + abs(min_value) + 1)) + else: + order_of_magnitude = np.floor(np.log10(series)) + + mode_order = max(order_of_magnitude.mode().iloc[0], 1) + mode_based_anomalies = ( + (order_of_magnitude < mode_order - mode_deviation) + | (order_of_magnitude > mode_order + mode_deviation) + ) + + freq_count = order_of_magnitude.value_counts() / series.count() + anomalous_orders = freq_count[freq_count <= threshold_freq].index + freq_based_anomalies = order_of_magnitude.isin(anomalous_orders) + + return mode_based_anomalies | freq_based_anomalies + + +def get_outlier_iqr(data, column_name): + q_high = data[column_name].quantile(0.75) + q_low = data[column_name].quantile(0.25) + iqr = q_high - q_low + lower_outlier = (data[column_name] < q_low - 1.5 * iqr) & (~pd.isnull(data[column_name])) + upper_outlier = (data[column_name] > q_high + 1.5 * iqr) & (~pd.isnull(data[column_name])) + return lower_outlier, upper_outlier + + +def get_outlier_z_score(data, column_name, threshold=2.5): + lower_limit = data[column_name].mean() - threshold * data[column_name].std() + upper_limit = data[column_name].mean() + threshold * data[column_name].std() + lower_outlier = (data[column_name] < lower_limit) & (~pd.isnull(data[column_name])) + upper_outlier = (data[column_name] > upper_limit) & (~pd.isnull(data[column_name])) + return lower_outlier, upper_outlier + + +def filter_variables_by_magnitude(df, feature_name, variables, min_order_of_magnitude=3): + def order_of_magnitude(num): + if num == 0: + return 0 + elif num < 0: + num = -num + return int(math.floor(math.log10(num))) + + valid_variables = [] + for var in variables: + var_values = df[df['variable_name'] == var][feature_name] + max_magnitude = order_of_magnitude(var_values.max()) + min_magnitude = order_of_magnitude(var_values.min()) + if max_magnitude - min_magnitude >= min_order_of_magnitude: + valid_variables.append(var) + return valid_variables + + +def get_box_cox_rescaled(series): + scaler = StandardScaler() + min_value = series.min() + box_cox = series + if series.nunique() > 1: + if min_value <= 0: + box_cox = box_cox + abs(min_value) + 1 + box_cox, _ = stats.boxcox(box_cox) + box_cox = scaler.fit_transform(box_cox.reshape(-1, 1)) + return box_cox + + +# --------------------------------------------------------------------------- +# Entropy helpers +# --------------------------------------------------------------------------- + +def calculate_list_entropy(column, unique_values, min_record_sample=10): + """ + Calculate the normalised entropy of a multi-value (list) column. + + Parameters + ---------- + column : pd.Series + Series of lists for a single responsible group. + unique_values : int + Global number of distinct answer options for the variable (used as + the normalisation denominator, so entropy is relative to the maximum + possible diversity across all responsibles, not just this group). + min_record_sample : int, optional + Minimum records required per unique value before entropy is computed. + Groups below this threshold return None (insufficient data). + + Returns + ------- + float or None + Normalised entropy in [0, 1] if conditions are met. + 0 for single-value distributions with enough samples. + None when the sample is too small. + + Notes + ----- + [FIX] Legacy used `np.log2(unique_values)` as the divisor, producing a + range of [0, ln(2)] ≈ [0, 0.693] instead of [0, 1]. scipy.stats.entropy + returns nats (natural log base), so the correct divisor is np.log(unique_values). + The binary anomaly flags produced by callers were unaffected because the + ln(2) factor cancelled in relative (median-based) comparisons, but the raw + entropy values were uninterpretable as a normalised measure. + """ + column = column[column != '##N/A##'] + flattened_series = column.explode() + prob_distribution = flattened_series.value_counts(normalize=True) + + if unique_values > 1 and flattened_series.shape[0] >= min_record_sample * unique_values: + # [FIX] Corrected: np.log(unique_values) matches scipy's natural-log base, + # yielding a true [0, 1] normalised entropy. + # Legacy used np.log2(unique_values), capping values at ln(2) ≈ 0.693. + entropy_ = entropy(prob_distribution.values) / np.log(unique_values) + elif unique_values == 1 and flattened_series.shape[0] >= min_record_sample * unique_values: + entropy_ = 0 + else: + entropy_ = None + + return entropy_ + + +def calculate_entropy(column, unique_values, min_record_sample=10): + """ + Calculate the normalised entropy of a column for a single responsible group. + + Parameters + ---------- + column : pd.Series + Feature values for a single responsible group; must be null-free + (callers are responsible for pre-filtering nulls before groupby). + unique_values : int + Global number of distinct values for the variable (used as the + normalisation denominator so that entropy is expressed relative to + the maximum possible diversity across all responsibles, not just + within this group). + min_record_sample : int, optional + Minimum records required per unique value before entropy is computed. + Groups below this threshold return None (insufficient data). + + Returns + ------- + float or None + Normalised entropy in [0, 1] if conditions are met. + 0 for single-value distributions with enough samples. + None when the sample is too small. + + Notes + ----- + [FIX] Legacy used `np.log2(unique_values)` as the divisor, producing a + range of [0, ln(2)] ≈ [0, 0.693] instead of [0, 1]. scipy.stats.entropy + returns nats (natural log base), so the correct divisor is np.log(unique_values). + The binary anomaly flags produced by callers were unaffected because the + ln(2) factor cancelled in relative (median-based) comparisons, but the raw + entropy values were uninterpretable as a normalised measure. + + [NOTE] column.shape[0] must reflect only non-null rows. Callers must apply + a null-excluding mask before groupby so that the sample-size threshold + (min_record_sample * unique_values) is not inflated by null entries. + """ + prob_distribution = column.value_counts(normalize=True) + + if unique_values > 1 and column.shape[0] >= min_record_sample * unique_values: + # [FIX] Corrected: np.log(unique_values) matches scipy's natural-log base, + # yielding a true [0, 1] normalised entropy. + # Legacy used np.log2(unique_values), capping values at ln(2) ≈ 0.693. + entropy_ = entropy(prob_distribution.values) / np.log(unique_values) + elif unique_values == 1 and column.shape[0] >= min_record_sample * unique_values: + entropy_ = 0 + else: + entropy_ = None + + return entropy_ + + +# --------------------------------------------------------------------------- +# Winsorize helper (unchanged from legacy) +# --------------------------------------------------------------------------- + +def adjustable_winsorize(data, initial_lower=0.05, initial_upper=0.05, step=0.01): + lower_limit = initial_lower + upper_limit = initial_upper + winsorized_data = winsorize(data, limits=[lower_limit, upper_limit]) + + while len(np.unique(winsorized_data)) <= 1 and (lower_limit > 0 or upper_limit > 0): + lower_limit = max(0, lower_limit - step) + upper_limit = max(0, upper_limit - step) + winsorized_data = winsorize(data, limits=[lower_limit, upper_limit]) + + return winsorized_data diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py index f12ac33..f4701e6 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py @@ -7,7 +7,10 @@ calculate_sequence_jump_score, calculate_first_decimal_score, calculate_answer_changed_score, - calculate_answer_removed_score, + # calculate_answer_removed_score is intentionally absent: s__answer_removed is + # computed at unit level from paradata_full by calculate_answer_removed_unit_score + # so that AnswerRemoved events for items deleted from microdata are not missed. + calculate_answer_removed_unit_score, calculate_answer_position_score, calculate_answer_selected_score, calculate_answer_duration_score, @@ -37,7 +40,8 @@ def calculate_item_scores(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> df_scored = calculate_sequence_jump_score(df_scored, parameters) df_scored = calculate_first_decimal_score(df_scored, parameters) df_scored = calculate_answer_changed_score(df_scored, parameters) - df_scored = calculate_answer_removed_score(df_scored, parameters) + # s__answer_removed is not computed here — see calculate_answer_removed_unit_score + # in calculate_unit_scores, which scores from paradata_full to match legacy coverage. df_scored = calculate_answer_position_score(df_scored, parameters) df_scored = calculate_answer_selected_score(df_scored, parameters) df_scored = calculate_answer_duration_score(df_scored, parameters) @@ -47,16 +51,37 @@ def calculate_item_scores(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> df_scored = calculate_gps_score(df_scored, parameters) return df_scored -def calculate_unit_scores(df_unit: pd.DataFrame, df_item_scores: pd.DataFrame, parameters: Dict[str, Any]) -> Tuple[pd.DataFrame, pd.DataFrame]: +def calculate_unit_scores(df_unit: pd.DataFrame, df_item_scores: pd.DataFrame, parameters: Dict[str, Any], paradata_full: pd.DataFrame = None) -> Tuple[pd.DataFrame, pd.DataFrame]: """ Aggregate item scores to unit, extract responsible scores, and calculate global risk. + + paradata_full is the full processed paradata stream (role=1, interviewing=True). + It is used to compute s__answer_removed at unit level directly from paradata, + matching legacy behaviour where items deleted from microdata are still counted. """ logger.info("Calculating Unit Scores and Global Risk...") - # 1. Aggregate item-level scores up to unit level + # 1. Aggregate item-level scores up to unit level. + # s__answer_removed is excluded from this aggregation (see aggregate_item_to_unit_scores); + # it is handled below using paradata_full to match legacy coverage. df_unit_scored = aggregate_item_to_unit_scores(df_unit, df_item_scores) + + # 2a. Score answer_removed at unit level from paradata_full. + # This replicates legacy make_score_unit__answer_removed which read from df_paradata + # directly and therefore included AnswerRemoved events for items later deleted from + # microdata. Falling back to the df_item-based mean when paradata_full is unavailable. + if paradata_full is not None and not paradata_full.empty: + unit_removed = calculate_answer_removed_unit_score(paradata_full, parameters) + df_unit_scored['s__answer_removed'] = df_unit_scored['interview__id'].map(unit_removed).fillna(0) + elif 's__answer_removed' in df_item_scores.columns: + logger.warning( + "paradata_full not available; falling back to df_item-based s__answer_removed " + "aggregation (may undercount removals for deleted items)." + ) + data = df_item_scores.groupby('interview__id')['s__answer_removed'].mean() + df_unit_scored['s__answer_removed'] = df_unit_scored['interview__id'].map(data).fillna(0) - # 2. Add pure unit-level calculations + # 2b. Add pure unit-level calculations df_unit_scored = calculate_unit_level_scores(df_unit_scored, parameters) # 3. Aggregate item-level scores up to responsible level diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py index 7957b54..7723b79 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py @@ -17,7 +17,11 @@ def create_pipeline(**kwargs) -> Pipeline: ), node( func=calculate_unit_scores, - inputs=["unit_features", "item_scores", "parameters"], + # paradata_full (4th arg) gives calculate_unit_scores access to ALL + # AnswerRemoved events, including those for items deleted from microdata, + # matching legacy make_score_unit__answer_removed behaviour. + # Real pipeline: replace "legacy_paradata_processed" with "paradata_processed". + inputs=["unit_features", "item_scores", "parameters", "legacy_paradata_processed"], outputs=["unit_risk_scores", "responsible_scores"], name="calculate_unit_scores_node", ), From a3795ab939d2ed07a813bc07cd2646026da39cb6 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Mon, 16 Mar 2026 13:39:21 +0000 Subject: [PATCH 32/70] Refactor feature processing and item processing functions to improve clarity and consistency; Remove the active paradata generation from the kedro node and from feature creation --- rissk/feature_processing_kedro.py | 104 ++++++------ rissk/item_processing_kedro.py | 151 ++++++++++-------- .../pipelines/feature_engineering/nodes.py | 10 +- .../pipelines/feature_engineering/pipeline.py | 1 + 4 files changed, 148 insertions(+), 118 deletions(-) diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index 1297b42..68080de 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -58,9 +58,9 @@ def _coerce_numeric_with_warning(df_item: pd.DataFrame, numeric_mask: pd.Series, return coerced -def get_df_time(df_active_paradata: pd.DataFrame) -> pd.DataFrame: +def get_df_time(df_paradata_full: pd.DataFrame) -> pd.DataFrame: """Calculates time differences and durations from paradata.""" - df_time = df_active_paradata.copy() + df_time = df_paradata_full.copy() # calculate time difference in seconds df_time['time_difference'] = df_time.groupby('interview__id')['timestamp_local'].diff() @@ -99,11 +99,11 @@ def get_df_time(df_active_paradata: pd.DataFrame) -> pd.DataFrame: return df_time -def get_df_sequence(df_active_paradata: pd.DataFrame) -> pd.DataFrame: +def get_df_sequence(df_paradata_full: pd.DataFrame) -> pd.DataFrame: """Calculates sequence-based features (jumps, previous answers).""" - # Filter for AnswerSet and get the last entry per index_col - mask = df_active_paradata['event'] == 'AnswerSet' - df_last = df_active_paradata[mask].groupby('index_col').last() + # Filter for AnswerSet and get the last entry per index_col (filter is already applied in base item table creation) + # mask = df_paradata_full['event'] == 'AnswerSet' + df_last = df_paradata_full.groupby('index_col').last().copy() # The groupby puts index_col in the index. # We need to sort by interview_id and order to reconstruct the sequence flow. @@ -228,7 +228,7 @@ def add_unit_time_features(df_unit: pd.DataFrame, df_time: pd.DataFrame, allowed # --- Base Table Creation --- -def create_base_item_table(microdata: pd.DataFrame, paradata_active: pd.DataFrame, parameters: dict) -> pd.DataFrame: +def create_base_item_table(microdata: pd.DataFrame, paradata_full: pd.DataFrame, parameters: dict) -> pd.DataFrame: """ Creates the base item table by merging microdata with paradata information. Equivalent to FeatureProcessing.make_df_item. @@ -248,33 +248,38 @@ def create_base_item_table(microdata: pd.DataFrame, paradata_active: pd.DataFram # 2. Select initial columns columns = ['value', "qtype", 'is_integer', 'qnr_seq', - 'n_answers', 'answer_sequence', - 'cascade_from_question_id', 'is_filtered_combobox', - 'index_col'] + item_level_columns + 'n_answers', 'answer_sequence', + 'cascade_from_question_id', 'is_filtered_combobox', + 'index_col'] + item_level_columns # Intersect with available columns to avoid KeyErrors - # columns = [c for c in columns if c in df_item.columns] df_item = df_item[columns].copy() # 3. Prepare Paradata for Merge # We want the *last* AnswerSet for each item paradata_columns = ['responsible', 'f__answer_hour_set', 'interviewing', 'tz_offset'] - available_para_cols = [c for c in paradata_columns if c in paradata_active.columns] + # available_para_cols = [c for c in paradata_columns if c in paradata_full.columns] - answer_set_mask = (paradata_active['event'] == 'AnswerSet') - - # # Already present in paradata_active from ingestion, but ensure it's there for merging - # if 'index_col' not in paradata_active.columns: - # paradata_active = make_index_col(paradata_active.copy()) + # The filter paradata_full['role'] == 1 is already applied in the paradata processing node, + # so all events in paradata_full should be from the interviewer. + paradata_full['question_scope'] = paradata_full['question_scope'].fillna('') + question_scope_mask = paradata_full['question_scope'].isin([0, '']) + answer_set_mask = (paradata_full['event'] == 'AnswerSet') + active_events_mask = [ + 'InterviewCreated', 'AnswerSet', 'AnswerRemoved', 'CommentSet', + 'Restarted', 'Resumed' # pause events, which have empty question scope + ] data_to_merge = ( - paradata_active[answer_set_mask] - .dropna(subset=['index_col']) # drop rows without index_col - .drop_duplicates(subset='index_col', keep='last') - ) - + paradata_full[answer_set_mask & question_scope_mask] + .dropna(subset=['index_col']) # drop rows without index_col + # keep the last AnswerSet per item, paradata is already sorted by interview__id and order in the processing node + .drop_duplicates(subset='index_col', keep='last') + [['index_col'] + paradata_columns] # select only necessary columns for merging + ) + # 4. Merge - df_item = df_item.merge(data_to_merge[available_para_cols + ['index_col']], how='left', on='index_col') + df_item = df_item.merge(data_to_merge[paradata_columns + ['index_col']], how='left', on='index_col') # 5. Filter for 'interviewing' == True (Supervisor Logic) # Remove items that are not in interviewing @@ -282,17 +287,17 @@ def create_base_item_table(microdata: pd.DataFrame, paradata_active: pd.DataFram # 6. Add Sequence Features if calculate_sequence: - df_sequence = get_df_sequence(paradata_active) + df_sequence = get_df_sequence(paradata_full[answer_set_mask & question_scope_mask]) df_item = add_sequence_features(df_item, df_sequence, allowed_features) # 7. Add Time Features if calculate_time: - df_time = get_df_time(paradata_active) + df_time = get_df_time(paradata_full[active_events_mask & question_scope_mask]) df_item = add_item_time_features(df_item, df_time, allowed_features, item_level_columns) return df_item -def create_base_unit_table(paradata_active: pd.DataFrame, parameters: dict) -> pd.DataFrame: +def create_base_unit_table(paradata_full: pd.DataFrame, parameters: dict) -> pd.DataFrame: """ Creates the base unit table (one row per interview). Equivalent to FeatureProcessing.make_df_unit. @@ -302,8 +307,18 @@ def create_base_unit_table(paradata_active: pd.DataFrame, parameters: dict) -> p # 1. Initialize from paradata columns = ['interview__id', 'responsible', 'qnr', 'qnr_version'] - # columns = [c for c in columns if c in paradata_active.columns] + # The filter paradata_full['role'] == 1 is already applied in the paradata processing node, + # so all events in paradata_full should be from the interviewer. + paradata_full['question_scope'] = paradata_full['question_scope'].fillna('') + question_scope_mask = paradata_full['question_scope'].isin([0, '']) + active_events_mask = [ + 'InterviewCreated', 'AnswerSet', 'AnswerRemoved', 'CommentSet', + 'Restarted', 'Resumed' # pause events, which have empty question scope + ] + paradata_active = paradata_full[active_events_mask & question_scope_mask].copy() + + df_unit = paradata_active[columns].copy() df_unit.drop_duplicates(inplace=True) @@ -440,42 +455,40 @@ def calc_pos(row): return df_item -def feat_answer_removed(df_item, **kwargs): +def feat_answer_removed(paradata_full): # f__answer_removed, answers removed (by interviewer, or by system as a result of interviewer action). # Matches legacy get_feature_item__answer_removed which uses self.df_paradata, but it appends the # feature to the item table instead of returning a separate dataframe. # (all events, role=1, interviewing=True). # The legacy method notes this feature may include items no longer in microdata. feature_name = 'f__answer_removed' - paradata_full = kwargs.get('paradata_full') - if paradata_full is None: - return df_item removed_mask = ( (paradata_full['event'] == 'AnswerRemoved') & - (paradata_full['role'] == 1) + (paradata_full['role'] == 1) # interviewer role is already filtered in paradata processing node ) - df_removed = paradata_full[removed_mask] + + df_removed = paradata_full[removed_mask].copy() if df_removed.empty: - return df_item + return df_removed # Align grouping grain with legacy helper exactly. group_cols = ['interview__id', 'responsible', 'variable_name', 'qnr_seq'] - if any(c not in df_removed.columns for c in group_cols) or any(c not in df_item.columns for c in group_cols): + if any(c not in df_removed.columns for c in group_cols): logger.warning( "%s: missing one or more legacy group columns (%s); skipping feature.", feature_name, group_cols, ) - return df_item + return df_removed - df_agg = df_removed.groupby(group_cols).agg( + df_agg_removed = df_removed.groupby(group_cols).agg( f__answer_removed=('order', 'count') ).reset_index() - # Keep item table cardinality while assigning legacy-grain counts. - df_item = df_item.merge(df_agg[group_cols + [feature_name]], how='left', on=group_cols) - return df_item + # # Keep item table cardinality while assigning legacy-grain counts. + # df_item = df_item.merge(df_agg_removed[group_cols + [feature_name]], how='left', on=group_cols) + return df_agg_removed def feat_answer_changed(df_item, **kwargs): @@ -486,13 +499,13 @@ def feat_answer_changed(df_item, **kwargs): combines both checks using a bitwise OR. """ feature_name = 'f__answer_changed' - paradata_active = kwargs.get('paradata_active') + paradata_full = kwargs.get('paradata_full') - if paradata_active is None: + if paradata_full is None: return df_item item_level_columns = ['interview__id', 'variable_name', 'roster_level'] - df_changed = paradata_active[paradata_active['event'] == 'AnswerSet'].copy() + df_changed = paradata_full[(paradata_full['event'] == 'AnswerSet') & (paradata_full['question_scope'].isin([0]))].copy() df_changed[feature_name] = False group_cols = [c for c in item_level_columns + ['index_col'] if c in df_changed.columns] @@ -671,10 +684,9 @@ def feat_gps(df_item, **kwargs): } -def enrich_item_features(df_item: pd.DataFrame, paradata_active: pd.DataFrame, paradata_full: pd.DataFrame, parameters: dict) -> pd.DataFrame: +def enrich_item_features(df_item: pd.DataFrame, paradata_full: pd.DataFrame, parameters: dict) -> pd.DataFrame: """ Applies feature engineering logic to the item table. - paradata_active: active interviewer events (self.df_active_paradata equivalent). paradata_full: all processed events, role=1, interviewing=True (self.df_paradata equivalent). """ logger.info("Enriching item features...") @@ -686,7 +698,7 @@ def enrich_item_features(df_item: pd.DataFrame, paradata_active: pd.DataFrame, p if func: logger.info(f"Calculating item feature: {feat_key}") try: - df_item = func(df_item, paradata_active=paradata_active, paradata_full=paradata_full) + df_item = func(df_item, paradata_full=paradata_full) except Exception as e: logger.warning(f"Failed to calculate {feat_key}: {e}") diff --git a/rissk/item_processing_kedro.py b/rissk/item_processing_kedro.py index 1ea44a8..ca32943 100644 --- a/rissk/item_processing_kedro.py +++ b/rissk/item_processing_kedro.py @@ -162,10 +162,11 @@ def calculate_gps_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd # running great-circle calculations for every pair. data['x'], data['y'], data['z'] = lat_lon_to_cartesian(data['f__gps_latitude'], data['f__gps_longitude']) # Accuracy is expected to accompany a GPS fix (Survey Solutions provides it). - # We convert `f__gps_accuracy` from metres → kilometres to match `lat_lon_to_cartesian` - # and use `fillna(0)` to avoid NaN radii. Revisit this behaviour because `query_ball_point` may - # return empty neighbor lists or raise when given NaN radii. - + # We convert `f__gps_accuracy` from metres → kilometres to match `lat_lon_to_cartesian`. + # `fillna(0)` is intentional here: it is a computational guard — a NaN radius would + # cause `query_ball_point` to raise or silently return empty results. Zero accuracy + # means we only use the base 10m radius for that point, which is a safe fallback. + # This is a parameter value, not a score output, so it is not subject to the NaN policy. data['accuracy'] = data['f__gps_accuracy'].fillna(0) / 1e3 # Build spatial index (KDTree) on 3D cartesian coords to count neighbours. @@ -224,10 +225,14 @@ def calculate_gps_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd model = LOF(contamination=contamination, n_neighbors=20) model.fit(data.loc[mask, coords_columns]) data.loc[mask, 's__gps_outlier'] = model.predict(data.loc[mask, coords_columns]) + # Extreme outlier rows excluded from model fitting keep NaN for s__gps_outlier — + # they are already classified as extreme outliers and the spatial model cannot + # evaluate them; NaN signals that no evaluation was possible for those points. else: - data['s__gps_outlier'] = 0 - - data['s__gps_outlier'] = data['s__gps_outlier'].fillna(0) + # All GPS points are extreme outliers (e.g. all 0,0). The spatial outlier model + # cannot run because there are no valid points to fit. s__gps_outlier = NaN + # for all, since no evaluation was possible. + data['s__gps_outlier'] = np.nan # Merge interview-level scores back to every row in the full long-format df. # Rows for interviews that had no GPS answers are left as NaN — they are not @@ -337,19 +342,17 @@ def calculate_answer_hour_set_score(df_item: pd.DataFrame, parameters: Dict[str, model = ECOD(contamination=contamination) model.fit(df[[feature_name]]) df[score_name] = model.predict(df[[feature_name]]) - # In case has detected "high frequencies anomalies", set them to 0 - df.loc[df['frequency'] <= df[df[score_name] == 0]['frequency'].min(), score_name] = 0 - - - # # In case ECOD has flagged high-frequency hours as anomalies, revert them to 0. - # # Guard against the degenerate case where every row is an outlier (no inliers), - # # which would make df[df[score_name] == 0]['frequency'].min() return NaN and - # # silently skip the correction via NaN comparison. - # inlier_mask = df[score_name] == 0 - # if inlier_mask.any(): - # min_inlier_rank = df.loc[inlier_mask, 'frequency'].min() - # df.loc[df['frequency'] <= min_inlier_rank, score_name] = 0 + # Revert high-frequency hours that ECOD incorrectly flagged as anomalies. + # Guard against the degenerate case where every row is an outlier (no inliers), + # which would make the unguarded expression return NaN and silently skip the + # correction. Legacy code has this silent failure; Kedro uses the explicit guard. + inlier_mask = df[score_name] == 0 + if inlier_mask.any(): + min_inlier_rank = df.loc[inlier_mask, 'frequency'].min() + df.loc[df['frequency'] <= min_inlier_rank, score_name] = 0 + # If no inliers exist (all rows flagged), scores remain as predicted — NaN was + # never introduced here since ECOD always returns 0/1, so no further action needed. # Assign scores back using index labels — safe regardless of index type or value df_out.loc[df.index, score_name] = df[score_name].values @@ -444,7 +447,10 @@ def calculate_answer_removed_unit_score( valid_variables = filter_variable_name_by_frequency(df, feature_name, frequency=100, min_unique_values=1) - df[score_name] = 0 + # Init to NaN: variables not passing the frequency filter keep NaN, indicating + # evaluation was not possible — unit-level groupby().mean() skips NaN so they + # don't contribute a spurious zero to the interview mean. + df[score_name] = np.nan contamination = get_contamination_parameter( parameters.get('features', {}), feature_name, @@ -461,6 +467,7 @@ def calculate_answer_removed_unit_score( df.loc[mask, score_name] = model.predict(df.loc[mask, [feature_name]]) # Aggregate to interview__id level matching legacy make_score_unit__answer_removed. + # groupby().mean() skips NaN rows, so only scored variables contribute. return df.groupby('interview__id')[score_name].mean() @@ -475,7 +482,8 @@ def calculate_answer_position_score(df_item: pd.DataFrame) -> pd.DataFrame: df[score_name] = np.nan return df - valid_variables = filter_variable_name_by_frequency(df[~pd.isnull(df[feature_name])], feature_name, frequency=100, min_unique_values=3) + valid_variables = filter_variable_name_by_frequency( + df[~pd.isnull(df[feature_name])], feature_name, frequency=100, min_unique_values=3) df[score_name] = np.nan for var in valid_variables: @@ -510,13 +518,16 @@ def calculate_answer_selected_score(df_item: pd.DataFrame, parameters: Dict[str, df[score_name + '_upper'] = np.nan return df - valid_data = df[~pd.isnull(df[feature_name])] - valid_variables = filter_variable_name_by_frequency(valid_data, feature_name, frequency=100, min_unique_values=3) + valid_variables = filter_variable_name_by_frequency( + df[~pd.isnull(df[feature_name])], feature_name, frequency=100, min_unique_values=3) score_name1 = score_name + '_lower' score_name2 = score_name + '_upper' + df[score_name1] = np.nan + df[score_name2] = np.nan df[score_name] = np.nan - + + contamination = get_contamination_parameter( parameters.get('features', {}), feature_name, @@ -532,8 +543,8 @@ def calculate_answer_selected_score(df_item: pd.DataFrame, parameters: Dict[str, model.fit(df.loc[mask, [feature_name]]) df.loc[mask, score_name] = model.predict(df.loc[mask, [feature_name]]) - non_anomalies = df.loc[mask & (df[score_name] == 0), feature_name] + if not non_anomalies.empty: min_good_value = non_anomalies.min() max_good_value = non_anomalies.max() @@ -547,6 +558,7 @@ def calculate_answer_selected_score(df_item: pd.DataFrame, parameters: Dict[str, df.drop(columns=[score_name], errors='ignore', inplace=True) return df + def calculate_answer_duration_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: feature_name = 'f__answer_duration' score_name = rename_feature(feature_name) @@ -558,9 +570,10 @@ def calculate_answer_duration_score(df_item: pd.DataFrame, parameters: Dict[str, df[score_name + '_lower'] = np.nan df[score_name + '_upper'] = np.nan return df - - valid_data = df[~pd.isnull(df[feature_name])] - valid_variables = filter_variable_name_by_frequency(valid_data, feature_name, frequency=100, min_unique_values=3) + + # Select only those variables that have at least three distinct values and more than one hundred records + valid_variables = filter_variable_name_by_frequency( + df[~pd.isnull(df[feature_name])], feature_name, frequency=100, min_unique_values=3) score_name1 = score_name + '_lower' score_name2 = score_name + '_upper' @@ -597,40 +610,38 @@ def calculate_answer_duration_score(df_item: pd.DataFrame, parameters: Dict[str, df.drop(columns=[score_name], errors='ignore', inplace=True) return df -def calculate_single_question_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: +def calculate_single_question_score(df_item: pd.DataFrame) -> pd.DataFrame: feature_name = 'f__single_question' score_name = rename_feature(feature_name) df = df_item.copy() - - if 'qtype' not in df.columns or 'n_answers' not in df.columns or 'value' not in df.columns: - return df + columns = ['qtype', 'n_answers', 'is_filtered_combobox', 'cascade_from_question_id'] + if any(col not in df.columns for col in columns + [feature_name]): + return df + # Mask specific for single questions without filter rules bypassing cascades single_question_mask = ( (df["qtype"] == 'SingleQuestion') & (df['n_answers'] > 1) & - (df.get('is_filtered_combobox', False) == False) & - (pd.isnull(df.get('cascade_from_question_id', np.nan))) + (df['is_filtered_combobox'] == False) & + (pd.isnull(df['cascade_from_question_id'])) ) df[score_name] = np.nan - valid_data = df[single_question_mask] + valid_data = df[single_question_mask]&df[~pd.isnull(df[feature_name])] if valid_data.empty: return df - + variables = filter_variable_name_by_frequency(valid_data, 'value', frequency=100, min_unique_values=3) for var in variables: - mask = (df['variable_name'] == var) & single_question_mask + mask = (df['variable_name'] == var) & single_question_mask & (~pd.isnull(df[feature_name])) if mask.sum() > 0: unique_values = df.loc[mask, 'value'].nunique() - try: - entropy_df = df[mask].groupby('responsible')['value'].apply( - calculate_entropy, unique_values=unique_values - ).reset_index() - except NameError: - continue + entropy_df = df[mask].groupby('responsible')['value'].apply( + calculate_entropy, unique_values=unique_values + ).reset_index() entropy_df = entropy_df[~pd.isnull(entropy_df['value'])] if entropy_df.shape[0] > 0: @@ -640,28 +651,27 @@ def calculate_single_question_score(df_item: pd.DataFrame, parameters: Dict[str, lambda x: 1 if x < median_value - 0.5 * median_value else 0) responsible_map = entropy_df.set_index('responsible')[score_name].to_dict() - df.loc[mask, score_name] = df.loc[mask, 'responsible'].map(responsible_map).fillna(0) + df.loc[mask, score_name] = df.loc[mask, 'responsible'].map(responsible_map) return df -def calculate_multi_option_question_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + +def calculate_multi_option_question_score(df_item: pd.DataFrame) -> pd.DataFrame: feature_name = 'f__multi_option_question' score_name = rename_feature(feature_name) df = df_item.copy() - if 'qtype' not in df.columns or 'value' not in df.columns: + if any(col not in df.columns for col in ['qtype', feature_name]): return df - multi_question_mask = (df["qtype"] == 'MultyOptionsQuestion') - valid_data = df[multi_question_mask] + multi_question_mask = (df["qtype"] == 'MultyOptionsQuestion') & (~pd.isnull(df[feature_name])) + valid_data = df[multi_question_mask].copy() df[score_name] = np.nan if valid_data.empty: return df - # Filter variables safely via counts - val_counts = valid_data['variable_name'].value_counts() - variables = val_counts[val_counts >= 100].index + variables = filter_variable_name_by_frequency(valid_data, 'value', frequency=100, min_unique_values=3) for var in variables: mask = (df['variable_name'] == var) & multi_question_mask @@ -669,12 +679,11 @@ def calculate_multi_option_question_score(df_item: pd.DataFrame, parameters: Dic # Need safely explode nested lists in values exploded_vals = df.loc[mask, 'value'].explode() unique_values = len([v for v in exploded_vals.unique() if v != '##N/A##']) - try: - entropy_df = df[mask].groupby('responsible')['value'].apply( - calculate_list_entropy, unique_values=unique_values, min_record_sample=5 - ).reset_index() - except NameError: - continue + + entropy_df = df[mask].groupby('responsible')['value'].apply( + calculate_list_entropy, unique_values=unique_values, min_record_sample=5 + ).reset_index() + entropy_df = entropy_df[~pd.isnull(entropy_df['value'])] @@ -685,11 +694,11 @@ def calculate_multi_option_question_score(df_item: pd.DataFrame, parameters: Dic lambda x: 1 if x < median_value - 0.5 * median_value else 0) responsible_map = entropy_df.set_index('responsible')[score_name].to_dict() - df.loc[mask, score_name] = df.loc[mask, 'responsible'].map(responsible_map).fillna(0) + df.loc[mask, score_name] = df.loc[mask, 'responsible'].map(responsible_map) return df -def calculate_first_digit_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: +def calculate_first_digit_score(df_item: pd.DataFrame) -> pd.DataFrame: feature_name = 'f__numeric_response' score_name = 's__first_digit' df = df_item.copy() @@ -703,14 +712,20 @@ def calculate_first_digit_score(df_item: pd.DataFrame, parameters: Dict[str, Any valid_data = df[~pd.isnull(df[feature_name])] valid_variables = filter_variable_name_by_frequency(valid_data, feature_name, frequency=100, min_unique_values=3) df[score_name] = np.nan + + valid_variables = filter_variables_by_magnitude(valid_data, feature_name, valid_variables, min_order_of_magnitude=3) - try: - valid_variables = filter_variables_by_magnitude(valid_data, feature_name, valid_variables, min_order_of_magnitude=3) - benford_jensen_df = apply_benford_tests( - valid_data, valid_variables, 'responsible', feature_name, apply_first_digit=True, minimum_sample=50 - ) - except NameError: - return df # dependencies missing + + # Computes the Jensen divergence for each variable_name and responsible on the first digit distribution. + # Jensen's divergence returns a value between (0, 1) of how much the first digit distribution + # of specific responsible is similar to the first digit distribution of all others. + # Higher the value higher is the difference. + # The Bendford Jensen divergence is calculated only on those responsible and variable_name + # who have at least 50 records. + # Once it is calculated, values that diverge from more than 50% from the median value get marked as "anomalous." + benford_jensen_df = apply_benford_tests( + valid_data, valid_variables, 'responsible', feature_name, apply_first_digit=True, minimum_sample=50 + ) if not benford_jensen_df.empty: variable_list = benford_jensen_df['variable_name'].unique() @@ -725,7 +740,9 @@ def calculate_first_digit_score(df_item: pd.DataFrame, parameters: Dict[str, Any mask = (df['variable_name'] == var) responsible_map = bj_df.set_index('responsible')[score_name].to_dict() - df.loc[mask, score_name] = df.loc[mask, 'responsible'].map(responsible_map).fillna(0) + # Responsibles absent from the map (below 50-record Benford threshold) + # keep NaN — evaluation was not possible for them. + df.loc[mask, score_name] = df.loc[mask, 'responsible'].map(responsible_map) return df diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py index 52dc338..df6f61e 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py @@ -78,18 +78,18 @@ def filter_active_paradata_node( Active paradata DataFrame: keep active events, prior rejection/review events, for questions with scope interviewer """ active_events = [ - 'InterviewCreated', 'AnswerSet', 'Resumed', - 'AnswerRemoved', 'CommentSet', 'Restarted' + 'InterviewCreated', 'AnswerSet', 'AnswerRemoved', 'CommentSet', + 'Restarted', 'Resumed' # pause events, which have empty question scope ] - # only keep events done by interview (in most cases this should be all, after above filters, # just in case supervisor or HQ answered something while interviewer answered on web mode) # keep active events, prior rejection/review events, for questions with scope interviewer # Filter conditions + paradata_processed['question_scope'] = paradata_processed['question_scope'].fillna('') # Fill NaN with empty string for consistent filtering active_mask = ( (paradata_processed['event'].isin(active_events)) & - (paradata_processed['question_scope'].isin([0, ''])) & - (paradata_processed['role'] == 1) + (paradata_processed['question_scope'].isin([0, ''])) & # question scope interviewer only, but fillna so pauses are added back in, as they have empty question scope + (paradata_processed['role'] == 1) # redundant given previous filtering ) vars_needed = [ diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py index 9967586..c895b4d 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py @@ -19,6 +19,7 @@ def create_pipeline(**kwargs) -> Pipeline: outputs="paradata_processed", name="process_paradata_node", ), + # This node is redundant and the filters will be moved to feature creation nodes node( func=filter_active_paradata_node, inputs=["paradata_processed", "parameters"], From e1ad49bc7514e8a056b5abfcc2cb24475784301d Mon Sep 17 00:00:00 2001 From: VJausovec Date: Mon, 16 Mar 2026 14:41:41 +0000 Subject: [PATCH 33/70] Refactor feature creation and processing pipelines to enhance handling of paradata; introduce a new node for removed answers and streamline event filtering logic. --- rissk/feature_processing_kedro.py | 86 +++++++++++-------- rissk/item_processing_kedro.py | 48 ++++++++++- .../pipelines/feature_creation/nodes.py | 42 +++++---- .../pipelines/feature_creation/pipeline.py | 31 ++++--- .../pipelines/feature_engineering/pipeline.py | 11 +-- 5 files changed, 146 insertions(+), 72 deletions(-) diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index 68080de..2ccc2f0 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -59,8 +59,28 @@ def _coerce_numeric_with_warning(df_item: pd.DataFrame, numeric_mask: pd.Series, return coerced def get_df_time(df_paradata_full: pd.DataFrame) -> pd.DataFrame: - """Calculates time differences and durations from paradata.""" - df_time = df_paradata_full.copy() + """Calculates time differences and durations from paradata. + + Mirrors the legacy df_active_paradata filter before computing time deltas: + - AnswerSet / AnswerRemoved / CommentSet: included only when question_scope == 0 + (interviewer-scope questions; supervisor-scope questions with scope == 1 are excluded). + - InterviewCreated / Resumed / Restarted: no question scope (NaN); included regardless. + - All other event types (Completed, ApprovalRequested, etc.): excluded. + + Computing .diff() on the full paradata would fragment time gaps with irrelevant events, + producing shorter (and wrong) durations for the active events that follow them. + """ + # Events that carry a question scope — keep only interviewer-scope (== 0). + # NaN scope (supervisor-originated or no-question events) is intentionally excluded here. + question_scope_events = ['AnswerSet', 'AnswerRemoved', 'CommentSet'] + # Events that have no question scope (pause / session events); always include. + no_scope_events = ['InterviewCreated', 'Resumed', 'Restarted'] + + active_mask = ( + (df_paradata_full['event'].isin(no_scope_events)) | + (df_paradata_full['event'].isin(question_scope_events) & (df_paradata_full['question_scope'] == 0)) + ) + df_time = df_paradata_full[active_mask].copy() # calculate time difference in seconds df_time['time_difference'] = df_time.groupby('interview__id')['timestamp_local'].diff() @@ -260,21 +280,18 @@ def create_base_item_table(microdata: pd.DataFrame, paradata_full: pd.DataFrame, paradata_columns = ['responsible', 'f__answer_hour_set', 'interviewing', 'tz_offset'] # available_para_cols = [c for c in paradata_columns if c in paradata_full.columns] - # The filter paradata_full['role'] == 1 is already applied in the paradata processing node, - # so all events in paradata_full should be from the interviewer. - paradata_full['question_scope'] = paradata_full['question_scope'].fillna('') - question_scope_mask = paradata_full['question_scope'].isin([0, '']) - answer_set_mask = (paradata_full['event'] == 'AnswerSet') - active_events_mask = [ - 'InterviewCreated', 'AnswerSet', 'AnswerRemoved', 'CommentSet', - 'Restarted', 'Resumed' # pause events, which have empty question scope - ] - + # Interviewer-scope AnswerSet events: scope==0 means interviewer, scope==1 means supervisor. + # Pause events (Resumed/Restarted) have NaN scope; no fillna needed — they are not AnswerSet events. + interviewer_answer_mask = ( + (paradata_full['event'] == 'AnswerSet') & + (paradata_full['question_scope'] == 0) + ) + data_to_merge = ( - paradata_full[answer_set_mask & question_scope_mask] - .dropna(subset=['index_col']) # drop rows without index_col + paradata_full[interviewer_answer_mask] + .dropna(subset=['index_col']) # drop rows without index_col # keep the last AnswerSet per item, paradata is already sorted by interview__id and order in the processing node - .drop_duplicates(subset='index_col', keep='last') + .drop_duplicates(subset='index_col', keep='last') [['index_col'] + paradata_columns] # select only necessary columns for merging ) @@ -287,12 +304,14 @@ def create_base_item_table(microdata: pd.DataFrame, paradata_full: pd.DataFrame, # 6. Add Sequence Features if calculate_sequence: - df_sequence = get_df_sequence(paradata_full[answer_set_mask & question_scope_mask]) + df_sequence = get_df_sequence(paradata_full[interviewer_answer_mask]) df_item = add_sequence_features(df_item, df_sequence, allowed_features) # 7. Add Time Features if calculate_time: - df_time = get_df_time(paradata_full[active_events_mask & question_scope_mask]) + # Pass full paradata; get_df_time filters by event type internally. + # This correctly includes pause events (Resumed/Restarted) which have NaN question_scope. + df_time = get_df_time(paradata_full) df_item = add_item_time_features(df_item, df_time, allowed_features, item_level_columns) return df_item @@ -308,30 +327,28 @@ def create_base_unit_table(paradata_full: pd.DataFrame, parameters: dict) -> pd. # 1. Initialize from paradata columns = ['interview__id', 'responsible', 'qnr', 'qnr_version'] - # The filter paradata_full['role'] == 1 is already applied in the paradata processing node, - # so all events in paradata_full should be from the interviewer. - paradata_full['question_scope'] = paradata_full['question_scope'].fillna('') - question_scope_mask = paradata_full['question_scope'].isin([0, '']) - active_events_mask = [ - 'InterviewCreated', 'AnswerSet', 'AnswerRemoved', 'CommentSet', - 'Restarted', 'Resumed' # pause events, which have empty question scope - ] - paradata_active = paradata_full[active_events_mask & question_scope_mask].copy() - - - df_unit = paradata_active[columns].copy() + # Use interviewer-scope AnswerSet events to seed unit identity rows. + # responsible is only reliably populated on AnswerSet events. + interviewer_answer_mask = ( + (paradata_full['event'] == 'AnswerSet') & + (paradata_full['question_scope'] == 0) + ) + + df_unit = paradata_full[interviewer_answer_mask][columns].copy() df_unit.drop_duplicates(inplace=True) - + # Filter valid responsible df_unit = df_unit[(df_unit['responsible'] != '') & (df_unit['responsible'].notna())] - + pause_features = ['f__pause_count', 'f__pause_duration', 'f__pause_list'] unit_time_features = ['f__total_duration', 'f__total_elapse', 'f__days_from_start', 'f__time_changed'] calculate_pause = any(f in allowed_features for f in pause_features) calculate_unit_time = any(f in allowed_features for f in unit_time_features) if calculate_pause or calculate_unit_time: - df_time = get_df_time(paradata_active) + # Pass full paradata so get_df_time correctly includes pause events (Resumed/Restarted) + # which have NaN question_scope and would be dropped by any scope filter. + df_time = get_df_time(paradata_full) if calculate_pause: df_unit = add_pause_features(df_unit, df_time, allowed_features) if calculate_unit_time: @@ -505,7 +522,7 @@ def feat_answer_changed(df_item, **kwargs): return df_item item_level_columns = ['interview__id', 'variable_name', 'roster_level'] - df_changed = paradata_full[(paradata_full['event'] == 'AnswerSet') & (paradata_full['question_scope'].isin([0]))].copy() + df_changed = paradata_full[(paradata_full['event'] == 'AnswerSet') & (paradata_full['question_scope'] == 0)].copy() df_changed[feature_name] = False group_cols = [c for c in item_level_columns + ['index_col'] if c in df_changed.columns] @@ -677,7 +694,8 @@ def feat_gps(df_item, **kwargs): 'answer_position': feat_answer_position, 'answer_changed': feat_answer_changed, 'answer_selected': feat_answer_selected, - 'answer_removed': feat_answer_removed, + # answer_removed is handled as a separate pipeline node outputting removed_answers parquet; + # it is NOT enriched into df_item here. 'comment_length': feat_comment_length, 'comment_set': feat_comment_set, 'gps': feat_gps, diff --git a/rissk/item_processing_kedro.py b/rissk/item_processing_kedro.py index ca32943..d52cf64 100644 --- a/rissk/item_processing_kedro.py +++ b/rissk/item_processing_kedro.py @@ -471,7 +471,53 @@ def calculate_answer_removed_unit_score( return df.groupby('interview__id')[score_name].mean() -def calculate_answer_position_score(df_item: pd.DataFrame) -> pd.DataFrame: +def calculate_answer_removed_score_from_df( + removed_answers: pd.DataFrame, + parameters: Dict[str, Any], +) -> pd.Series: + """Score answer-removal anomalies from the pre-aggregated removed_answers DataFrame. + + Takes the output of feat_answer_removed (columns: interview__id, responsible, + variable_name, qnr_seq, f__answer_removed) and applies the same ECOD scoring + logic as calculate_answer_removed_unit_score, without re-filtering paradata. + + Returns a Series indexed by interview__id → mean s__answer_removed score. + """ + feature_name = 'f__answer_removed' + score_name = rename_feature(feature_name) + + if removed_answers is None or removed_answers.empty: + return pd.Series(dtype=float) + + required_cols = ['interview__id', 'variable_name', feature_name] + if any(c not in removed_answers.columns for c in required_cols): + logger.warning( + "calculate_answer_removed_score_from_df: removed_answers is missing one or more " + "required columns %s; returning empty Series.", required_cols + ) + return pd.Series(dtype=float) + + df = removed_answers.copy() + + valid_variables = filter_variable_name_by_frequency(df, feature_name, frequency=100, min_unique_values=1) + + df[score_name] = np.nan + contamination = get_contamination_parameter( + parameters.get('features', {}), + feature_name, + automatic_contamination=parameters.get('automatic_contamination', False), + method='medfilt', + random_state=42, + ) + + for var in valid_variables: + mask = (df['variable_name'] == var) & (~pd.isnull(df[feature_name])) + if mask.sum() > 0: + model = ECOD(contamination=contamination) + model.fit(df.loc[mask, [feature_name]]) + df.loc[mask, score_name] = model.predict(df.loc[mask, [feature_name]]) + + return df.groupby('interview__id')[score_name].mean() feature_name = 'f__answer_position' score_name = rename_feature(feature_name) df = df_item.copy() diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py index a9143fc..70dbb66 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py @@ -10,41 +10,35 @@ create_base_unit_table, enrich_item_features, enrich_unit_features, + feat_answer_removed, ) logger = logging.getLogger(__name__) def create_base_item_table_node( - microdata: pd.DataFrame, - paradata_active: pd.DataFrame, + microdata: pd.DataFrame, + paradata_full: pd.DataFrame, parameters: Dict[str, Any] ) -> pd.DataFrame: - """ - Node wrapper for create_base_item_table. - """ - return create_base_item_table(microdata, paradata_active, parameters) + """Node wrapper for create_base_item_table.""" + return create_base_item_table(microdata, paradata_full, parameters) def create_base_unit_table_node( - paradata_active: pd.DataFrame, + paradata_full: pd.DataFrame, parameters: Dict[str, Any] ) -> pd.DataFrame: - """ - Node wrapper for create_base_unit_table. - """ - return create_base_unit_table(paradata_active, parameters) + """Node wrapper for create_base_unit_table.""" + return create_base_unit_table(paradata_full, parameters) def enrich_item_features_node( item_features_base: pd.DataFrame, - paradata_active: pd.DataFrame, paradata_full: pd.DataFrame, parameters: Dict[str, Any] ) -> pd.DataFrame: - """ - Node wrapper for enrich_item_features. - paradata_active: active interviewer events only (equivalent to self.df_active_paradata). + """Node wrapper for enrich_item_features. paradata_full: all processed events, role=1, interviewing=True (equivalent to self.df_paradata). """ - return enrich_item_features(item_features_base, paradata_active, paradata_full, parameters) + return enrich_item_features(item_features_base, paradata_full, parameters) def enrich_unit_features_node( unit_features_base: pd.DataFrame, @@ -52,8 +46,20 @@ def enrich_unit_features_node( paradata_full: pd.DataFrame, parameters: Dict[str, Any] ) -> pd.DataFrame: - """ - Node wrapper for enrich_unit_features. + """Node wrapper for enrich_unit_features. paradata_full: all processed events, role=1, interviewing=True (equivalent to self.df_paradata). """ return enrich_unit_features(unit_features_base, item_features, paradata_full, parameters) + +def build_removed_answers_node( + paradata_full: pd.DataFrame, + parameters: Dict[str, Any] +) -> pd.DataFrame: + """Extract and aggregate all AnswerRemoved events as a standalone dataset. + + This node produces the removed_answers parquet which captures AnswerRemoved + events for items that may no longer exist in microdata (deleted items), matching + legacy get_feature_item__answer_removed behaviour. The output is consumed by + the rissk_scoring pipeline to score s__answer_removed at unit level. + """ + return feat_answer_removed(paradata_full) diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py index cd6a80f..19b041d 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py @@ -5,45 +5,54 @@ create_base_unit_table_node, enrich_item_features_node, enrich_unit_features_node, + build_removed_answers_node, ) def create_pipeline(**kwargs) -> Pipeline: """Create the feature creation pipeline. Returns: - A pipeline that builds item and unit feature tables. + A pipeline that builds item and unit feature tables, and a removed_answers dataset. """ return pipeline([ node( func=create_base_item_table_node, - # inputs=["raw_microdata", "paradata_active", "parameters"], - # Legacy test data: - inputs=["legacy_microdata", "legacy_paradata_active", "parameters"], + # inputs=["raw_microdata", "paradata_processed", "parameters"], + # Legacy test data: + inputs=["legacy_microdata", "legacy_paradata_processed", "parameters"], outputs="item_features_base", name="create_base_item_table_node", ), node( func=create_base_unit_table_node, - # inputs=["paradata_active", "parameters"], - # Legacy test data: - inputs=["legacy_paradata_active", "parameters"], + # inputs=["paradata_processed", "parameters"], + # Legacy test data: + inputs=["legacy_paradata_processed", "parameters"], outputs="unit_features_base", name="create_base_unit_table_node", ), node( func=enrich_item_features_node, - # inputs=["item_features_base", "paradata_active", "paradata_processed", "parameters"], - # Legacy test data: - inputs=["item_features_base", "legacy_paradata_active", "legacy_paradata_processed", "parameters"], + # inputs=["item_features_base", "paradata_processed", "parameters"], + # Legacy test data: + inputs=["item_features_base", "legacy_paradata_processed", "parameters"], outputs="item_features", name="enrich_item_features_node", ), node( func=enrich_unit_features_node, # inputs=["unit_features_base", "item_features", "paradata_processed", "parameters"], - # Legacy test data: + # Legacy test data: inputs=["unit_features_base", "item_features", "legacy_paradata_processed", "parameters"], outputs="unit_features", name="enrich_unit_features_node", ), + node( + func=build_removed_answers_node, + # inputs=["paradata_processed", "parameters"], + # Legacy test data: + inputs=["legacy_paradata_processed", "parameters"], + outputs="removed_answers", + name="build_removed_answers_node", + ), ]) diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py index c895b4d..3a3ee23 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py @@ -2,7 +2,6 @@ from kedro.pipeline import Pipeline, node, pipeline from .nodes import ( process_paradata_node, - filter_active_paradata_node, ) @@ -19,11 +18,7 @@ def create_pipeline(**kwargs) -> Pipeline: outputs="paradata_processed", name="process_paradata_node", ), - # This node is redundant and the filters will be moved to feature creation nodes - node( - func=filter_active_paradata_node, - inputs=["paradata_processed", "parameters"], - outputs="paradata_active", - name="filter_active_paradata_node", - ), + # filter_active_paradata_node removed: each feature function now applies + # its own question_scope == 0 filter inline where needed. Pause events + # (Resumed, Restarted) have NaN question_scope and must not be dropped globally. ]) From 1697b0838a51c61fcbe4bd13268255083dccd600 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Mon, 16 Mar 2026 19:50:50 +0000 Subject: [PATCH 34/70] Refactor scoring pipeline to add separate removed_answers dataset; update related functions for improved handling of AnswerRemoved events and maintain legacy behavior for deleted items. --- rissk/feature_processing_kedro.py | 2 -- rissk_kedro/conf/base/catalog.yml | 13 ++++++++--- rissk_kedro/conf/base/globals.yml | 6 +++++ .../pipelines/rissk_scoring/nodes.py | 22 +++++++++---------- .../pipelines/rissk_scoring/pipeline.py | 7 +++--- 5 files changed, 30 insertions(+), 20 deletions(-) diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index 2ccc2f0..4c96222 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -67,8 +67,6 @@ def get_df_time(df_paradata_full: pd.DataFrame) -> pd.DataFrame: - InterviewCreated / Resumed / Restarted: no question scope (NaN); included regardless. - All other event types (Completed, ApprovalRequested, etc.): excluded. - Computing .diff() on the full paradata would fragment time gaps with irrelevant events, - producing shorter (and wrong) durations for the active events that follow them. """ # Events that carry a question scope — keep only interviewer-scope (== 0). # NaN scope (supervisor-originated or no-question events) is intentionally excluded here. diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index 1687757..5bd01f0 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -36,9 +36,9 @@ paradata_processed: type: pandas.ParquetDataset filepath: data/${globals:survey.name}/latest/30_PROCESSED/paradata_processed.parquet -paradata_active: - type: pandas.ParquetDataset - filepath: data/${globals:survey.name}/latest/30_PROCESSED/paradata_active.parquet +# paradata_active has been removed: the active-event filter is now applied inline +# within each function that needs it, using explicit event and question_scope masks. +# This avoids silently dropping pause events (Resumed/Restarted) which have NaN scope. # === LEGACY DATA FOR PIPELINE TESTING === @@ -78,6 +78,13 @@ unit_features: type: pandas.ParquetDataset filepath: data/${globals:survey.name}/latest/30_PROCESSED/unit_features.parquet +# Aggregated AnswerRemoved events (includes items deleted from microdata). +# Used by the rissk_scoring pipeline to compute s__answer_removed at unit level, +# matching legacy get_feature_item__answer_removed / make_score_unit__answer_removed. +removed_answers: + type: pandas.ParquetDataset + filepath: data/${globals:survey.name}/latest/30_PROCESSED/removed_answers.parquet + # === SCORING DataFrames === item_scores: diff --git a/rissk_kedro/conf/base/globals.yml b/rissk_kedro/conf/base/globals.yml index 5e88c34..6650ed3 100644 --- a/rissk_kedro/conf/base/globals.yml +++ b/rissk_kedro/conf/base/globals.yml @@ -21,3 +21,9 @@ survey: # questionnaires: # - name: "slchbs_saintlucia_2025" # VERSION: [5, 6, 7] + +# survey: +# name: "fbf house holduntitled folder" +# questionnaires: +# - name: "fbf_household" +# VERSION: [13] \ No newline at end of file diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py index f4701e6..418f6ed 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py @@ -8,9 +8,9 @@ calculate_first_decimal_score, calculate_answer_changed_score, # calculate_answer_removed_score is intentionally absent: s__answer_removed is - # computed at unit level from paradata_full by calculate_answer_removed_unit_score + # computed at unit level from the removed_answers dataset by calculate_answer_removed_score_from_df # so that AnswerRemoved events for items deleted from microdata are not missed. - calculate_answer_removed_unit_score, + calculate_answer_removed_score_from_df, calculate_answer_position_score, calculate_answer_selected_score, calculate_answer_duration_score, @@ -40,8 +40,8 @@ def calculate_item_scores(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> df_scored = calculate_sequence_jump_score(df_scored, parameters) df_scored = calculate_first_decimal_score(df_scored, parameters) df_scored = calculate_answer_changed_score(df_scored, parameters) - # s__answer_removed is not computed here — see calculate_answer_removed_unit_score - # in calculate_unit_scores, which scores from paradata_full to match legacy coverage. + # s__answer_removed is not computed here — see calculate_answer_removed_score_from_df + # in calculate_unit_scores, which scores from the removed_answers dataset to match legacy coverage. df_scored = calculate_answer_position_score(df_scored, parameters) df_scored = calculate_answer_selected_score(df_scored, parameters) df_scored = calculate_answer_duration_score(df_scored, parameters) @@ -51,13 +51,13 @@ def calculate_item_scores(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> df_scored = calculate_gps_score(df_scored, parameters) return df_scored -def calculate_unit_scores(df_unit: pd.DataFrame, df_item_scores: pd.DataFrame, parameters: Dict[str, Any], paradata_full: pd.DataFrame = None) -> Tuple[pd.DataFrame, pd.DataFrame]: +def calculate_unit_scores(df_unit: pd.DataFrame, df_item_scores: pd.DataFrame, parameters: Dict[str, Any], removed_answers: pd.DataFrame = None) -> Tuple[pd.DataFrame, pd.DataFrame]: """ Aggregate item scores to unit, extract responsible scores, and calculate global risk. - paradata_full is the full processed paradata stream (role=1, interviewing=True). - It is used to compute s__answer_removed at unit level directly from paradata, - matching legacy behaviour where items deleted from microdata are still counted. + removed_answers is the pre-aggregated AnswerRemoved dataset produced by build_removed_answers_node. + It is used to compute s__answer_removed at unit level, matching legacy behaviour where + items deleted from microdata (absent from df_item) are still counted. """ logger.info("Calculating Unit Scores and Global Risk...") @@ -70,12 +70,12 @@ def calculate_unit_scores(df_unit: pd.DataFrame, df_item_scores: pd.DataFrame, p # This replicates legacy make_score_unit__answer_removed which read from df_paradata # directly and therefore included AnswerRemoved events for items later deleted from # microdata. Falling back to the df_item-based mean when paradata_full is unavailable. - if paradata_full is not None and not paradata_full.empty: - unit_removed = calculate_answer_removed_unit_score(paradata_full, parameters) + if removed_answers is not None and not removed_answers.empty: + unit_removed = calculate_answer_removed_score_from_df(removed_answers, parameters) df_unit_scored['s__answer_removed'] = df_unit_scored['interview__id'].map(unit_removed).fillna(0) elif 's__answer_removed' in df_item_scores.columns: logger.warning( - "paradata_full not available; falling back to df_item-based s__answer_removed " + "removed_answers not available; falling back to df_item-based s__answer_removed " "aggregation (may undercount removals for deleted items)." ) data = df_item_scores.groupby('interview__id')['s__answer_removed'].mean() diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py index 7723b79..8a05ef9 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py @@ -17,11 +17,10 @@ def create_pipeline(**kwargs) -> Pipeline: ), node( func=calculate_unit_scores, - # paradata_full (4th arg) gives calculate_unit_scores access to ALL - # AnswerRemoved events, including those for items deleted from microdata, + # removed_answers gives calculate_unit_scores access to ALL AnswerRemoved events, + # including those for items deleted from microdata, # matching legacy make_score_unit__answer_removed behaviour. - # Real pipeline: replace "legacy_paradata_processed" with "paradata_processed". - inputs=["unit_features", "item_scores", "parameters", "legacy_paradata_processed"], + inputs=["unit_features", "item_scores", "parameters", "removed_answers"], outputs=["unit_risk_scores", "responsible_scores"], name="calculate_unit_scores_node", ), From 6a2ad51f9d442ab39c9e18535b222a8e73e16226 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Tue, 17 Mar 2026 13:46:34 +0000 Subject: [PATCH 35/70] Refactor code structure for improved readability and maintainability --- rissk/item_processing_kedro.py | 3 + rissk_kedro/conf/base/globals.yml | 2 +- .../src/rissk_kedro/test_ingestion.ipynb | 4084 ++++------------- 3 files changed, 840 insertions(+), 3249 deletions(-) diff --git a/rissk/item_processing_kedro.py b/rissk/item_processing_kedro.py index d52cf64..d1b496c 100644 --- a/rissk/item_processing_kedro.py +++ b/rissk/item_processing_kedro.py @@ -518,6 +518,9 @@ def calculate_answer_removed_score_from_df( df.loc[mask, score_name] = model.predict(df.loc[mask, [feature_name]]) return df.groupby('interview__id')[score_name].mean() + + +def calculate_answer_position_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: feature_name = 'f__answer_position' score_name = rename_feature(feature_name) df = df_item.copy() diff --git a/rissk_kedro/conf/base/globals.yml b/rissk_kedro/conf/base/globals.yml index 6650ed3..e0804e4 100644 --- a/rissk_kedro/conf/base/globals.yml +++ b/rissk_kedro/conf/base/globals.yml @@ -20,7 +20,7 @@ survey: # name: "slchbs" # questionnaires: # - name: "slchbs_saintlucia_2025" -# VERSION: [5, 6, 7] +# VERSION: [6, 7] # 5 is for testing empty data handling # survey: # name: "fbf house holduntitled folder" diff --git a/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb b/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb index 24212d2..87bef86 100644 --- a/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb +++ b/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 67, "id": "607ef013", "metadata": {}, "outputs": [], @@ -21,409 +21,525 @@ "import numpy as np\n", "from collections import Counter\n", "import math\n", + "import json\n", "from pandas.api import types as ptypes\n", "import pyarrow.parquet as pq\n", + "import ast\n", "\n", - "from rissk.config import DATA_DIR, RAW_DATA_DIR, PROCESSED_DATA_DIR, INTERIM_DATA_DIR, PROJ_ROOT" + "from rissk.config import DATA_DIR, RAW_DATA_DIR, PROCESSED_DATA_DIR, INTERIM_DATA_DIR, PROJ_ROOT\n", + "from rissk.utils.testing_utils import compare_parquet_files" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 68, "id": "a9c7966e", "metadata": {}, "outputs": [], "source": [ - "SURVEY = \"hies2024\"" + "# SURVEY = \"pmpmd\"\n", + "# SURVEY = \"hies2024\"\n", + "SURVEY = \"slchbs\"" ] }, { "cell_type": "code", - "execution_count": 3, - "id": "dc4569ea", + "execution_count": 69, + "id": "9a8dce83", "metadata": {}, "outputs": [], "source": [ - "# original files\n", - "df_para = pd.read_parquet(INTERIM_DATA_DIR.joinpath(\"paradata.parquet\"))\n", - "df_questionnaire = pd.read_parquet(PROCESSED_DATA_DIR.joinpath(\"questionnaire.parquet\"))\n", - "df_microdata = pd.read_parquet(PROCESSED_DATA_DIR.joinpath(\"microdata.parquet\"))" + "# This will remove the empty lists that were present in legacy data and are not present in the new Kedro outputs, \n", + "# to ensure a more apples-to-apples comparison of the microdata. \n", + "# It also handles stringified lists that may contain only-missing values (e.g., \"[nan, nan]\") by normalizing them to empty lists. \n", + "# The drop_rows option allows for optionally removing rows where the specified columns contain empty lists after normalization, \n", + "# which is relevant for the TextListQuestion rows in this test.\n", + "\n", + "def clean_empty_lists(df: pd.DataFrame, columns: list = None, drop_rows: bool = False) -> pd.DataFrame:\n", + " \"\"\"Normalize list-like cells and optionally drop rows where the specified columns\n", + " contain only-missing lists (e.g., [nan, nan] or stringified equivalents).\n", + "\n", + " Special handling for the token '##N/A##':\n", + " - If a list mixes real-missing values (NaN) and the token '##N/A##', treat the list\n", + " as empty (i.e., normalize to []).\n", + " - If a list contains only the token '##N/A##' (no real-missing), leave it as-is.\n", + "\n", + " Args:\n", + " df: input DataFrame\n", + " columns: list of column names to clean\n", + " drop_rows: if True, drop rows where any of the listed columns is an empty list\n", + " after normalization (i.e., [], or parsed [] from string like \"[nan]\").\n", + " \"\"\"\n", + " if (columns is None) or (len(columns) == 0):\n", + " print(\"No columns specified for cleaning empty lists. Returning original DataFrame.\")\n", + " return df\n", + "\n", + " def is_strict_missing(x):\n", + " \"\"\"True for NaN or common missing string tokens (excluding '##N/A##').\"\"\"\n", + " try:\n", + " if pd.isna(x):\n", + " return True\n", + " except Exception:\n", + " pass\n", + " if isinstance(x, str):\n", + " t = x.strip().strip('\\\"\\'')\n", + " if t.lower() in ('nan', 'none', 'null', ''):\n", + " return True\n", + " return False\n", + "\n", + " def is_na_token(x):\n", + " \"\"\"True for the explicit token '##N/A##' (trim quotes and whitespace).\"\"\"\n", + " if not isinstance(x, str):\n", + " return False\n", + " t = x.strip().strip('\\\"\\'')\n", + " return t == '##N/A##'\n", + "\n", + " def parse_if_list_str(x):\n", + " # Already a Python list\n", + " if isinstance(x, list):\n", + " # Determine membership types\n", + " if len(x) == 0:\n", + " return []\n", + " strict_missing_flags = [is_strict_missing(el) for el in x]\n", + " na_token_flags = [is_na_token(el) for el in x]\n", + " # If all elements are strict-missing -> empty\n", + " if all(strict_missing_flags):\n", + " return []\n", + " # If all elements are either strict-missing or na-token, and at least one strict-missing -> empty\n", + " if all(sm or nt for sm, nt in zip(strict_missing_flags, na_token_flags)) and any(strict_missing_flags):\n", + " return []\n", + " # Otherwise leave original list (including the case all are na-token)\n", + " return x\n", + "\n", + " # Not a string -> nothing to do\n", + " if not isinstance(x, str):\n", + " return x\n", + " s = x.strip()\n", + " # Not a list-like string\n", + " if not (s.startswith('[') and s.endswith(']')):\n", + " return x\n", + " # try json then ast\n", + " try:\n", + " val = json.loads(s)\n", + " except Exception:\n", + " try:\n", + " val = ast.literal_eval(s)\n", + " except Exception:\n", + " val = None\n", + " # If parsed to a Python list, evaluate missingness using same rules\n", + " if isinstance(val, list):\n", + " if len(val) == 0:\n", + " return []\n", + " strict_missing_flags = [is_strict_missing(el) for el in val]\n", + " na_token_flags = [is_na_token(el) for el in val]\n", + " if all(strict_missing_flags):\n", + " return []\n", + " if all(sm or nt for sm, nt in zip(strict_missing_flags, na_token_flags)) and any(strict_missing_flags):\n", + " return []\n", + " return val\n", + " # Handle unquoted or mixed token lists like \"[nan, '##N/A##']\" by manual parse\n", + " inner = s[1:-1].strip()\n", + " if inner == \"\":\n", + " return []\n", + " parts = [p.strip() for p in inner.split(',')]\n", + " if len(parts) > 0:\n", + " strict_missing_flags = [is_strict_missing(p.strip().strip('\\\"\\'')) for p in parts]\n", + " na_token_flags = [is_na_token(p.strip().strip('\\\"\\'')) for p in parts]\n", + " if all(strict_missing_flags):\n", + " return []\n", + " if all(sm or nt for sm, nt in zip(strict_missing_flags, na_token_flags)) and any(strict_missing_flags):\n", + " return []\n", + " # otherwise leave original string (do not attempt risky eval)\n", + " return x\n", + "\n", + " for col in columns:\n", + " if col not in df.columns:\n", + " continue\n", + " # If real lists are present, normalize them (and clean all-missing lists)\n", + " if df[col].apply(lambda x: isinstance(x, list)).any():\n", + " def clean_list_cell(x):\n", + " if isinstance(x, list):\n", + " if len(x) == 0:\n", + " return []\n", + " strict_missing_flags = [is_strict_missing(el) for el in x]\n", + " na_token_flags = [is_na_token(el) for el in x]\n", + " if all(strict_missing_flags):\n", + " return []\n", + " if all(sm or nt for sm, nt in zip(strict_missing_flags, na_token_flags)) and any(strict_missing_flags):\n", + " return []\n", + " return x\n", + " if isinstance(x, str):\n", + " return parse_if_list_str(x)\n", + " return x\n", + " df[col] = df[col].apply(clean_list_cell)\n", + " else:\n", + " df[col] = df[col].apply(parse_if_list_str)\n", + "\n", + " if drop_rows:\n", + " # Build mask for rows to drop: any specified column is an empty list\n", + " drop_mask = pd.Series(False, index=df.index)\n", + " for col in columns:\n", + " if col not in df.columns:\n", + " continue\n", + " drop_mask = drop_mask | df[col].apply(lambda x: isinstance(x, list) and len(x) == 0)\n", + " if drop_mask.any():\n", + " df = df.loc[~drop_mask].reset_index(drop=True)\n", + "\n", + " return df" ] }, { "cell_type": "code", - "execution_count": 4, - "id": "3d3ef86a", + "execution_count": null, + "id": "992b6482", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "61141b6a", "metadata": {}, "outputs": [], "source": [ - "# Kedro pipeline outputs\n", - "df_para_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"20_INTERIM\", \"paradata.parquet\"))\n", - "df_questionnaire_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"questionnaire.parquet\"))\n", - "df_microdata_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"microdata.parquet\"))" + "SURVEY = \"pmpmd\"\n", + "df_microdata = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"microdata.parquet\"))\n", + "df_microdata_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\",\"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"microdata.parquet\"))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "886fccc6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "236 [nan, '##N/A##', nan, '##N/A##', nan, '##N/A##...\n", + "Name: value, dtype: object\n" + ] + } + ], + "source": [ + "print(df_microdata['value'][236:237])" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "00061576", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TextListQuestion rows before: 4672\n", + "Rows to drop (TextListQuestion with empty list): 3267\n", + "TextListQuestion rows after: 1405\n" + ] + } + ], + "source": [ + "# Normalize TextListQuestion `value` cells and drop rows with only-missing lists\n", + "mask_TextListQuestion = df_microdata['qtype'] == 'TextListQuestion'\n", + "print('TextListQuestion rows before:', mask_TextListQuestion.sum())\n", + "# Normalize list-like cells in the subset (do not drop rows in the subset call)\n", + "df_microdata.loc[mask_TextListQuestion, 'value'] = (\n", + " clean_empty_lists(df_microdata.loc[mask_TextListQuestion].copy(), ['value'], drop_rows=False)['value']\n", + ")\n", + "# Drop rows where the 'value' column is an empty list for TextListQuestion rows\n", + "drop_mask = df_microdata['qtype'].eq('TextListQuestion') & df_microdata['value'].apply(lambda x: isinstance(x, list) and len(x) == 0)\n", + "print('Rows to drop (TextListQuestion with empty list):', drop_mask.sum())\n", + "if drop_mask.any():\n", + " df_microdata = df_microdata.loc[~drop_mask].reset_index(drop=True)\n", + " \n", + "print('TextListQuestion rows after:', (df_microdata['qtype'] == 'TextListQuestion').sum())" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "72fdc596", + "execution_count": 73, + "id": "dc4569ea", "metadata": {}, "outputs": [], "source": [ - "# Comparison utility inserted into notebook\n", - "def _dtype_map(dseries: pd.Series) -> Dict[str, str]:\n", - " return {col: str(dtype) for col, dtype in dseries.items()}\n", - "\n", - "\n", - "def _find_candidate_key(df_a: pd.DataFrame, df_b: pd.DataFrame, common_cols: List[str]) -> Optional[str]:\n", - " # Prefer obvious id-like columns\n", - " candidates = [c for c in common_cols if any(k in c.lower() for k in ['id', 'uuid', 'key', 'interview'])]\n", - " # fall back to all common cols\n", - " candidates = candidates + [c for c in common_cols if c not in candidates]\n", - " for c in candidates:\n", - " try:\n", - " if df_a[c].is_unique and df_b[c].is_unique:\n", - " return c\n", - " except Exception:\n", - " continue\n", - " return None\n", - "\n", - "\n", - "def _is_numeric_series(s: pd.Series) -> bool:\n", - " return ptypes.is_numeric_dtype(s.dtype)\n", + "def test_fast(SURVEY: str):\n", + " # original files\n", + " df_para = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"20_INTERIM\", \"paradata.parquet\"))\n", + " df_questionnaire = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"questionnaire.parquet\"))\n", + " df_microdata = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"microdata.parquet\"))\n", + " try:\n", + " df_para_processed = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"paradata.parquet\"))\n", + " # df_para_active = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"paradata_active.parquet\"))\n", + " except Exception as e:\n", + " print(f\"Error reading paradata_processed or paradata_active: {e}\")\n", + " df_para_processed = pd.DataFrame()\n", + " # df_para_active = pd.DataFrame()\n", "\n", + " # Normalize TextListQuestion `value` cells and drop rows with only-missing lists\n", + " mask_TextListQuestion = df_microdata['qtype'] == 'TextListQuestion'\n", + " print('TextListQuestion rows before:', mask_TextListQuestion.sum())\n", + " # Normalize list-like cells in the subset (do not drop rows in the subset call)\n", + " df_microdata.loc[mask_TextListQuestion, 'value'] = (\n", + " clean_empty_lists(df_microdata.loc[mask_TextListQuestion].copy(), ['value'], drop_rows=False)['value']\n", + " )\n", + " # Drop rows where the 'value' column is an empty list for TextListQuestion rows\n", + " drop_mask = df_microdata['qtype'].eq('TextListQuestion') & df_microdata['value'].apply(lambda x: isinstance(x, list) and len(x) == 0)\n", + " print('Rows to drop (TextListQuestion with empty list):', drop_mask.sum())\n", + " if drop_mask.any():\n", + " df_microdata = df_microdata.loc[~drop_mask].reset_index(drop=True)\n", + " print('TextListQuestion rows after:', (df_microdata['qtype'] == 'TextListQuestion').sum())\n", "\n", - "def _try_convert_numeric(val):\n", + " # Kedro pipeline outputs\n", + " df_para_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"20_INTERIM\", \"paradata.parquet\"))\n", + " df_questionnaire_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"questionnaire.parquet\"))\n", + " df_microdata_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"microdata.parquet\"))\n", " try:\n", - " if isinstance(val, str):\n", - " # Check if it looks like a list\n", - " if val.startswith('[') and val.endswith(']'):\n", - " # It's a list string. Compare as list?\n", - " # For now, just return as is (string) comparison\n", - " return val\n", - " return float(val)\n", - " except (ValueError, TypeError):\n", - " return val\n", - "\n", + " df_para_processed_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"paradata_processed.parquet\"))\n", + " # df_para_active_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"paradata_active.parquet\"))\n", + " except Exception as e:\n", + " print(f\"Error reading paradata_processed_kedro or paradata_active_kedro: {e}\")\n", + " df_para_processed_kedro = pd.DataFrame()\n", + " # df_para_active_kedro = pd.DataFrame()\n", "\n", - "def _compare_elementwise(a: pd.Series, b: pd.Series, atol: float, rtol: float) -> np.ndarray:\n", - " \"\"\"Return boolean mask where True indicates a != b (treating NaNs as equal).\n", - " Works for numeric (uses isclose) and non-numeric (stringified) series.\n", - " Has special handling for numeric-string mismatch (e.g. '1' vs '1.0').\n", - " \"\"\"\n", - " # Align lengths assumed equal and indexes aligned\n", - " \n", - " # helper for mixed types\n", - " def smart_compare(val_a, val_b):\n", - " if val_a == val_b:\n", - " return True\n", - " # checks for nan\n", + " for df_name, df_orig, df_kedro in [\n", + " (\"paradata\", df_para, df_para_kedro),\n", + " (\"questionnaire\", df_questionnaire, df_questionnaire_kedro),\n", + " (\"microdata\", df_microdata, df_microdata_kedro),\n", + " (\"paradata_processed\", df_para_processed, df_para_processed_kedro),\n", + " # (\"paradata_active\", df_para_active, df_para_active_kedro)\n", + " ]:\n", " try:\n", - " if np.isnan(val_a) and np.isnan(val_b):\n", - " return True\n", - " except:\n", - " pass\n", - " \n", - " # Try numeric conversion\n", - " try:\n", - " fa = float(val_a)\n", - " fb = float(val_b)\n", - " if np.isnan(fa) and np.isnan(fb):\n", - " return True\n", - " return np.isclose(fa, fb, atol=atol, rtol=rtol)\n", - " except (ValueError, TypeError):\n", - " # If conversion fails, strict string comparison was already done at start\n", - " return str(val_a) == str(val_b)\n", - "\n", - " # 1. If numeric series, use vectorized numeric comparison\n", - " if _is_numeric_series(a) and _is_numeric_series(b):\n", - " # convert to float with NaN preserved\n", - " a_f = a.astype(float)\n", - " b_f = b.astype(float)\n", - " # both NaN -> equal\n", - " both_nan = a_f.isna() & b_f.isna()\n", - " # use isclose (NaNs produce False) then invert and mask NaNs\n", - " close = np.isclose(a_f.fillna(np.nan), b_f.fillna(np.nan), atol=atol, rtol=rtol, equal_nan=True)\n", - " neq = ~close\n", - " neq[both_nan.values] = False\n", - " return neq\n", - " \n", - " # 2. For object/mixed series, use element-wise smart comparison\n", - " # This is slower but necessary for '1' vs '1.0' in object columns\n", - " # We can optimize by first checking string equality\n", - " a_s = a.fillna('__NA__').astype(str)\n", - " b_s = b.fillna('__NA__').astype(str)\n", - " \n", - " # Boolean mask of string mismatches\n", - " neq_mask = (a_s != b_s).to_numpy()\n", - " \n", - " # If no string mismatches, we are done\n", - " if not neq_mask.any():\n", - " return neq_mask\n", + " print(30 * \"=\" + f\" {df_name.upper()} \" + 30 * \"=\")\n", + " print('Shape:', f\"Original - {df_orig.shape}, Kedro - {df_kedro.shape}\")\n", + " if df_name in [\"questionnaire\", \"microdata\"]:\n", + " print('QNR Sequence:', f\"Original - {df_orig['qnr_seq'].nunique()}, Kedro - {df_kedro['qnr_seq'].nunique()}\")\n", + " print('QNR Version:', f\"Original - {df_orig['qnr_version'].unique()}, Kedro - {df_kedro['qnr_version'].unique()}\")\n", + " # print('QNR Version empty:', f\"Original - {df_orig['qnr_version'].isna().sum()}, Kedro - {df_kedro['qnr_version'].isna().sum()}\")\n", + " print('QNR:', f\"Original - {df_orig['qnr'].unique()}, Kedro - {df_kedro['qnr'].unique()}\")\n", + " if df_name == \"microdata\":\n", + " # print('Values:', f\"Original - {df_orig['value'].nunique()}, Kedro - {df_kedro['value'].nunique()}\")\n", + " print('interview__id:', f\"Original - {df_orig['interview__id'].nunique()}, Kedro - {df_kedro['interview__id'].nunique()}\")\n", + " print('interview_id by qnr', f\"Original - {df_orig.groupby(['qnr', 'qnr_version'])['interview__id'].nunique().to_dict()}, Kedro - {df_kedro.groupby(['qnr', 'qnr_version'])['interview__id'].nunique().to_dict()}\")\n", + " if df_name in [\"paradata\", \"paradata_processed\"]:\n", + " print('interview__id:', f\"Original - {df_orig['interview__id'].nunique()}, Kedro - {df_kedro['interview__id'].nunique()}\")\n", + " except Exception as e:\n", + " print(f\"Error comparing {df_name}: {e}\")\n", " \n", - " # For the mismatches, try numeric comparison\n", - " # Get indices of mismatches\n", - " mismatch_indices = np.where(neq_mask)[0]\n", - " \n", - " # Use list comprehension for the mismatched subset\n", - " a_vals = a.iloc[mismatch_indices].values\n", - " b_vals = b.iloc[mismatch_indices].values\n", - " \n", - " resolved_mask = []\n", - " for va, vb in zip(a_vals, b_vals):\n", - " # We need to check if they are \"close enough\"\n", - " is_equal = smart_compare(va, vb)\n", - " # If is_equal is True, then we flag it as FALSE (no difference)\n", - " resolved_mask.append(not is_equal)\n", - " \n", - " # Update the neq_mask\n", - " neq_mask[mismatch_indices] = resolved_mask\n", - " \n", - " return neq_mask\n", - "\n", - "\n", - "def compare_parquet_files(df_a: pd.DataFrame, df_b: pd.DataFrame, check: Optional[str] = None, atol: float = 1e-9, rtol: float = 1e-8) -> Tuple[bool, Dict[str, Any]]:\n", - " \"\"\"\n", - " Robust comparison of two DataFrames produced by different pipelines.\n", - "\n", - " - Basic checks (shape, columns, dtypes) always computed.\n", - " - `check` can be None, 'cells' or 'rows'.\n", - " - For 'cells': attempts label-aligned comparison using a detected unique key column\n", - " (preferred if present in both tables), otherwise aligns by index intersection when possible,\n", - " otherwise falls back to positional/overlap comparison. Reports columns that have any differing\n", - " cells and total number of differing cells compared.\n", - "\n", - " Returns: (same: bool, details: dict)\n", - " \"\"\"\n", - " if not isinstance(df_a, pd.DataFrame) or not isinstance(df_b, pd.DataFrame):\n", - " raise TypeError('compare_parquet_files expects pandas DataFrame inputs')\n", - "\n", - " details: Dict[str, Any] = {}\n", - "\n", - " # Basic metadata\n", - " shape_equal = df_a.shape == df_b.shape\n", - " details['shape'] = {'equal': bool(shape_equal), 'shape_a': df_a.shape, 'shape_b': df_b.shape}\n", - "\n", - " cols_a = list(df_a.columns)\n", - " cols_b = list(df_b.columns)\n", - " only_a = [c for c in cols_a if c not in cols_b]\n", - " only_b = [c for c in cols_b if c not in cols_a]\n", - " different_columns = list(dict.fromkeys(only_a + only_b))\n", - " columns_equal = len(different_columns) == 0\n", - " details['columns'] = {'different_columns': different_columns, 'equal': bool(columns_equal), 'only_in_a': only_a, 'only_in_b': only_b}\n", - "\n", - " dtypes_a = _dtype_map(df_a.dtypes)\n", - " dtypes_b = _dtype_map(df_b.dtypes)\n", - " common = [c for c in cols_a if c in cols_b]\n", - " dtype_mismatch = [c for c in common if dtypes_a.get(c) != dtypes_b.get(c)]\n", - " dtypes_equal = len(dtype_mismatch) == 0\n", - " details['dtypes'] = {'mismatched_columns': dtype_mismatch, 'equal': bool(dtypes_equal)}\n", - "\n", - " same = bool(shape_equal and columns_equal and dtypes_equal)\n", - "\n", - " # Normalize alias\n", - " if check == 'cell':\n", - " check = 'cells'\n", - "\n", - " # Automatic key detection (if any)\n", - " candidate_key = _find_candidate_key(df_a, df_b, common)\n", - " details['auto_key'] = candidate_key\n", - " # Determine columns to compare when using a key-based alignment.\n", - " # Exclude the key column(s) from the per-column comparison because\n", - " # setting the key as index will remove it from the DataFrame columns.\n", - " if candidate_key is None:\n", - " cols_to_compare = common\n", - " else:\n", - " if isinstance(candidate_key, (list, tuple)):\n", - " key_list = list(candidate_key)\n", - " else:\n", - " key_list = [candidate_key]\n", - " cols_to_compare = [c for c in common if c not in key_list]\n", - " # choose active columns for comparison depending on whether we will align by key\n", - " cols = cols_to_compare if candidate_key is not None else common\n", - "\n", - " # CELL-level comparison (position/label depending on alignment)\n", - " if check == 'cells':\n", - " cell_info: Dict[str, Any] = {'checked': True}\n", - " diff_df = None\n", - " if len(common) == 0:\n", - " cell_info['note'] = 'no common columns to compare'\n", - " cell_info['columns_with_differences'] = []\n", - " cell_info['total_cell_differences'] = 0\n", - " else:\n", - " # Prefer key-based alignment\n", - " if candidate_key is not None:\n", - " note = f\"aligned by key='{candidate_key}'\"\n", - " # set index by key and intersect\n", - " a_k = df_a.set_index(candidate_key)\n", - " b_k = df_b.set_index(candidate_key)\n", - " common_idx = a_k.index.intersection(b_k.index)\n", - " a_al = a_k.loc[common_idx, cols].fillna('__NA__')\n", - " b_al = b_k.loc[common_idx, cols].fillna('__NA__')\n", - " rows_compared = len(common_idx)\n", - " else:\n", - " # try index-based alignment if helpful\n", - " inter_idx = df_a.index.intersection(df_b.index)\n", - " if df_a.index.is_unique and df_b.index.is_unique and len(inter_idx) > 0:\n", - " note = 'aligned by index intersection'\n", - " a_al = df_a.reindex(index=inter_idx, columns=common).fillna('__NA__')\n", - " b_al = df_b.reindex(index=inter_idx, columns=common).fillna('__NA__')\n", - " rows_compared = len(inter_idx)\n", - " else:\n", - " # fall back to positional comparison over overlap\n", - " rows_to_compare = min(len(df_a), len(df_b))\n", - " note = 'positional comparison over overlapping rows'\n", - " a_al = df_a.iloc[:rows_to_compare][common].fillna('__NA__').reset_index(drop=True)\n", - " b_al = df_b.iloc[:rows_to_compare][common].fillna('__NA__').reset_index(drop=True)\n", - " rows_compared = rows_to_compare\n", - "\n", - " # perform elementwise comparison with tolerance on numeric cols\n", - " neq_mask = np.zeros((rows_compared, len(cols)), dtype=bool)\n", - " for j, col in enumerate(cols):\n", - " a_col = a_al[col]\n", - " b_col = b_al[col]\n", - " col_neq = _compare_elementwise(a_col, b_col, atol=atol, rtol=rtol)\n", - " neq_mask[:, j] = col_neq\n", - "\n", - " neq_df = pd.DataFrame(neq_mask, columns=cols)\n", - " cols_with_diff = neq_df.any(axis=0)\n", - " cols_with_diff_names = cols_with_diff[cols_with_diff].index.tolist()\n", - " total_cell_diffs = int(neq_df.values.sum())\n", - "\n", - " cell_info['columns_with_differences'] = cols_with_diff_names\n", - " cell_info['total_cell_differences'] = total_cell_diffs\n", - " cell_info['rows_compared'] = int(rows_compared)\n", - " cell_info['note'] = note\n", - " \n", - " # Generate difference dataframe if needed\n", - " if total_cell_diffs > 0:\n", - " diff_list = []\n", - " # Finding indices (row, col) of differences\n", - " rows, cols_idx = np.where(neq_mask)\n", - " for r, c in zip(rows, cols_idx):\n", - " col_name = cols[c]\n", - " # Get index label if available\n", - " idx_label = a_al.index[r]\n", - " val_a = a_al.iloc[r, c]\n", - " val_b = b_al.iloc[r, c]\n", - " diff_list.append({\n", - " 'index': idx_label,\n", - " 'column': col_name,\n", - " 'value_a': val_a,\n", - " 'value_b': val_b\n", - " })\n", - " diff_df = pd.DataFrame(diff_list)\n", - "\n", - " if total_cell_diffs > 0:\n", - " same = False\n", - " details['cell_compare'] = cell_info\n", - " if diff_df is not None:\n", - " details['diff_df'] = diff_df\n", - "\n", - " # ROW-level comparison\n", - " if check == 'rows':\n", - " row_info: Dict[str, Any] = {'checked': True}\n", - " if len(common) == 0:\n", - " row_info['note'] = 'no common columns to compare; cannot perform row membership check'\n", - " \n", - " else:\n", - " if candidate_key is not None:\n", - " # compare by key: count keys only in A/B and mismatched rows for common keys\n", - " a_k = df_a.set_index(candidate_key)[cols].fillna('__NA__')\n", - " b_k = df_b.set_index(candidate_key)[cols].fillna('__NA__')\n", - " keys_a = set(a_k.index)\n", - " keys_b = set(b_k.index)\n", - " keys_only_a = keys_a - keys_b\n", - " keys_only_b = keys_b - keys_a\n", - " common_keys = keys_a & keys_b\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "52774f11", + "metadata": {}, + "outputs": [], + "source": [ + "def test_cell(SURVEY: str, survey_details: dict = None):\n", + " if survey_details is None:\n", + " survey_details = {}\n", "\n", - " # count per-key mismatches\n", - " mismatched_keys = 0\n", - " for k in common_keys:\n", - " a_row = a_k.loc[k]\n", - " b_row = b_k.loc[k]\n", - " # elementwise comparison across common cols\n", - " neq_any = False\n", - " for col in cols:\n", - " if _compare_elementwise(pd.Series([a_row[col]]), pd.Series([b_row[col]]), atol=atol, rtol=rtol)[0]:\n", - " neq_any = True\n", - " break\n", - " if neq_any:\n", - " mismatched_keys += 1\n", + " # original files\n", + " df_para = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"20_INTERIM\", \"paradata.parquet\"))\n", + " df_para.sort_values(by=['qnr_version', 'qnr', 'interview__id', 'qnr_seq'], inplace=True, ignore_index=True)\n", "\n", - " row_info['keys_only_in_a'] = int(len(keys_only_a))\n", - " row_info['keys_only_in_b'] = int(len(keys_only_b))\n", - " row_info['mismatched_common_keys'] = int(mismatched_keys)\n", - " row_info['num_rows_different'] = int(len(keys_only_a) + len(keys_only_b) + mismatched_keys)\n", - " row_info['total_rows_a'] = len(df_a)\n", - " row_info['total_rows_b'] = len(df_b)\n", - " row_info['note'] = f\"compared by key='{candidate_key}'\"\n", + " df_questionnaire = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"questionnaire.parquet\"))\n", + " df_questionnaire.sort_values(by=['qnr_version', 'qnr', 'qnr_seq'], inplace=True, ignore_index=True)\n", + " print('questionnaire rows with categories', df_questionnaire['categories_id'].notna().sum())\n", "\n", - " if row_info['num_rows_different'] > 0:\n", - " same = False\n", - " else:\n", - " # multiset row comparison on common cols (stringified)\n", - " a_rows = df_a[common].fillna('__NA__').astype(str)\n", - " b_rows = df_b[common].fillna('__NA__').astype(str)\n", - " a_tuples = [tuple(r) for r in a_rows.values]\n", - " b_tuples = [tuple(r) for r in b_rows.values]\n", - " cnt_a = Counter(a_tuples)\n", - " cnt_b = Counter(b_tuples)\n", - " rows_a_not_b = sum(max(cnt_a[k] - cnt_b.get(k, 0), 0) for k in cnt_a)\n", - " rows_b_not_a = sum(max(cnt_b[k] - cnt_a.get(k, 0), 0) for k in cnt_b)\n", - " num_diff = int(rows_a_not_b + rows_b_not_a)\n", + " df_microdata = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"microdata.parquet\"))\n", + " \n", + " # Normalize TextListQuestion `value` cells and drop rows with only-missing lists\n", + " mask_TextListQuestion = df_microdata['qtype'] == 'TextListQuestion'\n", + " # print('TextListQuestion rows before:', mask_TextListQuestion.sum())\n", + " # Normalize list-like cells in the subset (do not drop rows in the subset call)\n", + " df_microdata.loc[mask_TextListQuestion, 'value'] = (\n", + " clean_empty_lists(df_microdata.loc[mask_TextListQuestion].copy(), ['value'], drop_rows=False)['value']\n", + " )\n", + " # Drop rows where the 'value' column is an empty list for TextListQuestion rows\n", + " drop_mask = df_microdata['qtype'].eq('TextListQuestion') & df_microdata['value'].apply(lambda x: isinstance(x, list) and len(x) == 0)\n", + " print('Rows to drop (TextListQuestion with empty list):', drop_mask.sum())\n", + " if drop_mask.any():\n", + " df_microdata = df_microdata.loc[~drop_mask].reset_index(drop=True)\n", + " # print('TextListQuestion rows after:', (df_microdata['qtype'] == 'TextListQuestion').sum())\n", "\n", - " row_info['rows_in_a_not_in_b'] = int(rows_a_not_b)\n", - " row_info['rows_in_b_not_in_a'] = int(rows_b_not_a)\n", - " row_info['num_rows_different'] = num_diff\n", - " row_info['total_rows_a'] = len(a_tuples)\n", - " row_info['total_rows_b'] = len(b_tuples)\n", - " row_info['note'] = 'multiset row comparison on common columns'\n", + " df_microdata.sort_values(by=['qnr_version', 'qnr', 'interview__id', 'qnr_seq'], inplace=True, ignore_index=True)\n", + " \n", + " try:\n", + " df_para_processed = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"paradata.parquet\"))\n", + " df_para_processed.sort_values(by=['qnr_version', 'qnr', 'interview__id'], inplace=True, ignore_index=True)\n", + " print('paradata_processed rows with categories', df_para_processed['categories'].notna().sum())\n", + " # df_para_active = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"paradata_active.parquet\"))\n", + " # df_para_active.sort_values(by=['qnr_version', 'qnr', 'interview__id'], inplace=True, ignore_index=True)\n", + " except Exception as e:\n", + " print(f\"Error reading paradata_processed or paradata_active: {e}\")\n", + " df_para_processed = pd.DataFrame()\n", + " # df_para_active = pd.DataFrame()\n", "\n", - " if num_diff > 0:\n", - " same = False\n", - " details['row_compare'] = row_info\n", + " # Kedro pipeline outputs\n", + " df_para_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"20_INTERIM\", \"paradata.parquet\"))\n", + " df_para_kedro.sort_values(by=['qnr_version', 'qnr', 'interview__id', 'qnr_seq'], inplace=True, ignore_index=True)\n", + " df_questionnaire_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"questionnaire.parquet\"))\n", + " df_questionnaire_kedro.sort_values(by=['qnr_version', 'qnr', 'qnr_seq'], inplace=True, ignore_index=True)\n", + " df_microdata_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"microdata.parquet\"))\n", + " df_microdata_kedro.sort_values(by=['qnr_version', 'qnr', 'interview__id', 'qnr_seq'], inplace=True, ignore_index=True)\n", + " \n", + " try:\n", + " df_para_processed_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"paradata_processed.parquet\"))\n", + " df_para_processed_kedro.sort_values(by=['qnr_version', 'qnr', 'interview__id'], inplace=True, ignore_index=True)\n", + " # df_para_active_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"paradata_active.parquet\"))\n", + " # df_para_active_kedro.sort_values(by=['qnr_version', 'qnr', 'interview__id'], inplace=True, ignore_index=True)\n", + " except Exception as e:\n", + " print(f\"Error reading paradata_processed_kedro or paradata_active_kedro: {e}\")\n", + " df_para_processed_kedro = pd.DataFrame()\n", + " # df_para_active_kedro = pd.DataFrame()\n", "\n", - " details['same'] = bool(same)\n", - " return bool(same), details\n" + " for df_name, df_orig, df_kedro in [\n", + " (\"paradata\", df_para, df_para_kedro),\n", + " (\"questionnaire\", df_questionnaire, df_questionnaire_kedro),\n", + " (\"microdata\", df_microdata, df_microdata_kedro),\n", + " (\"paradata_processed\", df_para_processed, df_para_processed_kedro),\n", + " # (\"paradata_active\", df_para_active, df_para_active_kedro)\n", + " ]:\n", + " try:\n", + " same, details = compare_parquet_files(df_kedro, df_orig, check='cells')\n", + " # store details per survey and per df_name (don't overwrite previous df_name entries)\n", + " survey_details.setdefault(SURVEY, {})[df_name] = details\n", + " print(30 * \"=\" + f\" {df_name.upper()} CELL COMPARISON \" + 30 * \"=\")\n", + " print(same)\n", + " print(details['shape'])\n", + " print(details['columns'])\n", + " print(details['dtypes'])\n", + " print(details['cell_compare'])\n", + " print('Number of cell differences:', details['cell_compare']['total_cell_differences'])\n", + " try:\n", + " print('Diff DF shape:', details[\"diff_df\"].shape)\n", + " # display(details[\"diff_df\"])\n", + " except Exception as e:\n", + " print(f\"Error displaying diff_df for {df_name}: {e}\")\n", + " except Exception as e:\n", + " print(f\"Error comparing {df_name}: {e}\")\n", + " return survey_details" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "792a94d3", + "execution_count": null, + "id": "61b067e7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=======================================================================================================\n", + "======================================== TESTING SURVEY: PMPMD ========================================\n", + "=======================================================================================================\n", + "TextListQuestion rows before: 4672\n", + "Rows to drop (TextListQuestion with empty list): 3267\n", + "TextListQuestion rows after: 1405\n" + ] + } + ], "source": [ - "_, details = compare_parquet_files(df_para_kedro, df_para, check='cells')\n" + "for SURVEY in [\"pmpmd\", \"hies2024\", \"slchbs\", \"fbf house holduntitled folder\"]:\n", + " print((80 + len(f\" TESTING SURVEY: {SURVEY.upper()} \")) * \"=\")\n", + " print(40 * \"=\" + f\" TESTING SURVEY: {SURVEY.upper()} \" + 40 * \"=\")\n", + " print((80 + len(f\" TESTING SURVEY: {SURVEY.upper()} \")) * \"=\")\n", + " test_fast(SURVEY)" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "0a094beb", + "execution_count": null, + "id": "8626e46b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'equal': False, 'shape_a': (78413, 11), 'shape_b': (78413, 27)}\n", - "{'different_columns': ['timestamp_local', 'qnr', 'qnr_version', 'qnr_seq', 'variable_name', 'qtype', 'question_type', 'answers', 'question_scope', 'yes_no_view', 'is_filtered_combobox', 'is_integer', 'cascade_from_question_id', 'answer_sequence', 'n_answers', 'question_sequence'], 'equal': False, 'only_in_a': [], 'only_in_b': ['timestamp_local', 'qnr', 'qnr_version', 'qnr_seq', 'variable_name', 'qtype', 'question_type', 'answers', 'question_scope', 'yes_no_view', 'is_filtered_combobox', 'is_integer', 'cascade_from_question_id', 'answer_sequence', 'n_answers', 'question_sequence']}\n", - "{'mismatched_columns': ['timestamp_utc', 'tz_offset'], 'equal': False}\n" + "=======================================================================================================\n", + "======================================== TESTING SURVEY: PMPMD ========================================\n", + "=======================================================================================================\n", + "questionnaire rows with categories 641\n", + "Rows to drop (TextListQuestion with empty list): 3267\n", + "Error reading paradata_processed or paradata_active: 'categories'\n", + "============================== PARADATA CELL COMPARISON ==============================\n", + "False\n", + "{'equal': True, 'shape_a': (1766171, 27), 'shape_b': (1766171, 27)}\n", + "{'different_columns': [], 'equal': True, 'only_in_a': [], 'only_in_b': []}\n", + "{'mismatched_columns': [], 'equal': True}\n", + "{'checked': True, 'columns_with_differences': ['answer_sequence', 'n_answers'], 'total_cell_differences': 900828, 'rows_compared': 1766171, 'note': 'aligned by index intersection'}\n", + "Number of cell differences: 900828\n", + "Diff DF shape: (900828, 4)\n", + "============================== QUESTIONNAIRE CELL COMPARISON ==============================\n", + "False\n", + "{'equal': True, 'shape_a': (3000, 38), 'shape_b': (3000, 38)}\n", + "{'different_columns': [], 'equal': True, 'only_in_a': [], 'only_in_b': []}\n", + "{'mismatched_columns': [], 'equal': True}\n", + "{'checked': True, 'columns_with_differences': ['answer_sequence', 'n_answers'], 'total_cell_differences': 1282, 'rows_compared': 3000, 'note': 'aligned by index intersection'}\n", + "Number of cell differences: 1282\n", + "Diff DF shape: (1282, 4)\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[66]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 4\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[32m40\u001b[39m * \u001b[33m\"\u001b[39m\u001b[33m=\u001b[39m\u001b[33m\"\u001b[39m + \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m TESTING SURVEY: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mSURVEY.upper()\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m \u001b[39m\u001b[33m\"\u001b[39m + \u001b[32m40\u001b[39m * \u001b[33m\"\u001b[39m\u001b[33m=\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 5\u001b[39m \u001b[38;5;28mprint\u001b[39m((\u001b[32m80\u001b[39m + \u001b[38;5;28mlen\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m TESTING SURVEY: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mSURVEY.upper()\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m \u001b[39m\u001b[33m\"\u001b[39m)) * \u001b[33m\"\u001b[39m\u001b[33m=\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m \u001b[43mtest_cell\u001b[49m\u001b[43m(\u001b[49m\u001b[43mSURVEY\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msurvey_details\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 8\u001b[39m survey_details\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[64]\u001b[39m\u001b[32m, line 68\u001b[39m, in \u001b[36mtest_cell\u001b[39m\u001b[34m(SURVEY, survey_details)\u001b[39m\n\u001b[32m 60\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m df_name, df_orig, df_kedro \u001b[38;5;129;01min\u001b[39;00m [\n\u001b[32m 61\u001b[39m (\u001b[33m\"\u001b[39m\u001b[33mparadata\u001b[39m\u001b[33m\"\u001b[39m, df_para, df_para_kedro),\n\u001b[32m 62\u001b[39m (\u001b[33m\"\u001b[39m\u001b[33mquestionnaire\u001b[39m\u001b[33m\"\u001b[39m, df_questionnaire, df_questionnaire_kedro),\n\u001b[32m (...)\u001b[39m\u001b[32m 65\u001b[39m \u001b[38;5;66;03m# (\"paradata_active\", df_para_active, df_para_active_kedro)\u001b[39;00m\n\u001b[32m 66\u001b[39m ]:\n\u001b[32m 67\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m68\u001b[39m same, details = \u001b[43mcompare_parquet_files\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_kedro\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_orig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mcells\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 69\u001b[39m \u001b[38;5;66;03m# store details per survey and per df_name (don't overwrite previous df_name entries)\u001b[39;00m\n\u001b[32m 70\u001b[39m survey_details.setdefault(SURVEY, {})[df_name] = details\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Work/Rowsquared/RISSK/rissk/rissk/utils/testing_utils.py:244\u001b[39m, in \u001b[36mcompare_parquet_files\u001b[39m\u001b[34m(df_a, df_b, check, atol, rtol)\u001b[39m\n\u001b[32m 242\u001b[39m a_col = a_al[col]\n\u001b[32m 243\u001b[39m b_col = b_al[col]\n\u001b[32m--> \u001b[39m\u001b[32m244\u001b[39m col_neq = \u001b[43m_compare_elementwise\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma_col\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mb_col\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43matol\u001b[49m\u001b[43m=\u001b[49m\u001b[43matol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrtol\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrtol\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 245\u001b[39m neq_mask[:, j] = col_neq\n\u001b[32m 247\u001b[39m neq_df = pd.DataFrame(neq_mask, columns=cols)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Work/Rowsquared/RISSK/rissk/rissk/utils/testing_utils.py:112\u001b[39m, in \u001b[36m_compare_elementwise\u001b[39m\u001b[34m(a, b, atol, rtol)\u001b[39m\n\u001b[32m 107\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m neq\n\u001b[32m 109\u001b[39m \u001b[38;5;66;03m# 2. For object/mixed series, use element-wise smart comparison\u001b[39;00m\n\u001b[32m 110\u001b[39m \u001b[38;5;66;03m# This is slower but necessary for '1' vs '1.0' in object columns\u001b[39;00m\n\u001b[32m 111\u001b[39m \u001b[38;5;66;03m# We can optimize by first checking string equality\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m112\u001b[39m a_s = \u001b[43ma\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfillna\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m__NA__\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 113\u001b[39m b_s = b.fillna(\u001b[33m'\u001b[39m\u001b[33m__NA__\u001b[39m\u001b[33m'\u001b[39m).astype(\u001b[38;5;28mstr\u001b[39m)\n\u001b[32m 115\u001b[39m \u001b[38;5;66;03m# Boolean mask of string mismatches\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/generic.py:6665\u001b[39m, in \u001b[36mNDFrame.astype\u001b[39m\u001b[34m(self, dtype, copy, errors)\u001b[39m\n\u001b[32m 6659\u001b[39m results = [\n\u001b[32m 6660\u001b[39m ser.astype(dtype, copy=copy, errors=errors) \u001b[38;5;28;01mfor\u001b[39;00m _, ser \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.items()\n\u001b[32m 6661\u001b[39m ]\n\u001b[32m 6663\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 6664\u001b[39m \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m6665\u001b[39m new_data = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_mgr\u001b[49m\u001b[43m.\u001b[49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 6666\u001b[39m res = \u001b[38;5;28mself\u001b[39m._constructor_from_mgr(new_data, axes=new_data.axes)\n\u001b[32m 6667\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m res.__finalize__(\u001b[38;5;28mself\u001b[39m, method=\u001b[33m\"\u001b[39m\u001b[33mastype\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/internals/managers.py:449\u001b[39m, in \u001b[36mBaseBlockManager.astype\u001b[39m\u001b[34m(self, dtype, copy, errors)\u001b[39m\n\u001b[32m 446\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m using_copy_on_write():\n\u001b[32m 447\u001b[39m copy = \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m449\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 450\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mastype\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 451\u001b[39m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 452\u001b[39m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 453\u001b[39m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 454\u001b[39m \u001b[43m \u001b[49m\u001b[43musing_cow\u001b[49m\u001b[43m=\u001b[49m\u001b[43musing_copy_on_write\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 455\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/internals/managers.py:363\u001b[39m, in \u001b[36mBaseBlockManager.apply\u001b[39m\u001b[34m(self, f, align_keys, **kwargs)\u001b[39m\n\u001b[32m 361\u001b[39m applied = b.apply(f, **kwargs)\n\u001b[32m 362\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m363\u001b[39m applied = \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 364\u001b[39m result_blocks = extend_blocks(applied, result_blocks)\n\u001b[32m 366\u001b[39m out = \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m).from_blocks(result_blocks, \u001b[38;5;28mself\u001b[39m.axes)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/internals/blocks.py:784\u001b[39m, in \u001b[36mBlock.astype\u001b[39m\u001b[34m(self, dtype, copy, errors, using_cow, squeeze)\u001b[39m\n\u001b[32m 781\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mCan not squeeze with more than one column.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 782\u001b[39m values = values[\u001b[32m0\u001b[39m, :] \u001b[38;5;66;03m# type: ignore[call-overload]\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m784\u001b[39m new_values = \u001b[43mastype_array_safe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 786\u001b[39m new_values = maybe_coerce_values(new_values)\n\u001b[32m 788\u001b[39m refs = \u001b[38;5;28;01mNone\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/dtypes/astype.py:237\u001b[39m, in \u001b[36mastype_array_safe\u001b[39m\u001b[34m(values, dtype, copy, errors)\u001b[39m\n\u001b[32m 234\u001b[39m dtype = dtype.numpy_dtype\n\u001b[32m 236\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m237\u001b[39m new_values = \u001b[43mastype_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 238\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[32m 239\u001b[39m \u001b[38;5;66;03m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[32m 240\u001b[39m \u001b[38;5;66;03m# trying to convert to float\u001b[39;00m\n\u001b[32m 241\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m errors == \u001b[33m\"\u001b[39m\u001b[33mignore\u001b[39m\u001b[33m\"\u001b[39m:\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/dtypes/astype.py:182\u001b[39m, in \u001b[36mastype_array\u001b[39m\u001b[34m(values, dtype, copy)\u001b[39m\n\u001b[32m 179\u001b[39m values = values.astype(dtype, copy=copy)\n\u001b[32m 181\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m182\u001b[39m values = \u001b[43m_astype_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 184\u001b[39m \u001b[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001b[39;00m\n\u001b[32m 185\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dtype, np.dtype) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values.dtype.type, \u001b[38;5;28mstr\u001b[39m):\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/dtypes/astype.py:96\u001b[39m, in \u001b[36m_astype_nansafe\u001b[39m\u001b[34m(arr, dtype, copy, skipna)\u001b[39m\n\u001b[32m 94\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m arr.ndim > \u001b[32m1\u001b[39m:\n\u001b[32m 95\u001b[39m arr = arr.ravel()\n\u001b[32m---> \u001b[39m\u001b[32m96\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mlib\u001b[49m\u001b[43m.\u001b[49m\u001b[43mensure_string_array\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 97\u001b[39m \u001b[43m \u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m=\u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert_na_value\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\n\u001b[32m 98\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m.reshape(shape)\n\u001b[32m 100\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m np.issubdtype(arr.dtype, np.floating) \u001b[38;5;129;01mand\u001b[39;00m dtype.kind \u001b[38;5;129;01min\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33miu\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m 101\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m _astype_float_to_int_nansafe(arr, dtype, copy)\n", + "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/lib.pyx:718\u001b[39m, in \u001b[36mpandas._libs.lib.ensure_string_array\u001b[39m\u001b[34m()\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/lib.pyx:832\u001b[39m, in \u001b[36mpandas._libs.lib.ensure_string_array\u001b[39m\u001b[34m()\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/numpy/_core/arrayprint.py:1721\u001b[39m, in \u001b[36m_array_str_implementation\u001b[39m\u001b[34m(a, max_line_width, precision, suppress_small, array2string)\u001b[39m\n\u001b[32m 1715\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m a.shape == ():\n\u001b[32m 1716\u001b[39m \u001b[38;5;66;03m# obtain a scalar and call str on it, avoiding problems for subclasses\u001b[39;00m\n\u001b[32m 1717\u001b[39m \u001b[38;5;66;03m# for which indexing with () returns a 0d instead of a scalar by using\u001b[39;00m\n\u001b[32m 1718\u001b[39m \u001b[38;5;66;03m# ndarray's getindex. Also guard against recursive 0d object arrays.\u001b[39;00m\n\u001b[32m 1719\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m _guarded_repr_or_str(np.ndarray.\u001b[34m__getitem__\u001b[39m(a, ()))\n\u001b[32m-> \u001b[39m\u001b[32m1721\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marray2string\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_line_width\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprecision\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msuppress_small\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m \u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/numpy/_core/arrayprint.py:773\u001b[39m, in \u001b[36marray2string\u001b[39m\u001b[34m(a, max_line_width, precision, suppress_small, separator, prefix, style, formatter, threshold, edgeitems, sign, floatmode, suffix, legacy)\u001b[39m\n\u001b[32m 619\u001b[39m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_array2string_dispatcher, module=\u001b[33m'\u001b[39m\u001b[33mnumpy\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 620\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34marray2string\u001b[39m(a, max_line_width=\u001b[38;5;28;01mNone\u001b[39;00m, precision=\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 621\u001b[39m suppress_small=\u001b[38;5;28;01mNone\u001b[39;00m, separator=\u001b[33m'\u001b[39m\u001b[33m \u001b[39m\u001b[33m'\u001b[39m, prefix=\u001b[33m\"\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 622\u001b[39m style=np._NoValue, formatter=\u001b[38;5;28;01mNone\u001b[39;00m, threshold=\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 623\u001b[39m edgeitems=\u001b[38;5;28;01mNone\u001b[39;00m, sign=\u001b[38;5;28;01mNone\u001b[39;00m, floatmode=\u001b[38;5;28;01mNone\u001b[39;00m, suffix=\u001b[33m\"\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 624\u001b[39m *, legacy=\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m 625\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 626\u001b[39m \u001b[33;03m Return a string representation of an array.\u001b[39;00m\n\u001b[32m 627\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 770\u001b[39m \n\u001b[32m 771\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m773\u001b[39m overrides = \u001b[43m_make_options_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprecision\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthreshold\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medgeitems\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 774\u001b[39m \u001b[43m \u001b[49m\u001b[43mmax_line_width\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msuppress_small\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 775\u001b[39m \u001b[43m \u001b[49m\u001b[43msign\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mformatter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfloatmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlegacy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 776\u001b[39m options = format_options.get().copy()\n\u001b[32m 777\u001b[39m options.update(overrides)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/numpy/_core/arrayprint.py:66\u001b[39m, in \u001b[36m_make_options_dict\u001b[39m\u001b[34m(precision, threshold, edgeitems, linewidth, suppress, nanstr, infstr, sign, formatter, floatmode, legacy, override_repr)\u001b[39m\n\u001b[32m 57\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_make_options_dict\u001b[39m(precision=\u001b[38;5;28;01mNone\u001b[39;00m, threshold=\u001b[38;5;28;01mNone\u001b[39;00m, edgeitems=\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 58\u001b[39m linewidth=\u001b[38;5;28;01mNone\u001b[39;00m, suppress=\u001b[38;5;28;01mNone\u001b[39;00m, nanstr=\u001b[38;5;28;01mNone\u001b[39;00m, infstr=\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 59\u001b[39m sign=\u001b[38;5;28;01mNone\u001b[39;00m, formatter=\u001b[38;5;28;01mNone\u001b[39;00m, floatmode=\u001b[38;5;28;01mNone\u001b[39;00m, legacy=\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 60\u001b[39m override_repr=\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m 61\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 62\u001b[39m \u001b[33;03m Make a dictionary out of the non-None arguments, plus conversion of\u001b[39;00m\n\u001b[32m 63\u001b[39m \u001b[33;03m *legacy* and sanity checks.\u001b[39;00m\n\u001b[32m 64\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m66\u001b[39m options = {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28;43mlocals\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m.items()) \u001b[38;5;28;01mif\u001b[39;00m v \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m}\n\u001b[32m 68\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m suppress \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 69\u001b[39m options[\u001b[33m'\u001b[39m\u001b[33msuppress\u001b[39m\u001b[33m'\u001b[39m] = \u001b[38;5;28mbool\u001b[39m(suppress)\n", + "\u001b[31mKeyboardInterrupt\u001b[39m: " ] } ], "source": [ - "print(details['shape'])\n", - "print(details['columns'])\n", - "print(details['dtypes'])" + "survey_details = {}\n", + "for SURVEY in [\"pmpmd\", \"hies2024\", \"slchbs\", \"fbf house holduntitled folder\"]:\n", + " print((80 + len(f\" TESTING SURVEY: {SURVEY.upper()} \")) * \"=\")\n", + " print(40 * \"=\" + f\" TESTING SURVEY: {SURVEY.upper()} \" + 40 * \"=\")\n", + " print((80 + len(f\" TESTING SURVEY: {SURVEY.upper()} \")) * \"=\")\n", + " test_cell(SURVEY, survey_details)\n", + "\n", + "survey_details" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46e9e7a3", + "metadata": {}, + "outputs": [], + "source": [ + "display(survey_details['hies2024']['microdata']['diff_df'])" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "5a2e9402", + "execution_count": null, + "id": "38d6eee4", "metadata": {}, "outputs": [ { @@ -448,7 +564,7 @@ { "name": "value_a", "rawType": "object", - "type": "string" + "type": "unknown" }, { "name": "value_b", @@ -456,362 +572,362 @@ "type": "unknown" } ], - "ref": "df32ed59-ae08-4a84-9798-997b419e5a62", + "ref": "11aea3b1-c4bb-4f17-8816-85a416a50e6a", "rows": [ [ "0", "0", - "timestamp_utc", - "2024-10-29T01:17:15.712", - "2024-10-29 01:17:15.712000" + "answer_sequence", + "[10140, 10142, 10147, 15218, 15220, 15233, 18256, 18258, 18264, 20026, 20030, 20031, 30042, 30045, 30049, 80110, 80115, 80119]", + "nan" ], [ "1", "0", - "tz_offset", - "11:00:00", - "0 days 11:00:00" + "n_answers", + "18.0", + "__NA__" ], [ "2", "1", - "timestamp_utc", - "2024-10-29T01:17:15.712", - "2024-10-29 01:17:15.712000" + "answer_sequence", + "[3004257, 3004259, 3004553, 3004951, 3004953, 8011055, 8011553, 8011951, 8011955, 8011957, 1014053, 1014055, 1014251, 1014253, 1014257, 1014259, 1014261, 1014751, 1014753, 1014755, 1522051, 1522053, 1522055, 1523353, 1523355, 1521851, 1521853, 2002661, 2003161, 2003059, 1825653, 1825657, 1825855, 1825857, 1825863, 1826453, 1826459]", + "nan" ], [ "3", "1", - "tz_offset", - "11:00:00", - "0 days 11:00:00" + "n_answers", + "37.0", + "__NA__" ], [ "4", - "2", - "timestamp_utc", - "2024-10-29T01:17:15.712", - "2024-10-29 01:17:15.712000" + "7", + "value", + "['Бат', 1, 'Цэцэг', 2]", + "['Бат', '##N/A##', 'Цэцэг', '##N/A##']" ], [ "5", - "2", - "tz_offset", - "11:00:00", - "0 days 11:00:00" + "19", + "answer_sequence", + "[1, 0]", + "nan" ], [ "6", - "3", - "timestamp_utc", - "2024-10-29T01:17:15.712", - "2024-10-29 01:17:15.712000" + "19", + "n_answers", + "2.0", + "__NA__" ], [ "7", - "3", - "tz_offset", - "11:00:00", - "0 days 11:00:00" + "22", + "answer_sequence", + "[1, 0]", + "nan" ], [ "8", - "4", - "timestamp_utc", - "2024-10-29T01:17:15.712", - "2024-10-29 01:17:15.712000" + "22", + "n_answers", + "2.0", + "__NA__" ], [ "9", - "4", - "tz_offset", - "11:00:00", - "0 days 11:00:00" + "23", + "answer_sequence", + "[1, 0]", + "nan" ], [ "10", - "5", - "timestamp_utc", - "2024-10-29T01:17:19.503", - "2024-10-29 01:17:19.503000" + "23", + "n_answers", + "2.0", + "__NA__" ], [ "11", - "5", - "tz_offset", - "11:00:00", - "0 days 11:00:00" + "24", + "answer_sequence", + "[1, 0]", + "nan" ], [ "12", - "6", - "timestamp_utc", - "2024-10-29T01:17:38.591", - "2024-10-29 01:17:38.591000" + "24", + "n_answers", + "2.0", + "__NA__" ], [ "13", - "6", - "tz_offset", - "11:00:00", - "0 days 11:00:00" + "25", + "answer_sequence", + "[1, 0]", + "nan" ], [ "14", - "7", - "timestamp_utc", - "2024-10-29T01:18:17.911", - "2024-10-29 01:18:17.911000" + "25", + "n_answers", + "2.0", + "__NA__" ], [ "15", - "7", - "tz_offset", - "11:00:00", - "0 days 11:00:00" + "26", + "answer_sequence", + "[1, 0]", + "nan" ], [ "16", - "8", - "timestamp_utc", - "2024-10-29T01:18:24.612", - "2024-10-29 01:18:24.612000" + "26", + "n_answers", + "2.0", + "__NA__" ], [ "17", - "8", - "tz_offset", - "11:00:00", - "0 days 11:00:00" + "27", + "answer_sequence", + "[1, 0]", + "nan" ], [ "18", - "9", - "timestamp_utc", - "2024-10-29T01:19:14.377", - "2024-10-29 01:19:14.377000" + "27", + 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"details['diff_df']" + "display(survey_details['pmpmd']['microdata']['diff_df'])" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "de3363a0", + "execution_count": null, + "id": "8d9879a4", "metadata": {}, "outputs": [], "source": [ - "_, details = compare_parquet_files(df_microdata_kedro, df_microdata, check='cells')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "4850564f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'equal': False, 'shape_a': (0, 0), 'shape_b': (23127, 41)}\n", - "{'different_columns': ['interview__id', 'roster_level', 'variable', 'value', 'filename', 'qnr', 'qnr_version', 'qnr_seq', 'variable_name', 'qtype', 'question_type', 'answers', 'children', 'condition_expression', 'hide_if_disabled', 'featured', 'instructions', 'properties', 'public_key', 'question_scope', 'question_text', 'stata_export_caption', 'variable_label', 'is_timestamp', 'validation_conditions', 'yes_no_view', 'is_filtered_combobox', 'is_integer', 'categories_id', 'title', 'is_roster', 'linked_to_roster_id', 'linked_to_question_id', 'cascade_from_question_id', 'parents', 'answer_sequence', 'n_answers', 'is_linked', 'parent_1', 'parent_2', 'question_sequence'], 'equal': False, 'only_in_a': [], 'only_in_b': ['interview__id', 'roster_level', 'variable', 'value', 'filename', 'qnr', 'qnr_version', 'qnr_seq', 'variable_name', 'qtype', 'question_type', 'answers', 'children', 'condition_expression', 'hide_if_disabled', 'featured', 'instructions', 'properties', 'public_key', 'question_scope', 'question_text', 'stata_export_caption', 'variable_label', 'is_timestamp', 'validation_conditions', 'yes_no_view', 'is_filtered_combobox', 'is_integer', 'categories_id', 'title', 'is_roster', 'linked_to_roster_id', 'linked_to_question_id', 'cascade_from_question_id', 'parents', 'answer_sequence', 'n_answers', 'is_linked', 'parent_1', 'parent_2', 'question_sequence']}\n", - "{'mismatched_columns': [], 'equal': True}\n", - "{'checked': True, 'note': 'no common columns to compare', 'columns_with_differences': [], 'total_cell_differences': 0}\n", - "Cells are the same\n" - ] - } - ], - "source": [ - "print(details['shape'])\n", - "print(details['columns'])\n", - "print(details['dtypes'])\n", - "print(details['cell_compare'])\n", - "try:\n", - " display(details['df_diff'])\n", - "except:\n", - " print('Cells are the same')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "beeb1d8e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(52, 36) (52, 36)\n" - ] - } - ], - "source": [ - "print(df_questionnaire.shape, df_questionnaire_kedro.shape)" + "def summary_table_for_survey(SURVEY: str, datasets=None):\n", + " \"\"\"Produce a summary DataFrame with requested comparison stats for a survey.\n", + "\n", + " Columns: SURVEY, df, shape_equal, shape_rows, shape_cols, dtype_equal,\n", + " different_columns, columns_with_differences, num_cell_differences, questionnaire_categories_count\n", + " \"\"\"\n", + " if datasets is None:\n", + " datasets = ['paradata', 'questionnaire', 'microdata', 'paradata_processed']\n", + " rows = []\n", + " for df_name in datasets:\n", + " try:\n", + " orig_path = PROJ_ROOT.joinpath('data', SURVEY, 'latest')\n", + " kedro_path = PROJ_ROOT.joinpath('rissk_kedro', 'data', SURVEY, 'latest')\n", + " if df_name == 'paradata':\n", + " df_orig = pd.read_parquet(orig_path.joinpath('20_INTERIM', 'paradata.parquet'))\n", + " df_kedro = pd.read_parquet(kedro_path.joinpath('20_INTERIM', 'paradata.parquet'))\n", + " else:\n", + " df_orig = pd.read_parquet(orig_path.joinpath('30_PROCESSED', f'{df_name}.parquet'))\n", + " df_kedro = pd.read_parquet(kedro_path.joinpath('30_PROCESSED', f'{df_name}.parquet'))\n", + "\n", + " same, details = compare_parquet_files(df_kedro, df_orig, check='cells')\n", + "\n", + " shape_equal = bool(details.get('shape', {}).get('equal', False))\n", + " shape_a = details.get('shape', {}).get('shape_a', (None, None))\n", + " try:\n", + " shape_rows = int(shape_a[0])\n", + " except Exception:\n", + " shape_rows = None\n", + " try:\n", + " shape_cols = int(shape_a[1])\n", + " except Exception:\n", + " shape_cols = None\n", + "\n", + " dtype_equal = bool(details.get('dtypes', {}).get('equal', False))\n", + " different_columns = details.get('columns', {}).get('different_columns', [])\n", + " cols_with_diff = details.get('cell_compare', {}).get('columns_with_differences', [])\n", + " num_cell_diffs = int(details.get('cell_compare', {}).get('total_cell_differences', 0))\n", + "\n", + " questionnaire_categories_count = None\n", + " if df_name == 'questionnaire':\n", + " try:\n", + " questionnaire_categories_count = int(df_orig['categories_id'].notna().sum())\n", + " except Exception:\n", + " questionnaire_categories_count = None\n", + "\n", + " rows.append({\n", + " 'SURVEY': SURVEY,\n", + " 'df': df_name,\n", + " 'shape_bool': shape_equal,\n", + " 'shape[0]': shape_rows,\n", + " 'shape[1]': shape_cols,\n", + " 'dtype_bool': dtype_equal,\n", + " 'different_columns': ','.join(map(str, different_columns)) if different_columns else '',\n", + " 'columns_with_differences': ','.join(map(str, cols_with_diff)) if cols_with_diff else '',\n", + " 'Number of cell differences': num_cell_diffs,\n", + " 'questionnaire_categories_count': questionnaire_categories_count,\n", + " })\n", + " except Exception as e:\n", + " rows.append({\n", + " 'SURVEY': SURVEY,\n", + " 'df': df_name,\n", + " 'shape_bool': False,\n", + " 'shape[0]': None,\n", + " 'shape[1]': None,\n", + " 'dtype_bool': False,\n", + " 'different_columns': '',\n", + " 'columns_with_differences': '',\n", + " 'Number of cell differences': None,\n", + " 'questionnaire_categories_count': None,\n", + " 'error': str(e)\n", + " })\n", + " return pd.DataFrame(rows)\n", + "\n", + "# Example: produce and display table for a survey\n", + "# summary_df = summary_table_for_survey('hies2024')\n", + "# display(summary_df)\n", + "# print('summary_table_for_survey defined — call it with a survey name to produce the table.')" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "2d466e2a", + "execution_count": null, + "id": "9fd680d3", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ True, True, True, True, True, True, True, True, True,\n", - " True, True, True, True, True, True, True, True, True,\n", - " True, True, True, True, True, True, True, True, True,\n", - " True, True, True, True, True, True, True, True, True])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "df_questionnaire.columns == df_questionnaire_kedro.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "1fddd0a2", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "object", - "type": "string" - }, - { - "name": "df_questionnaire", - "rawType": "object", - "type": "unknown" - }, - { - "name": "df_questionnaire_kedro", - "rawType": "object", - 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interview__idordereventresponsibleroletimestamp_utctz_offsetparametersparamanswer...question_typeanswersquestion_scopeyes_no_viewis_filtered_comboboxis_integercascade_from_question_idanswer_sequencen_answersquestion_sequence
0468fc58b1d4b4196af97bcbfbc5464bb1InterviewCreatedWEST_Sup20012024-10-29 01:17:15.7120 days 11:00:00NoneNoneNone...NaNNoneNaNNoneNoneNoneNonenanNaNNaN
1468fc58b1d4b4196af97bcbfbc5464bb2SupervisorAssignedWEST_Sup20012024-10-29 01:17:15.7120 days 11:00:00NoneNoneNone...NaNNoneNaNNoneNoneNoneNonenanNaNNaN
2468fc58b1d4b4196af97bcbfbc5464bb3InterviewModeChangedWEST_Sup20012024-10-29 01:17:15.7120 days 11:00:00CAPI||CAPI...NaNNoneNaNNoneNoneNoneNonenanNaNNaN
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" - ], - "text/plain": [ - " interview__id order event responsible \\\n", - "0 468fc58b1d4b4196af97bcbfbc5464bb 1 InterviewCreated WEST_Sup200 \n", - "1 468fc58b1d4b4196af97bcbfbc5464bb 2 SupervisorAssigned WEST_Sup200 \n", - "2 468fc58b1d4b4196af97bcbfbc5464bb 3 InterviewModeChanged WEST_Sup200 \n", - "3 468fc58b1d4b4196af97bcbfbc5464bb 4 InterviewerAssigned WEST_Sup200 \n", - "4 468fc58b1d4b4196af97bcbfbc5464bb 5 KeyAssigned None \n", - "\n", - " role timestamp_utc tz_offset parameters param \\\n", - "0 1 2024-10-29 01:17:15.712 0 days 11:00:00 None None \n", - "1 1 2024-10-29 01:17:15.712 0 days 11:00:00 None None \n", - "2 1 2024-10-29 01:17:15.712 0 days 11:00:00 CAPI|| CAPI \n", - "3 1 2024-10-29 01:17:15.712 0 days 11:00:00 WEST_Sup200 WEST_Sup200 \n", - "4 0 2024-10-29 01:17:15.712 0 days 11:00:00 66-54-06-24 66-54-06-24 \n", - "\n", - " answer ... question_type answers question_scope yes_no_view \\\n", - "0 None ... NaN None NaN None \n", - "1 None ... NaN None NaN None \n", - "2 ... NaN None NaN None \n", - "3 None ... NaN None NaN None \n", - "4 None ... NaN None NaN None \n", - "\n", - " is_filtered_combobox is_integer cascade_from_question_id answer_sequence \\\n", - "0 None None None nan \n", - "1 None None None nan \n", - "2 None None None nan \n", - "3 None None None nan \n", - "4 None None None nan \n", - "\n", - " n_answers question_sequence \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "\n", - "[5 rows x 27 columns]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_para.head(5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e10c6d7d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Old Compression: SNAPPY\n", - "New Compression: SNAPPY\n", - "5387\n", - "5405\n" - ] - } - ], - "source": [ - "# meta_old = pq.read_metadata(PROCESSED_DATA_DIR.joinpath(\"microdata.parquet\"))\n", - "# meta_new = pq.read_metadata(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"microdata.parquet\"))\n", - "\n", - "# print(f\"Old Compression: {meta_old.row_group(0).column(0).compression}\")\n", - "# print(f\"New Compression: {meta_new.row_group(0).column(0).compression}\")\n", + "# Run summaries for the four surveys, append into one table, and export as CSV\n", + "surveys = [\"pmpmd\", \"hies2024\", \"slchbs\", \"fbf house holduntitled folder\"]\n", + "all_dfs = []\n", + "for s in surveys:\n", + " try:\n", + " print(f'Generating summary for {s}...')\n", + " df = summary_table_for_survey(s)\n", + " # add survey column already present, ensure consistent order\n", + " all_dfs.append(df)\n", + " except Exception as e:\n", + " print(f'Error for {s}: {e}')\n", "\n", - "# print(len(meta_old.metadata[b'pandas']))\n", - "# print(len(meta_new.metadata[b'pandas']))" + "if len(all_dfs) > 0:\n", + " summary_all = pd.concat(all_dfs, ignore_index=True)\n", + " out_dir = PROJ_ROOT.joinpath('data','reports')\n", + " out_dir.mkdir(parents=True, exist_ok=True)\n", + " out_csv = out_dir.joinpath('comparison_summary_all_surveys.csv')\n", + " summary_all.to_csv(out_csv, index=False)\n", + " print(f'Wrote summary CSV to: {out_csv}')\n", + " display(summary_all)\n", + "else:\n", + " print('No summaries were produced.')" ] } ], From 498067006a8d8e3dbd25bf310935e8702087d199 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Sat, 21 Mar 2026 11:04:54 +0000 Subject: [PATCH 36/70] Refactor feature processing and pipeline definitions to enhance handling of paradata; reintroduce filter_active_paradata_node for legacy compatibility and improve numeric mask functionality. --- rissk/feature_processing_kedro.py | 41 ++++++++++++++++--- rissk_kedro/conf/base/catalog.yml | 8 ++-- .../pipelines/feature_creation/pipeline.py | 20 ++++----- .../pipelines/feature_engineering/nodes.py | 4 +- .../pipelines/feature_engineering/pipeline.py | 12 ++++-- 5 files changed, 61 insertions(+), 24 deletions(-) diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index 4c96222..a10d1b6 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -24,7 +24,7 @@ def make_index_col(df: pd.DataFrame) -> pd.DataFrame: df['index_col'] = df['index_col'].str.strip('_') return df -def get_numeric_mask(df_item: pd.DataFrame) -> pd.Series: +def get_numeric_mask(df_item: pd.DataFrame, filter_answer_values: bool) -> pd.Series: """Returns a boolean mask for valid numeric question rows, matching the legacy numeric_question_mask.""" sentinel_mask = _is_missing_numeric_sentinel(df_item['value']) mask = ( @@ -33,6 +33,9 @@ def get_numeric_mask(df_item: pd.DataFrame) -> pd.Series: (~pd.isnull(df_item['value'])) & (~sentinel_mask) ) + if filter_answer_values: + answer_mask = _is_answer_value(df_item['value'], df_item['answer_sequence']) + mask &= ~answer_mask return mask @@ -40,6 +43,33 @@ def _is_missing_numeric_sentinel(values: pd.Series) -> pd.Series: """Robustly detects the numeric missing-value sentinel across mixed object values.""" return pd.to_numeric(values, errors='coerce').eq(-999999999) +def _is_answer_value(values: pd.Series, answer_sequence: pd.Series) -> pd.Series: + """Returns True where the numeric value matches an item in the answer_sequence list. + + answer_sequence is expected to be the string-coerced form produced by + paradata['answer_sequence'].apply(str), e.g. "[1, 2]", "[0, -99]", "nan". + """ + def _row_is_answer(value, seq_str): + if not isinstance(seq_str, str) or seq_str in ('nan', 'None', ''): + return False + try: + items = ast.literal_eval(seq_str) + except (ValueError, SyntaxError): + return False + if not isinstance(items, list): + return False + numeric_val = pd.to_numeric(value, errors='coerce') + if pd.isna(numeric_val): + return False + return any(numeric_val == pd.to_numeric(item, errors='coerce') for item in items) + + return pd.Series( + [_row_is_answer(v, s) for v, s in zip(values, answer_sequence)], + index=values.index, + dtype=bool, + ) + + def _coerce_numeric_with_warning(df_item: pd.DataFrame, numeric_mask: pd.Series, feature_name: str) -> pd.Series: """Coerce numeric values and warn about rows that cannot be parsed.""" values = df_item.loc[numeric_mask, 'value'] @@ -63,7 +93,6 @@ def get_df_time(df_paradata_full: pd.DataFrame) -> pd.DataFrame: Mirrors the legacy df_active_paradata filter before computing time deltas: - AnswerSet / AnswerRemoved / CommentSet: included only when question_scope == 0 - (interviewer-scope questions; supervisor-scope questions with scope == 1 are excluded). - InterviewCreated / Resumed / Restarted: no question scope (NaN); included regardless. - All other event types (Completed, ApprovalRequested, etc.): excluded. @@ -370,7 +399,7 @@ def feat_string_length(df_item, **kwargs): def feat_numeric_response(df_item, **kwargs): # f__numeric_response, response, if NumericQuestions, else empty pd.NA feature_name = 'f__numeric_response' - numeric_mask = get_numeric_mask(df_item) + numeric_mask = get_numeric_mask(df_item=df_item, filter_answer_values=False) df_item[feature_name] = np.nan if numeric_mask.any(): numeric_values = _coerce_numeric_with_warning(df_item, numeric_mask, feature_name) @@ -380,7 +409,7 @@ def feat_numeric_response(df_item, **kwargs): def feat_first_digit(df_item, **kwargs): # f__first_digit, first digit of the response if numeric question else empty pd.NA feature_name = 'f__first_digit' - numeric_mask = get_numeric_mask(df_item) + numeric_mask = get_numeric_mask(df_item=df_item, filter_answer_values=True) df_item[feature_name] = pd.NA if numeric_mask.any(): numeric_values = _coerce_numeric_with_warning(df_item, numeric_mask, feature_name) @@ -393,7 +422,7 @@ def feat_last_digit(df_item, **kwargs): # f__last_digit, modulus of 10 of the response if numeric question else empty pd.NA feature_name = 'f__last_digit' # Use the same mask as legacy: excludes empty, null, and -999999999 - numeric_mask = get_numeric_mask(df_item) + numeric_mask = get_numeric_mask(df_item=df_item, filter_answer_values=True) df_item[feature_name] = pd.NA if numeric_mask.any(): @@ -415,7 +444,7 @@ def feat_first_decimal(df_item, **kwargs): if mask.any(): values = pd.to_numeric(df_item.loc[mask, 'value'], errors='coerce') - res = np.floor(values * 100) % 100 + res = np.floor(values * 10) % 10 df_item.loc[mask, feature_name] = res.astype('Int64') # Match legacy: ensure the full feature column uses nullable integer dtype. diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index 5bd01f0..fd484cc 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -36,9 +36,11 @@ paradata_processed: type: pandas.ParquetDataset filepath: data/${globals:survey.name}/latest/30_PROCESSED/paradata_processed.parquet -# paradata_active has been removed: the active-event filter is now applied inline -# within each function that needs it, using explicit event and question_scope masks. -# This avoids silently dropping pause events (Resumed/Restarted) which have NaN scope. +# paradata_active is provided for legacy compatibility. New feature functions may +# apply question_scope filters inline; legacy code expects this dataset. +paradata_active: + type: pandas.ParquetDataset + filepath: data/${globals:survey.name}/latest/30_PROCESSED/paradata_active.parquet # === LEGACY DATA FOR PIPELINE TESTING === diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py index 19b041d..7b86128 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py @@ -17,41 +17,41 @@ def create_pipeline(**kwargs) -> Pipeline: return pipeline([ node( func=create_base_item_table_node, - # inputs=["raw_microdata", "paradata_processed", "parameters"], + inputs=["raw_microdata", "paradata_processed", "parameters"], # Legacy test data: - inputs=["legacy_microdata", "legacy_paradata_processed", "parameters"], + # inputs=["legacy_microdata", "legacy_paradata_processed", "parameters"], outputs="item_features_base", name="create_base_item_table_node", ), node( func=create_base_unit_table_node, - # inputs=["paradata_processed", "parameters"], + inputs=["paradata_processed", "parameters"], # Legacy test data: - inputs=["legacy_paradata_processed", "parameters"], + # inputs=["legacy_paradata_processed", "parameters"], outputs="unit_features_base", name="create_base_unit_table_node", ), node( func=enrich_item_features_node, - # inputs=["item_features_base", "paradata_processed", "parameters"], + inputs=["item_features_base", "paradata_processed", "parameters"], # Legacy test data: - inputs=["item_features_base", "legacy_paradata_processed", "parameters"], + # inputs=["item_features_base", "legacy_paradata_processed", "parameters"], outputs="item_features", name="enrich_item_features_node", ), node( func=enrich_unit_features_node, - # inputs=["unit_features_base", "item_features", "paradata_processed", "parameters"], + inputs=["unit_features_base", "item_features", "paradata_processed", "parameters"], # Legacy test data: - inputs=["unit_features_base", "item_features", "legacy_paradata_processed", "parameters"], + # inputs=["unit_features_base", "item_features", "legacy_paradata_processed", "parameters"], outputs="unit_features", name="enrich_unit_features_node", ), node( func=build_removed_answers_node, - # inputs=["paradata_processed", "parameters"], + inputs=["paradata_processed", "parameters"], # Legacy test data: - inputs=["legacy_paradata_processed", "parameters"], + # inputs=["legacy_paradata_processed", "parameters"], outputs="removed_answers", name="build_removed_answers_node", ), diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py index df6f61e..3900543 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py @@ -85,10 +85,10 @@ def filter_active_paradata_node( # keep active events, prior rejection/review events, for questions with scope interviewer # Filter conditions - paradata_processed['question_scope'] = paradata_processed['question_scope'].fillna('') # Fill NaN with empty string for consistent filtering active_mask = ( (paradata_processed['event'].isin(active_events)) & - (paradata_processed['question_scope'].isin([0, ''])) & # question scope interviewer only, but fillna so pauses are added back in, as they have empty question scope + # question scope interviewer only. Pause events and interview-created have NaN scope + (paradata_processed['question_scope'].isin([0, None])) & (paradata_processed['role'] == 1) # redundant given previous filtering ) diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py index 3a3ee23..26af6d9 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py @@ -2,6 +2,7 @@ from kedro.pipeline import Pipeline, node, pipeline from .nodes import ( process_paradata_node, + filter_active_paradata_node, ) @@ -18,7 +19,12 @@ def create_pipeline(**kwargs) -> Pipeline: outputs="paradata_processed", name="process_paradata_node", ), - # filter_active_paradata_node removed: each feature function now applies - # its own question_scope == 0 filter inline where needed. Pause events - # (Resumed, Restarted) have NaN question_scope and must not be dropped globally. + node( + func=filter_active_paradata_node, + inputs=["paradata_processed", "parameters"], + outputs="paradata_active", + name="filter_active_paradata_node", + ), + # `filter_active_paradata_node` reinstated for compatibility with + # legacy functions that expect `paradata_active`. ]) From 5b8e6c287cff749e665ba250792eea34c80ba71c Mon Sep 17 00:00:00 2001 From: VJausovec Date: Tue, 24 Mar 2026 00:02:19 +0000 Subject: [PATCH 37/70] Refactor event handling in add_item_time_features and feature functions to normalize roster_level for AnswerRemoved events; align numeric masks with legacy behavior for consistency in data processing. --- rissk/feature_processing_kedro.py | 21 +++++++++++++++------ 1 file changed, 15 insertions(+), 6 deletions(-) diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index a10d1b6..68764b9 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -203,6 +203,11 @@ def add_item_time_features(df_item: pd.DataFrame, df_time: pd.DataFrame, allowed if selected_features: # Filter out empty variable_name (Pauses) df_time_filtered = df_time[df_time['variable_name'] != ''].copy() + # AnswerRemoved / CommentSet events have roster_level=None in paradata (no roster context + # is recorded on removal/comment events), while AnswerSet rows carry ''. Normalise to '' + # so they land in the same groupby bucket as the corresponding AnswerSet events, matching + # the legacy behaviour where process_paradata does fillna('') on the whole dataframe. + df_time_filtered['roster_level'] = df_time_filtered['roster_level'].fillna('') # Summarize on item level # Note: df_time might have multiple events per item (e.g. AnswerRemoved then AnswerSet) @@ -399,6 +404,8 @@ def feat_string_length(df_item, **kwargs): def feat_numeric_response(df_item, **kwargs): # f__numeric_response, response, if NumericQuestions, else empty pd.NA feature_name = 'f__numeric_response' + # Use the same mask as legacy: excludes empty, null, and -999999999 + # filter_answer_values=True would exclude values that match the answer options (legacy did not apply this filter) numeric_mask = get_numeric_mask(df_item=df_item, filter_answer_values=False) df_item[feature_name] = np.nan if numeric_mask.any(): @@ -409,7 +416,9 @@ def feat_numeric_response(df_item, **kwargs): def feat_first_digit(df_item, **kwargs): # f__first_digit, first digit of the response if numeric question else empty pd.NA feature_name = 'f__first_digit' - numeric_mask = get_numeric_mask(df_item=df_item, filter_answer_values=True) + # Use the same mask as legacy: excludes empty, null, and -999999999 + # filter_answer_values=True would exclude values that match the answer options (legacy did not apply this filter) + numeric_mask = get_numeric_mask(df_item=df_item, filter_answer_values=False) df_item[feature_name] = pd.NA if numeric_mask.any(): numeric_values = _coerce_numeric_with_warning(df_item, numeric_mask, feature_name) @@ -422,7 +431,8 @@ def feat_last_digit(df_item, **kwargs): # f__last_digit, modulus of 10 of the response if numeric question else empty pd.NA feature_name = 'f__last_digit' # Use the same mask as legacy: excludes empty, null, and -999999999 - numeric_mask = get_numeric_mask(df_item=df_item, filter_answer_values=True) + # filter_answer_values=True would exclude values that match the answer options (legacy did not apply this filter) + numeric_mask = get_numeric_mask(df_item=df_item, filter_answer_values=False) df_item[feature_name] = pd.NA if numeric_mask.any(): @@ -557,10 +567,9 @@ def feat_answer_changed(df_item, **kwargs): # --- Case 1: TextListQuestion and MultyOptionsQuestion (without yes_no_view mode) --- # Keep flow aligned with legacy while scoping masks to their intended qtypes. - list_mask = ( - (df_changed["qtype"] == 'TextListQuestion') & - (df_changed['yes_no_view'] == False) - ) if has_yes_no else (df_changed["qtype"] == 'TextListQuestion') + # TextListQuestion don't have yes_no_view mode. + list_mask = df_changed["qtype"] == 'TextListQuestion' + multi_mask = ( (df_changed["qtype"] == 'MultyOptionsQuestion') & (df_changed['yes_no_view'] == False) From 6032e73fe1c60ed01040f4da893b71516e635938 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Thu, 26 Mar 2026 10:49:34 +0000 Subject: [PATCH 38/70] Refactor scoring and feature processing to align with legacy behavior; enhance unit table creation with active paradata and conditional scoring execution based on feature flags. --- rissk/feature_processing_kedro.py | 15 +-- rissk/item_processing_kedro.py | 18 ++-- rissk_kedro/conf/base/parameters.yml | 8 +- .../pipelines/rissk_scoring/nodes.py | 91 ++++++++++++++----- 4 files changed, 92 insertions(+), 40 deletions(-) diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index 68764b9..da4698e 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -359,14 +359,17 @@ def create_base_unit_table(paradata_full: pd.DataFrame, parameters: dict) -> pd. # 1. Initialize from paradata columns = ['interview__id', 'responsible', 'qnr', 'qnr_version'] - # Use interviewer-scope AnswerSet events to seed unit identity rows. - # responsible is only reliably populated on AnswerSet events. - interviewer_answer_mask = ( - (paradata_full['event'] == 'AnswerSet') & - (paradata_full['question_scope'] == 0) + # Match legacy code and use active paradata to seed the unit table. + question_scope_events = ['AnswerSet', 'AnswerRemoved', 'CommentSet'] + # Events that have no question scope (pause / session events); always include. + no_scope_events = ['InterviewCreated', 'Resumed', 'Restarted'] + + active_mask = ( + (paradata_full['event'].isin(no_scope_events)) | + (paradata_full['event'].isin(question_scope_events) & (paradata_full['question_scope'] == 0)) ) - df_unit = paradata_full[interviewer_answer_mask][columns].copy() + df_unit = paradata_full[active_mask][columns].copy() df_unit.drop_duplicates(inplace=True) # Filter valid responsible diff --git a/rissk/item_processing_kedro.py b/rissk/item_processing_kedro.py index d1b496c..912d278 100644 --- a/rissk/item_processing_kedro.py +++ b/rissk/item_processing_kedro.py @@ -299,6 +299,7 @@ def calculate_first_decimal_score(df_item: pd.DataFrame, parameters: Dict[str, A ) for var in valid_variables: + # logger.info(f"Calculating {score_name} for variable: {var}") mask = (df['variable_name'] == var) & (~pd.isnull(df[feature_name])) if mask.sum() > 0: model = COF(contamination=contamination) @@ -665,7 +666,9 @@ def calculate_single_question_score(df_item: pd.DataFrame) -> pd.DataFrame: df = df_item.copy() columns = ['qtype', 'n_answers', 'is_filtered_combobox', 'cascade_from_question_id'] - if any(col not in df.columns for col in columns + [feature_name]): + # f__single_question is not a separately computed feature column — scoring works + # directly on 'value' with a qtype mask, matching legacy make_score__single_question. + if any(col not in df.columns for col in columns): return df # Mask specific for single questions without filter rules bypassing cascades @@ -677,14 +680,14 @@ def calculate_single_question_score(df_item: pd.DataFrame) -> pd.DataFrame: ) df[score_name] = np.nan - valid_data = df[single_question_mask]&df[~pd.isnull(df[feature_name])] + valid_data = df[single_question_mask].copy() if valid_data.empty: return df variables = filter_variable_name_by_frequency(valid_data, 'value', frequency=100, min_unique_values=3) for var in variables: - mask = (df['variable_name'] == var) & single_question_mask & (~pd.isnull(df[feature_name])) + mask = (df['variable_name'] == var) & single_question_mask if mask.sum() > 0: unique_values = df.loc[mask, 'value'].nunique() @@ -709,11 +712,13 @@ def calculate_multi_option_question_score(df_item: pd.DataFrame) -> pd.DataFrame feature_name = 'f__multi_option_question' score_name = rename_feature(feature_name) df = df_item.copy() - - if any(col not in df.columns for col in ['qtype', feature_name]): + + # f__multi_option_question is not a separately computed feature column — scoring works + # directly on 'value' with a qtype mask, matching legacy make_score__multi_option_question. + if 'qtype' not in df.columns: return df - multi_question_mask = (df["qtype"] == 'MultyOptionsQuestion') & (~pd.isnull(df[feature_name])) + multi_question_mask = (df["qtype"] == 'MultyOptionsQuestion') valid_data = df[multi_question_mask].copy() df[score_name] = np.nan @@ -764,7 +769,6 @@ def calculate_first_digit_score(df_item: pd.DataFrame) -> pd.DataFrame: valid_variables = filter_variables_by_magnitude(valid_data, feature_name, valid_variables, min_order_of_magnitude=3) - # Computes the Jensen divergence for each variable_name and responsible on the first digit distribution. # Jensen's divergence returns a value between (0, 1) of how much the first digit distribution # of specific responsible is similar to the first digit distribution of all others. diff --git a/rissk_kedro/conf/base/parameters.yml b/rissk_kedro/conf/base/parameters.yml index 8bfc90b..c1da7ca 100644 --- a/rissk_kedro/conf/base/parameters.yml +++ b/rissk_kedro/conf/base/parameters.yml @@ -105,7 +105,7 @@ features: string_length: use: true -# Output Configuration -output: - feature_score: true - unit_risk_score_path: "results/unit_risk_score.csv" \ No newline at end of file +# # Output Configuration +# output: +# feature_score: true +# unit_risk_score_path: "results/unit_risk_score.csv" \ No newline at end of file diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py index 418f6ed..20bcb43 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py @@ -34,24 +34,67 @@ def calculate_item_scores(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> """ Run item level scoring applying various mathematical models. f__answer_removed is already present in df_item from the feature creation pipeline. + Each scoring function is only executed when its corresponding feature has use: true + in parameters['features'], matching the feature creation pipeline behaviour. """ logger.info("Calculating Item Scores...") - df_scored = calculate_answer_hour_set_score(df_item, parameters) - df_scored = calculate_sequence_jump_score(df_scored, parameters) - df_scored = calculate_first_decimal_score(df_scored, parameters) - df_scored = calculate_answer_changed_score(df_scored, parameters) + features = parameters.get('features', {}) + df_scored = df_item + + if features.get('answer_hour_set', {}).get('use', False): + logger.info("Calculating answer_hour_set_score") + df_scored = calculate_answer_hour_set_score(df_scored, parameters) + + if features.get('sequence_jump', {}).get('use', False): + logger.info("Calculating sequence_jump_score") + df_scored = calculate_sequence_jump_score(df_scored, parameters) + + if features.get('first_decimal', {}).get('use', False): + logger.info("Calculating first_decimal_score") + df_scored = calculate_first_decimal_score(df_scored, parameters) + + if features.get('answer_changed', {}).get('use', False): + logger.info("Calculating answer_changed_score") + df_scored = calculate_answer_changed_score(df_scored, parameters) + # s__answer_removed is not computed here — see calculate_answer_removed_score_from_df # in calculate_unit_scores, which scores from the removed_answers dataset to match legacy coverage. - df_scored = calculate_answer_position_score(df_scored, parameters) - df_scored = calculate_answer_selected_score(df_scored, parameters) - df_scored = calculate_answer_duration_score(df_scored, parameters) - df_scored = calculate_single_question_score(df_scored, parameters) - df_scored = calculate_multi_option_question_score(df_scored, parameters) - df_scored = calculate_first_digit_score(df_scored, parameters) - df_scored = calculate_gps_score(df_scored, parameters) + + if features.get('answer_position', {}).get('use', False): + logger.info("Calculating answer_position_score") + df_scored = calculate_answer_position_score(df_scored, parameters) + + if features.get('answer_selected', {}).get('use', False): + logger.info("Calculating answer_selected_score") + df_scored = calculate_answer_selected_score(df_scored, parameters) + + if features.get('answer_duration', {}).get('use', False): + logger.info("Calculating answer_duration_score") + df_scored = calculate_answer_duration_score(df_scored, parameters) + + if features.get('single_question', {}).get('use', False): + logger.info("Calculating single_question_score") + df_scored = calculate_single_question_score(df_scored) + + if features.get('multi_option_question', {}).get('use', False): + logger.info("Calculating multi_option_question_score") + df_scored = calculate_multi_option_question_score(df_scored) + + if features.get('first_digit', {}).get('use', False): + logger.info("Calculating first_digit_score") + df_scored = calculate_first_digit_score(df_scored) + + if features.get('gps', {}).get('use', False): + logger.info("Calculating gps_score") + df_scored = calculate_gps_score(df_scored, parameters) + return df_scored -def calculate_unit_scores(df_unit: pd.DataFrame, df_item_scores: pd.DataFrame, parameters: Dict[str, Any], removed_answers: pd.DataFrame = None) -> Tuple[pd.DataFrame, pd.DataFrame]: +def calculate_unit_scores( + df_unit: pd.DataFrame, + df_item_scores: pd.DataFrame, + parameters: Dict[str, Any], removed_answers: pd.DataFrame = None + ) -> Tuple[pd.DataFrame, pd.DataFrame]: """ Aggregate item scores to unit, extract responsible scores, and calculate global risk. @@ -60,7 +103,8 @@ def calculate_unit_scores(df_unit: pd.DataFrame, df_item_scores: pd.DataFrame, p items deleted from microdata (absent from df_item) are still counted. """ logger.info("Calculating Unit Scores and Global Risk...") - + features = parameters.get('features', {}) + # 1. Aggregate item-level scores up to unit level. # s__answer_removed is excluded from this aggregation (see aggregate_item_to_unit_scores); # it is handled below using paradata_full to match legacy coverage. @@ -70,16 +114,17 @@ def calculate_unit_scores(df_unit: pd.DataFrame, df_item_scores: pd.DataFrame, p # This replicates legacy make_score_unit__answer_removed which read from df_paradata # directly and therefore included AnswerRemoved events for items later deleted from # microdata. Falling back to the df_item-based mean when paradata_full is unavailable. - if removed_answers is not None and not removed_answers.empty: - unit_removed = calculate_answer_removed_score_from_df(removed_answers, parameters) - df_unit_scored['s__answer_removed'] = df_unit_scored['interview__id'].map(unit_removed).fillna(0) - elif 's__answer_removed' in df_item_scores.columns: - logger.warning( - "removed_answers not available; falling back to df_item-based s__answer_removed " - "aggregation (may undercount removals for deleted items)." - ) - data = df_item_scores.groupby('interview__id')['s__answer_removed'].mean() - df_unit_scored['s__answer_removed'] = df_unit_scored['interview__id'].map(data).fillna(0) + if features.get('answer_removed', {}).get('use', False): + if removed_answers is not None and not removed_answers.empty: + unit_removed = calculate_answer_removed_score_from_df(removed_answers, parameters) + df_unit_scored['s__answer_removed'] = df_unit_scored['interview__id'].map(unit_removed).fillna(0) + elif 's__answer_removed' in df_item_scores.columns: + logger.warning( + "removed_answers not available; falling back to df_item-based s__answer_removed " + "aggregation (may undercount removals for deleted items)." + ) + data = df_item_scores.groupby('interview__id')['s__answer_removed'].mean() + df_unit_scored['s__answer_removed'] = df_unit_scored['interview__id'].map(data).fillna(0) # 2b. Add pure unit-level calculations df_unit_scored = calculate_unit_level_scores(df_unit_scored, parameters) From c8d968a9582a4b1a84d6b852555058cedde14a6f Mon Sep 17 00:00:00 2001 From: VJausovec Date: Thu, 26 Mar 2026 14:41:35 +0000 Subject: [PATCH 39/70] Refactor microdata handling in data ingestion pipeline; introduce raw microdata loading and merging with questionnaire metadata for improved data processing. --- rissk/utils/import_utils_kedro.py | 133 ++++++++---------- .../pipelines/data_ingestion/nodes.py | 48 ++++--- .../pipelines/data_ingestion/pipeline.py | 13 +- .../pipelines/feature_creation/pipeline.py | 2 +- 4 files changed, 98 insertions(+), 98 deletions(-) diff --git a/rissk/utils/import_utils_kedro.py b/rissk/utils/import_utils_kedro.py index 7203d89..c04616c 100644 --- a/rissk/utils/import_utils_kedro.py +++ b/rissk/utils/import_utils_kedro.py @@ -387,34 +387,40 @@ def replace_stata_missing(val): def get_microdata(data_path: Path, df_questionnaires: pd.DataFrame) -> pd.DataFrame: + raw = get_microdata_raw(data_path, df_questionnaires) + return merge_microdata_questionnaire(raw, df_questionnaires) + + +def get_microdata_raw(data_path: Path, df_questionnaires: pd.DataFrame) -> pd.DataFrame: + """Load microdata with transform_multi applied but without questionnaire metadata merge. + + Performs all processing steps of get_microdata (including multi-question transformation, + filtering, column normalisation and value stringification) up to but not including + the merge with questionnaire metadata. + """ drop_list = ['interview__key', 'sssys_irnd', 'has__errors', 'interview__status', 'assignment__id'] file_names = get_microdata_file_list(data_path) - # define multi/list question conditions if not df_questionnaires.empty: - # Use boolean indexing unlinked_mask = (df_questionnaires["qtype"] == 'MultyOptionsQuestion') & ( df_questionnaires['is_linked'] == False) linked_mask = (df_questionnaires["qtype"] == 'MultyOptionsQuestion') & ( df_questionnaires['is_linked'] == True) list_mask = (df_questionnaires["qtype"] == 'TextListQuestion') gps_mask = (df_questionnaires["qtype"] == 'GpsCoordinateQuestion') - - # extract multi/list question lists from conditions + multi_unlinked_vars = df_questionnaires.loc[unlinked_mask, 'variable_name'].tolist() multi_linked_vars = df_questionnaires.loc[linked_mask, 'variable_name'].tolist() list_vars = df_questionnaires.loc[list_mask, 'variable_name'].tolist() gps_vars = df_questionnaires.loc[gps_mask, 'variable_name'].tolist() - - # Iterate over each file + all_dfs = [] for file_name in file_names: df = read_microdata_file(data_path, file_name) if df.empty: continue - - #Efficient drop + cols_to_drop = [col for col in drop_list if col in df.columns] if cols_to_drop: df.drop(columns=cols_to_drop, inplace=True) @@ -425,7 +431,6 @@ def get_microdata(data_path: Path, df_questionnaires: pd.DataFrame) -> pd.DataFr df = transform_multi(df, list_vars, 'list') df = transform_multi(df, gps_vars, 'gps') - # create roster_level from __id columns if on roster level, else '' if main questionnaire file roster_ids = [col for col in df.columns if col.endswith("__id") and col != "interview__id"] if roster_ids: df['roster_level'] = df[roster_ids].apply(lambda row: ",".join(map(str, row)), axis=1) @@ -435,10 +440,10 @@ def get_microdata(data_path: Path, df_questionnaires: pd.DataFrame) -> pd.DataFr id_vars = ['interview__id', 'roster_level'] value_vars = [col for col in df.columns if col not in id_vars] - + if not value_vars: continue - + df_long = df.melt(id_vars=id_vars, value_vars=value_vars, var_name='variable', value_name='value') df_long['filename'] = file_name all_dfs.append(df_long) @@ -448,41 +453,20 @@ def get_microdata(data_path: Path, df_questionnaires: pd.DataFrame) -> pd.DataFr else: return pd.DataFrame() - # Filter invalid values - # Optimized filter: - # Check for empty string or NaN. Note: 'value' column is mixed type probably. - # Convert 'value' to string could simplify emptiness check but be careful with NaN - - # Vectorized check is faster than apply - # combined_df['value'] is likely object type - - # is_valid logic from legacy: - # if list: return True - # if string/other: value != '' and notna(value) - - # Since we can't easily vectorize types check mixed with lists in pandas, use apply only if needed - # But usually transform_multi returns lists for some columns. - def is_valid_fast(val): if val is None: return False - if isinstance(val, (list, tuple)): + if isinstance(val, (list, tuple)): if len(val) == 0: return False - # Filter out lists that contain only NaNs or empty strings return any(pd.notna(x) and x != '' for x in val) if isinstance(val, (np.ndarray,)): return val.size > 0 if isinstance(val, str) and val == '': return False - # Fallback for other types where equality might be array-like (though unlikely for scalars) - if hasattr(val, 'size') and hasattr(val, 'shape'): # duck typing for arrays - return val.size > 0 - + if hasattr(val, 'size') and hasattr(val, 'shape'): + return val.size > 0 try: - if pd.isna(val): return False + if pd.isna(val): return False except: - pass - - # Check for empty string equality safely + pass if str(val) == '': return False - return True combined_df = combined_df[combined_df['value'].apply(is_valid_fast)] @@ -495,50 +479,49 @@ def is_valid_fast(val): except ValueError: logger.warning(f"Could not set version for {data_path.name}") - if not df_questionnaires.empty: - # Merge setup - roster_columns = [c for c in combined_df.columns if '__id' in c and c != 'interview__id'] - - # Ensure join keys have matching types - # variable, qnr, qnr_version are strings/objects - - merge_on_left = ['variable', 'qnr', 'qnr_version'] - merge_on_right = ['variable_name', 'qnr', 'qnr_version'] - - combined_df = combined_df.merge( - df_questionnaires, - how='left', - left_on=merge_on_left, - right_on=merge_on_right - ) - - sort_cols = ['interview__id'] - if 'qnr_seq' in combined_df.columns: - sort_cols.append('qnr_seq') - sort_cols.extend(roster_columns) - - # Safe sort (ignore missing cols) - actual_sort_cols = [c for c in sort_cols if c in combined_df.columns] - combined_df.sort_values(actual_sort_cols, inplace=True) - combined_df.reset_index(drop=True, inplace=True) combined_df.columns = [normalize_column_name(c) for c in combined_df.columns] - # Normalize float values that are actually integers (e.g. 1.0 -> 1) before string conversion - # This ensures "107080102.0" becomes "107080102" matching legacy output + def normalize_and_stringify(val): if isinstance(val, float) and val.is_integer(): - return str(int(val)) + return str(int(val)) if isinstance(val, (list, tuple, np.ndarray)): - # If it's a list (from transform_multi), we might need to normalize internal floats tool? - # Legacy code just did astype(str), which calls str(val). - # str([1.0, 2.0]) -> "[1.0, 2.0]" - # str([1, 2]) -> "[1, 2]" - # So we might need to clean up lists too if we want exact match. - # However, let's stick to scalar normalization first as that's the primary complaint. - return str(val) + return str(val) return str(val) - # Use apply for robust conversion combined_df['value'] = combined_df['value'].apply(normalize_and_stringify) - + return combined_df + + +def merge_microdata_questionnaire(microdata_raw: pd.DataFrame, df_questionnaires: pd.DataFrame) -> pd.DataFrame: + """Merge raw microdata with questionnaire metadata and normalize column names. + + Produces output identical to get_microdata when combined with get_microdata_raw. + """ + if microdata_raw.empty or df_questionnaires.empty: + return microdata_raw + + merge_on_left = ['variable', 'qnr', 'qnr_version'] + merge_on_right = ['variable_name', 'qnr', 'qnr_version'] + + merged = microdata_raw.merge( + df_questionnaires, + how='left', + left_on=merge_on_left, + right_on=merge_on_right + ) + + sort_cols = [c for c in ['interview__id', 'qnr_seq', 'roster_level'] if c in merged.columns] + merged.sort_values(sort_cols, inplace=True) + merged.reset_index(drop=True, inplace=True) + merged.columns = [normalize_column_name(c) for c in merged.columns] + # Stringify properties dicts so serialisation is consistent regardless of whether + # the questionnaire was used from memory (1-step) or via a parquet round-trip (2-step). + # Pyarrow's struct union type adds extra None keys during the parquet round-trip; + # stringifying here prevents that discrepancy from propagating into the microdata output. + if 'properties' in merged.columns: + merged['properties'] = merged['properties'].apply( + lambda x: str(x) if isinstance(x, dict) else x + ) + return merged diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py index ba9acb4..e0fed2e 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py @@ -8,7 +8,8 @@ get_survey_info, get_questionnaire, get_paradata, - get_microdata + get_microdata_raw, + merge_microdata_questionnaire ) @@ -125,40 +126,43 @@ def load_questionnaire_node(file_paths: List[Path]) -> pd.DataFrame: if 'answer_sequence' in combined_df.columns: combined_df['answer_sequence'] = combined_df['answer_sequence'].apply(str) + if 'properties' in combined_df.columns: + combined_df['properties'] = combined_df['properties'].apply( + lambda x: str(x) if isinstance(x, dict) else x + ) return combined_df -def load_microdata_node(file_paths: List[Path]) -> pd.DataFrame: +def load_raw_microdata_node(file_paths: List[Path]) -> pd.DataFrame: """ - Loads microdata (answers) from extracted folders. - Independent node that generates its own questionnaire reference. + Loads raw microdata (answers) from extracted folders. + Applies multi-question transformation using questionnaire metadata but does not + merge questionnaire columns into the output. Values are normalized and stringified. """ - logger.info(f"Processing microdata for {len(file_paths)} paths") + logger.info(f"Processing raw microdata for {len(file_paths)} paths") survey_info = get_survey_info(file_paths) - + dfs_microdata = [] - + for survey_questionnaire, questionnaires_details in survey_info.items(): - for questionnaires_version, file_paths in questionnaires_details.items(): - tabular_path = file_paths.get('Tabular') + for questionnaires_version, file_paths_detail in questionnaires_details.items(): + tabular_path = file_paths_detail.get('Tabular') if not tabular_path: logger.warning( - f"Skipping microdata load for {survey_questionnaire} v{questionnaires_version}: " + f"Skipping raw microdata load for {survey_questionnaire} v{questionnaires_version}: " "missing Tabular export" ) continue try: - # We need the questionnaire map for variable types and structure df_questionnaires = get_questionnaire(tabular_path) - df_microdata = get_microdata(tabular_path, df_questionnaires) - + df_microdata = get_microdata_raw(tabular_path, df_questionnaires) dfs_microdata.append(df_microdata) - logger.info(f"Loaded microdata for {survey_questionnaire} v{questionnaires_version}") + logger.info(f"Loaded raw microdata for {survey_questionnaire} v{questionnaires_version}") except Exception as e: - logger.error(f"Failed to load microdata for {survey_questionnaire} v{questionnaires_version}. Skipping. Error: {str(e)}") + logger.error(f"Failed to load raw microdata for {survey_questionnaire} v{questionnaires_version}. Skipping. Error: {str(e)}") continue if not dfs_microdata: @@ -166,8 +170,14 @@ def load_microdata_node(file_paths: List[Path]) -> pd.DataFrame: combined_df = pd.concat(dfs_microdata) combined_df.reset_index(drop=True, inplace=True) - - if 'answer_sequence' in combined_df.columns: - combined_df['answer_sequence'] = combined_df['answer_sequence'].apply(str) - return combined_df + + +def merge_microdata_questionnaire_node(raw_microdata: pd.DataFrame, questionnaire: pd.DataFrame) -> pd.DataFrame: + """ + Merges raw microdata with questionnaire metadata and normalizes column names. + """ + logger.info("Merging raw microdata with questionnaire metadata") + merged = merge_microdata_questionnaire(raw_microdata, questionnaire) + merged.reset_index(drop=True, inplace=True) + return merged diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py index 45a092d..4dd9548 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py @@ -4,7 +4,8 @@ filter_extracted_survey_paths_node, load_paradata_node, load_questionnaire_node, - load_microdata_node + load_raw_microdata_node, + merge_microdata_questionnaire_node ) # catalog for path def create_pipeline(**kwargs) -> Pipeline: @@ -41,9 +42,15 @@ def create_pipeline(**kwargs) -> Pipeline: name="load_questionnaire_node" ), node( - func=load_microdata_node, + func=load_raw_microdata_node, inputs="file_paths", outputs="raw_microdata", - name="load_microdata_node" + name="load_raw_microdata_node" + ), + node( + func=merge_microdata_questionnaire_node, + inputs=["raw_microdata", "raw_questionnaire"], + outputs="microdata", + name="merge_microdata_questionnaire_node" ) ]) diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py index 7b86128..94844a9 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/pipeline.py @@ -17,7 +17,7 @@ def create_pipeline(**kwargs) -> Pipeline: return pipeline([ node( func=create_base_item_table_node, - inputs=["raw_microdata", "paradata_processed", "parameters"], + inputs=["microdata", "paradata_processed", "parameters"], # Legacy test data: # inputs=["legacy_microdata", "legacy_paradata_processed", "parameters"], outputs="item_features_base", From 15e9a76a18a1febf25bf9d676274a14874745077 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Fri, 27 Mar 2026 11:50:11 +0000 Subject: [PATCH 40/70] Enhance feature processing by adding questionnaire filtering; include qnr and qnr_version in item table and update aggregation logic for removed answers. --- rissk/feature_processing_kedro.py | 7 +- rissk_kedro/conf/base/catalog.yml | 4 + rissk_kedro/conf/base/globals.yml | 3 + .../src/rissk_kedro/pipeline_registry.py | 152 +++++++++++++++++- .../pipelines/feature_creation/nodes.py | 34 ++++ 5 files changed, 192 insertions(+), 8 deletions(-) diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index da4698e..b3555c9 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -302,7 +302,7 @@ def create_base_item_table(microdata: pd.DataFrame, paradata_full: pd.DataFrame, columns = ['value', "qtype", 'is_integer', 'qnr_seq', 'n_answers', 'answer_sequence', 'cascade_from_question_id', 'is_filtered_combobox', - 'index_col'] + item_level_columns + 'index_col', 'qnr', 'qnr_version'] + item_level_columns # Intersect with available columns to avoid KeyErrors df_item = df_item[columns].copy() @@ -530,7 +530,10 @@ def feat_answer_removed(paradata_full): return df_removed # Align grouping grain with legacy helper exactly. + # qnr and qnr_version are included so removed_answers carries questionnaire + # identity for per-questionnaire filtering downstream. group_cols = ['interview__id', 'responsible', 'variable_name', 'qnr_seq'] + extra_cols = [c for c in ['qnr', 'qnr_version'] if c in df_removed.columns] if any(c not in df_removed.columns for c in group_cols): logger.warning( "%s: missing one or more legacy group columns (%s); skipping feature.", @@ -539,7 +542,7 @@ def feat_answer_removed(paradata_full): ) return df_removed - df_agg_removed = df_removed.groupby(group_cols).agg( + df_agg_removed = df_removed.groupby(group_cols + extra_cols).agg( f__answer_removed=('order', 'count') ).reset_index() diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index fd484cc..43103c3 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -27,6 +27,10 @@ raw_questionnaire: filepath: data/${globals:survey.name}/latest/30_PROCESSED/questionnaire.parquet raw_microdata: + type: pandas.ParquetDataset + filepath: data/${globals:survey.name}/latest/20_INTERIM/microdata_raw.parquet + +microdata: type: pandas.ParquetDataset filepath: data/${globals:survey.name}/latest/30_PROCESSED/microdata.parquet diff --git a/rissk_kedro/conf/base/globals.yml b/rissk_kedro/conf/base/globals.yml index e0804e4..831900b 100644 --- a/rissk_kedro/conf/base/globals.yml +++ b/rissk_kedro/conf/base/globals.yml @@ -5,8 +5,11 @@ survey: questionnaires: - name: "snb_hies_hh" VERSION: [9, 10, 11] + # filter_var: None - name: "slbhies_listing" VERSION: [5, 6, 7] + # filter_var: None + # survey: # name: "pmpmd" diff --git a/rissk_kedro/src/rissk_kedro/pipeline_registry.py b/rissk_kedro/src/rissk_kedro/pipeline_registry.py index 0a10ab5..e98bdd7 100644 --- a/rissk_kedro/src/rissk_kedro/pipeline_registry.py +++ b/rissk_kedro/src/rissk_kedro/pipeline_registry.py @@ -1,15 +1,155 @@ """Project pipelines.""" -from kedro.framework.project import find_pipelines -from kedro.pipeline import Pipeline +from pathlib import Path +from typing import Callable + +import pandas as pd +import yaml +from kedro.pipeline import Pipeline, node, pipeline + +from rissk_kedro.pipelines.feature_creation.nodes import make_qnr_filter + + +def _load_questionnaire_names() -> list[str]: + """Read questionnaire names from conf/base/globals.yml at registry build time. + + pipeline_registry.py is imported before Kedro's ConfigLoader is available, so + globals.yml is read directly via yaml.safe_load. The path is resolved relative + to this file: src/rissk_kedro/ -> (parents[2]) -> rissk_kedro/ project root. + """ + globals_path = Path(__file__).parents[2] / "conf" / "base" / "globals.yml" + with globals_path.open() as fh: + globals_data = yaml.safe_load(fh) + questionnaires = globals_data.get("survey", {}).get("questionnaires", []) + return [q["name"] for q in questionnaires] + + +def _make_merge_node( + output_name: str, + input_names: list[str], + node_name: str, +) -> node: + """Build a node that pd.concat-s N MemoryDataset DataFrames into one output.""" + n = len(input_names) + + def merge_fn(*dfs): + non_empty = [df for df in dfs if df is not None and not df.empty] + if not non_empty: + return pd.DataFrame() + return pd.concat(non_empty, ignore_index=True) + + # Give the function a unique __name__ so Kedro uses it in the node label. + merge_fn.__name__ = node_name + + return node( + func=merge_fn, + inputs=input_names, + outputs=output_name, + name=node_name, + ) def register_pipelines() -> dict[str, Pipeline]: """Register the project's pipelines. - Returns: - A mapping from pipeline names to ``Pipeline`` objects. + Builds one filter + namespaced-scoring pipeline instance per questionnaire, + then adds a merge pipeline that concatenates per-questionnaire scored outputs + back into the same three catalog datasets (item_scores, unit_risk_scores, + responsible_scores) that exist today. Catalog is unchanged. """ - pipelines = find_pipelines(raise_errors=True) - pipelines["__default__"] = sum(pipelines.values()) + # Import sub-pipelines here to avoid circular imports at module level. + from rissk_kedro.pipelines.data_ingestion import create_pipeline as ingestion_pipeline + from rissk_kedro.pipelines.feature_engineering import create_pipeline as feature_engineering_pipeline + from rissk_kedro.pipelines.feature_creation import create_pipeline as feature_creation_pipeline + from rissk_kedro.pipelines.rissk_scoring import create_pipeline as scoring_pipeline + + qnr_names = _load_questionnaire_names() + + # ------------------------------------------------------------------ # + # Per-questionnaire filter + scoring pipelines # + # ------------------------------------------------------------------ # + per_qnr_pipelines: dict[str, Pipeline] = {} + + item_score_datasets: list[str] = [] + unit_score_datasets: list[str] = [] + resp_score_datasets: list[str] = [] + + for qnr_name in qnr_names: + # Sanitise the questionnaire name so it is a valid Python identifier / + # Kedro namespace component (spaces -> underscores, etc.). + ns = qnr_name.replace(" ", "_").replace("-", "_") + + # -- Filter node -------------------------------------------------- + filter_node = node( + func=make_qnr_filter(qnr_name), + inputs=["item_features", "unit_features", "removed_answers"], + outputs=[ + f"item_features__{ns}", + f"unit_features__{ns}", + f"removed_answers__{ns}", + ], + name=f"filter_features_{ns}_node", + ) + + # -- Namespaced scoring pipeline ---------------------------------- + # Explicit input/output mappings override namespacing for those keys so + # the filter outputs wire directly and the final scored dfs get unique names. + # parameters must be passed via the dedicated `parameters` arg — Kedro + # raises PipelineError if they appear in `inputs`. + namespaced_scoring = pipeline( + scoring_pipeline(), + namespace=ns, + inputs={ + "item_features": f"item_features__{ns}", + "unit_features": f"unit_features__{ns}", + "removed_answers": f"removed_answers__{ns}", + }, + parameters={"parameters": "parameters"}, + outputs={ + "item_scores": f"item_scores__{ns}", + "unit_risk_scores": f"unit_risk_scores__{ns}", + "responsible_scores": f"responsible_scores__{ns}", + }, + ) + + item_score_datasets.append(f"item_scores__{ns}") + unit_score_datasets.append(f"unit_risk_scores__{ns}") + resp_score_datasets.append(f"responsible_scores__{ns}") + + qnr_pipeline = Pipeline([filter_node]) + namespaced_scoring + per_qnr_pipelines[f"scoring_{ns}"] = qnr_pipeline + + # ------------------------------------------------------------------ # + # Merge pipeline — concat all per-qnr outputs into catalog datasets # + # ------------------------------------------------------------------ # + merge_pipeline = Pipeline([ + _make_merge_node("item_scores", item_score_datasets, "merge_item_scores_node"), + _make_merge_node("unit_risk_scores", unit_score_datasets, "merge_unit_scores_node"), + _make_merge_node("responsible_scores", resp_score_datasets, "merge_responsible_scores_node"), + ]) + + # ------------------------------------------------------------------ # + # Shared upstream pipelines # + # ------------------------------------------------------------------ # + ingestion = ingestion_pipeline() + feat_eng = feature_engineering_pipeline() + feat_creation = feature_creation_pipeline() + + all_scoring = sum(per_qnr_pipelines.values(), Pipeline([])) + merge_pipeline + + pipelines: dict[str, Pipeline] = {} + + # Named pipelines for selective runs + pipelines["data_ingestion"] = ingestion + pipelines["feature_engineering"] = feat_eng + pipelines["feature_creation"] = feat_creation + pipelines["scoring"] = all_scoring # filter + score + merge; skips ingestion/feature creation + + # Individual per-questionnaire scoring (without merge) — useful for debugging + for name, p in per_qnr_pipelines.items(): + pipelines[name] = p + + # Full run + pipelines["__default__"] = ingestion + feat_eng + feat_creation + all_scoring + return pipelines diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py index 70dbb66..315c55a 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py @@ -63,3 +63,37 @@ def build_removed_answers_node( the rissk_scoring pipeline to score s__answer_removed at unit level. """ return feat_answer_removed(paradata_full) + + +def make_qnr_filter(qnr_name: str): + """Factory that returns a filter function scoped to a single questionnaire. + + All three feature tables (item_features, unit_features, removed_answers) carry + a ``qnr`` column and are filtered directly on it. If ``removed_answers`` was + produced before the qnr column was added a fallback filter by interview__id is + applied automatically. + """ + def filter_features( + item_features: pd.DataFrame, + unit_features: pd.DataFrame, + removed_answers: pd.DataFrame, + ): + unit_filtered = unit_features[unit_features['qnr'] == qnr_name].copy() + item_filtered = item_features[item_features['qnr'] == qnr_name].copy() + if removed_answers is not None and not removed_answers.empty: + if 'qnr' in removed_answers.columns: + removed_filtered = removed_answers[removed_answers['qnr'] == qnr_name].copy() + else: + # fallback: removed_answers pre-dates the qnr column addition + valid_ids = set(unit_filtered['interview__id']) + removed_filtered = removed_answers[removed_answers['interview__id'].isin(valid_ids)].copy() + else: + removed_filtered = pd.DataFrame() + logger.info( + "filter_features_%s: %d interviews, %d item rows, %d removed_answer rows", + qnr_name, len(unit_filtered), len(item_filtered), len(removed_filtered), + ) + return item_filtered, unit_filtered, removed_filtered + + filter_features.__name__ = f"filter_features_{qnr_name}" + return filter_features From 43f5bb2b97da63b59ba988f16d530acf9319a793 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Sun, 29 Mar 2026 15:34:32 +0100 Subject: [PATCH 41/70] Refactor numeric response feature processing to apply answer value filtering; update related feature functions for consistency in handling numeric data. --- rissk/feature_processing_kedro.py | 12 ++++--- rissk/item_processing_kedro.py | 59 ++++++------------------------- 2 files changed, 18 insertions(+), 53 deletions(-) diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index b3555c9..3b4b1db 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -409,7 +409,7 @@ def feat_numeric_response(df_item, **kwargs): feature_name = 'f__numeric_response' # Use the same mask as legacy: excludes empty, null, and -999999999 # filter_answer_values=True would exclude values that match the answer options (legacy did not apply this filter) - numeric_mask = get_numeric_mask(df_item=df_item, filter_answer_values=False) + numeric_mask = get_numeric_mask(df_item=df_item, filter_answer_values=True) df_item[feature_name] = np.nan if numeric_mask.any(): numeric_values = _coerce_numeric_with_warning(df_item, numeric_mask, feature_name) @@ -421,7 +421,7 @@ def feat_first_digit(df_item, **kwargs): feature_name = 'f__first_digit' # Use the same mask as legacy: excludes empty, null, and -999999999 # filter_answer_values=True would exclude values that match the answer options (legacy did not apply this filter) - numeric_mask = get_numeric_mask(df_item=df_item, filter_answer_values=False) + numeric_mask = get_numeric_mask(df_item=df_item, filter_answer_values=True) df_item[feature_name] = pd.NA if numeric_mask.any(): numeric_values = _coerce_numeric_with_warning(df_item, numeric_mask, feature_name) @@ -435,7 +435,7 @@ def feat_last_digit(df_item, **kwargs): feature_name = 'f__last_digit' # Use the same mask as legacy: excludes empty, null, and -999999999 # filter_answer_values=True would exclude values that match the answer options (legacy did not apply this filter) - numeric_mask = get_numeric_mask(df_item=df_item, filter_answer_values=False) + numeric_mask = get_numeric_mask(df_item=df_item, filter_answer_values=True) df_item[feature_name] = pd.NA if numeric_mask.any(): @@ -451,8 +451,10 @@ def feat_last_digit(df_item, **kwargs): def feat_first_decimal(df_item, **kwargs): # f__first_decimal, first decimal digit if numeric question else empty pd.NA feature_name = 'f__first_decimal' - # mask: not integer and not empty - mask = (df_item['is_integer'] == False) & (df_item['value'] != '') + # mask: not integer, not empty & not mumeric sentinel + numeric_mask = get_numeric_mask(df_item=df_item, filter_answer_values=True) + mask_integer = (df_item['is_integer'] == False) & (df_item['value'] != '') & (~pd.isnull(df_item['value'])) + mask = numeric_mask & mask_integer df_item[feature_name] = pd.NA if mask.any(): diff --git a/rissk/item_processing_kedro.py b/rissk/item_processing_kedro.py index 912d278..780f294 100644 --- a/rissk/item_processing_kedro.py +++ b/rissk/item_processing_kedro.py @@ -87,38 +87,6 @@ def filter_columns( return index_col + keep_columns, drop_columns -def get_clean_pivot_table( - df_item: pd.DataFrame, - feature_name: str, - remove_low_freq_col: bool = True, - filter_conditions=None, - threshold: int = 100, - min_unique_values: int = 3, -) -> Tuple[pd.DataFrame, List[str]]: - """Create a pivot table handling columns and filtering.""" - index_col = ['interview__id', 'roster_level', 'responsible'] - data = df_item.copy() - - if filter_conditions is not None: - data = data.loc[filter_conditions] - - data = pd.pivot_table(data=data, index=index_col, columns='variable_name', - values=feature_name, fill_value=np.nan) - data = data.reset_index() - - if data.columns.nlevels > 1: - data.columns = [f'{col[0]}_{col[1]}'.rstrip('_') for col in data.columns] - - index_col = [col for col in index_col if col in data.columns] - keep_columns, drop_columns = filter_columns( - data, index_col, threshold=threshold, min_unique_values=min_unique_values - ) - - if remove_low_freq_col: - data = data[keep_columns].copy() - - return data, index_col - # --- SCORING FUNCTIONS BEGIN --- @@ -126,10 +94,12 @@ def calculate_gps_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd df = df_item.copy() score_cols = ['s__gps_proximity_counts', 's__gps_outlier', 's__gps_extreme_outlier'] required_columns = ['f__gps_latitude', 'f__gps_longitude', 'f__gps_accuracy'] - index_col = ['interview__id', 'roster_level', 'responsible'] + # variable_name is included so rows from different GPS questions remain distinct + # when multiple GPS variables exist for the same (interview, roster, responsible) + index_col = ['interview__id', 'roster_level', 'responsible', 'variable_name'] # If required GPS columns are missing, return original df - if any(col not in df.columns for col in required_columns): + if any(col not in df.columns for col in required_columns + ['variable_name']): return df gps_mask = (~pd.isnull(df['f__gps_latitude'])) & (~pd.isnull(df['f__gps_longitude'])) @@ -138,17 +108,10 @@ def calculate_gps_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd df[col] = np.nan return df - # Aggregate to one row per interview by averaging GPS columns across all GPS - # variable_names, matching the legacy pivot_table(aggfunc='mean') behaviour. - # When a questionnaire has multiple GPS questions this produces a mean - # coordinate; for single-GPS questionnaires the result is identical to the - # raw value. (This mirrors legacy pivot semantics where duplicates are - # collapsed by mean so spatial comparisons are one point per interview.) - data = ( - df.loc[gps_mask, index_col + required_columns] - .groupby(index_col, as_index=False)[required_columns] - .mean() - ) + # Keep each (interview, variable_name) as a separate GPS point so that + # questionnaires with multiple GPS variables pool all their points together + # for the outlier model without collapsing coordinates by mean. + data = df.loc[gps_mask, index_col + required_columns].copy() # Everything that has 0,0 as coordinates is considered an extreme outlier # (devices sometimes report 0,0 when a fix failed); mark these explicitly @@ -217,7 +180,7 @@ def calculate_gps_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd # model (a planar approximation). For larger geographic extents consider # switching to ['x','y','z'] or a geodesic distance measure. coords_columns = ['x', 'y'] - + # USE COF if dataset has less than 10000 samples else use LOF if data.loc[mask].shape[0] < 10000: model = COF(contamination=contamination) @@ -753,7 +716,7 @@ def calculate_multi_option_question_score(df_item: pd.DataFrame) -> pd.DataFrame return df def calculate_first_digit_score(df_item: pd.DataFrame) -> pd.DataFrame: - feature_name = 'f__numeric_response' + feature_name = 'f__first_digit' score_name = 's__first_digit' df = df_item.copy() @@ -763,7 +726,7 @@ def calculate_first_digit_score(df_item: pd.DataFrame) -> pd.DataFrame: df[score_name] = np.nan return df - valid_data = df[~pd.isnull(df[feature_name])] + valid_data = df[~pd.isnull(df[feature_name])].copy() valid_variables = filter_variable_name_by_frequency(valid_data, feature_name, frequency=100, min_unique_values=3) df[score_name] = np.nan From 0d948c3eed3cd8079fa7f04ad937f5f6ab7d3f41 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Sun, 29 Mar 2026 20:51:41 +0100 Subject: [PATCH 42/70] Monkey patches to generate legacy scoring for testing --- main_monkey_patch_features.py | 180 ++++++++++++++++++++++++++ main_monkey_patch_scores.py | 237 ++++++++++++++++++++++++++++++++++ 2 files changed, 417 insertions(+) create mode 100644 main_monkey_patch_features.py create mode 100644 main_monkey_patch_scores.py diff --git a/main_monkey_patch_features.py b/main_monkey_patch_features.py new file mode 100644 index 0000000..1c9e90b --- /dev/null +++ b/main_monkey_patch_features.py @@ -0,0 +1,180 @@ +import os +from omegaconf import DictConfig, OmegaConf +from hydra.core.hydra_config import HydraConfig +from rissk.unit_proccessing import * +from rissk.config import PROJ_ROOT +import hydra +# from memory_profiler import memory_usage +import warnings + +warnings.simplefilter(action='ignore', category=Warning) + + +def manage_path(config): + root_path = HydraConfig.get().runtime.cwd + if config['export_path'] is not None: + if os.path.isabs(config['export_path']) is False: + config['export_path'] = os.path.join(root_path, config['export_path']) + config['environment']['data']['externals'] = os.path.dirname(config['export_path']) + for key, value in config['environment']['data'].items(): + # Check if the value is a relative path + if not os.path.isabs(value): + # Convert the relative path to an absolute path + config['environment']['data'][key] = os.path.join(root_path, value) + config['surveys'] = [os.path.basename(config['export_path'])] + if os.path.isabs(config['output_file']) is False: + + config['output_file'] = os.path.join(root_path, config['output_file']) + return config + + +@hydra.main(config_path='configuration', version_base='1.1', config_name='main.yaml') +def unit_risk_score(config: DictConfig) -> None: + # print(OmegaConf.to_yaml(config)) + print("*" * 12) + config = manage_path(config) + + # --- MONKEY PATCH FOR TESTING --- + import pandas as pd + from rissk.unit_proccessing import UnitDataProcessing + + # Path to your existing files + SURVEY = "hies2024" + # SURVEY = "pmpmd" + # SURVEY = "slchbs" + # SURVEY = "fbf house holduntitled folder" + DATA_DIR = os.path.join(PROJ_ROOT, "rissk_kedro", "data", SURVEY, "latest", "30_PROCESSED") + + print(f"LOADING PARQUET FROM: {DATA_DIR}") + + # Load dataframes directly + microdata = pd.read_parquet(os.path.join(DATA_DIR, "microdata.parquet")) + paradata = pd.read_parquet(os.path.join(DATA_DIR, "paradata_processed.parquet")) + + # Manually initialize the class, skipping __init__ logic that breaks + # We use __new__ to create instance without calling __init__ + survey_class = UnitDataProcessing.__new__(UnitDataProcessing) + + # Manually set attributes that __init__ would set + survey_class.config = config + survey_class._limit_unit = config.get('limit_unit', None) + survey_class._allowed_features = ['f__' + k for k, v in config['features'].items() if v['use']] + survey_class.item_level_columns = ['interview__id', 'variable_name', 'roster_level'] + + # --- Prepare paradata (paradata_processed) for use as _df_paradata --- + # process_paradata() calls fillna('') so NaN question_scope values match the isin([0, '']) filter + # in the df_active_paradata property. Replicate that here. + paradata.fillna('', inplace=True) + + # Normalize qnr/qnr_version -> survey_name/survey_version if the extract uses alternate column names + alias_map = {'qnr': 'survey_name', 'qnr_version': 'survey_version'} + for src_col, dst_col in alias_map.items(): + if src_col in paradata.columns and dst_col not in paradata.columns: + print(f"Renaming paradata column {src_col} -> {dst_col}") + paradata = paradata.rename(columns={src_col: dst_col}) + + # Ensure survey_name / survey_version exist (fallback if still missing) + for col in ['survey_name', 'survey_version']: + if col not in paradata.columns: + print(f"Adding missing column to paradata: {col}") + paradata[col] = SURVEY if col == 'survey_name' else 'latest' + + # _df_paradata = processed paradata (equivalent to process_paradata() output). + # df_active_paradata property will derive the active subset from this automatically. + survey_class._df_paradata = paradata + + # BYPASS make_df_item call in __init__ and do it manually + print("Building Items (Legacy)...") + survey_class._df_item = survey_class.make_df_item(microdata) + + print("Building Units (Legacy)...") + survey_class._df_unit = survey_class.make_df_unit() + + print("Building Responsible...") + survey_class._df_resp = survey_class.make_df_responsible() + + # Numeric mask setup (copied from __init__) + survey_class.numeric_question_mask = ( + (survey_class._df_item["qtype"] == 'NumericQuestion') & + (survey_class._df_item['value'] != '') & + (~pd.isnull(survey_class._df_item['value'])) & + (survey_class._df_item['value'] != -999999999) + ) + + # Initialize score columns to None + survey_class._score_columns = None + + # Now standard flow + try: + # Calculate features (accessing properties triggers calculations) + print("Calculating Item Features...") + _ = survey_class.df_item + + print(f"saving item features to {DATA_DIR}/item_features_legacy.parquet") + survey_class._df_item.to_parquet(os.path.join(DATA_DIR, "item_features_legacy.parquet")) + + print("Calculating Unit Features...") + _ = survey_class.df_unit + + print(f"saving unit features to {DATA_DIR}/unit_features_legacy.parquet") + survey_class._df_unit.to_parquet(os.path.join(DATA_DIR, "unit_features_legacy.parquet")) + + print("Calculating Global Legacy Risk Scores...") + survey_class.make_global_score() + + # Persist final unit risk output from legacy code path. + unit_risk_cols = ['interview__id', 'responsible', 'unit_risk_score'] + unit_risk_df = survey_class._df_unit[unit_risk_cols].copy() + unit_risk_df['unit_risk_score'] = unit_risk_df['unit_risk_score'].round(2) + unit_risk_df.sort_values('unit_risk_score', inplace=True) + unit_risk_csv = os.path.join(DATA_DIR, "unit_risk_score_legacy.csv") + unit_risk_parquet = os.path.join(DATA_DIR, "unit_risk_score_legacy.parquet") + unit_risk_df.to_csv(unit_risk_csv, index=False) + unit_risk_df.to_parquet(unit_risk_parquet, index=False) + + # Build a merged score table to inspect item/unit/responsible scoring columns together. + resp_score_cols = [c for c in survey_class._df_resp.columns if c.startswith('s__')] + resp_view_cols = ['responsible', 'responsible_score'] + resp_score_cols + df_scores = survey_class._df_unit.merge( + survey_class._df_resp[resp_view_cols], + on='responsible', + how='left', + ) + score_cols = [c for c in df_scores.columns if c.startswith('s__')] + id_cols = [ + c for c in ['interview__id', 'responsible', 'survey_name', 'survey_version'] + if c in df_scores.columns + ] + final_cols = id_cols + ['unit_risk_score', 'responsible_score'] + sorted(score_cols) + final_cols = [c for c in final_cols if c in df_scores.columns] + df_scores = df_scores[final_cols] + + scores_csv = os.path.join(DATA_DIR, "scores_table_legacy.csv") + scores_parquet = os.path.join(DATA_DIR, "scores_table_legacy.parquet") + df_scores.to_csv(scores_csv, index=False) + df_scores.to_parquet(scores_parquet, index=False) + print(f"saved legacy unit risk to {unit_risk_csv}") + print(f"saved legacy score table to {scores_csv}") + + print("DONE. Legacy test data generated.") + return # Stop here + + except ValueError as e: + print(f"An error occurred: {e}") + + # --- END MONKEY PATCH --- + + try: + survey_class = UnitDataProcessing(config) + df_item = survey_class.df_item + df_unit = survey_class.df_unit + survey_class.make_global_score() + survey_class.save() + except ValueError as e: + print(f"An error occurred: {e}") + + +if __name__ == "__main__": + unit_risk_score() + # mem_usage = memory_usage(unit_risk_score) + # print(f"Memory usage (in MB): {max(mem_usage)}") diff --git a/main_monkey_patch_scores.py b/main_monkey_patch_scores.py new file mode 100644 index 0000000..813efa6 --- /dev/null +++ b/main_monkey_patch_scores.py @@ -0,0 +1,237 @@ +import os +from omegaconf import DictConfig, OmegaConf +from hydra.core.hydra_config import HydraConfig +from rissk.unit_proccessing import * +from rissk.config import PROJ_ROOT +import hydra +# from memory_profiler import memory_usage +import warnings + +warnings.simplefilter(action='ignore', category=Warning) + + +def manage_path(config): + root_path = HydraConfig.get().runtime.cwd + if config['export_path'] is not None: + if os.path.isabs(config['export_path']) is False: + config['export_path'] = os.path.join(root_path, config['export_path']) + config['environment']['data']['externals'] = os.path.dirname(config['export_path']) + for key, value in config['environment']['data'].items(): + # Check if the value is a relative path + if not os.path.isabs(value): + # Convert the relative path to an absolute path + config['environment']['data'][key] = os.path.join(root_path, value) + config['surveys'] = [os.path.basename(config['export_path'])] + if os.path.isabs(config['output_file']) is False: + + config['output_file'] = os.path.join(root_path, config['output_file']) + return config + + +@hydra.main(config_path='configuration', version_base='1.1', config_name='main.yaml') +def unit_risk_score(config: DictConfig) -> None: + # print(OmegaConf.to_yaml(config)) + print("*" * 12) + config = manage_path(config) + + # --- MONKEY PATCH FOR TESTING --- + # Loads Kedro feature-creation pipeline outputs (item_features, unit_features, + # removed_answers) and runs only the legacy scoring logic on top of them so + # the resulting scores can be compared to the Kedro scoring pipeline outputs. + import pandas as pd + from rissk.unit_proccessing import UnitDataProcessing + + # SURVEY = "hies2024" + # SURVEY = "pmpmd" + # SURVEY = "slchbs" + SURVEY = "fbf house holduntitled folder" + DATA_DIR = os.path.join(PROJ_ROOT, "rissk_kedro", "data", SURVEY, "latest", "30_PROCESSED") + SCORE_DIR = os.path.join(PROJ_ROOT, "rissk_kedro", "data", SURVEY, "latest", "40_SCORED") + + print(f"LOADING KEDRO FEATURE OUTPUTS FROM: {DATA_DIR}") + + # Load Kedro feature-creation pipeline outputs + df_item_kedro = pd.read_parquet(os.path.join(DATA_DIR, "item_features.parquet")) + df_unit_kedro = pd.read_parquet(os.path.join(DATA_DIR, "unit_features.parquet")) + df_removed_kedro = pd.read_parquet(os.path.join(DATA_DIR, "removed_answers.parquet")) + + # Manually initialize the class without calling __init__ + survey_class = UnitDataProcessing.__new__(UnitDataProcessing) + survey_class.config = config + survey_class._limit_unit = config.get('limit_unit', None) + survey_class._allowed_features = ['f__' + k for k, v in config['features'].items() if v['use']] + survey_class.item_level_columns = ['interview__id', 'variable_name', 'roster_level'] + + # Assign Kedro feature tables; strip any pre-existing s__* columns so that + # make_global_score starts from a clean slate. + survey_class._df_item = df_item_kedro.drop( + columns=[c for c in df_item_kedro.columns if c.startswith('s__')] + ) + survey_class._df_unit = df_unit_kedro.drop( + columns=[c for c in df_unit_kedro.columns if c.startswith('s__')] + ) + # df_unit_score property requires survey_name/survey_version; rename from Kedro column names + survey_class._df_unit.rename(columns={'qnr': 'survey_name', 'qnr_version': 'survey_version'}, inplace=True) + if 'survey_name' not in survey_class._df_unit.columns: + survey_class._df_unit['survey_name'] = SURVEY + if 'survey_version' not in survey_class._df_unit.columns: + survey_class._df_unit['survey_version'] = 'latest' + + # Build _df_resp from unique responsibles present in unit features + survey_class._df_resp = ( + df_unit_kedro[['responsible']] + .drop_duplicates() + .loc[lambda d: (d['responsible'] != '') & d['responsible'].notna()] + .copy() + ) + + # Numeric mask needed by several scoring methods accessed via self.df_item + survey_class.numeric_question_mask = ( + (survey_class._df_item["qtype"] == 'NumericQuestion') & + (survey_class._df_item['value'] != '') & + (~pd.isnull(survey_class._df_item['value'])) & + (survey_class._df_item['value'] != -999999999) + ) + + survey_class._score_columns = None + + # Patch get_feature_item__answer_removed so that make_score__answer_removed + # uses the Kedro-built removed_answers table instead of reading self.df_paradata. + survey_class.get_feature_item__answer_removed = lambda feature_name: df_removed_kedro.copy() + + try: + print("Calculating Legacy Risk Scores from Kedro feature outputs...") + + # Populate all s__* columns on _df_unit/_df_resp first, then sanitise before + # StandardScaler runs. Division-based scores (e.g. s__pause_duration = + # f__pause_duration / f__total_elapse) can produce inf when the denominator is 0. + _ = survey_class.df_unit_score + s_cols = [c for c in survey_class._df_unit.columns if c.startswith('s__')] + survey_class._df_unit[s_cols] = survey_class._df_unit[s_cols].replace( + [np.inf, -np.inf], np.nan + ) + + # Recompute _score_columns on sanitised data so make_global_score sees the + # correct set. For small surveys all scores can be constant/all-NaN after + # sanitisation, which would give StandardScaler an empty DataFrame. + score_cols_all = [c for c in survey_class._df_unit.columns if c.startswith('s__')] + survey_class._score_columns = ( + survey_class._df_unit[score_cols_all] + .columns[survey_class._df_unit[score_cols_all].nunique() > 1] + .tolist() + ) + if not survey_class._score_columns: + print("No score columns with sufficient variance — cannot compute global score for this survey.") + return + + # Determine whether the responsible-level score has enough variance to run PCA. + # make_responsible_score receives restricted_columns=_score_columns (unit-level), so + # only columns in _df_resp that are NOT in _score_columns are used. If they are all + # constant after fillna(0) the StandardScaler raises "at least one array or dtype". + _restricted = survey_class._score_columns + _resp_candidates = [ + c for c in survey_class._df_resp.columns + if not c.startswith('responsible') and c not in _restricted + ] + _resp_has_variance = ( + not survey_class._df_resp[_resp_candidates].fillna(0).loc[ + :, survey_class._df_resp[_resp_candidates].fillna(0).nunique() != 1 + ].empty + if _resp_candidates else False + ) + + survey_class.make_global_score(combine_resp_score=_resp_has_variance) + + # Build item-level score table (equivalent to Kedro calculate_item_scores output). + # answer_removed is excluded here matching Kedro behaviour (scored at unit level only). + # GPS is excluded due to its pivoted shape (already a WARNING in make_global_score). + print("Collecting item-level scores...") + id_cols = [c for c in ['interview__id', 'variable_name', 'roster_level', 'index_col'] + if c in survey_class._df_item.columns] + df_item_scores = survey_class._df_item[id_cols].copy() + merge_key = 'index_col' if 'index_col' in df_item_scores.columns \ + else ['interview__id', 'variable_name', 'roster_level'] + merge_cols = [merge_key] if isinstance(merge_key, str) else merge_key + + item_score_methods = [ + ('make_score__answer_hour_set', ['s__answer_hour_set']), + ('make_score__sequence_jump', ['s__sequence_jump']), + ('make_score__first_decimal', ['s__first_decimal']), + ('make_score__answer_changed', ['s__answer_changed']), + ('make_score__answer_position', ['s__answer_position']), + ('make_score__answer_selected', ['s__answer_selected_lower', 's__answer_selected_upper']), + ('make_score__answer_duration', ['s__answer_duration_lower', 's__answer_duration_upper']), + ('make_score__single_question', ['s__single_question']), + ('make_score__multi_option_question', ['s__multi_option_question']), + ('make_score__first_digit', ['s__first_digit']), + ] + for method_name, score_cols in item_score_methods: + try: + result = getattr(survey_class, method_name)() + available = [c for c in score_cols if c in result.columns] + if not available: + continue + result_slim = result[merge_cols + available].drop_duplicates(subset=merge_cols) + df_item_scores = df_item_scores.merge(result_slim, on=merge_key, how='left') + except Exception as e: + print(f"WARNING: item score {score_cols}: {e}") + + item_scores_parquet = os.path.join(SCORE_DIR, "item_scores_legacy.parquet") + df_item_scores.to_parquet(item_scores_parquet, index=False) + print(f"saved legacy item scores to {item_scores_parquet}") + + # Persist unit risk scores + unit_risk_cols = ['interview__id', 'responsible', 'unit_risk_score'] + unit_risk_df = survey_class._df_unit[unit_risk_cols].copy() + unit_risk_df['unit_risk_score'] = unit_risk_df['unit_risk_score'].round(2) + unit_risk_df.sort_values('unit_risk_score', inplace=True) + unit_risk_csv = os.path.join(SCORE_DIR, "unit_risk_score_legacy.csv") + unit_risk_parquet = os.path.join(SCORE_DIR, "unit_risk_score_legacy.parquet") + unit_risk_df.to_csv(unit_risk_csv, index=False) + unit_risk_df.to_parquet(unit_risk_parquet, index=False) + + # Build merged score table (unit + responsible scores) + resp_score_cols = [c for c in survey_class._df_resp.columns if c.startswith('s__')] + resp_id_cols = ['responsible'] + if 'responsible_score' in survey_class._df_resp.columns: + resp_id_cols.append('responsible_score') + resp_view_cols = resp_id_cols + resp_score_cols + df_scores = survey_class._df_unit.merge( + survey_class._df_resp[resp_view_cols], on='responsible', how='left', + ) + score_cols = [c for c in df_scores.columns if c.startswith('s__')] + id_cols = [c for c in ['interview__id', 'responsible', 'survey_name', 'survey_version'] + if c in df_scores.columns] + final_cols = id_cols + ['unit_risk_score', 'responsible_score'] + sorted(score_cols) + df_scores = df_scores[[c for c in final_cols if c in df_scores.columns]] + + scores_csv = os.path.join(SCORE_DIR, "scores_table_legacy.csv") + scores_parquet = os.path.join(SCORE_DIR, "scores_table_legacy.parquet") + df_scores.to_csv(scores_csv, index=False) + df_scores.to_parquet(scores_parquet, index=False) + print(f"saved legacy unit risk to {unit_risk_csv}") + print(f"saved legacy score table to {scores_csv}") + + print("DONE. Legacy scores from Kedro features generated.") + return # Stop here + + except ValueError as e: + print(f"An error occurred: {e}") + return # do not fall through to the regular UnitDataProcessing block + + # --- END MONKEY PATCH --- + + try: + survey_class = UnitDataProcessing(config) + df_item = survey_class.df_item + df_unit = survey_class.df_unit + survey_class.make_global_score() + survey_class.save() + except ValueError as e: + print(f"An error occurred: {e}") + + +if __name__ == "__main__": + unit_risk_score() + # mem_usage = memory_usage(unit_risk_score) + # print(f"Memory usage (in MB): {max(mem_usage)}") From 75bb61f5c1707fd6bd2fc8cea01a2901a65a690f Mon Sep 17 00:00:00 2001 From: VJausovec Date: Mon, 30 Mar 2026 20:42:37 +0100 Subject: [PATCH 43/70] Add consent filtering functionality to feature creation pipeline; refactor questionnaire loading and processing logic. --- rissk/feature_processing_kedro.py | 14 +++- rissk/item_processing_kedro.py | 26 +++++-- rissk/unit_processing_kedro.py | 4 +- rissk/utils/stats_utils_kedro.py | 27 +++++-- rissk_kedro/conf/base/globals.yml | 37 ++++++---- .../src/rissk_kedro/pipeline_registry.py | 58 ++++++++++++--- .../pipelines/feature_creation/nodes.py | 73 +++++++++++++++++++ .../pipelines/feature_engineering/nodes.py | 20 ++--- 8 files changed, 203 insertions(+), 56 deletions(-) diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index 3b4b1db..4155925 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -1,3 +1,4 @@ +import math import pandas as pd import numpy as np import ast @@ -36,6 +37,7 @@ def get_numeric_mask(df_item: pd.DataFrame, filter_answer_values: bool) -> pd.Se if filter_answer_values: answer_mask = _is_answer_value(df_item['value'], df_item['answer_sequence']) mask &= ~answer_mask + return mask @@ -425,9 +427,15 @@ def feat_first_digit(df_item, **kwargs): df_item[feature_name] = pd.NA if numeric_mask.any(): numeric_values = _coerce_numeric_with_warning(df_item, numeric_mask, feature_name) - # Take absolute value, convert to string, extract first character - vals = numeric_values.abs().astype(str).str[0] - df_item.loc[numeric_mask, feature_name] = pd.to_numeric(vals, errors='coerce').astype('Int64') + # Extract first significant digit using log10 (correct for values in (0,1)) + def _first_significant_digit(val): + val = abs(val) + if val == 0: + return 0 + power = math.floor(math.log10(val)) + return int(val / 10**power) + vals = numeric_values.apply(_first_significant_digit) + df_item.loc[numeric_mask, feature_name] = pd.array(vals, dtype='Int64') return df_item def feat_last_digit(df_item, **kwargs): diff --git a/rissk/item_processing_kedro.py b/rissk/item_processing_kedro.py index 780f294..77fe9b7 100644 --- a/rissk/item_processing_kedro.py +++ b/rissk/item_processing_kedro.py @@ -13,7 +13,7 @@ calculate_entropy, calculate_list_entropy, filter_variables_by_magnitude, - apply_benford_tests + apply_benford_tests, ) from rissk.detection_algorithms_kedro import lat_lon_to_cartesian @@ -716,22 +716,32 @@ def calculate_multi_option_question_score(df_item: pd.DataFrame) -> pd.DataFrame return df def calculate_first_digit_score(df_item: pd.DataFrame) -> pd.DataFrame: - feature_name = 'f__first_digit' + feature_name = 'f__numeric_response' + first_digit_feature = 'f__first_digit' score_name = 's__first_digit' df = df_item.copy() - if feature_name not in df.columns: + if feature_name not in df.columns or first_digit_feature not in df.columns: return df - if df[feature_name].dropna().empty: + if df[feature_name].dropna().empty or df[first_digit_feature].dropna().empty: df[score_name] = np.nan return df valid_data = df[~pd.isnull(df[feature_name])].copy() - valid_variables = filter_variable_name_by_frequency(valid_data, feature_name, frequency=100, min_unique_values=3) df[score_name] = np.nan - valid_variables = filter_variables_by_magnitude(valid_data, feature_name, valid_variables, min_order_of_magnitude=3) - + # f__first_digit is already computed by the feature pipeline and is NA for zeros + # and nulls, so filter_variable_name_by_frequency applied to it naturally restricts + # frequency and uniqueness counts to the nonzero Benford-eligible population. + # No need to recompute first digits here — f__numeric_response is only needed for + # the magnitude range check and for apply_benford_tests. + valid_variables = filter_variable_name_by_frequency( + df, first_digit_feature, frequency=100, min_unique_values=3 + ) + + benford_data = valid_data[valid_data[feature_name] != 0].copy() + valid_variables = filter_variables_by_magnitude(benford_data, feature_name, valid_variables, min_order_of_magnitude=3) + # Computes the Jensen divergence for each variable_name and responsible on the first digit distribution. # Jensen's divergence returns a value between (0, 1) of how much the first digit distribution # of specific responsible is similar to the first digit distribution of all others. @@ -740,7 +750,7 @@ def calculate_first_digit_score(df_item: pd.DataFrame) -> pd.DataFrame: # who have at least 50 records. # Once it is calculated, values that diverge from more than 50% from the median value get marked as "anomalous." benford_jensen_df = apply_benford_tests( - valid_data, valid_variables, 'responsible', feature_name, apply_first_digit=True, minimum_sample=50 + benford_data, valid_variables, 'responsible', feature_name, apply_first_digit=True, minimum_sample=50 ) if not benford_jensen_df.empty: diff --git a/rissk/unit_processing_kedro.py b/rissk/unit_processing_kedro.py index de262cd..04233c9 100644 --- a/rissk/unit_processing_kedro.py +++ b/rissk/unit_processing_kedro.py @@ -4,6 +4,8 @@ from typing import List, Dict, Any, Tuple from pyod.models.pca import PCA from pyod.models.iforest import IForest +from pyod.models.ecod import ECOD +from rissk.item_processing_kedro import get_contamination_parameter from sklearn.preprocessing import StandardScaler, MinMaxScaler, normalize logger = logging.getLogger(__name__) @@ -130,8 +132,6 @@ def aggregate_item_to_unit_scores(df_unit: pd.DataFrame, df_item_scores: pd.Data def calculate_unit_level_scores(df_unit: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: """Calculate scores that are purely derived from unit-level features.""" - from pyod.models.ecod import ECOD - from rissk.item_processing_kedro import get_contamination_parameter df = df_unit.copy() if 'f__time_changed' in df.columns: diff --git a/rissk/utils/stats_utils_kedro.py b/rissk/utils/stats_utils_kedro.py index 9c811df..5f4e169 100644 --- a/rissk/utils/stats_utils_kedro.py +++ b/rissk/utils/stats_utils_kedro.py @@ -68,9 +68,12 @@ def get_digit_frequecies(df, feature_name, apply_first_digit, minimum_sample=50) def first_digit(val): - """Extract the first digit from a value.""" + """Extract the first significant digit from a value using log10.""" val = abs(val) - return int(str(val)[0]) + if val == 0: + return 0 + power = math.floor(math.log10(val)) + return int(val / 10**power) def last_digit(val): @@ -157,18 +160,26 @@ def get_outlier_z_score(data, column_name, threshold=2.5): def filter_variables_by_magnitude(df, feature_name, variables, min_order_of_magnitude=3): + """Return variables whose nonzero absolute values span at least `min_order_of_magnitude` orders. + + Zeros are excluded because they are not part of the Benford domain and would + anchor min_magnitude at 0, distorting the apparent range. Negative values are + treated by absolute value: using raw min/max with sign inversion would reverse + the comparison for all-negative series (e.g. min=-1000, max=-0.01 would give + magnitude(max) - magnitude(min) = -2 - 3 = -5, always failing). + """ def order_of_magnitude(num): - if num == 0: - return 0 - elif num < 0: - num = -num + # num is guaranteed positive (abs applied by caller) return int(math.floor(math.log10(num))) valid_variables = [] for var in variables: var_values = df[df['variable_name'] == var][feature_name] - max_magnitude = order_of_magnitude(var_values.max()) - min_magnitude = order_of_magnitude(var_values.min()) + nonzero_abs = var_values[var_values != 0].abs() + if nonzero_abs.empty: + continue + max_magnitude = order_of_magnitude(nonzero_abs.max()) + min_magnitude = order_of_magnitude(nonzero_abs.min()) if max_magnitude - min_magnitude >= min_order_of_magnitude: valid_variables.append(var) return valid_variables diff --git a/rissk_kedro/conf/base/globals.yml b/rissk_kedro/conf/base/globals.yml index 831900b..62b01b3 100644 --- a/rissk_kedro/conf/base/globals.yml +++ b/rissk_kedro/conf/base/globals.yml @@ -1,14 +1,17 @@ # Survey Configuration (from env.yaml) -survey: - name: "hies2024" - questionnaires: - - name: "snb_hies_hh" - VERSION: [9, 10, 11] - # filter_var: None - - name: "slbhies_listing" - VERSION: [5, 6, 7] - # filter_var: None +# survey: +# name: "hies2024" +# questionnaires: +# - name: "snb_hies_hh" +# VERSION: [9, 10, 11] +# filter_var: null +# - name: "slbhies_listing" +# VERSION: [5, 6, 7] +# filter_var: null # Set to a single-key dict to filter by consent, e.g.: + # filter_var: {consent_q: "1"} + # Only interviews where paradata variable 'consent_q' has answer "1" are scored. + # The answer value must be a string (paradata answers are always strings). # survey: @@ -16,17 +19,21 @@ survey: # questionnaires: # - name: "pmpmd_community" # VERSION: [2, 3, 4, 5] +# filter_var: null # - name: "pmpmd_household" # VERSION: [4, 5, 6] +# filter_var: null -# survey: -# name: "slchbs" -# questionnaires: -# - name: "slchbs_saintlucia_2025" -# VERSION: [6, 7] # 5 is for testing empty data handling +survey: + name: "slchbs" + questionnaires: + - name: "slchbs_saintlucia_2025" + VERSION: [6, 7] # 5 is for testing empty data handling + filter_var: null # survey: # name: "fbf house holduntitled folder" # questionnaires: # - name: "fbf_household" -# VERSION: [13] \ No newline at end of file +# VERSION: [13] +# filter_var: null \ No newline at end of file diff --git a/rissk_kedro/src/rissk_kedro/pipeline_registry.py b/rissk_kedro/src/rissk_kedro/pipeline_registry.py index e98bdd7..1520bf2 100644 --- a/rissk_kedro/src/rissk_kedro/pipeline_registry.py +++ b/rissk_kedro/src/rissk_kedro/pipeline_registry.py @@ -7,7 +7,7 @@ import yaml from kedro.pipeline import Pipeline, node, pipeline -from rissk_kedro.pipelines.feature_creation.nodes import make_qnr_filter +from rissk_kedro.pipelines.feature_creation.nodes import make_qnr_filter, make_consent_filter def _load_questionnaire_names() -> list[str]: @@ -24,6 +24,18 @@ def _load_questionnaire_names() -> list[str]: return [q["name"] for q in questionnaires] +def _load_questionnaires() -> list[dict]: + """Return the full list of questionnaire config dicts from conf/base/globals.yml. + + Each dict may contain ``name``, ``VERSION``, and the optional + ``filter_var`` consent-filter setting. + """ + globals_path = Path(__file__).parents[2] / "conf" / "base" / "globals.yml" + with globals_path.open() as fh: + globals_data = yaml.safe_load(fh) + return globals_data.get("survey", {}).get("questionnaires", []) + + def _make_merge_node( output_name: str, input_names: list[str], @@ -63,7 +75,7 @@ def register_pipelines() -> dict[str, Pipeline]: from rissk_kedro.pipelines.feature_creation import create_pipeline as feature_creation_pipeline from rissk_kedro.pipelines.rissk_scoring import create_pipeline as scoring_pipeline - qnr_names = _load_questionnaire_names() + questionnaires = _load_questionnaires() # ------------------------------------------------------------------ # # Per-questionnaire filter + scoring pipelines # @@ -74,28 +86,54 @@ def register_pipelines() -> dict[str, Pipeline]: unit_score_datasets: list[str] = [] resp_score_datasets: list[str] = [] - for qnr_name in qnr_names: + for qnr_config in questionnaires: + qnr_name = qnr_config["name"] + # filter_var: dict like {variable_name: answer_value}, or None to skip. + filter_var = qnr_config.get("filter_var", None) + # Sanitise the questionnaire name so it is a valid Python identifier / # Kedro namespace component (spaces -> underscores, etc.). ns = qnr_name.replace(" ", "_").replace("-", "_") - # -- Filter node -------------------------------------------------- + # -- Questionnaire filter node ------------------------------------ + # Outputs use a _qnr__ suffix so the consent filter can write the + # canonical __{ns} names consumed by the scoring pipeline below. filter_node = node( func=make_qnr_filter(qnr_name), inputs=["item_features", "unit_features", "removed_answers"], + outputs=[ + f"item_features_qnr__{ns}", + f"unit_features_qnr__{ns}", + f"removed_answers_qnr__{ns}", + ], + name=f"filter_features_{ns}_node", + ) + + # -- Consent filter node ------------------------------------------ + # When filter_var is None the function is a pass-through. When set, + # it drops interviews that lack the required consent answer and emits + # a WARNING so the operator knows filtering is active. + consent_filter_node = node( + func=make_consent_filter(qnr_name, filter_var), + inputs=[ + f"item_features_qnr__{ns}", + f"unit_features_qnr__{ns}", + f"removed_answers_qnr__{ns}", + "paradata_processed", + ], outputs=[ f"item_features__{ns}", f"unit_features__{ns}", f"removed_answers__{ns}", ], - name=f"filter_features_{ns}_node", + name=f"filter_consent_{ns}_node", ) # -- Namespaced scoring pipeline ---------------------------------- # Explicit input/output mappings override namespacing for those keys so - # the filter outputs wire directly and the final scored dfs get unique names. - # parameters must be passed via the dedicated `parameters` arg — Kedro - # raises PipelineError if they appear in `inputs`. + # the consent-filter outputs wire directly and the final scored dfs get + # unique names. parameters must be passed via the dedicated `parameters` + # arg — Kedro raises PipelineError if they appear in `inputs`. namespaced_scoring = pipeline( scoring_pipeline(), namespace=ns, @@ -116,7 +154,7 @@ def register_pipelines() -> dict[str, Pipeline]: unit_score_datasets.append(f"unit_risk_scores__{ns}") resp_score_datasets.append(f"responsible_scores__{ns}") - qnr_pipeline = Pipeline([filter_node]) + namespaced_scoring + qnr_pipeline = Pipeline([filter_node, consent_filter_node]) + namespaced_scoring per_qnr_pipelines[f"scoring_{ns}"] = qnr_pipeline # ------------------------------------------------------------------ # @@ -143,7 +181,7 @@ def register_pipelines() -> dict[str, Pipeline]: pipelines["data_ingestion"] = ingestion pipelines["feature_engineering"] = feat_eng pipelines["feature_creation"] = feat_creation - pipelines["scoring"] = all_scoring # filter + score + merge; skips ingestion/feature creation + pipelines["rissk_scoring"] = all_scoring # filter + score + merge; skips ingestion/feature creation # Individual per-questionnaire scoring (without merge) — useful for debugging for name, p in per_qnr_pipelines.items(): diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py index 315c55a..02a97dc 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py @@ -97,3 +97,76 @@ def filter_features( filter_features.__name__ = f"filter_features_{qnr_name}" return filter_features + + +def make_consent_filter(qnr_name: str, filter_var): + """Factory that returns a consent-filter function for a single questionnaire. + + ``filter_var`` must be a dict with exactly one key-value pair + ``{variable_name: answer_value}`` (matching the legacy ``limit_unit`` shape), + or ``None`` to skip filtering entirely. + + When set, only interviews where ``variable_name == key`` and + ``str(value) == str(answer_value)`` are retained across all three feature + tables. A WARNING is emitted so operators know filtering is active. + """ + def filter_by_consent( + item_features: pd.DataFrame, + unit_features: pd.DataFrame, + removed_answers: pd.DataFrame, + paradata: pd.DataFrame, + ): + if filter_var is None: + return item_features, unit_features, removed_answers + + consent_variable = next(iter(filter_var)) + # Careful: paradata answer column is always a string, so cast the + # configured value to str — matching legacy filter_by_consent behaviour. + consent_value = str(filter_var[consent_variable]) + + logger.warning( + "filter_by_consent [%s]: consent filtering is ACTIVE — " + "keeping only interviews where '%s' == '%s'", + qnr_name, consent_variable, consent_value, + ) + + # Scope to this questionnaire before looking up approved interviews. + qnr_paradata = ( + paradata[paradata["qnr"] == qnr_name] + if "qnr" in paradata.columns + else paradata + ) + + cond1 = qnr_paradata["variable_name"] == consent_variable + cond2 = qnr_paradata["answer"] == consent_value + approved_ids = qnr_paradata.loc[cond1 & cond2, "interview__id"].unique() + + if len(approved_ids) == 0: + total_interviews = unit_features["interview__id"].nunique() + raise ValueError( + f"filter_by_consent [{qnr_name}]: filter_var " + f"{{'{consent_variable}': '{consent_value}'}} matched 0 interviews " + f"out of {total_interviews}. " + f"Check that the variable name and answer value are correct. " + f"Note: paradata answer values are always strings." + ) + + item_filtered = item_features[item_features["interview__id"].isin(approved_ids)].copy() + unit_filtered = unit_features[unit_features["interview__id"].isin(approved_ids)].copy() + + if removed_answers is not None and not removed_answers.empty: + removed_filtered = removed_answers[ + removed_answers["interview__id"].isin(approved_ids) + ].copy() + else: + removed_filtered = removed_answers + + logger.info( + "filter_by_consent [%s]: retained %d / %d interviews (%d item rows)", + qnr_name, len(unit_filtered), len(unit_features), len(item_filtered), + ) + + return item_filtered, unit_filtered, removed_filtered + + filter_by_consent.__name__ = f"filter_by_consent_{qnr_name}" + return filter_by_consent diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py index 3900543..0c42d28 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py @@ -48,19 +48,19 @@ def process_paradata_node( paradata.sort_values(['interview__id', 'order'], inplace=True) paradata.reset_index(drop=True, inplace=True) - # Limit Unit Logic - limit_unit = parameters.get('processing', {}).get('limit_unit') - if limit_unit is not None: - consent_variable = next(iter(limit_unit)) - consent_value = str(limit_unit[consent_variable]) + # # Limit Unit Logic + # limit_unit = parameters.get('processing', {}).get('limit_unit') + # if limit_unit is not None: + # consent_variable = next(iter(limit_unit)) + # consent_value = str(limit_unit[consent_variable]) - cond1 = (paradata['variable_name'] == consent_variable) - cond2 = (paradata['answer'] == consent_value) + # cond1 = (paradata['variable_name'] == consent_variable) + # cond2 = (paradata['answer'] == consent_value) - filtered_interview_id = paradata[cond1 & cond2]['interview__id'].unique() - paradata = paradata[paradata['interview__id'].isin(filtered_interview_id)].copy() + # filtered_interview_id = paradata[cond1 & cond2]['interview__id'].unique() + # paradata = paradata[paradata['interview__id'].isin(filtered_interview_id)].copy() - return paradata + # return paradata def filter_active_paradata_node( From 584f7327d1ae3d59fc5fdd19d1e9967927677c48 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Tue, 31 Mar 2026 21:29:41 +0100 Subject: [PATCH 44/70] changes to logging and clean-up --- .gitignore | 67 +++++++ ingestion_discrepancies.md | 27 --- main_monkey_patch_features.py | 180 ------------------ rissk/__init__.py | 4 +- rissk/feature_processing_kedro.py | 3 +- rissk/unit_processing_kedro.py | 34 +++- rissk/utils/import_utils_kedro.py | 3 +- rissk_kedro/conf/logging.yml | 3 +- .../pipelines/data_ingestion/nodes.py | 4 +- .../pipelines/feature_engineering/nodes.py | 6 +- .../pipelines/rissk_scoring/nodes.py | 17 +- 11 files changed, 127 insertions(+), 221 deletions(-) delete mode 100644 ingestion_discrepancies.md delete mode 100644 main_monkey_patch_features.py diff --git a/.gitignore b/.gitignore index 0074df5..f1cd355 100644 --- a/.gitignore +++ b/.gitignore @@ -266,3 +266,70 @@ ingestion_refactor_context.md rissk_kedro/stats.json rissk_kedro/feature_process_ploomber_pipeline_integration.md data_ingestion_function_changes.md +.vscode/mcp.json +rissk/utils/testing_utils.py +configuration/main.yaml +env.yaml +env.yaml +configuration/main.yaml +configuration/main.yaml +configuration/main.yaml +env.yaml +rissk_kedro/src/rissk_kedro/test_ingestion.ipynb +.gitignore +Feature_and_Unit_Process_refactor.md +main.py +main.py.bak +configuration/main.yaml +configuration/main.yaml +rissk_kedro/src/rissk_kedro/test_ingestion.ipynb +rissk_kedro/src/rissk_kedro/test_microdata.ipynb +rissk_kedro/src/rissk_kedro/test_ingestion.ipynb +env.yaml +rissk/utils/import_utils_kedro.py +rissk_kedro/conf/base/parameters.yml +rissk_kedro/src/rissk_kedro/test_item_creation.ipynb +rissk/feature_processing_kedro.py +Feature Creation Pipeline.md +rissk/Base Item Table.md +rissk_kedro/src/rissk_kedro/test_unti_creation.ipynb +scoring_refactor_instructions.md +Kedro_Scoring_Refactor.md +rissk_kedro/src/rissk_kedro/test_unit_creation.ipynb +rissk_kedro/src/rissk_kedro/test_ingestion.ipynb +rissk_kedro/src/rissk_kedro/test_item_scoring.ipynb +rissk/prompt.md +rissk_kedro/src/rissk_kedro/Score_NaN_Handling_and_Aggregation.md +rissk/Paradata vs Active Paradata.md +.gitignore +rissk_kedro/src/rissk_kedro/test_ingestion.ipynb +FEATURES_SCORES.md +FEATURES_SCORES.md +rissk_kedro/src/rissk_kedro/test_ingestion.ipynb +rissk_kedro/src/rissk_kedro/test_ingestion_bulk_read_in.ipynb +Scoring_Refactor_Review.md +rissk_kedro/conf/base/catalog.yml +rissk_kedro/src/rissk_kedro/microdata_drop_columns.md +FEATURES_SCORES.md +rissk/unit_scoring.md +rissk_kedro/conf/base/test_microdata_gps_answers.ipynb +rissk_kedro/src/rissk_kedro/test_item_creation_individual.ipynb +rissk_kedro/src/rissk_kedro/test_item_f__answer_changed.ipynb +rissk_kedro/src/rissk_kedro/test_item_f__answer_removed.ipynb +rissk_kedro/src/rissk_kedro/test_ingestion.ipynb +rissk_kedro/src/rissk_kedro/test_item_f__answer_selected.ipynb +rissk_kedro/src/rissk_kedro/test_item_s__first_decimal.ipynb +rissk_kedro/src/rissk_kedro/test_item_unit_creation.ipynb +rissk_kedro/src/rissk_kedro/test_score_item.ipynb +main_monkey_patch_item_unit.py +rissk_kedro/src/rissk_kedro/test_microdata_gps_answers.ipynb +FEATURES_SCORES_updated.md +rissk_kedro/src/rissk_kedro/test_ingestion.ipynb +.gitignore +rissk_kedro/src/rissk_kedro/test_scoring_gps.ipynb +rissk_kedro/src/rissk_kedro/test_scoring_gps_legacy.ipynb +rissk/Unit Scoring — Legacy vs Kedro Behavi.md +main_monkey_patch_scores.py +rissk_kedro/src/rissk_kedro/test_scoring_first_digit.ipynb +rissk_kedro/src/rissk_kedro/test_score_unit.ipynb +rissk_kedro/src/rissk_kedro/test_scoring_first_digit_digit_checks.ipynb diff --git a/ingestion_discrepancies.md b/ingestion_discrepancies.md deleted file mode 100644 index 8601a26..0000000 --- a/ingestion_discrepancies.md +++ /dev/null @@ -1,27 +0,0 @@ -# Data Ingestion Discrepancies: Ploomber (Legacy) vs Kedro (New) - -## Overview -This document tracks intentional data discrepancies between the legacy Ploomber pipeline and the new Kedro pipeline. These differences are accepted improve data quality or cleanliness. - -## 1. Microdata `value` Column Normalization - -### The Discrepancy -- **Legacy (Ploomber):** The `value` column in `microdata.parquet` contains a mix of format styles for integer-like values. - - Example: `1` (integer-like string) and `1.0` (float-like string) appear inconsistently for the same logical value. -- **New (Kedro):** The pipeline now explicitly normalizes values before conversion to string. - - Logic: If a float value `x` is equivalent to an integer (`x.is_integer() is True`), it is converted to an integer before stringification. - - Result: `1.0` becomes `"1"`. `1.5` remains `"1.5"`. - - Lists: This normalization is also applied to values inside list-strings (e.g., `"[1.0, 2.0]"` becomes `"[1, 2]"`). - -### Decision -**Status:** ACCEPTED (Intentional Deviation) - -We have chosen to keep the cleaner, normalized integer format in Kedro. -- **Reasoning:** - 1. The values are typically categorical codes (IDs, boolean flags like 0/1), where `1` is semantically more accurate than `1.0`. - 2. Mixed formatting in the legacy pipeline appears to be an artifact of how Pandas handles `NaN`s (forcing floats) rather than intentional data design. - 3. Uniform formatting simplifies downstream processing. - -### Downstream Implications -Any downstream code (feature engineering, analysis) that performs **exact string matching** against float-strings (e.g., `val == "1.0"`) may fail or return empty results. -- **Action Required:** Ensure downstream filtering uses type-safe comparisons (convert to float/int before comparing) or checks for the normalized string `"1"`. diff --git a/main_monkey_patch_features.py b/main_monkey_patch_features.py deleted file mode 100644 index 1c9e90b..0000000 --- a/main_monkey_patch_features.py +++ /dev/null @@ -1,180 +0,0 @@ -import os -from omegaconf import DictConfig, OmegaConf -from hydra.core.hydra_config import HydraConfig -from rissk.unit_proccessing import * -from rissk.config import PROJ_ROOT -import hydra -# from memory_profiler import memory_usage -import warnings - -warnings.simplefilter(action='ignore', category=Warning) - - -def manage_path(config): - root_path = HydraConfig.get().runtime.cwd - if config['export_path'] is not None: - if os.path.isabs(config['export_path']) is False: - config['export_path'] = os.path.join(root_path, config['export_path']) - config['environment']['data']['externals'] = os.path.dirname(config['export_path']) - for key, value in config['environment']['data'].items(): - # Check if the value is a relative path - if not os.path.isabs(value): - # Convert the relative path to an absolute path - config['environment']['data'][key] = os.path.join(root_path, value) - config['surveys'] = [os.path.basename(config['export_path'])] - if os.path.isabs(config['output_file']) is False: - - config['output_file'] = os.path.join(root_path, config['output_file']) - return config - - -@hydra.main(config_path='configuration', version_base='1.1', config_name='main.yaml') -def unit_risk_score(config: DictConfig) -> None: - # print(OmegaConf.to_yaml(config)) - print("*" * 12) - config = manage_path(config) - - # --- MONKEY PATCH FOR TESTING --- - import pandas as pd - from rissk.unit_proccessing import UnitDataProcessing - - # Path to your existing files - SURVEY = "hies2024" - # SURVEY = "pmpmd" - # SURVEY = "slchbs" - # SURVEY = "fbf house holduntitled folder" - DATA_DIR = os.path.join(PROJ_ROOT, "rissk_kedro", "data", SURVEY, "latest", "30_PROCESSED") - - print(f"LOADING PARQUET FROM: {DATA_DIR}") - - # Load dataframes directly - microdata = pd.read_parquet(os.path.join(DATA_DIR, "microdata.parquet")) - paradata = pd.read_parquet(os.path.join(DATA_DIR, "paradata_processed.parquet")) - - # Manually initialize the class, skipping __init__ logic that breaks - # We use __new__ to create instance without calling __init__ - survey_class = UnitDataProcessing.__new__(UnitDataProcessing) - - # Manually set attributes that __init__ would set - survey_class.config = config - survey_class._limit_unit = config.get('limit_unit', None) - survey_class._allowed_features = ['f__' + k for k, v in config['features'].items() if v['use']] - survey_class.item_level_columns = ['interview__id', 'variable_name', 'roster_level'] - - # --- Prepare paradata (paradata_processed) for use as _df_paradata --- - # process_paradata() calls fillna('') so NaN question_scope values match the isin([0, '']) filter - # in the df_active_paradata property. Replicate that here. - paradata.fillna('', inplace=True) - - # Normalize qnr/qnr_version -> survey_name/survey_version if the extract uses alternate column names - alias_map = {'qnr': 'survey_name', 'qnr_version': 'survey_version'} - for src_col, dst_col in alias_map.items(): - if src_col in paradata.columns and dst_col not in paradata.columns: - print(f"Renaming paradata column {src_col} -> {dst_col}") - paradata = paradata.rename(columns={src_col: dst_col}) - - # Ensure survey_name / survey_version exist (fallback if still missing) - for col in ['survey_name', 'survey_version']: - if col not in paradata.columns: - print(f"Adding missing column to paradata: {col}") - paradata[col] = SURVEY if col == 'survey_name' else 'latest' - - # _df_paradata = processed paradata (equivalent to process_paradata() output). - # df_active_paradata property will derive the active subset from this automatically. - survey_class._df_paradata = paradata - - # BYPASS make_df_item call in __init__ and do it manually - print("Building Items (Legacy)...") - survey_class._df_item = survey_class.make_df_item(microdata) - - print("Building Units (Legacy)...") - survey_class._df_unit = survey_class.make_df_unit() - - print("Building Responsible...") - survey_class._df_resp = survey_class.make_df_responsible() - - # Numeric mask setup (copied from __init__) - survey_class.numeric_question_mask = ( - (survey_class._df_item["qtype"] == 'NumericQuestion') & - (survey_class._df_item['value'] != '') & - (~pd.isnull(survey_class._df_item['value'])) & - (survey_class._df_item['value'] != -999999999) - ) - - # Initialize score columns to None - survey_class._score_columns = None - - # Now standard flow - try: - # Calculate features (accessing properties triggers calculations) - print("Calculating Item Features...") - _ = survey_class.df_item - - print(f"saving item features to {DATA_DIR}/item_features_legacy.parquet") - survey_class._df_item.to_parquet(os.path.join(DATA_DIR, "item_features_legacy.parquet")) - - print("Calculating Unit Features...") - _ = survey_class.df_unit - - print(f"saving unit features to {DATA_DIR}/unit_features_legacy.parquet") - survey_class._df_unit.to_parquet(os.path.join(DATA_DIR, "unit_features_legacy.parquet")) - - print("Calculating Global Legacy Risk Scores...") - survey_class.make_global_score() - - # Persist final unit risk output from legacy code path. - unit_risk_cols = ['interview__id', 'responsible', 'unit_risk_score'] - unit_risk_df = survey_class._df_unit[unit_risk_cols].copy() - unit_risk_df['unit_risk_score'] = unit_risk_df['unit_risk_score'].round(2) - unit_risk_df.sort_values('unit_risk_score', inplace=True) - unit_risk_csv = os.path.join(DATA_DIR, "unit_risk_score_legacy.csv") - unit_risk_parquet = os.path.join(DATA_DIR, "unit_risk_score_legacy.parquet") - unit_risk_df.to_csv(unit_risk_csv, index=False) - unit_risk_df.to_parquet(unit_risk_parquet, index=False) - - # Build a merged score table to inspect item/unit/responsible scoring columns together. - resp_score_cols = [c for c in survey_class._df_resp.columns if c.startswith('s__')] - resp_view_cols = ['responsible', 'responsible_score'] + resp_score_cols - df_scores = survey_class._df_unit.merge( - survey_class._df_resp[resp_view_cols], - on='responsible', - how='left', - ) - score_cols = [c for c in df_scores.columns if c.startswith('s__')] - id_cols = [ - c for c in ['interview__id', 'responsible', 'survey_name', 'survey_version'] - if c in df_scores.columns - ] - final_cols = id_cols + ['unit_risk_score', 'responsible_score'] + sorted(score_cols) - final_cols = [c for c in final_cols if c in df_scores.columns] - df_scores = df_scores[final_cols] - - scores_csv = os.path.join(DATA_DIR, "scores_table_legacy.csv") - scores_parquet = os.path.join(DATA_DIR, "scores_table_legacy.parquet") - df_scores.to_csv(scores_csv, index=False) - df_scores.to_parquet(scores_parquet, index=False) - print(f"saved legacy unit risk to {unit_risk_csv}") - print(f"saved legacy score table to {scores_csv}") - - print("DONE. Legacy test data generated.") - return # Stop here - - except ValueError as e: - print(f"An error occurred: {e}") - - # --- END MONKEY PATCH --- - - try: - survey_class = UnitDataProcessing(config) - df_item = survey_class.df_item - df_unit = survey_class.df_unit - survey_class.make_global_score() - survey_class.save() - except ValueError as e: - print(f"An error occurred: {e}") - - -if __name__ == "__main__": - unit_risk_score() - # mem_usage = memory_usage(unit_risk_score) - # print(f"Memory usage (in MB): {max(mem_usage)}") diff --git a/rissk/__init__.py b/rissk/__init__.py index 7368f87..1babcfc 100644 --- a/rissk/__init__.py +++ b/rissk/__init__.py @@ -1 +1,3 @@ -from rissk import config # noqa: F401 +# config is not auto-imported here to avoid module-level side effects (loguru logs, +# env.yaml reads) when rissk.* modules are imported during Kedro runs. +# Import rissk.config explicitly in legacy scripts that need it. diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index 4155925..5e9fd9d 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -30,7 +30,8 @@ def get_numeric_mask(df_item: pd.DataFrame, filter_answer_values: bool) -> pd.Se sentinel_mask = _is_missing_numeric_sentinel(df_item['value']) mask = ( (df_item["qtype"] == 'NumericQuestion') & - (df_item['value'] != '') & + # TODO remove '' !! + # (df_item['value'] != '') & (~pd.isnull(df_item['value'])) & (~sentinel_mask) ) diff --git a/rissk/unit_processing_kedro.py b/rissk/unit_processing_kedro.py index 04233c9..b795a57 100644 --- a/rissk/unit_processing_kedro.py +++ b/rissk/unit_processing_kedro.py @@ -65,7 +65,18 @@ def calculate_global_score(df_unit_scores: pd.DataFrame, df_resp_scores: pd.Data return df_unit df = df_unit[available_cols].copy() - df = pd.DataFrame(scaler.fit_transform(df), columns=available_cols) + + # Drop constant columns before StandardScaler — a constant column produces NaN after + # z-scoring (division by zero std), which would make IForest scores meaningless and + # MinMaxScaler produce NaN unit_risk_score for every interview. + # This mirrors legacy's `nunique() > 1` filter in df_unit_score. + varying_cols = [c for c in available_cols if df[c].nunique() > 1] + if not varying_cols: + logger.warning("All score columns are constant — cannot compute meaningful global risk score.") + return df_unit + + df = df[varying_cols] + df = pd.DataFrame(scaler.fit_transform(df), columns=varying_cols) model = IForest(random_state=42) model.fit(df.fillna(0)) @@ -79,12 +90,23 @@ def calculate_global_score(df_unit_scores: pd.DataFrame, df_resp_scores: pd.Data # Scale to 0-100 df_unit['unit_risk_score'] = scaler.fit_transform(df_unit[['unit_risk_score']]) - # Merge unit score with responsible score + # Merge unit score with responsible score. + # Only apply the multiplication when responsible_score has actual variance — if PCA + # on the responsible-level scores couldn't run (too few enumerators or all scores + # constant), responsible_score is all-zero, and multiplying produces a constant-zero + # column that MinMaxScaler turns into NaN for every interview. if combine_resp_score and 'responsible' in df_unit.columns and df_resp_scores is not None and 'responsible_score' in df_resp_scores.columns: - df_resp_map = df_resp_scores.set_index('responsible')['responsible_score'].to_dict() - df_unit['responsible_score'] = df_unit['responsible'].map(df_resp_map).fillna(0) - df_unit['unit_risk_score'] = df_unit['unit_risk_score'] * df_unit['responsible_score'] - df_unit['unit_risk_score'] = scaler.fit_transform(df_unit[['unit_risk_score']]) + resp_score_series = df_resp_scores['responsible_score'] + if resp_score_series.nunique() > 1: + df_resp_map = df_resp_scores.set_index('responsible')['responsible_score'].to_dict() + df_unit['responsible_score'] = df_unit['responsible'].map(df_resp_map).fillna(0) + df_unit['unit_risk_score'] = df_unit['unit_risk_score'] * df_unit['responsible_score'] + df_unit['unit_risk_score'] = scaler.fit_transform(df_unit[['unit_risk_score']]) + else: + logger.warning( + "responsible_score has no variance (likely too few enumerators or all scores constant); " + "skipping responsible-score multiplication to preserve interview-level unit_risk_score." + ) return df_unit diff --git a/rissk/utils/import_utils_kedro.py b/rissk/utils/import_utils_kedro.py index c04616c..c250c0f 100644 --- a/rissk/utils/import_utils_kedro.py +++ b/rissk/utils/import_utils_kedro.py @@ -7,10 +7,11 @@ import zipfile import shutil import json # Added json import +import logging import pandas as pd # Added pandas import import numpy as np # Added numpy import -from loguru import logger +logger = logging.getLogger(__name__) from rissk.utils.file_process_utils_kedro import ( get_file_parts, diff --git a/rissk_kedro/conf/logging.yml b/rissk_kedro/conf/logging.yml index 6fb6607..cda556c 100644 --- a/rissk_kedro/conf/logging.yml +++ b/rissk_kedro/conf/logging.yml @@ -36,8 +36,9 @@ loggers: kedro: level: INFO - pyspark_spaceflights: + rissk: level: INFO root: handlers: [rich, info_file_handler] + level: INFO diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py index e0fed2e..0f21e73 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py @@ -1,7 +1,9 @@ from pathlib import Path from typing import Any, Callable, Dict, List, Optional +import logging import pandas as pd -from loguru import logger + +logger = logging.getLogger(__name__) from rissk.utils.import_utils_kedro import ( extract_zip, filter_matching_folders, diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py index 0c42d28..b6d8e79 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py @@ -1,8 +1,10 @@ """Nodes for processing paradata and building features.""" +import logging import pandas as pd import numpy as np from typing import Dict -from loguru import logger + +logger = logging.getLogger(__name__) from rissk.feature_processing_kedro import make_index_col @@ -60,7 +62,7 @@ def process_paradata_node( # filtered_interview_id = paradata[cond1 & cond2]['interview__id'].unique() # paradata = paradata[paradata['interview__id'].isin(filtered_interview_id)].copy() - # return paradata + return paradata def filter_active_paradata_node( diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py index 20bcb43..3807fe0 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py @@ -148,5 +148,20 @@ def calculate_unit_scores( combine_resp_score=True, restricted_columns=restricted_columns ) - + + # 6. Merge responsible-level s__ columns back onto unit output. + # Legacy save() merges _df_resp (which holds s__single_question, + # s__multi_option_question, s__answer_position, s__first_digit) back + # onto _df_unit by responsible so those scores appear in the feature CSV. + resp_s_cols = [c for c in df_resp_scored.columns if c.startswith('s__')] + if resp_s_cols and 'responsible' in df_resp_scored.columns and not df_resp_scored.empty: + # Only bring in columns not already present at unit level + new_resp_cols = [c for c in resp_s_cols if c not in df_final_unit.columns] + if new_resp_cols: + df_final_unit = df_final_unit.merge( + df_resp_scored[['responsible'] + new_resp_cols], + on='responsible', + how='left' + ) + return df_final_unit, df_resp_scored From 03dd68d6f371b6a841edfff86866f161f59e9bd0 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Thu, 2 Apr 2026 13:44:40 +0100 Subject: [PATCH 45/70] Refactor data ingestion and feature creation pipelines; remove legacy feature engineering components and enhance logging for questionnaire processing. --- rissk/utils/stats_utils_kedro.py | 8 +- rissk_kedro/conf/base/catalog.yml | 45 +++----- .../src/rissk_kedro/pipeline_registry.py | 5 +- .../pipelines/data_ingestion/nodes.py | 109 ++++++++++++++---- .../pipelines/data_ingestion/pipeline.py | 15 ++- .../pipelines/feature_creation/nodes.py | 11 ++ .../pipelines/feature_engineering/__init__.py | 5 - .../pipelines/feature_engineering/nodes.py | 109 ------------------ .../pipelines/feature_engineering/pipeline.py | 30 ----- .../pipelines/rissk_scoring/nodes.py | 11 ++ 10 files changed, 143 insertions(+), 205 deletions(-) delete mode 100644 rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/__init__.py delete mode 100644 rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py delete mode 100644 rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py diff --git a/rissk/utils/stats_utils_kedro.py b/rissk/utils/stats_utils_kedro.py index 5f4e169..61932c1 100644 --- a/rissk/utils/stats_utils_kedro.py +++ b/rissk/utils/stats_utils_kedro.py @@ -68,9 +68,13 @@ def get_digit_frequecies(df, feature_name, apply_first_digit, minimum_sample=50) def first_digit(val): - """Extract the first significant digit from a value using log10.""" + """ + Extract the first significant digit from a value using log10. + Follow legacy behaviour of setting values with abs(val) < 1 to 0 + (non-Benford domain) and applying absolute value to negatives. + """ val = abs(val) - if val == 0: + if val < 1: return 0 power = math.floor(math.log10(val)) return int(val / 10**power) diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index 43103c3..45afcb2 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -18,9 +18,9 @@ extracted_survey_folders: type: rissk_kedro.datasets.PathDataset # === INGESTED DataFrames === -paradata_interim: +paradata_raw: type: pandas.ParquetDataset - filepath: data/${globals:survey.name}/latest/20_INTERIM/paradata.parquet + filepath: data/${globals:survey.name}/latest/20_INTERIM/paradata_raw.parquet raw_questionnaire: type: pandas.ParquetDataset @@ -34,36 +34,29 @@ microdata: type: pandas.ParquetDataset filepath: data/${globals:survey.name}/latest/30_PROCESSED/microdata.parquet -# === FEATURE ENGINEERING DataFrames === - paradata_processed: type: pandas.ParquetDataset filepath: data/${globals:survey.name}/latest/30_PROCESSED/paradata_processed.parquet -# paradata_active is provided for legacy compatibility. New feature functions may -# apply question_scope filters inline; legacy code expects this dataset. -paradata_active: - type: pandas.ParquetDataset - filepath: data/${globals:survey.name}/latest/30_PROCESSED/paradata_active.parquet - -# === LEGACY DATA FOR PIPELINE TESTING === -# Uncomment these and update pipeline.py inputs to test against legacy-produced data. -legacy_microdata: - type: pandas.ParquetDataset - filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/${globals:survey.name}/latest/30_PROCESSED/microdata.parquet -# -# # Equivalent to paradata_processed (output of process_paradata_node). -# # The legacy pipeline saved this as paradata.parquet (not paradata_processed.parquet). -legacy_paradata_processed: - type: pandas.ParquetDataset - filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/${globals:survey.name}/latest/30_PROCESSED/paradata.parquet -# -# # Equivalent to paradata_active (output of filter_active_paradata_node). -legacy_paradata_active: - type: pandas.ParquetDataset - filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/${globals:survey.name}/latest/30_PROCESSED/paradata_active.parquet +# # === LEGACY DATA FOR PIPELINE TESTING === +# # Uncomment these and update pipeline.py inputs to test against legacy-produced data. + +# legacy_microdata: +# type: pandas.ParquetDataset +# filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/${globals:survey.name}/latest/30_PROCESSED/microdata.parquet +# # +# # # Equivalent to paradata_processed (output of process_paradata_node). +# # # The legacy pipeline saved this as paradata.parquet (not paradata_processed.parquet). +# legacy_paradata_processed: +# type: pandas.ParquetDataset +# filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/${globals:survey.name}/latest/30_PROCESSED/paradata.parquet +# # +# # # Equivalent to paradata_active (output of filter_active_paradata_node). +# legacy_paradata_active: +# type: pandas.ParquetDataset +# filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/${globals:survey.name}/latest/30_PROCESSED/paradata_active.parquet # === FEATURE CREATION DataFrames === diff --git a/rissk_kedro/src/rissk_kedro/pipeline_registry.py b/rissk_kedro/src/rissk_kedro/pipeline_registry.py index 1520bf2..83cf8fb 100644 --- a/rissk_kedro/src/rissk_kedro/pipeline_registry.py +++ b/rissk_kedro/src/rissk_kedro/pipeline_registry.py @@ -71,7 +71,6 @@ def register_pipelines() -> dict[str, Pipeline]: """ # Import sub-pipelines here to avoid circular imports at module level. from rissk_kedro.pipelines.data_ingestion import create_pipeline as ingestion_pipeline - from rissk_kedro.pipelines.feature_engineering import create_pipeline as feature_engineering_pipeline from rissk_kedro.pipelines.feature_creation import create_pipeline as feature_creation_pipeline from rissk_kedro.pipelines.rissk_scoring import create_pipeline as scoring_pipeline @@ -170,7 +169,6 @@ def register_pipelines() -> dict[str, Pipeline]: # Shared upstream pipelines # # ------------------------------------------------------------------ # ingestion = ingestion_pipeline() - feat_eng = feature_engineering_pipeline() feat_creation = feature_creation_pipeline() all_scoring = sum(per_qnr_pipelines.values(), Pipeline([])) + merge_pipeline @@ -179,7 +177,6 @@ def register_pipelines() -> dict[str, Pipeline]: # Named pipelines for selective runs pipelines["data_ingestion"] = ingestion - pipelines["feature_engineering"] = feat_eng pipelines["feature_creation"] = feat_creation pipelines["rissk_scoring"] = all_scoring # filter + score + merge; skips ingestion/feature creation @@ -188,6 +185,6 @@ def register_pipelines() -> dict[str, Pipeline]: pipelines[name] = p # Full run - pipelines["__default__"] = ingestion + feat_eng + feat_creation + all_scoring + pipelines["__default__"] = ingestion + feat_creation + all_scoring return pipelines diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py index 0f21e73..3d564ea 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py @@ -1,18 +1,20 @@ from pathlib import Path from typing import Any, Callable, Dict, List, Optional import logging +import numpy as np import pandas as pd logger = logging.getLogger(__name__) from rissk.utils.import_utils_kedro import ( - extract_zip, + extract_zip, filter_matching_folders, - get_survey_info, - get_questionnaire, - get_paradata, + get_survey_info, + get_questionnaire, + get_paradata_raw, get_microdata_raw, merge_microdata_questionnaire ) +from rissk.feature_processing_kedro import make_index_col def extract_zip_files_node(survey_zip_partitions: Dict[str, Callable[[], Path]], zip_password: str) -> None: @@ -43,39 +45,43 @@ def filter_extracted_survey_paths_node(survey_partitions: Dict[str, Callable[[], using survey partition entries. This node does not perform extraction. """ + lines = ["=" * 55, " DATA INGESTION — Questionnaires to process", "=" * 55] + for q in questionnaires: + versions = ", ".join(str(v) for v in q.get("VERSION", [])) + lines.append(f" • {q['name']} | versions: [{versions}]") + lines.append("=" * 55) + logger.info("\n" + "\n".join(lines)) + logger.info(f"Collecting matching survey folders from {len(survey_partitions)} partition entries") return filter_matching_folders(survey_partitions, questionnaires) def load_paradata_node(file_paths: List[Path]) -> pd.DataFrame: """ - Loads paradata from extracted folders. - Independent node that generates its own questionnaire reference. + Loads raw paradata from extracted folders. + No questionnaire metadata is merged at this stage; column splitting, + timestamp parsing and version tagging are performed by get_paradata_raw. """ - logger.info(f"Processing paradata for {len(file_paths)} paths") + logger.info(f"Processing raw paradata for {len(file_paths)} paths") survey_info = get_survey_info(file_paths) - + dfs_paradata = [] - + for survey_questionnaire, questionnaires_details in survey_info.items(): - for questionnaires_version, file_paths in questionnaires_details.items(): - tabular_path = file_paths.get('Tabular') - paradata_path = file_paths.get('Paradata') + for questionnaires_version, file_paths_detail in questionnaires_details.items(): + paradata_path = file_paths_detail.get('Paradata') - if not tabular_path or not paradata_path: + if not paradata_path: logger.warning( f"Skipping paradata load for {survey_questionnaire} v{questionnaires_version}: " - f"missing required exports (Tabular={bool(tabular_path)}, Paradata={bool(paradata_path)})" + "missing Paradata export" ) continue try: - # We need the questionnaire map even for paradata processing - df_questionnaires = get_questionnaire(tabular_path) - df_paradata = get_paradata(paradata_path, df_questionnaires) - + df_paradata = get_paradata_raw(paradata_path) dfs_paradata.append(df_paradata) - logger.info(f"Loaded paradata for {survey_questionnaire} v{questionnaires_version}") + logger.info(f"Loaded raw paradata for {survey_questionnaire} v{questionnaires_version}") except Exception as e: logger.error(f"Failed to load paradata for {survey_questionnaire} v{questionnaires_version}. Skipping. Error: {str(e)}") continue @@ -85,12 +91,65 @@ def load_paradata_node(file_paths: List[Path]) -> pd.DataFrame: combined_df = pd.concat(dfs_paradata) combined_df.reset_index(drop=True, inplace=True) - - if 'answer_sequence' in combined_df.columns: - combined_df['answer_sequence'] = combined_df['answer_sequence'].apply(str) - return combined_df +def process_paradata_node( + paradata_raw: pd.DataFrame, + questionnaire: pd.DataFrame, + parameters: Dict, +) -> pd.DataFrame: + """ + Merges questionnaire metadata onto raw paradata, processes timestamps and + interviewing flags, makes the index column, and filters to active interviewer + events - producing the paradata_processed dataset consumed by feature creation. + """ + paradata = paradata_raw.copy() + + # 1. Merge questionnaire metadata + if not questionnaire.empty: + q_columns = [ + 'qnr_seq', 'variable_name', 'qtype', 'question_type', + 'answers', 'question_scope', + 'yes_no_view', 'is_filtered_combobox', + 'is_integer', 'cascade_from_question_id', + 'answer_sequence', 'n_answers', 'question_sequence', + 'qnr', 'qnr_version', + ] + q_columns = [c for c in q_columns if c in questionnaire.columns] + paradata = paradata.merge( + questionnaire[q_columns], + how='left', + left_on=['param', 'qnr', 'qnr_version'], + right_on=['variable_name', 'qnr', 'qnr_version'], + ) + + # 2. Stringify answer_sequence for parquet serialization (matches legacy behaviour) + if 'answer_sequence' in paradata.columns: + paradata['answer_sequence'] = paradata['answer_sequence'].apply(str) + + # 3. Calculate f__answer_hour_set + paradata['f__answer_hour_set'] = ( + paradata['timestamp_local'].dt.hour + + paradata['timestamp_local'].dt.round('30min').dt.minute / 60 + ) + + # 4. Calculate interviewing flag and filter to first-pass interviewer events + events_split = ['RejectedBySupervisor', 'OpenedBySupervisor', 'OpenedByHQ', 'RejectedByHQ'] + paradata['flag'] = paradata['event'].isin(events_split) + paradata['cumulative_flag'] = paradata.groupby('interview__id')['flag'].cumsum() + paradata['interviewing'] = np.where(paradata['cumulative_flag'] > 0, False, True) + paradata.drop(['flag', 'cumulative_flag'], axis=1, inplace=True) + paradata = paradata[(paradata['interviewing'] == True) & (paradata['role'] == 1)].copy() + + # 5. Make index column + paradata = make_index_col(paradata) + + # 6. Sort + paradata.sort_values(['interview__id', 'order'], inplace=True) + paradata.reset_index(drop=True, inplace=True) + + return paradata + def load_questionnaire_node(file_paths: List[Path]) -> pd.DataFrame: """ @@ -136,7 +195,7 @@ def load_questionnaire_node(file_paths: List[Path]) -> pd.DataFrame: return combined_df -def load_raw_microdata_node(file_paths: List[Path]) -> pd.DataFrame: +def load_raw_microdata_node(file_paths: List[Path], questionnaire: pd.DataFrame) -> pd.DataFrame: """ Loads raw microdata (answers) from extracted folders. Applies multi-question transformation using questionnaire metadata but does not @@ -159,7 +218,7 @@ def load_raw_microdata_node(file_paths: List[Path]) -> pd.DataFrame: continue try: - df_questionnaires = get_questionnaire(tabular_path) + df_questionnaires = questionnaire[questionnaire['qnr'] == survey_questionnaire] df_microdata = get_microdata_raw(tabular_path, df_questionnaires) dfs_microdata.append(df_microdata) logger.info(f"Loaded raw microdata for {survey_questionnaire} v{questionnaires_version}") diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py index 4dd9548..637757f 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py @@ -2,8 +2,9 @@ from .nodes import ( extract_zip_files_node, filter_extracted_survey_paths_node, - load_paradata_node, - load_questionnaire_node, + load_paradata_node, + process_paradata_node, + load_questionnaire_node, load_raw_microdata_node, merge_microdata_questionnaire_node ) @@ -32,7 +33,7 @@ def create_pipeline(**kwargs) -> Pipeline: node( func=load_paradata_node, inputs="file_paths", - outputs="paradata_interim", + outputs="paradata_raw", name="load_paradata_node" ), node( @@ -41,9 +42,15 @@ def create_pipeline(**kwargs) -> Pipeline: outputs="raw_questionnaire", name="load_questionnaire_node" ), + node( + func=process_paradata_node, + inputs=["paradata_raw", "raw_questionnaire", "parameters"], + outputs="paradata_processed", + name="process_paradata_node" + ), node( func=load_raw_microdata_node, - inputs="file_paths", + inputs=["file_paths", "raw_questionnaire"], outputs="raw_microdata", name="load_raw_microdata_node" ), diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py index 02a97dc..89505d8 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py @@ -21,6 +21,17 @@ def create_base_item_table_node( parameters: Dict[str, Any] ) -> pd.DataFrame: """Node wrapper for create_base_item_table.""" + questionnaires = parameters.get('survey', {}).get('questionnaires', []) + lines = [ + "=" * 55, + " FEATURE CREATION — Configuration", + "=" * 55, + " Questionnaires:", + ] + for q in questionnaires: + lines.append(f" • {q['name']}") + lines.append("=" * 55) + logger.info("\n" + "\n".join(lines)) return create_base_item_table(microdata, paradata_full, parameters) def create_base_unit_table_node( diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/__init__.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/__init__.py deleted file mode 100644 index 9fd7813..0000000 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -"""Feature engineering pipeline.""" - -from .pipeline import create_pipeline - -__all__ = ["create_pipeline"] diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py deleted file mode 100644 index b6d8e79..0000000 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/nodes.py +++ /dev/null @@ -1,109 +0,0 @@ -"""Nodes for processing paradata and building features.""" -import logging -import pandas as pd -import numpy as np -from typing import Dict - -logger = logging.getLogger(__name__) - -from rissk.feature_processing_kedro import make_index_col - - -def process_paradata_node( - paradata_interim: pd.DataFrame, - parameters: Dict -) -> pd.DataFrame: - """ - Process paradata timestamps, flags, and index creation. - - Args: - paradata_interim: Interim paradata DataFrame - parameters: Pipeline parameters - - Returns: - Processed paradata DataFrame - """ - paradata = paradata_interim.copy() - - # Calculate f__answer_hour_set - paradata['f__answer_hour_set'] = ( - paradata['timestamp_local'].dt.hour + - paradata['timestamp_local'].dt.round('30min').dt.minute / 60 - ) - - # Calculate interviewing flag - events_split = ['RejectedBySupervisor', 'OpenedBySupervisor', 'OpenedByHQ', 'RejectedByHQ'] - paradata['flag'] = paradata['event'].isin(events_split) - - # Count flagged events for each interview - paradata['cumulative_flag'] = paradata.groupby('interview__id')['flag'].cumsum() - paradata['interviewing'] = np.where(paradata['cumulative_flag'] > 0, False, True) - - # Filter interviewing == True AND role == 1 - paradata.drop(['flag', 'cumulative_flag'], axis=1, inplace=True) - paradata = paradata[(paradata['interviewing'] == True) & (paradata['role'] == 1)].copy() - - # Use shared helper to avoid drift with feature_processing_kedro - paradata = make_index_col(paradata) - - # Sort by interview__id, order - paradata.sort_values(['interview__id', 'order'], inplace=True) - paradata.reset_index(drop=True, inplace=True) - - # # Limit Unit Logic - # limit_unit = parameters.get('processing', {}).get('limit_unit') - # if limit_unit is not None: - # consent_variable = next(iter(limit_unit)) - # consent_value = str(limit_unit[consent_variable]) - - # cond1 = (paradata['variable_name'] == consent_variable) - # cond2 = (paradata['answer'] == consent_value) - - # filtered_interview_id = paradata[cond1 & cond2]['interview__id'].unique() - # paradata = paradata[paradata['interview__id'].isin(filtered_interview_id)].copy() - - return paradata - - -def filter_active_paradata_node( - paradata_processed: pd.DataFrame, - parameters: Dict -) -> pd.DataFrame: - """ - Filter paradata to active events. - - Args: - paradata_processed: Processed paradata DataFrame - parameters: Pipeline parameters - - Returns: - Active paradata DataFrame: keep active events, prior rejection/review events, for questions with scope interviewer - """ - active_events = [ - 'InterviewCreated', 'AnswerSet', 'AnswerRemoved', 'CommentSet', - 'Restarted', 'Resumed' # pause events, which have empty question scope - ] - # just in case supervisor or HQ answered something while interviewer answered on web mode) - # keep active events, prior rejection/review events, for questions with scope interviewer - - # Filter conditions - active_mask = ( - (paradata_processed['event'].isin(active_events)) & - # question scope interviewer only. Pause events and interview-created have NaN scope - (paradata_processed['question_scope'].isin([0, None])) & - (paradata_processed['role'] == 1) # redundant given previous filtering - ) - - vars_needed = [ - 'interview__id', 'order', 'event', 'responsible', 'role', 'tz_offset', - 'param', 'answer', 'roster_level', 'timestamp_local', 'variable_name', - 'question_sequence', 'question_scope', "qtype", 'question_type', - 'qnr', 'qnr_version', 'interviewing', 'yes_no_view', 'index_col', 'f__answer_hour_set' - ] - - # Only keep columns present in the dataframe - vars_needed = [col for col in vars_needed if col in paradata_processed.columns] - - df_para_active = paradata_processed.loc[active_mask, vars_needed].copy() - - return df_para_active \ No newline at end of file diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py deleted file mode 100644 index 26af6d9..0000000 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_engineering/pipeline.py +++ /dev/null @@ -1,30 +0,0 @@ -"""Feature engineering pipeline definition.""" -from kedro.pipeline import Pipeline, node, pipeline -from .nodes import ( - process_paradata_node, - filter_active_paradata_node, -) - - -def create_pipeline(**kwargs) -> Pipeline: - """Create the feature engineering pipeline. - - Returns: - A pipeline that processes paradata and builds features. - """ - return pipeline([ - node( - func=process_paradata_node, - inputs=["paradata_interim", "parameters"], - outputs="paradata_processed", - name="process_paradata_node", - ), - node( - func=filter_active_paradata_node, - inputs=["paradata_processed", "parameters"], - outputs="paradata_active", - name="filter_active_paradata_node", - ), - # `filter_active_paradata_node` reinstated for compatibility with - # legacy functions that expect `paradata_active`. - ]) diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py index 3807fe0..f38f472 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py @@ -37,6 +37,17 @@ def calculate_item_scores(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> Each scoring function is only executed when its corresponding feature has use: true in parameters['features'], matching the feature creation pipeline behaviour. """ + questionnaires = parameters.get('survey', {}).get('questionnaires', []) + lines = [ + "=" * 55, + " RISSK SCORING", + "=" * 55, + " Questionnaires:", + ] + for q in questionnaires: + lines.append(f" • {q['name']}") + lines.append("=" * 55) + logger.info("\n" + "\n".join(lines)) logger.info("Calculating Item Scores...") features = parameters.get('features', {}) df_scored = df_item From c7136071a9a0b46679f3a4ba801b0f5ddda31337 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Thu, 2 Apr 2026 21:55:55 +0100 Subject: [PATCH 46/70] Refactor paradata loading functions; separate raw loading logic and enhance documentation for clarity. --- rissk/feature_processing_kedro.py | 36 +++++++----------- rissk/utils/import_utils_kedro.py | 61 +++++++++++++++++++------------ 2 files changed, 52 insertions(+), 45 deletions(-) diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index 5e9fd9d..2d73a1f 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -4,18 +4,17 @@ import ast import logging +from rissk.utils.stats_utils_kedro import first_digit + logger = logging.getLogger(__name__) # --- Helper Functions --- def make_index_col(df: pd.DataFrame) -> pd.DataFrame: """Creates a unique index column based on interview_id, variable_name, and roster_level.""" - # Filter out columns with NaN and empty strings for the mask - # Using fillna('') to handle NaNs safely for string concatenation - - # Create mask for valid rows (not null and not empty string in key columns) - # Note: In Py3.13/Pandas 2.x, strict comparison rules apply. - + # fillna('') normalises NaN roster_level (non-roster items) and NaN variable_name + # to empty string before concatenation, matching legacy make_index_col behaviour. + # Trailing/leading underscores are stripped so non-roster items don't get a trailing '_'. df_temp = df[['interview__id', 'variable_name', 'roster_level']].fillna('').astype(str) # Concatenate columns @@ -78,7 +77,7 @@ def _coerce_numeric_with_warning(df_item: pd.DataFrame, numeric_mask: pd.Series, values = df_item.loc[numeric_mask, 'value'] coerced = pd.to_numeric(values, errors='coerce') - failed_mask = coerced.isna() & values.notna() & (values != '') + failed_mask = coerced.isna() & values.notna() failed_count = int(failed_mask.sum()) if failed_count > 0: sample_bad_values = values[failed_mask].astype(str).drop_duplicates().head(10).tolist() @@ -163,8 +162,8 @@ def get_df_sequence(df_paradata_full: pd.DataFrame) -> pd.DataFrame: # f__previous_question, f__previous_answer, f__previous_roster # Using shift on the group df_last['f__previous_question'] = df_last.groupby('interview__id')['variable_name'].shift() - df_last['f__previous_answer'] = df_last.groupby('interview__id')['answer'].shift().fillna('') - df_last['f__previous_roster'] = df_last.groupby('interview__id')['roster_level'].shift().fillna('') + df_last['f__previous_answer'] = df_last.groupby('interview__id')['answer'].shift().fillna(pd.NA) + df_last['f__previous_roster'] = df_last.groupby('interview__id')['roster_level'].shift().fillna(pd.NA) # f__sequence_jump # Calculate answer sequence (1, 2, 3...) based on actual occurrence @@ -204,8 +203,9 @@ def add_item_time_features(df_item: pd.DataFrame, df_time: pd.DataFrame, allowed selected_features = [f for f in time_features if f in allowed_features] if selected_features: - # Filter out empty variable_name (Pauses) - df_time_filtered = df_time[df_time['variable_name'] != ''].copy() + # Filter out pause events (Resumed/Restarted) which have variable_name=NaN in Kedro + # (legacy used '' after global fillna(''), but NaN is the correct sentinel here) + df_time_filtered = df_time[df_time['variable_name'].notna()].copy() # AnswerRemoved / CommentSet events have roster_level=None in paradata (no roster context # is recorded on removal/comment events), while AnswerSet rows carry ''. Normalise to '' # so they land in the same groupby bucket as the corresponding AnswerSet events, matching @@ -376,7 +376,7 @@ def create_base_unit_table(paradata_full: pd.DataFrame, parameters: dict) -> pd. df_unit.drop_duplicates(inplace=True) # Filter valid responsible - df_unit = df_unit[(df_unit['responsible'] != '') & (df_unit['responsible'].notna())] + df_unit = df_unit[df_unit['responsible'].notna()] pause_features = ['f__pause_count', 'f__pause_duration', 'f__pause_list'] unit_time_features = ['f__total_duration', 'f__total_elapse', 'f__days_from_start', 'f__time_changed'] @@ -428,14 +428,7 @@ def feat_first_digit(df_item, **kwargs): df_item[feature_name] = pd.NA if numeric_mask.any(): numeric_values = _coerce_numeric_with_warning(df_item, numeric_mask, feature_name) - # Extract first significant digit using log10 (correct for values in (0,1)) - def _first_significant_digit(val): - val = abs(val) - if val == 0: - return 0 - power = math.floor(math.log10(val)) - return int(val / 10**power) - vals = numeric_values.apply(_first_significant_digit) + vals = numeric_values.apply(first_digit) df_item.loc[numeric_mask, feature_name] = pd.array(vals, dtype='Int64') return df_item @@ -462,7 +455,7 @@ def feat_first_decimal(df_item, **kwargs): feature_name = 'f__first_decimal' # mask: not integer, not empty & not mumeric sentinel numeric_mask = get_numeric_mask(df_item=df_item, filter_answer_values=True) - mask_integer = (df_item['is_integer'] == False) & (df_item['value'] != '') & (~pd.isnull(df_item['value'])) + mask_integer = (df_item['is_integer'] == False) & (~pd.isnull(df_item['value'])) mask = numeric_mask & mask_integer df_item[feature_name] = pd.NA @@ -787,7 +780,6 @@ def feat_unit_number_answered(df_unit, item_features, **kwargs): (~pd.isnull(item_features['value'])) & (~sentinel_mask) & (item_features['value'] != '##N/A##') & - (item_features['value'] != '') & (item_features['qtype'] != 'Variable') ) df_agg = item_features[mask].groupby('interview__id').agg( diff --git a/rissk/utils/import_utils_kedro.py b/rissk/utils/import_utils_kedro.py index c250c0f..c2c1713 100644 --- a/rissk/utils/import_utils_kedro.py +++ b/rissk/utils/import_utils_kedro.py @@ -261,9 +261,13 @@ def read_paradata(survey_path: Path, delimiter='\t') -> pd.DataFrame: return df -def get_paradata(data_path: Path, df_questionnaires: pd.DataFrame) -> pd.DataFrame: +def get_paradata_raw(data_path: Path) -> pd.DataFrame: """ - Loads and processes a paradata file from the provided path and merges it with the questionnaire dataframe. + Loads and parses a raw paradata file from the provided path. + + Performs parameter splitting, timestamp computation, and questionnaire-version + tagging, but does NOT merge questionnaire metadata. Column names are + normalised before returning. """ try: df_para = read_paradata(data_path, delimiter='\t') @@ -273,8 +277,6 @@ def get_paradata(data_path: Path, df_questionnaires: pd.DataFrame) -> pd.DataFra if 'parameters' in df_para.columns: # split the parameter column - # Using n=1 to limit splits is correct - # Check if expand=True returns intended shape split_param = df_para['parameters'].str.split(r'\|\|', n=1, expand=True) if split_param.shape[1] == 2: df_para['param'] = split_param[0] @@ -282,14 +284,14 @@ def get_paradata(data_path: Path, df_questionnaires: pd.DataFrame) -> pd.DataFra else: df_para['param'] = df_para['parameters'] df_para['answer'] = None - - if 'answer' in df_para.columns and df_para['answer'].notna().any(): + + if 'answer' in df_para.columns and df_para['answer'].notna().any(): split_answer = df_para['answer'].str.rsplit(r'||', n=1, expand=True) if split_answer.shape[1] == 2: df_para['answer'] = split_answer[0] df_para['roster_level'] = split_answer[1] else: - df_para['roster_level'] = None # Or empty string + df_para['roster_level'] = None if 'timestamp_utc' in df_para.columns and 'tz_offset' in df_para.columns: df_para['timestamp_utc'] = pd.to_datetime(df_para['timestamp_utc']) @@ -306,28 +308,41 @@ def get_paradata(data_path: Path, df_questionnaires: pd.DataFrame) -> pd.DataFra except ValueError: logger.warning(f"Could not parse filename '{data_path.name}' for version info") - if not df_questionnaires.empty: - q_columns = ['qnr_seq', 'variable_name', "qtype", 'question_type', - 'answers', 'question_scope', - 'yes_no_view', 'is_filtered_combobox', - 'is_integer', 'cascade_from_question_id', - 'answer_sequence', 'n_answers', 'question_sequence', - 'qnr', 'qnr_version'] - - # Ensure columns exist in questionnaire df before selecting - q_columns = [c for c in q_columns if c in df_questionnaires.columns] - - # Merge - df_para = df_para.merge(df_questionnaires[q_columns], how='left', - left_on=['param', 'qnr', 'qnr_version'], - right_on=['variable_name', 'qnr', 'qnr_version']) - # Normalize column names df_para.columns = [normalize_column_name(c) for c in df_para.columns] return df_para +def get_paradata(data_path: Path, df_questionnaires: pd.DataFrame) -> pd.DataFrame: + """ + Loads and processes a paradata file from the provided path and merges it with the questionnaire dataframe. + + This is a backward-compatible wrapper around get_paradata_raw that additionally + merges questionnaire metadata columns onto the result. + """ + df_para = get_paradata_raw(data_path) + + if df_para.empty or df_questionnaires.empty: + return df_para + + q_columns = ['qnr_seq', 'variable_name', "qtype", 'question_type', + 'answers', 'question_scope', + 'yes_no_view', 'is_filtered_combobox', + 'is_integer', 'cascade_from_question_id', + 'answer_sequence', 'n_answers', 'question_sequence', + 'qnr', 'qnr_version'] + + # Ensure columns exist in questionnaire df before selecting + q_columns = [c for c in q_columns if c in df_questionnaires.columns] + + df_para = df_para.merge(df_questionnaires[q_columns], how='left', + left_on=['param', 'qnr', 'qnr_version'], + right_on=['variable_name', 'qnr', 'qnr_version']) + + return df_para + + def get_microdata_file_list(data_path: Path) -> list[str]: """ Get a list of microdata files in the specified directory. From 54a6efa649281bd29208c34662fd4bc8d4a44c1e Mon Sep 17 00:00:00 2001 From: VJausovec Date: Fri, 3 Apr 2026 11:06:33 +0100 Subject: [PATCH 47/70] Enhance GPS scoring logic; introduce s__gps flag in calculate_gps_score and update aggregation in unit processing to maintain legacy compatibility. --- rissk/item_processing_kedro.py | 7 +++++++ rissk/unit_processing_kedro.py | 10 ++++------ .../src/rissk_kedro/pipelines/rissk_scoring/nodes.py | 9 +++++++++ 3 files changed, 20 insertions(+), 6 deletions(-) diff --git a/rissk/item_processing_kedro.py b/rissk/item_processing_kedro.py index 77fe9b7..bebcb19 100644 --- a/rissk/item_processing_kedro.py +++ b/rissk/item_processing_kedro.py @@ -98,6 +98,13 @@ def calculate_gps_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd # when multiple GPS variables exist for the same (interview, roster, responsible) index_col = ['interview__id', 'roster_level', 'responsible', 'variable_name'] + # s__gps: integer flag (1 = GPS question, 0 = other). Set unconditionally so that + # aggregate_item_to_unit_scores can always sum it to the interview-level GPS question + # count, matching legacy make_score_unit__gps which read f__gps from df_item directly + # regardless of whether the GPS outlier model ran successfully. + if 'f__gps' in df.columns: + df['s__gps'] = df['f__gps'].astype(int) + # If required GPS columns are missing, return original df if any(col not in df.columns for col in required_columns + ['variable_name']): return df diff --git a/rissk/unit_processing_kedro.py b/rissk/unit_processing_kedro.py index b795a57..ba0e42f 100644 --- a/rissk/unit_processing_kedro.py +++ b/rissk/unit_processing_kedro.py @@ -139,16 +139,14 @@ def aggregate_item_to_unit_scores(df_unit: pd.DataFrame, df_item_scores: pd.Data df_out[score] = df_out['interview__id'].map(data).fillna(0) # 3. GPS specifics (if gps scores exist) - gps_features = ['s__gps_proximity_counts', 's__gps_outlier', 's__gps_extreme_outlier'] + # s__gps is the per-interview count of GPS-type questions (sum of the item-level + # boolean flag converted to int in calculate_gps_score), matching legacy + # make_score_unit__gps which summed f__gps from df_item. + gps_features = ['s__gps_proximity_counts', 's__gps_outlier', 's__gps_extreme_outlier', 's__gps'] for score in gps_features: if score in df_item_scores.columns: data = df_item_scores.groupby('interview__id')[score].sum() df_out[score] = df_out['interview__id'].map(data).fillna(0) - - # Legacy parity: s__gps is the sum of f__gps at interview level. - if 'f__gps' in df_item_scores.columns: - data = df_item_scores.groupby('interview__id')['f__gps'].sum() - df_out['s__gps'] = df_out['interview__id'].map(data).fillna(0) return df_out diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py index f38f472..c187599 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py @@ -99,6 +99,15 @@ def calculate_item_scores(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> logger.info("Calculating gps_score") df_scored = calculate_gps_score(df_scored, parameters) + # Keep only the columns needed for downstream unit/responsible scoring and output. + # - responsible: required by aggregate_item_to_responsible_scores (groupby + init) + # - s__gps is produced by calculate_gps_score (f__gps.astype(int)) when GPS is + # enabled and is picked up naturally by the s__ filter below. + id_cols = ['qnr', 'qnr_version', 'index_col', 'interview__id', 'variable_name', 'roster_level', 'responsible'] + score_cols = [c for c in df_scored.columns if c.startswith('s__')] + keep_cols = [c for c in id_cols + score_cols if c in df_scored.columns] + df_scored = df_scored[keep_cols] + return df_scored def calculate_unit_scores( From 087b306303a4292b0121dce3e41559abc5fd395c Mon Sep 17 00:00:00 2001 From: VJausovec Date: Sun, 5 Apr 2026 12:43:27 +0100 Subject: [PATCH 48/70] Changes to how legacy scoring data is generated for testing --- main_monkey_patch_scores.py | 380 +++++++++++++++++++++--------------- 1 file changed, 220 insertions(+), 160 deletions(-) diff --git a/main_monkey_patch_scores.py b/main_monkey_patch_scores.py index 813efa6..f7d7c01 100644 --- a/main_monkey_patch_scores.py +++ b/main_monkey_patch_scores.py @@ -41,10 +41,10 @@ def unit_risk_score(config: DictConfig) -> None: import pandas as pd from rissk.unit_proccessing import UnitDataProcessing - # SURVEY = "hies2024" + SURVEY = "hies2024" # SURVEY = "pmpmd" # SURVEY = "slchbs" - SURVEY = "fbf house holduntitled folder" + # SURVEY = "fbf house holduntitled folder" DATA_DIR = os.path.join(PROJ_ROOT, "rissk_kedro", "data", SURVEY, "latest", "30_PROCESSED") SCORE_DIR = os.path.join(PROJ_ROOT, "rissk_kedro", "data", SURVEY, "latest", "40_SCORED") @@ -55,169 +55,229 @@ def unit_risk_score(config: DictConfig) -> None: df_unit_kedro = pd.read_parquet(os.path.join(DATA_DIR, "unit_features.parquet")) df_removed_kedro = pd.read_parquet(os.path.join(DATA_DIR, "removed_answers.parquet")) - # Manually initialize the class without calling __init__ - survey_class = UnitDataProcessing.__new__(UnitDataProcessing) - survey_class.config = config - survey_class._limit_unit = config.get('limit_unit', None) - survey_class._allowed_features = ['f__' + k for k, v in config['features'].items() if v['use']] - survey_class.item_level_columns = ['interview__id', 'variable_name', 'roster_level'] - - # Assign Kedro feature tables; strip any pre-existing s__* columns so that - # make_global_score starts from a clean slate. - survey_class._df_item = df_item_kedro.drop( - columns=[c for c in df_item_kedro.columns if c.startswith('s__')] - ) - survey_class._df_unit = df_unit_kedro.drop( - columns=[c for c in df_unit_kedro.columns if c.startswith('s__')] - ) - # df_unit_score property requires survey_name/survey_version; rename from Kedro column names - survey_class._df_unit.rename(columns={'qnr': 'survey_name', 'qnr_version': 'survey_version'}, inplace=True) - if 'survey_name' not in survey_class._df_unit.columns: - survey_class._df_unit['survey_name'] = SURVEY - if 'survey_version' not in survey_class._df_unit.columns: - survey_class._df_unit['survey_version'] = 'latest' - - # Build _df_resp from unique responsibles present in unit features - survey_class._df_resp = ( - df_unit_kedro[['responsible']] - .drop_duplicates() - .loc[lambda d: (d['responsible'] != '') & d['responsible'].notna()] - .copy() - ) - - # Numeric mask needed by several scoring methods accessed via self.df_item - survey_class.numeric_question_mask = ( - (survey_class._df_item["qtype"] == 'NumericQuestion') & - (survey_class._df_item['value'] != '') & - (~pd.isnull(survey_class._df_item['value'])) & - (survey_class._df_item['value'] != -999999999) - ) - - survey_class._score_columns = None - - # Patch get_feature_item__answer_removed so that make_score__answer_removed - # uses the Kedro-built removed_answers table instead of reading self.df_paradata. - survey_class.get_feature_item__answer_removed = lambda feature_name: df_removed_kedro.copy() - - try: - print("Calculating Legacy Risk Scores from Kedro feature outputs...") - - # Populate all s__* columns on _df_unit/_df_resp first, then sanitise before - # StandardScaler runs. Division-based scores (e.g. s__pause_duration = - # f__pause_duration / f__total_elapse) can produce inf when the denominator is 0. - _ = survey_class.df_unit_score - s_cols = [c for c in survey_class._df_unit.columns if c.startswith('s__')] - survey_class._df_unit[s_cols] = survey_class._df_unit[s_cols].replace( - [np.inf, -np.inf], np.nan + # Get unique questionnaires present in the feature tables (mirrors pipeline_registry per-qnr loop) + qnr_names = df_unit_kedro['qnr'].dropna().unique().tolist() + print(f"Found {len(qnr_names)} questionnaire(s): {qnr_names}") + + all_item_scores: list = [] + all_unit_risk_dfs: list = [] + all_df_scores: list = [] + + for qnr_name in qnr_names: + print(f"\n--- Processing questionnaire: {qnr_name} ---") + + # Filter each feature table to this questionnaire only (mirrors make_qnr_filter) + df_item_qnr = df_item_kedro[df_item_kedro['qnr'] == qnr_name].copy() + df_unit_qnr = df_unit_kedro[df_unit_kedro['qnr'] == qnr_name].copy() + if df_removed_kedro is not None and not df_removed_kedro.empty: + if 'qnr' in df_removed_kedro.columns: + df_removed_qnr = df_removed_kedro[df_removed_kedro['qnr'] == qnr_name].copy() + else: + # fallback: removed_answers pre-dates the qnr column addition + valid_ids = set(df_unit_qnr['interview__id']) + df_removed_qnr = df_removed_kedro[df_removed_kedro['interview__id'].isin(valid_ids)].copy() + else: + df_removed_qnr = pd.DataFrame() + + if df_unit_qnr.empty: + print(f" No units found for {qnr_name}, skipping.") + continue + + # Manually initialize the class without calling __init__ + survey_class = UnitDataProcessing.__new__(UnitDataProcessing) + survey_class.config = config + survey_class._limit_unit = config.get('limit_unit', None) + survey_class._allowed_features = ['f__' + k for k, v in config['features'].items() if v['use']] + survey_class.item_level_columns = ['interview__id', 'variable_name', 'roster_level'] + + # Assign filtered feature tables; strip any pre-existing s__* columns so that + # make_global_score starts from a clean slate. + # reset_index(drop=True) is critical: boolean-filter slices of the parquet + # retain the original row positions (e.g. pmpmd_household rows may start at + # index 25 if pmpmd_individual occupies rows 0..24). make_global_score merges + # _df_unit with _df_resp and then assigns the result back by label — any row + # whose index label exceeds len(merged_df)-1 gets NaN, producing blank unit_risk_scores. + survey_class._df_item = df_item_qnr.drop( + columns=[c for c in df_item_qnr.columns if c.startswith('s__')] + ).reset_index(drop=True) + survey_class._df_unit = df_unit_qnr.drop( + columns=[c for c in df_unit_qnr.columns if c.startswith('s__')] + ).reset_index(drop=True) + # df_unit_score property requires survey_name/survey_version; rename from Kedro column names + survey_class._df_unit.rename(columns={'qnr': 'survey_name', 'qnr_version': 'survey_version'}, inplace=True) + if 'survey_name' not in survey_class._df_unit.columns: + survey_class._df_unit['survey_name'] = qnr_name + if 'survey_version' not in survey_class._df_unit.columns: + survey_class._df_unit['survey_version'] = 'latest' + + # Build _df_resp from unique responsibles present in this questionnaire's unit features + survey_class._df_resp = ( + df_unit_qnr[['responsible']] + .drop_duplicates() + .loc[lambda d: (d['responsible'] != '') & d['responsible'].notna()] + .reset_index(drop=True) + .copy() ) - # Recompute _score_columns on sanitised data so make_global_score sees the - # correct set. For small surveys all scores can be constant/all-NaN after - # sanitisation, which would give StandardScaler an empty DataFrame. - score_cols_all = [c for c in survey_class._df_unit.columns if c.startswith('s__')] - survey_class._score_columns = ( - survey_class._df_unit[score_cols_all] - .columns[survey_class._df_unit[score_cols_all].nunique() > 1] - .tolist() - ) - if not survey_class._score_columns: - print("No score columns with sufficient variance — cannot compute global score for this survey.") - return - - # Determine whether the responsible-level score has enough variance to run PCA. - # make_responsible_score receives restricted_columns=_score_columns (unit-level), so - # only columns in _df_resp that are NOT in _score_columns are used. If they are all - # constant after fillna(0) the StandardScaler raises "at least one array or dtype". - _restricted = survey_class._score_columns - _resp_candidates = [ - c for c in survey_class._df_resp.columns - if not c.startswith('responsible') and c not in _restricted - ] - _resp_has_variance = ( - not survey_class._df_resp[_resp_candidates].fillna(0).loc[ - :, survey_class._df_resp[_resp_candidates].fillna(0).nunique() != 1 - ].empty - if _resp_candidates else False + # Numeric mask needed by several scoring methods accessed via self.df_item + survey_class.numeric_question_mask = ( + (survey_class._df_item["qtype"] == 'NumericQuestion') & + (survey_class._df_item['value'] != '') & + (~pd.isnull(survey_class._df_item['value'])) & + (survey_class._df_item['value'] != -999999999) ) - survey_class.make_global_score(combine_resp_score=_resp_has_variance) - - # Build item-level score table (equivalent to Kedro calculate_item_scores output). - # answer_removed is excluded here matching Kedro behaviour (scored at unit level only). - # GPS is excluded due to its pivoted shape (already a WARNING in make_global_score). - print("Collecting item-level scores...") - id_cols = [c for c in ['interview__id', 'variable_name', 'roster_level', 'index_col'] - if c in survey_class._df_item.columns] - df_item_scores = survey_class._df_item[id_cols].copy() - merge_key = 'index_col' if 'index_col' in df_item_scores.columns \ - else ['interview__id', 'variable_name', 'roster_level'] - merge_cols = [merge_key] if isinstance(merge_key, str) else merge_key - - item_score_methods = [ - ('make_score__answer_hour_set', ['s__answer_hour_set']), - ('make_score__sequence_jump', ['s__sequence_jump']), - ('make_score__first_decimal', ['s__first_decimal']), - ('make_score__answer_changed', ['s__answer_changed']), - ('make_score__answer_position', ['s__answer_position']), - ('make_score__answer_selected', ['s__answer_selected_lower', 's__answer_selected_upper']), - ('make_score__answer_duration', ['s__answer_duration_lower', 's__answer_duration_upper']), - ('make_score__single_question', ['s__single_question']), - ('make_score__multi_option_question', ['s__multi_option_question']), - ('make_score__first_digit', ['s__first_digit']), - ] - for method_name, score_cols in item_score_methods: + survey_class._score_columns = None + + # Patch get_feature_item__answer_removed so that make_score__answer_removed + # uses the Kedro-built removed_answers table instead of reading self.df_paradata. + # Default argument captures df_removed_qnr at loop iteration time. + survey_class.get_feature_item__answer_removed = lambda feature_name, _r=df_removed_qnr: _r.copy() + + try: + print(f" Calculating Legacy Risk Scores for {qnr_name}...") + + # Populate all s__* columns on _df_unit/_df_resp first, then sanitise before + # StandardScaler runs. Division-based scores (e.g. s__pause_duration = + # f__pause_duration / f__total_elapse) can produce inf when the denominator is 0. + _ = survey_class.df_unit_score + s_cols = [c for c in survey_class._df_unit.columns if c.startswith('s__')] + survey_class._df_unit[s_cols] = survey_class._df_unit[s_cols].replace( + [np.inf, -np.inf], np.nan + ) + + # Recompute _score_columns on sanitised data so make_global_score sees the + # correct set. For small surveys all scores can be constant/all-NaN after + # sanitisation, which would give StandardScaler an empty DataFrame. + score_cols_all = [c for c in survey_class._df_unit.columns if c.startswith('s__')] + survey_class._score_columns = ( + survey_class._df_unit[score_cols_all] + .columns[survey_class._df_unit[score_cols_all].nunique() > 1] + .tolist() + ) + if not survey_class._score_columns: + print(f" No score columns with sufficient variance for {qnr_name} — skipping global score.") + continue + + # Determine whether the responsible-level score has enough variance to run PCA. + _restricted = survey_class._score_columns + _resp_candidates = [ + c for c in survey_class._df_resp.columns + if not c.startswith('responsible') and c not in _restricted + ] + _resp_has_variance = ( + not survey_class._df_resp[_resp_candidates].fillna(0).loc[ + :, survey_class._df_resp[_resp_candidates].fillna(0).nunique() != 1 + ].empty + if _resp_candidates else False + ) + + survey_class.make_global_score(combine_resp_score=_resp_has_variance) + + # Build item-level score table (equivalent to Kedro calculate_item_scores output). + # answer_removed is excluded here matching Kedro behaviour (scored at unit level only). + # GPS is excluded due to its pivoted shape (already a WARNING in make_global_score). + print(f" Collecting item-level scores for {qnr_name}...") + id_cols = [c for c in ['interview__id', 'variable_name', 'roster_level', 'index_col'] + if c in survey_class._df_item.columns] + df_item_scores = survey_class._df_item[id_cols].copy() + merge_key = 'index_col' if 'index_col' in df_item_scores.columns \ + else ['interview__id', 'variable_name', 'roster_level'] + merge_cols = [merge_key] if isinstance(merge_key, str) else merge_key + + item_score_methods = [ + ('make_score__answer_hour_set', ['s__answer_hour_set']), + ('make_score__sequence_jump', ['s__sequence_jump']), + ('make_score__first_decimal', ['s__first_decimal']), + ('make_score__answer_changed', ['s__answer_changed']), + ('make_score__answer_position', ['s__answer_position']), + ('make_score__answer_selected', ['s__answer_selected_lower', 's__answer_selected_upper']), + ('make_score__answer_duration', ['s__answer_duration_lower', 's__answer_duration_upper']), + ('make_score__single_question', ['s__single_question']), + ('make_score__multi_option_question', ['s__multi_option_question']), + ('make_score__first_digit', ['s__first_digit']), + ] + for method_name, score_cols in item_score_methods: + try: + result = getattr(survey_class, method_name)() + available = [c for c in score_cols if c in result.columns] + if not available: + continue + result_slim = result[merge_cols + available].drop_duplicates(subset=merge_cols) + df_item_scores = df_item_scores.merge(result_slim, on=merge_key, how='left') + except Exception as e: + print(f" WARNING: item score {score_cols}: {e}") + + all_item_scores.append(df_item_scores) + + # Collect unit risk scores for this questionnaire + unit_risk_cols = ['interview__id', 'responsible', 'unit_risk_score'] + unit_risk_df = survey_class._df_unit[unit_risk_cols].copy() + unit_risk_df['unit_risk_score'] = unit_risk_df['unit_risk_score'].round(2) + all_unit_risk_dfs.append(unit_risk_df) + + # Build merged score table (unit + responsible scores) for this questionnaire + resp_score_cols = [c for c in survey_class._df_resp.columns if c.startswith('s__')] + resp_id_cols = ['responsible'] + if 'responsible_score' in survey_class._df_resp.columns: + resp_id_cols.append('responsible_score') + resp_view_cols = resp_id_cols + resp_score_cols + df_scores_qnr = survey_class._df_unit.merge( + survey_class._df_resp[resp_view_cols], on='responsible', how='left', + ) + score_cols_final = [c for c in df_scores_qnr.columns if c.startswith('s__')] + id_cols_final = [c for c in ['interview__id', 'responsible', 'survey_name', 'survey_version'] + if c in df_scores_qnr.columns] + final_cols = id_cols_final + ['unit_risk_score', 'responsible_score'] + sorted(score_cols_final) + df_scores_qnr = df_scores_qnr[[c for c in final_cols if c in df_scores_qnr.columns]] + all_df_scores.append(df_scores_qnr) + + except ValueError as e: + print(f" ERROR in {qnr_name}: {e}") + continue + + # --- Merge per-questionnaire results (mirrors merge_pipeline in pipeline_registry) --- + if not all_item_scores: + print("No questionnaire produced results. Exiting.") + return + + df_item_scores_all = pd.concat(all_item_scores, ignore_index=True) + # Normalise any object-typed columns that became mixed after concat + for col in df_item_scores_all.columns: + if df_item_scores_all[col].dtype == object: try: - result = getattr(survey_class, method_name)() - available = [c for c in score_cols if c in result.columns] - if not available: - continue - result_slim = result[merge_cols + available].drop_duplicates(subset=merge_cols) - df_item_scores = df_item_scores.merge(result_slim, on=merge_key, how='left') - except Exception as e: - print(f"WARNING: item score {score_cols}: {e}") - - item_scores_parquet = os.path.join(SCORE_DIR, "item_scores_legacy.parquet") - df_item_scores.to_parquet(item_scores_parquet, index=False) - print(f"saved legacy item scores to {item_scores_parquet}") - - # Persist unit risk scores - unit_risk_cols = ['interview__id', 'responsible', 'unit_risk_score'] - unit_risk_df = survey_class._df_unit[unit_risk_cols].copy() - unit_risk_df['unit_risk_score'] = unit_risk_df['unit_risk_score'].round(2) - unit_risk_df.sort_values('unit_risk_score', inplace=True) - unit_risk_csv = os.path.join(SCORE_DIR, "unit_risk_score_legacy.csv") - unit_risk_parquet = os.path.join(SCORE_DIR, "unit_risk_score_legacy.parquet") - unit_risk_df.to_csv(unit_risk_csv, index=False) - unit_risk_df.to_parquet(unit_risk_parquet, index=False) - - # Build merged score table (unit + responsible scores) - resp_score_cols = [c for c in survey_class._df_resp.columns if c.startswith('s__')] - resp_id_cols = ['responsible'] - if 'responsible_score' in survey_class._df_resp.columns: - resp_id_cols.append('responsible_score') - resp_view_cols = resp_id_cols + resp_score_cols - df_scores = survey_class._df_unit.merge( - survey_class._df_resp[resp_view_cols], on='responsible', how='left', - ) - score_cols = [c for c in df_scores.columns if c.startswith('s__')] - id_cols = [c for c in ['interview__id', 'responsible', 'survey_name', 'survey_version'] - if c in df_scores.columns] - final_cols = id_cols + ['unit_risk_score', 'responsible_score'] + sorted(score_cols) - df_scores = df_scores[[c for c in final_cols if c in df_scores.columns]] - - scores_csv = os.path.join(SCORE_DIR, "scores_table_legacy.csv") - scores_parquet = os.path.join(SCORE_DIR, "scores_table_legacy.parquet") - df_scores.to_csv(scores_csv, index=False) - df_scores.to_parquet(scores_parquet, index=False) - print(f"saved legacy unit risk to {unit_risk_csv}") - print(f"saved legacy score table to {scores_csv}") - - print("DONE. Legacy scores from Kedro features generated.") - return # Stop here - - except ValueError as e: - print(f"An error occurred: {e}") - return # do not fall through to the regular UnitDataProcessing block + df_item_scores_all[col] = df_item_scores_all[col].astype(float) + except (ValueError, TypeError): + pass + item_scores_parquet = os.path.join(SCORE_DIR, "item_scores_legacy.parquet") + df_item_scores_all.to_parquet(item_scores_parquet, index=False) + print(f"Saved legacy item scores to {item_scores_parquet}") + + unit_risk_df_all = pd.concat(all_unit_risk_dfs, ignore_index=True) + unit_risk_df_all.sort_values('unit_risk_score', inplace=True) + unit_risk_csv = os.path.join(SCORE_DIR, "unit_risk_score_legacy.csv") + unit_risk_parquet = os.path.join(SCORE_DIR, "unit_risk_score_legacy.parquet") + unit_risk_df_all.to_csv(unit_risk_csv, index=False) + unit_risk_df_all.to_parquet(unit_risk_parquet, index=False) + + df_scores_all = pd.concat(all_df_scores, ignore_index=True) + # After concat across questionnaires, boolean/int columns can become object dtype. + # Cast any remaining object-typed s__* columns to float so pyarrow can write parquet. + for col in df_scores_all.columns: + if df_scores_all[col].dtype == object: + try: + df_scores_all[col] = df_scores_all[col].astype(float) + except (ValueError, TypeError): + pass # leave non-numeric object columns as-is + scores_csv = os.path.join(SCORE_DIR, "scores_table_legacy.csv") + scores_parquet = os.path.join(SCORE_DIR, "scores_table_legacy.parquet") + df_scores_all.to_csv(scores_csv, index=False) + df_scores_all.to_parquet(scores_parquet, index=False) + print(f"Saved legacy unit risk to {unit_risk_csv}") + print(f"Saved legacy score table to {scores_csv}") + + print("DONE. Legacy scores from Kedro features generated.") + return # Stop here — do not fall through to the regular UnitDataProcessing block # --- END MONKEY PATCH --- From 3c646a6ebdf19bcdf4358e56b0ecafc523be74ab Mon Sep 17 00:00:00 2001 From: VJausovec Date: Sun, 5 Apr 2026 12:44:11 +0100 Subject: [PATCH 49/70] Update GPS scoring logic to handle NaN values and drop feature columns from unit output --- rissk/item_processing_kedro.py | 32 +++++++++++-------- .../pipelines/rissk_scoring/nodes.py | 4 +++ 2 files changed, 22 insertions(+), 14 deletions(-) diff --git a/rissk/item_processing_kedro.py b/rissk/item_processing_kedro.py index bebcb19..e0355ff 100644 --- a/rissk/item_processing_kedro.py +++ b/rissk/item_processing_kedro.py @@ -98,12 +98,12 @@ def calculate_gps_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd # when multiple GPS variables exist for the same (interview, roster, responsible) index_col = ['interview__id', 'roster_level', 'responsible', 'variable_name'] - # s__gps: integer flag (1 = GPS question, 0 = other). Set unconditionally so that + # s__gps: integer flag (1 = GPS question, NaN = other). Set unconditionally so that # aggregate_item_to_unit_scores can always sum it to the interview-level GPS question # count, matching legacy make_score_unit__gps which read f__gps from df_item directly # regardless of whether the GPS outlier model ran successfully. if 'f__gps' in df.columns: - df['s__gps'] = df['f__gps'].astype(int) + df['s__gps'] = np.where(df['f__gps'].fillna(False).astype(bool), 1, np.nan) # If required GPS columns are missing, return original df if any(col not in df.columns for col in required_columns + ['variable_name']): @@ -730,24 +730,27 @@ def calculate_first_digit_score(df_item: pd.DataFrame) -> pd.DataFrame: if feature_name not in df.columns or first_digit_feature not in df.columns: return df - if df[feature_name].dropna().empty or df[first_digit_feature].dropna().empty: + + valid_data = df[ + ~pd.isnull(df[feature_name]) & ( + ~pd.isnull(df[first_digit_feature])) & ( + df[first_digit_feature] != 0) + ].copy() + + if valid_data.empty: df[score_name] = np.nan return df - - valid_data = df[~pd.isnull(df[feature_name])].copy() + # we need both f__numeric_response and f__first_digit to apply Benford tests, + # so filter to rows where both are present df[score_name] = np.nan - # f__first_digit is already computed by the feature pipeline and is NA for zeros - # and nulls, so filter_variable_name_by_frequency applied to it naturally restricts - # frequency and uniqueness counts to the nonzero Benford-eligible population. - # No need to recompute first digits here — f__numeric_response is only needed for - # the magnitude range check and for apply_benford_tests. + # f__first_digit is already computed by the feature pipeline valid_variables = filter_variable_name_by_frequency( - df, first_digit_feature, frequency=100, min_unique_values=3 + valid_data, first_digit_feature, frequency=100, min_unique_values=3 ) - benford_data = valid_data[valid_data[feature_name] != 0].copy() - valid_variables = filter_variables_by_magnitude(benford_data, feature_name, valid_variables, min_order_of_magnitude=3) + # Additionally, Benford's Law is most applicable to variables that span several orders of magnitude, + valid_variables = filter_variables_by_magnitude(valid_data, feature_name, valid_variables, min_order_of_magnitude=3) # Computes the Jensen divergence for each variable_name and responsible on the first digit distribution. # Jensen's divergence returns a value between (0, 1) of how much the first digit distribution @@ -756,8 +759,9 @@ def calculate_first_digit_score(df_item: pd.DataFrame) -> pd.DataFrame: # The Bendford Jensen divergence is calculated only on those responsible and variable_name # who have at least 50 records. # Once it is calculated, values that diverge from more than 50% from the median value get marked as "anomalous." + benford_jensen_df = apply_benford_tests( - benford_data, valid_variables, 'responsible', feature_name, apply_first_digit=True, minimum_sample=50 + valid_data, valid_variables, 'responsible', feature_name, apply_first_digit=True, minimum_sample=50 ) if not benford_jensen_df.empty: diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py index c187599..144cd8d 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py @@ -184,4 +184,8 @@ def calculate_unit_scores( how='left' ) + # Drop feature columns (f__*) from unit output — only scores and identifiers are needed. + feature_cols = [c for c in df_final_unit.columns if c.startswith('f__')] + df_final_unit = df_final_unit.drop(columns=feature_cols) + return df_final_unit, df_resp_scored From 8c31f0fd0a94919e11c714b3be2d252ff29dfb92 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Sun, 5 Apr 2026 18:04:37 +0100 Subject: [PATCH 50/70] Enhance pause feature handling by filling NaN values for count and duration; ensure legacy behavior for responsible score columns by filling NaNs with 0. --- rissk/feature_processing_kedro.py | 7 ++++++- .../src/rissk_kedro/pipelines/rissk_scoring/nodes.py | 5 +++++ 2 files changed, 11 insertions(+), 1 deletion(-) diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index 2d73a1f..07f1c27 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -251,7 +251,12 @@ def add_pause_features(df_unit: pd.DataFrame, df_time: pd.DataFrame, allowed_fea df_pause = df_pause[['interview__id'] + selected_features] df_unit = df_unit.merge(df_pause, how='left', on='interview__id') - + + # Fill NaNs for pause features: no pauses -> count=0, duration=0, empty list. + if 'f__pause_count' in selected_features: + df_unit['f__pause_count'] = df_unit['f__pause_count'].fillna(0).astype(int) + if 'f__pause_duration' in selected_features: + df_unit['f__pause_duration'] = df_unit['f__pause_duration'].fillna(0) if 'f__pause_list' in selected_features: # Ensure interviews absent in df_time also get an empty list after merge. df_unit['f__pause_list'] = df_unit['f__pause_list'].apply( diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py index 144cd8d..e8c0f32 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py @@ -183,6 +183,11 @@ def calculate_unit_scores( on='responsible', how='left' ) + # Guard: a responsible present in df_unit but absent from df_resp_scored + # (no item rows) would produce NaN for all resp-level score columns after + # the left join. Fill with 0 to match legacy behaviour where _df_resp always + # has an entry for every responsible and fillna(0) is applied at write time. + df_final_unit[new_resp_cols] = df_final_unit[new_resp_cols].fillna(0) # Drop feature columns (f__*) from unit output — only scores and identifiers are needed. feature_cols = [c for c in df_final_unit.columns if c.startswith('f__')] From 4e69ac0306214e30d98d47d1f3eb92725cc702fd Mon Sep 17 00:00:00 2001 From: VJausovec Date: Mon, 6 Apr 2026 01:02:58 +0100 Subject: [PATCH 51/70] Refactor calculate_unit_scores function to streamline scoring logic; clarify handling of removed_answers and improve legacy compatibility. --- .../pipelines/rissk_scoring/nodes.py | 102 ++++++++++-------- 1 file changed, 60 insertions(+), 42 deletions(-) diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py index e8c0f32..182f62b 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py @@ -111,13 +111,17 @@ def calculate_item_scores(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> return df_scored def calculate_unit_scores( - df_unit: pd.DataFrame, - df_item_scores: pd.DataFrame, + df_unit: pd.DataFrame, + df_item_scores: pd.DataFrame, parameters: Dict[str, Any], removed_answers: pd.DataFrame = None ) -> Tuple[pd.DataFrame, pd.DataFrame]: """ Aggregate item scores to unit, extract responsible scores, and calculate global risk. + This node receives data for a single questionnaire — the pipeline_registry filters + item_features / unit_features per questionnaire before invoking the scoring pipeline, + so no internal qnr loop is needed here. + removed_answers is the pre-aggregated AnswerRemoved dataset produced by build_removed_answers_node. It is used to compute s__answer_removed at unit level, matching legacy behaviour where items deleted from microdata (absent from df_item) are still counted. @@ -127,13 +131,13 @@ def calculate_unit_scores( # 1. Aggregate item-level scores up to unit level. # s__answer_removed is excluded from this aggregation (see aggregate_item_to_unit_scores); - # it is handled below using paradata_full to match legacy coverage. + # it is handled below using removed_answers to match legacy coverage. df_unit_scored = aggregate_item_to_unit_scores(df_unit, df_item_scores) - # 2a. Score answer_removed at unit level from paradata_full. + # 2a. Score answer_removed at unit level from removed_answers. # This replicates legacy make_score_unit__answer_removed which read from df_paradata # directly and therefore included AnswerRemoved events for items later deleted from - # microdata. Falling back to the df_item-based mean when paradata_full is unavailable. + # microdata. Falling back to the df_item-based mean when removed_answers is unavailable. if features.get('answer_removed', {}).get('use', False): if removed_answers is not None and not removed_answers.empty: unit_removed = calculate_answer_removed_score_from_df(removed_answers, parameters) @@ -145,52 +149,66 @@ def calculate_unit_scores( ) data = df_item_scores.groupby('interview__id')['s__answer_removed'].mean() df_unit_scored['s__answer_removed'] = df_unit_scored['interview__id'].map(data).fillna(0) - - # 2b. Add pure unit-level calculations + + # 2b. Add pure unit-level calculations (row-wise or by interview__id). df_unit_scored = calculate_unit_level_scores(df_unit_scored, parameters) - # 3. Aggregate item-level scores up to responsible level - df_resp_scored = pd.DataFrame() - df_resp_scored = aggregate_item_to_responsible_scores(df_resp_scored, df_item_scores) - - # 4. Calculate final responsible score via PCA - restricted_columns = parameters.get('unit_scoring', {}).get('restricted_columns', []) - df_resp_scored = calculate_responsible_score(df_resp_scored, restricted_columns) - - # Determine all scored columns dynamically (s_*) - score_columns = [col for col in df_unit_scored.columns if col.startswith('s__')] - - # 5. Calculate final global unit risk score - df_final_unit = calculate_global_score( - df_unit_scores=df_unit_scored, - df_resp_scores=df_resp_scored, + qnr_name = df_unit_scored['qnr'].iloc[0] if 'qnr' in df_unit_scored.columns and not df_unit_scored.empty else None + logger.info(f"Scoring questionnaire: {qnr_name!r} ({len(df_unit_scored)} interviews)") + + if df_unit_scored.empty: + logger.warning(f"No units found for questionnaire '{qnr_name}' — returning empty.") + return df_unit_scored, pd.DataFrame() + + # 3. Aggregate item scores to responsible level. + # Seed df_resp from unit_features responsibles (all responsibles with any interview + # activity), matching legacy which seeds _df_resp from df_active_paradata. + # Responsibles present in unit_features but absent from item_scores (no scoreable + # items) will have NaN in all score columns → filled to 0 before PCA, exactly as + # legacy make_responsible_score does via fillna(0). + df_resp_init = ( + df_unit_scored[['responsible']] + .drop_duplicates() + .loc[lambda d: (d['responsible'] != '') & d['responsible'].notna()] + .reset_index(drop=True) + .copy() + ) + df_resp = aggregate_item_to_responsible_scores(df_resp_init, df_item_scores) + + # 4. PCA-based responsible score. + # restricted_columns = ALL unit-level s__ columns (matching legacy make_responsible_score + # which receives restricted_columns=_score_columns, the full set including constant cols). + # This ensures any responsible-level feature that also appears at unit level (e.g. + # s__single_question, s__answer_position) is excluded from the resp PCA regardless of + # whether it has variance — exactly as legacy does. + score_columns = [c for c in df_unit_scored.columns if c.startswith('s__')] + df_resp = calculate_responsible_score(df_resp, score_columns) + + # 5. IForest global unit risk score. + df_unit_final = calculate_global_score( + df_unit_scores=df_unit_scored, + df_resp_scores=df_resp, score_columns=score_columns, combine_resp_score=True, - restricted_columns=restricted_columns + restricted_columns=None, ) # 6. Merge responsible-level s__ columns back onto unit output. - # Legacy save() merges _df_resp (which holds s__single_question, - # s__multi_option_question, s__answer_position, s__first_digit) back - # onto _df_unit by responsible so those scores appear in the feature CSV. - resp_s_cols = [c for c in df_resp_scored.columns if c.startswith('s__')] - if resp_s_cols and 'responsible' in df_resp_scored.columns and not df_resp_scored.empty: - # Only bring in columns not already present at unit level - new_resp_cols = [c for c in resp_s_cols if c not in df_final_unit.columns] + # Legacy save() merges _df_resp (s__single_question, s__multi_option_question, + # s__answer_position, s__first_digit) back onto _df_unit by responsible. + resp_s_cols = [c for c in df_resp.columns if c.startswith('s__')] + if resp_s_cols and 'responsible' in df_resp.columns: + new_resp_cols = [c for c in resp_s_cols if c not in df_unit_final.columns] if new_resp_cols: - df_final_unit = df_final_unit.merge( - df_resp_scored[['responsible'] + new_resp_cols], + df_unit_final = df_unit_final.merge( + df_resp[['responsible'] + new_resp_cols], on='responsible', - how='left' + how='left', ) - # Guard: a responsible present in df_unit but absent from df_resp_scored - # (no item rows) would produce NaN for all resp-level score columns after - # the left join. Fill with 0 to match legacy behaviour where _df_resp always - # has an entry for every responsible and fillna(0) is applied at write time. - df_final_unit[new_resp_cols] = df_final_unit[new_resp_cols].fillna(0) + df_unit_final[new_resp_cols] = df_unit_final[new_resp_cols].fillna(0) - # Drop feature columns (f__*) from unit output — only scores and identifiers are needed. - feature_cols = [c for c in df_final_unit.columns if c.startswith('f__')] - df_final_unit = df_final_unit.drop(columns=feature_cols) + # Drop feature columns (f__*) from unit output — only scores and identifiers needed. + feature_cols = [c for c in df_unit_final.columns if c.startswith('f__')] + df_unit_final = df_unit_final.drop(columns=feature_cols) - return df_final_unit, df_resp_scored + return df_unit_final, df_resp From a218820f9e95786c82268a49aac941b4bde0882a Mon Sep 17 00:00:00 2001 From: VJausovec Date: Mon, 6 Apr 2026 14:46:32 +0100 Subject: [PATCH 52/70] Refactor scoring functions to improve handling of edge cases; remove redundant functions and enhance output structure by adding 'qnr' to responsible scores. --- rissk/item_processing_kedro.py | 71 +------------------ rissk/unit_processing_kedro.py | 7 ++ .../pipelines/rissk_scoring/nodes.py | 4 ++ 3 files changed, 12 insertions(+), 70 deletions(-) diff --git a/rissk/item_processing_kedro.py b/rissk/item_processing_kedro.py index e0355ff..80e88b1 100644 --- a/rissk/item_processing_kedro.py +++ b/rissk/item_processing_kedro.py @@ -371,76 +371,7 @@ def calculate_answer_changed_score(df_item: pd.DataFrame, parameters: Dict[str, # f__answer_removed is merged back with how='left' — so those deleted items are # silently dropped, making an item-level s__answer_removed on df_item structurally # incomplete and potentially misleading. -# -# The authoritative score is computed at UNIT level from paradata_full directly by -# calculate_answer_removed_unit_score below, matching legacy coverage exactly. - - -def calculate_answer_removed_unit_score( - paradata_full: pd.DataFrame, - parameters: Dict[str, Any], -) -> pd.Series: - """Score answer-removal anomalies from paradata_full directly, matching legacy - make_score_unit__answer_removed which operated on self.df_paradata (NOT df_item). - - Items deleted from microdata — whose AnswerRemoved events are absent from df_item - because of the how='left' merge in feat_answer_removed — are included here, - eliminating the undercount introduced by the Kedro item-table path. - - Returns a Series indexed by interview__id → mean s__answer_removed score, - ready to be mapped directly into df_unit. - """ - feature_name = 'f__answer_removed' - score_name = rename_feature(feature_name) - - required_cols = ['event', 'role', 'order', 'interview__id', 'variable_name'] - if any(c not in paradata_full.columns for c in required_cols): - logger.warning( - "calculate_answer_removed_unit_score: paradata_full is missing one or more " - "required columns %s; returning empty Series.", required_cols - ) - return pd.Series(dtype=float) - - # Replicate legacy get_feature_item__answer_removed exactly. - removed_mask = (paradata_full['event'] == 'AnswerRemoved') & (paradata_full['role'] == 1) - df_removed = paradata_full[removed_mask] - - if df_removed.empty: - return pd.Series(dtype=float) - - # Match legacy groupby grain: (interview__id, responsible, variable_name, qnr_seq). - # qnr_seq may be absent in some paradata versions; fall back gracefully. - group_cols = [c for c in ['interview__id', 'responsible', 'variable_name', 'qnr_seq'] - if c in df_removed.columns] - df = df_removed.groupby(group_cols).agg( - f__answer_removed=('order', 'count') - ).reset_index() - - valid_variables = filter_variable_name_by_frequency(df, feature_name, frequency=100, min_unique_values=1) - - # Init to NaN: variables not passing the frequency filter keep NaN, indicating - # evaluation was not possible — unit-level groupby().mean() skips NaN so they - # don't contribute a spurious zero to the interview mean. - df[score_name] = np.nan - contamination = get_contamination_parameter( - parameters.get('features', {}), - feature_name, - automatic_contamination=parameters.get('automatic_contamination', False), - method='medfilt', - random_state=42, - ) - - for var in valid_variables: - mask = (df['variable_name'] == var) & (~pd.isnull(df[feature_name])) - if mask.sum() > 0: - model = ECOD(contamination=contamination) - model.fit(df.loc[mask, [feature_name]]) - df.loc[mask, score_name] = model.predict(df.loc[mask, [feature_name]]) - - # Aggregate to interview__id level matching legacy make_score_unit__answer_removed. - # groupby().mean() skips NaN rows, so only scored variables contribute. - return df.groupby('interview__id')[score_name].mean() - + def calculate_answer_removed_score_from_df( removed_answers: pd.DataFrame, diff --git a/rissk/unit_processing_kedro.py b/rissk/unit_processing_kedro.py index ba0e42f..3c62d92 100644 --- a/rissk/unit_processing_kedro.py +++ b/rissk/unit_processing_kedro.py @@ -256,6 +256,13 @@ def calculate_responsible_score(df_resp_features: pd.DataFrame, restricted_colum if df_pca_input.empty: df_resp['responsible_score'] = 0.0 return df_resp + + # PCA-based outlier scoring requires at least 2 varying columns to be meaningful: + # with only 1 component there are no minor eigenvectors to compute weighted + # reconstruction error against, so all scores would be identical. + if df_pca_input.shape[1] < 2: + df_resp['responsible_score'] = 0.0 + return df_resp df_pca_scaled = pd.DataFrame(scaler.fit_transform(df_pca_input), columns=df_pca_input.columns) diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py index 182f62b..ef8838d 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py @@ -211,4 +211,8 @@ def calculate_unit_scores( feature_cols = [c for c in df_unit_final.columns if c.startswith('f__')] df_unit_final = df_unit_final.drop(columns=feature_cols) + # Add qnr as the first column of the responsible scores output. + if qnr_name is not None and 'qnr' not in df_resp.columns: + df_resp.insert(0, 'qnr', qnr_name) + return df_unit_final, df_resp From 9886ab0f845713d38a26921ef7e823a86a4e703c Mon Sep 17 00:00:00 2001 From: VJausovec Date: Mon, 6 Apr 2026 15:45:57 +0100 Subject: [PATCH 53/70] add Kedro NiceGUI wrappers, requirements and set-up instructions --- rissk/utils/import_utils.py | 3 +- rissk_kedro/SETUP.md | 193 + rissk_kedro/app/__init__.py | 0 rissk_kedro/conf/base/catalog.yml | 30 +- rissk_kedro/conf/base/globals.yml | 39 +- rissk_kedro/conf/base/parameters.yml | 18 +- rissk_kedro/pyproject.toml | 24 +- rissk_kedro/requirements.txt | 42 +- rissk_kedro/run_gui.bat | 12 + rissk_kedro/run_gui.sh | 20 + .../src/rissk_kedro/pipeline_registry.py | 45 +- uv.lock | 3221 +++++++++++++++++ 12 files changed, 3563 insertions(+), 84 deletions(-) create mode 100644 rissk_kedro/SETUP.md create mode 100644 rissk_kedro/app/__init__.py create mode 100644 rissk_kedro/run_gui.bat create mode 100755 rissk_kedro/run_gui.sh create mode 100644 uv.lock diff --git a/rissk/utils/import_utils.py b/rissk/utils/import_utils.py index 641f7f6..7ed77aa 100644 --- a/rissk/utils/import_utils.py +++ b/rissk/utils/import_utils.py @@ -4,7 +4,8 @@ import pandas as pd import zipfile from io import BytesIO -from loguru import logger +import logging +logger = logging.getLogger(__name__) from pathlib import Path import re import os diff --git a/rissk_kedro/SETUP.md b/rissk_kedro/SETUP.md new file mode 100644 index 0000000..dc698e7 --- /dev/null +++ b/rissk_kedro/SETUP.md @@ -0,0 +1,193 @@ +# RISSK — Getting Started + +RISSK uses machine learning to score interviews from Survey Solutions export files, +flagging individual interviews most likely to contain unwanted interviewer behaviour. + +--- + +## Prerequisites + +- **Python 3.10 – 3.13** installed on your machine +- An internet connection for the initial install +- Survey Solutions export files (Main Survey Data + Paradata ZIPs) + +Verify your Python version: + +```bash +python --version +``` + +--- + +## Option A — uv (recommended for new users) + +[uv](https://docs.astral.sh/uv/) is a fast, self-contained Python package manager. +You do **not** need to manage virtual environments manually. + +### 1. Install uv + +**macOS / Linux:** +```bash +curl -LsSf https://astral.sh/uv/install.sh | sh +``` + +**Windows (PowerShell):** +```powershell +powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" +``` + +### 2. Get the RISSK code + +Clone with Git: +```bash +git clone https://github.com/rowsquared/rissk.git +cd rissk/rissk_kedro +``` + +Or download the ZIP from GitHub, unzip it, and navigate to the `rissk_kedro/` folder. + +### 3. Install dependencies + +```bash +uv sync +uv pip install "nicegui>=1.4" +``` + +### 4. Launch the GUI + +**macOS / Linux:** +```bash +bash run_gui.sh +``` + +**Windows:** +```bat +run_gui.bat +``` + +Your browser will open automatically at **http://localhost:8080**. + +--- + +## Option B — conda (for experienced users) + +### 1. Create and activate a conda environment + +```bash +conda create -n rissk python=3.13 +conda activate rissk +``` + +### 2. Get the code + +```bash +git clone https://github.com/rowsquared/rissk.git +cd rissk/rissk_kedro +``` + +### 3. Install dependencies + +```bash +pip install -e ".[gui]" +``` + +Or install manually: +```bash +pip install -r requirements.txt +pip install "nicegui>=1.4" +``` + +### 4. Launch the GUI + +```bash +bash run_gui.sh # macOS / Linux +run_gui.bat # Windows +``` + +--- + +## Using the GUI + +### Step 1 — Data folder + +Choose where RISSK will read and write survey data. + +- **Default (`data`):** keeps everything inside the `rissk_kedro/` project folder. +- **Absolute path:** point to any folder on your machine, e.g. `/Users/jane/surveys`. + +The GUI shows you the exact subfolder where ZIP files must be placed, e.g.: + +``` +/Users/jane/surveys/pmpmd/latest/10_RAW/ +``` + +Click **Create folder & Open** to create that folder and open it in your file manager. + +### Step 2 — Prepare your Survey Solutions exports + +Export from Survey Solutions and place the **unmodified ZIP files** in the folder shown: + +1. **Main Survey Data** — choose *Tab separated* or *Stata 14*, tick *Include meta information about questionnaire*. +2. **Paradata** — under *Data Type* select *Paradata*. + +> Export both files from the **same questionnaire version** consecutively. +> For multiple compatible versions, export each separately and place all ZIPs in the same folder. + +Do **not** rename, modify, or unzip the files. + +### Step 3 — Survey configuration + +- **Survey name:** exactly as it appears in Survey Solutions (e.g. `pmpmd`). +- **Questionnaires:** one row per questionnaire template. + - **Versions:** comma-separated list, e.g. `4, 5, 6`. + - **Consent filter (optional):** score only interviews where a specific paradata variable equals a required value (useful for surveys with a consent question). + +### Step 4 — Save & Run + +1. Click **Save configuration** on the Setup tab. +2. Switch to the **Run** tab. +3. Choose a pipeline stage (leave as *All* for a full run). +4. Click **Run RISSK** and monitor the live log. + +Results are written to: +``` +//latest/40_SCORED/unit_risk_scores.csv +``` + +### Advanced settings + +Access the **Advanced** tab to: +- Set a ZIP password (if your exports are password-protected) +- Toggle automatic contamination estimation +- Enable/disable individual features and adjust contamination thresholds + +--- + +## Running without the GUI (command line) + +Experienced users can run Kedro directly from the `rissk_kedro/` directory: + +```bash +# Full pipeline +kedro run + +# Individual stages +kedro run --pipeline data_ingestion +kedro run --pipeline feature_creation +kedro run --pipeline rissk_scoring +``` + +Configuration overrides go in `conf/local/globals.yml` and `conf/local/parameters.yml` +(these files are ignored by git). + +--- + +## Troubleshooting + +| Problem | Solution | +|---|---| +| `ModuleNotFoundError: nicegui` | Run `pip install "nicegui>=1.4"` in your active environment | +| Browser does not open | Open http://localhost:8080 manually | +| Pipeline fails with "No data found" | Check that ZIP files are in the correct subfolder (see Setup tab) | +| `kedro: command not found` | Activate your environment first (`conda activate rissk` or `source .venv/bin/activate`) | +| ZIPs not extracted | Make sure filenames are not modified; check the ZIP password setting if exports are protected | diff --git a/rissk_kedro/app/__init__.py b/rissk_kedro/app/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index 45afcb2..6014f09 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -4,7 +4,7 @@ # The source partitions (Zips) survey_zip_partitions: type: partitions.PartitionedDataset - path: data/${globals:survey.name}/latest/10_RAW + path: ${globals:data_root}/${globals:survey.name}/latest/10_RAW dataset: type: rissk_kedro.datasets.PathDataset filename_suffix: ".zip" @@ -13,30 +13,30 @@ survey_zip_partitions: # Used by downstream nodes to find the directories extracted_survey_folders: type: partitions.PartitionedDataset - path: data/${globals:survey.name}/latest/10_RAW + path: ${globals:data_root}/${globals:survey.name}/latest/10_RAW dataset: type: rissk_kedro.datasets.PathDataset # === INGESTED DataFrames === paradata_raw: type: pandas.ParquetDataset - filepath: data/${globals:survey.name}/latest/20_INTERIM/paradata_raw.parquet + filepath: ${globals:data_root}/${globals:survey.name}/latest/20_INTERIM/paradata_raw.parquet raw_questionnaire: type: pandas.ParquetDataset - filepath: data/${globals:survey.name}/latest/30_PROCESSED/questionnaire.parquet + filepath: ${globals:data_root}/${globals:survey.name}/latest/30_PROCESSED/questionnaire.parquet raw_microdata: type: pandas.ParquetDataset - filepath: data/${globals:survey.name}/latest/20_INTERIM/microdata_raw.parquet + filepath: ${globals:data_root}/${globals:survey.name}/latest/20_INTERIM/microdata_raw.parquet microdata: type: pandas.ParquetDataset - filepath: data/${globals:survey.name}/latest/30_PROCESSED/microdata.parquet + filepath: ${globals:data_root}/${globals:survey.name}/latest/30_PROCESSED/microdata.parquet paradata_processed: type: pandas.ParquetDataset - filepath: data/${globals:survey.name}/latest/30_PROCESSED/paradata_processed.parquet + filepath: ${globals:data_root}/${globals:survey.name}/latest/30_PROCESSED/paradata_processed.parquet @@ -62,38 +62,38 @@ paradata_processed: # === FEATURE CREATION DataFrames === item_features_base: type: pandas.ParquetDataset - filepath: data/${globals:survey.name}/latest/20_INTERIM/item_features_base.parquet + filepath: ${globals:data_root}/${globals:survey.name}/latest/20_INTERIM/item_features_base.parquet unit_features_base: type: pandas.ParquetDataset - filepath: data/${globals:survey.name}/latest/20_INTERIM/unit_features_base.parquet + filepath: ${globals:data_root}/${globals:survey.name}/latest/20_INTERIM/unit_features_base.parquet # Final Feature Tables (Input to Risk Scoring) item_features: type: pandas.ParquetDataset - filepath: data/${globals:survey.name}/latest/30_PROCESSED/item_features.parquet + filepath: ${globals:data_root}/${globals:survey.name}/latest/30_PROCESSED/item_features.parquet unit_features: type: pandas.ParquetDataset - filepath: data/${globals:survey.name}/latest/30_PROCESSED/unit_features.parquet + filepath: ${globals:data_root}/${globals:survey.name}/latest/30_PROCESSED/unit_features.parquet # Aggregated AnswerRemoved events (includes items deleted from microdata). # Used by the rissk_scoring pipeline to compute s__answer_removed at unit level, # matching legacy get_feature_item__answer_removed / make_score_unit__answer_removed. removed_answers: type: pandas.ParquetDataset - filepath: data/${globals:survey.name}/latest/30_PROCESSED/removed_answers.parquet + filepath: ${globals:data_root}/${globals:survey.name}/latest/30_PROCESSED/removed_answers.parquet # === SCORING DataFrames === item_scores: type: pandas.ParquetDataset - filepath: data/${globals:survey.name}/latest/40_SCORED/item_scores.parquet + filepath: ${globals:data_root}/${globals:survey.name}/latest/40_SCORED/item_scores.parquet unit_risk_scores: type: pandas.CSVDataset - filepath: data/${globals:survey.name}/latest/40_SCORED/unit_risk_scores.csv + filepath: ${globals:data_root}/${globals:survey.name}/latest/40_SCORED/unit_risk_scores.csv responsible_scores: type: pandas.CSVDataset - filepath: data/${globals:survey.name}/latest/40_SCORED/responsible_scores.csv + filepath: ${globals:data_root}/${globals:survey.name}/latest/40_SCORED/responsible_scores.csv diff --git a/rissk_kedro/conf/base/globals.yml b/rissk_kedro/conf/base/globals.yml index 62b01b3..44afb5a 100644 --- a/rissk_kedro/conf/base/globals.yml +++ b/rissk_kedro/conf/base/globals.yml @@ -1,5 +1,10 @@ # Survey Configuration (from env.yaml) +# Root folder that contains all survey data subfolders. +# Can be a relative path (relative to rissk_kedro/) or an absolute path. +# Override this in conf/local/globals.yml to point to your data on disk. +data_root: "data" + # survey: # name: "hies2024" # questionnaires: @@ -9,27 +14,29 @@ # - name: "slbhies_listing" # VERSION: [5, 6, 7] # filter_var: null # Set to a single-key dict to filter by consent, e.g.: - # filter_var: {consent_q: "1"} - # Only interviews where paradata variable 'consent_q' has answer "1" are scored. - # The answer value must be a string (paradata answers are always strings). +# # # # filter_var: {consent_q: "1"} +# # # # Only interviews where paradata variable 'consent_q' has answer "1" are scored. +# # # # The answer value must be a string (paradata answers are always strings). +survey: + name: "pmpmd" + questionnaires: + - name: "pmpmd_community" + VERSION: [2, 3, 4, 5] + filter_var: null + - name: "pmpmd_household" + VERSION: [4, 5, 6] + filter_var: null + + # survey: -# name: "pmpmd" +# name: "slchbs" # questionnaires: -# - name: "pmpmd_community" -# VERSION: [2, 3, 4, 5] -# filter_var: null -# - name: "pmpmd_household" -# VERSION: [4, 5, 6] -# filter_var: null +# - name: "slchbs_saintlucia_2025" +# VERSION: [6, 7] # 5 is for testing empty data handling +# filter_var: null -survey: - name: "slchbs" - questionnaires: - - name: "slchbs_saintlucia_2025" - VERSION: [6, 7] # 5 is for testing empty data handling - filter_var: null # survey: # name: "fbf house holduntitled folder" diff --git a/rissk_kedro/conf/base/parameters.yml b/rissk_kedro/conf/base/parameters.yml index c1da7ca..f2d2891 100644 --- a/rissk_kedro/conf/base/parameters.yml +++ b/rissk_kedro/conf/base/parameters.yml @@ -21,7 +21,7 @@ features: answer_hour_set: use: true parameters: - contamination: 0.11 + contamination: 0.1 answer_changed: use: true parameters: @@ -43,7 +43,7 @@ features: first_decimal: use: true parameters: - contamination: 0.11 + contamination: 0.1 frequency: 100 first_digit: use: true @@ -61,19 +61,19 @@ features: use: true sub_features: [gps_latitude, gps_longitude, gps_accuracy] parameters: - contamination: 0.11 + contamination: 0.1 pause_count: use: true parameters: - contamination: 0.11 + contamination: 0.1 pause_duration: use: true parameters: - contamination: 0.11 + contamination: 0.1 pause_list: use: true parameters: - contamination: 0.11 + contamination: 0.1 comment_length: use: true comment_set: @@ -85,15 +85,15 @@ features: number_answered: use: true parameters: - contamination: 0.11 + contamination: 0.1 total_duration: use: true parameters: - contamination: 0.11 + contamination: 0.1 total_elapse: use: true parameters: - contamination: 0.11 + contamination: 0.1 single_question: use: true multi_option_question: diff --git a/rissk_kedro/pyproject.toml b/rissk_kedro/pyproject.toml index a31a821..a3c4808 100644 --- a/rissk_kedro/pyproject.toml +++ b/rissk_kedro/pyproject.toml @@ -3,37 +3,43 @@ requires = ["flit_core >=3.2,<4"] build-backend = "flit_core.buildapi" [project] -name = "rissk" +# NOTE: named "rissk-pipeline" to avoid collision with the root "rissk" ML package +# in the uv workspace. The installed Python package is still "rissk_kedro". +name = "rissk-pipeline" version = "0.1.2" -description = "Automatically identify at-risk interviews from your Survey Solutions export files." +description = "Kedro pipeline for RISSK — Survey Quality Control." authors = [{ name = "rowsquared" }] license = { file = "LICENSE" } readme = "README.md" +requires-python = ">=3.13" classifiers = [ "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.13", "License :: OSI Approved :: MIT License", - "Operating System :: MacOS", ] -# Standard Dependencies (formerly requirements.txt) +# Minimal runtime dependencies — only what the pipeline actually imports. dependencies = [ + "rissk", # ML logic (workspace root package) "kedro==1.2.0", - "kedro-datasets[pandas,s3fs,excel,files]>=9.1.0", - "rpy2>=3.6.4", + "kedro-datasets[pandas]>=9.1.0", # parquet + CSV + PartitionedDataset "pandas>=2.2.3", "numpy>=2.1.0", "pyod>=1.1.5", - "loguru>=0.7.3", + "scipy>=1.10", + "scikit-learn>=1.5", + "pyyaml>=6.0", ] -# Replaces tests_require from setup.py [project.optional-dependencies] test = [ "pytest>=8.0", "pytest-cov", ] +# Tell uv where to find the "rissk" package (workspace sibling at repo root). +[tool.uv.sources] +rissk = { workspace = true } + [tool.kedro] package_name = "rissk_kedro" project_name = "rissk" diff --git a/rissk_kedro/requirements.txt b/rissk_kedro/requirements.txt index 4eab0d7..c4ac5bf 100644 --- a/rissk_kedro/requirements.txt +++ b/rissk_kedro/requirements.txt @@ -1,24 +1,24 @@ -# This file lists the dependencies for the Rissk Kedro project. +# RISSK Kedro — runtime dependencies +# Python >= 3.13 +# +# For uv users: uv sync (reads pyproject.toml, ignores this file) +# For conda/pip: pip install -r requirements.txt -# Kedro template dependencies -# python~=3.11 -ipython>=8.10 -jupyterlab>=3.0 -notebook -kedro[jupyter]~=1.2.0 -kedro-datasets[pandas-csvdataset, pandas-exceldataset, pandas-parquetdataset, pandas-statadataset, plotly-plotlydataset, plotly-jsondataset, matplotlib-matplotlibdataset, spark-sparkdataset]>=9.1 -kedro-viz>=6.7.0 -scikit-learn~=1.5.1 -seaborn~=0.12.1 +# --- Kedro framework --- +kedro==1.2.0 +# pandas extra covers ParquetDataset, CSVDataset and PartitionedDataset +kedro-datasets[pandas]>=9.1.0 -# Additional dependencies for the Rissk project -hydra-core>=1.3.2 -numpy>=1.24.4 -pandas>=2.0.3 -openpyxl>=3.1.2 -# scikit-learn>=1.3.0 -scipy>=1.10.1 -# seaborn>=0.12.2 -pyod>=1.1.0 -pythresh>=0.3.3 +# --- Core ML / data --- +pandas>=2.2.3 +numpy>=2.1.0 +scipy>=1.10 # cKDTree, stats, mstats, winsorize, entropy, distance_matrix +scikit-learn>=1.5 # StandardScaler, LabelEncoder, IsolationForest, PCA, NearestNeighbors +pyod>=1.1.5 # ECOD, COF, INNE, LOF, IForest, FILTER + +# --- Config --- +pyyaml>=6.0 + +# --- GUI --- +nicegui>=1.4 diff --git a/rissk_kedro/run_gui.bat b/rissk_kedro/run_gui.bat new file mode 100644 index 0000000..6af74db --- /dev/null +++ b/rissk_kedro/run_gui.bat @@ -0,0 +1,12 @@ +@echo off +REM RISSK GUI launcher for Windows +cd /d "%~dp0" + +python -c "import nicegui" 2>nul || pip install "nicegui>=1.4" + +echo. +echo Starting RISSK GUI... +echo Open your browser at: http://localhost:8080 +echo (Press Ctrl+C to stop) +echo. +python app\main.py diff --git a/rissk_kedro/run_gui.sh b/rissk_kedro/run_gui.sh new file mode 100755 index 0000000..91b9df3 --- /dev/null +++ b/rissk_kedro/run_gui.sh @@ -0,0 +1,20 @@ +#!/usr/bin/env bash +# RISSK GUI launcher +# Run this script from any directory — it always executes from rissk_kedro/. +set -e + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +cd "$SCRIPT_DIR" + +# Check for nicegui; install automatically if missing. +if ! python -c "import nicegui" 2>/dev/null; then + echo "NiceGUI not found. Installing..." + pip install "nicegui>=1.4" +fi + +echo "" +echo "Starting RISSK GUI..." +echo "Open your browser at: http://localhost:8080" +echo "(Press Ctrl+C to stop)" +echo "" +python app/main.py diff --git a/rissk_kedro/src/rissk_kedro/pipeline_registry.py b/rissk_kedro/src/rissk_kedro/pipeline_registry.py index 83cf8fb..195d011 100644 --- a/rissk_kedro/src/rissk_kedro/pipeline_registry.py +++ b/rissk_kedro/src/rissk_kedro/pipeline_registry.py @@ -10,30 +10,49 @@ from rissk_kedro.pipelines.feature_creation.nodes import make_qnr_filter, make_consent_filter -def _load_questionnaire_names() -> list[str]: - """Read questionnaire names from conf/base/globals.yml at registry build time. +def _read_globals() -> dict: + """Merge conf/base/globals.yml with conf/local/globals.yml (local takes precedence). pipeline_registry.py is imported before Kedro's ConfigLoader is available, so - globals.yml is read directly via yaml.safe_load. The path is resolved relative - to this file: src/rissk_kedro/ -> (parents[2]) -> rissk_kedro/ project root. + globals files are read directly via yaml.safe_load. The project root is resolved + relative to this file: src/rissk_kedro/ -> (parents[2]) -> rissk_kedro/. + The GUI writes user configuration to conf/local/globals.yml; this function ensures + those overrides are visible to the pipeline registry. """ - globals_path = Path(__file__).parents[2] / "conf" / "base" / "globals.yml" - with globals_path.open() as fh: - globals_data = yaml.safe_load(fh) - questionnaires = globals_data.get("survey", {}).get("questionnaires", []) + project_root = Path(__file__).parents[2] + + def _deep_merge(base: dict, override: dict) -> dict: + out = dict(base) + for k, v in override.items(): + if k in out and isinstance(out[k], dict) and isinstance(v, dict): + out[k] = _deep_merge(out[k], v) + else: + out[k] = v + return out + + base_path = project_root / "conf" / "base" / "globals.yml" + base = yaml.safe_load(base_path.read_text()) or {} + + local_path = project_root / "conf" / "local" / "globals.yml" + if local_path.exists(): + local = yaml.safe_load(local_path.read_text()) or {} + return _deep_merge(base, local) + return base + + +def _load_questionnaire_names() -> list[str]: + """Return questionnaire names from the merged globals config.""" + questionnaires = _read_globals().get("survey", {}).get("questionnaires", []) return [q["name"] for q in questionnaires] def _load_questionnaires() -> list[dict]: - """Return the full list of questionnaire config dicts from conf/base/globals.yml. + """Return the full list of questionnaire config dicts from the merged globals config. Each dict may contain ``name``, ``VERSION``, and the optional ``filter_var`` consent-filter setting. 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b/pyproject.toml index 037cfd8..a400393 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,11 +11,31 @@ authors = [ ] license = { file = "LICENSE" } readme = "README.md" +requires-python = ">=3.13" classifiers = [ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License" ] -requires-python = "~=3.9" + +# Minimal runtime dependencies for the rissk ML package. +dependencies = [ + "pandas>=2.2.3", + "numpy>=2.1.0", + "pyod>=1.1.5", + "scipy>=1.10", + "scikit-learn>=1.5", + "pyyaml>=6.0", +] + +[project.optional-dependencies] +# Install with: uv sync --extra gui +gui = [ + "nicegui>=1.4", +] + +# uv workspace: rissk_kedro/ is a member so `uv sync` installs both packages together. +[tool.uv.workspace] +members = ["rissk_kedro"] [tool.black] line-length = 99 From 112255673ee815f803f8c768cfc944b847d37b4a Mon Sep 17 00:00:00 2001 From: VJausovec Date: Mon, 6 Apr 2026 22:52:21 +0100 Subject: [PATCH 55/70] Enhance documentation and setup instructions; update Python version requirement, clarify scoring functions, and improve GUI launcher scripts. --- rissk/feature_processing_kedro.py | 42 +++++++--- rissk/item_processing_kedro.py | 83 ++++++++++++++++++- rissk/unit_processing_kedro.py | 33 ++++++-- rissk/utils/import_utils_kedro.py | 14 ++-- rissk_kedro/SETUP.md | 20 +++-- rissk_kedro/conf/base/globals.yml | 30 +++---- rissk_kedro/conf/base/parameters.yml | 7 +- rissk_kedro/run_gui.sh | 10 +++ .../pipelines/data_ingestion/nodes.py | 11 ++- run_gui.bat | 3 + run_gui.sh | 3 + 11 files changed, 199 insertions(+), 57 deletions(-) create mode 100644 run_gui.bat create mode 100755 run_gui.sh diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index 07f1c27..7e9e291 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -122,9 +122,12 @@ def get_df_time(df_paradata_full: pd.DataFrame) -> pd.DataFrame: # Using pd.NA for nullable integers/floats in pandas if column allows, or np.nan df_time.loc[df_time['time_difference'] < 0, 'time_difference'] = pd.NA - # time for answers/comments + # f__answer_duration: total time spent recording answers, i.e. the sum of all + # time-intervals from active events that conclude with AnswerSet or AnswerRemoved. df_time['f__answer_duration'] = df_time.loc[ df_time['event'].isin(['AnswerSet', 'AnswerRemoved']), 'time_difference'] + # f__comment_duration: total time spent on comments, i.e. the sum of all + # time-intervals from active events that conclude with CommentSet. df_time['f__comment_duration'] = df_time.loc[df_time['event'] == 'CommentSet', 'time_difference'] df_time['f__pause_duration'] = df_time.loc[df_time['event'].isin(['Resumed', 'Restarted']), 'time_difference'] @@ -135,8 +138,10 @@ def get_df_time(df_paradata_full: pd.DataFrame) -> pd.DataFrame: condition = (df_time['event'].isin(active_events)) & (df_time['time_difference'] < 30 * 60) df_time['f__total_duration'] = df_time.loc[condition, 'time_difference'] - # Starting timestamp per interview: min timestamp of the first AnswerSet event per interview. - # Using map on a pre-computed groupby result (matching the legacy approach). + # Starting timestamp per interview: minimum timestamp among AnswerSet events (not the + # global event minimum). If the device clock is adjusted later in the interview, the + # overall min of timestamp_local would return a misleadingly early start time. Anchoring + # to the first AnswerSet avoids this clock-adjustment artefact. start_time_map = df_time[df_time['event'] == 'AnswerSet'].groupby('interview__id')['timestamp_local'].min() df_time['f__starting_timestamp'] = df_time['interview__id'].map(start_time_map) @@ -165,21 +170,24 @@ def get_df_sequence(df_paradata_full: pd.DataFrame) -> pd.DataFrame: df_last['f__previous_answer'] = df_last.groupby('interview__id')['answer'].shift().fillna(pd.NA) df_last['f__previous_roster'] = df_last.groupby('interview__id')['roster_level'].shift().fillna(pd.NA) - # f__sequence_jump - # Calculate answer sequence (1, 2, 3...) based on actual occurrence + # f__sequence_jump: the change in the gap between questionnaire order and answer order + # from one answered question to the next. A non-zero value means the interviewer skipped + # questions or navigated backwards relative to the questionnaire sequence. + # Calculate answer sequence (1, 2, 3...) based on actual occurrence order. df_last['answer_sequence'] = df_last.groupby('interview__id').cumcount() + 1 - - # Diff between questionnaire sequence and answer sequence - # Ensure types are compatible + + # diff = questionnaire position - answer position; a constant diff means sequential + # answering, while a change in diff indicates skipping ahead or going backwards. df_last['question_sequence'] = pd.to_numeric(df_last['question_sequence'], errors='coerce').fillna(0) df_last['diff'] = df_last['question_sequence'] - df_last['answer_sequence'] - - # The 'jump' is the difference of the difference + + # The jump is how much the diff itself changed from one answer to the next. df_last['f__sequence_jump'] = df_last.groupby('interview__id')['diff'].diff() return df_last def add_sequence_features(df_item: pd.DataFrame, df_sequence: pd.DataFrame, allowed_features: list) -> pd.DataFrame: + """Merge sequence-based features from df_sequence onto df_item for enabled features.""" sequence_features = ['f__previous_question', 'f__previous_answer', 'f__previous_roster', 'f__sequence_jump'] @@ -198,6 +206,7 @@ def add_sequence_features(df_item: pd.DataFrame, df_sequence: pd.DataFrame, allo return df_item def add_item_time_features(df_item: pd.DataFrame, df_time: pd.DataFrame, allowed_features: list, item_level_columns: list) -> pd.DataFrame: + """Aggregate per-event answer/comment durations from df_time to item level and merge onto df_item.""" time_features = ['f__answer_duration', 'f__comment_duration'] selected_features = [f for f in time_features if f in allowed_features] @@ -234,6 +243,7 @@ def add_item_time_features(df_item: pd.DataFrame, df_time: pd.DataFrame, allowed return df_item def add_pause_features(df_unit: pd.DataFrame, df_time: pd.DataFrame, allowed_features: list) -> pd.DataFrame: + """Aggregate pause count, total duration, and duration list from df_time to the unit table.""" pause_features = ['f__pause_count', 'f__pause_duration', 'f__pause_list'] selected_features = [f for f in pause_features if f in allowed_features] @@ -266,6 +276,7 @@ def add_pause_features(df_unit: pd.DataFrame, df_time: pd.DataFrame, allowed_fea return df_unit def add_unit_time_features(df_unit: pd.DataFrame, df_time: pd.DataFrame, allowed_features: list) -> pd.DataFrame: + """Aggregate interview-level time features (total duration, elapse, days since start, clock shifts) onto df_unit.""" time_features = ['f__total_duration', 'f__total_elapse', 'f__days_from_start', 'f__time_changed'] selected_features = [f for f in time_features if f in allowed_features] @@ -475,6 +486,11 @@ def feat_first_decimal(df_item, **kwargs): return df_item def feat_answer_position(df_item, **kwargs): + """Compute f__answer_position: relative position of the selected answer within the answer list. + + Calculated only for SingleQuestion items with more than two options, excluding filtered + comboboxes and cascade children. Position is idx / (n_answers - 1), producing a value in [0, 1]. + """ # f__answer_position, relative position of the selected answer # only questions with more than two answers feature_name = 'f__answer_position' @@ -522,6 +538,12 @@ def calc_pos(row): return df_item def feat_answer_removed(paradata_full): + """Aggregate AnswerRemoved event counts per item into f__answer_removed. + + Returns a standalone DataFrame (not merged into df_item) because AnswerRemoved events + may reference items that were subsequently deleted from microdata and are therefore absent + from df_item. Consuming code scores this separately via calculate_answer_removed_score_from_df. + """ # f__answer_removed, answers removed (by interviewer, or by system as a result of interviewer action). # Matches legacy get_feature_item__answer_removed which uses self.df_paradata, but it appends the # feature to the item table instead of returning a separate dataframe. diff --git a/rissk/item_processing_kedro.py b/rissk/item_processing_kedro.py index 80e88b1..18ec66f 100644 --- a/rissk/item_processing_kedro.py +++ b/rissk/item_processing_kedro.py @@ -91,6 +91,17 @@ def filter_columns( # --- SCORING FUNCTIONS BEGIN --- def calculate_gps_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + """Score GPS coordinates for spatial outliers and coordinate proximity. + + Produces three scores on each row: + - s__gps_proximity_counts: number of other GPS points within 10 m (accounting for accuracy). + - s__gps_extreme_outlier: 1 if the point is a 0,0 fix or lies beyond p75 + 3.5*IQR of + the Cartesian distance distribution from the median survey location. + - s__gps_outlier: 1/0 from COF (< 10 000 points) or LOF (>= 10 000 points) fit on x/y coords. + + Also sets s__gps (integer flag: 1 = GPS question row, NaN = other) so that the unit-level + aggregation can count GPS questions per interview regardless of outlier model outcome. + """ df = df_item.copy() score_cols = ['s__gps_proximity_counts', 's__gps_outlier', 's__gps_extreme_outlier'] required_columns = ['f__gps_latitude', 'f__gps_longitude', 'f__gps_accuracy'] @@ -215,6 +226,11 @@ def calculate_gps_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd def calculate_sequence_jump_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + """Score sequence-jump anomalies per variable using the INNE isolation-based model. + + Only variables with at least 100 records and 3 distinct jump values are scored. + Rows for variables that don't meet the threshold keep s__sequence_jump = NaN. + """ feature_name = 'f__sequence_jump' score_name = rename_feature(feature_name) df = df_item.copy() @@ -246,6 +262,11 @@ def calculate_sequence_jump_score(df_item: pd.DataFrame, parameters: Dict[str, A def calculate_first_decimal_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + """Score first-decimal-digit anomalies per variable using the COF density model. + + Only variables with at least 100 records and 3 distinct first-decimal values are scored, + matching the legacy make_score__first_decimal filter. + """ feature_name = 'f__first_decimal' score_name = rename_feature(feature_name) df = df_item.copy() @@ -280,6 +301,12 @@ def calculate_first_decimal_score(df_item: pd.DataFrame, parameters: Dict[str, A def calculate_answer_hour_set_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + """Score hour-of-day anomalies using ECOD on f__answer_hour_set. + + ECOD is a parameter-free outlier detection algorithm based on empirical CDF functions. + Hours that ECOD flags but which are the most frequent in the dataset are reverted to 0 + (non-anomalous) because high-frequency hours cannot represent interviewer-level anomalies. + """ # Detect time set anomalies using ECOD algorithm. # ECOD is a parameter-free, highly interpretable outlier detection algorithm based on empirical CDF functions feature_name = 'f__answer_hour_set' @@ -331,6 +358,10 @@ def calculate_answer_hour_set_score(df_item: pd.DataFrame, parameters: Dict[str, def calculate_answer_changed_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + """Score answer-change anomalies per variable using ECOD. + + Only variables with at least 100 records and at least 1 distinct change value are scored. + """ feature_name = 'f__answer_changed' score_name = rename_feature(feature_name) df = df_item.copy() @@ -423,6 +454,12 @@ def calculate_answer_removed_score_from_df( def calculate_answer_position_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + """Score answer-position entropy per responsible per variable. + + Flags enumerators whose selected-answer-position distribution differs by more than 50% + from the median entropy across all enumerators for each variable. + Only variables with at least 100 records and 3 distinct position values are scored. + """ feature_name = 'f__answer_position' score_name = rename_feature(feature_name) df = df_item.copy() @@ -433,6 +470,10 @@ def calculate_answer_position_score(df_item: pd.DataFrame, parameters: Dict[str, df[score_name] = np.nan return df + # Score is computed at the responsible level: entropy of answer-position distributions + # measures whether an enumerator systematically favours certain positions (e.g. always + # first or always last option). This bias is only detectable across many interviews for + # the same enumerator, not within a single interview. valid_variables = filter_variable_name_by_frequency( df[~pd.isnull(df[feature_name])], feature_name, frequency=100, min_unique_values=3) df[score_name] = np.nan @@ -458,6 +499,12 @@ def calculate_answer_position_score(df_item: pd.DataFrame, parameters: Dict[str, return df def calculate_answer_selected_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + """Score the share of selected options in multi-option questions using ECOD. + + Splits the output into s__answer_selected_lower (too few options selected) and + s__answer_selected_upper (too many selected) based on the inlier range for each variable. + Only variables with at least 100 records and 3 distinct share values are scored. + """ feature_name = 'f__answer_selected' score_name = rename_feature(feature_name) df = df_item.copy() @@ -511,6 +558,12 @@ def calculate_answer_selected_score(df_item: pd.DataFrame, parameters: Dict[str, def calculate_answer_duration_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + """Score answer-duration anomalies per variable using ECOD. + + Splits output into s__answer_duration_lower (unusually fast) and s__answer_duration_upper + (unusually slow) relative to the inlier range for each variable. + Only variables with at least 100 records and 3 distinct duration values are scored. + """ feature_name = 'f__answer_duration' score_name = rename_feature(feature_name) df = df_item.copy() @@ -562,6 +615,13 @@ def calculate_answer_duration_score(df_item: pd.DataFrame, parameters: Dict[str, return df def calculate_single_question_score(df_item: pd.DataFrame) -> pd.DataFrame: + """Score single-answer question entropy per responsible per variable. + + Flags enumerators whose selected-answer distribution for a variable differs by more than 50% + from the median entropy across all enumerators, indicating a potential acquiescence bias. + Excludes filtered comboboxes and cascade questions. Only variables with at least 100 + records and 3 distinct answer values are scored. + """ feature_name = 'f__single_question' score_name = rename_feature(feature_name) df = df_item.copy() @@ -571,7 +631,11 @@ def calculate_single_question_score(df_item: pd.DataFrame) -> pd.DataFrame: # directly on 'value' with a qtype mask, matching legacy make_score__single_question. if any(col not in df.columns for col in columns): return df - + + # Score is computed at the responsible level: entropy of the selected-answer distribution + # reveals whether an enumerator consistently picks the same option across interviews. + # This acquiescence bias only emerges when comparing many interviews per enumerator. + # Mask specific for single questions without filter rules bypassing cascades single_question_mask = ( (df["qtype"] == 'SingleQuestion') & @@ -610,6 +674,12 @@ def calculate_single_question_score(df_item: pd.DataFrame) -> pd.DataFrame: def calculate_multi_option_question_score(df_item: pd.DataFrame) -> pd.DataFrame: + """Score multi-option question entropy per responsible per variable. + + Flags enumerators whose combination of selected answers for a variable differs by more than + 50% from the median list-entropy across all enumerators, indicating systematic subset selection. + Only variables with at least 100 records and 3 distinct answer combinations are scored. + """ feature_name = 'f__multi_option_question' score_name = rename_feature(feature_name) df = df_item.copy() @@ -619,6 +689,10 @@ def calculate_multi_option_question_score(df_item: pd.DataFrame) -> pd.DataFrame if 'qtype' not in df.columns: return df + # Score is computed at the responsible level: entropy of the combination of options + # selected across interviews exposes enumerators who systematically pick the same + # subset of answers for every respondent. + multi_question_mask = (df["qtype"] == 'MultyOptionsQuestion') valid_data = df[multi_question_mask].copy() @@ -654,6 +728,13 @@ def calculate_multi_option_question_score(df_item: pd.DataFrame) -> pd.DataFrame return df def calculate_first_digit_score(df_item: pd.DataFrame) -> pd.DataFrame: + """Score first-digit Benford's Law deviations per responsible per variable. + + Computes the Jensen divergence between an enumerator's first-digit distribution and + that of all other enumerators. Only numeric variables spanning at least 3 orders of + magnitude and enumerators with at least 50 records per variable are evaluated. + Enumerators whose divergence exceeds the median by more than 50% are flagged. + """ feature_name = 'f__numeric_response' first_digit_feature = 'f__first_digit' score_name = 's__first_digit' diff --git a/rissk/unit_processing_kedro.py b/rissk/unit_processing_kedro.py index 3c62d92..dae9d06 100644 --- a/rissk/unit_processing_kedro.py +++ b/rissk/unit_processing_kedro.py @@ -118,6 +118,8 @@ def aggregate_item_to_unit_scores(df_unit: pd.DataFrame, df_item_scores: pd.Data # Note: s__answer_removed is intentionally excluded here — it is scored # at unit level directly from paradata_full by calculate_answer_removed_unit_score # in calculate_unit_scores, so that items deleted from microdata are included. + # fillna(0): an interview absent from df_item_scores for a given feature has no + # scorable items, which means no anomaly was detected — the absence is not unknown. mean_scores = [ 's__answer_hour_set', 's__answer_changed', 's__first_decimal', 's__sequence_jump' @@ -151,16 +153,24 @@ def aggregate_item_to_unit_scores(df_unit: pd.DataFrame, df_item_scores: pd.Data return df_out def calculate_unit_level_scores(df_unit: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: - """Calculate scores that are purely derived from unit-level features.""" + """Calculate unit-level scores derived directly from unit-feature columns. + + These scores do not involve item-level aggregation; each is a simple transformation + of an existing unit feature (rescaling, rate normalisation, or ECOD outlier detection). + Only columns present in df_unit are processed; missing features are skipped silently. + """ df = df_unit.copy() if 'f__time_changed' in df.columns: + # Divide by 600 (seconds) to express device clock shifts in 10-minute units. df['s__time_changed'] = round(df['f__time_changed'].abs() / 600) - + if 'f__total_duration' in df.columns: + # Divide by 300 (seconds) to express total active interview time in 5-minute units. df['s__total_duration'] = round(df['f__total_duration'] / 300) - + if 'f__days_from_start' in df.columns: + # Convert days elapsed since the first interview in the dataset to weeks. df['s__days_from_start'] = (df['f__days_from_start'] / 7).astype(int) if 'f__total_elapse' in df.columns: @@ -192,11 +202,13 @@ def calculate_unit_level_scores(df_unit: pd.DataFrame, parameters: Dict[str, Any df.drop(columns=[score_name, 'f__total_elapse_scaled'], inplace=True, errors='ignore') if 'f__pause_duration' in df.columns and 'f__total_elapse' in df.columns: - df['s__pause_duration'] = np.where(df['f__total_elapse'] != 0, + # Express pause time as a fraction of the total elapsed interview time. + df['s__pause_duration'] = np.where(df['f__total_elapse'] != 0, df['f__pause_duration'] / df['f__total_elapse'], 0) - + if 'f__pause_count' in df.columns and 'f__number_answered' in df.columns: - df['s__pause_count'] = np.where(df['f__number_answered'] != 0, + # Express pause count as a rate per answered question, normalising for interview length. + df['s__pause_count'] = np.where(df['f__number_answered'] != 0, df['f__pause_count'] / df['f__number_answered'], 0) if 'f__number_answered' in df.columns: @@ -216,7 +228,11 @@ def aggregate_item_to_responsible_scores(df_resp: pd.DataFrame, df_item_scores: if df_out.empty: return df_out - # Mean across responsible directly + # s__single_question, s__multi_option_question, and s__answer_position are computed at + # the responsible level: they measure how uniformly an enumerator distributes answers + # across categories or positions, a pattern that only becomes detectable when pooling + # many interviews. Scores are averaged first within each variable, then across variables, + # to prevent high-answer-count questions from dominating the responsible-level signal. scores_double_mean = ['s__single_question', 's__multi_option_question', 's__answer_position'] for score in scores_double_mean: if score in df_item_scores.columns: @@ -225,6 +241,9 @@ def aggregate_item_to_responsible_scores(df_resp: pd.DataFrame, df_item_scores: if 'responsible' in df_out.columns: df_out[score] = df_out['responsible'].map(data).fillna(0) + # s__first_digit uses Jensen divergence from Benford's Law, which requires a large + # sample of numeric responses per enumerator to be statistically meaningful and is + # therefore aggregated at the responsible level rather than per interview. if 's__first_digit' in df_item_scores.columns: data = df_item_scores.groupby('responsible')['s__first_digit'].mean() if 'responsible' in df_out.columns: diff --git a/rissk/utils/import_utils_kedro.py b/rissk/utils/import_utils_kedro.py index c2c1713..2a2a678 100644 --- a/rissk/utils/import_utils_kedro.py +++ b/rissk/utils/import_utils_kedro.py @@ -71,14 +71,14 @@ def filter_matching_folders(partitions: Dict[str, Callable[[], Path]], questionn patterns = [] for q in questionnaires: name = q.get("name") - versions = q.get("VERSION", []) - if not name or not versions: + if not name: continue - - version_pattern = "|".join(map(str, versions)) - # Pattern: Matches start of string, the name, an underscore, - # one of the versions, and then an underscore or end of string. - # Example: ^slbhies_listing_(1|2|6)_.* + versions = q.get("VERSION", []) + # Empty VERSION list means "accept any integer version" + version_pattern = "|".join(map(str, versions)) if versions else r"\d+" + # Pattern: Matches start of string, the name, an underscore, + # one of the versions (or any integer), and then an underscore. + # Example: ^slbhies_listing_(1|2|6)_.* or ^slbhies_listing_(\d+)_.* regex = re.compile(rf"^{re.escape(name)}_({version_pattern})_.*") patterns.append(regex) diff --git a/rissk_kedro/SETUP.md b/rissk_kedro/SETUP.md index dc698e7..ab0dce3 100644 --- a/rissk_kedro/SETUP.md +++ b/rissk_kedro/SETUP.md @@ -7,7 +7,7 @@ flagging individual interviews most likely to contain unwanted interviewer behav ## Prerequisites -- **Python 3.10 – 3.13** installed on your machine +- **Python 3.13** installed on your machine - An internet connection for the initial install - Survey Solutions export files (Main Survey Data + Paradata ZIPs) @@ -41,16 +41,15 @@ powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | ie Clone with Git: ```bash git clone https://github.com/rowsquared/rissk.git -cd rissk/rissk_kedro +cd rissk ``` -Or download the ZIP from GitHub, unzip it, and navigate to the `rissk_kedro/` folder. +Or download the ZIP from GitHub, unzip it, and navigate to the `rissk/` folder. ### 3. Install dependencies ```bash -uv sync -uv pip install "nicegui>=1.4" +uv sync --extra gui ``` ### 4. Launch the GUI @@ -82,18 +81,18 @@ conda activate rissk ```bash git clone https://github.com/rowsquared/rissk.git -cd rissk/rissk_kedro +cd rissk ``` ### 3. Install dependencies ```bash -pip install -e ".[gui]" +pip install -e "rissk_kedro[gui]" ``` Or install manually: ```bash -pip install -r requirements.txt +pip install -r rissk_kedro/requirements.txt pip install "nicegui>=1.4" ``` @@ -167,6 +166,9 @@ Access the **Advanced** tab to: Experienced users can run Kedro directly from the `rissk_kedro/` directory: +```bash +cd rissk_kedro + ```bash # Full pipeline kedro run @@ -177,7 +179,7 @@ kedro run --pipeline feature_creation kedro run --pipeline rissk_scoring ``` -Configuration overrides go in `conf/local/globals.yml` and `conf/local/parameters.yml` +Configuration overrides go in `rissk_kedro/conf/local/globals.yml` and `rissk_kedro/conf/local/parameters.yml` (these files are ignored by git). --- diff --git a/rissk_kedro/conf/base/globals.yml b/rissk_kedro/conf/base/globals.yml index 44afb5a..43d7836 100644 --- a/rissk_kedro/conf/base/globals.yml +++ b/rissk_kedro/conf/base/globals.yml @@ -19,23 +19,23 @@ data_root: "data" # # # # The answer value must be a string (paradata answers are always strings). -survey: - name: "pmpmd" - questionnaires: - - name: "pmpmd_community" - VERSION: [2, 3, 4, 5] - filter_var: null - - name: "pmpmd_household" - VERSION: [4, 5, 6] - filter_var: null - - # survey: -# name: "slchbs" +# name: "pmpmd" # questionnaires: -# - name: "slchbs_saintlucia_2025" -# VERSION: [6, 7] # 5 is for testing empty data handling -# filter_var: null +# - name: "pmpmd_community" +# VERSION: [2, 3, 4, 5] +# filter_var: null +# - name: "pmpmd_household" +# VERSION: [4, 5, 6] +# filter_var: null + + +survey: + name: "slchbs" + questionnaires: + - name: "slchbs_saintlucia_2025" + VERSION: [6, 7] # 5 is for testing empty data handling + filter_var: null # survey: diff --git a/rissk_kedro/conf/base/parameters.yml b/rissk_kedro/conf/base/parameters.yml index f2d2891..87d1843 100644 --- a/rissk_kedro/conf/base/parameters.yml +++ b/rissk_kedro/conf/base/parameters.yml @@ -44,7 +44,6 @@ features: use: true parameters: contamination: 0.1 - frequency: 100 first_digit: use: true last_digit: @@ -104,8 +103,4 @@ features: use: true string_length: use: true - -# # Output Configuration -# output: -# feature_score: true -# unit_risk_score_path: "results/unit_risk_score.csv" \ No newline at end of file + \ No newline at end of file diff --git a/rissk_kedro/run_gui.sh b/rissk_kedro/run_gui.sh index 91b9df3..0a0bb5b 100755 --- a/rissk_kedro/run_gui.sh +++ b/rissk_kedro/run_gui.sh @@ -6,6 +6,16 @@ set -e SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" cd "$SCRIPT_DIR" +# Warn if running in the conda base environment +if [[ "${CONDA_DEFAULT_ENV}" == "base" ]]; then + echo "WARNING: You are running in the conda 'base' environment." + echo "It is strongly recommended to activate your project environment first:" + echo " conda activate rissk_py3_13_macos" + echo "" + read -r -p "Continue anyway? [y/N] " confirm + [[ "${confirm,,}" == "y" ]] || exit 1 +fi + # Check for nicegui; install automatically if missing. if ! python -c "import nicegui" 2>/dev/null; then echo "NiceGUI not found. Installing..." diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py index 3d564ea..a09a303 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py @@ -47,7 +47,8 @@ def filter_extracted_survey_paths_node(survey_partitions: Dict[str, Callable[[], """ lines = ["=" * 55, " DATA INGESTION — Questionnaires to process", "=" * 55] for q in questionnaires: - versions = ", ".join(str(v) for v in q.get("VERSION", [])) + ver_list = q.get("VERSION", []) + versions = ", ".join(str(v) for v in ver_list) if ver_list else "all" lines.append(f" • {q['name']} | versions: [{versions}]") lines.append("=" * 55) logger.info("\n" + "\n".join(lines)) @@ -133,7 +134,13 @@ def process_paradata_node( paradata['timestamp_local'].dt.round('30min').dt.minute / 60 ) - # 4. Calculate interviewing flag and filter to first-pass interviewer events + # 4. Calculate interviewing flag and filter to first-pass interviewer events. + # interviewing=True for all events that occurred before any Supervisor/HQ interaction, + # False for everything after. A rejection or review event resets the interpretive + # context: answers recorded afterwards belong to a different (post-review) pass and + # should not be scored as if they were the original interviewing session. + # The cumsum trick propagates the flag forward so every subsequent row in the same + # interview automatically receives interviewing=False once the first flagged event fires. events_split = ['RejectedBySupervisor', 'OpenedBySupervisor', 'OpenedByHQ', 'RejectedByHQ'] paradata['flag'] = paradata['event'].isin(events_split) paradata['cumulative_flag'] = paradata.groupby('interview__id')['flag'].cumsum() diff --git a/run_gui.bat b/run_gui.bat new file mode 100644 index 0000000..c5ba7d3 --- /dev/null +++ b/run_gui.bat @@ -0,0 +1,3 @@ +@echo off +REM RISSK GUI launcher — run from the repo root (rissk/) +call "%~dp0rissk_kedro\run_gui.bat" diff --git a/run_gui.sh b/run_gui.sh new file mode 100755 index 0000000..b7268f7 --- /dev/null +++ b/run_gui.sh @@ -0,0 +1,3 @@ +#!/usr/bin/env bash +# RISSK GUI launcher — run from the repo root (rissk/) +exec "$(dirname "${BASH_SOURCE[0]}")/rissk_kedro/run_gui.sh" "$@" From 74dd20c69079927cd2ac4fe08d5adacb94c786ed Mon Sep 17 00:00:00 2001 From: VJausovec Date: Tue, 7 Apr 2026 11:34:49 +0100 Subject: [PATCH 56/70] Rename first_decimal features and functions to first_decimals for clarity; update scoring logic to capture two decimal digits. --- rissk/feature_processing_kedro.py | 15 ++++++++++----- rissk/item_processing_kedro.py | 10 +++++----- rissk/unit_processing_kedro.py | 2 +- rissk_kedro/conf/base/parameters.yml | 2 +- .../rissk_kedro/pipelines/rissk_scoring/nodes.py | 8 ++++---- 5 files changed, 21 insertions(+), 16 deletions(-) diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index 7e9e291..f956ee5 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -466,9 +466,9 @@ def feat_last_digit(df_item, **kwargs): return df_item -def feat_first_decimal(df_item, **kwargs): - # f__first_decimal, first decimal digit if numeric question else empty pd.NA - feature_name = 'f__first_decimal' +def feat_first_decimals(df_item, **kwargs): + # f__first_decimals, first decimals if numeric question else empty pd.NA + feature_name = 'f__first_decimals' # mask: not integer, not empty & not mumeric sentinel numeric_mask = get_numeric_mask(df_item=df_item, filter_answer_values=True) mask_integer = (df_item['is_integer'] == False) & (~pd.isnull(df_item['value'])) @@ -477,7 +477,12 @@ def feat_first_decimal(df_item, **kwargs): if mask.any(): values = pd.to_numeric(df_item.loc[mask, 'value'], errors='coerce') - res = np.floor(values * 10) % 10 + # Intentional: capture the first TWO decimal digits (e.g. 3.47 → 47) rather than just + # the first (e.g. 4). Using two digits gives the COF model a finer-grained signal and + # materially reduces hash-collisions for values like x.10, x.20 … x.90 that would be + # indistinguishable if only a single decimal were retained. The feature is therefore + # named f__first_decimals (plural) to make this design choice visible at a glance. + res = np.floor(values * 100) % 100 df_item.loc[mask, feature_name] = res.astype('Int64') # Match legacy: ensure the full feature column uses nullable integer dtype. @@ -763,7 +768,7 @@ def feat_gps(df_item, **kwargs): 'numeric_response': feat_numeric_response, 'first_digit': feat_first_digit, 'last_digit': feat_last_digit, - 'first_decimal': feat_first_decimal, + 'first_decimals': feat_first_decimals, 'answer_position': feat_answer_position, 'answer_changed': feat_answer_changed, 'answer_selected': feat_answer_selected, diff --git a/rissk/item_processing_kedro.py b/rissk/item_processing_kedro.py index 18ec66f..57ce674 100644 --- a/rissk/item_processing_kedro.py +++ b/rissk/item_processing_kedro.py @@ -261,13 +261,13 @@ def calculate_sequence_jump_score(df_item: pd.DataFrame, parameters: Dict[str, A return df -def calculate_first_decimal_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: - """Score first-decimal-digit anomalies per variable using the COF density model. +def calculate_first_decimals_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd.DataFrame: + """Score anomalies in the first two decimal digits per variable using the COF density model. - Only variables with at least 100 records and 3 distinct first-decimal values are scored, - matching the legacy make_score__first_decimal filter. + f__first_decimals stores the first TWO decimal digits of each numeric response (e.g. 3.47 → 47). """ - feature_name = 'f__first_decimal' + # s__first_decimals is the anomaly score derived from f__first_decimals (two decimal digits). + feature_name = 'f__first_decimals' score_name = rename_feature(feature_name) df = df_item.copy() diff --git a/rissk/unit_processing_kedro.py b/rissk/unit_processing_kedro.py index dae9d06..a076bbd 100644 --- a/rissk/unit_processing_kedro.py +++ b/rissk/unit_processing_kedro.py @@ -122,7 +122,7 @@ def aggregate_item_to_unit_scores(df_unit: pd.DataFrame, df_item_scores: pd.Data # scorable items, which means no anomaly was detected — the absence is not unknown. mean_scores = [ 's__answer_hour_set', 's__answer_changed', - 's__first_decimal', 's__sequence_jump' + 's__first_decimals', 's__sequence_jump' ] for score in mean_scores: if score in df_item_scores.columns: diff --git a/rissk_kedro/conf/base/parameters.yml b/rissk_kedro/conf/base/parameters.yml index 87d1843..de0b4e6 100644 --- a/rissk_kedro/conf/base/parameters.yml +++ b/rissk_kedro/conf/base/parameters.yml @@ -40,7 +40,7 @@ features: use: true parameters: contamination: 0.1 - first_decimal: + first_decimals: use: true parameters: contamination: 0.1 diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py index ef8838d..23d052e 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py @@ -5,7 +5,7 @@ from rissk.item_processing_kedro import ( calculate_answer_hour_set_score, calculate_sequence_jump_score, - calculate_first_decimal_score, + calculate_first_decimals_score, calculate_answer_changed_score, # calculate_answer_removed_score is intentionally absent: s__answer_removed is # computed at unit level from the removed_answers dataset by calculate_answer_removed_score_from_df @@ -60,9 +60,9 @@ def calculate_item_scores(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> logger.info("Calculating sequence_jump_score") df_scored = calculate_sequence_jump_score(df_scored, parameters) - if features.get('first_decimal', {}).get('use', False): - logger.info("Calculating first_decimal_score") - df_scored = calculate_first_decimal_score(df_scored, parameters) + if features.get('first_decimals', {}).get('use', False): + logger.info("Calculating first_decimals_score") + df_scored = calculate_first_decimals_score(df_scored, parameters) if features.get('answer_changed', {}).get('use', False): logger.info("Calculating answer_changed_score") From 3b39af18a17856139858b879a1827b0d85997b5d Mon Sep 17 00:00:00 2001 From: VJausovec Date: Tue, 7 Apr 2026 14:43:47 +0100 Subject: [PATCH 57/70] Remove legacy data configurations and update feature parameters in YAML files for clarity and maintenance. --- rissk_kedro/conf/base/catalog.yml | 20 -------------------- rissk_kedro/conf/base/parameters.yml | 24 +++++++++--------------- 2 files changed, 9 insertions(+), 35 deletions(-) diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index 6014f09..04ac5ce 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -39,26 +39,6 @@ paradata_processed: filepath: ${globals:data_root}/${globals:survey.name}/latest/30_PROCESSED/paradata_processed.parquet - -# # === LEGACY DATA FOR PIPELINE TESTING === -# # Uncomment these and update pipeline.py inputs to test against legacy-produced data. - -# legacy_microdata: -# type: pandas.ParquetDataset -# filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/${globals:survey.name}/latest/30_PROCESSED/microdata.parquet -# # -# # # Equivalent to paradata_processed (output of process_paradata_node). -# # # The legacy pipeline saved this as paradata.parquet (not paradata_processed.parquet). -# legacy_paradata_processed: -# type: pandas.ParquetDataset -# filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/${globals:survey.name}/latest/30_PROCESSED/paradata.parquet -# # -# # # Equivalent to paradata_active (output of filter_active_paradata_node). -# legacy_paradata_active: -# type: pandas.ParquetDataset -# filepath: /Users/vanessa/Work/Rowsquared/RISSK/rissk/data/${globals:survey.name}/latest/30_PROCESSED/paradata_active.parquet - - # === FEATURE CREATION DataFrames === item_features_base: type: pandas.ParquetDataset diff --git a/rissk_kedro/conf/base/parameters.yml b/rissk_kedro/conf/base/parameters.yml index de0b4e6..8054e19 100644 --- a/rissk_kedro/conf/base/parameters.yml +++ b/rissk_kedro/conf/base/parameters.yml @@ -2,11 +2,7 @@ survey: # This tells Kedro to pull the entire block from globals.yml questionnaires: ${globals:survey.questionnaires} - -# # Ingestion Configuration -# ingestion: -# raw_data_path: "data/hies2024/latest/10_RAW" - + # Set the password for zip files in local/parameters.yml if needed. # It will override the base/parameters.yml setting. zip_password: null @@ -34,8 +30,6 @@ features: use: true parameters: contamination: 0.1 - answer_share_selected: - use: true answer_duration: use: true parameters: @@ -45,11 +39,11 @@ features: parameters: contamination: 0.1 first_digit: - use: true + use: true # numeric_response must be set to true also for this to work last_digit: - use: true + use: false numeric_response: - use: true + use: true sequence_jump: use: true parameters: @@ -70,15 +64,15 @@ features: parameters: contamination: 0.1 pause_list: - use: true + use: false parameters: contamination: 0.1 comment_length: - use: true + use: false comment_set: - use: true + use: false comment_duration: - use: true + use: false number_unanswered: use: true number_answered: @@ -102,5 +96,5 @@ features: answer_position: use: true string_length: - use: true + use: false \ No newline at end of file From 696acebe894c680ef0ada0f824d61502da16641f Mon Sep 17 00:00:00 2001 From: VJausovec Date: Tue, 7 Apr 2026 15:01:47 +0100 Subject: [PATCH 58/70] cleanup --- data_read_tes.ipynb | 58 - main_monkey_patch_scores.py | 297 ---- .../src/rissk_kedro/test_ingestion.ipynb | 1231 ----------------- 3 files changed, 1586 deletions(-) delete mode 100644 data_read_tes.ipynb delete mode 100644 main_monkey_patch_scores.py delete mode 100644 rissk_kedro/src/rissk_kedro/test_ingestion.ipynb diff --git a/data_read_tes.ipynb b/data_read_tes.ipynb deleted file mode 100644 index 4deedb5..0000000 --- a/data_read_tes.ipynb +++ /dev/null @@ -1,58 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "db65a927", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import os\n", - "from pathlib import Path" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "451a7f55", - "metadata": {}, - "outputs": [], - "source": [ - "root_path = os.getcwd()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c1e47644", - "metadata": {}, - "outputs": [], - "source": [ - "file_path = Path(root_path).joinpath('data', 'raw', 'slchbs_saintlucia_2025_6_STATA_All', 'slchbs_saintlucia_2025_6', 'slchbs_saintlucia_2025.dta')\n", - "df_test = pd.read_stata(file_path, convert_categoricals=False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "rissk_env_01", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.14" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/main_monkey_patch_scores.py b/main_monkey_patch_scores.py deleted file mode 100644 index f7d7c01..0000000 --- a/main_monkey_patch_scores.py +++ /dev/null @@ -1,297 +0,0 @@ -import os -from omegaconf import DictConfig, OmegaConf -from hydra.core.hydra_config import HydraConfig -from rissk.unit_proccessing import * -from rissk.config import PROJ_ROOT -import hydra -# from memory_profiler import memory_usage -import warnings - -warnings.simplefilter(action='ignore', category=Warning) - - -def manage_path(config): - root_path = HydraConfig.get().runtime.cwd - if config['export_path'] is not None: - if os.path.isabs(config['export_path']) is False: - config['export_path'] = os.path.join(root_path, config['export_path']) - config['environment']['data']['externals'] = os.path.dirname(config['export_path']) - for key, value in config['environment']['data'].items(): - # Check if the value is a relative path - if not os.path.isabs(value): - # Convert the relative path to an absolute path - config['environment']['data'][key] = os.path.join(root_path, value) - config['surveys'] = [os.path.basename(config['export_path'])] - if os.path.isabs(config['output_file']) is False: - - config['output_file'] = os.path.join(root_path, config['output_file']) - return config - - -@hydra.main(config_path='configuration', version_base='1.1', config_name='main.yaml') -def unit_risk_score(config: DictConfig) -> None: - # print(OmegaConf.to_yaml(config)) - print("*" * 12) - config = manage_path(config) - - # --- MONKEY PATCH FOR TESTING --- - # Loads Kedro feature-creation pipeline outputs (item_features, unit_features, - # removed_answers) and runs only the legacy scoring logic on top of them so - # the resulting scores can be compared to the Kedro scoring pipeline outputs. - import pandas as pd - from rissk.unit_proccessing import UnitDataProcessing - - SURVEY = "hies2024" - # SURVEY = "pmpmd" - # SURVEY = "slchbs" - # SURVEY = "fbf house holduntitled folder" - DATA_DIR = os.path.join(PROJ_ROOT, "rissk_kedro", "data", SURVEY, "latest", "30_PROCESSED") - SCORE_DIR = os.path.join(PROJ_ROOT, "rissk_kedro", "data", SURVEY, "latest", "40_SCORED") - - print(f"LOADING KEDRO FEATURE OUTPUTS FROM: {DATA_DIR}") - - # Load Kedro feature-creation pipeline outputs - df_item_kedro = pd.read_parquet(os.path.join(DATA_DIR, "item_features.parquet")) - df_unit_kedro = pd.read_parquet(os.path.join(DATA_DIR, "unit_features.parquet")) - df_removed_kedro = pd.read_parquet(os.path.join(DATA_DIR, "removed_answers.parquet")) - - # Get unique questionnaires present in the feature tables (mirrors pipeline_registry per-qnr loop) - qnr_names = df_unit_kedro['qnr'].dropna().unique().tolist() - print(f"Found {len(qnr_names)} questionnaire(s): {qnr_names}") - - all_item_scores: list = [] - all_unit_risk_dfs: list = [] - all_df_scores: list = [] - - for qnr_name in qnr_names: - print(f"\n--- Processing questionnaire: {qnr_name} ---") - - # Filter each feature table to this questionnaire only (mirrors make_qnr_filter) - df_item_qnr = df_item_kedro[df_item_kedro['qnr'] == qnr_name].copy() - df_unit_qnr = df_unit_kedro[df_unit_kedro['qnr'] == qnr_name].copy() - if df_removed_kedro is not None and not df_removed_kedro.empty: - if 'qnr' in df_removed_kedro.columns: - df_removed_qnr = df_removed_kedro[df_removed_kedro['qnr'] == qnr_name].copy() - else: - # fallback: removed_answers pre-dates the qnr column addition - valid_ids = set(df_unit_qnr['interview__id']) - df_removed_qnr = df_removed_kedro[df_removed_kedro['interview__id'].isin(valid_ids)].copy() - else: - df_removed_qnr = pd.DataFrame() - - if df_unit_qnr.empty: - print(f" No units found for {qnr_name}, skipping.") - continue - - # Manually initialize the class without calling __init__ - survey_class = UnitDataProcessing.__new__(UnitDataProcessing) - survey_class.config = config - survey_class._limit_unit = config.get('limit_unit', None) - survey_class._allowed_features = ['f__' + k for k, v in config['features'].items() if v['use']] - survey_class.item_level_columns = ['interview__id', 'variable_name', 'roster_level'] - - # Assign filtered feature tables; strip any pre-existing s__* columns so that - # make_global_score starts from a clean slate. - # reset_index(drop=True) is critical: boolean-filter slices of the parquet - # retain the original row positions (e.g. pmpmd_household rows may start at - # index 25 if pmpmd_individual occupies rows 0..24). make_global_score merges - # _df_unit with _df_resp and then assigns the result back by label — any row - # whose index label exceeds len(merged_df)-1 gets NaN, producing blank unit_risk_scores. - survey_class._df_item = df_item_qnr.drop( - columns=[c for c in df_item_qnr.columns if c.startswith('s__')] - ).reset_index(drop=True) - survey_class._df_unit = df_unit_qnr.drop( - columns=[c for c in df_unit_qnr.columns if c.startswith('s__')] - ).reset_index(drop=True) - # df_unit_score property requires survey_name/survey_version; rename from Kedro column names - survey_class._df_unit.rename(columns={'qnr': 'survey_name', 'qnr_version': 'survey_version'}, inplace=True) - if 'survey_name' not in survey_class._df_unit.columns: - survey_class._df_unit['survey_name'] = qnr_name - if 'survey_version' not in survey_class._df_unit.columns: - survey_class._df_unit['survey_version'] = 'latest' - - # Build _df_resp from unique responsibles present in this questionnaire's unit features - survey_class._df_resp = ( - df_unit_qnr[['responsible']] - .drop_duplicates() - .loc[lambda d: (d['responsible'] != '') & d['responsible'].notna()] - .reset_index(drop=True) - .copy() - ) - - # Numeric mask needed by several scoring methods accessed via self.df_item - survey_class.numeric_question_mask = ( - (survey_class._df_item["qtype"] == 'NumericQuestion') & - (survey_class._df_item['value'] != '') & - (~pd.isnull(survey_class._df_item['value'])) & - (survey_class._df_item['value'] != -999999999) - ) - - survey_class._score_columns = None - - # Patch get_feature_item__answer_removed so that make_score__answer_removed - # uses the Kedro-built removed_answers table instead of reading self.df_paradata. - # Default argument captures df_removed_qnr at loop iteration time. - survey_class.get_feature_item__answer_removed = lambda feature_name, _r=df_removed_qnr: _r.copy() - - try: - print(f" Calculating Legacy Risk Scores for {qnr_name}...") - - # Populate all s__* columns on _df_unit/_df_resp first, then sanitise before - # StandardScaler runs. Division-based scores (e.g. s__pause_duration = - # f__pause_duration / f__total_elapse) can produce inf when the denominator is 0. - _ = survey_class.df_unit_score - s_cols = [c for c in survey_class._df_unit.columns if c.startswith('s__')] - survey_class._df_unit[s_cols] = survey_class._df_unit[s_cols].replace( - [np.inf, -np.inf], np.nan - ) - - # Recompute _score_columns on sanitised data so make_global_score sees the - # correct set. For small surveys all scores can be constant/all-NaN after - # sanitisation, which would give StandardScaler an empty DataFrame. - score_cols_all = [c for c in survey_class._df_unit.columns if c.startswith('s__')] - survey_class._score_columns = ( - survey_class._df_unit[score_cols_all] - .columns[survey_class._df_unit[score_cols_all].nunique() > 1] - .tolist() - ) - if not survey_class._score_columns: - print(f" No score columns with sufficient variance for {qnr_name} — skipping global score.") - continue - - # Determine whether the responsible-level score has enough variance to run PCA. - _restricted = survey_class._score_columns - _resp_candidates = [ - c for c in survey_class._df_resp.columns - if not c.startswith('responsible') and c not in _restricted - ] - _resp_has_variance = ( - not survey_class._df_resp[_resp_candidates].fillna(0).loc[ - :, survey_class._df_resp[_resp_candidates].fillna(0).nunique() != 1 - ].empty - if _resp_candidates else False - ) - - survey_class.make_global_score(combine_resp_score=_resp_has_variance) - - # Build item-level score table (equivalent to Kedro calculate_item_scores output). - # answer_removed is excluded here matching Kedro behaviour (scored at unit level only). - # GPS is excluded due to its pivoted shape (already a WARNING in make_global_score). - print(f" Collecting item-level scores for {qnr_name}...") - id_cols = [c for c in ['interview__id', 'variable_name', 'roster_level', 'index_col'] - if c in survey_class._df_item.columns] - df_item_scores = survey_class._df_item[id_cols].copy() - merge_key = 'index_col' if 'index_col' in df_item_scores.columns \ - else ['interview__id', 'variable_name', 'roster_level'] - merge_cols = [merge_key] if isinstance(merge_key, str) else merge_key - - item_score_methods = [ - ('make_score__answer_hour_set', ['s__answer_hour_set']), - ('make_score__sequence_jump', ['s__sequence_jump']), - ('make_score__first_decimal', ['s__first_decimal']), - ('make_score__answer_changed', ['s__answer_changed']), - ('make_score__answer_position', ['s__answer_position']), - ('make_score__answer_selected', ['s__answer_selected_lower', 's__answer_selected_upper']), - ('make_score__answer_duration', ['s__answer_duration_lower', 's__answer_duration_upper']), - ('make_score__single_question', ['s__single_question']), - ('make_score__multi_option_question', ['s__multi_option_question']), - ('make_score__first_digit', ['s__first_digit']), - ] - for method_name, score_cols in item_score_methods: - try: - result = getattr(survey_class, method_name)() - available = [c for c in score_cols if c in result.columns] - if not available: - continue - result_slim = result[merge_cols + available].drop_duplicates(subset=merge_cols) - df_item_scores = df_item_scores.merge(result_slim, on=merge_key, how='left') - except Exception as e: - print(f" WARNING: item score {score_cols}: {e}") - - all_item_scores.append(df_item_scores) - - # Collect unit risk scores for this questionnaire - unit_risk_cols = ['interview__id', 'responsible', 'unit_risk_score'] - unit_risk_df = survey_class._df_unit[unit_risk_cols].copy() - unit_risk_df['unit_risk_score'] = unit_risk_df['unit_risk_score'].round(2) - all_unit_risk_dfs.append(unit_risk_df) - - # Build merged score table (unit + responsible scores) for this questionnaire - resp_score_cols = [c for c in survey_class._df_resp.columns if c.startswith('s__')] - resp_id_cols = ['responsible'] - if 'responsible_score' in survey_class._df_resp.columns: - resp_id_cols.append('responsible_score') - resp_view_cols = resp_id_cols + resp_score_cols - df_scores_qnr = survey_class._df_unit.merge( - survey_class._df_resp[resp_view_cols], on='responsible', how='left', - ) - score_cols_final = [c for c in df_scores_qnr.columns if c.startswith('s__')] - id_cols_final = [c for c in ['interview__id', 'responsible', 'survey_name', 'survey_version'] - if c in df_scores_qnr.columns] - final_cols = id_cols_final + ['unit_risk_score', 'responsible_score'] + sorted(score_cols_final) - df_scores_qnr = df_scores_qnr[[c for c in final_cols if c in df_scores_qnr.columns]] - all_df_scores.append(df_scores_qnr) - - except ValueError as e: - print(f" ERROR in {qnr_name}: {e}") - continue - - # --- Merge per-questionnaire results (mirrors merge_pipeline in pipeline_registry) --- - if not all_item_scores: - print("No questionnaire produced results. Exiting.") - return - - df_item_scores_all = pd.concat(all_item_scores, ignore_index=True) - # Normalise any object-typed columns that became mixed after concat - for col in df_item_scores_all.columns: - if df_item_scores_all[col].dtype == object: - try: - df_item_scores_all[col] = df_item_scores_all[col].astype(float) - except (ValueError, TypeError): - pass - item_scores_parquet = os.path.join(SCORE_DIR, "item_scores_legacy.parquet") - df_item_scores_all.to_parquet(item_scores_parquet, index=False) - print(f"Saved legacy item scores to {item_scores_parquet}") - - unit_risk_df_all = pd.concat(all_unit_risk_dfs, ignore_index=True) - unit_risk_df_all.sort_values('unit_risk_score', inplace=True) - unit_risk_csv = os.path.join(SCORE_DIR, "unit_risk_score_legacy.csv") - unit_risk_parquet = os.path.join(SCORE_DIR, "unit_risk_score_legacy.parquet") - unit_risk_df_all.to_csv(unit_risk_csv, index=False) - unit_risk_df_all.to_parquet(unit_risk_parquet, index=False) - - df_scores_all = pd.concat(all_df_scores, ignore_index=True) - # After concat across questionnaires, boolean/int columns can become object dtype. - # Cast any remaining object-typed s__* columns to float so pyarrow can write parquet. - for col in df_scores_all.columns: - if df_scores_all[col].dtype == object: - try: - df_scores_all[col] = df_scores_all[col].astype(float) - except (ValueError, TypeError): - pass # leave non-numeric object columns as-is - scores_csv = os.path.join(SCORE_DIR, "scores_table_legacy.csv") - scores_parquet = os.path.join(SCORE_DIR, "scores_table_legacy.parquet") - df_scores_all.to_csv(scores_csv, index=False) - df_scores_all.to_parquet(scores_parquet, index=False) - print(f"Saved legacy unit risk to {unit_risk_csv}") - print(f"Saved legacy score table to {scores_csv}") - - print("DONE. Legacy scores from Kedro features generated.") - return # Stop here — do not fall through to the regular UnitDataProcessing block - - # --- END MONKEY PATCH --- - - try: - survey_class = UnitDataProcessing(config) - df_item = survey_class.df_item - df_unit = survey_class.df_unit - survey_class.make_global_score() - survey_class.save() - except ValueError as e: - print(f"An error occurred: {e}") - - -if __name__ == "__main__": - unit_risk_score() - # mem_usage = memory_usage(unit_risk_score) - # print(f"Memory usage (in MB): {max(mem_usage)}") diff --git a/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb b/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb deleted file mode 100644 index 87bef86..0000000 --- a/rissk_kedro/src/rissk_kedro/test_ingestion.ipynb +++ /dev/null @@ -1,1231 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7a72d996", - "metadata": {}, - "source": [ - "Tests data ingestion is the same as original code by comparing questionnaire, microdata and paradata output." - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "607ef013", - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "import pandas as pd\n", - "from typing import Dict, Any, List, Optional, Tuple\n", - "import numpy as np\n", - "from collections import Counter\n", - "import math\n", - "import json\n", - "from pandas.api import types as ptypes\n", - "import pyarrow.parquet as pq\n", - "import ast\n", - "\n", - "from rissk.config import DATA_DIR, RAW_DATA_DIR, PROCESSED_DATA_DIR, INTERIM_DATA_DIR, PROJ_ROOT\n", - "from rissk.utils.testing_utils import compare_parquet_files" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "a9c7966e", - "metadata": {}, - "outputs": [], - "source": [ - "# SURVEY = \"pmpmd\"\n", - "# SURVEY = \"hies2024\"\n", - "SURVEY = \"slchbs\"" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "9a8dce83", - "metadata": {}, - "outputs": [], - "source": [ - "# This will remove the empty lists that were present in legacy data and are not present in the new Kedro outputs, \n", - "# to ensure a more apples-to-apples comparison of the microdata. \n", - "# It also handles stringified lists that may contain only-missing values (e.g., \"[nan, nan]\") by normalizing them to empty lists. \n", - "# The drop_rows option allows for optionally removing rows where the specified columns contain empty lists after normalization, \n", - "# which is relevant for the TextListQuestion rows in this test.\n", - "\n", - "def clean_empty_lists(df: pd.DataFrame, columns: list = None, drop_rows: bool = False) -> pd.DataFrame:\n", - " \"\"\"Normalize list-like cells and optionally drop rows where the specified columns\n", - " contain only-missing lists (e.g., [nan, nan] or stringified equivalents).\n", - "\n", - " Special handling for the token '##N/A##':\n", - " - If a list mixes real-missing values (NaN) and the token '##N/A##', treat the list\n", - " as empty (i.e., normalize to []).\n", - " - If a list contains only the token '##N/A##' (no real-missing), leave it as-is.\n", - "\n", - " Args:\n", - " df: input DataFrame\n", - " columns: list of column names to clean\n", - " drop_rows: if True, drop rows where any of the listed columns is an empty list\n", - " after normalization (i.e., [], or parsed [] from string like \"[nan]\").\n", - " \"\"\"\n", - " if (columns is None) or (len(columns) == 0):\n", - " print(\"No columns specified for cleaning empty lists. Returning original DataFrame.\")\n", - " return df\n", - "\n", - " def is_strict_missing(x):\n", - " \"\"\"True for NaN or common missing string tokens (excluding '##N/A##').\"\"\"\n", - " try:\n", - " if pd.isna(x):\n", - " return True\n", - " except Exception:\n", - " pass\n", - " if isinstance(x, str):\n", - " t = x.strip().strip('\\\"\\'')\n", - " if t.lower() in ('nan', 'none', 'null', ''):\n", - " return True\n", - " return False\n", - "\n", - " def is_na_token(x):\n", - " \"\"\"True for the explicit token '##N/A##' (trim quotes and whitespace).\"\"\"\n", - " if not isinstance(x, str):\n", - " return False\n", - " t = x.strip().strip('\\\"\\'')\n", - " return t == '##N/A##'\n", - "\n", - " def parse_if_list_str(x):\n", - " # Already a Python list\n", - " if isinstance(x, list):\n", - " # Determine membership types\n", - " if len(x) == 0:\n", - " return []\n", - " strict_missing_flags = [is_strict_missing(el) for el in x]\n", - " na_token_flags = [is_na_token(el) for el in x]\n", - " # If all elements are strict-missing -> empty\n", - " if all(strict_missing_flags):\n", - " return []\n", - " # If all elements are either strict-missing or na-token, and at least one strict-missing -> empty\n", - " if all(sm or nt for sm, nt in zip(strict_missing_flags, na_token_flags)) and any(strict_missing_flags):\n", - " return []\n", - " # Otherwise leave original list (including the case all are na-token)\n", - " return x\n", - "\n", - " # Not a string -> nothing to do\n", - " if not isinstance(x, str):\n", - " return x\n", - " s = x.strip()\n", - " # Not a list-like string\n", - " if not (s.startswith('[') and s.endswith(']')):\n", - " return x\n", - " # try json then ast\n", - " try:\n", - " val = json.loads(s)\n", - " except Exception:\n", - " try:\n", - " val = ast.literal_eval(s)\n", - " except Exception:\n", - " val = None\n", - " # If parsed to a Python list, evaluate missingness using same rules\n", - " if isinstance(val, list):\n", - " if len(val) == 0:\n", - " return []\n", - " strict_missing_flags = [is_strict_missing(el) for el in val]\n", - " na_token_flags = [is_na_token(el) for el in val]\n", - " if all(strict_missing_flags):\n", - " return []\n", - " if all(sm or nt for sm, nt in zip(strict_missing_flags, na_token_flags)) and any(strict_missing_flags):\n", - " return []\n", - " return val\n", - " # Handle unquoted or mixed token lists like \"[nan, '##N/A##']\" by manual parse\n", - " inner = s[1:-1].strip()\n", - " if inner == \"\":\n", - " return []\n", - " parts = [p.strip() for p in inner.split(',')]\n", - " if len(parts) > 0:\n", - " strict_missing_flags = [is_strict_missing(p.strip().strip('\\\"\\'')) for p in parts]\n", - " na_token_flags = [is_na_token(p.strip().strip('\\\"\\'')) for p in parts]\n", - " if all(strict_missing_flags):\n", - " return []\n", - " if all(sm or nt for sm, nt in zip(strict_missing_flags, na_token_flags)) and any(strict_missing_flags):\n", - " return []\n", - " # otherwise leave original string (do not attempt risky eval)\n", - " return x\n", - "\n", - " for col in columns:\n", - " if col not in df.columns:\n", - " continue\n", - " # If real lists are present, normalize them (and clean all-missing lists)\n", - " if df[col].apply(lambda x: isinstance(x, list)).any():\n", - " def clean_list_cell(x):\n", - " if isinstance(x, list):\n", - " if len(x) == 0:\n", - " return []\n", - " strict_missing_flags = [is_strict_missing(el) for el in x]\n", - " na_token_flags = [is_na_token(el) for el in x]\n", - " if all(strict_missing_flags):\n", - " return []\n", - " if all(sm or nt for sm, nt in zip(strict_missing_flags, na_token_flags)) and any(strict_missing_flags):\n", - " return []\n", - " return x\n", - " if isinstance(x, str):\n", - " return parse_if_list_str(x)\n", - " return x\n", - " df[col] = df[col].apply(clean_list_cell)\n", - " else:\n", - " df[col] = df[col].apply(parse_if_list_str)\n", - "\n", - " if drop_rows:\n", - " # Build mask for rows to drop: any specified column is an empty list\n", - " drop_mask = pd.Series(False, index=df.index)\n", - " for col in columns:\n", - " if col not in df.columns:\n", - " continue\n", - " drop_mask = drop_mask | df[col].apply(lambda x: isinstance(x, list) and len(x) == 0)\n", - " if drop_mask.any():\n", - " df = df.loc[~drop_mask].reset_index(drop=True)\n", - "\n", - " return df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "992b6482", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "61141b6a", - "metadata": {}, - "outputs": [], - "source": [ - "SURVEY = \"pmpmd\"\n", - "df_microdata = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"microdata.parquet\"))\n", - "df_microdata_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\",\"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"microdata.parquet\"))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "886fccc6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "236 [nan, '##N/A##', nan, '##N/A##', nan, '##N/A##...\n", - "Name: value, dtype: object\n" - ] - } - ], - "source": [ - "print(df_microdata['value'][236:237])" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "00061576", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TextListQuestion rows before: 4672\n", - "Rows to drop (TextListQuestion with empty list): 3267\n", - "TextListQuestion rows after: 1405\n" - ] - } - ], - "source": [ - "# Normalize TextListQuestion `value` cells and drop rows with only-missing lists\n", - "mask_TextListQuestion = df_microdata['qtype'] == 'TextListQuestion'\n", - "print('TextListQuestion rows before:', mask_TextListQuestion.sum())\n", - "# Normalize list-like cells in the subset (do not drop rows in the subset call)\n", - "df_microdata.loc[mask_TextListQuestion, 'value'] = (\n", - " clean_empty_lists(df_microdata.loc[mask_TextListQuestion].copy(), ['value'], drop_rows=False)['value']\n", - ")\n", - "# Drop rows where the 'value' column is an empty list for TextListQuestion rows\n", - "drop_mask = df_microdata['qtype'].eq('TextListQuestion') & df_microdata['value'].apply(lambda x: isinstance(x, list) and len(x) == 0)\n", - "print('Rows to drop (TextListQuestion with empty list):', drop_mask.sum())\n", - "if drop_mask.any():\n", - " df_microdata = df_microdata.loc[~drop_mask].reset_index(drop=True)\n", - " \n", - "print('TextListQuestion rows after:', (df_microdata['qtype'] == 'TextListQuestion').sum())" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "dc4569ea", - "metadata": {}, - "outputs": [], - "source": [ - "def test_fast(SURVEY: str):\n", - " # original files\n", - " df_para = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"20_INTERIM\", \"paradata.parquet\"))\n", - " df_questionnaire = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"questionnaire.parquet\"))\n", - " df_microdata = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"microdata.parquet\"))\n", - " try:\n", - " df_para_processed = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"paradata.parquet\"))\n", - " # df_para_active = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"paradata_active.parquet\"))\n", - " except Exception as e:\n", - " print(f\"Error reading paradata_processed or paradata_active: {e}\")\n", - " df_para_processed = pd.DataFrame()\n", - " # df_para_active = pd.DataFrame()\n", - "\n", - " # Normalize TextListQuestion `value` cells and drop rows with only-missing lists\n", - " mask_TextListQuestion = df_microdata['qtype'] == 'TextListQuestion'\n", - " print('TextListQuestion rows before:', mask_TextListQuestion.sum())\n", - " # Normalize list-like cells in the subset (do not drop rows in the subset call)\n", - " df_microdata.loc[mask_TextListQuestion, 'value'] = (\n", - " clean_empty_lists(df_microdata.loc[mask_TextListQuestion].copy(), ['value'], drop_rows=False)['value']\n", - " )\n", - " # Drop rows where the 'value' column is an empty list for TextListQuestion rows\n", - " drop_mask = df_microdata['qtype'].eq('TextListQuestion') & df_microdata['value'].apply(lambda x: isinstance(x, list) and len(x) == 0)\n", - " print('Rows to drop (TextListQuestion with empty list):', drop_mask.sum())\n", - " if drop_mask.any():\n", - " df_microdata = df_microdata.loc[~drop_mask].reset_index(drop=True)\n", - " print('TextListQuestion rows after:', (df_microdata['qtype'] == 'TextListQuestion').sum())\n", - "\n", - " # Kedro pipeline outputs\n", - " df_para_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"20_INTERIM\", \"paradata.parquet\"))\n", - " df_questionnaire_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"questionnaire.parquet\"))\n", - " df_microdata_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"microdata.parquet\"))\n", - " try:\n", - " df_para_processed_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"paradata_processed.parquet\"))\n", - " # df_para_active_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"paradata_active.parquet\"))\n", - " except Exception as e:\n", - " print(f\"Error reading paradata_processed_kedro or paradata_active_kedro: {e}\")\n", - " df_para_processed_kedro = pd.DataFrame()\n", - " # df_para_active_kedro = pd.DataFrame()\n", - "\n", - " for df_name, df_orig, df_kedro in [\n", - " (\"paradata\", df_para, df_para_kedro),\n", - " (\"questionnaire\", df_questionnaire, df_questionnaire_kedro),\n", - " (\"microdata\", df_microdata, df_microdata_kedro),\n", - " (\"paradata_processed\", df_para_processed, df_para_processed_kedro),\n", - " # (\"paradata_active\", df_para_active, df_para_active_kedro)\n", - " ]:\n", - " try:\n", - " print(30 * \"=\" + f\" {df_name.upper()} \" + 30 * \"=\")\n", - " print('Shape:', f\"Original - {df_orig.shape}, Kedro - {df_kedro.shape}\")\n", - " if df_name in [\"questionnaire\", \"microdata\"]:\n", - " print('QNR Sequence:', f\"Original - {df_orig['qnr_seq'].nunique()}, Kedro - {df_kedro['qnr_seq'].nunique()}\")\n", - " print('QNR Version:', f\"Original - {df_orig['qnr_version'].unique()}, Kedro - {df_kedro['qnr_version'].unique()}\")\n", - " # print('QNR Version empty:', f\"Original - {df_orig['qnr_version'].isna().sum()}, Kedro - {df_kedro['qnr_version'].isna().sum()}\")\n", - " print('QNR:', f\"Original - {df_orig['qnr'].unique()}, Kedro - {df_kedro['qnr'].unique()}\")\n", - " if df_name == \"microdata\":\n", - " # print('Values:', f\"Original - {df_orig['value'].nunique()}, Kedro - {df_kedro['value'].nunique()}\")\n", - " print('interview__id:', f\"Original - {df_orig['interview__id'].nunique()}, Kedro - {df_kedro['interview__id'].nunique()}\")\n", - " print('interview_id by qnr', f\"Original - {df_orig.groupby(['qnr', 'qnr_version'])['interview__id'].nunique().to_dict()}, Kedro - {df_kedro.groupby(['qnr', 'qnr_version'])['interview__id'].nunique().to_dict()}\")\n", - " if df_name in [\"paradata\", \"paradata_processed\"]:\n", - " print('interview__id:', f\"Original - {df_orig['interview__id'].nunique()}, Kedro - {df_kedro['interview__id'].nunique()}\")\n", - " except Exception as e:\n", - " print(f\"Error comparing {df_name}: {e}\")\n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "52774f11", - "metadata": {}, - "outputs": [], - "source": [ - "def test_cell(SURVEY: str, survey_details: dict = None):\n", - " if survey_details is None:\n", - " survey_details = {}\n", - "\n", - " # original files\n", - " df_para = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"20_INTERIM\", \"paradata.parquet\"))\n", - " df_para.sort_values(by=['qnr_version', 'qnr', 'interview__id', 'qnr_seq'], inplace=True, ignore_index=True)\n", - "\n", - " df_questionnaire = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"questionnaire.parquet\"))\n", - " df_questionnaire.sort_values(by=['qnr_version', 'qnr', 'qnr_seq'], inplace=True, ignore_index=True)\n", - " print('questionnaire rows with categories', df_questionnaire['categories_id'].notna().sum())\n", - "\n", - " df_microdata = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"microdata.parquet\"))\n", - " \n", - " # Normalize TextListQuestion `value` cells and drop rows with only-missing lists\n", - " mask_TextListQuestion = df_microdata['qtype'] == 'TextListQuestion'\n", - " # print('TextListQuestion rows before:', mask_TextListQuestion.sum())\n", - " # Normalize list-like cells in the subset (do not drop rows in the subset call)\n", - " df_microdata.loc[mask_TextListQuestion, 'value'] = (\n", - " clean_empty_lists(df_microdata.loc[mask_TextListQuestion].copy(), ['value'], drop_rows=False)['value']\n", - " )\n", - " # Drop rows where the 'value' column is an empty list for TextListQuestion rows\n", - " drop_mask = df_microdata['qtype'].eq('TextListQuestion') & df_microdata['value'].apply(lambda x: isinstance(x, list) and len(x) == 0)\n", - " print('Rows to drop (TextListQuestion with empty list):', drop_mask.sum())\n", - " if drop_mask.any():\n", - " df_microdata = df_microdata.loc[~drop_mask].reset_index(drop=True)\n", - " # print('TextListQuestion rows after:', (df_microdata['qtype'] == 'TextListQuestion').sum())\n", - "\n", - " df_microdata.sort_values(by=['qnr_version', 'qnr', 'interview__id', 'qnr_seq'], inplace=True, ignore_index=True)\n", - " \n", - " try:\n", - " df_para_processed = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"paradata.parquet\"))\n", - " df_para_processed.sort_values(by=['qnr_version', 'qnr', 'interview__id'], inplace=True, ignore_index=True)\n", - " print('paradata_processed rows with categories', df_para_processed['categories'].notna().sum())\n", - " # df_para_active = pd.read_parquet(PROJ_ROOT.joinpath(\"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"paradata_active.parquet\"))\n", - " # df_para_active.sort_values(by=['qnr_version', 'qnr', 'interview__id'], inplace=True, ignore_index=True)\n", - " except Exception as e:\n", - " print(f\"Error reading paradata_processed or paradata_active: {e}\")\n", - " df_para_processed = pd.DataFrame()\n", - " # df_para_active = pd.DataFrame()\n", - "\n", - " # Kedro pipeline outputs\n", - " df_para_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"20_INTERIM\", \"paradata.parquet\"))\n", - " df_para_kedro.sort_values(by=['qnr_version', 'qnr', 'interview__id', 'qnr_seq'], inplace=True, ignore_index=True)\n", - " df_questionnaire_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"questionnaire.parquet\"))\n", - " df_questionnaire_kedro.sort_values(by=['qnr_version', 'qnr', 'qnr_seq'], inplace=True, ignore_index=True)\n", - " df_microdata_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"microdata.parquet\"))\n", - " df_microdata_kedro.sort_values(by=['qnr_version', 'qnr', 'interview__id', 'qnr_seq'], inplace=True, ignore_index=True)\n", - " \n", - " try:\n", - " df_para_processed_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"paradata_processed.parquet\"))\n", - " df_para_processed_kedro.sort_values(by=['qnr_version', 'qnr', 'interview__id'], inplace=True, ignore_index=True)\n", - " # df_para_active_kedro = pd.read_parquet(PROJ_ROOT.joinpath(\"rissk_kedro\", \"data\", SURVEY, \"latest\", \"30_PROCESSED\", \"paradata_active.parquet\"))\n", - " # df_para_active_kedro.sort_values(by=['qnr_version', 'qnr', 'interview__id'], inplace=True, ignore_index=True)\n", - " except Exception as e:\n", - " print(f\"Error reading paradata_processed_kedro or paradata_active_kedro: {e}\")\n", - " df_para_processed_kedro = pd.DataFrame()\n", - " # df_para_active_kedro = pd.DataFrame()\n", - "\n", - " for df_name, df_orig, df_kedro in [\n", - " (\"paradata\", df_para, df_para_kedro),\n", - " (\"questionnaire\", df_questionnaire, df_questionnaire_kedro),\n", - " (\"microdata\", df_microdata, df_microdata_kedro),\n", - " (\"paradata_processed\", df_para_processed, df_para_processed_kedro),\n", - " # (\"paradata_active\", df_para_active, df_para_active_kedro)\n", - " ]:\n", - " try:\n", - " same, details = compare_parquet_files(df_kedro, df_orig, check='cells')\n", - " # store details per survey and per df_name (don't overwrite previous df_name entries)\n", - " survey_details.setdefault(SURVEY, {})[df_name] = details\n", - " print(30 * \"=\" + f\" {df_name.upper()} CELL COMPARISON \" + 30 * \"=\")\n", - " print(same)\n", - " print(details['shape'])\n", - " print(details['columns'])\n", - " print(details['dtypes'])\n", - " print(details['cell_compare'])\n", - " print('Number of cell differences:', details['cell_compare']['total_cell_differences'])\n", - " try:\n", - " print('Diff DF shape:', details[\"diff_df\"].shape)\n", - " # display(details[\"diff_df\"])\n", - " except Exception as e:\n", - " print(f\"Error displaying diff_df for {df_name}: {e}\")\n", - " except Exception as e:\n", - " print(f\"Error comparing {df_name}: {e}\")\n", - " return survey_details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "61b067e7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=======================================================================================================\n", - "======================================== TESTING SURVEY: PMPMD ========================================\n", - "=======================================================================================================\n", - "TextListQuestion rows before: 4672\n", - "Rows to drop (TextListQuestion with empty list): 3267\n", - "TextListQuestion rows after: 1405\n" - ] - } - ], - "source": [ - "for SURVEY in [\"pmpmd\", \"hies2024\", \"slchbs\", \"fbf house holduntitled folder\"]:\n", - " print((80 + len(f\" TESTING SURVEY: {SURVEY.upper()} \")) * \"=\")\n", - " print(40 * \"=\" + f\" TESTING SURVEY: {SURVEY.upper()} \" + 40 * \"=\")\n", - " print((80 + len(f\" TESTING SURVEY: {SURVEY.upper()} \")) * \"=\")\n", - " test_fast(SURVEY)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8626e46b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=======================================================================================================\n", - "======================================== TESTING SURVEY: PMPMD ========================================\n", - "=======================================================================================================\n", - "questionnaire rows with categories 641\n", - "Rows to drop (TextListQuestion with empty list): 3267\n", - "Error reading paradata_processed or paradata_active: 'categories'\n", - "============================== PARADATA CELL COMPARISON ==============================\n", - "False\n", - "{'equal': True, 'shape_a': (1766171, 27), 'shape_b': (1766171, 27)}\n", - "{'different_columns': [], 'equal': True, 'only_in_a': [], 'only_in_b': []}\n", - "{'mismatched_columns': [], 'equal': True}\n", - "{'checked': True, 'columns_with_differences': ['answer_sequence', 'n_answers'], 'total_cell_differences': 900828, 'rows_compared': 1766171, 'note': 'aligned by index intersection'}\n", - "Number of cell differences: 900828\n", - "Diff DF shape: (900828, 4)\n", - "============================== QUESTIONNAIRE CELL COMPARISON ==============================\n", - "False\n", - "{'equal': True, 'shape_a': (3000, 38), 'shape_b': (3000, 38)}\n", - "{'different_columns': [], 'equal': True, 'only_in_a': [], 'only_in_b': []}\n", - "{'mismatched_columns': [], 'equal': True}\n", - "{'checked': True, 'columns_with_differences': ['answer_sequence', 'n_answers'], 'total_cell_differences': 1282, 'rows_compared': 3000, 'note': 'aligned by index intersection'}\n", - "Number of cell differences: 1282\n", - "Diff DF shape: (1282, 4)\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[66]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 4\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[32m40\u001b[39m * \u001b[33m\"\u001b[39m\u001b[33m=\u001b[39m\u001b[33m\"\u001b[39m + \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m TESTING SURVEY: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mSURVEY.upper()\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m \u001b[39m\u001b[33m\"\u001b[39m + \u001b[32m40\u001b[39m * \u001b[33m\"\u001b[39m\u001b[33m=\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 5\u001b[39m \u001b[38;5;28mprint\u001b[39m((\u001b[32m80\u001b[39m + \u001b[38;5;28mlen\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m TESTING SURVEY: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mSURVEY.upper()\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m \u001b[39m\u001b[33m\"\u001b[39m)) * \u001b[33m\"\u001b[39m\u001b[33m=\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m \u001b[43mtest_cell\u001b[49m\u001b[43m(\u001b[49m\u001b[43mSURVEY\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msurvey_details\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 8\u001b[39m survey_details\n", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[64]\u001b[39m\u001b[32m, line 68\u001b[39m, in \u001b[36mtest_cell\u001b[39m\u001b[34m(SURVEY, survey_details)\u001b[39m\n\u001b[32m 60\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m df_name, df_orig, df_kedro \u001b[38;5;129;01min\u001b[39;00m [\n\u001b[32m 61\u001b[39m (\u001b[33m\"\u001b[39m\u001b[33mparadata\u001b[39m\u001b[33m\"\u001b[39m, df_para, df_para_kedro),\n\u001b[32m 62\u001b[39m (\u001b[33m\"\u001b[39m\u001b[33mquestionnaire\u001b[39m\u001b[33m\"\u001b[39m, df_questionnaire, df_questionnaire_kedro),\n\u001b[32m (...)\u001b[39m\u001b[32m 65\u001b[39m \u001b[38;5;66;03m# (\"paradata_active\", df_para_active, df_para_active_kedro)\u001b[39;00m\n\u001b[32m 66\u001b[39m ]:\n\u001b[32m 67\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m68\u001b[39m same, details = \u001b[43mcompare_parquet_files\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_kedro\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_orig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mcells\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 69\u001b[39m \u001b[38;5;66;03m# store details per survey and per df_name (don't overwrite previous df_name entries)\u001b[39;00m\n\u001b[32m 70\u001b[39m survey_details.setdefault(SURVEY, {})[df_name] = details\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Work/Rowsquared/RISSK/rissk/rissk/utils/testing_utils.py:244\u001b[39m, in \u001b[36mcompare_parquet_files\u001b[39m\u001b[34m(df_a, df_b, check, atol, rtol)\u001b[39m\n\u001b[32m 242\u001b[39m a_col = a_al[col]\n\u001b[32m 243\u001b[39m b_col = b_al[col]\n\u001b[32m--> \u001b[39m\u001b[32m244\u001b[39m col_neq = \u001b[43m_compare_elementwise\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma_col\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mb_col\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43matol\u001b[49m\u001b[43m=\u001b[49m\u001b[43matol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrtol\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrtol\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 245\u001b[39m neq_mask[:, j] = col_neq\n\u001b[32m 247\u001b[39m neq_df = pd.DataFrame(neq_mask, columns=cols)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Work/Rowsquared/RISSK/rissk/rissk/utils/testing_utils.py:112\u001b[39m, in \u001b[36m_compare_elementwise\u001b[39m\u001b[34m(a, b, atol, rtol)\u001b[39m\n\u001b[32m 107\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m neq\n\u001b[32m 109\u001b[39m \u001b[38;5;66;03m# 2. For object/mixed series, use element-wise smart comparison\u001b[39;00m\n\u001b[32m 110\u001b[39m \u001b[38;5;66;03m# This is slower but necessary for '1' vs '1.0' in object columns\u001b[39;00m\n\u001b[32m 111\u001b[39m \u001b[38;5;66;03m# We can optimize by first checking string equality\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m112\u001b[39m a_s = \u001b[43ma\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfillna\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m__NA__\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 113\u001b[39m b_s = b.fillna(\u001b[33m'\u001b[39m\u001b[33m__NA__\u001b[39m\u001b[33m'\u001b[39m).astype(\u001b[38;5;28mstr\u001b[39m)\n\u001b[32m 115\u001b[39m \u001b[38;5;66;03m# Boolean mask of string mismatches\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/generic.py:6665\u001b[39m, in \u001b[36mNDFrame.astype\u001b[39m\u001b[34m(self, dtype, copy, errors)\u001b[39m\n\u001b[32m 6659\u001b[39m results = [\n\u001b[32m 6660\u001b[39m ser.astype(dtype, copy=copy, errors=errors) \u001b[38;5;28;01mfor\u001b[39;00m _, ser \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.items()\n\u001b[32m 6661\u001b[39m ]\n\u001b[32m 6663\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 6664\u001b[39m \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m6665\u001b[39m new_data = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_mgr\u001b[49m\u001b[43m.\u001b[49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 6666\u001b[39m res = \u001b[38;5;28mself\u001b[39m._constructor_from_mgr(new_data, axes=new_data.axes)\n\u001b[32m 6667\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m res.__finalize__(\u001b[38;5;28mself\u001b[39m, method=\u001b[33m\"\u001b[39m\u001b[33mastype\u001b[39m\u001b[33m\"\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/internals/managers.py:449\u001b[39m, in \u001b[36mBaseBlockManager.astype\u001b[39m\u001b[34m(self, dtype, copy, errors)\u001b[39m\n\u001b[32m 446\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m using_copy_on_write():\n\u001b[32m 447\u001b[39m copy = \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m449\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 450\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mastype\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 451\u001b[39m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 452\u001b[39m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 453\u001b[39m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 454\u001b[39m \u001b[43m \u001b[49m\u001b[43musing_cow\u001b[49m\u001b[43m=\u001b[49m\u001b[43musing_copy_on_write\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 455\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/internals/managers.py:363\u001b[39m, in \u001b[36mBaseBlockManager.apply\u001b[39m\u001b[34m(self, f, align_keys, **kwargs)\u001b[39m\n\u001b[32m 361\u001b[39m applied = b.apply(f, **kwargs)\n\u001b[32m 362\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m363\u001b[39m applied = \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 364\u001b[39m result_blocks = extend_blocks(applied, result_blocks)\n\u001b[32m 366\u001b[39m out = \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m).from_blocks(result_blocks, \u001b[38;5;28mself\u001b[39m.axes)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/internals/blocks.py:784\u001b[39m, in \u001b[36mBlock.astype\u001b[39m\u001b[34m(self, dtype, copy, errors, using_cow, squeeze)\u001b[39m\n\u001b[32m 781\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mCan not squeeze with more than one column.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 782\u001b[39m values = values[\u001b[32m0\u001b[39m, :] \u001b[38;5;66;03m# type: ignore[call-overload]\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m784\u001b[39m new_values = \u001b[43mastype_array_safe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 786\u001b[39m new_values = maybe_coerce_values(new_values)\n\u001b[32m 788\u001b[39m refs = \u001b[38;5;28;01mNone\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/dtypes/astype.py:237\u001b[39m, in \u001b[36mastype_array_safe\u001b[39m\u001b[34m(values, dtype, copy, errors)\u001b[39m\n\u001b[32m 234\u001b[39m dtype = dtype.numpy_dtype\n\u001b[32m 236\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m237\u001b[39m new_values = \u001b[43mastype_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 238\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[32m 239\u001b[39m \u001b[38;5;66;03m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[32m 240\u001b[39m \u001b[38;5;66;03m# trying to convert to float\u001b[39;00m\n\u001b[32m 241\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m errors == \u001b[33m\"\u001b[39m\u001b[33mignore\u001b[39m\u001b[33m\"\u001b[39m:\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/dtypes/astype.py:182\u001b[39m, in \u001b[36mastype_array\u001b[39m\u001b[34m(values, dtype, copy)\u001b[39m\n\u001b[32m 179\u001b[39m values = values.astype(dtype, copy=copy)\n\u001b[32m 181\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m182\u001b[39m values = \u001b[43m_astype_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 184\u001b[39m \u001b[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001b[39;00m\n\u001b[32m 185\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dtype, np.dtype) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values.dtype.type, \u001b[38;5;28mstr\u001b[39m):\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/pandas/core/dtypes/astype.py:96\u001b[39m, in \u001b[36m_astype_nansafe\u001b[39m\u001b[34m(arr, dtype, copy, skipna)\u001b[39m\n\u001b[32m 94\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m arr.ndim > \u001b[32m1\u001b[39m:\n\u001b[32m 95\u001b[39m arr = arr.ravel()\n\u001b[32m---> \u001b[39m\u001b[32m96\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mlib\u001b[49m\u001b[43m.\u001b[49m\u001b[43mensure_string_array\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 97\u001b[39m \u001b[43m \u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m=\u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert_na_value\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\n\u001b[32m 98\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m.reshape(shape)\n\u001b[32m 100\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m np.issubdtype(arr.dtype, np.floating) \u001b[38;5;129;01mand\u001b[39;00m dtype.kind \u001b[38;5;129;01min\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33miu\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m 101\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m _astype_float_to_int_nansafe(arr, dtype, copy)\n", - "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/lib.pyx:718\u001b[39m, in \u001b[36mpandas._libs.lib.ensure_string_array\u001b[39m\u001b[34m()\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/lib.pyx:832\u001b[39m, in \u001b[36mpandas._libs.lib.ensure_string_array\u001b[39m\u001b[34m()\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/numpy/_core/arrayprint.py:1721\u001b[39m, in \u001b[36m_array_str_implementation\u001b[39m\u001b[34m(a, max_line_width, precision, suppress_small, array2string)\u001b[39m\n\u001b[32m 1715\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m a.shape == ():\n\u001b[32m 1716\u001b[39m \u001b[38;5;66;03m# obtain a scalar and call str on it, avoiding problems for subclasses\u001b[39;00m\n\u001b[32m 1717\u001b[39m \u001b[38;5;66;03m# for which indexing with () returns a 0d instead of a scalar by using\u001b[39;00m\n\u001b[32m 1718\u001b[39m \u001b[38;5;66;03m# ndarray's getindex. Also guard against recursive 0d object arrays.\u001b[39;00m\n\u001b[32m 1719\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m _guarded_repr_or_str(np.ndarray.\u001b[34m__getitem__\u001b[39m(a, ()))\n\u001b[32m-> \u001b[39m\u001b[32m1721\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marray2string\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_line_width\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprecision\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msuppress_small\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m \u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/numpy/_core/arrayprint.py:773\u001b[39m, in \u001b[36marray2string\u001b[39m\u001b[34m(a, max_line_width, precision, suppress_small, separator, prefix, style, formatter, threshold, edgeitems, sign, floatmode, suffix, legacy)\u001b[39m\n\u001b[32m 619\u001b[39m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_array2string_dispatcher, module=\u001b[33m'\u001b[39m\u001b[33mnumpy\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 620\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34marray2string\u001b[39m(a, max_line_width=\u001b[38;5;28;01mNone\u001b[39;00m, precision=\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 621\u001b[39m suppress_small=\u001b[38;5;28;01mNone\u001b[39;00m, separator=\u001b[33m'\u001b[39m\u001b[33m \u001b[39m\u001b[33m'\u001b[39m, prefix=\u001b[33m\"\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 622\u001b[39m style=np._NoValue, formatter=\u001b[38;5;28;01mNone\u001b[39;00m, threshold=\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 623\u001b[39m edgeitems=\u001b[38;5;28;01mNone\u001b[39;00m, sign=\u001b[38;5;28;01mNone\u001b[39;00m, floatmode=\u001b[38;5;28;01mNone\u001b[39;00m, suffix=\u001b[33m\"\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 624\u001b[39m *, legacy=\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m 625\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 626\u001b[39m \u001b[33;03m Return a string representation of an array.\u001b[39;00m\n\u001b[32m 627\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 770\u001b[39m \n\u001b[32m 771\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m773\u001b[39m overrides = \u001b[43m_make_options_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprecision\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthreshold\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medgeitems\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 774\u001b[39m \u001b[43m \u001b[49m\u001b[43mmax_line_width\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msuppress_small\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 775\u001b[39m \u001b[43m \u001b[49m\u001b[43msign\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mformatter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfloatmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlegacy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 776\u001b[39m options = format_options.get().copy()\n\u001b[32m 777\u001b[39m options.update(overrides)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/envs/rissk_py3_13_macos/lib/python3.13/site-packages/numpy/_core/arrayprint.py:66\u001b[39m, in \u001b[36m_make_options_dict\u001b[39m\u001b[34m(precision, threshold, edgeitems, linewidth, suppress, nanstr, infstr, sign, formatter, floatmode, legacy, override_repr)\u001b[39m\n\u001b[32m 57\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_make_options_dict\u001b[39m(precision=\u001b[38;5;28;01mNone\u001b[39;00m, threshold=\u001b[38;5;28;01mNone\u001b[39;00m, edgeitems=\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 58\u001b[39m linewidth=\u001b[38;5;28;01mNone\u001b[39;00m, suppress=\u001b[38;5;28;01mNone\u001b[39;00m, nanstr=\u001b[38;5;28;01mNone\u001b[39;00m, infstr=\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 59\u001b[39m sign=\u001b[38;5;28;01mNone\u001b[39;00m, formatter=\u001b[38;5;28;01mNone\u001b[39;00m, floatmode=\u001b[38;5;28;01mNone\u001b[39;00m, legacy=\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 60\u001b[39m override_repr=\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m 61\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 62\u001b[39m \u001b[33;03m Make a dictionary out of the non-None arguments, plus conversion of\u001b[39;00m\n\u001b[32m 63\u001b[39m \u001b[33;03m *legacy* and sanity checks.\u001b[39;00m\n\u001b[32m 64\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m66\u001b[39m options = {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28;43mlocals\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m.items()) \u001b[38;5;28;01mif\u001b[39;00m v \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m}\n\u001b[32m 68\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m suppress \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 69\u001b[39m options[\u001b[33m'\u001b[39m\u001b[33msuppress\u001b[39m\u001b[33m'\u001b[39m] = \u001b[38;5;28mbool\u001b[39m(suppress)\n", - "\u001b[31mKeyboardInterrupt\u001b[39m: " - ] - } - ], - "source": [ - "survey_details = {}\n", - "for SURVEY in [\"pmpmd\", \"hies2024\", \"slchbs\", \"fbf house holduntitled folder\"]:\n", - " print((80 + len(f\" TESTING SURVEY: {SURVEY.upper()} \")) * \"=\")\n", - " print(40 * \"=\" + f\" TESTING SURVEY: {SURVEY.upper()} \" + 40 * \"=\")\n", - " print((80 + len(f\" TESTING SURVEY: {SURVEY.upper()} \")) * \"=\")\n", - " test_cell(SURVEY, survey_details)\n", - "\n", - "survey_details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "46e9e7a3", - "metadata": {}, - "outputs": [], - "source": [ - "display(survey_details['hies2024']['microdata']['diff_df'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "38d6eee4", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "int64", - "type": "integer" - }, - { - "name": "index", - "rawType": "int64", - 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...............
6390225271549categories_id__NA__204f1d1c-5bae-414e-811d-fea87daf3712
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6390227271549parent_1V: RESULTB: MEMBERS
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6390229271549question_sequence479.030.0
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\n", - "
" - ], - "text/plain": [ - " index column \\\n", - "0 0 answer_sequence \n", - "1 0 n_answers \n", - "2 1 answer_sequence \n", - "3 1 n_answers \n", - "4 7 value \n", - "... ... ... \n", - "6390225 271549 categories_id \n", - "6390226 271549 parents \n", - "6390227 271549 parent_1 \n", - "6390228 271549 parent_2 \n", - "6390229 271549 question_sequence \n", - "\n", - " value_a \\\n", - "0 [10140, 10142, 10147, 15218, 15220, 15233, 182... \n", - "1 18.0 \n", - "2 [3004257, 3004259, 3004553, 3004951, 3004953, ... \n", - "3 37.0 \n", - "4 ['Бат', 1, 'Цэцэг', 2] \n", - "... ... \n", - "6390225 __NA__ \n", - "6390226 V: RESULT \n", - "6390227 V: RESULT \n", - "6390228 __NA__ \n", - "6390229 479.0 \n", - "\n", - " value_b \n", - "0 nan \n", - "1 __NA__ \n", - "2 nan \n", - "3 __NA__ \n", - "4 [Бат, ##N/A##, Цэцэг, ##N/A##] \n", - "... ... \n", - "6390225 204f1d1c-5bae-414e-811d-fea87daf3712 \n", - "6390226 B: MEMBERS > MEMBER \n", - "6390227 B: MEMBERS \n", - "6390228 MEMBER \n", - "6390229 30.0 \n", - "\n", - "[6390230 rows x 4 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(survey_details['pmpmd']['microdata']['diff_df'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8d9879a4", - "metadata": {}, - "outputs": [], - "source": [ - "def summary_table_for_survey(SURVEY: str, datasets=None):\n", - " \"\"\"Produce a summary DataFrame with requested comparison stats for a survey.\n", - "\n", - " Columns: SURVEY, df, shape_equal, shape_rows, shape_cols, dtype_equal,\n", - " different_columns, columns_with_differences, num_cell_differences, questionnaire_categories_count\n", - " \"\"\"\n", - " if datasets is None:\n", - " datasets = ['paradata', 'questionnaire', 'microdata', 'paradata_processed']\n", - " rows = []\n", - " for df_name in datasets:\n", - " try:\n", - " orig_path = PROJ_ROOT.joinpath('data', SURVEY, 'latest')\n", - " kedro_path = PROJ_ROOT.joinpath('rissk_kedro', 'data', SURVEY, 'latest')\n", - " if df_name == 'paradata':\n", - " df_orig = pd.read_parquet(orig_path.joinpath('20_INTERIM', 'paradata.parquet'))\n", - " df_kedro = pd.read_parquet(kedro_path.joinpath('20_INTERIM', 'paradata.parquet'))\n", - " else:\n", - " df_orig = pd.read_parquet(orig_path.joinpath('30_PROCESSED', f'{df_name}.parquet'))\n", - " df_kedro = pd.read_parquet(kedro_path.joinpath('30_PROCESSED', f'{df_name}.parquet'))\n", - "\n", - " same, details = compare_parquet_files(df_kedro, df_orig, check='cells')\n", - "\n", - " shape_equal = bool(details.get('shape', {}).get('equal', False))\n", - " shape_a = details.get('shape', {}).get('shape_a', (None, None))\n", - " try:\n", - " shape_rows = int(shape_a[0])\n", - " except Exception:\n", - " shape_rows = None\n", - " try:\n", - " shape_cols = int(shape_a[1])\n", - " except Exception:\n", - " shape_cols = None\n", - "\n", - " dtype_equal = bool(details.get('dtypes', {}).get('equal', False))\n", - " different_columns = details.get('columns', {}).get('different_columns', [])\n", - " cols_with_diff = details.get('cell_compare', {}).get('columns_with_differences', [])\n", - " num_cell_diffs = int(details.get('cell_compare', {}).get('total_cell_differences', 0))\n", - "\n", - " questionnaire_categories_count = None\n", - " if df_name == 'questionnaire':\n", - " try:\n", - " questionnaire_categories_count = int(df_orig['categories_id'].notna().sum())\n", - " except Exception:\n", - " questionnaire_categories_count = None\n", - "\n", - " rows.append({\n", - " 'SURVEY': SURVEY,\n", - " 'df': df_name,\n", - " 'shape_bool': shape_equal,\n", - " 'shape[0]': shape_rows,\n", - " 'shape[1]': shape_cols,\n", - " 'dtype_bool': dtype_equal,\n", - " 'different_columns': ','.join(map(str, different_columns)) if different_columns else '',\n", - " 'columns_with_differences': ','.join(map(str, cols_with_diff)) if cols_with_diff else '',\n", - " 'Number of cell differences': num_cell_diffs,\n", - " 'questionnaire_categories_count': questionnaire_categories_count,\n", - " })\n", - " except Exception as e:\n", - " rows.append({\n", - " 'SURVEY': SURVEY,\n", - " 'df': df_name,\n", - " 'shape_bool': False,\n", - " 'shape[0]': None,\n", - " 'shape[1]': None,\n", - " 'dtype_bool': False,\n", - " 'different_columns': '',\n", - " 'columns_with_differences': '',\n", - " 'Number of cell differences': None,\n", - " 'questionnaire_categories_count': None,\n", - " 'error': str(e)\n", - " })\n", - " return pd.DataFrame(rows)\n", - "\n", - "# Example: produce and display table for a survey\n", - "# summary_df = summary_table_for_survey('hies2024')\n", - "# display(summary_df)\n", - "# print('summary_table_for_survey defined — call it with a survey name to produce the table.')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9fd680d3", - "metadata": {}, - "outputs": [], - "source": [ - "# Run summaries for the four surveys, append into one table, and export as CSV\n", - "surveys = [\"pmpmd\", \"hies2024\", \"slchbs\", \"fbf house holduntitled folder\"]\n", - "all_dfs = []\n", - "for s in surveys:\n", - " try:\n", - " print(f'Generating summary for {s}...')\n", - " df = summary_table_for_survey(s)\n", - " # add survey column already present, ensure consistent order\n", - " all_dfs.append(df)\n", - " except Exception as e:\n", - " print(f'Error for {s}: {e}')\n", - "\n", - "if len(all_dfs) > 0:\n", - " summary_all = pd.concat(all_dfs, ignore_index=True)\n", - " out_dir = PROJ_ROOT.joinpath('data','reports')\n", - " out_dir.mkdir(parents=True, exist_ok=True)\n", - " out_csv = out_dir.joinpath('comparison_summary_all_surveys.csv')\n", - " summary_all.to_csv(out_csv, index=False)\n", - " print(f'Wrote summary CSV to: {out_csv}')\n", - " display(summary_all)\n", - "else:\n", - " print('No summaries were produced.')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "rissk_py3_13_macos", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 27cee4df523fb3d17b165e94bcdcb767d1564999 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Wed, 29 Apr 2026 14:57:10 +0100 Subject: [PATCH 59/70] Refactor get_numeric_mask function to remove commented-out code and suppress RuntimeWarnings in calculate_first_decimals_score for cleaner logging. Co-authored-by: Copilot --- rissk/feature_processing_kedro.py | 2 -- rissk/item_processing_kedro.py | 27 +++++++++++++++++++++++++-- rissk_kedro/conf/base/globals.yml | 24 ++++++++++++++++-------- 3 files changed, 41 insertions(+), 12 deletions(-) diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index f956ee5..842d549 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -29,8 +29,6 @@ def get_numeric_mask(df_item: pd.DataFrame, filter_answer_values: bool) -> pd.Se sentinel_mask = _is_missing_numeric_sentinel(df_item['value']) mask = ( (df_item["qtype"] == 'NumericQuestion') & - # TODO remove '' !! - # (df_item['value'] != '') & (~pd.isnull(df_item['value'])) & (~sentinel_mask) ) diff --git a/rissk/item_processing_kedro.py b/rissk/item_processing_kedro.py index 57ce674..23058fb 100644 --- a/rissk/item_processing_kedro.py +++ b/rissk/item_processing_kedro.py @@ -1,6 +1,7 @@ import pandas as pd import numpy as np import logging +import warnings from typing import List, Dict, Any, Tuple from pyod.models.thresholds import FILTER from pyod.models.ecod import ECOD @@ -294,8 +295,30 @@ def calculate_first_decimals_score(df_item: pd.DataFrame, parameters: Dict[str, mask = (df['variable_name'] == var) & (~pd.isnull(df[feature_name])) if mask.sum() > 0: model = COF(contamination=contamination) - model.fit(df.loc[mask, [feature_name]]) - df.loc[mask, score_name] = model.predict(df.loc[mask, [feature_name]]) + # COF on f__first_decimals produces several expected RuntimeWarnings due to + # degenerate neighbourhoods (many identical values, e.g. x.00): + # - pyod.models.cof: "divide by zero / invalid value encountered in scalar divide" + # — zero chaining distance causes a 0/0 in the COF score formula. + # - numpy._core._methods: "overflow encountered in multiply/reduce" + # — intermediate squared-distance arithmetic overflows before being clipped. + # COF handles these cases gracefully (producing NaN/inf scores that it then + # clips or ignores). They are intentional side-effects of applying a + # distance-based algorithm to heavily-tied data, not coding errors. + # We suppress all RuntimeWarnings from both modules for the duration of + # fit/predict to keep the log clean. + with warnings.catch_warnings(): + warnings.filterwarnings( + 'ignore', + category=RuntimeWarning, + module=r'pyod\.models\.cof', + ) + warnings.filterwarnings( + 'ignore', + category=RuntimeWarning, + module=r'numpy\._core\._methods', + ) + model.fit(df.loc[mask, [feature_name]]) + df.loc[mask, score_name] = model.predict(df.loc[mask, [feature_name]]) return df diff --git a/rissk_kedro/conf/base/globals.yml b/rissk_kedro/conf/base/globals.yml index 43d7836..5bc83e1 100644 --- a/rissk_kedro/conf/base/globals.yml +++ b/rissk_kedro/conf/base/globals.yml @@ -11,7 +11,11 @@ data_root: "data" # - name: "snb_hies_hh" # VERSION: [9, 10, 11] # filter_var: null + +# survey: +# name: "hies2024" # - name: "slbhies_listing" +# questionnaires: # VERSION: [5, 6, 7] # filter_var: null # Set to a single-key dict to filter by consent, e.g.: # # # # filter_var: {consent_q: "1"} @@ -25,17 +29,21 @@ data_root: "data" # - name: "pmpmd_community" # VERSION: [2, 3, 4, 5] # filter_var: null -# - name: "pmpmd_household" -# VERSION: [4, 5, 6] -# filter_var: null - survey: - name: "slchbs" + name: "pmpmd" questionnaires: - - name: "slchbs_saintlucia_2025" - VERSION: [6, 7] # 5 is for testing empty data handling - filter_var: null + - name: "pmpmd_household" + VERSION: [4, 5, 6] + filter_var: null + + +# survey: +# name: "slchbs" +# questionnaires: +# - name: "slchbs_saintlucia_2025" +# VERSION: [6, 7] # 5 is for testing empty data handling +# filter_var: null # survey: From ed7afe40f532960cc24b7987e0d9dd4ef7368ff0 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Thu, 30 Apr 2026 23:17:03 +0100 Subject: [PATCH 60/70] Refactor Kedro pipeline dependencies and update scoring logic - Updated `requirements.txt` to include new Kedro framework dependencies and remove outdated ones. - Refactored `item_processing_kedro.py` and `unit_processing_kedro.py` to add commented out legacy scoring functions not yet ported. - Modified `SETUP.md` for clearer instructions on environment setup and questionnaire configuration. - Adjusted `catalog.yml` to use `questionnaire.name` instead of `survey.name` for data paths. - Updated `globals.yml` to reflect questionnaire configuration instead of survey configuration. - Changed `parameters.yml` to align with new questionnaire structure. - Simplified `pipeline_registry.py` by removing questionnaire loading logic and directly registering pipelines. - Refined `data_ingestion` and `feature_creation` nodes to work with the new questionnaire structure. - Enhanced `rissk_scoring` pipeline to include consent filtering based on the updated questionnaire configuration. Co-authored-by: Copilot --- requirements.txt | 51 ++++- rissk/item_processing_kedro.py | 47 ++++ rissk/unit_processing_kedro.py | 55 +++++ rissk_kedro/SETUP.md | 37 ++-- rissk_kedro/conf/base/catalog.yml | 30 +-- rissk_kedro/conf/base/globals.yml | 83 +++---- rissk_kedro/conf/base/parameters.yml | 84 ++++--- .../src/rissk_kedro/pipeline_registry.py | 205 +----------------- .../pipelines/data_ingestion/nodes.py | 23 +- .../pipelines/data_ingestion/pipeline.py | 4 +- .../pipelines/feature_creation/nodes.py | 146 +++++-------- .../pipelines/rissk_scoring/nodes.py | 8 +- .../pipelines/rissk_scoring/pipeline.py | 21 +- 13 files changed, 338 insertions(+), 456 deletions(-) diff --git a/requirements.txt b/requirements.txt index 3f90f60..b20fe70 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,13 +1,40 @@ -hydra-core<=1.3.2 -numpy<=1.26.4 -pandas<=2.2.2 -openpyxl<=3.1.2 -scikit-learn<=1.4.2 -scipy<=1.13.0 -seaborn<=0.13.2 -pyod<=1.1.3 -pythresh<=0.3.6 -s3fs<=2024.3.1 -pyarrow<=15.0.2 -fsspec +# ============================================================ +# RISSK — Kedro pipeline dependencies +# Python >= 3.13 +# ============================================================ + +# --- Kedro framework --- +kedro==1.2.0 +kedro-datasets[pandas-csvdataset,pandas-exceldataset,pandas-parquetdataset,pandas-statadataset,s3fs]>=9.1.0 +kedro-viz>=12.3.0 + +# --- Core data --- +pandas>=2.2.3 +numpy>=2.1.0 +pyarrow>=18.0.0 + +# --- Scientific / ML --- +scipy>=1.13.0 +scikit-learn>=1.5.0 +pyod>=1.1.5 +pythresh>=0.3.6 + +# --- GUI --- +nicegui>=1.4 + +# --- Logging / progress --- +loguru>=0.7.3 +tqdm>=4.67.0 + +# --- Config --- +python-dotenv>=1.0.1 + +# --- Storage / cloud --- +boto3>=1.35.0 +s3fs>=2024.3.1 +openpyxl>=3.1.2 + +# --- Package itself --- +-e . +-e rissk_kedro/ diff --git a/rissk/item_processing_kedro.py b/rissk/item_processing_kedro.py index 23058fb..4607e1c 100644 --- a/rissk/item_processing_kedro.py +++ b/rissk/item_processing_kedro.py @@ -818,3 +818,50 @@ def calculate_first_digit_score(df_item: pd.DataFrame) -> pd.DataFrame: return df + +# def calculate_last_digit_score(df_item: pd.DataFrame) -> pd.DataFrame: +# """Score last-digit anomalies per responsible per variable (NOT YET PORTED). +# +# Legacy `make_score__last_digit` in ItemFeatureProcessing applied Benford-style +# p-value tests on the last-digit distribution of numeric responses using a +# pivot-table approach. The item-level scoring was already commented out in the +# legacy codebase (see make_score__last_digit in item_processing.py) and the +# corresponding unit aggregation make_score_unit__last_digit was a no-op stub +# (pass). Kept commented until the approach is redesigned for the long-format +# Kedro pipeline. +# """ +# feature_name = 'f__numeric_response' +# score_name = 's__last_digit' +# df = df_item.copy() +# +# if feature_name not in df.columns: +# return df +# +# valid_data = df[~pd.isnull(df[feature_name])].copy() +# # Filter by frequency and order of magnitude, matching legacy pivot-table filtering +# valid_variables = filter_variable_name_by_frequency( +# valid_data, feature_name, frequency=100, min_unique_values=3 +# ) +# valid_variables = filter_variables_by_magnitude( +# valid_data, feature_name, valid_variables, min_order_of_magnitude=3 +# ) +# +# df[score_name] = np.nan +# for var in valid_variables: +# mask = (df['variable_name'] == var) & (~pd.isnull(df[feature_name])) +# if mask.sum() == 0: +# continue +# # Legacy used apply_benford_tests with p-value threshold of 0.05 to flag +# # enumerators whose last-digit distribution deviates significantly from uniform. +# results_df = apply_benford_tests( +# df[mask], [var], 'responsible', feature_name, +# apply_first_digit=False, minimum_sample=50, +# ) +# if results_df is not None and not results_df.empty and 'p-value' in results_df.columns: +# score_col = var + '_last_digit' +# results_df[score_col] = results_df['p-value'].apply(lambda x: 1 if x <= 0.05 else 0) +# responsible_map = results_df.set_index('responsible')[score_col].to_dict() +# df.loc[mask, score_name] = df.loc[mask, 'responsible'].map(responsible_map) +# +# return df + diff --git a/rissk/unit_processing_kedro.py b/rissk/unit_processing_kedro.py index a076bbd..776aee1 100644 --- a/rissk/unit_processing_kedro.py +++ b/rissk/unit_processing_kedro.py @@ -293,3 +293,58 @@ def calculate_responsible_score(df_resp_features: pd.DataFrame, restricted_colum # Merge back to original resp mapping return df_resp.merge(df_grouped[['responsible', 'responsible_score']], on='responsible', how='left') + + +# def aggregate_feature_unit__comments( +# df_item: pd.DataFrame, +# df_unit: pd.DataFrame, +# ) -> pd.DataFrame: +# """Aggregate comment-related features from item level to unit level (NOT YET PORTED). +# +# Legacy `make_feature_unit__comments` in UnitDataProcessing populated f__comments_set +# and f__comment_length on the unit frame by summing item-level values per interview__id. +# The function was already commented out in the legacy codebase and the features were +# not active in the pipeline. Kept commented here for reference. +# """ +# df_out = df_unit.copy() +# columns_to_check = ['f__comments_set', 'f__comment_length'] +# if any(col not in df_out.columns for col in columns_to_check): +# if any(col in df_item.columns for col in columns_to_check): +# df_unit_comment = df_item.groupby('interview__id').agg( +# f__comments_set=('f__comments_set', 'sum'), +# f__comment_length=('f__comment_length', 'sum'), +# ).reset_index() +# df_out['f__comments_set'] = df_out['interview__id'].map( +# df_unit_comment.set_index('interview__id')['f__comments_set'] +# ) +# df_out['f__comment_length'] = df_out['interview__id'].map( +# df_unit_comment.set_index('interview__id')['f__comment_length'] +# ) +# return df_out + + +# def aggregate_feature_unit__number_answers( +# df_unit: pd.DataFrame, +# df_active_paradata: pd.DataFrame, +# df_questionnaire: pd.DataFrame, +# ) -> pd.DataFrame: +# """Aggregate number-of-distinct-answers feature to unit level (NOT YET PORTED). +# +# Legacy `make_feature_unit__number_answers` in UnitDataProcessing computed the ratio +# of distinct variable_names answered per interview over the total question count in +# the questionnaire, sourced from df_active_paradata. The function was already +# commented out in the legacy codebase. Kept commented here for reference. +# """ +# df_out = df_unit.copy() +# answer_per_interview_df = ( +# df_active_paradata.groupby('interview__id')['variable_name'] +# .nunique() +# .reset_index() +# ) +# total_questions = df_questionnaire[ +# df_questionnaire['qtype'].str.contains('Question') +# ]['qtype'].count() +# df_out['f__number_answers'] = df_out['interview__id'].map( +# answer_per_interview_df.set_index('interview__id')['variable_name'] / total_questions +# ) +# return df_out diff --git a/rissk_kedro/SETUP.md b/rissk_kedro/SETUP.md index ab0dce3..ac880d4 100644 --- a/rissk_kedro/SETUP.md +++ b/rissk_kedro/SETUP.md @@ -70,33 +70,23 @@ Your browser will open automatically at **http://localhost:8080**. ## Option B — conda (for experienced users) -### 1. Create and activate a conda environment - -```bash -conda create -n rissk python=3.13 -conda activate rissk -``` - -### 2. Get the code +### 1. Get the code ```bash git clone https://github.com/rowsquared/rissk.git cd rissk ``` -### 3. Install dependencies +### 2. Create and activate the conda environment ```bash -pip install -e "rissk_kedro[gui]" +conda env create -f environment_kedro.yml +conda activate rissk_kedro ``` -Or install manually: -```bash -pip install -r rissk_kedro/requirements.txt -pip install "nicegui>=1.4" -``` +This installs Python 3.13, all pipeline dependencies, and the RISSK package in one step. -### 4. Launch the GUI +### 3. Launch the GUI ```bash bash run_gui.sh # macOS / Linux @@ -117,7 +107,7 @@ Choose where RISSK will read and write survey data. The GUI shows you the exact subfolder where ZIP files must be placed, e.g.: ``` -/Users/jane/surveys/pmpmd/latest/10_RAW/ +/Users/jane/surveys/pmpmd_household/latest/10_RAW/ ``` Click **Create folder & Open** to create that folder and open it in your file manager. @@ -134,12 +124,13 @@ Export from Survey Solutions and place the **unmodified ZIP files** in the folde Do **not** rename, modify, or unzip the files. -### Step 3 — Survey configuration +### Step 3 — Questionnaire configuration + +- **Questionnaire name:** the template name exactly as it appears in Survey Solutions (e.g. `pmpmd_household`). This is also used as the data folder name. +- **Versions:** comma-separated list of version numbers to process, e.g. `4, 5, 6`. +- **Consent filter (optional):** score only interviews where a specific paradata variable equals a required value (useful for surveys with a consent question). -- **Survey name:** exactly as it appears in Survey Solutions (e.g. `pmpmd`). -- **Questionnaires:** one row per questionnaire template. - - **Versions:** comma-separated list, e.g. `4, 5, 6`. - - **Consent filter (optional):** score only interviews where a specific paradata variable equals a required value (useful for surveys with a consent question). +To switch to a different questionnaire, update the name in the Setup tab and save — or edit `questionnaire.name` directly in `conf/local/globals.yml`. ### Step 4 — Save & Run @@ -150,7 +141,7 @@ Do **not** rename, modify, or unzip the files. Results are written to: ``` -//latest/40_SCORED/unit_risk_scores.csv +//latest/40_SCORED/unit_risk_scores.csv ``` ### Advanced settings diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index 04ac5ce..6393704 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -4,7 +4,7 @@ # The source partitions (Zips) survey_zip_partitions: type: partitions.PartitionedDataset - path: ${globals:data_root}/${globals:survey.name}/latest/10_RAW + path: ${globals:data_root}/${globals:questionnaire.name}/latest/10_RAW dataset: type: rissk_kedro.datasets.PathDataset filename_suffix: ".zip" @@ -13,67 +13,67 @@ survey_zip_partitions: # Used by downstream nodes to find the directories extracted_survey_folders: type: partitions.PartitionedDataset - path: ${globals:data_root}/${globals:survey.name}/latest/10_RAW + path: ${globals:data_root}/${globals:questionnaire.name}/latest/10_RAW dataset: type: rissk_kedro.datasets.PathDataset # === INGESTED DataFrames === paradata_raw: type: pandas.ParquetDataset - filepath: ${globals:data_root}/${globals:survey.name}/latest/20_INTERIM/paradata_raw.parquet + filepath: ${globals:data_root}/${globals:questionnaire.name}/latest/20_INTERIM/paradata_raw.parquet raw_questionnaire: type: pandas.ParquetDataset - filepath: ${globals:data_root}/${globals:survey.name}/latest/30_PROCESSED/questionnaire.parquet + filepath: ${globals:data_root}/${globals:questionnaire.name}/latest/30_PROCESSED/questionnaire.parquet raw_microdata: type: pandas.ParquetDataset - filepath: ${globals:data_root}/${globals:survey.name}/latest/20_INTERIM/microdata_raw.parquet + filepath: ${globals:data_root}/${globals:questionnaire.name}/latest/20_INTERIM/microdata_raw.parquet microdata: type: pandas.ParquetDataset - filepath: ${globals:data_root}/${globals:survey.name}/latest/30_PROCESSED/microdata.parquet + filepath: ${globals:data_root}/${globals:questionnaire.name}/latest/30_PROCESSED/microdata.parquet paradata_processed: type: pandas.ParquetDataset - filepath: ${globals:data_root}/${globals:survey.name}/latest/30_PROCESSED/paradata_processed.parquet + filepath: ${globals:data_root}/${globals:questionnaire.name}/latest/30_PROCESSED/paradata_processed.parquet # === FEATURE CREATION DataFrames === item_features_base: type: pandas.ParquetDataset - filepath: ${globals:data_root}/${globals:survey.name}/latest/20_INTERIM/item_features_base.parquet + filepath: ${globals:data_root}/${globals:questionnaire.name}/latest/20_INTERIM/item_features_base.parquet unit_features_base: type: pandas.ParquetDataset - filepath: ${globals:data_root}/${globals:survey.name}/latest/20_INTERIM/unit_features_base.parquet + filepath: ${globals:data_root}/${globals:questionnaire.name}/latest/20_INTERIM/unit_features_base.parquet # Final Feature Tables (Input to Risk Scoring) item_features: type: pandas.ParquetDataset - filepath: ${globals:data_root}/${globals:survey.name}/latest/30_PROCESSED/item_features.parquet + filepath: ${globals:data_root}/${globals:questionnaire.name}/latest/30_PROCESSED/item_features.parquet unit_features: type: pandas.ParquetDataset - filepath: ${globals:data_root}/${globals:survey.name}/latest/30_PROCESSED/unit_features.parquet + filepath: ${globals:data_root}/${globals:questionnaire.name}/latest/30_PROCESSED/unit_features.parquet # Aggregated AnswerRemoved events (includes items deleted from microdata). # Used by the rissk_scoring pipeline to compute s__answer_removed at unit level, # matching legacy get_feature_item__answer_removed / make_score_unit__answer_removed. removed_answers: type: pandas.ParquetDataset - filepath: ${globals:data_root}/${globals:survey.name}/latest/30_PROCESSED/removed_answers.parquet + filepath: ${globals:data_root}/${globals:questionnaire.name}/latest/30_PROCESSED/removed_answers.parquet # === SCORING DataFrames === item_scores: type: pandas.ParquetDataset - filepath: ${globals:data_root}/${globals:survey.name}/latest/40_SCORED/item_scores.parquet + filepath: ${globals:data_root}/${globals:questionnaire.name}/latest/40_SCORED/item_scores.parquet unit_risk_scores: type: pandas.CSVDataset - filepath: ${globals:data_root}/${globals:survey.name}/latest/40_SCORED/unit_risk_scores.csv + filepath: ${globals:data_root}/${globals:questionnaire.name}/latest/40_SCORED/unit_risk_scores.csv responsible_scores: type: pandas.CSVDataset - filepath: ${globals:data_root}/${globals:survey.name}/latest/40_SCORED/responsible_scores.csv + filepath: ${globals:data_root}/${globals:questionnaire.name}/latest/40_SCORED/responsible_scores.csv diff --git a/rissk_kedro/conf/base/globals.yml b/rissk_kedro/conf/base/globals.yml index 5bc83e1..ea2e055 100644 --- a/rissk_kedro/conf/base/globals.yml +++ b/rissk_kedro/conf/base/globals.yml @@ -1,54 +1,39 @@ -# Survey Configuration (from env.yaml) +# Questionnaire Configuration -# Root folder that contains all survey data subfolders. +# Root folder that contains all questionnaire data subfolders. # Can be a relative path (relative to rissk_kedro/) or an absolute path. # Override this in conf/local/globals.yml to point to your data on disk. data_root: "data" -# survey: -# name: "hies2024" -# questionnaires: -# - name: "snb_hies_hh" -# VERSION: [9, 10, 11] -# filter_var: null - -# survey: -# name: "hies2024" -# - name: "slbhies_listing" -# questionnaires: -# VERSION: [5, 6, 7] -# filter_var: null # Set to a single-key dict to filter by consent, e.g.: -# # # # filter_var: {consent_q: "1"} -# # # # Only interviews where paradata variable 'consent_q' has answer "1" are scored. -# # # # The answer value must be a string (paradata answers are always strings). - - -# survey: -# name: "pmpmd" -# questionnaires: -# - name: "pmpmd_community" -# VERSION: [2, 3, 4, 5] -# filter_var: null - -survey: - name: "pmpmd" - questionnaires: - - name: "pmpmd_household" - VERSION: [4, 5, 6] - filter_var: null - - -# survey: -# name: "slchbs" -# questionnaires: -# - name: "slchbs_saintlucia_2025" -# VERSION: [6, 7] # 5 is for testing empty data handling -# filter_var: null - - -# survey: -# name: "fbf house holduntitled folder" -# questionnaires: -# - name: "fbf_household" -# VERSION: [13] -# filter_var: null \ No newline at end of file +# questionnaire: +# name: "slbhies_listing" +# VERSION: [5, 6, 7] +# filter_var: null # Set to a single-key dict to filter by consent, e.g.: +# # filter_var: {consent_q: "1"} +# # Only interviews where paradata variable 'consent_q' has answer "1" are scored. +# # The answer value must be a string (paradata answers are always strings). + +questionnaire: + name: "snb_hies_hh" + VERSION: [9, 10, 11] + filter_var: null + +# questionnaire: +# name: "pmpmd_community" +# VERSION: [2, 3, 4, 5] +# filter_var: null + +# questionnaire: +# name: "pmpmd_household" +# VERSION: [4, 5, 6] +# filter_var: null + +# questionnaire: +# name: "slchbs_saintlucia_2025" +# VERSION: [6, 7] # 5 is for testing empty data handling +# filter_var: null + +# questionnaire: +# name: "fbf_household" +# VERSION: [13] +# filter_var: null \ No newline at end of file diff --git a/rissk_kedro/conf/base/parameters.yml b/rissk_kedro/conf/base/parameters.yml index 8054e19..46fbd23 100644 --- a/rissk_kedro/conf/base/parameters.yml +++ b/rissk_kedro/conf/base/parameters.yml @@ -1,31 +1,28 @@ -# Survey Configuration has to be defined in globals.yml -survey: - # This tells Kedro to pull the entire block from globals.yml - questionnaires: ${globals:survey.questionnaires} - +# Questionnaire configuration — defined in globals.yml +questionnaire: ${globals:questionnaire} + # Set the password for zip files in local/parameters.yml if needed. # It will override the base/parameters.yml setting. zip_password: null -# Processing Parameters -processing: - limit_unit: null # Set to filter by consent if needed - automatic_contamination: false - # Feature Engineering Configuration (from configuration/main.yaml) +# contamination: expected proportion of outliers in clean data (0.01–0.50). +# Set to 'auto' for any feature to let the algorithm estimate the threshold +# automatically from the data instead of using a fixed value. features: + # ── Item-level scored features ────────────────────────────────────────── answer_hour_set: use: true parameters: - contamination: 0.1 + contamination: auto answer_changed: use: true parameters: - contamination: 0.1 + contamination: auto answer_removed: use: true parameters: - contamination: 0.1 + contamination: auto # scored at unit level from removed_answers dataset answer_selected: use: true parameters: @@ -39,62 +36,57 @@ features: parameters: contamination: 0.1 first_digit: - use: true # numeric_response must be set to true also for this to work - last_digit: - use: false - numeric_response: - use: true + use: true # requires numeric_response: true sequence_jump: use: true parameters: contamination: 0.1 - time_changed: - use: true gps: use: true sub_features: [gps_latitude, gps_longitude, gps_accuracy] parameters: contamination: 0.1 + answer_position: + use: true + single_question: + use: true + multi_option_question: + use: true + + # ── Unit-level scored features ────────────────────────────────────────── + # Note: time_changed, pause_count, pause_duration are scored whenever the + # feature column is present; use: false only suppresses feature creation. + time_changed: + use: true pause_count: use: true - parameters: - contamination: 0.1 pause_duration: use: true - parameters: - contamination: 0.1 - pause_list: - use: false - parameters: - contamination: 0.1 - comment_length: - use: false - comment_set: - use: false - comment_duration: - use: false number_unanswered: use: true number_answered: use: true - parameters: - contamination: 0.1 total_duration: use: true - parameters: - contamination: 0.1 total_elapse: use: true parameters: contamination: 0.1 - single_question: - use: true - multi_option_question: - use: true days_from_start: use: true - answer_position: - use: true + + # ── Feature creation only — not scored ───────────────────────────────── + numeric_response: + use: true # required for first_digit; does not produce a score itself + last_digit: + use: false # feature built but no scoring function implemented + pause_list: + use: false # feature built but no scoring function implemented + comment_length: + use: false # feature built but no scoring function implemented + comment_set: + use: false # feature built but no scoring function implemented + comment_duration: + use: false # feature built but no scoring function implemented string_length: - use: false - \ No newline at end of file + use: false # feature built but no scoring function implemented diff --git a/rissk_kedro/src/rissk_kedro/pipeline_registry.py b/rissk_kedro/src/rissk_kedro/pipeline_registry.py index 195d011..ed77bb4 100644 --- a/rissk_kedro/src/rissk_kedro/pipeline_registry.py +++ b/rissk_kedro/src/rissk_kedro/pipeline_registry.py @@ -1,209 +1,22 @@ """Project pipelines.""" -from pathlib import Path -from typing import Callable - -import pandas as pd -import yaml -from kedro.pipeline import Pipeline, node, pipeline - -from rissk_kedro.pipelines.feature_creation.nodes import make_qnr_filter, make_consent_filter - - -def _read_globals() -> dict: - """Merge conf/base/globals.yml with conf/local/globals.yml (local takes precedence). - - pipeline_registry.py is imported before Kedro's ConfigLoader is available, so - globals files are read directly via yaml.safe_load. The project root is resolved - relative to this file: src/rissk_kedro/ -> (parents[2]) -> rissk_kedro/. - The GUI writes user configuration to conf/local/globals.yml; this function ensures - those overrides are visible to the pipeline registry. - """ - project_root = Path(__file__).parents[2] - - def _deep_merge(base: dict, override: dict) -> dict: - out = dict(base) - for k, v in override.items(): - if k in out and isinstance(out[k], dict) and isinstance(v, dict): - out[k] = _deep_merge(out[k], v) - else: - out[k] = v - return out - - base_path = project_root / "conf" / "base" / "globals.yml" - base = yaml.safe_load(base_path.read_text()) or {} - - local_path = project_root / "conf" / "local" / "globals.yml" - if local_path.exists(): - local = yaml.safe_load(local_path.read_text()) or {} - return _deep_merge(base, local) - return base - - -def _load_questionnaire_names() -> list[str]: - """Return questionnaire names from the merged globals config.""" - questionnaires = _read_globals().get("survey", {}).get("questionnaires", []) - return [q["name"] for q in questionnaires] - - -def _load_questionnaires() -> list[dict]: - """Return the full list of questionnaire config dicts from the merged globals config. - - Each dict may contain ``name``, ``VERSION``, and the optional - ``filter_var`` consent-filter setting. - """ - return _read_globals().get("survey", {}).get("questionnaires", []) - - -def _make_merge_node( - output_name: str, - input_names: list[str], - node_name: str, -) -> node: - """Build a node that pd.concat-s N MemoryDataset DataFrames into one output.""" - n = len(input_names) - - def merge_fn(*dfs): - non_empty = [df for df in dfs if df is not None and not df.empty] - if not non_empty: - return pd.DataFrame() - return pd.concat(non_empty, ignore_index=True) - - # Give the function a unique __name__ so Kedro uses it in the node label. - merge_fn.__name__ = node_name - - return node( - func=merge_fn, - inputs=input_names, - outputs=output_name, - name=node_name, - ) +from kedro.pipeline import Pipeline def register_pipelines() -> dict[str, Pipeline]: - """Register the project's pipelines. - - Builds one filter + namespaced-scoring pipeline instance per questionnaire, - then adds a merge pipeline that concatenates per-questionnaire scored outputs - back into the same three catalog datasets (item_scores, unit_risk_scores, - responsible_scores) that exist today. Catalog is unchanged. - """ - # Import sub-pipelines here to avoid circular imports at module level. + """Register the project's pipelines.""" from rissk_kedro.pipelines.data_ingestion import create_pipeline as ingestion_pipeline from rissk_kedro.pipelines.feature_creation import create_pipeline as feature_creation_pipeline from rissk_kedro.pipelines.rissk_scoring import create_pipeline as scoring_pipeline - questionnaires = _load_questionnaires() - - # ------------------------------------------------------------------ # - # Per-questionnaire filter + scoring pipelines # - # ------------------------------------------------------------------ # - per_qnr_pipelines: dict[str, Pipeline] = {} - - item_score_datasets: list[str] = [] - unit_score_datasets: list[str] = [] - resp_score_datasets: list[str] = [] - - for qnr_config in questionnaires: - qnr_name = qnr_config["name"] - # filter_var: dict like {variable_name: answer_value}, or None to skip. - filter_var = qnr_config.get("filter_var", None) - - # Sanitise the questionnaire name so it is a valid Python identifier / - # Kedro namespace component (spaces -> underscores, etc.). - ns = qnr_name.replace(" ", "_").replace("-", "_") - - # -- Questionnaire filter node ------------------------------------ - # Outputs use a _qnr__ suffix so the consent filter can write the - # canonical __{ns} names consumed by the scoring pipeline below. - filter_node = node( - func=make_qnr_filter(qnr_name), - inputs=["item_features", "unit_features", "removed_answers"], - outputs=[ - f"item_features_qnr__{ns}", - f"unit_features_qnr__{ns}", - f"removed_answers_qnr__{ns}", - ], - name=f"filter_features_{ns}_node", - ) - - # -- Consent filter node ------------------------------------------ - # When filter_var is None the function is a pass-through. When set, - # it drops interviews that lack the required consent answer and emits - # a WARNING so the operator knows filtering is active. - consent_filter_node = node( - func=make_consent_filter(qnr_name, filter_var), - inputs=[ - f"item_features_qnr__{ns}", - f"unit_features_qnr__{ns}", - f"removed_answers_qnr__{ns}", - "paradata_processed", - ], - outputs=[ - f"item_features__{ns}", - f"unit_features__{ns}", - f"removed_answers__{ns}", - ], - name=f"filter_consent_{ns}_node", - ) - - # -- Namespaced scoring pipeline ---------------------------------- - # Explicit input/output mappings override namespacing for those keys so - # the consent-filter outputs wire directly and the final scored dfs get - # unique names. parameters must be passed via the dedicated `parameters` - # arg — Kedro raises PipelineError if they appear in `inputs`. - namespaced_scoring = pipeline( - scoring_pipeline(), - namespace=ns, - inputs={ - "item_features": f"item_features__{ns}", - "unit_features": f"unit_features__{ns}", - "removed_answers": f"removed_answers__{ns}", - }, - parameters={"parameters": "parameters"}, - outputs={ - "item_scores": f"item_scores__{ns}", - "unit_risk_scores": f"unit_risk_scores__{ns}", - "responsible_scores": f"responsible_scores__{ns}", - }, - ) - - item_score_datasets.append(f"item_scores__{ns}") - unit_score_datasets.append(f"unit_risk_scores__{ns}") - resp_score_datasets.append(f"responsible_scores__{ns}") - - qnr_pipeline = Pipeline([filter_node, consent_filter_node]) + namespaced_scoring - per_qnr_pipelines[f"scoring_{ns}"] = qnr_pipeline - - # ------------------------------------------------------------------ # - # Merge pipeline — concat all per-qnr outputs into catalog datasets # - # ------------------------------------------------------------------ # - merge_pipeline = Pipeline([ - _make_merge_node("item_scores", item_score_datasets, "merge_item_scores_node"), - _make_merge_node("unit_risk_scores", unit_score_datasets, "merge_unit_scores_node"), - _make_merge_node("responsible_scores", resp_score_datasets, "merge_responsible_scores_node"), - ]) - - # ------------------------------------------------------------------ # - # Shared upstream pipelines # - # ------------------------------------------------------------------ # ingestion = ingestion_pipeline() feat_creation = feature_creation_pipeline() + scoring = scoring_pipeline() - all_scoring = sum(per_qnr_pipelines.values(), Pipeline([])) + merge_pipeline - - pipelines: dict[str, Pipeline] = {} - - # Named pipelines for selective runs - pipelines["data_ingestion"] = ingestion - pipelines["feature_creation"] = feat_creation - pipelines["rissk_scoring"] = all_scoring # filter + score + merge; skips ingestion/feature creation - - # Individual per-questionnaire scoring (without merge) — useful for debugging - for name, p in per_qnr_pipelines.items(): - pipelines[name] = p - - # Full run - pipelines["__default__"] = ingestion + feat_creation + all_scoring + return { + "__default__": ingestion + feat_creation + scoring, + "data_ingestion": ingestion, + "feature_creation": feat_creation, + "rissk_scoring": scoring, + } - return pipelines diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py index a09a303..92e3ad0 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py @@ -39,22 +39,25 @@ def extract_zip_files_node(survey_zip_partitions: Dict[str, Callable[[], Path]], # logger.debug(f"Skipping non-zip partition: {partition_id}") -def filter_extracted_survey_paths_node(survey_partitions: Dict[str, Callable[[], Any]], questionnaires: List[Dict]) -> List[Path]: +def filter_extracted_survey_paths_node(survey_partitions: Dict[str, Callable[[], Any]], questionnaire: Dict) -> List[Path]: """ Return extracted folder paths matching questionnaire/version patterns - using survey partition entries. + using partition entries. This node does not perform extraction. """ - lines = ["=" * 55, " DATA INGESTION — Questionnaires to process", "=" * 55] - for q in questionnaires: - ver_list = q.get("VERSION", []) - versions = ", ".join(str(v) for v in ver_list) if ver_list else "all" - lines.append(f" • {q['name']} | versions: [{versions}]") - lines.append("=" * 55) + ver_list = questionnaire.get("VERSION", []) + versions = ", ".join(str(v) for v in ver_list) if ver_list else "all" + lines = [ + "=" * 55, + " DATA INGESTION — Questionnaire to process", + "=" * 55, + f" • {questionnaire['name']} | versions: [{versions}]", + "=" * 55, + ] logger.info("\n" + "\n".join(lines)) - logger.info(f"Collecting matching survey folders from {len(survey_partitions)} partition entries") - return filter_matching_folders(survey_partitions, questionnaires) + logger.info(f"Collecting matching folders from {len(survey_partitions)} partition entries") + return filter_matching_folders(survey_partitions, [questionnaire]) def load_paradata_node(file_paths: List[Path]) -> pd.DataFrame: diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py index 637757f..b2802d2 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/pipeline.py @@ -24,8 +24,8 @@ def create_pipeline(**kwargs) -> Pipeline: node( func=filter_extracted_survey_paths_node, inputs=[ - "extracted_survey_folders", # This is where the extracted folders are passed. - "params:survey.questionnaires", + "extracted_survey_folders", + "params:questionnaire", ], outputs="file_paths", name="filter_extracted_survey_paths_node" diff --git a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py index 89505d8..9a0493d 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/feature_creation/nodes.py @@ -21,16 +21,14 @@ def create_base_item_table_node( parameters: Dict[str, Any] ) -> pd.DataFrame: """Node wrapper for create_base_item_table.""" - questionnaires = parameters.get('survey', {}).get('questionnaires', []) + questionnaire = parameters.get('questionnaire', {}) lines = [ "=" * 55, " FEATURE CREATION — Configuration", "=" * 55, - " Questionnaires:", + f" Questionnaire: {questionnaire.get('name', 'unknown')}", + "=" * 55, ] - for q in questionnaires: - lines.append(f" • {q['name']}") - lines.append("=" * 55) logger.info("\n" + "\n".join(lines)) return create_base_item_table(microdata, paradata_full, parameters) @@ -76,42 +74,14 @@ def build_removed_answers_node( return feat_answer_removed(paradata_full) -def make_qnr_filter(qnr_name: str): - """Factory that returns a filter function scoped to a single questionnaire. - - All three feature tables (item_features, unit_features, removed_answers) carry - a ``qnr`` column and are filtered directly on it. If ``removed_answers`` was - produced before the qnr column was added a fallback filter by interview__id is - applied automatically. - """ - def filter_features( - item_features: pd.DataFrame, - unit_features: pd.DataFrame, - removed_answers: pd.DataFrame, - ): - unit_filtered = unit_features[unit_features['qnr'] == qnr_name].copy() - item_filtered = item_features[item_features['qnr'] == qnr_name].copy() - if removed_answers is not None and not removed_answers.empty: - if 'qnr' in removed_answers.columns: - removed_filtered = removed_answers[removed_answers['qnr'] == qnr_name].copy() - else: - # fallback: removed_answers pre-dates the qnr column addition - valid_ids = set(unit_filtered['interview__id']) - removed_filtered = removed_answers[removed_answers['interview__id'].isin(valid_ids)].copy() - else: - removed_filtered = pd.DataFrame() - logger.info( - "filter_features_%s: %d interviews, %d item rows, %d removed_answer rows", - qnr_name, len(unit_filtered), len(item_filtered), len(removed_filtered), - ) - return item_filtered, unit_filtered, removed_filtered - - filter_features.__name__ = f"filter_features_{qnr_name}" - return filter_features - - -def make_consent_filter(qnr_name: str, filter_var): - """Factory that returns a consent-filter function for a single questionnaire. +def filter_by_consent( + item_features: pd.DataFrame, + unit_features: pd.DataFrame, + removed_answers: pd.DataFrame, + paradata: pd.DataFrame, + filter_var, +): + """Filter feature tables to interviews that match the consent variable. ``filter_var`` must be a dict with exactly one key-value pair ``{variable_name: answer_value}`` (matching the legacy ``limit_unit`` shape), @@ -121,63 +91,47 @@ def make_consent_filter(qnr_name: str, filter_var): ``str(value) == str(answer_value)`` are retained across all three feature tables. A WARNING is emitted so operators know filtering is active. """ - def filter_by_consent( - item_features: pd.DataFrame, - unit_features: pd.DataFrame, - removed_answers: pd.DataFrame, - paradata: pd.DataFrame, - ): - if filter_var is None: - return item_features, unit_features, removed_answers - - consent_variable = next(iter(filter_var)) - # Careful: paradata answer column is always a string, so cast the - # configured value to str — matching legacy filter_by_consent behaviour. - consent_value = str(filter_var[consent_variable]) - - logger.warning( - "filter_by_consent [%s]: consent filtering is ACTIVE — " - "keeping only interviews where '%s' == '%s'", - qnr_name, consent_variable, consent_value, + if filter_var is None: + return item_features, unit_features, removed_answers + + consent_variable = next(iter(filter_var)) + # Careful: paradata answer column is always a string, so cast the + # configured value to str — matching legacy filter_by_consent behaviour. + consent_value = str(filter_var[consent_variable]) + + logger.warning( + "filter_by_consent: consent filtering is ACTIVE — " + "keeping only interviews where '%s' == '%s'", + consent_variable, consent_value, + ) + + cond1 = paradata["variable_name"] == consent_variable + cond2 = paradata["answer"] == consent_value + approved_ids = paradata.loc[cond1 & cond2, "interview__id"].unique() + + if len(approved_ids) == 0: + total_interviews = unit_features["interview__id"].nunique() + raise ValueError( + f"filter_by_consent: filter_var " + f"{{'{consent_variable}': '{consent_value}'}} matched 0 interviews " + f"out of {total_interviews}. " + f"Check that the variable name and answer value are correct. " + f"Note: paradata answer values are always strings." ) - # Scope to this questionnaire before looking up approved interviews. - qnr_paradata = ( - paradata[paradata["qnr"] == qnr_name] - if "qnr" in paradata.columns - else paradata - ) + item_filtered = item_features[item_features["interview__id"].isin(approved_ids)].copy() + unit_filtered = unit_features[unit_features["interview__id"].isin(approved_ids)].copy() - cond1 = qnr_paradata["variable_name"] == consent_variable - cond2 = qnr_paradata["answer"] == consent_value - approved_ids = qnr_paradata.loc[cond1 & cond2, "interview__id"].unique() - - if len(approved_ids) == 0: - total_interviews = unit_features["interview__id"].nunique() - raise ValueError( - f"filter_by_consent [{qnr_name}]: filter_var " - f"{{'{consent_variable}': '{consent_value}'}} matched 0 interviews " - f"out of {total_interviews}. " - f"Check that the variable name and answer value are correct. " - f"Note: paradata answer values are always strings." - ) - - item_filtered = item_features[item_features["interview__id"].isin(approved_ids)].copy() - unit_filtered = unit_features[unit_features["interview__id"].isin(approved_ids)].copy() - - if removed_answers is not None and not removed_answers.empty: - removed_filtered = removed_answers[ - removed_answers["interview__id"].isin(approved_ids) - ].copy() - else: - removed_filtered = removed_answers - - logger.info( - "filter_by_consent [%s]: retained %d / %d interviews (%d item rows)", - qnr_name, len(unit_filtered), len(unit_features), len(item_filtered), - ) + if removed_answers is not None and not removed_answers.empty: + removed_filtered = removed_answers[ + removed_answers["interview__id"].isin(approved_ids) + ].copy() + else: + removed_filtered = removed_answers - return item_filtered, unit_filtered, removed_filtered + logger.info( + "filter_by_consent: retained %d / %d interviews (%d item rows)", + len(unit_filtered), len(unit_features), len(item_filtered), + ) - filter_by_consent.__name__ = f"filter_by_consent_{qnr_name}" - return filter_by_consent + return item_filtered, unit_filtered, removed_filtered diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py index 23d052e..0a27892 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py @@ -37,16 +37,14 @@ def calculate_item_scores(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> Each scoring function is only executed when its corresponding feature has use: true in parameters['features'], matching the feature creation pipeline behaviour. """ - questionnaires = parameters.get('survey', {}).get('questionnaires', []) + questionnaire = parameters.get('questionnaire', {}) lines = [ "=" * 55, " RISSK SCORING", "=" * 55, - " Questionnaires:", + f" Questionnaire: {questionnaire.get('name', 'unknown')}", + "=" * 55, ] - for q in questionnaires: - lines.append(f" • {q['name']}") - lines.append("=" * 55) logger.info("\n" + "\n".join(lines)) logger.info("Calculating Item Scores...") features = parameters.get('features', {}) diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py index 8a05ef9..c0fbf62 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py @@ -1,6 +1,7 @@ """Rissk scoring pipeline definition.""" from kedro.pipeline import Pipeline, node, pipeline from .nodes import calculate_item_scores, calculate_unit_scores +from rissk_kedro.pipelines.feature_creation.nodes import filter_by_consent def create_pipeline(**kwargs) -> Pipeline: """Create the scoring pipeline. @@ -9,9 +10,25 @@ def create_pipeline(**kwargs) -> Pipeline: A pipeline that calculates item and unit risk scores. """ return pipeline([ + node( + func=filter_by_consent, + inputs=[ + "item_features", + "unit_features", + "removed_answers", + "paradata_processed", + "params:questionnaire.filter_var", + ], + outputs=[ + "item_features_filtered", + "unit_features_filtered", + "removed_answers_filtered", + ], + name="filter_consent_node", + ), node( func=calculate_item_scores, - inputs=["item_features", "parameters"], + inputs=["item_features_filtered", "parameters"], outputs="item_scores", name="calculate_item_scores_node", ), @@ -20,7 +37,7 @@ def create_pipeline(**kwargs) -> Pipeline: # removed_answers gives calculate_unit_scores access to ALL AnswerRemoved events, # including those for items deleted from microdata, # matching legacy make_score_unit__answer_removed behaviour. - inputs=["unit_features", "item_scores", "parameters", "removed_answers"], + inputs=["unit_features_filtered", "item_scores", "parameters", "removed_answers_filtered"], outputs=["unit_risk_scores", "responsible_scores"], name="calculate_unit_scores_node", ), From 57f71dd54b28332f5b158fcedf708a9ff0eacc14 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Mon, 4 May 2026 22:17:49 +0100 Subject: [PATCH 61/70] Enhance logging and error handling for data loading and processing functions; update environment configuration for clarity and maintenance. Co-authored-by: Copilot --- environment_kedro.yml | 13 +++++--- rissk/feature_processing_kedro.py | 24 +++++++++++++- .../pipelines/data_ingestion/nodes.py | 31 +++++++++++++++++-- .../pipelines/rissk_scoring/nodes.py | 10 ++++++ 4 files changed, 69 insertions(+), 9 deletions(-) diff --git a/environment_kedro.yml b/environment_kedro.yml index 27ad7f0..cb8da31 100644 --- a/environment_kedro.yml +++ b/environment_kedro.yml @@ -1,19 +1,19 @@ -name: rissk_py3_13_macos +name: rissk_kedro channels: - conda-forge dependencies: - python=3.13 - # R Core - 3.13 compatible binaries + # R Core - 3.13 compatible binaries (not used in pipeline code) - r-base>=4.4 - r-ggplot2 - r-dplyr - r-tidyr - r-readr - r-stringr - # Graphviz System Deps (Crucial for macOS) + # Graphviz System Deps - graphviz - python-graphviz - - pydot # Highly recommended for macOS compatibility + - pydot - pip - pip: # Framework @@ -21,7 +21,10 @@ dependencies: - kedro-viz>=12.3.0 - kedro-datasets[pandas,s3fs,excel,files]>=9.1.0 - # Core Stack (Optimized for Apple Silicon) + # GUI + - nicegui>=1.4 + + # Core Stack - rpy2>=3.6.4 - pandas>=2.2.3 - numpy>=2.1.0 diff --git a/rissk/feature_processing_kedro.py b/rissk/feature_processing_kedro.py index 842d549..ff1ea9f 100644 --- a/rissk/feature_processing_kedro.py +++ b/rissk/feature_processing_kedro.py @@ -303,7 +303,14 @@ def create_base_item_table(microdata: pd.DataFrame, paradata_full: pd.DataFrame, Equivalent to FeatureProcessing.make_df_item. """ logger.info("Creating base item table...") - + + if microdata.empty: + logger.error( + "create_base_item_table: microdata is empty — all microdata files were missing " + "or contained no data rows. Cannot build item table. Returning empty DataFrame." + ) + return pd.DataFrame() + item_level_columns = ['interview__id', 'variable_name', 'roster_level'] allowed_features = ['f__' + k for k, v in parameters['features'].items() if v.get('use', False)] @@ -371,6 +378,14 @@ def create_base_unit_table(paradata_full: pd.DataFrame, parameters: dict) -> pd. Equivalent to FeatureProcessing.make_df_unit. """ logger.info("Creating base unit table...") + + if paradata_full.empty: + logger.error( + "create_base_unit_table: paradata_full is empty — no paradata to build unit table from. " + "Returning empty DataFrame." + ) + return pd.DataFrame() + allowed_features = ['f__' + k for k, v in parameters['features'].items() if v.get('use', False)] # 1. Initialize from paradata @@ -554,6 +569,13 @@ def feat_answer_removed(paradata_full): # The legacy method notes this feature may include items no longer in microdata. feature_name = 'f__answer_removed' + if paradata_full.empty or 'event' not in paradata_full.columns: + logger.warning( + "feat_answer_removed: paradata_full is empty or missing the 'event' column — " + "no AnswerRemoved events to process. Returning empty DataFrame." + ) + return pd.DataFrame() + removed_mask = ( (paradata_full['event'] == 'AnswerRemoved') & (paradata_full['role'] == 1) # interviewer role is already filtered in paradata processing node diff --git a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py index 92e3ad0..9433603 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/data_ingestion/nodes.py @@ -85,7 +85,13 @@ def load_paradata_node(file_paths: List[Path]) -> pd.DataFrame: try: df_paradata = get_paradata_raw(paradata_path) dfs_paradata.append(df_paradata) - logger.info(f"Loaded raw paradata for {survey_questionnaire} v{questionnaires_version}") + if df_paradata.empty: + logger.warning( + f"Empty data returned for paradata {survey_questionnaire} v{questionnaires_version}: " + "file may be empty or corrupt" + ) + else: + logger.info(f"Loaded raw paradata for {survey_questionnaire} v{questionnaires_version}") except Exception as e: logger.error(f"Failed to load paradata for {survey_questionnaire} v{questionnaires_version}. Skipping. Error: {str(e)}") continue @@ -107,6 +113,13 @@ def process_paradata_node( interviewing flags, makes the index column, and filters to active interviewer events - producing the paradata_processed dataset consumed by feature creation. """ + if paradata_raw.empty: + logger.error( + "process_paradata_node: paradata_raw is empty — all paradata files were missing " + "or contained no data rows. Cannot process paradata. Returning empty DataFrame." + ) + return pd.DataFrame() + paradata = paradata_raw.copy() # 1. Merge questionnaire metadata @@ -184,7 +197,13 @@ def load_questionnaire_node(file_paths: List[Path]) -> pd.DataFrame: try: df_questionnaires = get_questionnaire(tabular_path) dfs_questionnaires.append(df_questionnaires) - logger.info(f"Loaded questionnaire for {survey_questionnaire} v{questionnaires_version}") + if df_questionnaires.empty: + logger.warning( + f"Empty data returned for questionnaire {survey_questionnaire} v{questionnaires_version}: " + "file may be empty or corrupt" + ) + else: + logger.info(f"Loaded questionnaire for {survey_questionnaire} v{questionnaires_version}") except Exception as e: logger.error(f"Failed to load questionnaire for {survey_questionnaire} v{questionnaires_version}. Skipping. Error: {str(e)}") continue @@ -231,7 +250,13 @@ def load_raw_microdata_node(file_paths: List[Path], questionnaire: pd.DataFrame) df_questionnaires = questionnaire[questionnaire['qnr'] == survey_questionnaire] df_microdata = get_microdata_raw(tabular_path, df_questionnaires) dfs_microdata.append(df_microdata) - logger.info(f"Loaded raw microdata for {survey_questionnaire} v{questionnaires_version}") + if df_microdata.empty: + logger.warning( + f"Empty data returned for microdata {survey_questionnaire} v{questionnaires_version}: " + "file may be empty or corrupt" + ) + else: + logger.info(f"Loaded raw microdata for {survey_questionnaire} v{questionnaires_version}") except Exception as e: logger.error(f"Failed to load raw microdata for {survey_questionnaire} v{questionnaires_version}. Skipping. Error: {str(e)}") continue diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py index 0a27892..f8e7f18 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py @@ -47,6 +47,11 @@ def calculate_item_scores(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> ] logger.info("\n" + "\n".join(lines)) logger.info("Calculating Item Scores...") + + if df_item.empty: + logger.warning("calculate_item_scores: item features DataFrame is empty — no items to score. Returning empty DataFrame.") + return df_item + features = parameters.get('features', {}) df_scored = df_item @@ -125,6 +130,11 @@ def calculate_unit_scores( items deleted from microdata (absent from df_item) are still counted. """ logger.info("Calculating Unit Scores and Global Risk...") + + if df_unit.empty: + logger.warning("calculate_unit_scores: unit features DataFrame is empty — no units to score. Returning empty DataFrames.") + return pd.DataFrame(), pd.DataFrame() + features = parameters.get('features', {}) # 1. Aggregate item-level scores up to unit level. From fb61d888e914471c5c42fc83a3d6ae2c0e0e58d9 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Wed, 6 May 2026 11:50:01 +0100 Subject: [PATCH 62/70] GPS - extreme outlier only if both latitude AND longitude 0 responsible score: if don't fill with zero before multiplying (if 1 responsible is missing combined score if NaN, same as legacy) transform_multi - minor tweak for texlist questions where we keep N/A if question is enabled and all answers are N/A. This was previously dropped. In practice no such questions exist in test data so effect negligible. Co-authored-by: Copilot --- rissk/item_processing_kedro.py | 6 +++--- rissk/unit_processing_kedro.py | 20 ++++++++++---------- rissk/utils/file_process_utils_kedro.py | 3 ++- 3 files changed, 15 insertions(+), 14 deletions(-) diff --git a/rissk/item_processing_kedro.py b/rissk/item_processing_kedro.py index 4607e1c..e7add2c 100644 --- a/rissk/item_processing_kedro.py +++ b/rissk/item_processing_kedro.py @@ -135,9 +135,9 @@ def calculate_gps_score(df_item: pd.DataFrame, parameters: Dict[str, Any]) -> pd # Everything that has 0,0 as coordinates is considered an extreme outlier # (devices sometimes report 0,0 when a fix failed); mark these explicitly # so they can be excluded from median/distance calculations. - data['s__gps_extreme_outlier'] = 0 - data.loc[data['f__gps_latitude'] == 0.0, 's__gps_extreme_outlier'] = 1 - data.loc[data['f__gps_longitude'] == 0.0, 's__gps_extreme_outlier'] = 1 + data['s__gps_extreme_outlier'] = ( + (data['f__gps_latitude'] == 0.0) & (data['f__gps_longitude'] == 0.0) + ).astype(int) # Convert lat/lon into 3D Cartesian coordinates on a sphere (units = km). # Using Cartesian coords lets KDTree operate in Euclidean space instead of diff --git a/rissk/unit_processing_kedro.py b/rissk/unit_processing_kedro.py index 776aee1..db8c9e6 100644 --- a/rissk/unit_processing_kedro.py +++ b/rissk/unit_processing_kedro.py @@ -91,20 +91,20 @@ def calculate_global_score(df_unit_scores: pd.DataFrame, df_resp_scores: pd.Data df_unit['unit_risk_score'] = scaler.fit_transform(df_unit[['unit_risk_score']]) # Merge unit score with responsible score. - # Only apply the multiplication when responsible_score has actual variance — if PCA - # on the responsible-level scores couldn't run (too few enumerators or all scores - # constant), responsible_score is all-zero, and multiplying produces a constant-zero - # column that MinMaxScaler turns into NaN for every interview. + # Only apply the multiplication when responsible_score was successfully computed + # (not all-NaN) and has actual variance. If PCA couldn't run (too few columns or + # all scores constant), responsible_score is NaN — multiplying would wipe out all + # IForest-derived unit scores. if combine_resp_score and 'responsible' in df_unit.columns and df_resp_scores is not None and 'responsible_score' in df_resp_scores.columns: resp_score_series = df_resp_scores['responsible_score'] - if resp_score_series.nunique() > 1: + if resp_score_series.notna().any() and resp_score_series.nunique() > 1: df_resp_map = df_resp_scores.set_index('responsible')['responsible_score'].to_dict() - df_unit['responsible_score'] = df_unit['responsible'].map(df_resp_map).fillna(0) + df_unit['responsible_score'] = df_unit['responsible'].map(df_resp_map) df_unit['unit_risk_score'] = df_unit['unit_risk_score'] * df_unit['responsible_score'] df_unit['unit_risk_score'] = scaler.fit_transform(df_unit[['unit_risk_score']]) else: logger.warning( - "responsible_score has no variance (likely too few enumerators or all scores constant); " + "responsible_score is NaN or has no variance (likely too few enumerators or all scores constant); " "skipping responsible-score multiplication to preserve interview-level unit_risk_score." ) @@ -264,7 +264,7 @@ def calculate_responsible_score(df_resp_features: pd.DataFrame, restricted_colum columns = [col for col in df_resp.columns if not col.startswith('responsible') and (not restricted_columns or col not in restricted_columns)] if not columns: - df_resp['responsible_score'] = 0.0 + df_resp['responsible_score'] = np.nan return df_resp df_grouped = df_resp.groupby('responsible')[columns].mean().reset_index() @@ -273,14 +273,14 @@ def calculate_responsible_score(df_resp_features: pd.DataFrame, restricted_colum df_pca_input = df_pca_input.loc[:, df_pca_input.nunique() != 1] if df_pca_input.empty: - df_resp['responsible_score'] = 0.0 + df_resp['responsible_score'] = np.nan return df_resp # PCA-based outlier scoring requires at least 2 varying columns to be meaningful: # with only 1 component there are no minor eigenvectors to compute weighted # reconstruction error against, so all scores would be identical. if df_pca_input.shape[1] < 2: - df_resp['responsible_score'] = 0.0 + df_resp['responsible_score'] = np.nan return df_resp df_pca_scaled = pd.DataFrame(scaler.fit_transform(df_pca_input), columns=df_pca_input.columns) diff --git a/rissk/utils/file_process_utils_kedro.py b/rissk/utils/file_process_utils_kedro.py index 0fbec87..ebedae4 100644 --- a/rissk/utils/file_process_utils_kedro.py +++ b/rissk/utils/file_process_utils_kedro.py @@ -126,7 +126,8 @@ def normalize(v): return clean_sub transformation = [remove_unset_value(x) - if x else float('nan') for x in transformation] if transformation_type != 'gps' else [ + if x else ('##N/A##' if transformation_type == 'list' else float('nan')) + for x in transformation] if transformation_type != 'gps' else [ x if x else '' for x in transformation] transformed_df[var] = transformation # Add the transformation to the transformed DataFrame df = df.drop(related_cols, axis=1) # Drop the original columns From daf7ae65fa7b6c9a1061ae7679717955bc4942b2 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Wed, 6 May 2026 13:03:48 +0100 Subject: [PATCH 63/70] Rename unit_risk_scores to unit_rissk_scores in catalog and pipeline; ensure responsible_score presence in calculations and reorder columns in final csv output Co-authored-by: Copilot --- rissk_kedro/conf/base/catalog.yml | 4 +-- .../pipelines/rissk_scoring/nodes.py | 30 +++++++++++++++++-- .../pipelines/rissk_scoring/pipeline.py | 2 +- 3 files changed, 30 insertions(+), 6 deletions(-) diff --git a/rissk_kedro/conf/base/catalog.yml b/rissk_kedro/conf/base/catalog.yml index 6393704..b7a226a 100644 --- a/rissk_kedro/conf/base/catalog.yml +++ b/rissk_kedro/conf/base/catalog.yml @@ -70,9 +70,9 @@ item_scores: type: pandas.ParquetDataset filepath: ${globals:data_root}/${globals:questionnaire.name}/latest/40_SCORED/item_scores.parquet -unit_risk_scores: +unit_rissk_scores: type: pandas.CSVDataset - filepath: ${globals:data_root}/${globals:questionnaire.name}/latest/40_SCORED/unit_risk_scores.csv + filepath: ${globals:data_root}/${globals:questionnaire.name}/latest/40_SCORED/unit_rissk_scores.csv responsible_scores: type: pandas.CSVDataset diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py index f8e7f18..f778945 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/nodes.py @@ -1,4 +1,5 @@ import pandas as pd +import numpy as np from typing import Dict, Any, Tuple import logging @@ -219,8 +220,31 @@ def calculate_unit_scores( feature_cols = [c for c in df_unit_final.columns if c.startswith('f__')] df_unit_final = df_unit_final.drop(columns=feature_cols) - # Add qnr as the first column of the responsible scores output. - if qnr_name is not None and 'qnr' not in df_resp.columns: - df_resp.insert(0, 'qnr', qnr_name) + # Always ensure responsible_score is present (may be absent when PCA was skipped). + if 'responsible_score' not in df_unit_final.columns: + df_unit_final['responsible_score'] = np.nan + + # Apply column ordering for unit_rissk_scores: + # interview__id, qnr, responsible, qnr_version, unit_risk_score, responsible_score, + # IForest s__ cols, responsible s__ cols, any remaining cols. + lead_cols = ['interview__id', 'qnr', 'responsible', 'qnr_version', 'unit_risk_score', 'responsible_score'] + iforest_s = [c for c in score_columns if c in df_unit_final.columns] + resp_s_ordered_unit = [c for c in resp_s_cols if c in df_unit_final.columns] + ordered_unit = [c for c in lead_cols if c in df_unit_final.columns] + ordered_unit += iforest_s + ordered_unit += [c for c in resp_s_ordered_unit if c not in ordered_unit] + ordered_unit += [c for c in df_unit_final.columns if c not in ordered_unit] + df_unit_final = df_unit_final[ordered_unit] + + # Apply column ordering for responsible_scores: responsible, responsible_score, s__ cols. + # qnr is intentionally excluded from the responsible_scores output. + if 'responsible_score' not in df_resp.columns: + df_resp['responsible_score'] = np.nan + resp_lead = ['responsible', 'responsible_score'] + resp_s_ordered = [c for c in df_resp.columns if c.startswith('s__')] + ordered_resp = [c for c in resp_lead if c in df_resp.columns] + ordered_resp += resp_s_ordered + ordered_resp += [c for c in df_resp.columns if c not in ordered_resp and c != 'qnr'] + df_resp = df_resp[ordered_resp] return df_unit_final, df_resp diff --git a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py index c0fbf62..5916c0f 100644 --- a/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py +++ b/rissk_kedro/src/rissk_kedro/pipelines/rissk_scoring/pipeline.py @@ -38,7 +38,7 @@ def create_pipeline(**kwargs) -> Pipeline: # including those for items deleted from microdata, # matching legacy make_score_unit__answer_removed behaviour. inputs=["unit_features_filtered", "item_scores", "parameters", "removed_answers_filtered"], - outputs=["unit_risk_scores", "responsible_scores"], + outputs=["unit_rissk_scores", "responsible_scores"], name="calculate_unit_scores_node", ), ]) From 8f7aceca7b5ce5c0239e186c71f3c6f9456993c1 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Wed, 6 May 2026 22:24:30 +0100 Subject: [PATCH 64/70] Remove deprecated configuration files and clean up unused directories; update requirements to remove loguru dependency. --- .gitignore | 48 ++++ configuration/__init__.py | 0 .../environment/notebook_environment.yaml | 11 - configuration/main.yaml | 101 -------- notebooks/__init__.py | 0 notebooks/experiments/.gitkeep | 0 notebooks/exploration/.gitkeep | 0 pipelines/.gitkeep | 0 pipelines/feature_engineering/.gitkeep | 0 .../10_process_paradata.py | 91 -------- .../11_process_paradata_active.py | 47 ---- .../feature_engineering/12_process_items.py | 60 ----- pipelines/generic/.gitkeep | 0 pipelines/hooks.py | 46 ---- pipelines/ingestion/.gitkeep | 0 pipelines/ingestion/01_get_dataframes.py | 80 ------- pipelines/pipelines.rst | 178 -------------- pipelines/processing/.gitkeep | 0 reports/.gitkeep | 0 reports/figures/.gitkeep | 0 reports/notebooks/.gitkeep | 0 requirements.txt | 1 - results/.gitkeep | 0 rissk_kedro/conf/base/globals.yml | 23 +- rissk_kedro/docs/source/conf.py | 221 ------------------ rissk_kedro/docs/source/index.rst | 19 -- 26 files changed, 60 insertions(+), 866 deletions(-) delete mode 100644 configuration/__init__.py delete mode 100644 configuration/environment/notebook_environment.yaml delete mode 100644 configuration/main.yaml delete mode 100644 notebooks/__init__.py delete mode 100644 notebooks/experiments/.gitkeep delete mode 100644 notebooks/exploration/.gitkeep delete mode 100644 pipelines/.gitkeep delete mode 100644 pipelines/feature_engineering/.gitkeep delete mode 100644 pipelines/feature_engineering/10_process_paradata.py delete mode 100644 pipelines/feature_engineering/11_process_paradata_active.py delete mode 100644 pipelines/feature_engineering/12_process_items.py delete mode 100644 pipelines/generic/.gitkeep delete mode 100644 pipelines/hooks.py delete mode 100644 pipelines/ingestion/.gitkeep delete mode 100644 pipelines/ingestion/01_get_dataframes.py delete mode 100644 pipelines/pipelines.rst delete mode 100644 pipelines/processing/.gitkeep delete mode 100644 reports/.gitkeep delete mode 100644 reports/figures/.gitkeep delete mode 100644 reports/notebooks/.gitkeep delete mode 100644 results/.gitkeep delete mode 100644 rissk_kedro/docs/source/conf.py delete mode 100644 rissk_kedro/docs/source/index.rst diff --git a/.gitignore b/.gitignore index f1cd355..1a3551c 100644 --- a/.gitignore +++ b/.gitignore @@ -333,3 +333,51 @@ main_monkey_patch_scores.py rissk_kedro/src/rissk_kedro/test_scoring_first_digit.ipynb rissk_kedro/src/rissk_kedro/test_score_unit.ipynb rissk_kedro/src/rissk_kedro/test_scoring_first_digit_digit_checks.ipynb +prompt.md +markdown_docs/Base Item Table.md +markdown_docs/ingestion_discrepancies.md +markdown_docs/microdata_drop_columns.md +markdown_docs/Paradata vs Active Paradata.md +markdown_docs/Score_NaN_Handling_and_Aggregation.md +rissk_kedro/src/rissk_kedro/test_ingestion_separate_questionnaire.ipynb +rissk_kedro/src/rissk_kedro/test_ingestion.ipynb +rissk_kedro/src/rissk_kedro/test_scoring_first_decimal.ipynb +.gitignore +rissk_kedro/docs/source/copy_audit.md +markdown_docs/unit_scoring.md +markdown_docs/Unit Scoring — Legacy vs Kedro Behavi.md +markdown_docs/unit_scoring_legacy_kedro.md +markdown_docs/unscored_features.md +rissk_kedro/src/rissk_kedro/test_score_iforest.ipynb +rissk_kedro/src/rissk_kedro/test_pyod_PCA.ipynb +rissk_kedro/src/rissk_kedro/test_ingestion.ipynb +pipelines/feature_engineering/11_process_paradata_active.py +rissk_kedro/notebooks/legacy_data_generation/main_monkey_patch_unit_score.py +rissk_kedro/notebooks/testing/data_read_tes.ipynb +rissk_kedro/notebooks/testing/test_ingestion_bulk_read_in.ipynb +rissk_kedro/notebooks/testing/test_ingestion_separate_questionnaire.ipynb +rissk_kedro/notebooks/testing/test_ingestion.ipynb +rissk_kedro/notebooks/testing/test_item_creation_individual.ipynb +rissk_kedro/notebooks/testing/test_item_f__answer_changed.ipynb +rissk_kedro/notebooks/testing/test_item_f__answer_removed.ipynb +rissk_kedro/notebooks/testing/test_item_f__answer_selected.ipynb +rissk_kedro/notebooks/testing/test_item_s__first_decimal.ipynb +rissk_kedro/notebooks/testing/test_item_scoring.ipynb +rissk_kedro/notebooks/testing/test_item_unit_creation.ipynb +rissk_kedro/notebooks/testing/test_microdata_gps_answers.ipynb +rissk_kedro/notebooks/testing/test_microdata.ipynb +rissk_kedro/notebooks/testing/test_pyod_PCA.ipynb +rissk_kedro/notebooks/testing/test_score_iforest.ipynb +rissk_kedro/notebooks/testing/test_score_item.ipynb +rissk_kedro/notebooks/testing/test_score_unit.ipynb +rissk_kedro/notebooks/testing/test_scoring_first_decimal.ipynb +rissk_kedro/notebooks/testing/test_scoring_first_digit_digit_checks.ipynb +rissk_kedro/notebooks/testing/test_scoring_first_digit.ipynb +rissk_kedro/notebooks/testing/test_scoring_gps_legacy.ipynb +rissk_kedro/notebooks/testing/test_scoring_gps.ipynb +rissk_kedro/notebooks/testing/test_unit_creation.ipynb +rissk_kedro/notebooks/testing/test_scoring_first_decimal_method_comparisson.ipynb +markdown_docs/Kedro_vs_Legacy_Changelog.md +rissk_kedro/notebooks/testing/test_ingestion_microdata.ipynb +requirements_legacy.txt +requirements.txt diff --git a/configuration/__init__.py b/configuration/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/configuration/environment/notebook_environment.yaml b/configuration/environment/notebook_environment.yaml deleted file mode 100644 index 350301d..0000000 --- a/configuration/environment/notebook_environment.yaml +++ /dev/null @@ -1,11 +0,0 @@ -data: - externals: data/externals - raw: data/raw - processed: data/processed - final: data/final - results: results - - -reload: true -extract: true -save_to_disk: false \ No newline at end of file diff --git a/configuration/main.yaml b/configuration/main.yaml deleted file mode 100644 index cd7ee7c..0000000 --- a/configuration/main.yaml +++ /dev/null @@ -1,101 +0,0 @@ -# @package _global_ -version_base: 0.01 -defaults: - - environment/notebook_environment - -export_path: . - -output_file: results/unit_risk_score.csv -feature_score: true - -surveys: [snb_hies_hh] # [ifad_tunesia] -survey_version: [9, 10, 11] #'EndlineFINALV106_1' -password: null -limit_unit: null - -automatic_contamination: false -features: - answer_hour_set: - use: true - parameters: - contamination: 0.11 - answer_changed: - use: true - parameters: - contamination: 0.1 - answer_removed: - use: true - parameters: - contamination: 0.1 - answer_selected: - use: true - parameters: - contamination: 0.1 - answer_share_selected: - use: false - answer_duration: - use: true - parameters: - contamination: 0.1 - first_decimal: - use: true - parameters: - contamination: 0.11 - frequency: 100 - first_digit: - use: true - last_digit: - use: false - numeric_response: - use: true - sequence_jump: - use: true - parameters: - contamination: 0.1 - time_changed: - use: true - gps: - use: true - sub_features: [gps_latitude, gps_longitude, gps_accuracy] - parameters: - contamination: 0.11 - pause_count: - use: true - parameters: - contamination: 0.11 - pause_duration: - use: true - parameters: - contamination: 0.11 - pause_list: - use: false - number_unanswered: - use: false - number_answered: - use: true - parameters: - contamination: 0.11 - total_duration: - use: true - parameters: - contamination: 0.11 - total_elapse: - use: false - parameters: - contamination: 0.11 - single_question: - use: true - multi_option_question: - use: true - days_from_start: - use: false - answer_position: - use: false - comment_length: - use: false - comment_set: - use: false - comment_duration: - use: false - string_length: - use: false diff --git a/notebooks/__init__.py b/notebooks/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/notebooks/experiments/.gitkeep b/notebooks/experiments/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/notebooks/exploration/.gitkeep b/notebooks/exploration/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/pipelines/.gitkeep b/pipelines/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/pipelines/feature_engineering/.gitkeep b/pipelines/feature_engineering/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/pipelines/feature_engineering/10_process_paradata.py b/pipelines/feature_engineering/10_process_paradata.py deleted file mode 100644 index 7bbf950..0000000 --- a/pipelines/feature_engineering/10_process_paradata.py +++ /dev/null @@ -1,91 +0,0 @@ -# --- -# jupyter: -# jupytext: -# cell_metadata_filter: tags,-all -# custom_cell_magics: kql -# text_representation: -# extension: .py -# format_name: percent -# format_version: '1.3' -# jupytext_version: 1.11.2 -# --- - -# %% tags=["parameters"] -# declare a list tasks whose products you want to use as inputs -upstream = ['01_get_dataframes'] -product = None -limit_unit = None - -# %% -import pandas as pd -import numpy as np - -# %% -paradata = pd.read_parquet(upstream['01_get_dataframes']['paradata']) -#paradata.fillna('', inplace=True) - -paradata['f__answer_hour_set'] = (paradata['timestamp_local'].dt.hour + paradata['timestamp_local'].dt.round('30min').dt.minute / 60) - -# interviewing, True prior to Supervisor/HQ interaction, else False -events_split = ['RejectedBySupervisor', 'OpenedBySupervisor', 'OpenedByHQ', 'RejectedByHQ'] - -# Create a flag indicating whether each row has an event in `events_split` -paradata['flag'] = paradata['event'].isin(events_split) - -# %% -# Use `groupby` and `cumsum` to count how many flagged events occur for each group -# If the count is greater than 0, then the 'interviewing' column should be False -paradata['cumulative_flag'] = paradata.groupby('interview__id')['flag'].cumsum() -paradata['interviewing'] = np.where(paradata['cumulative_flag'] > 0, False, True) - - -# %% -def make_index_col(df): - - # Filter out columns with NaN and empty strings - mask = (~df[['interview__id', 'variable_name', 'roster_level']].isnull()) & \ - (df[['interview__id', 'variable_name', 'roster_level']] != '') - - # Use the mask to replace invalid values with an empty string - filtered_df = df.where(mask, '') - - # Concatenate the columns with an underscore separator - df['index_col'] = filtered_df['interview__id'].astype(str) + "_" + \ - filtered_df['variable_name'].astype(str) + "_" + \ - filtered_df['roster_level'].astype(str) - - # Remove trailing and leading underscores if they exist - df['index_col'] = df['index_col'].str.strip('_') - return df.copy() - - -# %% -# Cleanup the intermediate columns -paradata.drop(['flag', 'cumulative_flag'], axis=1, inplace=True) -paradata = paradata[(paradata['interviewing'] == True) & (paradata['role'] == 1)].copy() - -paradata = make_index_col(paradata) -paradata.sort_values(['interview__id', 'order'], inplace=True) -paradata.reset_index(inplace=True) - -# %% -# if limit_unit is not None: -# consent_variable = next(iter(limit_unit)) # Get the first (and only) key in the dictionary -# # Careful! Answer value is a string in paradata. -# # Therefore also consent_value must be set to a string. -# consent_value = str(imit_unit[consent_variable]) - -# cond1 = (paradata['variable_name'] == consent_variable) -# cond2 = (paradata['answer'] == consent_value) - -# filtered_interview_id = paradata[cond1 & cond2]['interview__id'].unique() - -# paradata = paradata[paradata['interview__id'].isin(filtered_interview_id)].copy() - -# %% -if 'answer_sequence' in paradata.columns: - paradata['answer_sequence'] = paradata['answer_sequence'].apply(str) -paradata_file = product['paradata'] -paradata.to_parquet(paradata_file) - -# %% \ No newline at end of file diff --git a/pipelines/feature_engineering/11_process_paradata_active.py b/pipelines/feature_engineering/11_process_paradata_active.py deleted file mode 100644 index 2655320..0000000 --- a/pipelines/feature_engineering/11_process_paradata_active.py +++ /dev/null @@ -1,47 +0,0 @@ -# --- -# jupyter: -# jupytext: -# cell_metadata_filter: tags,-all -# custom_cell_magics: kql -# text_representation: -# extension: .py -# format_name: percent -# format_version: '1.3' -# jupytext_version: 1.11.2 -# --- - -# %% tags=["parameters"] -# declare a list tasks whose products you want to use as inputs -upstream = ['10_process_paradata'] -product = None -limit_unit = None - -# %% -import pandas as pd -import numpy as np - -# %% -paradata = pd.read_parquet(upstream['10_process_paradata']['paradata']) -# %% -# df_para_active, active events, prior rejection/review events, for questions with scope interviewer - -active_events = ['InterviewCreated', 'AnswerSet', 'Resumed', 'AnswerRemoved', 'CommentSet', 'Restarted'] -# only keep events done by interview (in most cases this should be all, after above filters, -# just in case supervisor or HQ answered something while interviewer answered on web mode) -# keep active events, prior rejection/review events, for questions with scope interviewer -active_mask = (paradata['event'].isin(active_events)) & \ - (paradata['question_scope'].isin([0, ''])) & \ - (paradata['role'] == 1) - -vars_needed = ['interview__id', 'order', 'event', 'responsible', 'role', 'tz_offset', - 'param', 'answer', 'roster_level', 'timestamp_local', 'variable_name', - 'question_sequence', 'question_scope', "qtype", 'question_type', - 'qnr', 'qnr_version', 'interviewing', 'yes_no_view', 'index_col', 'f__answer_hour_set' - ] - -df_para_active = paradata.loc[active_mask, vars_needed] - -# %% -paradata_active_file = product['paradata_active'] -with open(paradata_active_file, 'wb') as f: - df_para_active.to_parquet(f) diff --git a/pipelines/feature_engineering/12_process_items.py b/pipelines/feature_engineering/12_process_items.py deleted file mode 100644 index 5cf4d2d..0000000 --- a/pipelines/feature_engineering/12_process_items.py +++ /dev/null @@ -1,60 +0,0 @@ -# --- -# jupyter: -# jupytext: -# cell_metadata_filter: tags,-all -# custom_cell_magics: kql -# text_representation: -# extension: .py -# format_name: percent -# format_version: '1.3' -# jupytext_version: 1.11.2 -# --- - -# %% tags=["parameters"] -# declare a list tasks whose products you want to use as inputs -upstream = ['01_get_dataframes', '10_process_paradata', '11_process_paradata_active'] -product = None -limit_unit = None - -# %% -import pandas as pd -import numpy as np - -# %% -microdata = pd.read_parquet(upstream['01_get_dataframes']['microdata']) -df_active_paradata = pd.read_parquet(upstream['11_process_paradata_active']['paradata_active']) - -# %% -allowed_features = ['f__' + k for k, v in config['features'].items() if v['use']] -item_level_columns = ['interview__id', 'variable_name', 'roster_level'] -df_paradata = self.process_paradata(paradata) -print('Paradata Processed') -_df_item = self.make_df_item(microdata) - -# %% [markdown] -# ### Make df_item - -# %% -microdata = make_index_col(microdata) -df_item = microdata[['value', "qtype", 'is_integer', 'qnr_seq', - 'n_answers', 'answer_sequence', - 'cascade_from_question_id', 'is_filtered_combobox', - 'index_col'] + item_level_columns] - -paradata_columns = ['responsible', 'f__answer_hour_set', 'interviewing', 'tz_offset'] -# merge microdata with active pardata and keep only the last answer set -answer_set_mask = (df_active_paradata['event'] == 'AnswerSet') -data = df_active_paradata[answer_set_mask].drop_duplicates(subset='index_col', keep='last') -df_item = df_item.merge(data[paradata_columns + ['index_col']], how='left', - on='index_col') -# Remove items that are not in interviewing -df_item = df_item[df_item['interviewing'] == True] -df_item = add_sequence_features(df_item) - -df_item = .add_item_time_features(df_item) - - -# %% -paradata_active_file = product['paradata_active'] -with open(paradata_active_file, 'wb') as f: - df_para_active.to_parquet(f) diff --git a/pipelines/generic/.gitkeep b/pipelines/generic/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/pipelines/hooks.py b/pipelines/hooks.py deleted file mode 100644 index 6584af9..0000000 --- a/pipelines/hooks.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -DAG-level and task-level hooks. All arguments in the functions are optional -""" - - -def dag_level_on_render(my_param): - """Executed after the pipeline renders (before execution) - """ - print(f'rendered DAG! my_param={my_param}') - - -def dag_level_on_finish(dag, report): - """Executes after the pipeline runs all tasks - """ - print(f'Finished executing pipeline {dag}, report:\n{report}') - - -def dag_level_on_failure(traceback): - """Executes if the pipeline fails - """ - if traceback.get('build'): - print('Pipeline execution failed while running the tasks!') - - if traceback.get('on_finish'): - print('Pipeline execution failed while executing an on_finish hook!') - - -def on_render(my_param, task, client, product, params): - """Executed after the task renders (before execution) - """ - print(f'Finished rendering {task.name} with my_param {my_param}, ' - f'client {client}, product {product}, and task params {params}') - - -def on_finish(task, client, product, params): - """Executes after the task runs - """ - print(f'Finished running {task.name} with client {client}, ' - f'product {product} and params {params}') - - -def on_failure(task, client, product, params): - """Executes if the task fails - """ - print(f'{task.name} with client {client}, ' - f'product {product} and params {params} failed!') \ No newline at end of file diff --git a/pipelines/ingestion/.gitkeep b/pipelines/ingestion/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/pipelines/ingestion/01_get_dataframes.py b/pipelines/ingestion/01_get_dataframes.py deleted file mode 100644 index ec87b98..0000000 --- a/pipelines/ingestion/01_get_dataframes.py +++ /dev/null @@ -1,80 +0,0 @@ -# --- -# jupyter: -# jupytext: -# cell_metadata_filter: tags,-all -# custom_cell_magics: kql -# text_representation: -# extension: .py -# format_name: percent -# format_version: '1.3' -# jupytext_version: 1.11.2 -# kernelspec: -# display_name: rissk -# language: python -# name: python3 -# --- - -# %% tags=["parameters"] -# declare a list tasks whose products you want to use as inputs -upstream = None -product = None -# %% [markdown] -# # Get Paradata, Microdata and Questionaire - -# %% tags=[] -from rissk.config import SURVEY, QUESTIONAIRE, RAW_DATA_DIR -from rissk.utils.import_utils import get_zip_files, extract_zip, get_survey_info, get_dataframes - - -# %% [markdown] -# ## Extract Zip file - -# %% -zip_files = get_zip_files(RAW_DATA_DIR, SURVEY, QUESTIONAIRE) - -survey_paths = [] -for zip_file in zip_files: - project_path = zip_file.with_suffix('') - survey_paths.append(project_path) - extract_zip(zip_file, project_path) - -# %% [markdown] -# ## Map Questionaire to paths - -# %% - -survey_info = get_survey_info(survey_paths) - - -# %% [markdown] -# ## Get Dataframes - -# %% -dfs_paradata, dfs_questionnaires, dfs_microdata = get_dataframes(survey_info) - -# %% [markdown] -# ## Save Dataframes - -# %% - -paradata_file = product['paradata'] -questionnaire_file = product['questionnaire'] -microdata_file = product['microdata'] - - - - -if 'answer_sequence' in dfs_paradata.columns: - dfs_paradata['answer_sequence'] = dfs_paradata['answer_sequence'].apply(str) -dfs_paradata.to_parquet(paradata_file) - -if 'answer_sequence' in dfs_questionnaires.columns: - dfs_questionnaires['answer_sequence'] = dfs_questionnaires['answer_sequence'].apply(str) -dfs_questionnaires.to_parquet(questionnaire_file) - - -if 'answer_sequence' in dfs_microdata.columns: - dfs_microdata['answer_sequence'] = dfs_microdata['answer_sequence'].apply(str) -dfs_microdata.to_parquet(microdata_file) - -# %% diff --git a/pipelines/pipelines.rst b/pipelines/pipelines.rst deleted file mode 100644 index baa29d2..0000000 --- a/pipelines/pipelines.rst +++ /dev/null @@ -1,178 +0,0 @@ -Configuration (``dev``/``prod``) -============================================ - -In the previous guide (:doc:`../user-guide/parametrized`), we saw how to use an -``env.yaml`` file to parametrize our pipeline and switch parameters from the -command line. - -Sometimes we want to change all the parameters at once. The most common -scenario is to change configuration during development and production. - -For example, say you're working on a Machine Learning pipeline whose -``pipeline.yaml`` looks like this: - -.. code-block:: yaml - :class: text-editor - :name: pipeline-yaml - - tasks: - - - source: get.py - product: - nb: get.ipynb - data: raw.csv - params: - sample_pct: '' - - - source: get.py - product: - nb: get.ipynb - data: raw.csv - - - source: get.py - product: - nb: get.ipynb - data: raw.csv - - -The pipeline above has one placeholder ``''``, which controls -which percentage of raw data to download. You may want to develop locally with a -fraction of the data, say 20%, to iterate quickly. To -`smoke test `_ quickly, -you may run it with a smaller sample, say 1%. Finally, to train a model, you'll -use 100% of the data. - -.. tip:: - - You can use placeholders (e.g., ````) anywhere in the - ``pipeline.yaml`` file. Another typical use case is to switch the product - location (e.g., ``product: '/some-data.csv'``. - - -By default, Ploomber looks for an ``env.yaml``. To enable rapid local -development with 20% of the data, you may create an ``env.yaml`` file like this: - -.. code-block:: yaml - :class: text-editor - - sample_pct: 20 - -For smoke testing, ``env.test.yaml``: - -.. code-block:: yaml - :class: text-editor - - sample_pct: 1 - -And for training, ``env.train.yaml``: - -.. code-block:: yaml - :class: text-editor - - sample_pct: 100 - -To switch configurations, you can set the ``PLOOMBER_ENV_FILENAME`` environment variable -to ``env.test.yaml`` in the testing environment and to ``env.train.yaml`` in -the training environment. - -Whenever ``PLOOMBER_ENV_FILENAME`` has a value, Ploomber uses it and looks for a file -with such a name. Note that this must be a filename, not a path since Ploomber -expects ``env.yaml`` files to exist in the same folder as the ``pipeline.yaml`` -file. For example, if you're on Linux or macOS: - -.. code-block:: console - - export PLOOMBER_ENV_FILENAME=env.train.yaml && ploomber build - - -.. important:: - - If you're using the Jupyter integration and want to see the changes - reflected in the injected cell, you need to shut down Jupyter - set ``PLOOMBER_ENV_FILENAME``, and start Jupyter again. - - -Managing multiple pipelines ---------------------------- - -If your project has more than one pipeline, they'll likely need -different ``env.yaml`` files. - -Say you have two pipelines, one for training a model (``pipeline.yaml``) and -one for serving it (``pipeline.serve.yaml``). You can create an ``env.yaml`` -file to parametrize ``pipeline.yaml`` and an ``env.serve.yaml`` to parametrize -``pipeline.serve.yaml``: - -.. code-block:: sh - - project/ - pipeline.yaml - pipeline.serve.yaml - env.yaml - env.serve.yaml - -The general rule is as follows: When loading a ``pipeline.{name}.yaml``, -extract the ``{name}`` portion. Then look for a ``env.{name}.yaml`` file, if -such file doesn't exist, look for an ``env.yaml`` file. Note that the -``PLOOMBER_ENV_FILENAME`` environment variable overrides this process. - -Alternatively, you may separate the pipelines into different directories, and -put an ``env.yaml`` on each one: - -.. code-block:: sh - - project-a/ - pipeline.yaml - env.yaml - project-b/ - pipeline.yaml - env.yaml - - -``env.yaml`` composition (DRY) ------------------------------- - -.. note:: New in version 0.18 - -In many cases, your development and production environment configuration share -many values in common. To keep them simple, you may create an ``env.yaml`` -(development configuration) and have your ``env.prod.yaml`` (production -configuration) inherit from it: - -.. code-block:: yaml - :class: text-editor - :name: env-yaml - - key: value - key_another: dev-value - - -Then in your ``env.prod.yaml``: - -.. code-block:: yaml - :class: text-editor - - meta: - # import development config - import_from: env.yaml - - # no need to declare key: value here, it'll be imported from env.yaml - - # overwrite value - key_another: production-value - -Note that if the value in ``import_from`` is a relative path, it is considered -so relative to the location of the env file (in our case ``env.prod.yaml``). - -You can switch values in ``env.yaml`` from the command line, to see how: - -.. code-block:: console - - ploomber build --help - - -Example, if you have a ``key`` in your ``env.yaml``: - -.. code-block:: console - - ploomber build --env--key new-value \ No newline at end of file diff --git a/pipelines/processing/.gitkeep b/pipelines/processing/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/reports/.gitkeep b/reports/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/reports/figures/.gitkeep b/reports/figures/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/reports/notebooks/.gitkeep b/reports/notebooks/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/requirements.txt b/requirements.txt index b20fe70..9a11d62 100644 --- a/requirements.txt +++ b/requirements.txt @@ -23,7 +23,6 @@ pythresh>=0.3.6 nicegui>=1.4 # --- Logging / progress --- -loguru>=0.7.3 tqdm>=4.67.0 # --- Config --- diff --git a/results/.gitkeep b/results/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/rissk_kedro/conf/base/globals.yml b/rissk_kedro/conf/base/globals.yml index ea2e055..129a3f3 100644 --- a/rissk_kedro/conf/base/globals.yml +++ b/rissk_kedro/conf/base/globals.yml @@ -5,18 +5,19 @@ # Override this in conf/local/globals.yml to point to your data on disk. data_root: "data" -# questionnaire: -# name: "slbhies_listing" -# VERSION: [5, 6, 7] -# filter_var: null # Set to a single-key dict to filter by consent, e.g.: -# # filter_var: {consent_q: "1"} -# # Only interviews where paradata variable 'consent_q' has answer "1" are scored. -# # The answer value must be a string (paradata answers are always strings). - questionnaire: - name: "snb_hies_hh" - VERSION: [9, 10, 11] - filter_var: null + name: "slbhies_listing" + VERSION: [5, 6, 7] + filter_var: null # Set to a single-key dict to filter by consent, e.g.: + # filter_var: {consent_q: "1"} + # Only interviews where paradata variable 'consent_q' has answer "1" are scored. + # The answer value must be a string (paradata answers are always strings). + + +# questionnaire: +# name: "snb_hies_hh" +# VERSION: [9, 10, 11] +# filter_var: null # questionnaire: # name: "pmpmd_community" diff --git a/rissk_kedro/docs/source/conf.py b/rissk_kedro/docs/source/conf.py deleted file mode 100644 index 592c48d..0000000 --- a/rissk_kedro/docs/source/conf.py +++ /dev/null @@ -1,221 +0,0 @@ -#!/usr/bin/env python3 - - -# rissk_kedro documentation build -# configuration file, created by sphinx-quickstart. -# -# This file is execfile()d with the current directory set to its -# containing dir. -# -# Note that not all possible configuration values are present in this -# autogenerated file. -# -# All configuration values have a default; values that are commented out -# serve to show the default. - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# -import re - -from rissk_kedro import __version__ as release - -# -- Project information ----------------------------------------------------- - -project = "rissk_kedro" -author = "Kedro" - -# The short X.Y version. -version = re.match(r"^([0-9]+\.[0-9]+).*", release).group(1) - -# -- General configuration --------------------------------------------------- - -# If your documentation needs a minimal Sphinx version, state it here. -# -# needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - "sphinx.ext.autodoc", - "sphinx.ext.napoleon", - "sphinx_autodoc_typehints", - "sphinx.ext.doctest", - "sphinx.ext.todo", - "sphinx.ext.coverage", - "sphinx.ext.ifconfig", - "sphinx.ext.viewcode", - "sphinx.ext.mathjax", - "nbsphinx", - "sphinx_copybutton", - "myst_parser", -] - -# enable autosummary plugin (table of contents for modules/classes/class -# methods) -autosummary_generate = True - -# Add any paths that contain templates here, relative to this directory. -templates_path = ["_templates"] - -# The suffix(es) of source filenames. -# You can specify multiple suffix as a list of string: -# -source_suffix = {".rst": "restructuredtext", ".md": "markdown"} - -# The master toctree document. -master_doc = "index" - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# -# This is also used if you do content translation via gettext catalogs. -# Usually you set "language" from the command line for these cases. -language = "en" - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path . -exclude_patterns = ["_build", "**.ipynb_checkpoints"] - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = "sphinx" - -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -html_theme = "sphinx_rtd_theme" - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -# -html_theme_options = {"collapse_navigation": False, "style_external_links": True} - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ["_static"] - -# Custom sidebar templates, must be a dictionary that maps document names -# to template names. -# -# The default sidebars (for documents that don't match any pattern) are -# defined by theme itself. Builtin themes are using these templates by -# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', -# 'searchbox.html']``. -# -# html_sidebars = {} - -html_show_sourcelink = False - -# Removes, from all docs, the copyright footer. -html_show_copyright = False - -# -- Options for HTMLHelp output --------------------------------------------- - -# Output file base name for HTML help builder. -htmlhelp_basename = "rissk_kedrodoc" - -# -- Options for LaTeX output ------------------------------------------------ - -latex_elements = { - # The paper size ('letterpaper' or 'a4paper'). - # - # 'papersize': 'letterpaper', - # - # The font size ('10pt', '11pt' or '12pt'). - # - # 'pointsize': '10pt', - # - # Additional stuff for the LaTeX preamble. - # - # 'preamble': '', - # - # Latex figure (float) alignment - # - # 'figure_align': 'htbp', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, -# author, documentclass [howto, manual, or own class]). -latex_documents = [ - ( - master_doc, - "rissk_kedro.tex", - "rissk_kedro Documentation", - "Kedro", - "manual", - ) -] - -# -- Options for manual page output ------------------------------------------ - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [ - ( - master_doc, - "rissk_kedro", - "rissk_kedro Documentation", - [author], - 1, - ) -] - -# -- Options for Texinfo output ---------------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - ( - master_doc, - "rissk_kedro", - "rissk_kedro Documentation", - author, - "rissk_kedro", - "Project rissk_kedro codebase.", - "Data-Science", - ) -] - -# -- Options for todo extension ---------------------------------------------- - -# If true, `todo` and `todoList` produce output, else they produce nothing. -todo_include_todos = False - -# -- Extension configuration ------------------------------------------------- - -# nbsphinx_prolog = """ -# see here for prolog/epilog details: -# https://nbsphinx.readthedocs.io/en/0.3.1/prolog-and-epilog.html -# """ - -# -- NBconvert kernel config ------------------------------------------------- -nbsphinx_kernel_name = "python3" - - -def remove_arrows_in_examples(lines): - for i, line in enumerate(lines): - lines[i] = line.replace(">>>", "") - - -def autodoc_process_docstring(app, what, name, obj, options, lines): - remove_arrows_in_examples(lines) - - -def skip(app, what, name, obj, skip, options): - if name == "__init__": - return False - return skip - - -def setup(app): - app.connect("autodoc-process-docstring", autodoc_process_docstring) - app.connect("autodoc-skip-member", skip) diff --git a/rissk_kedro/docs/source/index.rst b/rissk_kedro/docs/source/index.rst deleted file mode 100644 index 3df98ca..0000000 --- a/rissk_kedro/docs/source/index.rst +++ /dev/null @@ -1,19 +0,0 @@ -.. rissk_kedro documentation master file, created by sphinx-quickstart. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Welcome to project rissk_kedro's API docs! -============================================= - -.. toctree:: - :maxdepth: 4 - - modules - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` From 98674fb8a1fcaa87abac88551f8093acf5b434ac Mon Sep 17 00:00:00 2001 From: VJausovec Date: Wed, 6 May 2026 22:36:19 +0100 Subject: [PATCH 65/70] Refactor project structure and update dependencies - Removed obsolete env.yaml and environment_kedro.yml files. - Updated environment.yml to reflect new package versions and dependencies. - Deleted main.py, pipeline.yaml, and rissk_readme.ipynb as part of project restructuring. - Cleaned up unnecessary imports and code related to previous project setup. --- Kedro_vs_Legacy_Changelog.md | 717 +++++++++++++++++++++++++++++++++ env.yaml | 7 - environment.yml | 58 ++- environment_kedro.yml | 40 -- main.py | 48 --- pipeline.yaml | 29 -- rissk_kedro/notebooks/.gitkeep | 0 rissk_readme.ipynb | 164 -------- 8 files changed, 745 insertions(+), 318 deletions(-) create mode 100644 Kedro_vs_Legacy_Changelog.md delete mode 100644 env.yaml delete mode 100644 environment_kedro.yml delete mode 100644 main.py delete mode 100644 pipeline.yaml delete mode 100644 rissk_kedro/notebooks/.gitkeep delete mode 100644 rissk_readme.ipynb diff --git a/Kedro_vs_Legacy_Changelog.md b/Kedro_vs_Legacy_Changelog.md new file mode 100644 index 0000000..63a44cf --- /dev/null +++ b/Kedro_vs_Legacy_Changelog.md @@ -0,0 +1,717 @@ +# RISSK: Kedro Pipeline vs Legacy — Full Changelog + +This document details every meaningful change between the legacy Ploomber/Python 3.9 pipeline (`rissk/`) and the new Kedro pipeline (`rissk_kedro/`). Changes are separated into **architectural changes**, **intentional behavioural changes**, and **bug fixes**. + +--- + +## Table of Contents + +1. [Environment & Infrastructure](#1-environment--infrastructure) +2. [Architecture Overview](#2-architecture-overview) +3. [Data Ingestion](#3-data-ingestion) +4. [Feature Creation](#4-feature-creation) +5. [Item Scoring](#5-item-scoring) +6. [Unit Scoring](#6-unit-scoring) +7. [Unscored / Missing Features](#7-unscored--missing-features) +8. [Bug Fixes](#8-bug-fixes) + +--- + +## 1. Environment & Infrastructure + +| Aspect | Legacy | Kedro | +|---|---|---| +| Orchestrator | Ploomber (`pipeline.yaml`) | Kedro 1.2.0 | +| Python | 3.9 | 3.13 | +| NumPy | 1.26 | 2.3 | +| Pandas | 2.2 | 2.3 | +| SciPy | 1.13 | 1.17 | +| scikit-learn | 1.6 | 1.8 | +| Architecture | OOP class hierarchy with stateful `self.*` DataFrames | Functional pure-function nodes; data flows via Kedro Data Catalog | +| File I/O | `df.to_csv()`, direct `open()` calls, manual pickle | Kedro Dataset abstractions only | +| Configuration | Hardcoded defaults inside classes | `conf/base/parameters.yml` | +| Type hints | `List`, `Dict` from `typing` | Native `list`, `dict` (Python 3.10+ style) via `from __future__ import annotations` | +| Legacy protection | — | All Kedro rewrites are in new `*_kedro.py` files; legacy files are never modified | + +--- + +## 2. Architecture Overview + +### Legacy — Three-Level Class Hierarchy + +``` +FeatureProcessing (feature_processing.py) — data loading, base dataframes + └── ItemFeatureProcessing (item_processing.py) — item-level feature & score calculations + └── UnitDataProcessing (unit_proccessing.py) — unit/responsible scores + global risk +``` + +State was maintained on the class instance (`self._df_item`, `self._df_unit`, `self._df_resp`). Methods were discovered dynamically via `dir()` and `getattr()` naming conventions (`make_feature_item__*`, `make_score_unit__*`). + +### Kedro — Functional Pipeline Graph + +``` +data_ingestion → feature_creation → rissk_scoring +``` + +Each pipeline is a DAG of pure functions. DataFrames and parameters flow explicitly as node inputs/outputs. No shared mutable state. Intermediate datasets are persisted to the Data Catalog. + +--- + +## 3. Data Ingestion + +### 3.1 Structural Changes + +#### Microdata split into two pipeline steps +- **Legacy:** Microdata is loaded and merged with questionnaire metadata in a single step → `microdata.parquet`. +- **Kedro:** Split into two nodes: + 1. `load_raw_microdata_node` — reads raw files, applies `transform_multi` (requires questionnaire data), melts to long format, sets `qnr`/`qnr_version`, applies `normalize_column_name`. Output: `raw_microdata` (`20_INTERIM/microdata_raw.parquet`). + 2. `merge_microdata_questionnaire_node` — merges `raw_microdata` with questionnaire metadata. Output: `microdata` (`30_PROCESSED/microdata.parquet`). +- **Downstream impact:** All downstream pipelines consume `microdata` from step 2, which is structurally identical to the legacy output. `raw_microdata` is a new intermediate dataset not present in legacy. + +#### Unzip as a separate node +- **Legacy:** Unzip logic was inlined inside data processing functions. +- **Kedro:** `extract_zip` is its own dedicated node, called optionally before any data processing. + +#### Each output dataframe is independently constructed +- **Legacy:** Outputs could share mutable state or be derived from one another. +- **Kedro:** `questionnaire`, `paradata_processed`, and `microdata` are each produced by a dedicated node with no hidden dependencies. + +#### Skip/ignore empty files +- **Legacy:** A missing paradata or questionnaire export for a specific version would raise an exception and abort the run. +- **Kedro:** Each load node checks for the presence of `Paradata` or `Tabular` exports per version (both questionnaire and microdata loading use the `Tabular` key — there is no separate Questionnaire or Microdata export type). Four failure modes are handled: + - **Missing export folder** — logs `WARNING` and skips that version. + - **File exists but has no data rows** (header-only, valid file) — `pd.read_csv` / `read_stata` succeeds and returns a 0-row DataFrame; the node detects the empty result and logs a `WARNING` (`"file may be empty or corrupt"`). For paradata, the 0-row DataFrame preserves column schema and contributes no rows to the combined output. For microdata, `get_microdata_raw` skips header-only files entirely (returning a column-less empty DataFrame), so the node always warns for empty microdata files. + - **File exists but is corrupt or unreadable** — the utility function catches the parse exception and returns an empty DataFrame; `get_paradata_raw` and `read_microdata_file` log `ERROR`, while `read_json_questionnaire` (called by `get_questionnaire`) logs `WARNING`. The node then appends the empty DataFrame and logs a `WARNING`. The empty DataFrame contributes no rows to the final output. + - **Unrecoverable exception in the node itself** — caught by the node's outer `try/except`, logs `ERROR`, and continues to the next version. +- If **all** versions are empty or missing, the combined output for that stage is an empty DataFrame. Downstream nodes detect this and log `ERROR` or `WARNING` before returning early: + - `process_paradata_node` — logs `ERROR` if `paradata_raw` is entirely empty and returns `pd.DataFrame()`. + - `build_removed_answers_node` (`feat_answer_removed`) — logs `WARNING` if `paradata_processed` is empty or missing the `'event'` column and returns `pd.DataFrame()`. + - `create_base_item_table` — logs `ERROR` if `microdata` is entirely empty and returns `pd.DataFrame()`. + - `create_base_unit_table` — logs `ERROR` if `paradata_full` is entirely empty and returns `pd.DataFrame()`. + - `calculate_item_scores` and `calculate_unit_scores` — log `WARNING` if their input feature tables are empty and return empty DataFrames without attempting model fitting. +- The pipeline therefore signals all-empty data clearly through `ERROR`/`WARNING` log messages and completes without a cryptic crash. + +#### VERSION `[]` includes all available versions +- **Legacy:** `VERSION` in `env.yaml` required explicit version numbers. +- **Kedro:** An empty list `VERSION: []` now means "include all versions found in the survey folder". Explicit version lists still work as before. + ```yaml + questionnaires: + - name: "slchbs_saintlucia_2025" + VERSION: [] # all available versions + ``` + +#### Questionnaire no longer loaded inside paradata ingestion +- **Legacy:** Paradata loading was coupled with questionnaire metadata (`answer_sequence`, `n_answers`). +- **Kedro:** Questionnaire is loaded independently. `answer_sequence` and `n_answers` are now correctly populated via the linked category data from the questionnaire JSON — in legacy these columns were always empty because categories in Excel files were never matched to the questionnaire (extension mismatch + dashes in JSON names). + +#### Processed paradata has no `index` column +- **Legacy:** `paradata_processed` carried an `index` column (redundant row numbering). +- **Kedro:** The `index` column is dropped; `reset_index(drop=True)` is used after concatenation. + +#### Unzip limitation +- The `extract_zip` node only works on **direct Survey Solutions downloads**. On macOS, re-zipping a folder introduces an extra directory layer that breaks the hardcoded path resolution. Re-zipped files must be extracted manually. + +### 3.2 Intentional Behavioural Changes + +#### `active_mask` question_scope — filter moved into feature creation +- **Legacy (OOP):** Filled all NaN `question_scope` values with `''` globally via `fillna('')`, then filtered `question_scope in [0, '']`. This correctly let pause events (`Resumed`, `Restarted`) and interview-level events (`InterviewCreated`) through because they have no question scope (NaN → `''` after fill). +- **Legacy (Ploomber):** The `fillna('')` step was missing. The `question_scope in [0, '']` filter dropped all pause and interview-level events. No pause or interview creation events reached `paradata_active` — a silent data loss. +- **Kedro:** The `paradata_active` dataset was removed entirely. Feature creation functions consume `paradata_processed` directly and apply per-event-type filters inline: + - `AnswerSet`, `AnswerRemoved`, `CommentSet` → filter `question_scope == 0` + - `Resumed`, `Restarted`, `InterviewCreated` → no `question_scope` filter (these events have `NaN` scope and are included as-is) + This correctly includes pause events in unit-level time features while excluding supervisor-scoped question events. + +#### `limit_unit` / `filter_by_consent` — now active before scoring +- **Legacy (Ploomber):** The `filter_by_consent` function existed in `feature_processing.py` but was commented out in the Ploomber pipeline (`10_process_paradata`). The `limit_unit` config option had no effect. +- **Kedro:** `filter_by_consent` is an explicit node in the `rissk_scoring` pipeline, running before `calculate_item_scores`. It is configured via `filter_var` in `globals.yml` (per questionnaire). The value must be a single-key dict `{variable_name: answer_value}` — interviews where that variable in paradata does **not** match the answer value are dropped from `item_features`, `unit_features`, and `removed_answers` before scoring. Set to `null` (default) to skip filtering. If `filter_var` is set but matches 0 interviews, the pipeline raises a `ValueError` rather than silently producing empty results. + +#### Microdata `value` column normalization (integer floats) +- **Legacy:** `value` column contained mixed formats — e.g., `"1"` and `"1.0"` for the same logical integer value (artefact of Pandas NaN-forced float conversion). +- **Kedro:** Explicitly normalizes: if a float is integer-equivalent (`x.is_integer() == True`), it is converted to `int` before stringification. `"1.0"` → `"1"`. Also applied inside list-strings: `"[1.0, 2.0]"` → `"[1, 2]"`. +- **Downstream impact:** No current code in the pipeline performs exact string matching against numeric values in the `value` column — all comparisons use non-numeric sentinels (`'##N/A##'`, `-999999999`) or type-based checks (`pd.isnull`, `isinstance`). This note is a caution for future extensions: any new code performing exact string matching against numeric values (e.g., `val == "1.0"`) should use type-safe comparisons instead. + +### 3.3 Utility Function Changes (`import_utils_kedro.py`) + +#### `extract_zip` — complete rewrite +- **Legacy:** Read the entire zip into memory (`BytesIO`) before extracting — inefficient and risky for large datasets. +- **Kedro:** Streams extraction directly from disk. Adds **directory traversal security checks** (prevents zip-slip attacks). + +#### `get_survey_info` +- **Legacy:** Relied on implicit list appends and looser dictionary construction. +- **Kedro:** Uses `setdefault` for cleaner dictionary building. Added explicit `try-except` around filename parsing so one malformed file does not crash the whole pipeline. + +#### `get_questionnaire` / `read_json_questionnaire` +- **Legacy:** Used `os.path.join` and bare `open()`. +- **Kedro:** Fully converted to `pathlib.Path`. Added explicit UTF-8 encoding on all JSON reads (required for cross-platform Python 3.13 compatibility). + +#### `get_paradata` / `read_paradata` +- **Legacy:** `read_paradata` uses `os.path.join` and bare `open()` with no encoding hint; `pd.read_csv` has no `low_memory` flag. Parameter splitting uses an unguarded two-step `str.split` / `str.rsplit` on the `parameters` column — if the column is absent or a row has no `||` delimiter the assignment raises. +- **Kedro:** `read_paradata` converts to `pathlib.Path` and adds `encoding='utf-8'` (matching the questionnaire change) and `low_memory=False` to suppress `DtypeWarning` on mixed-type data. The parameter splitting in `get_paradata_raw` still uses `str.split` / `str.rsplit` but wraps each step with a `.shape[1] == 2` guard and a `.notna().any()` check before the answer split, so malformed or missing parameter values no longer raise. + +#### `get_microdata` +- **Legacy:** `drop_list` and `is_valid` are both local nested definitions inside `get_microdata`. `is_valid` accepts **any non-empty list unconditionally** (`isinstance(value, list): return True`) and rejects only empty strings and NaN scalars. Filtering uses `.apply(is_valid)`. +- **Kedro:** `get_microdata` is split into `get_microdata_raw` (transform + filter + stringify) and `merge_microdata_questionnaire` (questionnaire metadata join). Filtering still uses `.apply(is_valid_fast)` — not vectorized. `is_valid_fast` is more defensive: handles `None`, empty lists/tuples/arrays, and uses a try/except around `pd.isna` to avoid `TypeError: boolean value of NA is ambiguous` on Pandas 2.x nullable types. Key behavioural difference: `is_valid_fast` rejects lists where every element is NaN or empty (rather than accepting all non-empty lists), which is what drives the TextListQuestion all-NaN filtering in §3.3 Case A. + +#### `transform_multi` — MultyOptionsQuestion (linked) bug fix +- **Legacy:** For linked `MultyOptionsQuestion`, the comparison `sub = [ele if ele != [] else '##N/A##' for ele in sub]` evaluated to `True` for integers and `False` for objects in NumPy < 2.x. The result was that a list like `[[3, -999..., -999, ...], [1, -999..., ...]]` collapsed to `[##N/A##, ##N/A##, ...]` — discarding the one valid answer per row. +- **Kedro:** Fixed to produce `[[3], [1], ...]` — preserving the valid answer and correctly filtering out missing-coded values. + +#### `TextListQuestion` disabled-question filtering + +Two distinct cases arise from how Survey Solutions exports `TextListQuestion` data. Both are handled differently between legacy and Kedro. + +**Case A — Disabled question (questionnaire logic prevented it from being shown):** +- Export: all columns are system missing (NaN). NaN passes the `!= '##N/A##'` mask in `transform_multi`, so NaN entries are collected into the list and `remove_unset_value` preserves them → `[nan, nan, nan, ...]`. +- **Legacy:** `is_valid` accepts any list unconditionally → row kept in `df_item` with `value = [nan, nan, nan, ...]`. `~pd.isnull(value)` is `True` (a list object is not null) → incorrectly counted as **answered** in `f__number_answered`. +- **Kedro:** `is_valid_fast` rejects all-NaN lists → row absent from `df_item` → not counted. Correct behaviour. + +**Case B — Unanswered but enabled question (shown to the interviewer, left blank):** +- Export: all columns are `'##N/A##'`. The `!= '##N/A##'` mask blocks all columns → nothing is appended → `x` stays `[]`. The outer dispatch `if x else ...` short-circuits on the falsy `[]` before `remove_unset_value` is ever called. +- **Legacy:** Produces `float('nan')` → dropped by `is_valid` → row absent from `df_item` → **not counted in `f__number_unanswered`**. This is a bug: the question was shown and left blank, so it should be counted as unanswered. +- **Kedro (fixed):** The dispatch now returns `'##N/A##'` for `transformation_type == 'list'` when the list is empty: `'##N/A##' if transformation_type == 'list' else float('nan')`. The scalar `'##N/A##'` survives `is_valid_fast`, the row lands in `df_item`, and is counted in `f__number_unanswered`. See also Bug 18. +- **Practical impact:** No interviews with unanswered-but-enabled `TextListQuestion` items were found in the test data of over 2000 interviews. The fix is correct in principle but is expected to have no real-world effect in the surveys tested. + +**Net effect on features (both cases combined):** +- `f__number_answered` is lower in Kedro (Case A: disabled questions no longer counted as answered). No disabled `TextListQuestion` rows were found in the test data of over 2000 interviews, so the effect may be minor in practice. +- `f__number_unanswered` is higher in Kedro (Case B: unanswered-but-enabled questions now correctly counted). No such cases were found in the test data of over 2000 interviews — this fix is expected to have no real-world effect in practice. +- `s__pause_count = f__pause_count / f__number_answered` — smaller denominator in Kedro → higher value for affected interviews (Case A only). No such interviews were found in the test data of over 2000 interviews. +- `s__number_answered` is directly lower in Kedro (Case A, same source as `f__number_answered`). No such cases were found in the test data of over 2000 interviews, so the effect is expected to be minor in practice. +- All other features are unaffected: numeric/position/selection features filter by question type and exclude `TextListQuestion`; time and `f__answer_changed` features operate on paradata events, not `df_item` rows. + +#### `MultyOptionsQuestion` ComboBox category matching fix +- **Legacy (`file_process_utils.py`):** `get_categories` indexes the categories dict by `file.name` (full filename, e.g. `"mycat.xlsx"`). `update_df_categories` then looks up `row['CategoriesId']` directly — but the questionnaire JSON stores `CategoriesId` as the stem only (e.g. `"mycat"`). Because `"mycat" != "mycat.xlsx"`, the lookup **always fails** — no ComboBox question ever gets `n_answers` or `answer_sequence` populated. +- **Kedro (`file_process_utils_kedro.py`):** Two fixes: + 1. `get_categories` now keys by `file.stem` (no extension), matching the format of `CategoriesId` in the JSON. + 2. `update_df_categories` strips unicode dash characters (`unicodedata.category(c) == 'Pd'`) from both the `CategoriesId` value and the dictionary keys before comparing, handling cases where the JSON uses a different unicode dash character than the filename. + +--- + +## 4. Feature Creation + +### 4.1 Structural Changes + +#### OOP to functional architecture +- **Legacy:** Class methods (`make_feature_item__*`, `make_feature_unit__*`) mutated `self._df_item` / `self._df_unit` in-place. Discovery via dynamic `getattr`. +- **Kedro:** Stateless functions in `feature_processing_kedro.py`. Feature dispatch uses an explicit dictionary (`ITEM_FEATURE_MAP`, `UNIT_FEATURE_MAP`) instead of `getattr`. Easier to debug; no hidden side effects. + +#### Explicit pipeline nodes +Four dedicated nodes replace the monolithic class initialization: +1. `create_base_item_table_node` — microdata + paradata_processed → base item table +2. `create_base_unit_table_node` — paradata_processed → base unit table +3. `enrich_item_features_node` — base item table + paradata_processed → item features +4. `enrich_unit_features_node` — unit base + item features + paradata_processed → unit features +5. `build_removed_answers_node` — paradata_processed → removed_answers (for `answer_removed` scoring) + +#### Active paradata node removed +- **Legacy (Ploomber):** A separate `filter_active_paradata` step produced `paradata_active` as a distinct dataset consumed by all feature nodes. +- **Kedro:** The `paradata_active` intermediate dataset no longer exists. All feature creation nodes receive `paradata_processed` directly and apply the correct per-event-type filters inline (see §3.2). + +#### `df_item` carries questionnaire name and version columns +- **Legacy:** `df_item` had no questionnaire identifier. +- **Kedro:** Two extra columns (`qnr`, `qnr_version`) are propagated from microdata into `df_item` for informational purposes (logging, output labeling, joining results across questionnaires). Scoring is always run independently per questionnaire. + +### 4.2 Intentional Behavioural Changes + +#### `f__first_decimal` renamed to `f__first_decimals` +- The feature always extracted the **first two decimal digits**, but was misnamed `f__first_decimal` (singular) in legacy. +- **Kedro:** Renamed to `f__first_decimals` to accurately reflect what is computed. + +#### Numeric sentinel filter for `f__numeric_response`, `f__first_digit`, `f__last_digit`, `f__first_decimals` +- **Legacy:** Questions of type `NumericQuestion` can have predefined special-meaning answers (e.g. `NONE=0`, `NO PHONE NUMBER=9999999`, `Don't know=-99`). These were treated as real numeric responses in all numeric features. +- **Kedro:** A sentinel detection flag is introduced. When enabled (default: `True` for all numeric features), values that appear in the question's `answers` list (predefined option set) are excluded before computing the feature. This prevents sentinel codes from polluting first-digit distributions, decimal patterns, and numeric response statistics. +- **Important note:** The filter flag for `f__numeric_response` and `f__first_digit` must be set to the same value, because `f__first_digit` score calculation uses `f__numeric_response` for the Benford frequency/magnitude filters. `f__first_digit` itself is only used to count unique leading digits. + +#### `f__first_digit` — interval `[-1, 1]` mapped to first digit `0` +- Values where `|val| < 1` (e.g. `0.12`) cannot have a meaningful first significant digit under Benford's Law. Both legacy and Kedro assign `first_digit = 0` for this range. This behaviour is preserved explicitly in Kedro. + +#### `f__answer_selected` — list parsing via `ast.literal_eval` +- **Legacy:** Uses a bare `isinstance(val, list)` check — returns NaN if the value is not already a Python list object (see §4.3 for the bug this causes). +- **Kedro:** Applies `ast.literal_eval(str(val))` before checking length. This handles both native list objects and string-serialized lists (e.g. `"[1, 2]"`), and also wraps in try/except to return NaN on unparseable values. + +#### `f__gps` — minor robustness improvement only +- **Legacy:** Splits value string by `,`, assigns named columns, sets `f__gps = True/False` boolean flag plus `f__gps_latitude`, `f__gps_longitude`, `f__gps_accuracy`. +- **Kedro:** Identical behaviour — `f__gps = True/False` boolean flag is still set. The only change is a defensive `if gps_data.shape[1] >= 3` guard before reading the coordinate columns, preventing an `IndexError` if the CSV split produces fewer columns than expected. + +### 4.3 Legacy Bugs Fixed in Kedro — Feature Creation + +The following items were **broken in the legacy pipeline** and are **corrected in Kedro**. They are not "remaining differences" — the Kedro values are the correct ones. + +#### `f__answer_position` — always returned NaN in legacy +- **Legacy (both OOP and Ploomber):** `get_microdata` unconditionally converts all values to strings (`astype(str)`) before returning. `make_feature_item__answer_position` then checks `row['value'] in row['answer_sequence']` where `value` is a string (e.g. `"3"`) and `answer_sequence` is a list of integers (e.g. `[1, 2, 3, 4]`). The membership check always fails — `f__answer_position` is always NaN in both legacy pipelines. +- **Kedro:** Three changes fix the lookup: (1) `answer_sequence` is parsed back to a Python list via `ast.literal_eval(str(...))`, (2) `value` is converted to numeric via `pd.to_numeric(..., errors='coerce')`, and (3) if all sequence values are integers and the numeric value is integer-equivalent, it is cast to `int` to ensure type-safe membership testing. Results verified manually against a subset of answers across four surveys. + +#### `f__answer_selected` — always returned NaN in legacy +- **Legacy (both OOP and Ploomber):** `get_microdata` unconditionally applies `astype(str)` to the `value` column before returning, converting Python list objects (e.g. `[1, 2, 3]` from `transform_multi`) into their string representations (e.g. `"[1, 2, 3]"`). By the time `make_feature_item__answer_selected` runs, `isinstance(value, list)` is always `False` — no parquet round-trip is needed for this to fail. The legacy function never extracted a selection count. +- **Kedro:** `value` is converted back to a list via `ast.literal_eval(str(val))` before checking length. Also adds an explicit `n_answers > 0` guard (replaces the missing `is_linked` flag check) before dividing by `n_answers`. + +#### `f__answer_changed` — `yes_list` changes ignored for `MultyOptionsQuestion` with yes/no view +- **Legacy:** For `MultyOptionsQuestion` with `yes_no_view == True`, the yes_list change check was immediately overwritten by the no_list check — two consecutive `.loc` assignments on the same `yesno_mask`. Only removal of `no` answers was ever counted; changes to `yes` answers were silently discarded. +- **Kedro fix:** Both checks are combined with bitwise OR before a single assignment: `yes_changed | no_changed`. A change in either list is now correctly counted. `multi_mask` is also narrowed to `qtype == 'MultyOptionsQuestion'` explicitly (no practical effect in legacy since `TextListQuestion` rows have `NaN` for `yes_no_view` which does not match `== False`, but makes intent clear). +- **Impact:** `f__answer_changed` will be higher in Kedro for interviews with `MultyOptionsQuestion` where `yes` answers were changed independently of `no` answers. + +#### `f__number_answered` / `f__number_unanswered` — sentinel comparison against string values +- **Legacy (both OOP and Ploomber):** `get_microdata` stringifies the entire `value` column via `astype(str)` before returning. The sentinel comparisons `value == -999999999` and `value != -999999999` then compare strings to an integer, which is always `False`/`True` respectively. As a result: (1) `f__number_answered` **includes** sentinel-coded unanswered items (they pass the `!= -999999999` guard), (2) `f__number_unanswered` **never** counts them (the `== -999999999` check never matches a string). +- **Kedro:** Uses `_is_missing_numeric_sentinel`: `pd.to_numeric(values, errors='coerce').eq(-999999999)`. This coerces the string `"-999999999"` to the float `-999999999.0` before comparing, which correctly identifies the sentinel regardless of string/numeric type. +- **Downstream impact:** `f__number_unanswered` is higher in Kedro (now correctly counts sentinel-coded unanswered items); `f__number_answered` is lower. + +### 4.4 Missing Features (present in legacy, absent in Kedro) + +The following features exist in the legacy `ITEM_FEATURE_MAP` equivalent but are **entirely absent** from the Kedro feature map. If enabled in `parameters.yml`, they silently produce no output (the map lookup returns `None` and is skipped without error): + +| Feature | Legacy Location | +|---|---| +| `f__comment_length` | `feature_processing.py` | +| `f__comment_set` | `feature_processing.py` | +| `f__answer_removed` (item-level) | `item_processing.py` | + +Missing from `UNIT_FEATURE_MAP`: + +| Feature | Legacy Location | +|---|---| +| `f__translation_positions` | `feature_processing.py` | + +--- + +## 5. Item Scoring + +### 5.1 Structural Changes + +#### Scoring is enforced per questionnaire at the pipeline level +- **Legacy:** Scoring ran over whatever data was loaded in a single run. In practice this was typically a single questionnaire, but the code did not enforce this. +- **Kedro:** One questionnaire is configured at a time via `questionnaire.name` in `conf/base/globals.yml`. All catalog paths resolve to that questionnaire's data folder. To run a different questionnaire, the `globals.yml` entry is changed and the pipeline re-run. Per-questionnaire isolation is enforced through configuration, not through automatic multi-questionnaire dispatch. + +#### OOP `make_score__*` → pure functions +Each legacy `make_score__*` method is now a standalone function in `item_processing_kedro.py`, accepting `df_item` and parameters, returning a modified `df_item`. No `self` attributes. + +#### Score initialization: `0` → `np.nan` +- **Legacy:** Pre-filters rows with non-null feature values; initializes score column to `0` (explicit "no anomaly") for all returned rows. +- **Kedro:** Works on the full `df_item`. Initializes score column to `np.nan`. Only rows belonging to variables that pass the frequency/uniqueness threshold receive `0`/`1` from the model. All other rows remain `NaN`. +- **Semantic change:** `NaN` means "this item was not evaluated" rather than "no anomaly detected". This distinction is preserved at item level but resolved to `0` at unit level via `.fillna(0)` after `groupby().mean()`. + +#### `s__answer_removed` — moved from item-level to unit-level +- **Legacy:** `make_score__answer_removed` calls `get_feature_item__answer_removed`, which reads directly from `self.df_paradata` (not `df_item`). It counts `AnswerRemoved` events per `(interview__id, responsible, variable_name, qnr_seq)` — including events for items no longer present in microdata. The result is an item-level DataFrame scored by ECOD per variable. +- **Kedro:** `calculate_answer_removed_unit_score` also reads directly from `paradata_full` (equivalent coverage). The difference is architectural: instead of producing item-level scores that are later aggregated, Kedro computes the unit-level score directly — returning a Series indexed by `interview__id` that is mapped into `df_unit`, bypassing the item table entirely. + +### 5.2 Intentional Behavioural Changes + +#### `filter_variable_name_by_frequency` — stricter NaN filtering in Kedro +- **Legacy:** Counts unique values as `len(group[feature_name].unique())` and frequency as `len(group) > frequency`. Neither excludes NaN from the count, so NaN counts as a unique value and inflates the frequency. +- **Kedro:** Filters NaN values before computing both `nunique()` and `count()`. Variables with exactly 100 non-NaN entries pass in Kedro (the boundary is inclusive) but fail in legacy (legacy uses `>`, so 100 entries fail and 101 pass). For example, a variable with exactly 100 entries is evaluated in Kedro but not in legacy. + +#### `s__first_digit` — frequency and magnitude filter changed +- **Legacy:** Frequency filter uses all non-NaN `f__numeric_response` values (including zeros). A variable with ~100 entries of `1`, `10`, and `1000` would pass. +- **Kedro:** Frequency filter uses `f__first_digit` excluding zeros (values in `[-1, 1]` map to first digit `0` and are excluded from the frequency count). Magnitude filter also excludes `|val| < 1` from the order-of-magnitude evaluation. +- **Impact:** Variables that are mostly zeros or have a narrow magnitude range are excluded in Kedro but may have been included in legacy. The effect of this is that fewer variables get scored. Overall it is likely minimal as the there are further filters in the Benford scoring functions. + +#### `s__first_digit` — score mapped to entire `df_item` +- **Legacy:** Scores were mapped back only to the non-NaN entries in `df_item` (filtered subset). +- **Kedro:** Scores are mapped back to the **entire `df_item`**. NaN entries for a variable can receive a score if they belong to a `(responsible, variable_name)` pair that was evaluated. This has no effect on unit/responsible aggregation (NaN items don't contribute to means) but produces different item-level score tables. + +#### `f__first_decimals` COF warning suppression +- **Legacy:** No warning suppression. +- **Kedro:** COF on `f__first_decimals` produces expected `RuntimeWarning`s from `pyod.models.cof` (divide-by-zero in chaining distance) and `numpy._core._methods` (overflow in squared-distance arithmetic) when the data contains many identical values (e.g. `x.00`). These are handled gracefully by COF internally. `warnings.filterwarnings('ignore', ...)` is applied to both modules for the duration of `fit`/`predict` to keep the log clean. `INNE` and `IForest` were tested as alternatives but COF produces better results, so COF is retained despite the warnings. Adding jitter to COF was also tested but produced incorrect results — common values such as `0` were incorrectly flagged as outliers because jitter breaks the tied-distance structure that COF relies on to score high-frequency decimal patterns. + +#### Responsible score guard — minimum 2 columns with variance +- **Legacy:** Constant columns are removed before fitting PCA (`df_resp.loc[:, df_resp.nunique() != 1]`), but there is no guard against 0 or 1 varying columns remaining. With 0 columns, `StandardScaler.fit_transform` raises. With 1 column, PCA produces scores but they are based on a single component — reconstruction error has no minor eigenvectors to measure against, so all scores are effectively identical. +- **Kedro:** After dropping constant columns, an explicit `if df_pca_input.shape[1] < 2:` guard sets `responsible_score = NaN` and returns early (also applies when no columns remain at all). A second guard in `combine_unit_scores` (`if resp_score_series.notna().any() and resp_score_series.nunique() > 1:`) skips the responsible score multiplication when scores are all-NaN or constant, preserving the IForest-derived unit scores as-is. A warning is logged. +- **Impact:** Surveys where all score columns are constant, or only one varies (e.g. listing surveys with very little interviewer variation), no longer raise or produce misleading responsible scores. + +#### `answer_hour_set` high-frequency correction — guarded +- **Legacy:** `df.loc[df[score_name] == 0]['frequency'].min()` — if all rows are flagged as anomalies, this returns `NaN`, so the correction silently never fires. +- **Kedro:** Explicit guard `if inlier_mask.any():` — when all rows are anomalies, the `if` branch is skipped. ECOD predictions are preserved as-is. Silent failure is eliminated. + +#### GPS aggregation — per variable → per interview +- **Legacy:** `make_score__gps` calls `get_clean_pivot_table`, which pivots `df_item` on `variable_name`, producing multi-level columns like `f__gps_latitude_gps_q1`, `f__gps_latitude_gps_q2`. `replace_with_feature_name` then renames all of them to `f__gps_latitude` — creating duplicate column names. Any column access (`data['f__gps_latitude']`) returns a DataFrame instead of a Series, and the subsequent `lat_lon_to_cartesian` call crashes. Legacy **only works correctly if there is exactly one GPS variable**. +- **Kedro:** Keeps each `(interview__id, variable_name)` row as a distinct GPS point — no grouping, no mean. All GPS points across all GPS variables are pooled together into the KDTree/COF/LOF model, and scores are written back to their original rows. Multiple GPS variables are handled correctly; each variable's coordinates contribute independently to the outlier model. + +#### GPS — `s__gps_extreme_outlier` latitude check overwritten by longitude check +- **Legacy intent:** Flag a point as an extreme outlier only when **both** `latitude == 0` and `longitude == 0` (the comment reads "0,0 as coordinates" — a failed GPS fix). +- **Legacy bug:** Three consecutive assignments: first sets the column to `0`, second sets it to `1` where `latitude == 0`, third sets it to `1` where `longitude == 0` — **overwriting** the latitude result entirely. Only `longitude == 0` is ever actually flagged; a point at `(lat=0, lon=5)` is incorrectly flagged as an outlier, while a point at `(lat=5, lon=0)` is also incorrectly flagged. A point at `(lat=0, lon=0)` is flagged correctly but only by coincidence. +- **Kedro (fixed):** Single vectorised expression: `(latitude == 0) & (longitude == 0)`. A point is flagged only when **both** coordinates are zero — matching the original intent. +- **Impact:** Legacy flags any point with `longitude == 0` (regardless of latitude) and misses points where only `latitude == 0`. Kedro correctly flags only true `(0, 0)` fixes. + +#### GPS — accuracy and search radius divided by `1e6` instead of `1e3` +- **Legacy:** `data['accuracy'] = data['f__gps_accuracy'] / 1e6` and `radius = 10 / 1e6`. `lat_lon_to_cartesian` returns coordinates in kilometres (Earth radius = 6371 km). Accuracy (in metres) should be converted to km by dividing by `1e3`. Using `1e6` instead makes both the per-point accuracy term and the base search radius ~1000× too small — effectively zero. `s__gps_proximity_counts` is almost always `0` in legacy regardless of how close the GPS points are. +- **Kedro:** `data['accuracy'] = data['f__gps_accuracy'].fillna(0) / 1e3` and `radius = 10 / 1e3`. Correct metres → km conversion. Neighbours within 10 m + device accuracy are counted properly. +- **Impact:** `s__gps_proximity_counts` will be substantially higher in Kedro for surveys where interviewers collected GPS points at the same or nearby locations. + +#### GPS — extreme outlier edge case +- **Legacy:** When all GPS points are extreme outliers, the COF/LOF model may throw an exception or produce unpredictable results. +- **Kedro:** Sets score to `NaN` (evaluation not possible) — handled cleanly. + +#### Entropy normalization fix +- **Legacy:** `calculate_entropy` divides by `np.log2(unique_values)`. +- **Kedro:** Divides by `np.log(unique_values)` (natural log). Raw entropy values in Kedro are correctly normalized to [0, 1]; legacy values are in [0, ln(2)] ≈ [0, 0.693]. Since entropy is only used in a relative median comparison (`x < median − 0.5 × median`), the ln(2) factor cancels and no responsibles are flagged differently. Raw entropy values differ. + +#### `s__last_digit` — not implemented +- **Legacy:** `make_score__last_digit` was commented out. +- **Kedro:** Not implemented (consistent with legacy). + +#### `s__answer_removed` — fallback path undercounts (Kedro) +- **Primary path:** `calculate_answer_removed_unit_score` reads from `paradata_full` — complete coverage including deleted items. +- **Fallback path (when `removed_answers` is `None`):** Falls back to `df_item`-based aggregation using a `how='left'` join on the microdata item table. Items deleted from microdata post-collection are absent from `df_item`, so their `AnswerRemoved` events are silently dropped — systematic undercount. +- **Impact:** Only relevant if `removed_answers` dataset is unavailable. The primary path is equivalent to legacy. In practice this should never trigger. + +### 5.3 Scoring Coverage + +| Score | Kedro Implementation | Notes | +|---|---|---| +| `s__answer_hour_set` | ✅ Implemented | Guarded high-freq correction | +| `s__sequence_jump` | ✅ Faithful port | — | +| `s__first_decimal` | ✅ Faithful port | — | +| `s__answer_changed` | ✅ Faithful port | Feature bug fixed (see §4.3); scoring logic identical | +| `s__answer_removed` | ✅ Implemented (unit-level only) | Direct from `paradata_full`; no item-level score | +| `s__answer_position` | ✅ Faithful port | — | +| `s__answer_selected_lower/upper` | ✅ Faithful port | Intermediate `s__answer_selected` dropped | +| `s__answer_duration_lower/upper` | ✅ Faithful port | — | +| `s__single_question` | ✅ Faithful port | — | +| `s__multi_option_question` | ✅ Faithful port (bug fixed) | See Bug Fix §8.2 | +| `s__first_digit` | ✅ Faithful port | — | +| `s__gps_proximity_counts`, `s__gps_outlier`, `s__gps_extreme_outlier` | ✅ Implemented (bugs fixed) | See §5.2: accuracy `/1e6` bug, extreme-outlier overwrite bug, aggregation change | +| `s__last_digit` | ❌ Not implemented | Commented out in legacy too | + +--- + +## 6. Unit Scoring + +### 6.1 Structural Changes + +#### OOP `make_score_unit__*` → pure function nodes +All unit-level score methods have been ported to pure functions in `unit_processing_kedro.py`. Aggregation and global modeling logic (IForest, PCA, windsorization) are now explicit node functions. + +#### `windsorize_95_percentile` — non-mutating +- **Legacy:** No `.copy()` call; mutates the input DataFrame. +- **Kedro:** Uses `df_out = df.copy()` — input is never mutated. Adds `is_numeric_dtype` guard before operating. + +### 6.2 Intentional Behavioural Changes + +#### `s__pause_count` — division by zero +- **Legacy:** No zero-division guard; produces `NaN` or `inf` when `f__number_answered == 0`. +- **Kedro:** `np.where(f__number_answered != 0, ..., 0)` — returns `0` when `f__number_answered == 0`. + +#### `s__pause_duration` — division by zero +- **Legacy:** No zero-division guard; produces `NaN`/`inf` when `f__total_elapse == 0`. +- **Kedro:** Returns `0` when `f__total_elapse == 0`. + +#### `s__answer_hour_set` — missing `fillna(0)` in legacy +- **Legacy:** Interviews not appearing in the grouped data (no hour-set events) get `NaN`. +- **Kedro:** `fillna(0)` applied uniformly. Interviews with no hour-set events get `0`. + +#### Responsible score variance guard +- **Legacy:** Always multiplies by `responsible_score` even when it is constant (all-zero), which causes MinMaxScaler to produce `NaN` for all rows. +- **Kedro:** Guards with `if resp_score_series.nunique() > 1:`. If `responsible_score` is constant, the multiplication is skipped and a warning is logged. IForest-derived scores are preserved. + +#### ECOD on `f__total_elapse` — NaN rows +- **Legacy:** Fits ECOD on all rows, including any `NaN` in `f__total_elapse` — may error or produce incorrect scores. +- **Kedro:** Filters to `valid_mask` before fitting ECOD; NaN rows are handled cleanly. + +#### `s__time_changed`, `s__total_duration`, `s__days_from_start` — missing feature column guard +- **Legacy:** No existence check — raises `KeyError` if the required feature column is absent from `df_unit` (e.g. a survey type that produces no paradata events for that feature). The exception is caught by the `df_unit_score` property loop with a generic `print` warning; the actual error is discarded. +- **Kedro:** Explicit `if 'f__...' in df.columns:` guard — skips cleanly and emits a warning. + +#### `windsorize_95_percentile` assignment (pandas ≥ 2.0 compatibility) +- **Legacy:** `self._df_unit['unit_risk_score'] = windsorize_95_percentile(self.df_unit[['unit_risk_score']].copy())` — assigns a DataFrame to a Series column, which raises `ValueError` in pandas ≥ 2.0. +- **Kedro:** Correctly extracts the Series: `df_unit['unit_risk_score'] = windsorize_95_percentile(df_unit[['unit_risk_score']])['unit_risk_score']`. + +#### Responsible score — `_df_resp` granularity preserved +- **Legacy:** `make_responsible_score` replaces the entire `_df_resp` with the grouped result (`self._df_resp = self._df_resp.groupby(...).mean()`), permanently losing per-interview rows. +- **Kedro:** Operates on a copy and merges back, preserving original granularity. + +### 6.3 NaN vs 0 Divergence — Frequency-Filtered Scores + +The `0` (legacy) vs `NaN` (Kedro) item-score initialization only materially affects scores that use `filter_variable_name_by_frequency`. Items belonging to low-frequency variables receive `0` in legacy and `NaN` in Kedro. At unit level, `groupby().mean()` skips NaN by default, so the unit mean denominator differs: legacy includes those zero-scored items in the mean; Kedro excludes them. + +**Scores with frequency filter — unit/responsible means can diverge:** + +| Score | Aggregation | Filter threshold | Divergence risk | +|---|---|---|---| +| `s__sequence_jump` | unit mean | 100 records, 3 unique | Medium — jumps are sparse, many variables fail | +| `s__first_decimal` | unit mean | 100 records, 3 unique | Medium — only numeric questions | +| `s__answer_changed` | unit mean | 100 records, 1 unique | Lower — low bar, most variables pass | +| `s__answer_selected_lower/upper` | unit mean | 100 records, 3 unique | Medium | +| `s__answer_duration_lower/upper` | unit mean | 100 records, 3 unique | Medium | +| `s__answer_position` | responsible mean | 100 records, 3 unique | Medium | +| `s__single_question` | responsible mean | 100 records, 3 unique | Medium | +| `s__multi_option_question` | responsible mean | Legacy: 100 records only (no unique filter); Kedro: 100 records + 3 unique | Medium — Kedro additionally excludes variables with < 3 unique answer combos | +| `s__first_digit` | responsible mean | 100 records, 3 unique + 3-magnitude filter | **High** — strictest filter; most numerics excluded | + +`s__first_digit` carries the highest risk for **responsible scoring / PCA** because its filter is strictest. `s__sequence_jump` and `s__answer_selected/duration` carry the highest risk for **unit scoring / IForest** because sequence jumps are naturally sparse. + +`s__answer_removed` is excluded from this table: in Kedro it is computed directly at unit level from `paradata_full` without going through item-level scoring at all (see §5.1). The NaN/0 item-initialization divergence mechanism does not apply; any difference in values is due to the architectural change, not score initialization. + +**Scores without frequency filter — unit means are equivalent:** +`s__answer_hour_set`, `s__time_changed`, `s__total_duration`, `s__days_from_start`, `s__total_elapse_lower/upper`, `s__pause_duration`, `s__pause_count`, `s__number_answered`, `s__number_unanswered`, `s__gps_*`. + +--- + +### 6.4 Summary — Scenarios Producing Different Outputs + +| Scenario | Legacy output | Kedro output | +|---|---|---| +| Interview with no `answer_hour_set` events | `s__answer_hour_set = NaN` | `= 0` | +| `f__total_elapse == 0` | `s__pause_duration = NaN/inf` | `= 0` | +| `f__number_answered == 0` | `s__pause_count = NaN/inf` | `= 0` | +| All `responsible_score` constant | `unit_risk_score = NaN` (all rows) | Preserved from IForest | +| NaN rows in `f__total_elapse` | ECOD fit may include NaN | ECOD fit excludes NaN | +| pandas ≥ 2.0 windsorize assignment | `ValueError` | Correct | +| Survey missing feature column (e.g. no GPS) | `KeyError` caught with generic print; error discarded | Clean skip with warning | +| First digit Benford with zeros in `f__numeric_response` | Included in sample | Excluded from sample | +| `s__answer_removed` with `removed_answers = None` | Full coverage from paradata | Systematic undercount (fallback to `df_item`) | +--- + +## 7. Unscored / Missing Features + +The following features are enabled (`use: true`) in `parameters.yml` but are **never consumed by any Kedro scoring function**. They compute silently and their values do not appear in `item_scores` or `unit_risk_score`: + +| Feature | Calculation status | Scoring status | Notes | +|---|---|---|---| +| `last_digit` | ✅ Calculated | ❌ Not scored | Legacy scoring functions exist but were never ported to Kedro | +| `answer_share_selected` | ❌ Not calculated | ❌ Not scored | Removed from Kedro `parameters.yml` entirely — no feature function ever existed; orphaned entry superseded by `answer_selected` | +| `comment_length` | ✅ Calculated | ❌ Not scored | Legacy scoring exists but was commented out | +| `comment_set` | ✅ Calculated | ❌ Not scored | Same as above | +| `comment_duration` | ✅ Calculated | ❌ Not scored | No `s__comment_duration` is produced anywhere | +| `pause_list` | ✅ Calculated | ❌ Not scored | Computes a list of pause durations; nothing consumes it for scoring | +| `string_length` | ✅ Calculated | ❌ Not scored | No scoring function; GUI also marks it false by default | + +`answer_share_selected` has already been removed from the Kedro `parameters.yml`. The remaining entries (`last_digit`, `comment_length`, `comment_set`, `comment_duration`, `pause_list`, `string_length`) are still present and could be candidates for removal to reduce confusion. They remain for now in case some of the scoring functions will be re-instated in the future. + +--- + +## 8. Bug Fixes + +The following bugs exist in the legacy code and are corrected in the Kedro pipeline. + +--- + +### Bug 1 — `f__pause_count` inflated by `'size'` aggregation + +**File:** `rissk/feature_processing.py` +**Severity:** High — massively inflates `f__pause_count` + +**Legacy:** Uses `('f__pause_duration', 'size')` in the aggregation, which counts **all rows in the group** regardless of NaN. Since `f__pause_duration` is NaN for non-pause events, this counts every paradata row per interview as a "pause". + +**Kedro fix:** Uses `('f__pause_duration', 'count')` which counts only **non-NaN rows** — i.e., actual pause events. + +--- + +### Bug 2 — `make_score__multi_option_question`: silent no-op initialization + +**File:** `rissk/item_processing.py` +**Severity:** Medium — score column silently stays uninitialized + +**Legacy:** +```python +df.loc[score_name] = 0 # Bug: row assignment, not column assignment +``` +`df.loc[score_name]` on an integer-indexed DataFrame adds a **spurious row** with the score name string as the index label, setting all existing columns to `0`. The score column is never created. The column is then implicitly created by the later `df.loc[mask, score_name] = ...` inside the loop — but only for rows that match a variable with enough records. Rows outside any valid variable get `NaN` instead of the intended `0`. + +**Kedro fix:** +```python +df[score_name] = np.nan # Correct column assignment; consistent with NaN initialization elsewhere +``` + +--- + +### Bug 3 — `feat_answer_changed`: yes_list change overwritten by no_list check + +**File:** `rissk/feature_processing.py` +**Severity:** High — `yes_list`-only changes are never flagged + +**Legacy:** Applies the `yes_list` change check and then immediately overwrites the same column with the `no_list` check via two separate `.loc` assignments on the same mask. Any interview where only the yes answers changed is never flagged. + +**Kedro fix:** Both `yes_list` and `no_list` checks are combined with a bitwise OR before assignment. + +--- + +### Bug 4 — `make_score_unit__pause_count`: dead `pause_mask` variable + +**File:** `rissk/unit_proccessing.py` +**Severity:** Medium — no zero-division guard + +**Legacy:** +```python +pause_mask = ~pd.isnull(self._df_unit[feature_name]) # computed but never used +self._df_unit[score_name] = self._df_unit[feature_name] / self._df_unit['f__number_answered'] +# → NaN or inf when f__number_answered == 0 +``` +`pause_mask` is defined but never applied. Division proceeds over all rows including null `f__pause_count`, with no guard against `f__number_answered == 0`. + +**Kedro fix:** +```python +np.where(df['f__number_answered'] != 0, df['f__pause_count'] / df['f__number_answered'], 0) +``` + +--- + +### Bug 5 — `make_score_unit__total_elapse`: destructive in-place mutation of feature column + +**File:** `rissk/unit_proccessing.py` +**Severity:** High — corrupts `f__total_elapse` for all downstream consumers + +**Legacy:** +```python +self._df_unit[feature_name] = round(self._df_unit[feature_name] / 300) # permanently overwrites +``` +This permanently replaces `f__total_elapse` with the scaled value (`f__total_elapse / 300`) before ECOD is fitted. Any downstream consumer that runs after this method — including `make_score_unit__pause_duration`, which computes `f__pause_duration / f__total_elapse` — receives the scaled value. This causes `s__pause_duration` to be ~300× larger than intended. + +**Kedro fix:** Uses a temporary `f__total_elapse_scaled` column for the ECOD fitting step and drops it afterwards. `f__total_elapse` is never modified. + +--- + +### Bug 6 — `windsorize_95_percentile`: DataFrame assigned to Series column (pandas ≥ 2.0) + +**File:** `rissk/unit_proccessing.py` +**Severity:** High — crashes on pandas ≥ 2.0 + +**Legacy:** +```python +self._df_unit['unit_risk_score'] = windsorize_95_percentile(self.df_unit[['unit_risk_score']].copy()) +# windsorize returns a DataFrame; assigning a DataFrame to a column raises ValueError in pandas ≥ 2.0 +``` + +**Kedro fix:** +```python +df_unit['unit_risk_score'] = windsorize_95_percentile(df_unit[['unit_risk_score']])['unit_risk_score'] +``` + +--- + +### Bug 7 — `make_responsible_score`: responsible score multiplication with no variance guard + +**File:** `rissk/unit_proccessing.py` +**Severity:** Medium — can zero out all `unit_risk_score` values + +**Legacy:** Calls `make_responsible_score()` and multiplies without checking if `responsible_score` has any variance. When there are too few interviewers, PCA produces a constant responsible score of 0, and multiplying all unit risk scores by 0 → all zeros → MinMaxScaler produces all-NaN. + +**Kedro fix:** Guards with `if resp_score_series.nunique() > 1:`. Logs a warning and skips the multiplication when responsible score is constant. + +--- + +### Bug 8 — `transform_multi`: MultyOptionsQuestion (linked) collapses valid answers to `##N/A##` + +**File:** `rissk/utils/import_utils.py` +**Severity:** High — valid responses replaced with missing-value sentinel + +**Legacy:** The list comprehension `[ele if ele != [] else '##N/A##' for ele in sub]` compared `ele != []` against integer values. In NumPy < 2.x this comparison returned `True` for integers and `False` for objects, causing all elements to be treated as empty lists. A linked multi-option list `[[3, -999, -999, ...], [1, -999, ...]]` collapsed to `[##N/A##, ##N/A##, ...]`. + +**Kedro fix (`import_utils_kedro.py`):** Replaced the comparison with an explicit type check and value filter. Valid integer answers are preserved; missing-coded values (`-999999999`) are stripped. Result: `[[3], [1], ...]`. + +--- + +### Bug 9 — `MultyOptionsQuestion` ComboBox: category files never matched + +**File:** `rissk/utils/import_utils.py` +**Severity:** Medium — ComboBox answer options always unlinked + +**Legacy:** Category files for ComboBox questions are Excel files (e.g. `my-categories.xlsx`). `get_categories` keys the categories dict by `file.name` (full filename, e.g. `"my-categories.xlsx"`), but the questionnaire JSON stores `CategoriesId` as the stem only (e.g. `"my-categories"`). Because `"my-categories" != "my-categories.xlsx"`, the lookup in `update_df_categories` **always fails** — no ComboBox question ever gets `n_answers` or `answer_sequence` populated. + +**Kedro fix (`file_process_utils_kedro.py`):** Two fixes: + 1. `get_categories` keys by `file.stem` (no extension), matching the format of `CategoriesId` in the JSON. + 2. `update_df_categories` strips unicode dash characters (`unicodedata.category(c) == 'Pd'`) from both the `CategoriesId` value and the dictionary keys before comparing, handling cases where the JSON uses a different unicode dash variant than the filename. + +--- + +### Bug 10 — `make_score__gps`: `s__gps_extreme_outlier` latitude check overwritten by longitude check + +**File:** `rissk/unit_proccessing.py` +**Severity:** Medium — only longitude == 0 is flagged as extreme outlier + +**Legacy:** `s__gps_extreme_outlier` is set twice sequentially — first for zero latitude, then for zero longitude. The second assignment overwrites the first. Only points with `longitude == 0` end up flagged; `latitude == 0` points are silently cleared. + +**Kedro fix:** Both latitude and longitude zero-checks are combined with AND before assigning `s__gps_extreme_outlier`. + +--- + +### Bug 11 — `make_score__gps`: distance units divide by `1e6` instead of `1e3` + +**File:** `rissk/unit_proccessing.py` +**Severity:** Medium — GPS accuracy and proximity radius are 1000× too small + +**Legacy:** +```python +data['accuracy'] = data['f__gps_accuracy'] / 1e6 # Should be / 1e3 (metres → km) +radius = 10 / 1e6 # Should be 10 / 1e3 = 0.01 km +``` +The comments state the values are in kilometres, but dividing by `1e6` produces microkm. The 10-metre proximity radius becomes 0.00001 km instead of 0.01 km, making the proximity count almost always zero. + +**Kedro fix:** Both `accuracy` and `radius` divide by `1e3` (correct km conversion). + +--- + +### Bug 12 — `feat_answer_position`: always NaN in legacy Ploomber pipeline + +**File:** `rissk/feature_processing.py` (Ploomber execution) +**Severity:** High — feature is always NaN in production pipeline + +**Legacy:** `get_microdata` calls `.astype(str)` on all values unconditionally. `f__answer_position` checks membership with `value in answer_sequence` where `answer_sequence` is a list of integers. Because `value` is now a string, the check always fails. + +**Kedro fix:** `answer_sequence` is parsed back from its string representation to a Python list of integers before the membership check. Results spot-checked manually against four surveys and confirmed correct. + +--- + +### Bug 13 — `feat_answer_selected`: always NaN in legacy Ploomber pipeline + +**File:** `rissk/feature_processing.py` (Ploomber execution) +**Severity:** High — feature is always NaN in production pipeline + +**Legacy:** `isinstance(value, list)` is always `False` when values have been serialized to parquet and read back as strings (e.g. `"[1, 2]"`). The function never enters the selection-count branch. + +**Kedro fix:** `value` is converted back to a Python list before the `isinstance` check. Also adds `n_answers > 0` guard to handle edge cases where `n_answers` is zero or NaN (replaces the missing `is_linked` flag check). + +--- + +### Bug 14 — `get_clean_pivot_table`: float `0.2` passed as integer threshold + +**File:** `rissk/unit_proccessing.py` +**Severity:** Low — GPS `filter_columns` filter disabled by incorrect type + +**Legacy:** `get_clean_pivot_table` calls `filter_columns(threshold=0.2)`, but `filter_columns` expects an integer count threshold (default 100). A threshold of `0.2` means "keep columns with more than 0.2 non-NaN values" which is effectively always true — the filter is a no-op. Whether `0.2` was intended as a proportion or was simply a typo is unclear. + +**Kedro fix:** Uses the integer default of `100` (consistent with all other `filter_columns` calls in the codebase). The GPS filter using this threshold is currently disabled in Kedro anyway. + +### Bug 15 — `transform_multi`: unanswered `TextListQuestion` silently dropped instead of counted as unanswered + +**File:** `rissk/utils/file_process_utils_kedro.py` (Kedro fix only; legacy bug retained) +**Severity:** Very Low — `f__number_unanswered` understated for interviews with unanswered `TextListQuestion` items. No such cases were found in the test data of over 2000 interviews; this fix is correct in principle but is expected to have no real-world effect in practice. + +**Root cause:** When all columns for a `TextListQuestion` row are `'##N/A##'` (the interviewer was shown the question but left it blank), the `transform_multi` column loop appends nothing and `x` remains `[]`. The outer dispatch line: +```python +[remove_unset_value(x) if x else float('nan') for x in transformation] +``` +short-circuits on the falsy `[]` and returns `float('nan')` — bypassing `remove_unset_value` entirely. `float('nan')` is then dropped by `is_valid` / `is_valid_fast`, so the row never reaches `df_item` and is never counted by `f__number_unanswered`. + +Note: `remove_unset_value` in legacy does contain logic that would return `'##N/A##'` for an empty input list, but it is unreachable because of the `if x` short-circuit. + +**Kedro fix:** Changed the dispatch to: +```python +[remove_unset_value(x) if x else ('##N/A##' if transformation_type == 'list' else float('nan')) for x in transformation] +``` +For `transformation_type == 'list'`, an empty list now produces the scalar `'##N/A##'` instead of `float('nan')`. This value survives `is_valid_fast`, the row lands in `df_item` with `value = '##N/A##'`, and is correctly counted in `f__number_unanswered`. All other transformation types (`unlinked`, `linked`) continue to produce `float('nan')` for empty lists, as an empty list there means no selection was made — correct behaviour. + +--- + +## 9. GUI / App Changes + +The NiceGUI-based application was updated to reflect the scoring changes: + +### Features removed entirely +- `answer_share_selected`: No implementation exists at any level (feature or scoring). Removed from `parameters.yml` and GUI. + +### Features set to `false` and removed from GUI controls +These features are calculated but not scored. They remain in `parameters.yml` (set to `false`) as placeholders for potential future use, but are hidden from the GUI to avoid confusion: + +| Feature | Reason hidden | +|---|---| +| `last_digit` | Legacy scoring was commented out; not ported | +| `comment_length` | Legacy scoring was commented out; not ported | +| `comment_duration` | No scoring function exists | +| `comment_set` | Legacy scoring was commented out; not ported | +| `pause_list` | Computed but not consumed by any score | +| `string_length` | No scoring function; already defaulted to `false` | + +### `numeric_response` linked to `first_digit` in GUI +- `f__numeric_response` is required by the `s__first_digit` score calculation (Benford law uses `f__numeric_response` for frequency/magnitude filtering). Their `use` flags must be set to the same value. +- In the GUI, enabling/disabling `numeric_response` also enables/disables `first_digit`. A comment in `parameters.yml` documents this dependency. + +--- + diff --git a/env.yaml b/env.yaml deleted file mode 100644 index 52d08c2..0000000 --- a/env.yaml +++ /dev/null @@ -1,7 +0,0 @@ -SURVEY: "hies2024" -QUESTIONAIRE: - - name: "snb_hies_hh" - VERSION: [9, 10, 11] - - name: "slbhies_listing" - VERSION: [6, 7] -limit_unit: None \ No newline at end of file diff --git a/environment.yml b/environment.yml index cb2c699..cb8da31 100644 --- a/environment.yml +++ b/environment.yml @@ -1,42 +1,40 @@ -name: rissk +name: rissk_kedro channels: - conda-forge - - defaults dependencies: - - python=3.9 - # R and its dependencies - - r-base + - python=3.13 + # R Core - 3.13 compatible binaries (not used in pipeline code) + - r-base>=4.4 - r-ggplot2 - r-dplyr - r-tidyr - - r-shiny - r-readr - - r-irkernel # For running R in Jupyter Notebooks - r-stringr - # Interoperability - - rpy2 # For using R within Python - # Graphing libs + # Graphviz System Deps - graphviz - - pygraphviz - # Other tools + - python-graphviz + - pydot - pip - pip: - - jupyter_contrib_nbextensions - - awscli - - botocore - - loguru==0.7.3 - - tqdm==4.67.1 - - pandas==2.2.2 - - seaborn==0.13.2 - - docutils==0.16 - - openpyxl==3.1.2 - - pyarrow==15.0.2 - - pyod==1.1.3 - - python-dotenv==1.0.1 - - pythresh==0.3.6 - - ploomber==0.23.3 - - ipywidgets==8.1.5 - - typer==0.15.1 - - boto3==1.35.88 - - botocore==1.35.88 + # Framework + - kedro==1.2.0 + - kedro-viz>=12.3.0 + - kedro-datasets[pandas,s3fs,excel,files]>=9.1.0 + + # GUI + - nicegui>=1.4 + + # Core Stack + - rpy2>=3.6.4 + - pandas>=2.2.3 + - numpy>=2.1.0 + - pyarrow>=18.0.0 + + # Analysis Tools + - pyod>=1.1.5 + - pythresh>=1.0.3 + - loguru>=0.7.3 + - tqdm>=4.67.0 + - boto3>=1.35.0 + - python-dotenv>=1.0.1 - -e . \ No newline at end of file diff --git a/environment_kedro.yml b/environment_kedro.yml deleted file mode 100644 index cb8da31..0000000 --- a/environment_kedro.yml +++ /dev/null @@ -1,40 +0,0 @@ -name: rissk_kedro -channels: - - conda-forge -dependencies: - - python=3.13 - # R Core - 3.13 compatible binaries (not used in pipeline code) - - r-base>=4.4 - - r-ggplot2 - - r-dplyr - - r-tidyr - - r-readr - - r-stringr - # Graphviz System Deps - - graphviz - - python-graphviz - - pydot - - pip - - pip: - # Framework - - kedro==1.2.0 - - kedro-viz>=12.3.0 - - kedro-datasets[pandas,s3fs,excel,files]>=9.1.0 - - # GUI - - nicegui>=1.4 - - # Core Stack - - rpy2>=3.6.4 - - pandas>=2.2.3 - - numpy>=2.1.0 - - pyarrow>=18.0.0 - - # Analysis Tools - - pyod>=1.1.5 - - pythresh>=1.0.3 - - loguru>=0.7.3 - - tqdm>=4.67.0 - - boto3>=1.35.0 - - python-dotenv>=1.0.1 - - -e . \ No newline at end of file diff --git a/main.py b/main.py deleted file mode 100644 index fcb838d..0000000 --- a/main.py +++ /dev/null @@ -1,48 +0,0 @@ -import os -from omegaconf import DictConfig, OmegaConf -from hydra.core.hydra_config import HydraConfig -from rissk.unit_proccessing import * -import hydra -# from memory_profiler import memory_usage -import warnings - -warnings.simplefilter(action='ignore', category=Warning) - - -def manage_path(config): - root_path = HydraConfig.get().runtime.cwd - if config['export_path'] is not None: - if os.path.isabs(config['export_path']) is False: - config['export_path'] = os.path.join(root_path, config['export_path']) - config['environment']['data']['externals'] = os.path.dirname(config['export_path']) - for key, value in config['environment']['data'].items(): - # Check if the value is a relative path - if not os.path.isabs(value): - # Convert the relative path to an absolute path - config['environment']['data'][key] = os.path.join(root_path, value) - config['surveys'] = [os.path.basename(config['export_path'])] - if os.path.isabs(config['output_file']) is False: - - config['output_file'] = os.path.join(root_path, config['output_file']) - return config - - -@hydra.main(config_path='configuration', version_base='1.1', config_name='main.yaml') -def unit_risk_score(config: DictConfig) -> None: - # print(OmegaConf.to_yaml(config)) - print("*" * 12) - config = manage_path(config) - try: - survey_class = UnitDataProcessing(config) - df_item = survey_class.df_item - df_unit = survey_class.df_unit - survey_class.make_global_score() - survey_class.save() - except ValueError as e: - print(f"An error occurred: {e}") - - -if __name__ == "__main__": - unit_risk_score() - # mem_usage = memory_usage(unit_risk_score) - # print(f"Memory usage (in MB): {max(mem_usage)}") diff --git a/pipeline.yaml b/pipeline.yaml deleted file mode 100644 index 4a041b5..0000000 --- a/pipeline.yaml +++ /dev/null @@ -1,29 +0,0 @@ -executor: serial -# clients: -# # configures a dag-level File client -# File: rissk.clients.get_s3 # you can switch to clients.get_s3 or clients.get_gcloud - -tasks: - - source: pipelines/ingestion/01_get_dataframes.py - product: - nb: outputs/01_get_dataframes.ipynb - paradata: data/{{SURVEY}}/latest/20_INTERIM/paradata.parquet - questionnaire: data/{{SURVEY}}/latest/30_PROCESSED/questionnaire.parquet - microdata: data/{{SURVEY}}/latest/30_PROCESSED/microdata.parquet - # TODO! Need to find a way to add a json file with metadata for automatic trigger i.e. right now need to do `dag.build(force=True)` to force the run. If there was a josn file that changes, there would no need of `force=True` - # params: - # resources_: - # # whenever the JSON file changes, my-script.py runs again - # file: data/{{SURVEY}}//10_RAW/{{QUESTIONAIRE[0].name}}_{{QUESTIONAIRE[0].VERSION[0]}}_Paradata_All/export__info.json - - source: pipelines/feature_engineering/10_process_paradata.py - product: - nb: outputs/10_process_paradata.ipynb - paradata: data/{{SURVEY}}/latest/30_PROCESSED/paradata.parquet - - source: pipelines/feature_engineering/11_process_paradata_active.py - product: - nb: outputs/11_process_paradata_active.ipynb - paradata_active: data/{{SURVEY}}/latest/30_PROCESSED/paradata_active.parquet - params: - limit_unit: '{{limit_unit}}' - - diff --git a/rissk_kedro/notebooks/.gitkeep b/rissk_kedro/notebooks/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/rissk_readme.ipynb b/rissk_readme.ipynb deleted file mode 100644 index e3e60d1..0000000 --- a/rissk_readme.ipynb +++ /dev/null @@ -1,164 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "6172834c", - "metadata": {}, - "source": [ - "# Generate Paradata, Microdata in long format." - ] - }, - { - "cell_type": "markdown", - "id": "7f19542f", - "metadata": {}, - "source": [ - "### Download Data from storage system" - ] - }, - { - "cell_type": "markdown", - "id": "973b2cfa", - "metadata": {}, - "source": [ - "Please Note that you need [aws cli](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html#getting-started-install-instructions) installed and set up with credentials to download and uplaod the data. If you do not need the sync from S3, you can simply comment with `#` the next line.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "abe307ea", - "metadata": {}, - "outputs": [], - "source": [ - "! make sync_data_down " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "125e07c4", - "metadata": {}, - "outputs": [], - "source": [ - "from rissk.config import PROJ_ROOT, DATA_DIR\n", - "from ploomber.executors import Serial\n", - "from ploomber.spec import DAGSpec\n", - "import shutil\n", - "from pathlib import Path\n", - "from loguru import logger" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "63193e35-6fc4-4a6a-b823-d0f277bb7143", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "spec = DAGSpec(PROJ_ROOT / 'pipeline.yaml')\n", - "dag = spec.to_dag()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e4e5e4a7", - "metadata": {}, - "outputs": [], - "source": [ - "dag.executor = Serial(build_in_subprocess=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5d49aa3c", - "metadata": {}, - "outputs": [], - "source": [ - "build = dag.build(force=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b6d330f3", - "metadata": {}, - "outputs": [], - "source": [ - "dag.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "841e608c-e493-4b28-87c9-2dee5dcc7cc7", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "build" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5ca16421", - "metadata": {}, - "outputs": [], - "source": [ - "# Upload new data to S3\n", - "! make sync_data_up " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "59236ca8", - "metadata": {}, - "outputs": [], - "source": [ - "# Clean Local file to avoid disk overusage\n", - "if DATA_DIR.is_dir():\n", - " # Iterate over the directory's contents\n", - " for item in DATA_DIR.iterdir():\n", - " if item.is_dir():\n", - " shutil.rmtree(DATA_DIR / item) # Remove directory and all its contents\n", - " logger.info(f\"Successfully cleared local directory: {item}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "rissk", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.21" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 645191a9b99d9e390901a509c2422b4348a171dd Mon Sep 17 00:00:00 2001 From: VJausovec Date: Wed, 6 May 2026 22:48:00 +0100 Subject: [PATCH 66/70] Add RISSK GUI for Kedro pipeline configuration and execution --- .gitignore | 115 +------ rissk_kedro/app/main.py | 668 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 671 insertions(+), 112 deletions(-) create mode 100644 rissk_kedro/app/main.py diff --git a/.gitignore b/.gitignore index 1a3551c..bf0c1fa 100644 --- a/.gitignore +++ b/.gitignore @@ -259,125 +259,16 @@ cython_debug/ # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ -embedded-assets/tmpo_c6_8gw.html -ingestion_nstructions.md .gitignore -ingestion_refactor_context.md rissk_kedro/stats.json -rissk_kedro/feature_process_ploomber_pipeline_integration.md -data_ingestion_function_changes.md .vscode/mcp.json -rissk/utils/testing_utils.py -configuration/main.yaml -env.yaml -env.yaml -configuration/main.yaml -configuration/main.yaml -configuration/main.yaml -env.yaml -rissk_kedro/src/rissk_kedro/test_ingestion.ipynb -.gitignore -Feature_and_Unit_Process_refactor.md -main.py -main.py.bak -configuration/main.yaml configuration/main.yaml -rissk_kedro/src/rissk_kedro/test_ingestion.ipynb -rissk_kedro/src/rissk_kedro/test_microdata.ipynb -rissk_kedro/src/rissk_kedro/test_ingestion.ipynb -env.yaml -rissk/utils/import_utils_kedro.py -rissk_kedro/conf/base/parameters.yml -rissk_kedro/src/rissk_kedro/test_item_creation.ipynb -rissk/feature_processing_kedro.py -Feature Creation Pipeline.md -rissk/Base Item Table.md -rissk_kedro/src/rissk_kedro/test_unti_creation.ipynb -scoring_refactor_instructions.md -Kedro_Scoring_Refactor.md -rissk_kedro/src/rissk_kedro/test_unit_creation.ipynb -rissk_kedro/src/rissk_kedro/test_ingestion.ipynb -rissk_kedro/src/rissk_kedro/test_item_scoring.ipynb rissk/prompt.md -rissk_kedro/src/rissk_kedro/Score_NaN_Handling_and_Aggregation.md -rissk/Paradata vs Active Paradata.md -.gitignore -rissk_kedro/src/rissk_kedro/test_ingestion.ipynb -FEATURES_SCORES.md FEATURES_SCORES.md -rissk_kedro/src/rissk_kedro/test_ingestion.ipynb -rissk_kedro/src/rissk_kedro/test_ingestion_bulk_read_in.ipynb -Scoring_Refactor_Review.md rissk_kedro/conf/base/catalog.yml -rissk_kedro/src/rissk_kedro/microdata_drop_columns.md -FEATURES_SCORES.md -rissk/unit_scoring.md -rissk_kedro/conf/base/test_microdata_gps_answers.ipynb -rissk_kedro/src/rissk_kedro/test_item_creation_individual.ipynb -rissk_kedro/src/rissk_kedro/test_item_f__answer_changed.ipynb -rissk_kedro/src/rissk_kedro/test_item_f__answer_removed.ipynb -rissk_kedro/src/rissk_kedro/test_ingestion.ipynb -rissk_kedro/src/rissk_kedro/test_item_f__answer_selected.ipynb -rissk_kedro/src/rissk_kedro/test_item_s__first_decimal.ipynb -rissk_kedro/src/rissk_kedro/test_item_unit_creation.ipynb -rissk_kedro/src/rissk_kedro/test_score_item.ipynb -main_monkey_patch_item_unit.py -rissk_kedro/src/rissk_kedro/test_microdata_gps_answers.ipynb FEATURES_SCORES_updated.md -rissk_kedro/src/rissk_kedro/test_ingestion.ipynb -.gitignore -rissk_kedro/src/rissk_kedro/test_scoring_gps.ipynb -rissk_kedro/src/rissk_kedro/test_scoring_gps_legacy.ipynb -rissk/Unit Scoring — Legacy vs Kedro Behavi.md -main_monkey_patch_scores.py -rissk_kedro/src/rissk_kedro/test_scoring_first_digit.ipynb -rissk_kedro/src/rissk_kedro/test_score_unit.ipynb -rissk_kedro/src/rissk_kedro/test_scoring_first_digit_digit_checks.ipynb prompt.md -markdown_docs/Base Item Table.md -markdown_docs/ingestion_discrepancies.md -markdown_docs/microdata_drop_columns.md -markdown_docs/Paradata vs Active Paradata.md -markdown_docs/Score_NaN_Handling_and_Aggregation.md -rissk_kedro/src/rissk_kedro/test_ingestion_separate_questionnaire.ipynb -rissk_kedro/src/rissk_kedro/test_ingestion.ipynb -rissk_kedro/src/rissk_kedro/test_scoring_first_decimal.ipynb -.gitignore -rissk_kedro/docs/source/copy_audit.md -markdown_docs/unit_scoring.md -markdown_docs/Unit Scoring — Legacy vs Kedro Behavi.md -markdown_docs/unit_scoring_legacy_kedro.md -markdown_docs/unscored_features.md -rissk_kedro/src/rissk_kedro/test_score_iforest.ipynb -rissk_kedro/src/rissk_kedro/test_pyod_PCA.ipynb -rissk_kedro/src/rissk_kedro/test_ingestion.ipynb -pipelines/feature_engineering/11_process_paradata_active.py -rissk_kedro/notebooks/legacy_data_generation/main_monkey_patch_unit_score.py -rissk_kedro/notebooks/testing/data_read_tes.ipynb -rissk_kedro/notebooks/testing/test_ingestion_bulk_read_in.ipynb -rissk_kedro/notebooks/testing/test_ingestion_separate_questionnaire.ipynb -rissk_kedro/notebooks/testing/test_ingestion.ipynb -rissk_kedro/notebooks/testing/test_item_creation_individual.ipynb -rissk_kedro/notebooks/testing/test_item_f__answer_changed.ipynb -rissk_kedro/notebooks/testing/test_item_f__answer_removed.ipynb -rissk_kedro/notebooks/testing/test_item_f__answer_selected.ipynb -rissk_kedro/notebooks/testing/test_item_s__first_decimal.ipynb -rissk_kedro/notebooks/testing/test_item_scoring.ipynb -rissk_kedro/notebooks/testing/test_item_unit_creation.ipynb -rissk_kedro/notebooks/testing/test_microdata_gps_answers.ipynb -rissk_kedro/notebooks/testing/test_microdata.ipynb -rissk_kedro/notebooks/testing/test_pyod_PCA.ipynb -rissk_kedro/notebooks/testing/test_score_iforest.ipynb -rissk_kedro/notebooks/testing/test_score_item.ipynb -rissk_kedro/notebooks/testing/test_score_unit.ipynb -rissk_kedro/notebooks/testing/test_scoring_first_decimal.ipynb -rissk_kedro/notebooks/testing/test_scoring_first_digit_digit_checks.ipynb -rissk_kedro/notebooks/testing/test_scoring_first_digit.ipynb -rissk_kedro/notebooks/testing/test_scoring_gps_legacy.ipynb -rissk_kedro/notebooks/testing/test_scoring_gps.ipynb -rissk_kedro/notebooks/testing/test_unit_creation.ipynb -rissk_kedro/notebooks/testing/test_scoring_first_decimal_method_comparisson.ipynb -markdown_docs/Kedro_vs_Legacy_Changelog.md -rissk_kedro/notebooks/testing/test_ingestion_microdata.ipynb +markdown_docs/ requirements_legacy.txt -requirements.txt +rissk_kedro/notebooks/ +rissk/utils/testing_utils.py diff --git a/rissk_kedro/app/main.py b/rissk_kedro/app/main.py new file mode 100644 index 0000000..5e3f18c --- /dev/null +++ b/rissk_kedro/app/main.py @@ -0,0 +1,668 @@ +"""RISSK GUI — browser-based configuration and launcher for the Kedro pipeline. + +Run from the rissk_kedro/ directory: + python app/main.py +Then open http://localhost:8080 in your browser. +""" + +from __future__ import annotations + +import asyncio +import os +import platform +import sys +from copy import deepcopy +from pathlib import Path + +import yaml +from nicegui import app, ui + +# ── Paths ────────────────────────────────────────────────────────────────── +PROJECT_ROOT = Path(__file__).resolve().parent.parent # rissk_kedro/ +BASE_CONF = PROJECT_ROOT / "conf" / "base" +LOCAL_CONF = PROJECT_ROOT / "conf" / "local" + +# ── Pipeline choices ──────────────────────────────────────────────────────── +PIPELINES: dict[str, str | None] = { + "All (full run)": None, + "Data Ingestion only": "data_ingestion", + "Feature Creation only": "feature_creation", + "Scoring only": "rissk_scoring", +} + +# ── Feature definitions ───────────────────────────────────────────────────── +# Each tuple: (key, has_contamination, default_contamination, extra_param) +# extra_param is (param_name, default_value) or None +FEATURE_DEFS: list[tuple[str, bool, float | None, tuple[str, int] | None]] = [ + ("answer_hour_set", True, 0.10, None), + ("answer_changed", True, 0.10, None), + ("answer_removed", True, 0.10, None), + ("answer_selected", True, 0.10, None), + ("answer_duration", True, 0.10, None), + ("first_decimals", True, 0.10, None), + ("first_digit", False, None, None), + ("numeric_response", False, None, None), + ("sequence_jump", True, 0.10, None), + ("time_changed", False, None, None), + ("gps", True, 0.10, None), + ("pause_count", True, 0.10, None), + ("pause_duration", True, 0.10, None), + ("number_unanswered", False, None, None), + ("number_answered", True, 0.10, None), + ("total_duration", True, 0.10, None), + ("total_elapse", True, 0.10, None), + ("single_question", False, None, None), + ("multi_option_question", False, None, None), + ("days_from_start", False, None, None), + ("answer_position", False, None, None), +] + +# ── Config I/O helpers ────────────────────────────────────────────────────── + +def _deep_merge(base: dict, override: dict) -> dict: + out = dict(base) + for k, v in override.items(): + if k in out and isinstance(out[k], dict) and isinstance(v, dict): + out[k] = _deep_merge(out[k], v) + else: + out[k] = v + return out + + +def _read_globals() -> dict: + base = yaml.safe_load((BASE_CONF / "globals.yml").read_text()) or {} + local_path = LOCAL_CONF / "globals.yml" + if local_path.exists(): + local = yaml.safe_load(local_path.read_text()) or {} + return _deep_merge(base, local) + return base + + +def _read_parameters() -> dict: + base = yaml.safe_load((BASE_CONF / "parameters.yml").read_text()) or {} + local_path = LOCAL_CONF / "parameters.yml" + if local_path.exists(): + local = yaml.safe_load(local_path.read_text()) or {} + return _deep_merge(base, local) + return base + + +def _write_local_globals(data: dict) -> None: + LOCAL_CONF.mkdir(exist_ok=True) + with open(LOCAL_CONF / "globals.yml", "w") as f: + yaml.dump(data, f, default_flow_style=False, sort_keys=False, allow_unicode=True) + + +def _write_local_parameters(data: dict) -> None: + LOCAL_CONF.mkdir(exist_ok=True) + with open(LOCAL_CONF / "parameters.yml", "w") as f: + f.write("# Generated by RISSK GUI — do not edit manually\n") + yaml.dump(data, f, default_flow_style=False, sort_keys=False, allow_unicode=True) + + +def _load_feature_state(params: dict) -> dict: + """Parse features block from a parameters dict into a flat state dict.""" + feats_raw = params.get("features", {}) + state: dict[str, dict] = {} + for key, has_cont, default_cont, extra in FEATURE_DEFS: + raw = feats_raw.get(key, {}) + entry: dict = {"use": raw.get("use", True)} + if has_cont: + entry["contamination"] = ( + raw.get("parameters", {}).get("contamination") or default_cont + ) + if extra: + pname, pdefault = extra + entry[pname] = raw.get("parameters", {}).get(pname, pdefault) + state[key] = entry + return state + + +def _feature_state_to_params(state: dict) -> dict: + """Serialise feature state back to the parameters.yml features block format.""" + features: dict = {} + for key, has_cont, _, extra in FEATURE_DEFS: + s = state[key] + entry: dict = {"use": s["use"]} + feat_params: dict = {} + if has_cont: + cont = s.get("contamination") or 0.10 + feat_params["contamination"] = ( + "auto" if str(cont).strip().lower() == "auto" + else round(float(cont), 4) + ) + if extra: + pname, pdefault = extra + feat_params[pname] = int(s.get(pname) or pdefault) + if feat_params: + entry["parameters"] = feat_params + if key == "gps": + entry["sub_features"] = ["gps_latitude", "gps_longitude", "gps_accuracy"] + features[key] = entry + return features + + +def _open_folder(path: Path) -> None: + path.mkdir(parents=True, exist_ok=True) + system = platform.system() + if system == "Darwin": + os.system(f'open "{path}"') + elif system == "Windows": + os.startfile(str(path)) + else: + os.system(f'xdg-open "{path}"') + + +# ── Initial state ─────────────────────────────────────────────────────────── + +def _initial_state() -> dict: + glb = _read_globals() + params = _read_parameters() + return { + "data_root": glb.get("data_root", "data"), + "questionnaire": deepcopy(glb.get("questionnaire", {"name": "", "VERSION": [], "filter_var": None})), + "zip_password": params.get("zip_password") or "", + "features": _load_feature_state(params), + "pipeline": "All (full run)", + } + + +# ── UI ────────────────────────────────────────────────────────────────────── + +def main() -> None: + state = _initial_state() + + # ── config builders ────────────────────────────────────────────────── + def _build_globals() -> dict: + return { + "data_root": state["data_root"], + "questionnaire": { + "name": state["questionnaire"].get("name", ""), + "VERSION": state["questionnaire"].get("VERSION", []), + "filter_var": state["questionnaire"].get("filter_var"), + }, + } + + def _build_parameters() -> dict: + return { + "zip_password": state["zip_password"].strip() or None, + "features": _feature_state_to_params(state["features"]), + } + + def save_config() -> None: + _write_local_globals(_build_globals()) + _write_local_parameters(_build_parameters()) + ui.notify("Configuration saved to conf/local/", type="positive", position="top") + + def _zip_path() -> Path: + root = Path(state["data_root"]) + name = state["questionnaire"].get("name") or "" + return root / name / "latest" / "10_RAW" + + # ── page ───────────────────────────────────────────────────────────── + with ui.header(elevated=True).classes("items-center gap-3 px-6 bg-indigo-800 text-white"): + ui.icon("analytics", size="lg") + ui.label("RISSK — Survey Quality Control").classes("text-xl font-bold tracking-wide") + ui.space() + ui.label(f"Project: {PROJECT_ROOT.name}").classes("text-sm opacity-70") + ui.button(icon="refresh", on_click=ui.navigate.reload).props( + "flat round color=white size=sm" + ).tooltip("Reload config from disk") + ui.button(icon="power_settings_new", on_click=app.shutdown).props( + "flat round color=white size=sm" + ).tooltip("Shut down RISSK (stops the server)") + + with ui.tabs().classes("w-full bg-indigo-50") as tabs: + tab_setup = ui.tab("Setup", icon="folder_open") + tab_features = ui.tab("Features", icon="tune") + tab_run = ui.tab("Run", icon="play_arrow") + tab_advanced = ui.tab("Advanced", icon="settings") + + with ui.tab_panels(tabs, value=tab_setup).classes("w-full"): + + # ── SETUP TAB ──────────────────────────────────────────────────── + with ui.tab_panel(tab_setup): + with ui.column().classes("max-w-3xl mx-auto py-6 px-4 gap-6 w-full"): + + # ── Step 1: Data folder ────────────────────────────────── + with ui.card().classes("w-full"): + ui.label("Step 1 — Data folder").classes("text-base font-semibold") + ui.label( + "Root folder where RISSK stores survey data. " + "Default is 'data' (inside the rissk_kedro/ project folder). " + "Use an absolute path to store data elsewhere, e.g. /Users/jane/surveys." + ).classes("text-sm text-gray-500 mb-3") + + data_root_inp = ( + ui.input("Data root folder", value=state["data_root"]) + .classes("w-full") + .tooltip( + "Relative path resolved from rissk_kedro/. " + "Or absolute, e.g. /Users/jane/survey_exports" + ) + ) + + # ── Step 2: Questionnaire configuration ────────────────── + with ui.card().classes("w-full"): + ui.label("Step 2 — Questionnaire configuration").classes("text-base font-semibold") + ui.label( + "Configure the questionnaire to score. " + "Use only letters, numbers, or underscores (no spaces or special characters) for the name." + ).classes("text-sm text-gray-500 mb-3") + + import re as _re + + _SAFE_NAME = _re.compile(r'^[A-Za-z0-9_-]+$') + + qnr_name_error = ui.label("").classes("text-sm text-red-500 hidden") + + with ui.row().classes("w-full items-start gap-3 flex-wrap"): + qnr_name_inp = ( + ui.input( + "Questionnaire name", + value=state["questionnaire"].get("name", ""), + placeholder="e.g. pmpmd_household", + ) + .classes("flex-1 min-w-60") + .tooltip("Template name in Survey Solutions") + ) + + ver_str = ", ".join(str(v) for v in state["questionnaire"].get("VERSION", [])) + ver_inp = ( + ui.input( + "Versions", + value=ver_str, + placeholder="4, 5, 6 (empty = all)", + ) + .classes("w-44") + .tooltip( + "Comma-separated integers, e.g. 4, 5, 6. " + "Leave empty to include all versions found in the folder." + ) + ) + + has_filter = state["questionnaire"].get("filter_var") is not None + filter_sw = ui.switch("Consent filter", value=has_filter).tooltip( + "Only score interviews where a specific paradata variable matches a required answer" + ) + + fv = state["questionnaire"].get("filter_var") or {} + fv_key = list(fv.keys())[0] if fv else "" + fv_val = list(fv.values())[0] if fv else "" + filter_row = ui.row().classes("items-center gap-2 mt-1") + with filter_row: + fv_key_inp = ( + ui.input("Variable name", value=fv_key) + .classes("w-44") + .tooltip("Paradata variable to check for consent") + ) + fv_val_inp = ( + ui.input("Required answer (string)", value=fv_val) + .classes("w-44") + .tooltip("Answer value must be a string, e.g. '1'") + ) + filter_row.set_visibility(has_filter) + + # live zip-path preview + zip_path_label = ui.label(str(_zip_path())).classes( + "font-mono text-sm bg-gray-100 rounded px-3 py-2 w-full mt-3" + ) + + with ui.row().classes("mt-2 gap-2"): + ui.button( + "Create folder & Open", + icon="folder_open", + on_click=lambda: _open_folder(_zip_path()), + ).props("outline size=sm") + + def _refresh_zip_path() -> None: + zip_path_label.set_text(str(_zip_path())) + + def _on_data_root(e) -> None: + state["data_root"] = e.value.strip() + _refresh_zip_path() + _write_local_globals(_build_globals()) + + def _on_qnr_name(e) -> None: + raw = e.value.strip() + if raw and not _SAFE_NAME.match(raw): + qnr_name_error.set_text( + "⚠ Avoid spaces and special characters — use letters, numbers, _ or - only." + ) + qnr_name_error.classes(remove="hidden") + else: + qnr_name_error.classes(add="hidden") + state["questionnaire"]["name"] = raw + _refresh_zip_path() + _write_local_globals(_build_globals()) + + def _on_ver(e) -> None: + try: + state["questionnaire"]["VERSION"] = [ + int(x.strip()) for x in e.value.split(",") if x.strip() + ] + except ValueError: + ui.notify("Versions must be integers separated by commas.", type="warning") + + def _on_fv_key(e) -> None: + fv_current = state["questionnaire"].get("filter_var") or {} + old_val = list(fv_current.values())[0] if fv_current else "" + new_key = e.value.strip() + state["questionnaire"]["filter_var"] = {new_key: old_val} if new_key else None + + def _on_fv_val(e) -> None: + fv_current = state["questionnaire"].get("filter_var") or {} + old_key = list(fv_current.keys())[0] if fv_current else "" + state["questionnaire"]["filter_var"] = {old_key: e.value} if old_key else None + + def _on_filter_sw(e) -> None: + filter_row.set_visibility(e.value) + if not e.value: + state["questionnaire"]["filter_var"] = None + elif state["questionnaire"].get("filter_var") is None: + state["questionnaire"]["filter_var"] = {} + + data_root_inp.on_value_change(_on_data_root) + qnr_name_inp.on_value_change(_on_qnr_name) + ver_inp.on_value_change(_on_ver) + fv_key_inp.on_value_change(_on_fv_key) + fv_val_inp.on_value_change(_on_fv_val) + filter_sw.on_value_change(_on_filter_sw) + + with ui.expansion( + "How to export from Survey Solutions", icon="help_outline" + ).classes("w-full mt-3"): + ui.markdown( + """ +**1. Export Main Survey Data** + +In Survey Solutions go to *Survey → Export Data → Main survey data*. +Choose format **Tab separated** or **Stata 14** and tick +**Include meta information about questionnaire**. Download the ZIP. + +**2. Export Paradata** + +In Survey Solutions go to *Survey → Export Data*. +Under *Data Type* select **Paradata**. Download the ZIP. + +> Export both ZIPs from the **same version** of the questionnaire consecutively. + +**3. Place the ZIP files** + +Put **both** ZIPs into the folder shown above. +Do **not** rename, modify, or unzip the files. + +For **multiple questionnaire versions** (e.g. versions 4, 5, 6): +export each version separately and place all ZIPs in the same folder. +List the version numbers above. + """ + ) + + with ui.row().classes("w-full justify-end"): + ui.button("Save configuration", icon="save", on_click=save_config).props( + "color=primary" + ) + + # ── RUN TAB ────────────────────────────────────────────────────── + with ui.tab_panel(tab_run): + with ui.column().classes("max-w-3xl mx-auto py-6 px-4 gap-4 w-full"): + + ui.label("Run the pipeline").classes("text-base font-semibold") + + with ui.card().classes("w-full bg-amber-50"): + with ui.row().classes("items-center gap-2"): + ui.icon("info", color="amber-8") + ui.label( + "Save your configuration first (Setup tab), " + "and make sure your ZIP files are in the folder shown there " + "before running Data Ingestion." + ).classes("text-sm") + + pipeline_sel = ui.select( + list(PIPELINES.keys()), + value=state["pipeline"], + label="Pipeline stage", + ).classes("w-72") + pipeline_sel.on_value_change(lambda e: state.update({"pipeline": e.value})) + + with ui.row().classes("items-center gap-3"): + run_btn = ui.button("Run RISSK", icon="play_arrow").props("color=green") + stop_btn = ui.button("Stop", icon="stop").props("color=red outline") + stop_btn.set_visibility(False) + + log_output = ui.log(max_lines=800).classes( + "w-full h-96 font-mono text-xs border rounded" + ) + + # process holder (list used as mutable container in closures) + _proc: list[asyncio.subprocess.Process] = [] + + async def _do_run() -> None: + save_config() + pipeline_id = PIPELINES[state["pipeline"]] + cmd = [sys.executable, "-m", "kedro", "run"] + if pipeline_id: + cmd += ["--pipeline", pipeline_id] + + log_output.clear() + log_output.push(f"$ {' '.join(cmd)}\n") + + run_btn.set_enabled(False) + stop_btn.set_visibility(True) + + try: + proc = await asyncio.create_subprocess_exec( + *cmd, + cwd=str(PROJECT_ROOT), + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.STDOUT, + ) + _proc.clear() + _proc.append(proc) + + async for line in proc.stdout: + log_output.push( + line.decode("utf-8", errors="replace").rstrip() + ) + + await proc.wait() + if proc.returncode == 0: + log_output.push("\n✓ Pipeline completed successfully.") + ui.notify("Done!", type="positive", position="top") + else: + log_output.push( + f"\n✗ Pipeline failed (exit code {proc.returncode})." + ) + ui.notify( + "Pipeline failed — check the log above.", + type="negative", + position="top", + ) + except Exception as exc: + log_output.push(f"\nError: {exc}") + ui.notify(str(exc), type="negative", position="top") + finally: + run_btn.set_enabled(True) + stop_btn.set_visibility(False) + _proc.clear() + + def _do_stop() -> None: + if _proc: + _proc[0].terminate() + ui.notify("Stop signal sent.", type="warning", position="top") + + run_btn.on("click", _do_run) + stop_btn.on("click", _do_stop) + + # ── FEATURES TAB ───────────────────────────────────────────────── + with ui.tab_panel(tab_features): + with ui.column().classes("max-w-3xl mx-auto py-6 px-4 gap-4 w-full"): + + ui.label("Score selection").classes("text-base font-semibold") + ui.label( + "Choose which scores to calculate and adjust contamination thresholds " + "(expected proportion of outliers in clean data, 0.01–0.50, or 'auto'). " + "Default: all enabled, contamination = 0.10." + ).classes("text-sm text-gray-500") + + with ui.card().classes("w-full"): + # bulk toggle row + with ui.row().classes("items-center gap-4 pb-2 border-b"): + ui.label("Select all").classes("text-sm font-medium") + + def _set_all(value: bool) -> None: + for k in state["features"]: + state["features"][k]["use"] = value + render_features.refresh() + + ui.button("All on", icon="check_box", on_click=lambda: _set_all(True)).props("outline size=sm") + ui.button("All off", icon="check_box_outline_blank", on_click=lambda: _set_all(False)).props("outline size=sm") + + # header + with ui.row().classes( + "w-full items-center gap-2 px-2 py-1 font-medium text-sm text-gray-600 border-b" + ): + ui.label("Feature").classes("flex-1") + ui.label("Use").classes("w-14 text-center") + ui.label("Contamination").classes("w-32 text-center") + ui.label("Extra param").classes("w-24 text-center") + + # Features that must always share the same on/off state. + # Format: {key: partner_key} + _LINKED_PAIRS = { + "first_digit": "numeric_response", + "numeric_response": "first_digit", + } + + @ui.refreshable + def render_features() -> None: + for key, has_cont, _, extra in FEATURE_DEFS: + fs = state["features"][key] + row_bg = "" if fs["use"] else "bg-gray-50 opacity-60" + with ui.row().classes( + f"w-full items-center gap-2 px-2 py-1 border-b border-gray-100 {row_bg}" + ): + label_text = key.replace("_", " ").title() + if key in _LINKED_PAIRS: + with ui.row().classes("flex-1 items-center gap-1"): + ui.label(label_text).classes("text-sm") + ui.icon("link", size="xs", color="indigo-4").tooltip( + f"Linked with {_LINKED_PAIRS[key].replace('_', ' ').title()} — " + "both must be enabled together." + ) + else: + ui.label(label_text).classes("flex-1 text-sm") + + sw = ui.switch(value=fs["use"]).props("dense color=indigo") + + def _on_sw(e, k=key): + state["features"][k]["use"] = e.value + partner = _LINKED_PAIRS.get(k) + if partner: + state["features"][partner]["use"] = e.value + action = "enabled" if e.value else "disabled" + ui.notify( + f"{k.replace('_', ' ').title()} and " + f"{partner.replace('_', ' ').title()} " + f"have both been {action} — they are required together.", + type="warning", + position="top", + timeout=4000, + ) + render_features.refresh() + + sw.on_value_change(_on_sw) + + if has_cont: + cont_inp = ( + ui.input( + value=str(fs["contamination"]), + placeholder="0.10 or auto", + ) + .classes("w-32") + .props("dense") + ) + + def _on_cont(e, k=key): + v = e.value.strip().lower() if e.value else "" + if v == "auto": + state["features"][k]["contamination"] = "auto" + else: + try: + state["features"][k]["contamination"] = float(v) + except ValueError: + pass # ignore invalid input mid-typing + + cont_inp.on_value_change(_on_cont) + else: + ui.label("—").classes("w-32 text-center text-gray-400 text-sm") + + if extra: + pname, _ = extra + extra_inp = ( + ui.number( + value=fs.get(pname, extra[1]), + min=1, + step=1, + format="%.0f", + ) + .classes("w-24") + .props("dense") + .tooltip(pname) + ) + + def _on_extra(e, k=key, pn=pname): + if e.value is not None: + state["features"][k][pn] = int(e.value) + + extra_inp.on_value_change(_on_extra) + else: + ui.label("").classes("w-24") + + render_features() + + with ui.row().classes("w-full justify-end"): + ui.button("Save configuration", icon="save", on_click=save_config).props( + "color=primary" + ) + + # ── ADVANCED TAB ───────────────────────────────────────────────── + with ui.tab_panel(tab_advanced): + with ui.column().classes("max-w-3xl mx-auto py-6 px-4 gap-4 w-full"): + + ui.label("Advanced settings").classes("text-base font-semibold") + + # Zip password + with ui.card().classes("w-full"): + ui.label("ZIP password").classes("font-medium") + ui.label( + "Only required if your Survey Solutions export ZIPs are password-protected. " + "Leave blank if not applicable." + ).classes("text-sm text-gray-500 mb-2") + pwd_inp = ( + ui.input( + "Password", + value=state["zip_password"], + password=True, + password_toggle_button=True, + ) + .classes("w-72") + ) + pwd_inp.on_value_change(lambda e: state.update({"zip_password": e.value})) + + with ui.row().classes("w-full justify-end"): + ui.button("Save configuration", icon="save", on_click=save_config).props( + "color=primary" + ) + + ui.run( + title="RISSK", + port=8080, + reload=False, + show=True, + favicon="📊", + ) + + +if __name__ in {"__main__", "__mp_main__"}: + main() From 4479c33ec43604da940652c1399c003d91d6b414 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Wed, 6 May 2026 22:56:54 +0100 Subject: [PATCH 67/70] CLEANUP - Remove legacy feature generation, item processing, plotting, and unit processing modules to streamline the codebase and improve maintainability. --- rissk/clients.py | 25 -- rissk/config.py | 62 ---- rissk/dataset.py | 29 -- rissk/detection_algorithms.py | 222 ------------- rissk/feature_processing.py | 602 ---------------------------------- rissk/features.py | 29 -- rissk/item_processing.py | 456 ------------------------- rissk/plots.py | 29 -- rissk/unit_proccessing.py | 345 ------------------- 9 files changed, 1799 deletions(-) delete mode 100644 rissk/clients.py delete mode 100644 rissk/config.py delete mode 100644 rissk/dataset.py delete mode 100644 rissk/detection_algorithms.py delete mode 100644 rissk/feature_processing.py delete mode 100644 rissk/features.py delete mode 100644 rissk/item_processing.py delete mode 100644 rissk/plots.py delete mode 100644 rissk/unit_proccessing.py diff --git a/rissk/clients.py b/rissk/clients.py deleted file mode 100644 index c17a582..0000000 --- a/rissk/clients.py +++ /dev/null @@ -1,25 +0,0 @@ -from rissk.config import SURVEY, DATA_DIR -from ploomber.clients import LocalStorageClient, GCloudStorageClient, S3Client - - -def get_local(): - """Returns local client - """ - return LocalStorageClient(DATA_DIR, path_to_project_root=DATA_DIR) - - -def get_s3(): - """Returns S3 client - """ - # assumes your environment is already configured, you may also pass the - # json_credentials_path - return S3Client(bucket_name='surveytool', parent=f'{SURVEY}/latest') - - -def get_gcloud(): - """Returns google cloud storage client - """ - # assumes your environment is already configured, you may also pass the - # json_credentials_path - return GCloudStorageClient(bucket_name='surveytool', - parent=f'{SURVEY}/latest') \ No newline at end of file diff --git a/rissk/config.py b/rissk/config.py deleted file mode 100644 index d800307..0000000 --- a/rissk/config.py +++ /dev/null @@ -1,62 +0,0 @@ -from pathlib import Path -import yaml -import os -from dotenv import load_dotenv -from loguru import logger - -# Load environment variables from .env file if it exists -load_dotenv() - -# Paths -PROJ_ROOT = Path(__file__).parent.parent -logger.info(f"PROJ_ROOT path is: {PROJ_ROOT}") - -env_file_path = PROJ_ROOT / 'env.yaml' -def parse_questionnaire(env_var) -> list: - # Ensure QUESTIONAIRE is always a list of dictionaries - questionnaire = env_var.get('QUESTIONAIRE', []) - if isinstance(questionnaire, list): - parsed_questionnaire = [] - for item in questionnaire: - if isinstance(item, dict): - name = item.get('name') - version = item.get('VERSION', []) - if isinstance(version, int): - version = [version] - parsed_questionnaire.append({ - 'name': name, - 'VERSION': version - }) - return parsed_questionnaire - -with open(env_file_path, 'r') as file: - env_data = yaml.safe_load(file) - -# Load variables -SURVEY = env_data.get('SURVEY') -QUESTIONAIRE = parse_questionnaire(env_data) # Parse the new structure - -logger.info(f"Avaliable Questionnaires") -for item in QUESTIONAIRE: - name = item['name'] - versions = item['VERSION'] - logger.info(f"Questionnaire: {name} - Versions: {versions}") - - -DATA_DIR = PROJ_ROOT / "data" / SURVEY / "latest" - -EXTERNAL_DATA_DIR = DATA_DIR / "00_EXTERNAL" -RAW_DATA_DIR = DATA_DIR / "10_RAW" -INTERIM_DATA_DIR = DATA_DIR / "20_INTERIM" -PROCESSED_DATA_DIR = DATA_DIR / "30_PROCESSED" - - -# If tqdm is installed, configure loguru with tqdm.write -# https://github.com/Delgan/loguru/issues/135 -try: - from tqdm import tqdm - - logger.remove(0) - logger.add(lambda msg: tqdm.write(msg, end=""), colorize=True) -except ModuleNotFoundError: - pass diff --git a/rissk/dataset.py b/rissk/dataset.py deleted file mode 100644 index ac2e152..0000000 --- a/rissk/dataset.py +++ /dev/null @@ -1,29 +0,0 @@ -from pathlib import Path - -import typer -from loguru import logger -from tqdm import tqdm - -from rissk.config import PROCESSED_DATA_DIR, RAW_DATA_DIR - -app = typer.Typer() - - -@app.command() -def main( - # ---- REPLACE DEFAULT PATHS AS APPROPRIATE ---- - input_path: Path = RAW_DATA_DIR / "dataset.csv", - output_path: Path = PROCESSED_DATA_DIR / "dataset.csv", - # ---------------------------------------------- -): - # ---- REPLACE THIS WITH YOUR OWN CODE ---- - logger.info("Processing dataset...") - for i in tqdm(range(10), total=10): - if i == 5: - logger.info("Something happened for iteration 5.") - logger.success("Processing dataset complete.") - # ----------------------------------------- - - -if __name__ == "__main__": - app() diff --git a/rissk/detection_algorithms.py b/rissk/detection_algorithms.py deleted file mode 100644 index 5707fef..0000000 --- a/rissk/detection_algorithms.py +++ /dev/null @@ -1,222 +0,0 @@ -import pandas as pd -import numpy as np -from sklearn.preprocessing import LabelEncoder -from sklearn.ensemble import IsolationForest -from sklearn.preprocessing import OneHotEncoder -from sklearn.neighbors import NearestNeighbors -from scipy.spatial import distance_matrix -from scipy.stats import mstats -import math - - -def lat_lon_to_cartesian(lat, lon, R=6371): - """ - Convert lat, lon into 3D cartesian coordinates - - Parameters: - lat, lon: latitude and longitude in degrees - R: radius of the Earth (default is in kilometers) - - Returns: - x, y, z: 3D cartesian coordinates - """ - lat, lon = np.radians(lat), np.radians(lon) - x = R * np.cos(lat) * np.cos(lon) - y = R * np.cos(lat) * np.sin(lon) - z = R * np.sin(lat) - return x, y, z - - -def haversine(lat1, lon1, lat2, lon2): - """Calculate the great circle distance in kilometers between two points - on the earth (specified in decimal degrees)""" - # convert decimal degrees to radians - lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2]) - - # haversine formula - dlon = lon2 - lon1 - dlat = lat2 - lat1 - a = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2 - c = 2 * np.arcsin(np.sqrt(a)) - r = 6371 # Radius of earth in kilometers. Use 3956 for miles. Determines return value units. - return c * r - - -def check_distance(data, min_distance=20, lat='f__gps_latitude_GPS', lon='f___gps_longitude_GPS'): - df = data.copy() - df.reset_index(inplace=True) - df['is_too_close'] = False - - # Calculate the pairwise distances between all GPS coordinates - distances = distance_matrix(df[[lat, lon]].values, df[[lat, lon]].values) - distances = np.triu(distances) # Only keep the upper triangular part (excluding the diagonal) - - # Find pairs of coordinates that are closer than 20 meters - too_close_indices = np.argwhere(distances < min_distance) - - # Update 'is_too_close' column based on the pairs of coordinates that are too close - for i, j in too_close_indices: - if i != j: - df.at[i, 'is_too_close'] = True - df.at[j, 'is_too_close'] = True - return df - - -# Create a function to report the limits of the Z-Score -def z_score_limits(df, column_name): - """ returns the upper and lower limits of the Z-score """ - - # Compute the limits - upper_limit = df[column_name].mean() + 2.5 * df[column_name].std() - lower_limit = df[column_name].mean() - 2.5 * df[column_name].std() - - # Round and return the limits - upper_limit = round(upper_limit, 2) - lower_limit = round(lower_limit, 2) - - return lower_limit, upper_limit - - -def log_transformation_function(df, column_name): - """ Conduct a log transformation of a variable """ - # Replace the values with log-transformed values - df[[column_name]] = df[[column_name]].apply(np.log) - - -def fix_anomalies(data, col, threshold_percentage=0.3): - # If same column value is marked according to a distinct responsible both 1 and -1 than unset all anomalies - data['anomaly'] = data[col].replace(data.groupby(col)['anomaly'].max().to_dict()) - - # same if there is more than 30% of responsible that have that anomaly, set it to one - # Get all responsible that been marked anomalus for a specific value - grouped_df = data[data['anomaly'] == -1].groupby(col)['responsible'].nunique().reset_index(name='count') - # Compute the percentage - grouped_df['anomaly_percentage'] = grouped_df['count'] / data['responsible'].nunique() - update_anomalies_list = grouped_df[grouped_df['anomaly_percentage'] >= threshold_percentage][col].values - data.loc[data[col].isin(update_anomalies_list), 'anomaly'] = 1 - return data - - -def find_anomalies(df, index_col=['interview__id', 'roster_level', 'responsible'], overwrite_col=True, - contamination=0.1): - index_col = [col for col in index_col if col in df.columns] - df['index_col'] = df[index_col].apply(lambda row: '_'.join([str(row[col]) for col in index_col]), axis=1) - - for col in df.drop(columns=index_col + ['index_col']).columns: - # col = 'age_adult'#df_sequence_jump.columns[9] - data = df[~pd.isnull(df[col])].copy() - - onehot_encoder = OneHotEncoder() - responsible_encoded = onehot_encoder.fit_transform(data[['responsible']]).toarray() - # Extract the 'jump' and 'responsible_label' columns as features - # encoded_df = pd.DataFrame(responsible_encoded, columns=onehot_encoder.get_feature_names(['responsible'])) - encoded_df = pd.DataFrame(responsible_encoded, columns=onehot_encoder.get_feature_names_out(['responsible'])) - - # Combine the one-hot encoded DataFrame with the original DataFrame (excluding 'responsible') - encoded_df[col] = data[col].values - X = encoded_df.values.copy() # data[[col, 'responsible_label']].copy() - # Initialize and fit the Isolation Forest model - model = IsolationForest(contamination=contamination, - random_state=42) # Adjust contamination based on your anomaly threshold - # model = GaussianMixture(n_components=2, random_state=42) - # model = HBOS(n_bins=5) - # model = CBLOF(contamination=0.05, n_clusters=3) - model.fit(X) - # Predict the anomalies (1 for normal, -1 for anomalies) - anomaly_predictions = model.predict(X) - # anomaly_scores = model.decision_function(X) - # Add the anomaly predictions as a new column in the DataFrame - data['anomaly'] = anomaly_predictions - # data['anomaly_scores'] = anomaly_predictions - data = fix_anomalies(data, col, 0.6) - data['anomaly'] = data['anomaly'].replace({1: 0, -1: 1}) - if overwrite_col: - df[col] = df['index_col'].map(data.set_index('index_col')['anomaly']) - else: - df[col + '_anomaly'] = df['index_col'].map(data.set_index('index_col')['anomaly']) - df.drop(columns=['index_col'], inplace=True) - columns = df.drop(columns=index_col).columns - df = df.groupby('interview__id')[columns].sum() - df = df.reset_index() - return df - - -def find_outliers(df, index_col=['interview__id', 'roster_level', 'responsible']): - df['index_col'] = df[index_col].apply(lambda row: '_'.join([str(row[col]) for col in index_col]), axis=1) - - for col in df.drop(columns=index_col + ['index_col']).columns: - # col = 'age_adult'#df_sequence_jump.columns[9] - data = df[~pd.isnull(df[col])].copy() - - q_high = data[col].quantile(0.75) - q_low = data[col].quantile(0.25) - iqr = q_high - q_low - data['anomaly'] = 0 - data.loc[(data[col] < q_low - 1.5 * iqr) | (data[col] > q_high + 1.5 * iqr), 'anomaly'] = 1 - - df[col] = df['index_col'].map(data.set_index('index_col')['anomaly']) - - df.drop(columns=['index_col'], inplace=True) - return df - - -def find_consecutive_anomalies(df, index_col=['interview__id', 'roster_level', 'responsible']): - df['index_col'] = df[index_col].apply(lambda row: '_'.join([str(row[col]) for col in index_col]), axis=1) - - for col in df.drop(columns=index_col + ['index_col']).columns: - # col = 'age_adult'#df_sequence_jump.columns[9] - data = df[~pd.isnull(df[col])].copy() - - q_high = data[col].quantile(0.75) - q_low = data[col].quantile(0.25) - iqr = q_high - q_low - data['anomaly'] = 0 - data.loc[(data[col] < q_low - 1.5 * iqr) | (data[col] > q_high + 1.5 * iqr), 'anomaly'] = 1 - - q_high = data[data['anomaly'] == 0][col].quantile(0.75) - q_low = data[data['anomaly'] == 0][col].quantile(0.25) - iqr = q_high - q_low - data.loc[(data[col] < q_low - 1.5 * iqr), 'anomaly'] = 1 - - # data = fix_anomalies(data, col, 0.6) - - df[col] = df['index_col'].map(data.set_index('index_col')['anomaly']) - - df.drop(columns=['index_col'], inplace=True) - return df - - -def detect_duration_outliers_by_magnitude(data, column_name): - data = data[~pd.isnull(data[column_name])].copy() - # data = data.copy() - # data[column_name].fillna(0, inplace=True) - - # Create bins based on the order of magnitude - orders_of_magnitude = np.floor(np.log10(data[column_name] + 1)) - data['magnitude_order'] = orders_of_magnitude - - # Calculate the 10th last percentile range for the counts - # Let's assume that 1 order of magnitude is still not an outlier - upper_bound = max(1, data['magnitude_order'].quantile(0.9)) # Q3 + 1.5 * IQR - - # Mark the corresponding values as outliers - data['is_extreme_outlier'] = False - data.loc[(data['magnitude_order'] > upper_bound), 'is_extreme_outlier'] = True - - q1 = data[data['is_extreme_outlier'] == False][column_name].quantile(0.25) - q3 = data[data['is_extreme_outlier'] == False][column_name].quantile(0.75) - iqr = q3 - q1 - lower_bound = q1 - 1.5 * iqr - upper_bound = q3 + 1.5 * iqr - # Mark the corresponding values as outliers - data['is_outlier'] = False - # data.loc[(data[column_name] < lower_bound) | (data[column_name] > upper_bound), 'is_outlier'] = True - data.loc[(data[column_name] > upper_bound), 'is_outlier'] = True - # Remove the temporary magnitude_order column - data.drop(columns=['magnitude_order'], inplace=True) - max_non_outlier = data[data['is_outlier'] == False][column_name].max() - # winsorized_data = mstats.winsorize(data, limits=[0.0, max_non_outlier]) - data.loc[data['is_outlier'] == True, column_name] = max_non_outlier - return data - - diff --git a/rissk/feature_processing.py b/rissk/feature_processing.py deleted file mode 100644 index 5f5536a..0000000 --- a/rissk/feature_processing.py +++ /dev/null @@ -1,602 +0,0 @@ -#from rissk.import_manager import * -from rissk.utils.import_utils import * - - -class FeatureProcessing(object): - - def __init__(self, config): - self.config = config - - self._reload = self.config['environment']['reload'] - self._save_to_disk = self.config['environment']['save_to_disk'] - - self._source_path = self.config['environment']['data']['externals'] - self._raw_path = self.config['environment']['data']['raw'] - self._processed_path = self.config['environment']['data']['processed'] - self._final_path = self.config['environment']['data']['final'] - - self._limit_unit = self.config['limit_unit'] - - self._survey_names = self.config['surveys'] - self._survey_version = self.config['survey_version'] - - - - if self.config.get('zip_password'): - self.zip_password = self.config['password'].encode() - - survey_info = get_survey_info(config) - - - paradata, questionnaire, microdata = get_dataframes(survey_info, self._raw_path, self._processed_path, config, - reload=self._reload, - save_to_disk=self._save_to_disk) - - print('Data Loaded') - self._allowed_features = ['f__' + k for k, v in config['features'].items() if v['use']] - self.item_level_columns = ['interview__id', 'variable_name', 'roster_level'] - self._df_paradata = self.process_paradata(paradata) - print('Paradata Processed') - self._df_item = self.make_df_item(microdata) - print('Items Build') - self._df_unit = self.make_df_unit() - print('Unit Build') - self._df_resp = self.make_df_responsible() - # Define ask that get recurrently used - self.numeric_question_mask = ( - (self._df_item["qtype"] == 'NumericQuestion') & - (self._df_item['value'] != '') & - (~pd.isnull(self._df_item['value'])) & - (self._df_item['value'] != -999999999) - ) - - @staticmethod - def rename_feature(feature_name, starting_string='f', new_string='s'): - starting_string = starting_string + '__' - new_string = new_string + '__' - new_variable_name = feature_name.replace(starting_string, new_string) \ - if feature_name.startswith(starting_string) else feature_name - return new_variable_name - - @property - def df_item(self): - for method_name in self.get_make_methods(method_type='feature', level='item'): - feature_name = method_name.replace('make_feature_item', 'f') - if feature_name in self._allowed_features and feature_name not in self._df_item.columns: - try: - print(f"Processing {feature_name}...") - getattr(self, method_name)(feature_name) - # print(f"{feature_name} Processed") - except Exception as e: - print("WARNING: FEATURE ITEM: {} won't be used in further calculation".format(feature_name)) - return self._df_item - - @property - def df_unit(self): - for method_name in self.get_make_methods(method_type='feature', level='unit'): - feature_name = method_name.replace('make_feature_unit', 'f') - if feature_name in self._allowed_features and feature_name not in self._df_unit.columns: - try: - print(f"Processing {feature_name} ...") - getattr(self, method_name)(feature_name) - except Exception as e: - print("WARNING: FEATURE UNIT: {}, It won't be used in further calculation".format(feature_name)) - return self._df_unit - - @property - def df_active_paradata(self): - # df_para_active, active events, prior rejection/review events, for questions with scope interviewer - - active_events = ['InterviewCreated', 'AnswerSet', 'Resumed', 'AnswerRemoved', 'CommentSet', 'Restarted'] - # only keep events done by interview (in most cases this should be all, after above filters, - # just in case supervisor or HQ answered something while interviewer answered on web mode) - # keep active events, prior rejection/review events, for questions with scope interviewer - active_mask = (self.df_paradata['event'].isin(active_events)) & \ - (self.df_paradata['question_scope'].isin([0, ''])) & \ - (self.df_paradata['role'] == 1) - - vars_needed = ['interview__id', 'order', 'event', 'responsible', 'role', 'tz_offset', - 'param', 'answer', 'roster_level', 'timestamp_local', 'variable_name', - 'question_sequence', 'question_scope', "qtype", 'question_type', - 'survey_name', 'survey_version', 'interviewing', 'yes_no_view', 'index_col', 'f__answer_hour_set' - ] - - df_para_active = self.df_paradata.loc[active_mask, vars_needed] - return df_para_active - - @property - def df_paradata(self): - return self._df_paradata - - @property - def df_microdata(self): - paradata, questionnaire, microdata = self.get_dataframes(reload=self._reload, - save_to_disk=self._save_to_disk) - return microdata - - @property - def df_questionnaire(self): - paradata, questionnaire, microdata = self.get_dataframes(reload=self._reload, - save_to_disk=self._save_to_disk) - return questionnaire - - def make_index_col(self, df): - - # Filter out columns with NaN and empty strings - mask = (~df[['interview__id', 'variable_name', 'roster_level']].isnull()) & \ - (df[['interview__id', 'variable_name', 'roster_level']] != '') - - # Use the mask to replace invalid values with an empty string - filtered_df = df.where(mask, '') - - # Concatenate the columns with an underscore separator - df['index_col'] = filtered_df['interview__id'].astype(str) + "_" + \ - filtered_df['variable_name'].astype(str) + "_" + \ - filtered_df['roster_level'].astype(str) - - # Remove trailing and leading underscores if they exist - df['index_col'] = df['index_col'].str.strip('_') - return df - - def make_df_item(self, microdata): - - microdata = self.make_index_col(microdata) - df_item = microdata[['value', "qtype", 'is_integer', 'qnr_seq', - 'n_answers', 'answer_sequence', - 'cascade_from_question_id', 'is_filtered_combobox', - 'index_col'] + self.item_level_columns] - - paradata_columns = ['responsible', 'f__answer_hour_set', 'interviewing', 'tz_offset'] - # merge microdata with active pardata and keep only the last answer set - answer_set_mask = (self.df_active_paradata['event'] == 'AnswerSet') - data = self.df_active_paradata[answer_set_mask].drop_duplicates(subset='index_col', keep='last') - df_item = df_item.merge(data[paradata_columns + ['index_col']], how='left', - on='index_col') - # Remove items that are not in interviewing - df_item = df_item[df_item['interviewing'] == True] - df_item = self.add_sequence_features(df_item) - - df_item = self.add_item_time_features(df_item) - - return df_item.copy() - - def add_sequence_features(self, df_item): - # Define the list of features depending on sequences - sequence_features = ['f__previous_question', 'f__previous_answer', - 'f__previous_roster', 'f__sequence_jump'] - if any(col in self._allowed_features for col in sequence_features): - df_sequence = self.get_df_sequence() - # Remove non-selected features - sequence_features = ['index_col'] + [f for f in sequence_features if f in self._allowed_features] - df_sequence = df_sequence[sequence_features] - # Merge with df_item - - df_item = df_item.merge(df_sequence, how='left', on='index_col') - return df_item - - def add_item_time_features(self, df_item): - # Define the list of features depending on time - time_features = ['f__answer_duration', 'f__comment_duration'] - if any(col in self._allowed_features for col in time_features): - df_time = self.get_df_time() - # Remove records that have variable_name as empty string, i.e. Pauses - df_time = df_time[df_time['variable_name'] != ''] - # summarize on item level - df_time = df_time.groupby(self.item_level_columns + ['index_col']).agg( - f__answer_duration=('f__answer_duration', 'sum'), - f__comment_duration=('f__comment_duration', 'sum'), - ).reset_index() - - # Remove non-selected features - time_features = ['index_col'] + [f for f in time_features if f in self._allowed_features] - df_time = df_time[time_features] - # Merge with df_item - df_item = df_item.merge(df_time, how='left', on='index_col') - return df_item - - def get_df_time(self): - # f__answer_duration, total time spent to record answers, i.e., - # sum of all time-intervals from active events ending with the item being AnswerSet or AnswerRemoved - # f__comment_duration, total time spent to comment, i.e., - # sum of all time-intervals from active events ending with the item being CommentSet - ###### ITEM features - df_time = self.df_active_paradata - - # calculate time difference in seconds - df_time['time_difference'] = df_time.groupby('interview__id')['timestamp_local'].diff() - df_time['time_difference'] = df_time['time_difference'].dt.total_seconds() - df_time['f__time_changed'] = np.where(df_time['time_difference'] < -180, df_time['time_difference'], np.nan) - df_time.loc[df_time['time_difference'] < 0, 'time_difference'] = pd.NA - # time for answers/comments - df_time['f__answer_duration'] = df_time.loc[ - df_time['event'].isin(['AnswerSet', 'AnswerRemoved']), 'time_difference'] - df_time['f__comment_duration'] = df_time.loc[df_time['event'] == 'CommentSet', 'time_difference'] - - df_time['f__pause_duration'] = df_time.loc[df_time['event'].isin(['Resumed', 'Restarted']), 'time_difference'] - - ###### UNIT features - active_events = ['AnswerSet', 'AnswerRemoved', 'CommentSet', 'Resumed', 'Restarted'] - # Calculate the total duration of active events for all events with less than 30 minutes - df_time['f__total_duration'] = df_time.loc[(df_time['event'].isin(active_events) & ( - df_time['time_difference'] < 30 * 60)), 'time_difference'] - - # Get the min date from the min question sequesce as there might be some time setting - # change later that would change the starting date if just looking at the min of timestamp_local - starting_timestamp = df_time[df_time['event'].isin(['AnswerSet'])].groupby('interview__id')[ - 'timestamp_local'].min() - df_time['f__starting_timestamp'] = df_time['interview__id'].map(starting_timestamp) - - min_date = df_time['f__starting_timestamp'].min() - df_time['f__days_from_start'] = abs( - (df_time['timestamp_local'] - min_date).dt.days) # / (max_date-min_date).days - - return df_time - - def get_df_sequence(self): - - df_last = self.df_active_paradata[self.df_active_paradata['event'] == 'AnswerSet'].groupby( - 'index_col').last() - df_last = df_last.sort_values(['interview__id', 'order']).reset_index() - - # f__previous_question, f__previous_answer, f__previous_roster for previous answer set - df_last['f__previous_question'] = df_last.groupby('interview__id')['variable_name'].shift( - fill_value=pd.NA) - df_last['f__previous_answer'] = df_last.groupby('interview__id')['answer'].shift( - fill_value='') - df_last['f__previous_roster'] = df_last.groupby('interview__id')['roster_level'].shift( - fill_value='') - # f__sequence_jump, Difference between actual answer sequence and - # question sequence in the questionnaire, in difference to previous question - df_last['answer_sequence'] = df_last.groupby('interview__id').cumcount() + 1 - df_last['diff'] = df_last['question_sequence'] - df_last['answer_sequence'] - df_last['f__sequence_jump'] = df_last.groupby('interview__id')['diff'].diff() - - return df_last - - def process_paradata(self, paradata): - - # streamline missing (empty, NaN) to '', important to identify duplicates in terms of the roster below - paradata.fillna('', inplace=True) - - paradata['f__answer_hour_set'] = (paradata['timestamp_local'].dt.hour + paradata[ - 'timestamp_local'].dt.round( - '30min').dt.minute / 60) - - # interviewing, True prior to Supervisor/HQ interaction, else False - events_split = ['RejectedBySupervisor', 'OpenedBySupervisor', 'OpenedByHQ', 'RejectedByHQ'] - # Create a flag indicating whether each row has an event in `events_split` - paradata['flag'] = paradata['event'].isin(events_split) - - # Use `groupby` and `cumsum` to count how many flagged events occur for each group - # If the count is greater than 0, then the 'interviewing' column should be False - paradata['cumulative_flag'] = paradata.groupby('interview__id')['flag'].cumsum() - paradata['interviewing'] = np.where(paradata['cumulative_flag'] > 0, False, True) - - # Cleanup the intermediate columns - paradata.drop(['flag', 'cumulative_flag'], axis=1, inplace=True) - paradata = paradata[(paradata['interviewing'] == True) & (paradata['role'] == 1)].copy() - - paradata = self.make_index_col(paradata) - paradata.sort_values(['interview__id', 'order'], inplace=True) - paradata.reset_index(inplace=True) - - paradata = self.filter_by_consent(paradata) - - return paradata - - def filter_by_consent(self, paradata): - if self._limit_unit is not None: - consent_variable = next(iter(self._limit_unit)) # Get the first (and only) key in the dictionary - # Careful! Answer value is a string in paradata. - # Therefore also consent_value must be set to a string. - consent_value = str(self._limit_unit[consent_variable]) - - cond1 = (paradata['variable_name'] == consent_variable) - cond2 = (paradata['answer'] == consent_value) - - filtered_interview_id = paradata[cond1 & cond2]['interview__id'].unique() - - paradata = paradata[paradata['interview__id'].isin(filtered_interview_id)].copy() - - return paradata - - def make_df_unit(self): - df_unit = self.df_active_paradata[['interview__id', 'responsible', 'survey_name', 'survey_version']].copy() - df_unit.drop_duplicates(inplace=True) - df_unit = df_unit[(df_unit['responsible'] != '') & (~pd.isnull(df_unit['responsible']))] - df_unit = self.add_pause_features(df_unit) - df_unit = self.add_unit_time_features(df_unit) - return df_unit - - def make_df_responsible(self): - df_resp = self.df_active_paradata[['responsible']].copy() - df_resp.drop_duplicates(inplace=True) - df_resp = df_resp[(df_resp['responsible'] != '') & (~pd.isnull(df_resp['responsible']))] - return df_resp - - def save_data(self, df, file_name): - - target_dir = os.path.join(self._raw_path, self._survey_names) - survey_path = os.path.join(target_dir, self._survey_version) - processed_data_path = os.path.join(survey_path, 'processed_data') - df.to_pickle(os.path.join(processed_data_path, f'{file_name}.pkl')) - - def get_make_methods(self, method_type='feature', level='item'): - return [method for method in dir(self) if method.startswith(f"make_{method_type}_{level}__") - and callable(getattr(self, method))] - - ###### Feature item methods - def make_feature_item__string_length(self, feature_name): - # f__string_length, length of string answer, if TextQuestions else empty pd.NA - text_question_mask = (self._df_item["qtype"] == 'TextQuestion') - self._df_item[feature_name] = pd.NA - self._df_item.loc[text_question_mask, feature_name] = self._df_item.loc[ - text_question_mask, 'value'].str.len().astype('Int64') - - def make_feature_item__numeric_response(self, feature_name): - # f__numeric_response, response, if NumericQuestions, else empty pd.NA - self._df_item[feature_name] = np.nan - self._df_item.loc[self.numeric_question_mask, feature_name] = \ - self._df_item[self.numeric_question_mask]['value'].astype( - float) - - def make_feature_item__first_digit(self, feature_name): - # f__first_digit, first digit of the response if numeric question else empty pd.NA - self._df_item[feature_name] = pd.NA - self._df_item.loc[self.numeric_question_mask, feature_name] = \ - pd.to_numeric(self._df_item.loc[self.numeric_question_mask, 'value']).abs().astype(str).str[0].astype( - 'Int64') - - def make_feature_item__last_digit(self, feature_name): - # f__last_digit, modulus of 10 of the response if numeric question else empty pd.NA - self._df_item[feature_name] = pd.NA - - def extract_last_digit(x): - if x >= 1: # Check if the value has at least two digits - return x % 10 # Return the last digit - else: - return pd.NA - - self._df_item.loc[self.numeric_question_mask, feature_name] = pd.to_numeric( - self._df_item.loc[self.numeric_question_mask, 'value']).astype('int64') - - self._df_item.loc[self.numeric_question_mask, feature_name] = self._df_item.loc[ - self.numeric_question_mask, feature_name].apply(extract_last_digit) - - def make_feature_item__first_decimal(self, feature_name): - # f__first_decimal, first decimal digit if numeric question else empty pd.NA - decimal_question_mask = (self._df_item['is_integer'] == False) & (self._df_item['value'] != '') - self._df_item[feature_name] = pd.NA - values = self._df_item.loc[decimal_question_mask, 'value'].astype(float) - self._df_item.loc[decimal_question_mask, feature_name] = np.floor(values * 100) % 100 - self._df_item[feature_name] = self._df_item[feature_name].astype('Int64') - - def make_feature_item__answer_position(self, feature_name): - # f__rel_answer_position, relative position of the selected answer - # only questions with more than two answers - single_question_mask = ((self._df_item["qtype"] == 'SingleQuestion') - & (self._df_item['n_answers'] > 2) - & (self._df_item['is_filtered_combobox'] == False) - & (pd.isnull(self._df_item['cascade_from_question_id']))) - - def answer_position(row): - value = None - if (row['value'] in row['answer_sequence']) and pd.notnull(row['value']): - value = round(row['answer_sequence'].index(row['value']) / (row['n_answers'] - 1), 3) - return value - - self._df_item.loc[single_question_mask, feature_name] = ( - self._df_item.loc[single_question_mask].apply(answer_position, axis=1)) - - def get_feature_item__answer_removed(self, feature_name): - # This method cannot be used to directly insert the feature within df_item as the item - # might no longer exist in microdata, but only in paradta - # f__answer_removed, answers removed (by interviewer, or by system as a result of interviewer action). - removed_mask = (self.df_paradata['event'] == 'AnswerRemoved') & (self.df_paradata['role'] == 1) - df_item_removed = self.df_paradata[removed_mask] - - df_item_removed = df_item_removed.groupby(['interview__id', 'responsible', 'variable_name', 'qnr_seq', ]).agg( - f__answer_removed=('order', 'count'), - ) - return df_item_removed.reset_index() - - def make_feature_item__answer_changed(self, feature_name): - - df_changed_temp = self.df_active_paradata[self.df_active_paradata['event'] == 'AnswerSet'] - df_changed_temp[feature_name] = False - - # list and multi-select questions (without yes_no_mode) - list_mask = (df_changed_temp["qtype"] == 'TextListQuestion') - multi_mask = (df_changed_temp['yes_no_view'] == False) - df_changed_temp['answer_list'] = pd.NA - df_changed_temp.loc[list_mask, 'answer_list'] = df_changed_temp.loc[list_mask, 'answer'].str.split('|') - df_changed_temp.loc[multi_mask, 'answer_list'] = df_changed_temp.loc[multi_mask, 'answer'].str.split( - ', |\\|') - df_changed_temp['prev_answer_list'] = df_changed_temp.groupby(self.item_level_columns + ['index_col'])[ - 'answer_list'].shift() - answers_mask = df_changed_temp['prev_answer_list'].notna() - df_changed_temp.loc[answers_mask, feature_name] = df_changed_temp.loc[answers_mask].apply( - lambda row: not set(row['prev_answer_list']).issubset(set(row['answer_list'])), axis=1) - - # single answer question - df_changed_temp['prev_answer'] = df_changed_temp.groupby(self.item_level_columns + ['index_col'])[ - 'answer'].shift() - single_answer_mask = (~df_changed_temp["qtype"].isin(['MultyOptionsQuestion', 'TextListQuestion'])) & \ - (df_changed_temp['prev_answer'].notna()) & \ - (df_changed_temp['answer'] != df_changed_temp['prev_answer']) - df_changed_temp.loc[single_answer_mask, feature_name] = True - - # yes_no_view questions - yesno_mask = (df_changed_temp['yes_no_view'] == True) - df_filtered = df_changed_temp[yesno_mask].copy() - df_filtered[['yes_list', 'no_list']] = df_filtered['answer'].str.split('|', expand=True) - df_filtered['yes_list'] = df_filtered['yes_list'].str.split(', ').apply( - lambda x: [] if x == [''] or x is None else x) - df_filtered['no_list'] = df_filtered['no_list'].str.split(', ').apply( - lambda x: [] if x == [''] or x is None else x) - df_filtered['prev_yes_list'] = df_filtered.groupby(self.item_level_columns + ['index_col'])['yes_list'].shift( - fill_value=[]) - df_filtered['prev_no_list'] = df_filtered.groupby(self.item_level_columns + ['index_col'])['no_list'].shift( - fill_value=[]) - df_changed_temp.loc[yesno_mask, feature_name] = df_filtered.apply( - lambda row: not set(row['prev_yes_list']).issubset(set(row['yes_list'])), axis=1) - df_changed_temp.loc[yesno_mask, feature_name] = df_filtered.apply( - lambda row: not set(row['prev_no_list']).issubset(set(row['no_list'])), axis=1) - - # count on item level - df_changed_temp = df_changed_temp.groupby('index_col')[feature_name].sum().reset_index() - self._df_item[feature_name] = self._df_item['index_col'].map( - df_changed_temp.set_index('index_col')[feature_name]) - - def make_feature_item__answer_selected(self, feature_name): - # f__answers_selected, number of answers selected in a multi-answer or list question - multi_list_mask = self._df_item["qtype"].isin(['MultyOptionsQuestion']) - - # Function to calculate the number of elements in a list or return nan - def count_elements_or_nan(val): - if isinstance(val, list): - return len(val) - else: - return np.nan - - self._df_item.loc[multi_list_mask, feature_name] = self._df_item.loc[multi_list_mask, 'value'].apply( - count_elements_or_nan) - # f__share_selected, share between answers selected, and available answers (only for unlinked questions) - self._df_item[feature_name] = self._df_item[feature_name] / self._df_item['n_answers'] - - def make_feature_item__comment_length(self, feature_name): - # f__comment_length - comment_mask = (self.df_paradata['event'] == 'CommentSet') & \ - (self.df_paradata['role'] == 1) - - df_item_comment = self.df_paradata[comment_mask].copy() - df_item_comment[feature_name] = df_item_comment['answer'].str.len() - df_item_comment = df_item_comment.groupby('index_col').agg( - f__comment_length=(feature_name, 'sum'), - ) - self._df_item[feature_name] = self._df_item['index_col'].map( - df_item_comment[feature_name]) - - def make_feature_item__comment_set(self, feature_name): - # f__comments_set - comment_mask = (self.df_paradata['event'] == 'CommentSet') & \ - (self.df_paradata['role'] == 1) - - df_item_comment = self.df_paradata[comment_mask].copy() - df_item_comment = df_item_comment.groupby('index_col').agg( - f__comment_set=('order', 'count'), - ) - self._df_item[feature_name] = self._df_item['index_col'].map( - df_item_comment[feature_name]) - - def make_feature_item__gps(self, feature_name): - # f__gps_latitude, f__gps_longitude, f__gps_accuracy - gps_mask = self._df_item["qtype"] == 'GpsCoordinateQuestion' - gps_df = self._df_item.loc[gps_mask, 'value'].str.split(',', expand=True) - gps_df.columns = ['gps__Latitude', 'gps__Longitude', 'gps__Accuracy', 'gps__altitude', 'gps__timestamp_utc'] - self._df_item[feature_name] = False - self._df_item.loc[gps_mask, feature_name] = True - self._df_item.loc[gps_mask, 'f__gps_latitude'] = pd.to_numeric(gps_df['gps__Latitude'], errors='coerce') - self._df_item.loc[gps_mask, 'f__gps_longitude'] = pd.to_numeric(gps_df['gps__Longitude'], errors='coerce') - self._df_item.loc[gps_mask, 'f__gps_accuracy'] = pd.to_numeric(gps_df['gps__Accuracy'], errors='coerce') - drop_columns = [col for col in self._df_item.columns if col.startswith('gps__')] - self._df_item.drop(columns=drop_columns, inplace=True) - - ##### UNIT item methods - - def make_feature_unit__number_answered(self, feature_name): - answer_set_mask = ((~pd.isnull(self._df_item['value'])) - & (self._df_item['value'] != -999999999) - & (self._df_item['value'] != '##N/A##') - & (self._df_item['value'] != '') - & (self._df_item["qtype"] != 'Variable') - ) - df_answer_set = self._df_item[answer_set_mask] - df_answer_set = df_answer_set.groupby('interview__id').agg( - f__number_answered=('value', 'count') - ) - self._df_unit[feature_name] = self._df_unit['interview__id'].map( - df_answer_set[feature_name]) - self._df_unit[feature_name].fillna(0, inplace=True) - - def make_feature_unit__number_unanswered(self, feature_name): - answer_unset_mask = ( - (self._df_item['value'] == -999999999) - | (self._df_item['value'] == '##N/A##') - ) & (self._df_item["qtype"] != 'Variable') - df_answer_set = self._df_item[answer_unset_mask] - df_answer_set = df_answer_set.groupby('interview__id').agg( - f__number_unanswered=('value', 'count') - ) - self._df_unit[feature_name] = self._df_unit['interview__id'].map( - df_answer_set[feature_name]) - # Set to zero if not answered is not present - self._df_unit[feature_name].fillna(0, inplace=True) - - def make_feature_unit__translation_positions(self, feature_name): - - trans_mask = (self.df_paradata['event'].isin(['AnswerSet', 'TranslationSwitched'])) - - df_trans_temp = self.df_paradata.loc[ - trans_mask, ['interview__id', 'order', 'event', 'param']].copy().reset_index() - df_trans_temp['seq'] = df_trans_temp.groupby('interview__id').cumcount() + 1 - - # Define a function to calculate the relative positions - def relative_translation_positions(group): - total_rows = len(group) - translation_position = group.loc[group['event'] == 'TranslationSwitched', 'seq'] - relative_positions = [pos / total_rows for pos in translation_position] - return relative_positions - - # Group by 'interview__id' and apply the function - df_trans_temp = df_trans_temp.groupby('interview__id').apply( - relative_translation_positions).reset_index().rename(columns={0: feature_name}) - - self._df_unit[feature_name] = self._df_unit['interview__id'].map( - df_trans_temp.set_index('interview__id')[feature_name]) - - def add_pause_features(self, df_unit): - # Define the list of features depending on sequences - pause_features = ['f__pause_count', 'f__pause_duration', - 'f__pause_list'] - if any(col in self._allowed_features for col in pause_features): - df_pause = self.get_df_time() - df_pause = df_pause.groupby('interview__id').agg( - f__pause_count=('f__pause_duration', 'size'), # Count all occurrences - f__pause_duration=('f__pause_duration', 'sum'), # Sum non-null values - f__pause_list=('f__pause_duration', lambda x: x.tolist()) - ) - - df_pause = df_pause.reset_index() - # Remove non-selected features - pause_features = ['interview__id'] + [f for f in pause_features if f in self._allowed_features] - df_pause = df_pause[pause_features] - # Merge with df_item - - df_unit = df_unit.merge(df_pause, how='left', on='interview__id') - return df_unit - - def add_unit_time_features(self, df_unit): - # Define the list of features depending on time - time_features = ['f__total_duration', 'f__total_elapse', 'f__days_from_start', 'f__time_changed'] - if any(col in self._allowed_features for col in time_features): - df_time = self.get_df_time() - - df_dur = df_time.groupby('interview__id').agg( - f__total_duration=('f__total_duration', 'sum'), - f__total_elapse=('timestamp_local', lambda x: (x.max() - x.min()).total_seconds()), - f__time_changed=('f__time_changed', 'sum'), - f__days_from_start=('f__days_from_start', 'min'), - ) - - df_dur = df_dur.reset_index() - # Remove non-selected features - time_features = ['interview__id'] + [f for f in time_features if f in self._allowed_features] - df_dur = df_dur[time_features] - - # # convert total_duration and total_elapseinto minutes - # df_dur['f__total_duration'] = df_dur['f__total_duration'] / 60 - # df_dur['f__total_elapse'] = df_dur['f__total_elapse'] / 60 - # Merge with df_item - df_unit = df_unit.merge(df_dur, how='left', on='interview__id') - return df_unit diff --git a/rissk/features.py b/rissk/features.py deleted file mode 100644 index a93a0f6..0000000 --- a/rissk/features.py +++ /dev/null @@ -1,29 +0,0 @@ -from pathlib import Path - -import typer -from loguru import logger -from tqdm import tqdm - -from rissk.config import PROCESSED_DATA_DIR - -app = typer.Typer() - - -@app.command() -def main( - # ---- REPLACE DEFAULT PATHS AS APPROPRIATE ---- - input_path: Path = PROCESSED_DATA_DIR / "dataset.csv", - output_path: Path = PROCESSED_DATA_DIR / "features.csv", - # ----------------------------------------- -): - # ---- REPLACE THIS WITH YOUR OWN CODE ---- - logger.info("Generating features from dataset...") - for i in tqdm(range(10), total=10): - if i == 5: - logger.info("Something happened for iteration 5.") - logger.success("Features generation complete.") - # ----------------------------------------- - - -if __name__ == "__main__": - app() diff --git a/rissk/item_processing.py b/rissk/item_processing.py deleted file mode 100644 index 3ad1ab1..0000000 --- a/rissk/item_processing.py +++ /dev/null @@ -1,456 +0,0 @@ -from rissk.feature_processing import * -from rissk.detection_algorithms import * -from rissk.utils.stats_utils import * -from scipy.spatial import cKDTree -from sklearn.cluster import DBSCAN -from pyod.models.ecod import ECOD -from pyod.models.cof import COF -from pyod.models.lof import LOF -from pyod.models.inne import INNE -from scipy import stats -from sklearn.preprocessing import StandardScaler -from pyod.models.thresholds import FILTER - - -class ItemFeatureProcessing(FeatureProcessing): - - def __init__(self, config): - super().__init__(config) - - def get_contamination_parameter(self, feature_name, method='medfilt', random_state=42): - f_name = feature_name.replace('f__', '') - contamination = self.config.features.get(f_name, {}).get('parameters', {}).get('contamination') - if contamination is None or contamination == 'auto' or self.config.automatic_contamination is True: - return FILTER(method=method, random_state=random_state) - else: - return contamination - - @staticmethod - def filter_variable_name_by_frequency(df, feature_name, frequency=100, min_unique_values=3): - # Select only those variables that have at least 'min_unique_values' distinct values and more than one - # 'frequency' records - valid_variables = df.groupby('variable_name').filter(lambda group: - len(group[feature_name].unique()) >= min_unique_values - and len(group) > frequency) - # Get the unique variable names that meet the conditions - variables = valid_variables['variable_name'].unique() - return variables - - @staticmethod - def filter_columns(data, index_col, threshold=100): - drop_columns = [] - keep_columns = [] - for col in data.columns: - if (data[col].nunique() < 3 or data[col].count() < threshold) and col not in index_col: - drop_columns.append(col) - else: - keep_columns.append(col) - return keep_columns, drop_columns - - def get_clean_pivot_table(self, feature_name, remove_low_freq_col=True, filter_conditions=None, threshold=0.2): - index_col = ['interview__id', 'roster_level', 'responsible'] - data = self.df_item - if filter_conditions is not None: - data = data.loc[filter_conditions] - data = pd.pivot_table(data=data, index=index_col, columns='variable_name', - values=feature_name, fill_value=np.NAN) - data = data.reset_index() - # Define again index_col after pivoting in case of some column missing - if data.columns.nlevels > 1: - data.columns = [f'{col[0]}_{col[1]}'.rstrip('_') for col in data.columns] - - index_col = [col for col in index_col if col in data.columns] - keep_columns, drop_columns = self.filter_columns(data, index_col, threshold=threshold) - if remove_low_freq_col: - data = data[keep_columns] - - return data, index_col - - def make_score__gps(self): - feature_name = ['f__gps_latitude', 'f__gps_longitude', 'f__gps_accuracy'] - data, index_col = self.get_clean_pivot_table(feature_name, remove_low_freq_col=False) - - def replace_with_feature_name(columns, feature_names): - for i, s in enumerate(columns): - for sub in feature_names: - if sub in s: - columns[i] = sub - break - return columns - - data.columns = replace_with_feature_name(list(data.columns), feature_name) - data = data.reset_index() - # Everything that has 0,0 as coordinates is an outlier - data['s__gps_extreme_outlier'] = 0 - data['s__gps_extreme_outlier'] = data['f__gps_latitude'].apply(lambda x: 1 if x == 0.000000 else 0) - data['s__gps_extreme_outlier'] = data['f__gps_longitude'].apply(lambda x: 1 if x == 0.000000 else 0) - - - # Convert lat, lon to 3D cartesian coordinates - data['x'], data['y'], data['z'] = lat_lon_to_cartesian(data['f__gps_latitude'], - data['f__gps_longitude']) - - # Convert accuracy from meters to kilometers - data['accuracy'] = data['f__gps_accuracy'] / 1e6 - - # Create KDTree - tree = cKDTree(data[['x', 'y', 'z']]) - - # Convert 10 meters to kilometers, the unit of the Earth's radius - radius = 10 / 1e6 - - # Query for counts accounting for accuracy - counts = [len(tree.query_ball_point(xyz, r=radius + accuracy)) - 1 for xyz, accuracy in - zip(data[['x', 'y', 'z']].values, data['accuracy'])] - - data['s__gps_proximity_counts'] = counts - coords_columns = ['x', 'y'] - # Identify extreme spatial outliers - - mask = (data['s__gps_extreme_outlier'] < 1) - - median_x = data[mask].drop_duplicates(subset='x')['x'].median() - median_y = data[mask].drop_duplicates(subset='y')['y'].median() - median_z = data[mask].drop_duplicates(subset='z')['z'].median() - - # Calculate distances from the median point - data['distance_to_median'] = np.sqrt((data[mask]['x'] - median_x) ** 2 + - (data[mask]['y'] - median_y) ** 2 + - (data[mask]['z'] - median_z) ** 2 - ) - - # Set a threshold (e.g., 95th percentile of distances) - # Everything that is above 30 + the median distance is an outlier - p75 = data[mask]['distance_to_median'].quantile(0.75) - median = data[mask]['distance_to_median'].median() - range_75 = p75 - median - threshold = p75 + 3.5 * range_75 - - - data.loc[mask, 's__gps_extreme_outlier'] = data[mask]['distance_to_median'] > threshold - - # # Make a further cleaning with dbscan - # coords_columns = ['x', 'y'] - # model = DBSCAN(eps=0.5, min_samples=5) - # model.fit(data[mask][coords_columns]) - # data.loc[mask, 'outlier'] = model.fit_predict(data[mask][coords_columns]) - # data['s__gps_extreme_outlier'] = data.apply( - # lambda row: 1 if row['outlier'] == -1 or row['s__gps_extreme_outlier'] == 1 else 0, 1) - - # USE COF if dataset hase less than 20000 samples else use LOF - contamination = self.get_contamination_parameter('f__gps', method='medfilt', random_state=42) - if data[mask].shape[0] < 10000: - model = COF(contamination=contamination) - else: - model = LOF(contamination=contamination, n_neighbors=20) - model.fit(data[mask][coords_columns]) - data.loc[mask, 's__gps_outlier'] = model.predict(data[mask][coords_columns]) - - return data.drop(columns=['x', 'y', 'z', 'accuracy', 'distance_to_median', 'outlier'], errors='ignore') - - def make_score__sequence_jump(self): - feature_name = 'f__sequence_jump' - score_name = self.rename_feature(feature_name) - df = self.df_item[~pd.isnull(self.df_item[feature_name])].copy() - # Select only those variables that have at least three distinct values and more than one hundred records - valid_variables = self.filter_variable_name_by_frequency(df, feature_name, frequency=100, min_unique_values=3) - df[score_name] = 0 - contamination = self.get_contamination_parameter(feature_name) - for var in valid_variables: - mask = (df['variable_name'] == var) - model = INNE(contamination=contamination, random_state=42) - model.fit(df[mask][[feature_name]]) - df.loc[mask, score_name] = model.predict(df[mask][[feature_name]]) - return df - - def make_score__first_decimal(self): - - feature_name = 'f__first_decimal' - score_name = self.rename_feature(feature_name) - df = self.df_item[~pd.isnull(self.df_item[feature_name])].copy() - # Select only those variables that have at least three distinct values and more than one hundred records - valid_variables = self.filter_variable_name_by_frequency(df, feature_name, frequency=100, min_unique_values=3) - df[score_name] = 0 - for var in valid_variables: - mask = (df['variable_name'] == var) - contamination = self.get_contamination_parameter(feature_name, method='medfilt', random_state=42) - model = COF(contamination=contamination) - model.fit(df[mask][[feature_name]]) - df.loc[mask, score_name] = model.predict(df[mask][[feature_name]]) - return df - - def make_score__answer_hour_set(self): - # Detect time set anomalies using ECOD algorithm. - # ECOD is a parameter-free, highly interpretable outlier detection algorithm based on empirical CDF functions - feature_name = 'f__answer_hour_set' - score_name = self.rename_feature(feature_name) - df = self.df_item[~pd.isnull(self.df_item[feature_name])]#.copy() - - # Sorting the DataFrame based on the 'frequency' answer_hour_set in descending order - sorted_hours = df[feature_name].value_counts().index - hour_to_rank = {hour: rank for rank, hour in enumerate(sorted_hours)} - # Create a frequecy column - df['frequency'] = df[feature_name].map(hour_to_rank) - - # IDENTIFY Outliers by ECOD anomaly detection model - contamination = self.get_contamination_parameter(feature_name) - model = ECOD(contamination=contamination) - model.fit(df[[feature_name]]) - df[score_name] = model.predict(df[[feature_name]]) - - # In case has detected "high frequencies anomalies", set them to 0 - df.loc[df['frequency'] <= df[df[score_name] == 0]['frequency'].min(), score_name] = 0 - df.drop(columns=['frequency'], inplace=True) - return df - - def make_score__answer_changed(self): - feature_name = 'f__answer_changed' - score_name = self.rename_feature(feature_name) - df = self.df_item[~pd.isnull(self.df_item[feature_name])]#.copy() - # Select only those variables that have at least 1 distinct values and more than one hundred records - valid_variables = self.filter_variable_name_by_frequency(df, feature_name, frequency=100, min_unique_values=1) - df[score_name] = 0 - contamination = self.get_contamination_parameter(feature_name, method='medfilt', random_state=42) - for var in valid_variables: - mask = (df['variable_name'] == var) - - model = ECOD(contamination=contamination) - model.fit(df[mask][[feature_name]]) - df.loc[mask, score_name] = model.predict(df[mask][[feature_name]]) - return df - - def make_score__answer_removed(self): - feature_name = 'f__answer_removed' - score_name = self.rename_feature(feature_name) - df = self.get_feature_item__answer_removed(feature_name) - # Select only those variables that have at least 1 distinct values and more than one hundred records - valid_variables = self.filter_variable_name_by_frequency(df, feature_name, frequency=100, min_unique_values=1) - df[score_name] = 0 - contamination = self.get_contamination_parameter(feature_name, method='medfilt', random_state=42) - for var in valid_variables: - mask = (df['variable_name'] == var) - - model = ECOD(contamination=contamination) - model.fit(df[mask][[feature_name]]) - df.loc[mask, score_name] = model.predict(df[mask][[feature_name]]) - return df - - def make_score__answer_position(self): - # answer_position is calculated at responsible level - feature_name = 'f__answer_position' - score_name = self.rename_feature(feature_name) - - df = self.df_item[~pd.isnull(self.df_item[feature_name])].copy() - # Select only those variables that have at least three distinct values and more than one hundred records - valid_variables = self.filter_variable_name_by_frequency(df, feature_name, frequency=100, min_unique_values=3) - df[score_name] = 0 - for var in valid_variables: - mask = (df['variable_name'] == var) - unique_values = df[mask][feature_name].nunique() - entropy_df = df[mask].groupby('responsible')[feature_name].apply(calculate_entropy, - unique_values=unique_values, - min_record_sample=10) - entropy_df = entropy_df.reset_index() - entropy_df = entropy_df[~pd.isnull(entropy_df[feature_name])] - - if entropy_df.shape[0] > 0: - entropy_df.sort_values(feature_name, inplace=True, ascending=False) - - median_value = entropy_df[feature_name].median() - - median_value = entropy_df[feature_name].median() - entropy_df[score_name] = entropy_df[feature_name].apply( - lambda x: 1 if x < median_value - 50 / 100 * median_value else 0) - df.loc[mask, score_name] = df[mask]['responsible'].map(entropy_df.set_index('responsible')[score_name]) - return df - - def make_score__answer_selected(self): - feature_name = 'f__answer_selected' - score_name = self.rename_feature(feature_name) - df = self.df_item[~pd.isnull(self.df_item[feature_name])].copy() - # Select only those variables that have at least three distinct values and more than one hundred records - valid_variables = self.filter_variable_name_by_frequency(df, feature_name, frequency=100, min_unique_values=3) - df[score_name] = 0 - for var in valid_variables: - mask = (df['variable_name'] == var) - contamination = self.get_contamination_parameter(feature_name, method='medfilt', random_state=42) - model = ECOD(contamination=contamination) - model.fit(df[mask][[feature_name]]) - score_name1 = score_name + '_lower' - score_name2 = score_name + '_upper' - - df.loc[mask, score_name] = model.predict(df[mask][[feature_name]]) - min_good_value = df[(df[score_name] == 0) & mask][feature_name].min() - max_good_value = df[(df[score_name] == 0) & mask][feature_name].max() - - df.loc[mask, score_name1] = 0 - df.loc[mask, score_name2] = 0 - - df.loc[mask & (df[mask][feature_name] < min_good_value), score_name1] = 1 - df.loc[mask & (df[mask][feature_name] > max_good_value), score_name2] = 1 - df.drop(columns=[score_name], inplace=True) - return df - - def make_score__answer_duration(self): - feature_name = 'f__answer_duration' - score_name = self.rename_feature(feature_name) - df = self.df_item[~pd.isnull(self.df_item[feature_name])]#.copy() - # Select only those variables that have at least three distinct values and more than one hundred records - valid_variables = self.filter_variable_name_by_frequency(df, feature_name, frequency=100, min_unique_values=3) - - score_name1 = score_name + '_lower' - score_name2 = score_name + '_upper' - - df[score_name1] = 0 - df[score_name2] = 0 - for var in valid_variables: - mask = (df['variable_name'] == var) - contamination = self.get_contamination_parameter(feature_name, method='medfilt', random_state=42) - model = ECOD(contamination=contamination) - model.fit(df[mask][[feature_name]]) - - df.loc[mask, score_name] = model.predict(df[mask][[feature_name]]) - - min_good_value = df[(df[score_name] == 0) & mask][feature_name].min() - max_good_value = df[(df[score_name] == 0) & mask][feature_name].max() - - df.loc[mask, score_name1] = 0 - df.loc[mask, score_name2] = 0 - - df.loc[mask & (df[mask][feature_name] < min_good_value), score_name1] = 1 - df.loc[mask & (df[mask][feature_name] > max_good_value), score_name2] = 1 - - df.drop(columns=[score_name], inplace=True) - - return df - - def make_score__single_question(self): - # Answer single question is calculated at responsible level - - feature_name = 'f__single_question' - score_name = self.rename_feature(feature_name) - - single_question_mask = ((self.df_item["qtype"] == 'SingleQuestion') - & (self.df_item['n_answers'] > 1) - & (self.df_item['is_filtered_combobox'] == False) - & (pd.isnull(self.df_item['cascade_from_question_id']))) - - df = self.df_item[single_question_mask].copy() - # Select only those variables that have at least three distinct values and more than one hundred records - - variables = self.filter_variable_name_by_frequency(df, 'value', frequency=100, min_unique_values=3) - df[score_name] = 0 - for var in variables: - mask = (df['variable_name'] == var) - unique_values = df[mask]['value'].nunique() - entropy_df = df[mask].groupby('responsible')['value'].apply(calculate_entropy, unique_values=unique_values) - entropy_df = entropy_df.reset_index() - entropy_df = entropy_df[~pd.isnull(entropy_df['value'])] - - if entropy_df.shape[0] > 0: - entropy_df.sort_values('value', inplace=True, ascending=False) - - median_value = entropy_df['value'].median() - - median_value = entropy_df['value'].median() - entropy_df[score_name] = entropy_df['value'].apply( - lambda x: 1 if x < median_value - 50 / 100 * median_value else 0) - df.loc[mask, score_name] = df[mask]['responsible'].map(entropy_df.set_index('responsible')[score_name]) - - return df - - def make_score__multi_option_question(self): - feature_name = 'f__multi_option_question' - # Answer single question is calculated at responsible level - - score_name = self.rename_feature(feature_name) - - multi_question_mask = (self.df_item["qtype"] == 'MultyOptionsQuestion').copy() - - df = self.df_item[multi_question_mask].copy() - # Select only those variables that have at least three distinct values and more than one hundred records - valid_variables = df.groupby('variable_name').filter(lambda x: len(x) >= 100) - # Get the unique variable names that meet the conditions - variables = valid_variables['variable_name'].unique() - - df.loc[score_name] = 0 - for var in variables: - mask = (df['variable_name'] == var) - unique_values = len([v for v in df[mask]['value'].explode().unique() if v != '##N/A##']) - entropy_df = df[mask].groupby('responsible')['value'].apply(calculate_list_entropy, - unique_values=unique_values, - min_record_sample=5) - entropy_df = entropy_df.reset_index() - entropy_df = entropy_df[~pd.isnull(entropy_df['value'])] - - if entropy_df.shape[0] > 0: - entropy_df.sort_values('value', inplace=True, ascending=False) - - median_value = entropy_df['value'].median() - - median_value = entropy_df['value'].median() - entropy_df[score_name] = entropy_df['value'].apply( - lambda x: 1 if x < median_value - 50 / 100 * median_value else 0) - df.loc[mask, score_name] = df[mask]['responsible'].map(entropy_df.set_index('responsible')[score_name]) - - return df - - def make_score__first_digit(self): - feature_name = 'f__numeric_response' - score_name = 's__first_digit' - df = self.df_item[~pd.isnull(self.df_item[feature_name])].copy() - # Select only those variables that have at least three distinct values and more than one hundred records - valid_variables = self.filter_variable_name_by_frequency(df, feature_name, frequency=100, min_unique_values=3) - - # Select only those variables that have at least three different order of magnitude - valid_variables = filter_variables_by_magnitude(df, feature_name, valid_variables, min_order_of_magnitude=3) - - # Computes the Jensen divergence for each variable_name and responsible on the first digit distribution. - # Jensen's divergence returns a value between (0, 1) of how much the first digit distribution - # of specific responsible is similar to the first digit distribution of all others. - # Higher the value higher is the difference. - # The Bendford Jensen divergence is calculated only on those responsible and variable_name - # who have at least 50 records. - # Once it is calculated, values that diverge from more than 50% from the median value get marked as "anomalus." - benford_jensen_df = apply_benford_tests(df, valid_variables, 'responsible', feature_name, - apply_first_digit=True, minimum_sample=50) - - df[score_name] = 0 - variable_list = benford_jensen_df['variable_name'].unique() - for var in variable_list: - - bj_mask = (benford_jensen_df['variable_name'] == var) & (~pd.isnull(benford_jensen_df[feature_name])) - bj_df = benford_jensen_df[bj_mask].copy() - if bj_df.shape[0] > 0: - bj_df.sort_values(feature_name, inplace=True, ascending=True) - - median_value = bj_df[feature_name].median() - # If the distribution has a jensen difference grater than 50% - # from the median value, mark it as "anomalus" - bj_df[score_name] = bj_df[feature_name].apply( - lambda x: 1 if x > median_value + 50 / 100 * median_value else 0) - - - mask = (df['variable_name'] == var) - df.loc[mask, score_name] = df[mask]['responsible'].map(bj_df.set_index('responsible')[score_name]) - return df - - # def make_score__last_digit(self): - # feature = 'f__last_digit' - # pivot_table, index_col = self.get_clean_pivot_table('f__numeric_response', remove_low_freq_col=True) - # columns = [] - # for col in pivot_table.drop(columns=['interview__id', 'roster_level', 'responsible']).columns: - # data = pivot_table[~pd.isnull(pivot_table[col])].copy() - # new_col = filter_columns_by_magnitude(data.drop(columns=['interview__id', 'roster_level', 'responsible']), - # 3).columns - # columns += list(new_col) - # columns = list(set(columns)) - # - # data = pd.DataFrame(pivot_table.responsible.unique(), columns=['responsible']) - # for col in columns: - # results_df = apply_benford_tests(pivot_table[['responsible'] + columns], 'responsible', col) - # results_df[col + feature.replace('f__', '__')] = results_df['p-value'].apply(lambda x: x <= 0.05) - # data[col + feature.replace('f__', '__')] = results_df['responsible'].map( - # results_df.set_index('responsible')[col + feature.replace('f__', '__')]) - # - # return data diff --git a/rissk/plots.py b/rissk/plots.py deleted file mode 100644 index 15486f0..0000000 --- a/rissk/plots.py +++ /dev/null @@ -1,29 +0,0 @@ -from pathlib import Path - -import typer -from loguru import logger -from tqdm import tqdm - -from rissk.config import FIGURES_DIR, PROCESSED_DATA_DIR - -app = typer.Typer() - - -@app.command() -def main( - # ---- REPLACE DEFAULT PATHS AS APPROPRIATE ---- - input_path: Path = PROCESSED_DATA_DIR / "dataset.csv", - output_path: Path = FIGURES_DIR / "plot.png", - # ----------------------------------------- -): - # ---- REPLACE THIS WITH YOUR OWN CODE ---- - logger.info("Generating plot from data...") - for i in tqdm(range(10), total=10): - if i == 5: - logger.info("Something happened for iteration 5.") - logger.success("Plot generation complete.") - # ----------------------------------------- - - -if __name__ == "__main__": - app() diff --git a/rissk/unit_proccessing.py b/rissk/unit_proccessing.py deleted file mode 100644 index 13bed79..0000000 --- a/rissk/unit_proccessing.py +++ /dev/null @@ -1,345 +0,0 @@ -from rissk.item_processing import * -from rissk.detection_algorithms import * -from sklearn.preprocessing import StandardScaler, MinMaxScaler, Normalizer -from sklearn.preprocessing import normalize -# from sklearn.decomposition import PCA -from pyod.models.pca import PCA -from pyod.models.iforest import IForest - - -def windsorize_95_percentile(df): - """ - Windsorize values in all columns of the DataFrame that are above the 95th percentile. - - Args: - - df (pd.DataFrame): Input DataFrame - - Returns: - - pd.DataFrame: Windsorized DataFrame - """ - for column in df.columns: - # Calculate the 95th percentile for the column - percentile_95 = df[column].quantile(0.95) - - # Set values above the 95th percentile to the value at the 95th percentile - df[column] = df[column].apply(lambda x: min(x, percentile_95)) - - return df - - -class UnitDataProcessing(ItemFeatureProcessing): - - def __init__(self, config): - super().__init__(config) - self._score_columns = None - - @property - def df_unit_score(self): - for method_name in self.get_make_methods(method_type='score', level='unit'): - feature_name = method_name.replace('make_score_unit__', 'f__') - score_name = self.rename_feature(feature_name) - if feature_name in self._allowed_features and self._score_columns is None: - try: - print('Processing Score {}...'.format(score_name)) - getattr(self, method_name)(feature_name) - # print('Score{} Processed'.format(feature_name)) - except Exception as e: - print("WARNING: SCORE: {} won't be used in further calculation".format(score_name)) - - score_columns = [col for col in self._df_unit if - col.startswith('s__')] # and col.replace('s__','f__') in self._allowed_features] - # Remove columns with only nan or constant values - self._score_columns = self._df_unit[score_columns].columns[self._df_unit[score_columns].nunique() > 1].tolist() - return self._df_unit[['interview__id', 'responsible', 'survey_name', 'survey_version', ] + self._score_columns] - - def make_global_score(self, combine_resp_score=True, restricted_columns=None): - self._df_unit['unit_risk_score'] = 0 - scaler = StandardScaler() - df = self.df_unit_score[self._score_columns] - columns = self._score_columns - if restricted_columns is not None: - columns = [col for col in self._score_columns if col not in restricted_columns] - # df = windsorize_95_percentile(self.df_unit_score[columns].copy()) - df = df[columns].copy() - df = pd.DataFrame(scaler.fit_transform(df), columns=columns) - model = IForest(random_state=42) - model.fit(df.fillna(0)) - scaler = MinMaxScaler(feature_range=(0, 100)) - self._df_unit['unit_risk_score'] = model.decision_scores_ - - self._df_unit['unit_risk_score'] = windsorize_95_percentile(self.df_unit[['unit_risk_score']].copy()) - - self._df_unit['unit_risk_score'] = scaler.fit_transform(self._df_unit[['unit_risk_score']]) - - # Merge unit score with responsible score - if combine_resp_score: - # Make responsible Score - self.make_responsible_score(restricted_columns=columns) - merged_df = self._df_unit.merge(self._df_resp[['responsible', 'responsible_score']], how='left', - on='responsible') - self._df_unit['unit_risk_score'] = merged_df['unit_risk_score'] * merged_df['responsible_score'] - - self._df_unit['unit_risk_score'] = scaler.fit_transform(self._df_unit[['unit_risk_score']]) - - def make_responsible_score(self, restricted_columns): - scaler = StandardScaler() - columns = [col for col in self._df_resp.columns - if col.startswith('responsible') is False and col not in restricted_columns] - - self._df_resp = self._df_resp.groupby('responsible')[columns].mean() - self._df_resp = self._df_resp.reset_index() - - df_resp = self._df_resp[columns].fillna(0) - # Remove columns with constant values - df_resp = df_resp.loc[:, df_resp.nunique() != 1] - df_resp = pd.DataFrame(scaler.fit_transform(df_resp), columns=df_resp.columns) - - model = PCA(random_state=42) - model.fit(df_resp) - self._df_resp['responsible_score'] = model.decision_scores_ # function(df1) - # scaler = MinMaxScaler(feature_range=(0, 1)) - # self._df_resp['responsible_score'] = scaler.fit_transform(self._df_resp[['responsible_score']]) - self._df_resp['responsible_score'] = normalize(self._df_resp[['responsible_score']], norm='l1', axis=0) - - def save(self): - df = self._df_unit[['interview__id', 'responsible', 'unit_risk_score']] # .copy() - df['unit_risk_score'] = df['unit_risk_score'].round(2) - df.sort_values('unit_risk_score', inplace=True) - file_name = "_".join([self.config.surveys[0], self.config.survey_version[0], 'unit_risk_score']) + ".csv" - output_path = self.config['output_file'].split('.')[0] + '.csv' - df.to_csv(output_path, index=False) - print(f'SUCCESS! you can find the unit risk score output file in {output_path}') - if self.config['feature_score']: - - columns = [col for col in self._df_resp.columns if col.startswith('responsible') is False] - - sorted_columns = sorted(self._score_columns + columns) - - merged_df = self._df_unit.merge(self._df_resp, how='left', - on='responsible') - - merged_df = merged_df[['interview__id', 'responsible', 'survey_name', 'survey_version'] + sorted_columns] - output_path = self.config['output_file'].split('.')[0] + '_feature_score.csv' - merged_df.to_csv(output_path, index=False) - print(f'You can find the unit feature score file in {output_path}') - - def make_score_unit__numeric_response(self, feature_name): - pass - - def make_score_unit__last_digit(self, feature_name): - pass - - def make_score_unit__single_question(self, feature_name): - score_name = self.rename_feature(feature_name) - # single_question is calculated at responsible level - data = self.make_score__single_question() - data = data.groupby(['responsible', 'variable_name']).agg({score_name: 'mean'}) - data = data.reset_index() - data = data.groupby('responsible').agg({score_name: 'mean'}) - self._df_resp[score_name] = self._df_resp['responsible'].map(data[score_name]) - # Fill with 0's for missing values - self._df_resp[score_name].fillna(0, inplace=True) - - def make_score_unit__multi_option_question(self, feature_name): - score_name = self.rename_feature(feature_name) - # multi_option_question is calculated at responsible level - data = self.make_score__multi_option_question() - data = data.groupby(['responsible', 'variable_name']).agg({score_name: 'mean'}) - data = data.reset_index() - data = data.groupby('responsible').agg({score_name: 'mean'}) - self._df_resp[score_name] = self._df_resp['responsible'].map(data[score_name]) - # Fill with 0's for missing values - self._df_resp[score_name].fillna(0, inplace=True) - - def make_score_unit__answer_hour_set(self, feature_name): - data = self.make_score__answer_hour_set() - score_name = self.rename_feature(feature_name) - # Get the ratio of anomalies per interview__id over the total number of answer set - data = data.groupby(['interview__id']).agg({score_name: 'mean'}) - self._df_unit[score_name] = self._df_unit['interview__id'].map(data[score_name]) - - def make_score_unit__answer_removed(self, feature_name): - data = self.make_score__answer_removed() - score_name = self.rename_feature(feature_name) - data = data.groupby(['interview__id']).agg({score_name: 'mean'}) - self._df_unit[score_name] = self._df_unit['interview__id'].map(data[score_name]) - # Fill with 0's for missing values - self._df_unit[score_name].fillna(0, inplace=True) - - def make_score_unit__answer_changed(self, feature_name): - data = self.make_score__answer_changed() - score_name = self.rename_feature(feature_name) - # take the max number of anomaly for each question, i.e. 'roster_level' + 'variable_name' - data = data.groupby(['interview__id']).agg({score_name: 'mean'}) - self._df_unit[score_name] = self._df_unit['interview__id'].map(data[score_name]) - # Fill with 0's for missing values - self._df_unit[score_name].fillna(0, inplace=True) - - def make_score_unit__answer_position(self, feature_name): - score_name = self.rename_feature(feature_name) - # answer_position is calculated at responsible level - data = self.make_score__answer_position() - data = data.groupby(['responsible', 'variable_name']).agg({score_name: 'mean'}) - data = data.reset_index() - data = data.groupby('responsible')[score_name].mean() - self._df_resp[score_name] = self._df_resp['responsible'].map(data) - # Fill with 0's for missing values - self._df_resp[score_name].fillna(0, inplace=True) - - def make_score_unit__answer_selected(self, feature_name): - score_name = self.rename_feature(feature_name) - score_name1 = score_name + '_lower' - score_name2 = score_name + '_upper' - data = self.make_score__answer_selected() - data = data.groupby(['interview__id']).agg({score_name1: 'mean', score_name2: 'mean'}) - data = data.reset_index() - self._df_unit[score_name1] = self._df_unit['interview__id'].map(data.set_index('interview__id')[score_name1]) - self._df_unit[score_name2] = self._df_unit['interview__id'].map(data.set_index('interview__id')[score_name2]) - # Fill with 0's for missing values - self._df_unit[score_name1].fillna(0, inplace=True) - self._df_unit[score_name2].fillna(0, inplace=True) - - def make_score_unit__answer_duration(self, feature_name): - score_name = self.rename_feature(feature_name) - score_name1 = score_name + '_lower' - score_name2 = score_name + '_upper' - data = self.make_score__answer_duration() - data = data.groupby(['interview__id']).agg({score_name1: 'mean', score_name2: 'mean'}) - data = data.reset_index() - self._df_unit[score_name1] = self._df_unit['interview__id'].map(data.set_index('interview__id')[score_name1]) - self._df_unit[score_name2] = self._df_unit['interview__id'].map(data.set_index('interview__id')[score_name2]) - # Fill with 0's for missing values - self._df_unit[score_name1].fillna(0, inplace=True) - self._df_unit[score_name2].fillna(0, inplace=True) - - def make_score_unit__first_decimal(self, feature_name): - score_name = self.rename_feature(feature_name) - data = self.make_score__first_decimal() - data = data.groupby(['interview__id']).agg({score_name: 'mean'}) - - self._df_unit[score_name] = self._df_unit['interview__id'].map(data[score_name]) - # Fill with 0's for missing values. It means "No anomalies detected" - self._df_unit[score_name].fillna(0, inplace=True) - - def make_score_unit__first_digit(self, feature_name): - score_name = self.rename_feature(feature_name) - data = self.make_score__first_digit() - data = data.groupby(['responsible']).agg({score_name: 'mean'}) - - self._df_resp[score_name] = self._df_resp['responsible'].map(data[score_name]) - # Fill with 0's for missing values - self._df_resp[score_name].fillna(0, inplace=True) - - def make_score_unit__sequence_jump(self, feature_name): - score_name = feature_name.replace('f__', 's__') - data = self.make_score__sequence_jump() - data = data.groupby(['interview__id']).agg({score_name: 'mean'}) - - self._df_unit[score_name] = self._df_unit['interview__id'].map(data[score_name]) - # Fill with 0's for missing values. It means "No anomalies detected" - self._df_unit[score_name].fillna(0, inplace=True) - - def make_score_unit__time_changed(self, feature_name): - score_name = self.rename_feature(feature_name) - # round to 10 min - self._df_unit[score_name] = round(self._df_unit['f__time_changed'].abs()/600) - - def make_score_unit__total_duration(self, feature_name): - score_name = self.rename_feature(feature_name) - # transform Total duration into 10 minutes values - self._df_unit[score_name] = round(self._df_unit[feature_name] / 300) # / self._df_unit['f__number_answered'] - - def make_score_unit__days_from_start(self, feature_name): - score_name = self.rename_feature(feature_name) - self._df_unit[score_name] = (self._df_unit[feature_name] / 7).astype(int) - - def make_score_unit__total_elapse(self, feature_name): - score_name = self.rename_feature(feature_name) - self._df_unit[feature_name] = round(self._df_unit[feature_name] / 300) - contamination = self.get_contamination_parameter(feature_name, method='medfilt', random_state=42) - - model = ECOD(contamination=contamination) - model.fit(self._df_unit[[feature_name]]) - self._df_unit[score_name] = model.predict(self._df_unit[[feature_name]]) - - score_name1 = score_name + '_lower' - score_name2 = score_name + '_upper' - min_good_value = self._df_unit[(self._df_unit[score_name] == 0)][feature_name].min() - max_good_value = self._df_unit[(self._df_unit[score_name] == 0)][feature_name].max() - - self._df_unit[score_name1] = 0 - self._df_unit[score_name2] = 0 - - self._df_unit.loc[(self._df_unit[feature_name] < min_good_value), score_name1] = 1 - self._df_unit.loc[(self._df_unit[feature_name] > max_good_value), score_name2] = 1 - - self._df_unit.drop(columns=[score_name], inplace=True) - - def make_score_unit__pause_duration(self, feature_name): - - score_name = self.rename_feature(feature_name) - # transform Total duration into 10 minutes values - self._df_unit[score_name] = self._df_unit[feature_name] / self._df_unit['f__total_elapse'] - - def make_score_unit__pause_count(self, feature_name): - score_name = self.rename_feature(feature_name) - pause_mask = ~pd.isnull(self._df_unit[feature_name]) - self._df_unit[score_name] = self._df_unit[feature_name] / self._df_unit['f__number_answered'] - - def make_score_unit__number_answered(self, feature_name): - score_name = self.rename_feature(feature_name) - self._df_unit[score_name] = self._df_unit[feature_name] - - def make_score_unit__number_unanswered(self, feature_name): - score_name = self.rename_feature(feature_name) - self._df_unit[score_name] = self._df_unit[feature_name] - - def make_score_unit__gps(self, feature_name): - data = self.make_score__gps() - features = ['s__gps_proximity_counts', 's__gps_outlier', 's__gps_extreme_outlier'] - - data = data.groupby('interview__id')[features].sum() - data = data.reset_index() - - self._df_unit['s__gps_proximity_counts'] = self._df_unit['interview__id'].map( - data.set_index('interview__id')['s__gps_proximity_counts'] - ) - - self._df_unit['s__gps_outlier'] = self._df_unit['interview__id'].map( - data.set_index('interview__id')['s__gps_outlier'] - ) - self._df_unit['s__gps_extreme_outlier'] = self._df_unit['interview__id'].map( - data.set_index('interview__id')['s__gps_extreme_outlier'] - ) - - data = self.df_item.groupby('interview__id')[feature_name].sum() - score_name = feature_name.replace('f__', 's__') - self._df_unit[score_name] = self._df_unit['interview__id'].map(data) - - self._df_unit['s__gps_proximity_counts'].fillna(0, inplace=True) - self._df_unit['s__gps_outlier'].fillna(0, inplace=True) - self._df_unit['s__gps_extreme_outlier'].fillna(0, inplace=True) - - # def make_feature_unit__comments(self): - # columns_to_check = ['f__comments_set', 'f__comment_length'] - # if any(col not in self._df_unit.columns for col in columns_to_check): - # # f__comments_set, f_comment_length - # df_unit_comment = self.df_item.groupby('interview__id').agg( - # f__comments_set=('f__comments_set', 'sum'), - # f__comment_length=('f__comment_length', 'sum') - # ).reset_index() - # - # self._df_unit['f__comments_set'] = self._df_unit['interview__id'].map( - # df_unit_comment.set_index('interview__id')['f__comments_set'] - # ) - # - # self._df_unit['f__comment_length'] = self._df_unit['interview__id'].map( - # df_unit_comment.set_index('interview__id')['f__comment_length'] - # ) - # - # def make_feature_unit__number_answers(self): - # answer_per_interview_df = self.df_active_paradata.groupby('interview__id').variable_name.nunique() - # answer_per_interview_df = answer_per_interview_df.reset_index() - # total_questions = self.df_questionnaire[self.df_questionnaire["qtype"].str.contains('Question')]["qtype"].count() - # self._df_unit['f__number_answers'] = self.df_item['interview__id'].map( - # answer_per_interview_df.set_index('interview__id')['variable_name'] / total_questions) From 009c83ec25cc27d8b6eaff33de98cfa2ed06d7ae Mon Sep 17 00:00:00 2001 From: VJausovec Date: Wed, 6 May 2026 23:22:48 +0100 Subject: [PATCH 68/70] Refactor: Remove unused utility files and scripts - Deleted `file_manager_utils.py`, `file_process_utils.py`, `import_utils.py`, `stats_utils.py` as they are no longer needed. - Removed `run_gui.bat` and `run_gui.sh` scripts for GUI launching. - Cleaned up `setup.py` and `setup.cfg` as part of the project restructuring. - Updated `SETUP.md` to reflect changes in GUI launch instructions. Co-authored-by: Copilot --- Makefile | 100 ------ rissk/utils/chart_utils.py | 102 ------ rissk/utils/file_manager_utils.py | 75 ----- rissk/utils/file_process_utils.py | 255 --------------- rissk/utils/import_utils.py | 495 ------------------------------ rissk/utils/stats_utils.py | 248 --------------- rissk_kedro/SETUP.md | 5 +- run_gui.bat | 3 - run_gui.sh | 3 - setup.cfg | 6 - setup.py | 20 -- 11 files changed, 4 insertions(+), 1308 deletions(-) delete mode 100644 Makefile delete mode 100644 rissk/utils/chart_utils.py delete mode 100644 rissk/utils/file_manager_utils.py delete mode 100644 rissk/utils/file_process_utils.py delete mode 100644 rissk/utils/import_utils.py delete mode 100644 rissk/utils/stats_utils.py delete mode 100644 run_gui.bat delete mode 100755 run_gui.sh delete mode 100644 setup.cfg delete mode 100644 setup.py diff --git a/Makefile b/Makefile deleted file mode 100644 index f884dc4..0000000 --- a/Makefile +++ /dev/null @@ -1,100 +0,0 @@ -################################################################################# -# GLOBALS # -################################################################################# - -PROJECT_NAME = rissk -PYTHON_VERSION = 3.10 -PYTHON_INTERPRETER = python - -# Extract SURVEY value from env.yaml -SURVEY := $(shell $(PYTHON_INTERPRETER) -c "import yaml; print(yaml.safe_load(open('env.yaml'))['SURVEY'])") - - -################################################################################# -# COMMANDS # -################################################################################# - - -## Install Python Dependencies -.PHONY: requirements -requirements: - conda env update --name $(PROJECT_NAME) --file environment.yml --prune - - - R -e "IRkernel::installspec(user = TRUE)" - - -## Delete all compiled Python files -.PHONY: clean -clean: - find . -type f -name "*.py[co]" -delete - find . -type d -name "__pycache__" -delete - -## Lint using flake8 and black (use `make format` to do formatting) -.PHONY: lint -lint: - flake8 rissk - isort --check --diff --profile black rissk - black --check --config pyproject.toml rissk - -## Format source code with black -.PHONY: format -format: - black --config pyproject.toml rissk - - -## Download Data from storage system -.PHONY: sync_data_down -sync_data_down: - aws s3 sync s3://surveytool/$(SURVEY)/latest/ \ - data/$(SURVEY)/latest \ - --exclude "*" \ - --include "*.zip" - -## Upload Data to storage system -.PHONY: sync_data_up -sync_data_up: - aws s3 sync data/$(SURVEY)/latest \ - s3://surveytool/$(SURVEY)/latest \ - --exclude "*.m4a" \ - --exclude "10_RAW/*" \ - --exclude "20_INTERIM/*" \ - --include "20_INTERIM/**/paradata.parquet" \ - --include "10_RAW/**/Questionnaire/*" - -## Set up python (R) interpreter environment -.PHONY: create_environment -create_environment: - conda env create --name $(PROJECT_NAME) -f environment.yml - - @echo ">>> conda env created. Activate with:\nconda activate $(PROJECT_NAME)" - - -################################################################################# -# PROJECT RULES # -################################################################################# - - -## Make Dataset -.PHONY: data -data: requirements - $(PYTHON_INTERPRETER) rissk/dataset.py - - -################################################################################# -# Self Documenting Commands # -################################################################################# - -.DEFAULT_GOAL := help - -define PRINT_HELP_PYSCRIPT -import re, sys; \ -lines = '\n'.join([line for line in sys.stdin]); \ -matches = re.findall(r'\n## (.*)\n[\s\S]+?\n([a-zA-Z_-]+):', lines); \ -print('Available rules:\n'); \ -print('\n'.join(['{:25}{}'.format(*reversed(match)) for match in matches])) -endef -export PRINT_HELP_PYSCRIPT - -help: - @$(PYTHON_INTERPRETER) -c "${PRINT_HELP_PYSCRIPT}" < $(MAKEFILE_LIST) diff --git a/rissk/utils/chart_utils.py b/rissk/utils/chart_utils.py deleted file mode 100644 index ee869b0..0000000 --- a/rissk/utils/chart_utils.py +++ /dev/null @@ -1,102 +0,0 @@ -import seaborn as sns -import pandas as pd -import matplotlib.pyplot as plt - - -import seaborn as sns -import pandas as pd -import matplotlib.pyplot as plt - - -def make_top_perc_chart(df, target_label, plot_first_percentiles=False, plot_perc_overall=False): - - df = df.sort_values(by='unit_risk_score', ascending=False) - - df['cumulative_true'] = df[target_label].cumsum() - df['cumulative_count'] = range(1, len(df) + 1) - - # 4. Calculate percentages - df['percentage_of_true'] = df['cumulative_true'] / df['cumulative_count'] * 100 - percentage_of_true_overall = df['survey_label'].sum() / df['survey_label'].count() * 100 - df['percentage_of_records'] = df['cumulative_count'] / len(df) * 100 - fig, ax1 = plt.subplots() - - # 5. Plotting - ax1.plot(df['percentage_of_records'], df['percentage_of_true']) - ax1.axhline(y=percentage_of_true_overall, color='orange', linestyle='--') - - if plot_first_percentiles: - mask_5 = df['percentage_of_records'] <= 5 - percentage_of_true_5 = df[mask_5]['survey_label'].sum() / df[mask_5]['survey_label'].count() * 100 - mask_10 = df['percentage_of_records'] <= 10 - percentage_of_true_10 = df[mask_10]['survey_label'].sum() / df[mask_10]['survey_label'].count() * 100 - mask_15 = df['percentage_of_records'] <= 15 - percentage_of_true_15 = df[mask_15]['survey_label'].sum() / df[mask_15]['survey_label'].count() * 100 - mask_20 = df['percentage_of_records'] <= 20 - percentage_of_true_20 = df[mask_20]['survey_label'].sum() / df[mask_20]['survey_label'].count() * 100 - mask_25 = df['percentage_of_records'] <= 25 - percentage_of_true_25 = df[mask_25]['survey_label'].sum() / df[mask_25]['survey_label'].count() * 100 - - ax1.axvline(x=5, color='c', linestyle='--', alpha=0.3) - ax1.axvline(x=10, color='c', linestyle='--', alpha=0.3) - ax1.axvline(x=15, color='c', linestyle='--', alpha=0.3) - ax1.axvline(x=20, color='c', linestyle='--', alpha=0.3) - #ax1.axvline(x=25, color='c', linestyle='--', alpha=0.3) - - # Add text near the vertical line - ax1.text(50, percentage_of_true_overall - 5, 'Total Percentage of Artificial Fakes', rotation=0, - verticalalignment='center', color='black') - - ax1.text(2, 20, f'{str(round(percentage_of_true_5))}% within 5%', rotation=90, verticalalignment='center', - color='black') - ax1.text(7, 30, f'{str(round(percentage_of_true_10))}% within 10%', rotation=90, verticalalignment='center', - color='black') - ax1.text(15, 70, f'{str(round(percentage_of_true_15))}% within 15%', rotation=90, verticalalignment='center', - color='black') - ax1.text(22, 75, f'{str(round(percentage_of_true_20))}% within 20%', rotation=90, verticalalignment='center', - color='black') - - if plot_perc_overall: - ax2 = ax1.twinx() - ax2.plot(df['percentage_of_records'], df['cumulative_true'] / df[target_label].sum() * 100, color='green', alpha=0.3) - ax2.set_ylim([0, 100]) - ax2.set_ylabel('Percentage overall of artificial fakes (%)') - - ax1.set_ylim([0, 100]) - ax1.set_xlabel('Top N% of Interviews (%)') - ax1.set_ylabel('Percentage of artificial fakes (%)') - plt.title('Percentage of Artificial Fakes in Top Records') - ax1.grid(True) - plt.show() - - - - - -def make_score_perc_chart(df, target_label, plot_first_percentiles=False, plot_perc_overall=False): - df = df.sort_values(by='unit_risk_score', ascending=False) - - - df['cumulative_count'] = range(1, len(df) + 1) - - df['percentage_of_records'] = df['cumulative_count'] / len(df) * 100 - fig, ax1 = plt.subplots() - - # 5. Plotting - ax1.plot(df['percentage_of_records'], df['percentage_of_true']) - - - - - if plot_perc_overall: - ax2 = ax1.twinx() - ax2.plot(df['percentage_of_records'], df['cumulative_true'] / df[target_label].sum() * 100, color='green', alpha=0.3) - ax2.set_ylim([0, 100]) - ax2.set_ylabel('Percentage overall of artificial fakes (%)') - - ax1.set_ylim([0, 100]) - ax1.set_xlabel('Top N% of Interviews (%)') - ax1.set_ylabel('Percentage of artificial fakes (%)') - plt.title('Percentage of Artificial Fakes in Top Records') - ax1.grid(True) - plt.show() diff --git a/rissk/utils/file_manager_utils.py b/rissk/utils/file_manager_utils.py deleted file mode 100644 index c0597c1..0000000 --- a/rissk/utils/file_manager_utils.py +++ /dev/null @@ -1,75 +0,0 @@ -import os -import s3fs - - -def fs_isdir(path, key=None, secret=None, is_local=True): - """ - Check if a file or directory exists at the specified path. - - This function supports both local and S3 file systems. For S3 like file system, it requires credentials (key and secret). - - Parameters: - path (str): The path of the file or directory to check. - key (str, optional): The AWS access key ID. Default is None. - secret (str, optional): The AWS secret access key. Default is None. - is_local (bool, optional): A flag indicating if the file system is local or S3. Default is True. - **kwargs: Arbitrary keyword arguments for s3fs.S3FileSystem. - - Returns: - bool: True if the file or directory exists, False otherwise. - """ - if is_local is True: - return os.path.isdir(path) - else: - fs = s3fs.S3FileSystem(anon=False, key=key, secret=secret) - return fs.isdir(path) - - - -def fs_exists(path, key=None, secret=None, is_local=True): - """ - Check if a file or directory exists at the specified path. - - This function supports both local and S3 file systems. For S3 like file system, it requires credentials (key and secret). - - Parameters: - path (str): The path of the file or directory to check. - key (str, optional): The AWS access key ID. Default is None. - secret (str, optional): The AWS secret access key. Default is None. - is_local (bool, optional): A flag indicating if the file system is local or S3. Default is True. - **kwargs: Arbitrary keyword arguments for s3fs.S3FileSystem. - - Returns: - bool: True if the file or directory exists, False otherwise. - """ - if is_local is True: - return os.path.exists(path) - else: - fs = s3fs.S3FileSystem(anon=False, key=key, secret=secret) - return fs.exists(path) - - -def fs_mkrdir(path, key=None, secret=None, is_local=True): - if is_local is True: - os.makedirs(path, exist_ok=True) - else: - fs = s3fs.S3FileSystem(anon=False, key=key, secret=secret) - fs.mkdir(path, exist_ok=True) - - -def fs_listdir(path, key=None, secret=None, is_local=True): - if is_local is True: - return os.listdir(path) - else: - fs = s3fs.S3FileSystem(anon=False, key=key, secret=secret) - return fs.ls(path) - - -def fs_open(file_path, key=None, secret=None, mode='r', is_local=True): - if is_local is True: - return open(file_path, mode) - else: - fs = s3fs.S3FileSystem(anon=False, key=key, secret=secret) - return fs.open(file_path, mode) - - diff --git a/rissk/utils/file_process_utils.py b/rissk/utils/file_process_utils.py deleted file mode 100644 index de6900a..0000000 --- a/rissk/utils/file_process_utils.py +++ /dev/null @@ -1,255 +0,0 @@ -import os -from pathlib import Path -import pandas as pd -from typing import Dict -import re - - - -def set_qnr_version(df, survey_project, project_version): - df['qnr'] = survey_project - df['qnr_version'] = project_version - return df - - -def normalize_column_name(s): - """ - This function converts any string with capital letters to a string all lowercase with a "_" before any previously capital letter. - - Parameters: - s (str): The string to convert. - - Returns: - new_s (str): The converted string. - """ - new_s = "" - for i, char in enumerate(s): - if char.isupper(): - # Add underscore only if it's not the first or last character - if i != 0 and i != len(s) - 1: - new_s += "_" - new_s += char.lower() - else: - new_s += char - return new_s - - -def transform_multi(df, variable_list, transformation_type): - """ - This function takes a DataFrame and a list of variable names and applies a transformation depending on - transformation_type to the variables in the DataFrame that start with the given variable names. - - The transformation can be either 'unlinked,' 'linked,' 'list,' or 'gps.' - - Parameters: - df (DataFrame): The DataFrame to be transformed. - variable_list (list): The list of variable names to be transformed. - transformation_type (str): The type of transformation to apply. Must be 'unlinked,' 'linked,' 'list,' or 'gps.' - - Returns: - DataFrame: The transformed DataFrame. - - Raises: - ValueError: If transformation_type is not 'unlinked,' 'linked,' 'list,' or 'gps.' - """ - if transformation_type not in ['unlinked', 'linked', 'list', 'gps']: - raise ValueError("transformation_type must be either 'unlinked', 'linked', 'list', or 'gps'") - - transformed_df = pd.DataFrame(index=df.index) # DataFrame for storing transformations - - for var in variable_list: - if var in df.columns: - # Drop the target column, should it exist (only text list question on a linked roster) - df = df.drop(var, axis=1) - - related_cols = [col for col in df.columns if col.startswith(f"{var}__")] - - if related_cols: - transformation = [[] for _ in range(len(df))] \ - if transformation_type != 'gps' \ - else ['' for _ in range(len(df))] - - for col in related_cols: - - if transformation_type == 'unlinked': - suffix = int(col.split('__')[1].replace('n', '-')) - mask = df[col] > 0 - transformation = [x + [suffix] if mask.iloc[i] else x for i, x in enumerate(transformation)] - elif transformation_type == 'linked': - # !NOTE! if you add the (df[col] != -999999999) filter it removes also list that not only - # contains -999... - mask = (df[col].notna()) # & (df[col] != -999999999) - transformation = [x + [df.at[i, col]] if mask.iloc[i] else x for i, x in enumerate(transformation)] - elif transformation_type == 'list': - mask = (df[col] != '##N/A##') & (df[col] != '') - transformation = [x + [df.at[i, col]] if mask.iloc[i] else x for i, x in enumerate(transformation)] - elif transformation_type == 'gps': - transformation = [x + (',' if x else '') + (str(df.at[i, col]) - if pd.notna(df.at[i, col]) - and df.at[i, col] not in ['##N/A##', -999999999] - else '') for i, x in enumerate(transformation)] - - def remove_unset_value(sub_list): - sub = list(filter(lambda v: v not in [-999999999, '##N/A##'], sub_list)) - sub = [ele if ele != [] else '##N/A##' for ele in sub] - sub = sub if sub != [] and list(set(sub)) != ['##N/A##'] else '##N/A##' - return sub - - transformation = [remove_unset_value(x) - if x else float('nan') for x in transformation] if transformation_type != 'gps' else [ - x if x else '' for x in transformation] - transformed_df[var] = transformation # Add the transformation to the transformed DataFrame - df = df.drop(related_cols, axis=1) # Drop the original columns - - df = pd.concat([df, transformed_df], axis=1) # Concatenate the original DataFrame with the transformations - - return df.copy() - - -def process_json_structure(children, parent_group_title, counter, question_data): - """ - This function processes the JSON structure of a questionnaire, collecting information about the questions. - - Parameters: - children (list): The children nodes in the current JSON structure. - parent_group_title (str): The title of the parent group for the current child nodes. - counter (int): A counter to keep track of the sequence of questions. - question_data (list): A list where data about each question is appended as a dictionary. - - Returns: - counter (int): The updated counter value after processing all children nodes. - - """ - for child in children: - if "$type" in child: - question_data.append({ - "qnr_seq": counter, - "VariableName": child.get("VariableName"), - "qtype": child["$type"], - "QuestionType": child.get("QuestionType"), - "Answers": child.get("Answers"), - "Children": child.get("Children"), - "ConditionExpression": child.get("ConditionExpression"), - "HideIfDisabled": child.get("HideIfDisabled"), - "Featured": child.get("Featured"), - "Instructions": child.get("Instructions"), - "Properties": child.get("Properties"), - "PublicKey": child.get("PublicKey"), - "QuestionScope": child.get("QuestionScope"), - "QuestionText": child.get("QuestionText"), - "StataExportCaption": child.get("StataExportCaption"), - "VariableLabel": child.get("VariableLabel"), - "IsTimestamp": child.get("IsTimestamp"), - "ValidationConditions": child.get("ValidationConditions"), - "YesNoView": child.get("YesNoView"), - "IsFilteredCombobox": child.get("IsFilteredCombobox"), - "IsInteger": child.get("IsInteger"), - "CategoriesId": child.get("CategoriesId"), - "Title": child.get("Title"), - "IsRoster": child.get("IsRoster"), - "LinkedToRosterId": child.get("LinkedToRosterId"), - "LinkedToQuestionId": child.get("LinkedToQuestionId"), - "CascadeFromQuestionId": child.get("CascadeFromQuestionId"), - "parents": parent_group_title - }) - counter += 1 - - if "Children" in child: - child_group_title = child.get("Title", "") - counter = process_json_structure(child["Children"], parent_group_title + " > " + child_group_title, counter, - question_data) - - return counter - - -def get_categories(directory: Path) -> Dict[str, Dict[str, list]]: - """ - This function retrieves categories from Excel files within a directory. - - Parameters: - directory (Path): The directory where the category Excel files are stored. - - Returns: - Dict[str, Dict[str, list]]: A dictionary containing category data. Each key represents a filename, and each value is - another dictionary containing 'n_answers' and 'answer_sequence' which represents the number of answers and the - sequence of the answer IDs respectively. - """ - categories = {} - - # List all Excel files in the directory - files = directory.glob('*.xlsx') # Finds .xlsx files - files = list(files) + list(directory.glob('*.xls')) # Adds .xls files - - for file in files: - df = pd.read_excel(file) - n_answers = df.shape[0] - answer_sequence = df['id'].tolist() - categories[file.name] = {'n_answers': n_answers, 'answer_sequence': answer_sequence} - - return categories - - -def update_df_categories(row, categories): - """ - This function updates a DataFrame row with category information if applicable. - - Parameters: - row (Series): The Questioner DataFrame row to be updated. - categories (dict): A dictionary containing category data, keys are 'CategoriesId'. - - Returns: - Series: The updated DataFrame row. - - """ - if row['CategoriesId'] in categories: - row['n_answers'] = categories[row['CategoriesId']]['n_answers'] - row['answer_sequence'] = categories[row['CategoriesId']]['answer_sequence'] - return row - -def parse_filename(filename: str): - """ - Parses a filename based on the pattern ___. - - Parameters: - filename (str): The filename to parse. - - Returns: - dict: A dictionary containing 'questionnaire', 'version', 'format', and 'status'. - """ - # Regex pattern to match the filename structure - pattern = r"^(?P.+)_(?P[0-9]+)_(?P.+)_(?P.+)$" - - match = re.match(pattern, filename) - if not match: - raise ValueError(f"Filename '{filename}' does not match the expected pattern.") - - components = match.groupdict() - - # Extract components as a list - components = [ - match.group('questionnaire'), - match.group('version'), - match.group('format'), - match.group('status') - ] - return components - - -def get_file_parts(filename): - - questionnaire, version, file_format, interview_status = parse_filename(filename) - try: - version = int(version) - except ValueError: - raise ValueError(f"ERROR: {filename} Not a valid Survey Solutions export file. Version not found.") - - # Test input file has the correct name - if file_format not in ["Tabular", "STATA", "SPSS", "Paradata"]: - raise ValueError(f"ERROR: {filename} Not a valid Survey Solutions export file. Export type not found") - - if interview_status not in ["Approved", "InterviewerAssigned", "ApprovedBySupervisor", "ApprovedByHQ", "All", - 'ApprovedByHeadquarters']: - raise ValueError(f"ERROR: {filename} Not a valid Survey Solutions export file. Interview status not found.") - - file_format = file_format if file_format == 'Paradata' else 'Tabular' - return questionnaire, version, file_format, interview_status diff --git a/rissk/utils/import_utils.py b/rissk/utils/import_utils.py deleted file mode 100644 index 7ed77aa..0000000 --- a/rissk/utils/import_utils.py +++ /dev/null @@ -1,495 +0,0 @@ -import numpy as np -import json -import pyarrow as pa -import pandas as pd -import zipfile -from io import BytesIO -import logging -logger = logging.getLogger(__name__) -from pathlib import Path -import re -import os -from typing import List, Dict, Optional -from rissk.utils.file_process_utils import (get_file_parts, transform_multi, - set_qnr_version, normalize_column_name, - process_json_structure, get_categories, - update_df_categories) - - - - -def get_zip_files(data_dir: Path, survey: str, questionnaires: List[Dict[str, List[int]]]) -> List[Path]: - """ - Retrieves a list of zip files from the specified directory that match the given pattern. - - Parameters: - - data_dir (Path): The directory to search for zip files. - - survey (str): The survey name to match in the file names. - - questionnaires (List[Dict[str, List[int]]]): A list of dictionaries, each containing a - 'name' of the questionnaire and a 'VERSION' list to match in the file names. - - Returns: - - List[Path]: A list of matching zip file paths. - """ - matching_files = [] - - # Iterate through each questionnaire and its associated versions - for questionnaire in questionnaires: - name = questionnaire.get('name') - versions = questionnaire.get('VERSION', []) - - # Compile a regex pattern for matching files - version_pattern = "|".join(map(str, versions)) - pattern = re.compile(rf"{name}_({version_pattern})_.*\.zip") - - # List and filter files in the specified directory - matching_files.extend( - file_path - for file_path in data_dir.iterdir() - if pattern.match(file_path.name) - ) - - return matching_files - - -def extract_zip(file_source_path: Path, file_dest_path: Path, password: Optional[str] = None): - """ - Extracts a zip file to the specified destination path. - If nested zip files are encountered, they are extracted recursively. - - Parameters: - - file_source_path (Path): Path to the source zip file. - - file_dest_path (Path): Destination directory where files will be extracted. - - password (str, optional): Password for encrypted zip files. - """ - if password is None: - password = os.getenv('PASSWORD', None) - - try: - with file_source_path.open(mode='rb') as f: - zip_data = BytesIO(f.read()) - - with zipfile.ZipFile(zip_data) as zip_ref: - for file_info in zip_ref.infolist(): - file_name = file_info.filename - file_path = file_dest_path / file_name - - if file_info.is_dir(): - file_path.mkdir(parents=True, exist_ok=True) - else: - extracted_data = zip_ref.read(file_name, pwd=password.encode() if password else None) - if file_name.endswith('.zip'): - nested_dir = file_path.with_suffix('') - nested_dir.mkdir(parents=True, exist_ok=True) - nested_zip_path = nested_dir / file_path.name - with nested_zip_path.open(mode='wb') as nested_f: - nested_f.write(extracted_data) - extract_zip(nested_zip_path, nested_dir) # Recursively extract nested zip file - else: - file_path.parent.mkdir(parents=True, exist_ok=True) - with file_path.open(mode='wb') as extracted_f: - extracted_f.write(extracted_data) - - logger.info(f'Zip file {file_source_path} extracted successfully to {file_dest_path}') - except zipfile.BadZipFile: - logger.error(f'Error: The file {file_source_path} is not a zip file or it is corrupted.') - except RuntimeError as e: - logger.error(f'Error: A runtime error occurred - {e}') - except Exception as e: - logger.error(f'An unexpected error occurred: {e}') - - -def get_from_dir(dir_name: str, info: str) -> str: - """ - Extract information from a directory name formatted as '___'. - - Parameters: - dir_name (str): The directory name to parse. - info (str): The type of information to extract ('questionaire', 'version', 'format', 'status'). - - Returns: - str: The extracted information. - - Raises: - ValueError: If the info parameter is not one of 'questionaire', 'version', 'format', 'status'. - IndexError: If the directory name does not have the expected format. - """ - # Map info to the corresponding index - info_index = { - 'questionaire': 0, - 'version': 1, - 'format': 2, - 'status': 3 - } - - if info not in info_index: - raise ValueError("info parameter must be one of 'questionaire', 'version', 'format', 'status'") - - # Reverse split to handle potential underscores in QUESTIONAIRE - parts = dir_name.rsplit('_', 3) - - if len(parts) < 4: - raise IndexError("Directory name does not have the expected format '___'") - - return parts[info_index[info]] - -def assign_type(df, dtypes): - for column in dtypes.index: - df[column] = df[column].astype(dtypes[column]) - return df - - -def get_survey_info(survey_files): - - survey_info = {} - - for survey_path in survey_files: - filename = survey_path.name - questionnaire, version, file_format, interview_status = get_file_parts(filename) - qnr_version = f"{questionnaire}_{str(version)}" - - survey_info[questionnaire] = survey_info.get(questionnaire, {}) - survey_info[questionnaire][qnr_version] = survey_info[questionnaire].get(qnr_version, {}) - survey_info[questionnaire][qnr_version][file_format] = survey_path - return survey_info - - - -def save_parquet(df, file_path): - with open(file_path, 'wb') as f: - if 'answer_sequence' in df.columns: - df['answer_sequence'] = df['answer_sequence'].apply(str) - df.to_parquet(f) - - -def read_microdata_files(s_path, file_name): - file_path = os.path.join(s_path, file_name) - if file_name.endswith('.dta'): - try: - with open(file_path, 'rb') as f: - df = pd.read_stata(f, convert_categoricals=False, convert_missing=True) - # Manage missing values - df = df.where(df.astype(str) != '.a', -999999999) # replace '.a' with -999999999 to match tabular export - df = df.where(df.astype(str) != '.', np.nan) # replace '.' with np.nan - except Exception as e: - print(f"Error reading {file_path}: {e}") - else: - with open(file_path) as f: - df = pd.read_csv(f, delimiter='\t') - return df - - -def get_microdata_file_list(data_path: Path) -> List[str]: - """ - Get a list of microdata files in the specified directory, excluding certain files and extensions. - - Parameters: - data_path (Path): The directory path to search for files. - - Returns: - List[str]: A list of file names that match the criteria. - """ - excluded_files = ('interview__', 'assignment__', 'paradata.tab') - excluded_extensions = ('.dta', '.tab') - - # List comprehension to filter files - file_names = [ - file.name for file in data_path.iterdir() - if file.is_file() and file.suffix in excluded_extensions and not any(file.name.startswith(prefix) for prefix in excluded_files) - ] - - return file_names - - -def get_microdata(data_path, df_questionnaires): - drop_list = ['interview__key', 'sssys_irnd', 'has__errors', 'interview__status', 'assignment__id'] - - file_names = get_microdata_file_list(data_path) - - # define multi/list question conditions - if df_questionnaires.empty is False: - unlinked_mask = (df_questionnaires["qtype"] == 'MultyOptionsQuestion') & ( - df_questionnaires['is_linked'] == False) - linked_mask = (df_questionnaires["qtype"] == 'MultyOptionsQuestion') & (df_questionnaires['is_linked'] == True) - list_mask = (df_questionnaires["qtype"] == 'TextListQuestion') - gps_mask = (df_questionnaires["qtype"] == 'GpsCoordinateQuestion') - - # extract multi/list question lists from conditions - multi_unlinked_vars = df_questionnaires.loc[unlinked_mask, 'variable_name'].tolist() - multi_linked_vars = df_questionnaires.loc[linked_mask, 'variable_name'].tolist() - list_vars = df_questionnaires.loc[list_mask, 'variable_name'].tolist() - gps_vars = df_questionnaires.loc[gps_mask, 'variable_name'].tolist() - - # Iterate over each file - all_dfs = [] - for file_name in file_names: - - df = read_microdata_files(data_path, file_name) - # drop system-generated columns - df.drop(columns=[col for col in drop_list if col in df.columns], inplace=True) - - # transform multi/list questions - if df_questionnaires.empty is False: - df = transform_multi(df, multi_unlinked_vars, 'unlinked') - df = transform_multi(df, multi_linked_vars, 'linked') - df = transform_multi(df, list_vars, 'list') - df = transform_multi(df, gps_vars, 'gps') - - # create roster_level from __id columns if on roster level, else '' if main questionnaire file - roster_ids = [col for col in df.columns if col.endswith("__id") and col != "interview__id"] - if roster_ids: - df['roster_level'] = df[roster_ids].apply(lambda row: ",".join(map(str, row)), axis=1) - df.drop(columns=roster_ids, inplace=True) - else: - df['roster_level'] = '' - - id_vars = ['interview__id', 'roster_level'] - value_vars = [col for col in df.columns if col not in id_vars] - df_long = df.melt(id_vars=id_vars, value_vars=value_vars, var_name='variable', value_name='value') - df_long['filename'] = file_name - - all_dfs.append(df_long) - if len(all_dfs) > 0: - - combined_df = pd.concat(all_dfs, ignore_index=True) - else: - combined_df = pd.DataFrame() - - # Drop column with null or empty string in value - # Function to check if the value is not an empty string or NaN - def is_valid(value): - if isinstance(value, list): - return True # bool(value) # Not an empty list - return value != '' and pd.notna(value) # Not an empty string or NaN - - # Keep rows where the 'value' column passes the is_valid check - combined_df = combined_df[combined_df['value'].apply(is_valid)] - - - questionaire_name = get_from_dir(data_path.name, 'questionaire') - qnr_version = get_from_dir(data_path.name, 'version') - combined_df = set_qnr_version(combined_df, questionaire_name, qnr_version) - - # Manage the case questionnaires are not available for the survey - if df_questionnaires.empty is False: - roster_columns = [c for c in combined_df.columns if '__id' in c and c != 'interview__id'] - combined_df = combined_df.merge(df_questionnaires, how='left', - left_on=['variable', 'qnr', 'qnr_version'], - right_on=['variable_name', 'qnr', 'qnr_version']).sort_values( - ['interview__id', 'qnr_seq'] + roster_columns) - - combined_df.reset_index(drop=True, inplace=True) - - # Normalize columns - combined_df.columns = [normalize_column_name(c) for c in combined_df.columns] - - # Set value column to string for type compatibility - combined_df['value'] = combined_df['value'].astype(str) - return combined_df - - -def get_questionnaire_map(raw_path): - questionnaire_map = {} - questionnaire_list = os.listdir(raw_path) - for questionnaire in questionnaire_list: - if questionnaire.endswith('.json'): - file_name = os.path.basename(questionnaire) - questionnaire_id = file_name.split('_')[0].replace('-', '') - qnr_version = questionnaire.split('_')[1].replace('.json', '') - questionnaire_map[questionnaire_id] = { - 'file_name': file_name, - 'qnr_version': qnr_version, - 'file_path': os.path.join(raw_path, file_name) - } - return questionnaire_map - - -def get_questionnaire_id(extracted_path): - file_path = os.path.join(extracted_path, 'export__info.json') - with open(file_path, mode='r') as f: - data = json.load(f) - return data.get('QuestionnaireId').split("$")[0] - - -def read_json_questionnaire(survey_path, questionnaire_path=None): - if questionnaire_path is None: - file_path = os.path.join(survey_path, 'Questionnaire/content/document.json') - else: - questionnaire_id = get_questionnaire_id(survey_path) - questionnaire_map = get_questionnaire_map(questionnaire_path) - file_path = questionnaire_map.get(questionnaire_id).get('file_path') - with open(file_path, 'r') as f: - data = json.load(f) - return data - - -def read_paradata(survey_path, delimiter='\t'): - file_path = os.path.join(survey_path, 'paradata.tab') - with open(file_path, 'r') as f: - df = pd.read_csv(f, delimiter=delimiter) - return df - -def get_questionnaire(data_path: Path, questionnaire_path: Optional[Path] = None) -> pd.DataFrame: - """ - This function loads and processes a questionnaire from a JSON file located at the specified path. - It also handles the categorization of the data. - - Parameters: - data_path (Path): The path to the directory containing the questionnaire and categories data. - questionnaire_path (Optional[Path]): The path to the questionnaire JSON file. - - Returns: - pd.DataFrame: A processed DataFrame containing the questionnaire data. - """ - q_data = read_json_questionnaire(data_path, questionnaire_path=questionnaire_path) - - qnr_df = pd.DataFrame() - - if q_data is not None: - question_data = [] - question_counter = 0 - - process_json_structure(q_data["Children"], "", question_counter, question_data) - - qnr_df = pd.DataFrame(question_data) - qnr_df['answer_sequence'] = qnr_df['Answers'].apply( - lambda x: [int(item['AnswerValue']) for item in x] if x else np.nan) - qnr_df['n_answers'] = qnr_df['Answers'].apply(lambda x: len(x) if x else np.nan) - qnr_df['is_linked'] = (qnr_df['LinkedToRosterId'].notna()) | (qnr_df['LinkedToQuestionId'].notna()) - qnr_df['parents'] = qnr_df['parents'].str.lstrip(' > ') - split_columns = qnr_df['parents'].str.split(' > ', expand=True) - split_columns.columns = [f"parent_{i + 1}" for i in range(split_columns.shape[1])] - qnr_df = pd.concat([qnr_df, split_columns], axis=1) - qmask = qnr_df['QuestionScope'] == 0 - qnr_df['question_sequence'] = qmask.cumsum() - qnr_df.loc[~qmask, 'question_sequence'] = None - - categories_path = data_path / 'Questionnaire' / 'content' / 'Categories' - - if categories_path.exists(): - categories = get_categories(categories_path) - qnr_df = qnr_df.apply(lambda row: update_df_categories(row, categories), axis=1) - - qnr_df.reset_index(drop=True, inplace=True) - # Normalize columns - qnr_df.columns = [normalize_column_name(c) for c in qnr_df.columns] - - questionaire_name = get_from_dir(data_path.name, 'questionaire') - qnr_version = get_from_dir(data_path.name, 'version') - qnr_df = set_qnr_version(qnr_df, questionaire_name, qnr_version) - return qnr_df - - -def get_paradata(data_path, df_questionnaires): - """ - This function loads and processes a paradata file from the provided path and merges it with the questionnaire dataframe. - The function also generates a date-time column from the timestamp and marks whether the answer has changed. - - Parameters: - para_path (str): A string path to the paradata .csv file. - df_questionnaires (DataFrame): A Pandas DataFrame containing the questionnaire data. - - Returns: - df_para (DataFrame): A processed DataFrame containing the merged data from the paradata file and the questionnaire DataFrame. - - """ - df_para = read_paradata(data_path, delimiter='\t') - - # split the parameter column, first from the left, then from the right to avoid potential data entry issues - df_para[['param', 'answer']] = df_para['parameters'].str.split('\|\|', n=1, expand=True) - df_para[['answer', 'roster_level']] = df_para['answer'].str.rsplit('||', n=1, expand=True) - - #df_para['roster_level'] = df_para['roster_level'].str.replace("|","") # if yes/no questions are answered with yes for the first time, "|" will appear in roster - - # generate date-time, TZ not yet considered - df_para['timestamp_utc'] = pd.to_datetime(df_para['timestamp_utc']) - df_para['tz_offset'] = pd.to_timedelta(df_para['tz_offset'].str.replace(':', ' hours ') + ' minutes') - # Adjust the date column by the timezone offset - df_para['timestamp_local'] = df_para['timestamp_utc'] + df_para['tz_offset'] - - - questionaire_name = get_from_dir(data_path.name, 'questionaire') - qnr_version = get_from_dir(data_path.name, 'version') - - df_para = set_qnr_version(df_para, questionaire_name, qnr_version) - - #Merge with questionnaire data - if df_questionnaires.empty is False: - q_columns = ['qnr_seq', 'variable_name', "qtype", 'question_type', - 'answers', 'question_scope', - 'yes_no_view', 'is_filtered_combobox', - 'is_integer', 'cascade_from_question_id', - 'answer_sequence', 'n_answers', 'question_sequence', - 'qnr', 'qnr_version'] - df_para = df_para.merge(df_questionnaires[q_columns], how='left', - left_on=['param', 'qnr', 'qnr_version'], - right_on=['variable_name', 'qnr', 'qnr_version']) - - # Normalize column names - df_para.columns = [normalize_column_name(c) for c in df_para.columns] - return df_para - - -def get_dataframes(survey_info): - """ - Returns dataframes of the paradata, questionnaires, and microdata. - - Parameters: - save_to_disk: A boolean indicating whether to save the dataframes to disk. - reload: A boolean indicating whether to reload the data. - - Returns: - df_paradata, df_questionnaires, df_microdata: Dataframes containing the paradata, questionnaires, and microdata from the different surveys defined in the config. - """ - dfs_paradata = [] - dfs_questionnaires = [] - dfs_microdata = [] - - for survey_questionnaire, questionnaires_details in survey_info.items(): - for questionnaires_version, file_paths in questionnaires_details.items(): - tabular_path = file_paths['Tabular'] - paradata_path = file_paths['Paradata'] - - try: - df_questionnaires = get_questionnaire(tabular_path) - except Exception as e: - logger.error(f"Failed to load questionnaire for {survey_questionnaire} version {questionnaires_version} from {tabular_path}: {str(e)}") - raise - - try: - df_paradata = get_paradata(paradata_path, df_questionnaires) - except Exception as e: - logger.error(f"Failed to load paradata for {survey_questionnaire} version {questionnaires_version} from {paradata_path}: {str(e)}") - raise - - try: - df_microdata = get_microdata(tabular_path, df_questionnaires) - except Exception as e: - logger.error(f"Failed to load microdata for {survey_questionnaire} version {questionnaires_version} from {tabular_path}: {str(e)}") - raise - - logger.info(f"{survey_questionnaire} with version {questionnaires_version} loaded. " - f"\n" - f"Paradata shape: {df_paradata.shape} " - f"Questionnaires shape: {df_questionnaires.shape} " - f"Microdata shape: {df_microdata.shape} ") - - dfs_paradata.append(df_paradata) - dfs_questionnaires.append(df_questionnaires) - dfs_microdata.append(df_microdata) - - # create unique dataframe with all surveys - try: - dfs_paradata = pd.concat(dfs_paradata) - dfs_questionnaires = pd.concat(dfs_questionnaires) - dfs_microdata = pd.concat(dfs_microdata) - except Exception as e: - logger.error(f"Failed to concatenate dataframes: {str(e)}") - raise - - dfs_paradata.reset_index(drop=True, inplace=True) - dfs_questionnaires.reset_index(drop=True, inplace=True) - dfs_microdata.reset_index(drop=True, inplace=True) - - return dfs_paradata, dfs_questionnaires, dfs_microdata - diff --git a/rissk/utils/stats_utils.py b/rissk/utils/stats_utils.py deleted file mode 100644 index 63f5f27..0000000 --- a/rissk/utils/stats_utils.py +++ /dev/null @@ -1,248 +0,0 @@ -import math -import pandas as pd -import numpy as np -from scipy.stats import entropy -from scipy import stats -from sklearn.preprocessing import StandardScaler -from scipy.stats import chisquare, fisher_exact -from collections import Counter -from scipy.stats.mstats import winsorize - - -def jensen_shannon_divergence(p, q): - m = 0.5 * (p + q) - return 0.5 * (entropy(p, m) + entropy(q, m)) - - -def jensen_shannon_distance(p, q): - return np.sqrt(jensen_shannon_divergence(p, q)) - - -def get_digit_frequecies(df, feature_name, apply_first_digit, minimum_sample=50): - digit_mask = (df[feature_name] != 0) - if apply_first_digit: - total_digit_values = df[digit_mask][feature_name].apply(first_digit) - else: - total_digit_values = df[digit_mask][feature_name].apply(last_digit) - total_digit_count = Counter(total_digit_values) - total_digit_count = [total_digit_count.get(i, 0) for i in range(1, 10)] - if sum(total_digit_count) < minimum_sample: - total_digit_freq = None - else: - total_digit_freq = [v / sum(total_digit_count) for v in total_digit_count] - # DO not consider samples with size less than minimum_sample - - return total_digit_freq - - -def first_digit(val): - """Extract the first digit from a value.""" - val = abs(val) - return int(str(val)[0]) - - -def last_digit(val): - """Extract the first digit from a value.""" - return int(str(int(val))[-1]) - - -def apply_benford_tests(df, valid_variables, responsible_col, feature_name, apply_first_digit=True, minimum_sample=50): - responsible_list = df[responsible_col].unique() - results = [] - for var in valid_variables: - variable_mask = df['variable_name'] == var - for resp in responsible_list: - score = None - resp_mask = (df[responsible_col] == resp) - total_digit_count = get_digit_frequecies(df[variable_mask & (~resp_mask)], feature_name, apply_first_digit, - minimum_sample=minimum_sample) - resp_digit_count = get_digit_frequecies(df[variable_mask & resp_mask], feature_name, apply_first_digit, - minimum_sample=minimum_sample) - if resp_digit_count is not None and total_digit_count is not None: - # _, p_value = chisquare(total_digit_count, resp_digit_count) - # score = p_value < 0.05 - score = jensen_shannon_distance(np.array(total_digit_count), np.array(resp_digit_count)) - results.append((resp, var, score)) - return pd.DataFrame(results, columns=[responsible_col, 'variable_name', feature_name]) - - -def get_outlier_by_magnitude(series, mode_deviation=3, threshold_freq=0.02): - """ - Detects values that are anomalies based on their order of magnitude. - - Args: - - series (pd.Series): Series of numeric values. - - mode_deviation (int): Maximum allowable deviation from the mode's order of magnitude. - - threshold_freq (int): Maximum frequency for an order of magnitude to be considered anomalous. - - Returns: - - pd.Series: Boolean Series with True for anomalies and False for normal values. - """ - - # Compute order of magnitude for each value - min_value = series.min() - if min_value <= 0: - order_of_magnitude = np.floor(np.log10(series + abs(min_value) + 1)) - else: - order_of_magnitude = np.floor(np.log10(series)) - - # Using Mode-based method - mode_order = max(order_of_magnitude.mode().iloc[0], 1) - - mode_based_anomalies = ( - (order_of_magnitude < mode_order - mode_deviation) | (order_of_magnitude > mode_order + mode_deviation)) - - # Using Histogram/Frequency count-based method - freq_count = order_of_magnitude.value_counts() / series.count() - anomalous_orders = freq_count[freq_count <= threshold_freq].index - freq_based_anomalies = order_of_magnitude.isin(anomalous_orders) - - # Combine results - anomalies = mode_based_anomalies | freq_based_anomalies - - return anomalies - - -def get_outlier_iqr(data, column_name): - q_high = data[column_name].quantile(0.75) - q_low = data[column_name].quantile(0.25) - iqr = q_high - q_low - lower_outlier = (data[column_name] < q_low - 1.5 * iqr) & (~pd.isnull(data[column_name])) - upper_outlier = (data[column_name] > q_high + 1.5 * iqr) & (~pd.isnull(data[column_name])) - return lower_outlier, upper_outlier - - -def get_outlier_z_score(data, column_name, threshold=2.5): - # Compute the limits - lower_limit = data[column_name].mean() - threshold * data[column_name].std() - upper_limit = data[column_name].mean() + threshold * data[column_name].std() - - lower_outlier = (data[column_name] < lower_limit) & (~pd.isnull(data[column_name])) - upper_outlier = (data[column_name] > upper_limit) & (~pd.isnull(data[column_name])) - - return lower_outlier, upper_outlier - - -def filter_variables_by_magnitude(df, feature_name, variables, min_order_of_magnitude=3): - # Define a function to calculate order of magnitude - def order_of_magnitude(num): - if num == 0: - return 0 - elif num < 0: - num = -num - return int(math.floor(math.log10(num))) - - # Find columns that span at least min_order_of_magnitude - valid_variables = [] - for var in variables: - var_values = df[df['variable_name'] == var][feature_name] # Remove NaNs to avoid issues - max_magnitude = order_of_magnitude(var_values.max()) - min_magnitude = order_of_magnitude(var_values.min()) - - if max_magnitude - min_magnitude >= min_order_of_magnitude: - valid_variables.append(var) - - return valid_variables - - -def get_box_cox_rescaled(series): - scaler = StandardScaler() - min_value = series.min() - box_cox = series - if series.nunique() > 1: - if min_value <= 0: - box_cox = box_cox + abs(min_value) + 1 - box_cox, _ = stats.boxcox(box_cox) - box_cox = scaler.fit_transform(box_cox.reshape(-1, 1)) - return box_cox - - -def calculate_list_entropy(column, unique_values, min_record_sample=10): - """ - Calculate the normalized entropy of a given column. - - Parameters: - - column (pd.Series): The column for which the entropy is calculated. - - unique_values (int): The number of unique values in the column. - - min_record_sample (int, optional): The minimum sample size required - relative to the number of unique values. Defaults to 10. - - Returns: - - float or None: Returns normalized entropy if conditions are met, - 0 for single value distributions with enough samples, - otherwise None. - """ - - column = column[column != '##N/A##'] - flattened_series = column.explode() - # Compute the probability distribution of unique values in the column - # This uses value counts and then normalizes the counts to get probabilities - prob_distribution = flattened_series.value_counts(normalize=True) - - # Check conditions to calculate normalized entropy: - # 1. There should be more than one unique value - # 2. The number of records should be above a certain threshold - # based on the number of unique values - if unique_values > 1 and flattened_series.shape[0] >= min_record_sample * unique_values: - entropy_ = entropy(prob_distribution.values) / np.log2(unique_values) - # Check conditions where entropy is 0: - # 1. Only one unique value is present in the distribution - # 2. The number of records meets the required threshold - elif unique_values == 1 and flattened_series.shape[0] >= min_record_sample * unique_values: - entropy_ = 0 - # If none of the above conditions are met, return None - else: - entropy_ = None - - return entropy_ - - -def calculate_entropy(column, unique_values, min_record_sample=10): - """ - Calculate the normalized entropy of a given column. - - Parameters: - - column (pd.Series): The column for which the entropy is calculated. - - unique_values (int): The number of unique values in the column. - - min_record_sample (int, optional): The minimum sample size required - relative to the number of unique values. Defaults to 10. - - Returns: - - float or None: Returns normalized entropy if conditions are met, - 0 for single value distributions with enough samples, - otherwise None. - """ - - # Compute the probability distribution of unique values in the column - # This uses value counts and then normalizes the counts to get probabilities - prob_distribution = column.value_counts(normalize=True) - - # Check conditions to calculate normalized entropy: - # 1. There should be more than one unique value - # 2. The number of records should be above a certain threshold - # based on the number of unique values - if unique_values > 1 and column.shape[0] >= min_record_sample * unique_values: - entropy_ = entropy(prob_distribution.values) / np.log2(unique_values) - # Check conditions where entropy is 0: - # 1. Only one unique value is present in the distribution - # 2. The number of records meets the required threshold - elif unique_values == 1 and column.shape[0] >= min_record_sample * unique_values: - entropy_ = 0 - # If none of the above conditions are met, return None - else: - entropy_ = None - - return entropy_ - - -def adjustable_winsorize(data, initial_lower=0.05, initial_upper=0.05, step=0.01): - lower_limit = initial_lower - upper_limit = initial_upper - winsorized_data = winsorize(data, limits=[lower_limit, upper_limit]) - - while len(np.unique(winsorized_data)) <= 1 and (lower_limit > 0 or upper_limit > 0): - lower_limit = max(0, lower_limit - step) - upper_limit = max(0, upper_limit - step) - winsorized_data = winsorize(data, limits=[lower_limit, upper_limit]) - - return winsorized_data diff --git a/rissk_kedro/SETUP.md b/rissk_kedro/SETUP.md index ac880d4..440e63c 100644 --- a/rissk_kedro/SETUP.md +++ b/rissk_kedro/SETUP.md @@ -54,6 +54,8 @@ uv sync --extra gui ### 4. Launch the GUI +Run from the repo root (`rissk/`) or from inside `rissk_kedro/` — both work: + **macOS / Linux:** ```bash bash run_gui.sh @@ -88,6 +90,8 @@ This installs Python 3.13, all pipeline dependencies, and the RISSK package in o ### 3. Launch the GUI +Run from inside `rissk_kedro/`: + ```bash bash run_gui.sh # macOS / Linux run_gui.bat # Windows @@ -160,7 +164,6 @@ Experienced users can run Kedro directly from the `rissk_kedro/` directory: ```bash cd rissk_kedro -```bash # Full pipeline kedro run diff --git a/run_gui.bat b/run_gui.bat deleted file mode 100644 index c5ba7d3..0000000 --- a/run_gui.bat +++ /dev/null @@ -1,3 +0,0 @@ -@echo off -REM RISSK GUI launcher — run from the repo root (rissk/) -call "%~dp0rissk_kedro\run_gui.bat" diff --git a/run_gui.sh b/run_gui.sh deleted file mode 100755 index b7268f7..0000000 --- a/run_gui.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/usr/bin/env bash -# RISSK GUI launcher — run from the repo root (rissk/) -exec "$(dirname "${BASH_SOURCE[0]}")/rissk_kedro/run_gui.sh" "$@" diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index 404bbd8..0000000 --- a/setup.cfg +++ /dev/null @@ -1,6 +0,0 @@ -[flake8] -ignore = E731,E266,E501,C901,W503 -max-line-length = 99 -exclude = .git,notebooks,references,models,data -[ploomber] -entry-point = pipeline.yaml \ No newline at end of file diff --git a/setup.py b/setup.py deleted file mode 100644 index 40cafa1..0000000 --- a/setup.py +++ /dev/null @@ -1,20 +0,0 @@ -from setuptools import find_packages, setup - -# Read requirements from requirements.txt file -with open('requirements.txt') as f: - requirements = f.read().splitlines() - -setup( - name='rissk', - version='0.1.2', - description='Automatically identify at-risk interviews from your Survey Solutions export files.', - author='rowsquared', - long_description=open('README.md').read(), - long_description_content_type='text/markdown', - license='MIT', - packages=find_packages(), - #install_requires=requirements, - setup_requires=['pytest-runner'], - tests_require=['pytest'], - test_suite='tests', -) \ No newline at end of file From 78817ab2b50ea18216ac3d199d1aafb489fa435a Mon Sep 17 00:00:00 2001 From: VJausovec Date: Wed, 6 May 2026 23:40:38 +0100 Subject: [PATCH 69/70] Update README and SETUP documentation for RISSK Kedro pipeline: enhance installation instructions, clarify GUI launch commands, and correct output file naming. Co-authored-by: Copilot --- README.md | 351 +++++++++++++++++++++++++++++++++++++++++- rissk_kedro/README.md | 130 +--------------- rissk_kedro/SETUP.md | 16 +- 3 files changed, 353 insertions(+), 144 deletions(-) diff --git a/README.md b/README.md index d1df331..36a7f35 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,348 @@ -### Download Data from storage system -Please Note that you need [aws cli](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html#getting-started-install-instructions) installed and set up with credentials to download and uplaod the data. If you do not need the sync from S#, you can simply comment with `#` the next line. +![RISSK Logo](images/rissk.png) +# What is RISSK? -# How to Install RISSK -1. go to terminal inside the rissk folder and run +RISSK utilizes machine learning algorithms to generate a **Unit Risk Score (URS)** from your **[Survey Solutions](https://mysurvey.solutions/en/)** export files. This score indicates the likelihood of unwanted interviewer behaviour in individual interviews. It is a valuable tool to prioritize suspicious interviews for verification exercises such as back-checking or audio audits. Designed to be generic, RISSK can be easily integrated into the monitoring systems of most CAPI or CATI surveys conducted using Survey Solutions. Setting up and running RISSK on your local machine is straightforward and platform-independent. Running locally, it ensures the privacy and security of your survey data. Explore further details in the chapters below. + +- [Getting started](#getting-started) +- [Advanced use](#advanced-use) +- [Interpretation](#interpretation) +- [Survey integration](#survey-integration) +- [Limitations](#limitations) +- [Confirmation of results](#confirmation-of-results) +- [Process description](#process-description) +- [Roadmap](#roadmap) + +# Getting started + +These instructions will guide you on how to install and run RISSK on your local machine. For full installation details see [SETUP.md](rissk_kedro/SETUP.md). + +## Prerequisites + +- **Python 3.13** installed on your machine +- An internet connection for the initial install +- Survey Solutions export files (Main Survey Data + Paradata ZIPs) + +Verify your Python version: + +```bash +python --version +``` + +## Setup + +### Option A — uv (recommended) + +[uv](https://docs.astral.sh/uv/) is a fast, self-contained Python package manager. You do **not** need to manage virtual environments manually. + +1. **Install uv** — [instructions](https://docs.astral.sh/uv/getting-started/installation/) +2. **Get the code**: +```bash +git clone https://github.com/rowsquared/rissk.git +cd rissk +``` +3. **Install dependencies**: +```bash +uv sync --extra gui +``` +4. **Launch the GUI**: +```bash +bash rissk_kedro/run_gui.sh # macOS / Linux +rissk_kedro\run_gui.bat # Windows +``` + +### Option B — conda + +```bash +git clone https://github.com/rowsquared/rissk.git +cd rissk +conda env create -f environment.yml +conda activate rissk_kedro +bash rissk_kedro/run_gui.sh # macOS / Linux +rissk_kedro\run_gui.bat # Windows +``` + +Your browser will open automatically at **http://localhost:8080**. + +## Setting up export folder + +In the GUI **Setup** tab, choose a data folder and note the subfolder shown (e.g. `pmpmd_household/latest/10_RAW/`). Place your unmodified Survey Solutions ZIP files there. + +**Export data from Survey Solutions:** +- **Main Survey Data** — choose *Tab separated* or *Stata 14*, tick *Include meta information about questionnaire*.
Click to see a screenshot of the selected export options for Main Survey Data.![Export options Main Survey Data](images/export_main.png)
+- **Paradata** — under *Data Type* select *Paradata*.
Click to see a screenshot of the selected export options for Paradata.![Export options Paradata](images/export_para.png)
+ +Export both files from the **same questionnaire version** consecutively. For multiple compatible versions, export each separately and place all ZIPs in the same folder. **Do not rename, modify, or unzip the files.** + +## Running RISSK + +1. In the GUI **Setup** tab, enter your questionnaire name and version numbers, then click **Save configuration**. +2. Switch to the **Run** tab and click **Run RISSK**. +3. Monitor progress in the live log. Results are written to: +``` +//latest/40_SCORED/unit_rissk_scores.csv +``` + +**Command-line alternative** (from the `rissk_kedro/` directory): ```bash -$ make create_environment +kedro run +``` + +# Advanced use + +## Exporting feature scores +By default, RISSK exports only `unit_rissk_scores.csv` containing the `unit_risk_score` for each interview. To also export individual feature scores, enable **Feature score export** in the GUI **Advanced** tab before running. + +The additional output file `item_scores.parquet` contains the detailed feature scores for each interview. For guidance on how to interpret each feature score, refer to [FEATURES_SCORES.md](FEATURES_SCORES.md). + +## Excluding features + +By default, RISSK includes all available features when calculating the Unit Risk Score (URS). To exclude a specific feature, open the GUI **Advanced** tab and toggle the feature off before running. + +Alternatively, edit `rissk_kedro/conf/local/parameters.yml` directly: + +```yaml +features: + answer_changed: + use: false +``` + +## Adjusting contamination level + +Default contamination values have been set based on our testing data. To override them, edit `rissk_kedro/conf/local/parameters.yml`: + +```yaml +features: + answer_changed: + use: true + parameters: + contamination: 0.12 ``` -2. run -```bash -$ conda activate rissk + +Or adjust per-feature thresholds in the GUI **Advanced** tab. + +## Automatically determining contamination level + +The `medfilt` thresholding method can automatically determine contamination levels for each algorithm. This increases memory use and runtime but improved RISSK's effectiveness in our [experiment](#confirmation-of-results). + +Enable this in the GUI **Advanced** tab (**Automatic contamination**), or set it in `rissk_kedro/conf/local/parameters.yml`: + +```yaml +processing: + automatic_contamination: true ``` + +# Interpretation + +RISSK generates `unit_rissk_scores.csv`, which contains the following variables for each interview: `interview__id`, `responsible` and `unit_risk_score`. + +The `unit_risk_score` ranges from 0 to 100. A higher score suggests that the interview exhibits more anomalies and is therefore at a greater risk of containing problematic interviewer behavior. Interviews with elevated URS should be prioritized for verification and review. + +To identify these anomalies, RISSK analyzes the following features: +- Interview timing (hour of the day) +- Duration of the interview and individual questions +- Geographical location (if GPS questions are set) +- Sequence of questions asked +- Modifications to question answers +- Pauses during the interview +- Statistical properties of answers (variance, entropy, etc.) + +For more information on how URS is calculated, refer to chapter [Process description](#process-description). For a detailed breakdown of all features and scores, consult [FEATURES_SCORES.md](FEATURES_SCORES.md). + +> [!WARNING] +> The URS is **not** definitive proof of interviewer misconduct. It may include **false positives**, where legitimate interviews receive high scores due to unusual circumstances, and **false negatives**, where problematic interviews receive low scores because they contain few or no detectable anomalies. To conclusively identify interviewer misconduct, further verification and review is required. See [Survey integration](#survey-integration) for more details. + +The URS is a _relative_ measure, influenced by the data patterns in the set of interviews within the Survey Solutions export files. Therefore, scores will change when RISSK is run again with different data. When comparing URS between interviews, ensure that the scores were generated using the same set of export files. Direct comparison of URS values between different surveys is not advised. + +By design, RISSK considers only the interview data up to the first interaction by a Supervisor or HQ role to eliminate the influence of confounding post-interview actions. This means that if substantial parts of an interview are completed after this point, the URS may not accurately reflect the interview's risk level. Consequently, modifying an interview after rejection will not improve its URS. + +Note that RISSK does not currently take into account outstanding error messages or interviewer comments. These elements are easily accessible in the Survey Solutions interface and should ideally be reviewed systematically. + +# Survey integration + +RISSK serves as a useful tool to prioritize at-risk interviews in your quality assurance processes, such as in-depth reviews, back-checks, or audio auditing. Additionally, the Unit Risk Score (URS) can be monitored by interviewer and over time to identify trends. This chapter outlines general guidance on how to integrate RISSK into your survey. For advice specific to your context, please [list an issue](https://github.com/rowsquared/rissk/issues/new/choose) (use label 'questions') or reach out to the authors. + +> [!WARNING] +> While RISSK enhances the data quality assurance system of a survey, it should not replace other essential components such as back-checks or audio audits. + +**Frequency** + +Ideally, RISSK should be executed—and its results reviewed and acted upon—regularly during fieldwork to detect and address issues promptly. For most surveys, a frequency ranging from **daily** to **weekly** is advisable. This usually means that RISSK's output will need to be processed and reviewed in batches. + +**System integration** + +If possible, integrate RISSK into your survey's data management and monitoring system. This allows for automated execution and the consolidation of various monitoring indicators. For example: +- Run RISSK as part of the scripts that export from Survey Solutions. +- Use the output to identify interviews for review/verification and add them to the backlog for supervisors or data monitors. +- Incorporate URS values into your monitoring dashboard alongside other indicators. + +## Interview prioritization + +The URS is specifically designed to guide the **initial** review or verification of an interview. It only takes into account data collected before any interactions by a Supervisor or HQ role. Any actions taken by the interviewer after the first review or rejection should be monitored through other means. + +For each batch of interviews, it's most efficient to prioritize those with the **highest URS values** for review or verification, as they are most likely to contain issues. In our [experiment](#confirmation-of-results), 82% of interviews that fell within the top 5% of URS values were fabricated. However, because the URS can also include false negatives it may be beneficial to include some interviews with lower URS values in your review process. For instance, one approach could be to review or verify 10% of interviews with the highest URS in each batch, along with an additional random 5% drawn from the remaining pool. This could also be tailored to focus on specific interviewers or other criteria. + +## Review/Verification + +The process of reviewing or verifying interviews can involve various activities: + +- External verification through back-check interviews or audio audits. +- In-depth examination of the interview and its paradata. +- Direct queries or confrontations with interviewers. +- Direct observation of future interviews. + +It is advisable to keep a structured record of the outcome of the review/verification. Specifically, document for each interview whether it was found to contain problematic behaviour and, if so, describe the nature of the identified issues. This information can help you to finetune the composition of the URS (see chapter [Advanced use](#advanced-use) for details). The authors would also appreciate receiving these outcomes, together with the output file, to continue improving RISSK. + +If problematic interviewer misbehaviour is confirmed, timely and appropriate consequences should ensue. These can range from a stern warning (akin to a "yellow card") to the loss of a bonus or even dismissal in cases of intentional misconduct such as data fabrication. For unintentional mistakes or if an interviewer is struggling, tailored feedback, explanatory phone calls, or in-person retraining may be necessary. Failure to address issues promptly can lead to persistent problems during fieldwork, negatively impacting data quality. + +## Feedback to interviewers + +Informing interviewers that their activities are closely monitored and that an algorithm is used to flag suspicious cases typically offers two benefits: + +1. It encourages better performance, as people generally perform better when they understand the significance of their work. +2. It acts as a deterrent against misconduct, as there is a real risk of detection and subsequent consequences. + +However, it's crucial to exercise caution in your communication with interviewers. Specifically, **do NOT** reveal details about how RISSK operates, such as the specific features analyzed or the scores influencing the Unit Risk Score (URS). Doing so could allow interviewers to adjust their behavior to evade detection. + +For instance, providing feedback like, _"Your interview was flagged because it took place at night"_, could lead interviewers to falsify data during regular working hours or manipulate device time settings. Instead, opt for a generic initial inquiry, asking for details about the flagged interview and then cross-referencing this information with paradata and additional input from respondents or supervisors. For example, you might say, _"Your interview has been flagged and is currently under investigation. Could you please provide all the details about when it was conducted, with whom, how many visits were needed, any challenging aspects, pauses, and so forth?"_ + +Additionally, aim to provide feedback that is both **useful and actionable**. Generalized statements like, _"Your interview scored high; stop doing whatever you're doing wrong,"_ are not helpful. Instead, try to identify the underlying issues through verification or review and tailor your feedback accordingly. For instance, you could say, _"We've noticed that your interviews are unusually short and involve fewer households engaged in agriculture. If the respondent says 'No' in Q1, make sure to probe for XYZ. If the respondent mentions ABC, it should also be considered as a 'Yes' in Q1."_ + +## Monitoring + +To use the URS as a monitoring indicator, average `unit_risk_score` by interviewer (and/or team) and over time (week/month), and visualize it e.g. as part of a survey monitoring dashboard. While individual interviews by one interviewer may not score high enough to be reviewed/verified, a repeated high average score over time for one interviewer may signal potential issues and the need to take action. Monitoring the average URS by interviewer and time also helps to check if interviewers have adjusted to feedback or warnings (lower URS post-intervention) or continue to produce problematic interviews (equal or higher URS). + +> [!IMPORTANT] +> If your survey deploys multiple questionnaire templates, run RISSK separately for each one. + +# Limitations + +- **Majority behavior assumption**: RISSK assumes that the majority of interviews are conducted as desired, using this as a baseline for normal behavior. If a survey has an extreme level of problematic interviewer behavior, the scores may become unreliable. + +- **Low number of interviews**: Some scores require a minimum number of observations to be calculated. Anomaly detection functions better with more observations. While there are only few interviews, e.g., during the first few days of fieldwork, the scores are less effective and reliable. + +- **Interviewing events only**: RISSK considers only the interview data up to the first interaction by a Supervisor or HQ role. This means that if substantial parts of an interview are completed after this point, the URS may not accurately reflect the interview's risk level. If you're using [partial synchronization](https://docs.mysurvey.solutions/headquarters/config/admin-settings/) it's recommended that Supervisor and HQ roles refrain from opening interview files before they are completed to maintain reliability. + +- **System Requirements**: RISSK has been tested on a laptop with 16GB of RAM, processing paradata files up to 1GB in size. Larger datasets may require more advanced hardware. + +- **Data Format**: As of now, RISSK doesn't support SPSS format for microdata exports from Survey Solutions. Use STATA or TAB formats instead. + +- **Version Compatibility**: RISSK is designed to support export formats from Survey Solutions version 23.06 and later. While it may be possible to use RISSK with earlier versions, such compatibility has not been officially tested. + +- **Non-Contact and non-response**: Interviews containing non-contact or non-response cases can distort the URS, as these often follow an atypical path through the questionnaire. + +- **Interviews in Progress**: When using the online Interviewer, incomplete interviews that are still in progress can be included in the analysis, potentially distorting the URS. To minimize this issue, it's advisable to run RISSK during periods when there is minimal interviewing activity, such as during nighttime hours. + +- **Question Types**: No microdata based features have been developed for barcode, picture, audio, and geography questions. These question types are only considered through their related events in the paradata. + +- **Survey Modes**: RISSK is designed for CAPI or CATI modes. It has not been tested for CAWI mode in Survey Solutions. + +# Confirmation of results + +To rigorously test the RISSK package's effectiveness in identifying high-risk interviews, we conducted an experiment using both real and artificially created "fake" interviews. These fake interviews were designed to mimic various types of problematic interviewer behavior. + +## Methodology + +We utilized a real CATI survey dataset (specific details are confidential). To this, we added 77 artificial fake interviews created by 11 interviewers just after completion of fieldwork, each following one of seven scenarios designed to induce different types of interviewer behavior. Here are the scenarios: + +1. **Non-incentivized, uninformed**. Pretend you are interviewing and fill in the questionnaire. +2. **Incentivized, uninformed**. Fake as good as you can, try not to get caught. +3. Same as Scenario 2 (to generate more cases). +4. **Incentivized, real timing**. Fake as good as you can, try to be realistic in timings. +5. **Incentivized, real answers**. Fake as good as you can, try to set as real answers as possible. +6. **Non-incentivized, low effort**. Fake without putting effort. +7. **Incentivized, speed**. Fake as fast as possible. + +These artificial fake interviews were then mixed with 268 real interviews, creating a test set of 345 interviews. Real interviews for this survey are believed to be of general low-risk, as they were conducted by a small team of interviewers with a trusted, long-term relationship, incentives to perform well and deterrents to do badly, as well as a good data monitoring structure in place. Furthermore, interviewers were aware that the data they collected would be used to validate secondary data and that discrepancies would be investigated. Nevertheless, it could not be ruled out that some real interviews contained problematic interviewer behaviour. + +## Metrics + +To measure RISSK's utility in a practical survey setting, we sorted all interviews by `unit_risk_score`, select the top _N%_ - as would be done if using the URS to prioritize interviews for review/verification - and calculate the share of artificial fake interviews `share_urs`. We compare this to `share_rand`, the share of artificial fakes one would obtain if selecting _N%_ at random, which is equal to the prevalence of artificial fakes in the data. We also calculate the ratio of `share_urs/share_rand` measuring how many more artificial fake interviews are contained in the selection guided by URS vs a random selection. + +## Results + +The table below summarizes the results for the top 5, 10, 15 and 20 percent: + +| N | Share selecting top URS
(share_urs) | Share selecting at random
(share_rand) | Ratio
(share_urs/share_rand) | +|-----:|----------------------------------------:|-----------------------------------------------:|---------------------------------:| +| 5% | 82% | 22.4% | 3.7 | +| 10% | 56% | 22.4% | 2.5 | +| 15% | 43% | 22.4% | 1.9 | +| 20% | 41% | 22.4% | 1.9 | + +In our test, selecting the top 5% of interviews based on their URS yielded 3.7 times more artificial fakes than if selected randomly. This ratio decreases as we select a larger percentage of interviews, but at 1.9 for 20% continues to be significantly higher within the range of review/verification ratios common in surveys. + +In the chart below, the blue line (with the y-axis on the left) illustrates how `share_urs` varies as we increase the number of interviews selected, ranging from 1 to 100 of all interviews. The orange horizontal line, set at 22.4%, represents `share_rand`. The green line (with the y-axis on the right) indicates the percentage of all artificially created fake interviews contained within the top N% of interviews, sorted by their URS. As the chart shows, over two-thirds of all artificial interviews are found within the top 40% of interviews when sorted by URS. + +![experiment](images/final_output_no_automatic_contamintation.png) + +The results presented above were obtained by running RISSK with its default settings. The chart below shows results obtained using the automatic contamination option. This option enables the system to automatically determine the contamination levels employed by the relevant algorithms during score calculations. In our tests, using the automatic contamination level showed slightly weaker performance in the 0-10% range but surpassed the default settings in the 10-20% range. + +![experiment](images/final_output_automatic_contamination.png) + +Please note that our results are based on the classification of interviews as either real or artificially created, according to the experiment's design. While none of the artificially created interviews can be devoid of issues, some of the real interviews with relatively high `unit_risk_score` may also contain problematic behavior. This could potentially increase the `share_urs` value, further demonstrating the utility of the tool in identifying at-risk interviews. + +> [!NOTE] +> The effectiveness is likely to differ between surveys as it depends on the nature of problematic interviews. + +# Process description + +This chapter outlines the key steps that the RISSK pipeline follows to generate the Unit Risk Score (URS). + +## Data preparation + +1. **Unzipping files**. The pipeline scans the configured data folder for Survey Solutions export ZIP files (Main Survey Data in Tab or Stata format, and Paradata). Each version's files are extracted to a subfolder, and `Questionnaire/content.zip` is further unzipped. + +2. **Constructing questionnaire data**. For each version, a dataframe `df_questionnaire` is constructed from `Questionnaire/content/document.json` and the Excel files in `Questionnaire/content/Categories`. This dataframe has one row per questionnaire item (questions, variables, subsections) with columns for each item's properties (question type, variable name, etc.). + +3. **Constructing microdata**. All microdata export files are identified (excluding `interview__*` and `assignment__*` files). For each file: + - The data is loaded into a dataframe. + - If loaded from Stata, non-response values are adjusted to match the tabular export format. + - Columns related to multi-variable questions are consolidated into single columns. + - System-generated variables are removed. + - The dataframe is reshaped to long format and all version-specific dataframes are appended together. + - All rows relating to disabled questions or Survey Solutions variables are dropped. + - Question properties from `df_questionnaire` are merged. + +4. **Constructing paradata**. Each version's paradata file is loaded into a dataframe. The `parameters` column is split and question properties are merged in from `df_questionnaire`. + +5. **Appending versions**. The questionnaire, microdata, and paradata dataframes from all versions are appended to create comprehensive dataframes for each. + +## Indicator generation + +6. **Isolating interviewing events**. The paradata and microdata are filtered to focus solely on what we term _interviewing events_ — the initial interview process, prior to any corrections or updates after the first intervention by Supervisor or HQ roles. + - In the paradata of every interview, the first event of type `['RejectedBySupervisor', 'OpenedBySupervisor', 'RejectedByHQ', 'OpenedByHQ']` is identified and all subsequent events are removed. + - The remaining paradata is merged into the microdata, retaining only data points that correspond to interviewing events. + +7. **Constructing features**. Various features are derived from the refined paradata and microdata. These features can either be _unit-level_ (referring to the entire interview) or _item-level_ (pertaining to the answer of an individual question on a roster instance/row). Features are absolute values, such as the time spent on a particular question measured in seconds. For a detailed explanation of how each feature is calculated, refer to [FEATURES_SCORES.md](FEATURES_SCORES.md). + +## Generating scores + +8. **Evaluation and score calculation**. Individual features are evaluated and corresponding scores are calculated. For an in-depth understanding of all scores, please consult [FEATURES_SCORES.md](FEATURES_SCORES.md). Generally, scores are categorized into three types: + - **Type 1 Score — Item-level features aggregated to unit**: Anomalies are initially detected at the item level, such as identifying atypical hours of the day for a question to have been answered. Subsequently, the proportion of anomalies within each interview is calculated. + - **Type 2 Score — Unit-level features to unit-level**: Features are directly transformed at the unit level without any aggregation. The specific transformation depends on the nature of the feature. + - **Type 3 Score — Item-level features aggregated to interviewer**: Anomalies are first identified at the item level, grouped by both `variable_name` and `interviewer`. Then, the proportion of anomalies is calculated at the interviewer level. All interviews conducted by the same interviewer share the same Type 3 scores. + +9. **Score aggregation and normalization**. Individual scores are synthesized through the following steps: + - Type 3 Scores are aggregated using [Principal Component Analysis](https://en.wikipedia.org/wiki/Principal_component_analysis) (PCA), which is well-suited for the distribution of Type 3 Scores. + - Type 1 and 2 Scores are aggregated using [Isolation Forest](https://en.wikipedia.org/wiki/Isolation_forest). Due to the multiple distinct types of distributions present in Type 1 and 2 Scores, Isolation Forest was preferred over PCA. + - Results from the PCA and Isolation Forest are combined by a normalized product. + - This product is then [winsorized](https://en.wikipedia.org/wiki/Winsorizing) to mitigate the impact of extreme outliers. + - Finally, the winsorized product is [rescaled](https://en.wikipedia.org/wiki/Feature_scaling#Rescaling_(min-max_normalization)) to a range of 0–100, rendering the resulting `unit_risk_score` easy to interpret. + +# Roadmap +We have planned a series of enhancements and additions to RISSK, which are maintained as [issues](https://github.com/rowsquared/rissk/issues). They can be categorized into the following broad areas: + +- **Deploy RISSK:** + - Expand its application across diverse survey types and contexts. + - Actively seek feedback to refine the tool, address bugs, and gather more evidence on its efficacy. +- **Improve usability**: + - Provide additional outputs, including standardized summary reports and dashboards. +- **Refine methodology**: + - Minimize both false positives and negatives to bolster the reliability of the tool. + - Experiment with feature engineering, score design, and aggregation methods with more testing data. +- **Make RISSK learn**: + - Develop standardized framework for users to record review/verification outcomes. + - Produce an anonymized output format, allowing users to share data that can help refine RISSK's algorithms. + - For individual surveys: Establish a feedback loop enabling RISSK to adapt based on previous verification outcomes. + - Across different surveys: With standard verification outcome and access to more testing data, alternative methodologies can be explored, such as training neural networks, to enhance RISSK's prediction accuracy. diff --git a/rissk_kedro/README.md b/rissk_kedro/README.md index ccac267..aade981 100644 --- a/rissk_kedro/README.md +++ b/rissk_kedro/README.md @@ -1,129 +1,3 @@ -### Download Data from storage system -Please Note that you need [aws cli](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html#getting-started-install-instructions) installed and set up with credentials to download and uplaod the data. If you do not need the sync from S#, you can simply comment with `#` the next line. +# RISSK Kedro Pipeline - -# How to Install RISSK -1. go to terminal inside the rissk folder and run -```bash -$ make create_environment -``` -2. run -```bash -$ conda activate rissk -``` - -# rissk_kedro - -[![Powered by Kedro](https://img.shields.io/badge/powered_by-kedro-ffc900?logo=kedro)](https://kedro.org) - -## Overview - -This is your new Kedro project with PySpark setup, which was generated using `kedro 1.2.0`. - -Take a look at the [Kedro documentation](https://docs.kedro.org) to get started. - -## Rules and guidelines - -In order to get the best out of the template: - -* Don't remove any lines from the `.gitignore` file we provide -* Make sure your results can be reproduced by following a [data engineering convention](https://docs.kedro.org/en/stable/faq/faq.html#what-is-data-engineering-convention) -* Don't commit data to your repository -* Don't commit any credentials or your local configuration to your repository. Keep all your credentials and local configuration in `conf/local/` - -## Configuration & Secrets - -### Data Encryption -To handle password-protected zip files in the ingestion pipeline, you must provide the password locally. -Do NOT commit this password to version control. - -1. Create or edit `conf/local/parameters.yml` (this file is git-ignored). -2. Add the following key: - -```yaml -zip_password: "your_actual_password_here" -``` - -If this value is left as `null` (or missing from local config), the system will attempt to read the `PASSWORD` environment variable. - -## How to install dependencies - -Declare any dependencies in `requirements.txt` for `pip` installation. - -To install them, run: - -``` -pip install -r requirements.txt -``` - -## How to run your Kedro pipeline - -You can run your Kedro project with: - -``` -kedro run -``` - -## How to test your Kedro project - -Have a look at the files `tests/test_run.py` and `tests/pipelines/data_science/test_pipeline.py` for instructions on how to write your tests. Run the tests as follows: - -``` -pytest -``` - -You can configure the coverage threshold in your project's `pyproject.toml` file under the `[tool.coverage.report]` section. - -## Project dependencies - -To see and update the dependency requirements for your project use `requirements.txt`. Install the project requirements with `pip install -r requirements.txt`. - -[Further information about project dependencies](https://docs.kedro.org/en/stable/kedro_project_setup/dependencies.html#project-specific-dependencies) - -## How to work with Kedro and notebooks - -> Note: Using `kedro jupyter` or `kedro ipython` to run your notebook provides these variables in scope: `catalog`, `context`, `pipelines` and `session`. -> -> Jupyter, JupyterLab, and IPython are already included in the project requirements by default, so once you have run `pip install -r requirements.txt` you will not need to take any extra steps before you use them. - -### Jupyter -To use Jupyter notebooks in your Kedro project, you need to install Jupyter: - -``` -pip install jupyter -``` - -After installing Jupyter, you can start a local notebook server: - -``` -kedro jupyter notebook -``` - -### JupyterLab -To use JupyterLab, you need to install it: - -``` -pip install jupyterlab -``` - -You can also start JupyterLab: - -``` -kedro jupyter lab -``` - -### IPython -And if you want to run an IPython session: - -``` -kedro ipython -``` - -### How to ignore notebook output cells in `git` -To automatically strip out all output cell contents before committing to `git`, you can use tools like [`nbstripout`](https://github.com/kynan/nbstripout). For example, you can add a hook in `.git/config` with `nbstripout --install`. This will run `nbstripout` before anything is committed to `git`. - -> *Note:* Your output cells will be retained locally. - -## Package your Kedro project - -[Further information about building project documentation and packaging your project](https://docs.kedro.org/en/stable/tutorial/package_a_project.html) +See [SETUP.md](SETUP.md) for installation and usage instructions. diff --git a/rissk_kedro/SETUP.md b/rissk_kedro/SETUP.md index 440e63c..391c30c 100644 --- a/rissk_kedro/SETUP.md +++ b/rissk_kedro/SETUP.md @@ -54,16 +54,16 @@ uv sync --extra gui ### 4. Launch the GUI -Run from the repo root (`rissk/`) or from inside `rissk_kedro/` — both work: +From the `rissk_kedro/` directory: **macOS / Linux:** ```bash -bash run_gui.sh +bash rissk_kedro/run_gui.sh ``` **Windows:** ```bat -run_gui.bat +rissk_kedro\run_gui.bat ``` Your browser will open automatically at **http://localhost:8080**. @@ -82,7 +82,7 @@ cd rissk ### 2. Create and activate the conda environment ```bash -conda env create -f environment_kedro.yml +conda env create -f environment.yml conda activate rissk_kedro ``` @@ -90,11 +90,11 @@ This installs Python 3.13, all pipeline dependencies, and the RISSK package in o ### 3. Launch the GUI -Run from inside `rissk_kedro/`: +From the `rissk_kedro/` directory: ```bash -bash run_gui.sh # macOS / Linux -run_gui.bat # Windows +bash rissk_kedro/run_gui.sh # macOS / Linux +rissk_kedro\run_gui.bat # Windows ``` --- @@ -145,7 +145,7 @@ To switch to a different questionnaire, update the name in the Setup tab and sav Results are written to: ``` -//latest/40_SCORED/unit_risk_scores.csv +//latest/40_SCORED/unit_rissk_scores.csv ``` ### Advanced settings From 9f6628cef1605b825de995854d2912334a07d646 Mon Sep 17 00:00:00 2001 From: VJausovec Date: Wed, 6 May 2026 23:46:02 +0100 Subject: [PATCH 70/70] Remove loguru dependency from environment.yml --- environment.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/environment.yml b/environment.yml index cb8da31..1294b05 100644 --- a/environment.yml +++ b/environment.yml @@ -33,7 +33,6 @@ dependencies: # Analysis Tools - pyod>=1.1.5 - pythresh>=1.0.3 - - loguru>=0.7.3 - tqdm>=4.67.0 - boto3>=1.35.0 - python-dotenv>=1.0.1