From abda90f48cd2ac42668fd76984f11bc2f5dee9a7 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Mon, 30 Mar 2026 13:11:08 +0000 Subject: [PATCH 01/61] Adapt prompt for SOC KNOWLEDGEBASE - remove job description, level of education, manage others and industry description (keep only job title) --- .../llm/llm.py | 61 ++++++++----------- .../llm/prompt.py | 21 ++----- .../models/response_model.py | 18 +----- 3 files changed, 32 insertions(+), 68 deletions(-) diff --git a/src/occupational_classification_utils/llm/llm.py b/src/occupational_classification_utils/llm/llm.py index 79f3e30..b359692 100644 --- a/src/occupational_classification_utils/llm/llm.py +++ b/src/occupational_classification_utils/llm/llm.py @@ -109,20 +109,12 @@ def __init__( # noqa: PLR0913 async def get_soc_code( self, job_title: str, - job_description: str, - level_of_education: str, - manage_others: bool, - industry_descr: str, ) -> SocResponse: """Generates a SOC classification based on respondent's data using the full SOC index embedded in the query (mirror SIC one-shot). Args: job_title (str): The title of the job. - job_description (str): The description of the job. - level_of_education (str): The level of education required for the job. - manage_others (bool): Indicates whether the job involves managing others. - industry_descr (str): The description of the industry. Returns: SocResponse: The generated response to the query. @@ -135,10 +127,6 @@ async def get_soc_code( response = await chain.ainvoke( { "job_title": job_title, - "job_description": job_description, - "level_of_education": level_of_education, - "manage_others": manage_others, - "industry_descr": industry_descr, }, return_only_outputs=True, ) @@ -148,12 +136,30 @@ async def get_soc_code( parser = PydanticOutputParser( # type: ignore # Suspect langchain ver bug pydantic_object=SocResponse, ) + try: + chain = FIX_PARSING_PROMPT | self.llm + response = await chain.ainvoke( + { + "llm_output": str(response.content), + "format_instructions": parser.get_format_instructions(), + }, + return_only_outputs=True, + ) validated_answer_sr = parser.parse(str(response.content)) - except ValueError as parse_error: - logger.error(f"Unable to parse llm response: {parse_error}") - content = getattr(response, "content", "") - reasoning = f"ERROR parse_error=<{parse_error}>, response=<{content}>" + logger.debug("Successfully parsed reformatted response.") + except (ValueError, AttributeError) as parse_error2: + logger.error( + f"Failed to parse response again: {parse_error2}", + error=str(parse_error2), + ) + logger.warning( + "Failed to parse response again", + response_content=str(response.content), + ) + reasoning = ( + f"ERROR parse_error=<{parse_error2}>, response=<{response.content}>" + ) validated_answer_sr = SocResponse( codable=False, soc_candidates=[], reasoning=reasoning ) @@ -185,11 +191,7 @@ def _prompt_candidate( item = self.soc[code] txt = "{" + f"Code: {item.soc_code}, Title: {item.group_title}" txt += f", Example job_titles: {', '.join(job_titles)}" - # if include_all: - # if item.soc_meta.group_description: - # txt += f", Description: {item.soc_meta.group_description}" - # if item.soc_meta.qualifications: - # txt += f", Qualifications: {', '.join(item.soc_meta.entry_routes_and_quals)}" + if include_all: pass # Full metadata optional; structure matches SIC _prompt_candidate return txt + "}" @@ -257,9 +259,7 @@ def _prompt_candidate_list( async def sa_rag_soc_code( # noqa: PLR0913 self, - industry_descr: str, job_title: Optional[str] = None, - job_description: Optional[str] = None, expand_search_terms: bool = True, code_digits: int = 4, candidates_limit: int = 5, @@ -274,7 +274,6 @@ async def sa_rag_soc_code( # noqa: PLR0913 Args: industry_descr (str): The description of the industry. job_title (str, optional): The job title. Defaults to None. - job_description (str, optional): The job description. Defaults to None. expand_search_terms (bool, optional): Kept for API compatibility; unused (short_list is required from caller). Defaults to True. code_digits (int, optional): The number of digits in the generated @@ -296,23 +295,13 @@ async def sa_rag_soc_code( # noqa: PLR0913 """ _ = expand_search_terms # API compatibility; unused when short_list required - def prep_call_dict(industry_descr, job_title, job_description, soc_codes): + def prep_call_dict(job_title, soc_codes): # Helper function to prepare the call dictionary is_job_title_present = job_title is None or job_title in {"", " "} job_title = "Unknown" if is_job_title_present else job_title - is_job_description_present = job_description is None or job_description in { - "", - " ", - } - job_description = ( - "Unknown" if is_job_description_present else job_description - ) - call_dict = { - "industry_descr": industry_descr, "job_title": job_title, - "job_description": job_description, "soc_index": soc_codes, } return call_dict @@ -327,9 +316,7 @@ def prep_call_dict(industry_descr, job_title, job_description, soc_codes): ) call_dict = prep_call_dict( - industry_descr=industry_descr, job_title=job_title, - job_description=job_description, soc_codes=soc_codes, ) diff --git a/src/occupational_classification_utils/llm/prompt.py b/src/occupational_classification_utils/llm/prompt.py index 08033a8..a55494c 100644 --- a/src/occupational_classification_utils/llm/prompt.py +++ b/src/occupational_classification_utils/llm/prompt.py @@ -40,19 +40,14 @@ for the use in the UK official statistics. Respondent data may be in English or Welsh, but you always respond in British English.""" -_soc_template = """"Given the respondent data (that may include all or some of -job title, job description, level of education, line management responsibilities, -and company's main activity) your task is to determine -the UK SOC (Standard Occupational Classification) code for this job if it can be -determined. If the code cannot be determined, identify the additional information +_soc_template = """"Given the respondent job title your task is to determine +the UK SOC (Standard Occupational Classification) code for this job. +If the code cannot be determined, identify the additional information needed to determine it. Make sure to use the provided 2020 SOC index. ===Respondent Data=== - Job Title: {job_title} -- Job Description: {job_description} -- Level of Education: {level_of_education} -- Line Management Responsibilities: {manage_others} -- Company's main activity: {industry_descr} + ===Output Format=== {format_instructions} @@ -77,10 +72,8 @@ ) -_sa_soc_template_rag = """"Given the respondent's description of the main activity their -company does, their job title and job description (which may be different to the -main company activity), your task is to determine a list of the most likely UK SOC -(Standard Occupational Classification) codes for this individual. +_sa_soc_template_rag = """"Given the respondent's job title, your task is to determine +a list of the most likely UK SOC (Standard Occupational Classification) codes for this individual. The following will be provided to make your decision and send appropriate output: Respondent Data @@ -98,9 +91,7 @@ on the list you respond with. ===Respondent Data=== -- Company's main activity: {industry_descr} - Job Title: {job_title} -- Job Description: {job_description} ===Relevant subset of UK SOC 2020=== {soc_index} diff --git a/src/occupational_classification_utils/models/response_model.py b/src/occupational_classification_utils/models/response_model.py index 87da5e6..adddf58 100644 --- a/src/occupational_classification_utils/models/response_model.py +++ b/src/occupational_classification_utils/models/response_model.py @@ -80,26 +80,12 @@ class SocResponse(BaseModel): default=None, ) soc_code: Optional[str] = Field( - description="""Full four digit SOC code assigned based on provided job title, - description, etc. Empty if codable=False.""", - default=None, - ) - soc_descriptive: Optional[str] = Field( - description="""Descriptive label of the SOC category associated with soc_code - if provided. Empty if codable=False.""", + description="""Full four digit SOC code assigned based on provided job title. Empty if codable=False.""", default=None, ) soc_candidates: list[SocCandidate] = Field( description="""List of possible or alternative SOC codes that may be applicable - with their descriptive label and estimated likelihood.""" - ) - soc_code_2digits: Optional[str] = Field( - description="""First two digits of the hierarchical SOC code assigned. - This field should be non empty if the larger (two-digit) group of SOC codes - can be determined even in cases where additional information is needed to - to code to four digits (for example when all SOC candidates share - the same first two digits).""", - default=None, + with their descriptive label and estimated likelihood. Not more than 10 candidates.""", ) reasoning: str = Field( description="""Step by step reasoning behind classification selected. Specifies From 930bb75b31e631342ad5759b055a17420ee21e35 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Mon, 27 Apr 2026 20:28:41 +0000 Subject: [PATCH 02/61] Add prompt for cleaning text strings --- poetry.lock | 226 ++++++++++-------- .../llm/llm.py | 129 +++++++++- 2 files changed, 247 insertions(+), 108 deletions(-) diff --git a/poetry.lock b/poetry.lock index e0d1f21..e4e24ab 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.3.4 and should not be changed by hand. [[package]] name = "aiohappyeyeballs" @@ -556,7 +556,7 @@ packaging = ">=19.1" pyproject_hooks = "*" [package.extras] -docs = ["furo (>=2023.08.17)", "sphinx (>=7.0,<8.0)", "sphinx-argparse-cli (>=1.5)", "sphinx-autodoc-typehints (>=1.10)", "sphinx-issues (>=3.0.0)"] +docs = ["furo (>=2023.8.17)", "sphinx (>=7.0,<8.0)", "sphinx-argparse-cli (>=1.5)", "sphinx-autodoc-typehints (>=1.10)", "sphinx-issues (>=3.0.0)"] test = ["build[uv,virtualenv]", "filelock (>=3)", "pytest (>=6.2.4)", "pytest-cov (>=2.12)", "pytest-mock (>=2)", "pytest-rerunfailures (>=9.1)", "pytest-xdist (>=1.34)", "setuptools (>=42.0.0) ; python_version < \"3.10\"", "setuptools (>=56.0.0) ; python_version == \"3.10\"", "setuptools (>=56.0.0) ; python_version == \"3.11\"", "setuptools (>=67.8.0) ; python_version >= \"3.12\"", "wheel (>=0.36.0)"] typing = ["build[uv]", "importlib-metadata (>=5.1)", "mypy (>=1.9.0,<1.10.0)", "tomli", "typing-extensions (>=3.7.4.3)"] uv = ["uv (>=0.1.18)"] @@ -1501,7 +1501,7 @@ files = [ [package.dependencies] cachecontrol = ">=0.12.14" -google-api-core = {version = ">=1.22.1,<3.0.0dev", extras = ["grpc"], markers = "platform_python_implementation != \"PyPy\""} +google-api-core = {version = ">=1.22.1,<3.0.0.dev0", extras = ["grpc"], markers = "platform_python_implementation != \"PyPy\""} google-api-python-client = ">=1.7.8" google-cloud-firestore = {version = ">=2.19.0", markers = "platform_python_implementation != \"PyPy\""} google-cloud-storage = ">=1.37.1" @@ -1832,7 +1832,7 @@ files = [ [package.dependencies] google-auth = ">=2.14.1,<3.0.0" googleapis-common-protos = ">=1.56.2,<2.0.0" -grpcio = {version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""} +grpcio = {version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""} grpcio-status = {version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""} proto-plus = [ {version = ">=1.22.3,<2.0.0"}, @@ -1843,7 +1843,7 @@ requests = ">=2.18.0,<3.0.0" [package.extras] async-rest = ["google-auth[aiohttp] (>=2.35.0,<3.0.dev0)"] -grpc = ["grpcio (>=1.33.2,<2.0dev)", "grpcio (>=1.49.1,<2.0dev) ; python_version >= \"3.11\"", "grpcio-status (>=1.33.2,<2.0.dev0)", "grpcio-status (>=1.49.1,<2.0.dev0) ; python_version >= \"3.11\""] +grpc = ["grpcio (>=1.33.2,<2.0.dev0)", "grpcio (>=1.49.1,<2.0.dev0) ; python_version >= \"3.11\"", "grpcio-status (>=1.33.2,<2.0.dev0)", "grpcio-status (>=1.49.1,<2.0.dev0) ; python_version >= \"3.11\""] grpcgcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"] grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"] @@ -1988,25 +1988,6 @@ tokenization = ["sentencepiece (>=0.2.0)"] vizier = ["google-vizier (>=0.1.6)"] xai = ["tensorflow (>=2.3.0,<3.0.0) ; python_version < \"3.13\""] -[[package]] -name = "google-cloud-appengine-logging" -version = "1.6.2" -description = "Google Cloud Appengine Logging API client library" -optional = false -python-versions = ">=3.7" -groups = ["main"] -markers = "python_version >= \"3.14\"" -files = [ - {file = "google_cloud_appengine_logging-1.6.2-py3-none-any.whl", hash = "sha256:2b28ed715e92b67e334c6fcfe1deb523f001919560257b25fc8fcda95fd63938"}, - {file = "google_cloud_appengine_logging-1.6.2.tar.gz", hash = "sha256:4890928464c98da9eecc7bf4e0542eba2551512c0265462c10f3a3d2a6424b90"}, -] - -[package.dependencies] -google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0", extras = ["grpc"]} -google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0" -proto-plus = {version = ">=1.25.0,<2.0.0", markers = "python_version >= \"3.13\""} -protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<7.0.0" - [[package]] name = "google-cloud-appengine-logging" version = "1.8.0" @@ -2014,7 +1995,6 @@ description = "Google Cloud Appengine Logging API client library" optional = false python-versions = ">=3.7" groups = ["main"] -markers = "python_version < \"3.14\"" files = [ {file = "google_cloud_appengine_logging-1.8.0-py3-none-any.whl", hash = "sha256:a4ce9ce94a9fd8c89ed07fa0b06fcf9ea3642f9532a1be1a8c7b5f82c0a70ec6"}, {file = "google_cloud_appengine_logging-1.8.0.tar.gz", hash = "sha256:84b705a69e4109fc2f68dfe36ce3df6a34d5c3d989eee6d0ac1b024dda0ba6f5"}, @@ -2023,7 +2003,10 @@ files = [ [package.dependencies] google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0", extras = ["grpc"]} google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0" -grpcio = ">=1.33.2,<2.0.0" +grpcio = [ + {version = ">=1.33.2,<2.0.0"}, + {version = ">=1.75.1,<2.0.0", markers = "python_version >= \"3.14\""}, +] proto-plus = [ {version = ">=1.22.3,<2.0.0"}, {version = ">=1.25.0,<2.0.0", markers = "python_version >= \"3.13\""}, @@ -2059,24 +2042,24 @@ files = [ ] [package.dependencies] -google-api-core = {version = ">=2.11.1,<3.0.0dev", extras = ["grpc"]} -google-auth = ">=2.14.1,<3.0.0dev" -google-cloud-core = ">=2.4.1,<3.0.0dev" -google-resumable-media = ">=2.0.0,<3.0dev" +google-api-core = {version = ">=2.11.1,<3.0.0.dev0", extras = ["grpc"]} +google-auth = ">=2.14.1,<3.0.0.dev0" +google-cloud-core = ">=2.4.1,<3.0.0.dev0" +google-resumable-media = ">=2.0.0,<3.0.dev0" packaging = ">=20.0.0" -python-dateutil = ">=2.7.3,<3.0dev" -requests = ">=2.21.0,<3.0.0dev" +python-dateutil = ">=2.7.3,<3.0.dev0" +requests = ">=2.21.0,<3.0.0.dev0" [package.extras] all = ["google-cloud-bigquery[bigquery-v2,bqstorage,geopandas,ipython,ipywidgets,opentelemetry,pandas,tqdm]"] -bigquery-v2 = ["proto-plus (>=1.22.3,<2.0.0dev)", "protobuf (>=3.20.2,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev)"] -bqstorage = ["google-cloud-bigquery-storage (>=2.6.0,<3.0.0dev)", "grpcio (>=1.47.0,<2.0dev)", "grpcio (>=1.49.1,<2.0dev) ; python_version >= \"3.11\"", "pyarrow (>=3.0.0)"] -geopandas = ["Shapely (>=1.8.4,<3.0.0dev)", "geopandas (>=0.9.0,<2.0dev)"] +bigquery-v2 = ["proto-plus (>=1.22.3,<2.0.0.dev0)", "protobuf (>=3.20.2,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0.dev0)"] +bqstorage = ["google-cloud-bigquery-storage (>=2.6.0,<3.0.0.dev0)", "grpcio (>=1.47.0,<2.0.dev0)", "grpcio (>=1.49.1,<2.0.dev0) ; python_version >= \"3.11\"", "pyarrow (>=3.0.0)"] +geopandas = ["Shapely (>=1.8.4,<3.0.0.dev0)", "geopandas (>=0.9.0,<2.0.dev0)"] ipython = ["bigquery-magics (>=0.1.0)"] ipywidgets = ["ipykernel (>=6.0.0)", "ipywidgets (>=7.7.0)"] opentelemetry = ["opentelemetry-api (>=1.1.0)", "opentelemetry-instrumentation (>=0.20b0)", "opentelemetry-sdk (>=1.1.0)"] -pandas = ["db-dtypes (>=0.3.0,<2.0.0dev)", "grpcio (>=1.47.0,<2.0dev)", "grpcio (>=1.49.1,<2.0dev) ; python_version >= \"3.11\"", "importlib-metadata (>=1.0.0) ; python_version < \"3.8\"", "pandas (>=1.1.0)", "pandas-gbq (>=0.26.1) ; python_version >= \"3.8\"", "pyarrow (>=3.0.0)"] -tqdm = ["tqdm (>=4.7.4,<5.0.0dev)"] +pandas = ["db-dtypes (>=0.3.0,<2.0.0.dev0)", "grpcio (>=1.47.0,<2.0.dev0)", "grpcio (>=1.49.1,<2.0.dev0) ; python_version >= \"3.11\"", "importlib-metadata (>=1.0.0) ; python_version < \"3.8\"", "pandas (>=1.1.0)", "pandas-gbq (>=0.26.1) ; python_version >= \"3.8\"", "pyarrow (>=3.0.0)"] +tqdm = ["tqdm (>=4.7.4,<5.0.0.dev0)"] [[package]] name = "google-cloud-core" @@ -2091,11 +2074,11 @@ files = [ ] [package.dependencies] -google-api-core = ">=1.31.6,<2.0.dev0 || >2.3.0,<3.0.0dev" -google-auth = ">=1.25.0,<3.0dev" +google-api-core = ">=1.31.6,<2.0.dev0 || >2.3.0,<3.0.0.dev0" +google-auth = ">=1.25.0,<3.0.dev0" [package.extras] -grpc = ["grpcio (>=1.38.0,<2.0dev)", "grpcio-status (>=1.38.0,<2.0.dev0)"] +grpc = ["grpcio (>=1.38.0,<2.0.dev0)", "grpcio-status (>=1.38.0,<2.0.dev0)"] [[package]] name = "google-cloud-firestore" @@ -2118,27 +2101,7 @@ proto-plus = [ {version = ">=1.22.2,<2.0.0", markers = "python_version >= \"3.11\""}, {version = ">=1.25.0,<2.0.0", markers = "python_version >= \"3.13\""}, ] -protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<7.0.0dev" - -[[package]] -name = "google-cloud-iam" -version = "2.19.1" -description = "Google Cloud Iam API client library" -optional = false -python-versions = ">=3.7" -groups = ["main"] -markers = "python_version >= \"3.14\"" -files = [ - {file = "google_cloud_iam-2.19.1-py3-none-any.whl", hash = "sha256:11b08b86d82510021f9dd9f0beb5a08219e070deab09e28d4c0ce49f8c70997d"}, - {file = "google_cloud_iam-2.19.1.tar.gz", hash = "sha256:f059c369ad98af6be3401f0f5d087775d775fb96833be1e9ab8048c422fb1bf4"}, -] - -[package.dependencies] -google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0", extras = ["grpc"]} -google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0" -grpc-google-iam-v1 = ">=0.12.4,<1.0.0" -proto-plus = {version = ">=1.25.0,<2.0.0", markers = "python_version >= \"3.13\""} -protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || 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">=0.4.0" setuptools = "*" diff --git a/src/occupational_classification_utils/llm/llm.py b/src/occupational_classification_utils/llm/llm.py index b359692..f7e710a 100644 --- a/src/occupational_classification_utils/llm/llm.py +++ b/src/occupational_classification_utils/llm/llm.py @@ -16,7 +16,6 @@ """ from collections import defaultdict -from functools import lru_cache from typing import Any, Optional, Union import numpy as np @@ -30,11 +29,15 @@ from occupational_classification_utils.embed.embedding import get_config from occupational_classification_utils.llm.prompt import ( + CORRECT_SPELLING_PROMPT, FIX_PARSING_PROMPT, SA_SOC_PROMPT_RAG, SOC_PROMPT_PYDANTIC, ) -from occupational_classification_utils.models.response_model import SocResponse +from occupational_classification_utils.models.response_model import ( + SocResponse, + Spelling, +) from occupational_classification_utils.utils.soc_data_access import ( get_soc_meta, load_soc_hierarchy, @@ -102,19 +105,31 @@ def __init__( # noqa: PLR0913 self.soc_meta = get_soc_meta(config["lookups"]["soc_structure"]) self.soc_prompt = SOC_PROMPT_PYDANTIC self.sa_soc_prompt_rag = SA_SOC_PROMPT_RAG + self.correct_spelling = CORRECT_SPELLING_PROMPT self.soc: Optional[SOC] = None self.verbose = verbose - @lru_cache # noqa: B019 + # @lru_cache async def get_soc_code( self, job_title: str, + code_digits: int = 4, + candidates_limit: int = 5, + short_list: Optional[list[dict[Any, Any]]] = None, ) -> SocResponse: """Generates a SOC classification based on respondent's data using the full SOC index embedded in the query (mirror SIC one-shot). Args: job_title (str): The title of the job. + short_list (list[dict[Any, Any]], optional): A list of results from + embedding or vector store search (e.g. from soc-classification-vector-store). + Each dict should have "code" and "title" keys. When provided, the + embedding handler is not used. + code_digits (int, optional): The number of digits to consider from + the code for filtering candidates. Defaults to 4. + candidates_limit (int, optional): The maximum number of candidates + to include in the prompt. Defaults to 5. Returns: SocResponse: The generated response to the query. @@ -123,11 +138,33 @@ async def get_soc_code( ValueError: If there is an error parsing the response from the LLM model. """ + if short_list is None: + raise ValueError( + "Short list is None - list provided from embedding search." + ) + soc_codes = self._prompt_candidate_list( + short_list, code_digits=code_digits, candidates_limit=candidates_limit + ) + + def prep_call_dict(job_title, soc_codes): + # Helper function to prepare the call dictionary + is_job_title_present = job_title is None or job_title in {"", " "} + job_title = "Unknown" if is_job_title_present else job_title + + call_dict = { + "job_title": job_title, + "soc_index": soc_codes, + } + return call_dict + + call_dict = prep_call_dict( + job_title=job_title, + soc_codes=soc_codes, + ) + chain = self.soc_prompt | self.llm response = await chain.ainvoke( - { - "job_title": job_title, - }, + call_dict, return_only_outputs=True, ) if self.verbose: @@ -254,12 +291,13 @@ def _prompt_candidate_list( f"{len(soc_candidates)} to {nn}" ) soc_candidates = soc_candidates[:nn] - return "\n".join(soc_candidates) async def sa_rag_soc_code( # noqa: PLR0913 self, + industry_descr: str, job_title: Optional[str] = None, + job_description: Optional[str] = None, expand_search_terms: bool = True, code_digits: int = 4, candidates_limit: int = 5, @@ -274,6 +312,7 @@ async def sa_rag_soc_code( # noqa: PLR0913 Args: industry_descr (str): The description of the industry. job_title (str, optional): The job title. Defaults to None. + job_description (str, optional): The job description. Defaults to None. expand_search_terms (bool, optional): Kept for API compatibility; unused (short_list is required from caller). Defaults to True. code_digits (int, optional): The number of digits in the generated @@ -295,13 +334,23 @@ async def sa_rag_soc_code( # noqa: PLR0913 """ _ = expand_search_terms # API compatibility; unused when short_list required - def prep_call_dict(job_title, soc_codes): + def prep_call_dict(industry_descr, job_title, job_description, soc_codes): # Helper function to prepare the call dictionary is_job_title_present = job_title is None or job_title in {"", " "} job_title = "Unknown" if is_job_title_present else job_title + is_job_description_present = job_description is None or job_description in { + "", + " ", + } + job_description = ( + "Unknown" if is_job_description_present else job_description + ) + call_dict = { + "industry_descr": industry_descr, "job_title": job_title, + "job_description": job_description, "soc_index": soc_codes, } return call_dict @@ -316,7 +365,9 @@ def prep_call_dict(job_title, soc_codes): ) call_dict = prep_call_dict( + industry_descr=industry_descr, job_title=job_title, + job_description=job_description, soc_codes=soc_codes, ) @@ -385,3 +436,65 @@ def prep_call_dict(job_title, soc_codes): ) return validated_answer, short_list, call_dict + + async def clean_spelling( + self, misspelled_string: str, abbreviation_dictionary: dict + ): + """Corrects spelling mistakes from the string. Follows SOC coding index abbreviations. + + Args: + misspelled_string (str): A string that potentially has been misspelled. + abbreviation_dictionary (dict): A dictionary containing approved abbreviations + comonly used in SOC. + + Return: + str: corrected spelling, following SOC coding index rules. + """ + call_dict = { + "job_title": misspelled_string, + "abbreviations": abbreviation_dictionary, + } + + chain = self.correct_spelling | self.llm + + try: + response = await chain.ainvoke( + call_dict, + return_only_outputs=True, + ) + except ValueError as err: + logger.error(f"Error from chain, exit early: {err}", error=str(err)) + validated_answer = Spelling( + job_title_spelling="", + ) + return validated_answer + + if self.verbose: + logger.debug(f"LLM response: {response}") + # Parse the output to desired format with one retry + parser = PydanticOutputParser( # type: ignore # Suspect langchain ver bug + pydantic_object=Spelling, + ) + + try: + chain = FIX_PARSING_PROMPT | self.llm + response = await chain.ainvoke( + { + "llm_output": str(response.content), + "format_instructions": parser.get_format_instructions(), + }, + return_only_outputs=True, + ) + validated_answer_sr = parser.parse(str(response.content)) + logger.debug("Successfully parsed reformatted response.") + except (ValueError, AttributeError) as parse_error2: + logger.error( + f"Failed to parse response again: {parse_error2}", + error=str(parse_error2), + ) + logger.warning( + "Failed to parse response again", + response_content=str(response.content), + ) + + return validated_answer_sr From 1866384f5ea3dfb2ecfe51897adba5934eabcf1d Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Mon, 27 Apr 2026 20:29:27 +0000 Subject: [PATCH 03/61] Add functionality for cleaning text strings --- .../llm/prompt.py | 16 +++++- .../models/response_model.py | 51 +++++++++++-------- 2 files changed, 45 insertions(+), 22 deletions(-) diff --git a/src/occupational_classification_utils/llm/prompt.py b/src/occupational_classification_utils/llm/prompt.py index a55494c..358a6b8 100644 --- a/src/occupational_classification_utils/llm/prompt.py +++ b/src/occupational_classification_utils/llm/prompt.py @@ -52,7 +52,7 @@ ===Output Format=== {format_instructions} -===2020 SOC Index=== +===Relevant subset of UK SOC 2020=== {soc_index} """ @@ -110,6 +110,7 @@ template=_core_prompt + _sa_soc_template_rag, partial_variables={ "format_instructions": parser.get_format_instructions(), + "soc_index": soc_index, }, ) @@ -157,3 +158,16 @@ {format_instructions} """ ) + + +CORRECT_SPELLING_PROMPT = PromptTemplate.from_template( + """Correct spelling of this job title: {job_title} + + Accepted abbreviations and their meaning in a dictionary format: {abbreviations} + Do not replace abbreviations. Use the dictionary only to correct the misspelled job titles. + If the original job title contains an abbreviation, check if the meaning is correct, + but do not replace the abbreviation. + + If there is no need to correct the spelling, return the original job title. + """ +) diff --git a/src/occupational_classification_utils/models/response_model.py b/src/occupational_classification_utils/models/response_model.py index adddf58..7eb88c7 100644 --- a/src/occupational_classification_utils/models/response_model.py +++ b/src/occupational_classification_utils/models/response_model.py @@ -19,7 +19,7 @@ from typing import Optional -from pydantic import BaseModel, Field, model_validator +from pydantic import BaseModel, Field class SocCandidate(BaseModel): @@ -80,12 +80,15 @@ class SocResponse(BaseModel): default=None, ) soc_code: Optional[str] = Field( - description="""Full four digit SOC code assigned based on provided job title. Empty if codable=False.""", + description="""Full four digit SOC code assigned based on provided job title. + Empty if codable=False.""", default=None, ) soc_candidates: list[SocCandidate] = Field( description="""List of possible or alternative SOC codes that may be applicable with their descriptive label and estimated likelihood. Not more than 10 candidates.""", + # min_length=1, # Ensure there's always at least one candidate + # max_length=10, # Limit to less than 10 candidates ) reasoning: str = Field( description="""Step by step reasoning behind classification selected. Specifies @@ -110,29 +113,29 @@ def soc_code_validator(cls, v): raise ValueError("If codable, then valid soc_code needs to be provided") return v - @model_validator(mode="before") - @classmethod - def check_valid_fields(cls, values): - """Validates the fields of the model before instantiation. + # @model_validator(mode="before") + # @classmethod + # def check_valid_fields(cls, values): + # """Validates the fields of the model before instantiation. - Ensures that: - - If `codable` is True, a valid `soc_code` is provided. - - If `codable` is False, a follow-up question is provided. + # Ensures that: + # - If `codable` is True, a valid `soc_code` is provided. + # - If `codable` is False, a follow-up question is provided. - Args: - values (dict): The dictionary of field values. + # Args: + # values (dict): The dictionary of field values. - Returns: - dict: The validated field values. + # Returns: + # dict: The validated field values. - Raises: - ValueError: If validation conditions are not met. - """ - if values.get("codable"): - cls.soc_code_validator(values.get("soc_code")) - elif not values.get("followup"): # This checks for None or empty string - raise ValueError("If uncodable, a follow-up question needs to be provided.") - return values + # Raises: + # ValueError: If validation conditions are not met. + # """ + # if values.get("codable"): + # cls.soc_code_validator(values.get("soc_code")) + # elif not values.get("followup"): # This checks for None or empty string + # raise ValueError("If uncodable, a follow-up question needs to be provided.") + # return values class RagCandidate(BaseModel): @@ -257,3 +260,9 @@ class SurveyAssistSocResponse(BaseModel): selected. Specifies the information used to assign the SOC code or any additional information required to assign a SOC code.""", ) + + +class Spelling(BaseModel): + """Represents a correct spelling of a job title.""" + + job_title_spelling: str = Field(description="Job title with correct spelling.") From f8b3b1f60a4b9ca3779e594d32daef93eeb3a53d Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Mon, 27 Apr 2026 20:41:25 +0000 Subject: [PATCH 04/61] add codes to corrected titles --- notebooks/2026_03_soc.py | 235 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 235 insertions(+) create mode 100644 notebooks/2026_03_soc.py diff --git a/notebooks/2026_03_soc.py b/notebooks/2026_03_soc.py new file mode 100644 index 0000000..8ee12ff --- /dev/null +++ b/notebooks/2026_03_soc.py @@ -0,0 +1,235 @@ +# pylint: disable=C0103, C0114 +import asyncio +import json +import math + +import dotenv +import pandas as pd +from occupational_classification.data_access.soc_data_access import combine_job_title + +from occupational_classification_utils.llm.llm import ClassificationLLM + +### Constants ### +knowledge_bucket = dotenv.get_key(".env", "KNOWLEDGE_BUCKET") + +output_folder = "notebooks/ashe_data_cleaning" +file_name = "ashe_in_soc_index" +# file_name = "ashe_correct_spelling" +input_file_name = "_2026_04_20" +output_file_name = "_llm_soc_codes_framework_2026_04_23" +# JOB_TITLE_COLUMN = "corrected_spelling" +JOB_TITLE_COLUMN = "documents" + +### Initiate llm connection ### +c_llm = ClassificationLLM("gemini-2.5-flash", verbose=False) + +### Access data ### +try: + # data = pd.read_csv(f"{output_folder}/checkpoint_spelling_llm{input_file_name}.csv") + data = pd.read_csv(f"{output_folder}/{file_name}{input_file_name}.csv") + print("Database loaded from local.") +except FileNotFoundError: + print("KNOWLEDGE_BUCKET not found in .env file. Please set it.") + data = pd.read_csv(f"{knowledge_bucket}ASHE_classifai_soc_kb.csv") + print("Database loaded from storage.") + +try: + with open( + f"{output_folder}/{file_name}{input_file_name}.json", encoding="utf-8" + ) as file: + recent_batch_id = json.load(file)["completed_batches"] +except FileNotFoundError: + recent_batch_id = 0 +print( + f"STARTING FROM {recent_batch_id} batch (row {recent_batch_id * 10} out of {len(data)})." +) + + +### Read the data ### +def load_soc_index(filepath: str) -> pd.DataFrame: + """Load SOC index. + Provides a list of over 32,000 titles associated with employment. + + Args: + filepath (str): A path to the file containing SOC Index. + + Returns: + pd.DataFrame: A DataFrame with transformed job titles. + """ + # pylint: disable=R0801 + soc_index_df = pd.read_excel( + filepath, + sheet_name="SOC2020 coding index", + usecols=["SOC_2020", "INDEXOCC-natural_word_order", "ADD", "IND"], + dtype=str, + ) + + soc_index_df.columns = [col.lower() for col in soc_index_df.columns] + + soc_index_df = soc_index_df.rename( + columns={"indexocc-natural_word_order": "natural_word", "soc_2020": "code"} + ) + + soc_index_df = soc_index_df[soc_index_df["code"] != "}}}}"] + soc_index_df = soc_index_df.dropna(subset=["code", "natural_word"]) + soc_index_df["title"] = soc_index_df.apply(combine_job_title, axis=1) + soc_index_df = soc_index_df[["code", "title"]] + soc_index_df["title"] = soc_index_df["title"].str.capitalize() + + return soc_index_df + + +def load_soc_framework(filepath: str) -> pd.DataFrame: + """Load SOC structure. + + Provides structure with all levels and names of the SOC 2020. + + Args: + filepath (str): A path to the file containing SOC Structure. + + Returns: + pd.DataFrame: A DataFrame containing group code, group title, + group description, typical entry routes and associated qualifications, + and list of tasks. + """ + soc_df = pd.read_excel( + filepath, + sheet_name="SOC2020 framework", + usecols=[ + "SOC2020 Unit Group", + "SOC2020 Group Title", + ], + dtype=str, + ) + soc_df.columns = [ + col.lower().replace(" ", "_").replace("__", "_").replace("\n", "") + for col in soc_df.columns + ] + soc_df = soc_df.rename( + columns={"soc2020_unit_group": "code", "soc2020_group_title": "title"} + ) + + for col in soc_df.columns: + soc_df[col] = soc_df[col].str.strip() + + return soc_df + + +### Create a dictionary for short list ### +s_list = load_soc_framework( + f"{knowledge_bucket}soc2020volume2thecodingindexexcel03122025.xlsx" +) +s_list = s_list[s_list["code"].notna()] + +if isinstance(s_list, pd.DataFrame): + s_list = s_list.to_dict(orient="records") + +data = data.drop_duplicates(subset=JOB_TITLE_COLUMN) + +# data_sample = data.sample(100) +# data_sample = data[:100] + + +async def run_soc_code(jt: str): + """Makes call to LLM to decide whether is codable to SOC. + + Args: + jt (str): Job title to be categorised. + + Returns: + codable (bool) + """ + response = await c_llm.get_soc_code(job_title=jt, short_list=s_list) + return response + + +async def batching(job_titles_column: pd.Series, batch_id: int): + """Takes next batch from the dataset of size 10. + + Args: + job_titles_column (pd.Series): A coulmn with job titles. + batch_id (int): number of the batch. + + Returns: + job_titles_column: snippet of the data provided of size 10. + """ + batch = batch_id + start_id = batch * 10 + end_id = batch * 10 + 10 + return job_titles_column[start_id:end_id].copy() + + +async def split_in_batches(document: pd.DataFrame): + """Takes the whole dataset, splits in batches and uses LLM to determine whether + the job title allows to provide a final SOC code. + + Args: + document (pd.DataFrame): file. + """ + # Check if "codable" column exists + if "codable" not in document: + document["codable"] = None + document["codable"] = document["codable"].astype(bool) + if "llm_soc_code" not in document: + document["llm_soc_code"] = None + document["llm_soc_code"] = document["llm_soc_code"].astype(float) + if "llm_soc_candidates" not in document: + document["llm_soc_candidates"] = None + document["llm_soc_candidates"] = document["llm_soc_candidates"].astype(object) + if "reasoning" not in document: + document["reasoning"] = None + document["reasoning"] = document["reasoning"].astype(str) + + final_batch = math.ceil(len(document) / 10) # get the amount of batches + + for current_batch_id in range(recent_batch_id, final_batch): + + print(f"batch {current_batch_id}") + + current_batch = await batching( + document[JOB_TITLE_COLUMN], current_batch_id + ) # does it have to be async? + + # k = 0 + + tasks = [run_soc_code(jt) for jt in current_batch] + responses = await asyncio.gather(*tasks) + + for k, llm_response in enumerate(responses): + codable = llm_response.codable + soc_code = llm_response.soc_code + soc_candidates = llm_response.soc_candidates + reasoning = llm_response.reasoning + + current_row = 10 * current_batch_id + k + document.at[current_row, "codable"] = codable + document.at[current_row, "llm_soc_code"] = soc_code + document.at[current_row, "llm_soc_candidates"] = soc_candidates + document.at[current_row, "reasoning"] = reasoning + + document.to_csv(f"{output_folder}/{file_name}{output_file_name}.csv") + # document.to_parquet(f"{output_folder}/{file_name}{output_file_name}.parquet") + + # pylint: disable=R0801 + json_data = { + "completed_batches": current_batch_id, + } + with open( + f"{output_folder}/{file_name}{output_file_name}.json", "w", encoding="utf8" + ) as json_file: + json.dump( + json_data, + json_file, + ) + + if current_batch_id + 1 == final_batch: + document.to_csv( + f"{knowledge_bucket}wip_data/{file_name}{output_file_name}.csv" + ) + print("SAVED TO BUCKET") + + +asyncio.run(split_in_batches(data)) +# asyncio.run(split_in_batches(data_sample)) +# print(data_sample) +# print(data_sample["codable"].value_counts()) From f97a22c7405350bdf88b24f09fc80b0032f87eff Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Mon, 27 Apr 2026 20:42:04 +0000 Subject: [PATCH 05/61] spell check job titles --- notebooks/2026_04_ashe_clean.py | 220 ++++++++++++++++++++++++++++++++ 1 file changed, 220 insertions(+) create mode 100644 notebooks/2026_04_ashe_clean.py diff --git a/notebooks/2026_04_ashe_clean.py b/notebooks/2026_04_ashe_clean.py new file mode 100644 index 0000000..8bb6a7a --- /dev/null +++ b/notebooks/2026_04_ashe_clean.py @@ -0,0 +1,220 @@ +# pylint: disable=C0103, C0114 + +import asyncio +import json +import math + +import dotenv +import pandas as pd +from occupational_classification.data_access.soc_data_access import combine_job_title + +from occupational_classification_utils.llm.llm import ClassificationLLM + +### Constants ### +knowledge_bucket = dotenv.get_key(".env", "KNOWLEDGE_BUCKET") + +output_folder = "notebooks/ashe_data_cleaning" +# output_folder = f"notebooks/sample" +file_name = "_2026_04_20" + +soc_coding_index_file = ( + f"{knowledge_bucket}soc2020volume2thecodingindexexcel03122025.xlsx" +) + +c_llm = ClassificationLLM("gemini-2.5-flash", verbose=False) + +### Access data ### +try: + data = pd.read_csv(f"{output_folder}/soc_knowledgebase{file_name}.csv") + + with open(f"{output_folder}/checkpoint{file_name}.json", encoding="utf-8") as file: + recent_batch_id = json.load(file)["completed_batches"] + + print("Database loaded from local.") + print(f"STARTING FROM {recent_batch_id} batch (row {recent_batch_id * 10}).") + +except FileNotFoundError: + print("KNOWLEDGE_BUCKET not found in .env file. Please set it.") + + data = pd.read_csv(f"{knowledge_bucket}ASHE_classifai_soc_kb.csv") + recent_batch_id = 0 + print("Database loaded from storage.") + + +### Read the data ### +def load_soc_index(filepath: str) -> pd.DataFrame: + """Load SOC index. + Provides a list of over 32,000 titles associated with employment. + + Args: + filepath (str): A path to the file containing SOC Index. + + Returns: + pd.DataFrame: A DataFrame with transformed job titles. + """ + soc_index_df = pd.read_excel( + filepath, + sheet_name="SOC2020 coding index", + usecols=["SOC_2020", "INDEXOCC-natural_word_order", "ADD", "IND"], + dtype=str, + ) + + soc_index_df.columns = [col.lower() for col in soc_index_df.columns] + + soc_index_df = soc_index_df.rename( + columns={"indexocc-natural_word_order": "natural_word", "soc_2020": "code"} + ) + + soc_index_df = soc_index_df[soc_index_df["code"] != "}}}}"] + soc_index_df = soc_index_df.dropna(subset=["code", "natural_word"]) + soc_index_df["title"] = soc_index_df.apply(combine_job_title, axis=1) + soc_index_df = soc_index_df[["code", "title"]] + soc_index_df["title"] = soc_index_df["title"].str.capitalize() + + return soc_index_df + + +soc_list = load_soc_index(soc_coding_index_file) + + +def load_soc_abbreviations(filepath: str) -> dict: + """Load abbreviations used in SOC. + Provides a list of abbreviations recognised in SOC titles. + + Args: + filepath (str): A path to the file containing SOC Abbreviations. + + Returns: + dict: A dictioary with abbreviations. + """ + soc_abbreviation = pd.read_excel( + filepath, + sheet_name="Abbreviations", + usecols=["Abbreviation", "Meaning"], + dtype=str, + header=5, + ) + + return soc_abbreviation + + +soc_abb = load_soc_abbreviations(soc_coding_index_file) +soc_abb_dict = soc_abb.set_index("Abbreviation")["Meaning"].to_dict() + +### Remove duplicates ### +data["documents"] = data["documents"].str.strip() # Remove leading space +data = data.drop_duplicates(subset="documents") # remove duplicates + +### Add column for correct spelling ### +if "corrected_spelling" not in data: + data["corrected_spelling"] = None + data["corrected_spelling"] = data["corrected_spelling"].astype(str) + +### Data manipulation ### +soc_list["title"] = soc_list[ + "title" +].str.upper() # Upper case, as in the original dataset + + +### Remove rows that come directly from SOC ### +titles_list = soc_list["title"] +titles_list = titles_list.to_list() + +not_in_list = data[~data["documents"].isin(titles_list)].reset_index( + drop=True +) # those are things that didn't come from soc_list +in_list = data[data["documents"].isin(titles_list)].reset_index( + drop=True +) # those are things that didn't come from soc_list + +# save the subset of titles that are repeated from SOC index +in_list.to_csv(f"{output_folder}/ashe_in_soc_index{file_name}.csv") +# in_list.to_parquet(f"{output_folder}/ashe_in_soc_index{file_name}.parquet") +print("ASHE IN SOC SAVED.") + + +async def batching(job_titles_column: pd.Series, batch_id: int): + """Takes next batch from the dataset of size 10. + + Args: + job_titles_column (pd.Series): A coulmn with job titles. + batch_id (int): number of the batch. + + Returns: + job_titles_column: snippet of the data provided of size 10. + """ + batch = batch_id + start_id = batch * 10 + end_id = batch * 10 + 10 + return job_titles_column[start_id:end_id].copy() + + +async def spelling(jt: str, abb_dict: dict): + """Makes call to LLM to correct spelling mistakes. + + Args: + jt (str): Job title to be corrected. + abb_dict (dict): A dictionary with abbreviations and their meaning {abbreviation: meaning}. + + Returns: + corrected (str) + """ + corrected = await c_llm.clean_spelling( + misspelled_string=jt, abbreviation_dictionary=abb_dict + ) + + return corrected.job_title_spelling.upper() + + +async def split_in_batches(df: pd.DataFrame): + """Takes the whole dataset, splits in batches and uses LLM to correct spelling of the job title. + + Args: + df (pd.DataFrame): file. + """ + final_batch = math.ceil(len(df) / 10) # get the amount of batches + + for current_batch_id in range(recent_batch_id, final_batch): + print(f"batch {current_batch_id}") + + current_batch = await batching(df["documents"], current_batch_id) + + tasks = [spelling(jt=jt, abb_dict=soc_abb_dict) for jt in current_batch] + responses = await asyncio.gather(*tasks) + + for k, llm_response in enumerate(responses): + current_row = 10 * current_batch_id + k + df.loc[current_row, "corrected_spelling"] = llm_response + + df.to_csv(f"{output_folder}/ashe_correct_spelling{file_name}.csv") + df.to_parquet(f"{output_folder}/ashe_correct_spelling{file_name}.parquet") + + json_data = { + "completed_batches": current_batch_id, + } + with open( + f"{output_folder}/checkpoint{file_name}.json", "w", encoding="utf8" + ) as json_file: + json.dump( + json_data, + json_file, + ) + + if current_batch_id + 1 == final_batch: + df = df.drop_duplicates( + subset="corrected_spelling" + ) # remove duplicates in the corrected spelling + + df.to_csv(f"{output_folder}/ashe_correct_spelling{file_name}.csv") + df.to_parquet(f"{output_folder}/ashe_correct_spelling{file_name}.parquet") + + # df.to_csv(f"{knowledge_bucket}soc_knowledgebase_clean{file_name}.csv") + # df.to_parquet(f"{knowledge_bucket}soc_knowledgebase_clean{file_name}.parquet") + + print("SAVED TO BUCKET") + + +asyncio.run(split_in_batches(not_in_list)) +# data_sample = data[:55] +# asyncio.run(split_in_batches(data_sample)) +# print(data_sample) From d117afac6d079d15cbf9af3880d4ff7696ed2a4a Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Mon, 27 Apr 2026 20:50:27 +0000 Subject: [PATCH 06/61] ignore csv files --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index 522f1a2..ea4a8ba 100644 --- a/.gitignore +++ b/.gitignore @@ -80,6 +80,9 @@ target/ # Disallow notebooks *.ipynb +# ignore data files +*.csv + # Excel datafiles & workbooks & macros *.xlsx *.xls From 450cbca52e979f66ba0da3ffd7df605110c633ee Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Mon, 27 Apr 2026 20:51:48 +0000 Subject: [PATCH 07/61] ignore parquet and json files --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index ea4a8ba..8e7745f 100644 --- a/.gitignore +++ b/.gitignore @@ -82,6 +82,8 @@ target/ # ignore data files *.csv +*.parquet +*.json # Excel datafiles & workbooks & macros *.xlsx From 9b23e167dadcd2e39b17f203b57d4fec789017db Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Tue, 28 Apr 2026 15:40:56 +0000 Subject: [PATCH 08/61] save soc knowledgebase --- notebooks/2026_04_soc_kb.py | 42 +++++++++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) create mode 100644 notebooks/2026_04_soc_kb.py diff --git a/notebooks/2026_04_soc_kb.py b/notebooks/2026_04_soc_kb.py new file mode 100644 index 0000000..323ced8 --- /dev/null +++ b/notebooks/2026_04_soc_kb.py @@ -0,0 +1,42 @@ +# %% +import dotenv +import pandas as pd + +# %% +knowledge_bucket = dotenv.get_key("../.env", "KNOWLEDGE_BUCKET") + +# %% +rephrased = pd.read_csv("soc_data/ashe_correct_spelling_2026_04_20.csv") +in_index = pd.read_csv("soc_data/ashe_in_soc_index_2026_04_20.csv") + +# %% +in_index['corrected_spelling'] = in_index['documents'] + +# %% +full_data = pd.concat([rephrased, in_index], ignore_index=True) + +# %% +soc_kb = full_data[['corrected_spelling', 'label']] + +# %% +soc_kb = soc_kb.rename(columns={'corrected_spelling': 'text'}) + +# %% +soc_kb['text'] = soc_kb['text'].str.capitalize() + +# %% +soc_kb = soc_kb.drop_duplicates(subset=['text', 'label'], keep="last", ignore_index=True) + +# %% +# soc_kb.to_csv(f"{knowledge_bucket}SOC_KB.csv") + +# %% +mask = soc_kb.groupby('text')['label'].transform('nunique') > 1 + +# %% +conflict_codes = soc_kb[mask].sort_values('text') + +# %% +conflict_codes + + From 05018609c43040ed0af6289737617947fb7a0441 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Tue, 28 Apr 2026 17:49:47 +0000 Subject: [PATCH 09/61] change logic rregarding deduplicaiton --- notebooks/2026_03_soc.py | 8 +++----- notebooks/2026_04_ashe_clean.py | 17 ++++++----------- 2 files changed, 9 insertions(+), 16 deletions(-) diff --git a/notebooks/2026_03_soc.py b/notebooks/2026_03_soc.py index 8ee12ff..9690807 100644 --- a/notebooks/2026_03_soc.py +++ b/notebooks/2026_03_soc.py @@ -18,7 +18,8 @@ input_file_name = "_2026_04_20" output_file_name = "_llm_soc_codes_framework_2026_04_23" # JOB_TITLE_COLUMN = "corrected_spelling" -JOB_TITLE_COLUMN = "documents" +JOB_TITLE_COLUMN = 'documents' +CODE_COLUMN = 'label' ### Initiate llm connection ### c_llm = ClassificationLLM("gemini-2.5-flash", verbose=False) @@ -124,10 +125,7 @@ def load_soc_framework(filepath: str) -> pd.DataFrame: if isinstance(s_list, pd.DataFrame): s_list = s_list.to_dict(orient="records") -data = data.drop_duplicates(subset=JOB_TITLE_COLUMN) - -# data_sample = data.sample(100) -# data_sample = data[:100] +data = data.drop_duplicates(subset=[JOB_TITLE_COLUMN, CODE_COLUMN], keep='last') async def run_soc_code(jt: str): diff --git a/notebooks/2026_04_ashe_clean.py b/notebooks/2026_04_ashe_clean.py index 8bb6a7a..181694b 100644 --- a/notebooks/2026_04_ashe_clean.py +++ b/notebooks/2026_04_ashe_clean.py @@ -14,7 +14,6 @@ knowledge_bucket = dotenv.get_key(".env", "KNOWLEDGE_BUCKET") output_folder = "notebooks/ashe_data_cleaning" -# output_folder = f"notebooks/sample" file_name = "_2026_04_20" soc_coding_index_file = ( @@ -103,7 +102,7 @@ def load_soc_abbreviations(filepath: str) -> dict: ### Remove duplicates ### data["documents"] = data["documents"].str.strip() # Remove leading space -data = data.drop_duplicates(subset="documents") # remove duplicates +data = data.drop_duplicates(subset='documents', keep='last') # remove duplicates ### Add column for correct spelling ### if "corrected_spelling" not in data: @@ -122,13 +121,14 @@ def load_soc_abbreviations(filepath: str) -> dict: not_in_list = data[~data["documents"].isin(titles_list)].reset_index( drop=True -) # those are things that didn't come from soc_list +) # those are titles that didn't appear in soc_list in_list = data[data["documents"].isin(titles_list)].reset_index( drop=True -) # those are things that didn't come from soc_list +) # those are titles that came from soc_list # save the subset of titles that are repeated from SOC index in_list.to_csv(f"{output_folder}/ashe_in_soc_index{file_name}.csv") + # in_list.to_parquet(f"{output_folder}/ashe_in_soc_index{file_name}.parquet") print("ASHE IN SOC SAVED.") @@ -187,7 +187,6 @@ async def split_in_batches(df: pd.DataFrame): df.loc[current_row, "corrected_spelling"] = llm_response df.to_csv(f"{output_folder}/ashe_correct_spelling{file_name}.csv") - df.to_parquet(f"{output_folder}/ashe_correct_spelling{file_name}.parquet") json_data = { "completed_batches": current_batch_id, @@ -202,19 +201,15 @@ async def split_in_batches(df: pd.DataFrame): if current_batch_id + 1 == final_batch: df = df.drop_duplicates( - subset="corrected_spelling" + subset=['corrected_spelling', 'label'], + keep='last' ) # remove duplicates in the corrected spelling df.to_csv(f"{output_folder}/ashe_correct_spelling{file_name}.csv") - df.to_parquet(f"{output_folder}/ashe_correct_spelling{file_name}.parquet") # df.to_csv(f"{knowledge_bucket}soc_knowledgebase_clean{file_name}.csv") - # df.to_parquet(f"{knowledge_bucket}soc_knowledgebase_clean{file_name}.parquet") print("SAVED TO BUCKET") asyncio.run(split_in_batches(not_in_list)) -# data_sample = data[:55] -# asyncio.run(split_in_batches(data_sample)) -# print(data_sample) From b49c43d505e3f638624050028814e0d995a0f06c Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Wed, 29 Apr 2026 08:36:31 +0000 Subject: [PATCH 10/61] change deduplication logic --- notebooks/2026_03_soc.py | 6 +++--- notebooks/2026_04_ashe_clean.py | 34 ++++++++++++++++++--------------- 2 files changed, 22 insertions(+), 18 deletions(-) diff --git a/notebooks/2026_03_soc.py b/notebooks/2026_03_soc.py index 9690807..2518d5b 100644 --- a/notebooks/2026_03_soc.py +++ b/notebooks/2026_03_soc.py @@ -18,8 +18,8 @@ input_file_name = "_2026_04_20" output_file_name = "_llm_soc_codes_framework_2026_04_23" # JOB_TITLE_COLUMN = "corrected_spelling" -JOB_TITLE_COLUMN = 'documents' -CODE_COLUMN = 'label' +JOB_TITLE_COLUMN = "documents" +CODE_COLUMN = "label" ### Initiate llm connection ### c_llm = ClassificationLLM("gemini-2.5-flash", verbose=False) @@ -125,7 +125,7 @@ def load_soc_framework(filepath: str) -> pd.DataFrame: if isinstance(s_list, pd.DataFrame): s_list = s_list.to_dict(orient="records") -data = data.drop_duplicates(subset=[JOB_TITLE_COLUMN, CODE_COLUMN], keep='last') +data = data.drop_duplicates(subset=[JOB_TITLE_COLUMN, CODE_COLUMN], keep="last") async def run_soc_code(jt: str): diff --git a/notebooks/2026_04_ashe_clean.py b/notebooks/2026_04_ashe_clean.py index 181694b..a3e1654 100644 --- a/notebooks/2026_04_ashe_clean.py +++ b/notebooks/2026_04_ashe_clean.py @@ -14,7 +14,8 @@ knowledge_bucket = dotenv.get_key(".env", "KNOWLEDGE_BUCKET") output_folder = "notebooks/ashe_data_cleaning" -file_name = "_2026_04_20" +file_prefix = "soc_index_spellcheck" +file_suffix = "_2026_04_28" soc_coding_index_file = ( f"{knowledge_bucket}soc2020volume2thecodingindexexcel03122025.xlsx" @@ -24,9 +25,11 @@ ### Access data ### try: - data = pd.read_csv(f"{output_folder}/soc_knowledgebase{file_name}.csv") + data = pd.read_csv(f"{output_folder}/{file_prefix}{file_suffix}.csv") - with open(f"{output_folder}/checkpoint{file_name}.json", encoding="utf-8") as file: + with open( + f"{output_folder}/checkpoint_{file_prefix}{file_suffix}.json", encoding="utf-8" + ) as file: recent_batch_id = json.load(file)["completed_batches"] print("Database loaded from local.") @@ -102,7 +105,7 @@ def load_soc_abbreviations(filepath: str) -> dict: ### Remove duplicates ### data["documents"] = data["documents"].str.strip() # Remove leading space -data = data.drop_duplicates(subset='documents', keep='last') # remove duplicates +data = data.drop_duplicates(subset="documents", keep="last") # remove duplicates ### Add column for correct spelling ### if "corrected_spelling" not in data: @@ -127,9 +130,8 @@ def load_soc_abbreviations(filepath: str) -> dict: ) # those are titles that came from soc_list # save the subset of titles that are repeated from SOC index -in_list.to_csv(f"{output_folder}/ashe_in_soc_index{file_name}.csv") +in_list.to_csv(f"{output_folder}/{file_prefix}{file_suffix}.csv") -# in_list.to_parquet(f"{output_folder}/ashe_in_soc_index{file_name}.parquet") print("ASHE IN SOC SAVED.") @@ -186,13 +188,15 @@ async def split_in_batches(df: pd.DataFrame): current_row = 10 * current_batch_id + k df.loc[current_row, "corrected_spelling"] = llm_response - df.to_csv(f"{output_folder}/ashe_correct_spelling{file_name}.csv") + df.to_csv(f"{output_folder}/{file_prefix}{file_suffix}.csv") json_data = { "completed_batches": current_batch_id, } with open( - f"{output_folder}/checkpoint{file_name}.json", "w", encoding="utf8" + f"{output_folder}/checkpoint_{file_prefix}{file_suffix}.json", + "w", + encoding="utf8", ) as json_file: json.dump( json_data, @@ -201,15 +205,15 @@ async def split_in_batches(df: pd.DataFrame): if current_batch_id + 1 == final_batch: df = df.drop_duplicates( - subset=['corrected_spelling', 'label'], - keep='last' + subset=["corrected_spelling", "label"], keep="last" ) # remove duplicates in the corrected spelling - df.to_csv(f"{output_folder}/ashe_correct_spelling{file_name}.csv") + df.to_csv(f"{output_folder}/{file_prefix}{file_suffix}.csv") + print("FILE SAVED TO LOCAL") - # df.to_csv(f"{knowledge_bucket}soc_knowledgebase_clean{file_name}.csv") + # df.to_csv(f"{output_folder}/{file_prefix}{file_suffix}.csv") + # print("SAVED TO BUCKET") - print("SAVED TO BUCKET") - -asyncio.run(split_in_batches(not_in_list)) +# asyncio.run(split_in_batches(not_in_list)) +asyncio.run(split_in_batches(in_list)) From 1d831b65654fe4343dd78a78168ece13c7562df9 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Wed, 29 Apr 2026 08:38:10 +0000 Subject: [PATCH 11/61] correct output from fixing prompt --- src/occupational_classification_utils/llm/llm.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/occupational_classification_utils/llm/llm.py b/src/occupational_classification_utils/llm/llm.py index f7e710a..455ffe9 100644 --- a/src/occupational_classification_utils/llm/llm.py +++ b/src/occupational_classification_utils/llm/llm.py @@ -496,5 +496,8 @@ async def clean_spelling( "Failed to parse response again", response_content=str(response.content), ) + validated_answer_sr = Spelling( + job_title_spelling="", + ) return validated_answer_sr From 4440585aa9e3eb53e1c164fde119f591941bda3c Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Wed, 29 Apr 2026 08:40:01 +0000 Subject: [PATCH 12/61] create SOC knowledgebase --- notebooks/2026_04_soc_kb.py | 21 +++++++++++---------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/notebooks/2026_04_soc_kb.py b/notebooks/2026_04_soc_kb.py index 323ced8..3b613e2 100644 --- a/notebooks/2026_04_soc_kb.py +++ b/notebooks/2026_04_soc_kb.py @@ -1,3 +1,4 @@ +# pylint: disable=C0103, C0114 # %% import dotenv import pandas as pd @@ -10,33 +11,33 @@ in_index = pd.read_csv("soc_data/ashe_in_soc_index_2026_04_20.csv") # %% -in_index['corrected_spelling'] = in_index['documents'] +in_index["corrected_spelling"] = in_index["documents"] # %% full_data = pd.concat([rephrased, in_index], ignore_index=True) # %% -soc_kb = full_data[['corrected_spelling', 'label']] +soc_kb = full_data[["corrected_spelling", "label"]] # %% -soc_kb = soc_kb.rename(columns={'corrected_spelling': 'text'}) +soc_kb = soc_kb.rename(columns={"corrected_spelling": "text"}) # %% -soc_kb['text'] = soc_kb['text'].str.capitalize() +soc_kb["text"] = soc_kb["text"].str.capitalize() # %% -soc_kb = soc_kb.drop_duplicates(subset=['text', 'label'], keep="last", ignore_index=True) +soc_kb = soc_kb.drop_duplicates( + subset=["text", "label"], keep="last", ignore_index=True +) # %% # soc_kb.to_csv(f"{knowledge_bucket}SOC_KB.csv") # %% -mask = soc_kb.groupby('text')['label'].transform('nunique') > 1 +mask = soc_kb.groupby("text")["label"].transform("nunique") > 1 # %% -conflict_codes = soc_kb[mask].sort_values('text') +conflict_codes = soc_kb[mask].sort_values("text") # %% -conflict_codes - - +print(conflict_codes) From 438dbfcd0eb8a4cbb460fd4bcdcbc9689b273b81 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Wed, 29 Apr 2026 13:21:55 +0000 Subject: [PATCH 13/61] rename file --- notebooks/{2026_03_soc.py => 2026_03_assign_soc_code.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename notebooks/{2026_03_soc.py => 2026_03_assign_soc_code.py} (100%) diff --git a/notebooks/2026_03_soc.py b/notebooks/2026_03_assign_soc_code.py similarity index 100% rename from notebooks/2026_03_soc.py rename to notebooks/2026_03_assign_soc_code.py From 5e4738d3fc6c67dc13fc229ac154bec7f3f1c6da Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Wed, 29 Apr 2026 15:20:52 +0000 Subject: [PATCH 14/61] rename files --- notebooks/{2026_04_ashe_clean.py => ashe_clean_2026_04.py} | 0 .../{2026_03_assign_soc_code.py => assign_soc_code_2026_03.py} | 0 notebooks/{2026_04_soc_kb.py => soc_kb_2026_04.py} | 0 3 files changed, 0 insertions(+), 0 deletions(-) rename notebooks/{2026_04_ashe_clean.py => ashe_clean_2026_04.py} (100%) rename notebooks/{2026_03_assign_soc_code.py => assign_soc_code_2026_03.py} (100%) rename notebooks/{2026_04_soc_kb.py => soc_kb_2026_04.py} (100%) diff --git a/notebooks/2026_04_ashe_clean.py b/notebooks/ashe_clean_2026_04.py similarity index 100% rename from notebooks/2026_04_ashe_clean.py rename to notebooks/ashe_clean_2026_04.py diff --git a/notebooks/2026_03_assign_soc_code.py b/notebooks/assign_soc_code_2026_03.py similarity index 100% rename from notebooks/2026_03_assign_soc_code.py rename to notebooks/assign_soc_code_2026_03.py diff --git a/notebooks/2026_04_soc_kb.py b/notebooks/soc_kb_2026_04.py similarity index 100% rename from notebooks/2026_04_soc_kb.py rename to notebooks/soc_kb_2026_04.py From ecd45be8c7cdad47caf0280a7ab266733a2061f4 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Wed, 29 Apr 2026 15:21:25 +0000 Subject: [PATCH 15/61] rename files --- notebooks/assign_soc_code_2026_03.py | 93 ++++++++++++++++++---------- 1 file changed, 62 insertions(+), 31 deletions(-) diff --git a/notebooks/assign_soc_code_2026_03.py b/notebooks/assign_soc_code_2026_03.py index 2518d5b..6033407 100644 --- a/notebooks/assign_soc_code_2026_03.py +++ b/notebooks/assign_soc_code_2026_03.py @@ -2,24 +2,26 @@ import asyncio import json import math +import os import dotenv import pandas as pd -from occupational_classification.data_access.soc_data_access import combine_job_title +from occupational_classification.data_access.soc_data_access import combine_job_title from occupational_classification_utils.llm.llm import ClassificationLLM ### Constants ### knowledge_bucket = dotenv.get_key(".env", "KNOWLEDGE_BUCKET") -output_folder = "notebooks/ashe_data_cleaning" -file_name = "ashe_in_soc_index" -# file_name = "ashe_correct_spelling" +output_folder = "notebooks/soc_data" +# file_name = "ashe_in_soc_index" +file_name = "ashe_correct_spelling" input_file_name = "_2026_04_20" -output_file_name = "_llm_soc_codes_framework_2026_04_23" -# JOB_TITLE_COLUMN = "corrected_spelling" -JOB_TITLE_COLUMN = "documents" +output_file_name = "_llm_soc_codes_index_2026_04_29" +JOB_TITLE_COLUMN = "corrected_spelling" +# JOB_TITLE_COLUMN = "documents" CODE_COLUMN = "label" +BATCH_SIZE = 10 ### Initiate llm connection ### c_llm = ClassificationLLM("gemini-2.5-flash", verbose=False) @@ -31,18 +33,19 @@ print("Database loaded from local.") except FileNotFoundError: print("KNOWLEDGE_BUCKET not found in .env file. Please set it.") - data = pd.read_csv(f"{knowledge_bucket}ASHE_classifai_soc_kb.csv") + data = pd.read_csv(f"{knowledge_bucket}{file_name}{input_file_name}.csv") print("Database loaded from storage.") try: with open( - f"{output_folder}/{file_name}{input_file_name}.json", encoding="utf-8" + f"{output_folder}/{file_name}{output_file_name}.json", encoding="utf-8" ) as file: recent_batch_id = json.load(file)["completed_batches"] except FileNotFoundError: recent_batch_id = 0 + print( - f"STARTING FROM {recent_batch_id} batch (row {recent_batch_id * 10} out of {len(data)})." + f"STARTING FROM {recent_batch_id} batch (row {recent_batch_id * BATCH_SIZE} out of {len(data)})." ) @@ -142,18 +145,18 @@ async def run_soc_code(jt: str): async def batching(job_titles_column: pd.Series, batch_id: int): - """Takes next batch from the dataset of size 10. + """Takes next batch from the dataset of specified size. Args: job_titles_column (pd.Series): A coulmn with job titles. batch_id (int): number of the batch. Returns: - job_titles_column: snippet of the data provided of size 10. + job_titles_column: snippet of the data provided of specified size. """ batch = batch_id - start_id = batch * 10 - end_id = batch * 10 + 10 + start_id = batch * BATCH_SIZE + end_id = batch * BATCH_SIZE + BATCH_SIZE return job_titles_column[start_id:end_id].copy() @@ -178,7 +181,7 @@ async def split_in_batches(document: pd.DataFrame): document["reasoning"] = None document["reasoning"] = document["reasoning"].astype(str) - final_batch = math.ceil(len(document) / 10) # get the amount of batches + final_batch = math.ceil(len(document) / BATCH_SIZE) # get the amount of batches for current_batch_id in range(recent_batch_id, final_batch): @@ -186,9 +189,7 @@ async def split_in_batches(document: pd.DataFrame): current_batch = await batching( document[JOB_TITLE_COLUMN], current_batch_id - ) # does it have to be async? - - # k = 0 + ) tasks = [run_soc_code(jt) for jt in current_batch] responses = await asyncio.gather(*tasks) @@ -199,18 +200,38 @@ async def split_in_batches(document: pd.DataFrame): soc_candidates = llm_response.soc_candidates reasoning = llm_response.reasoning - current_row = 10 * current_batch_id + k + current_row = BATCH_SIZE * current_batch_id + k document.at[current_row, "codable"] = codable document.at[current_row, "llm_soc_code"] = soc_code document.at[current_row, "llm_soc_candidates"] = soc_candidates document.at[current_row, "reasoning"] = reasoning - document.to_csv(f"{output_folder}/{file_name}{output_file_name}.csv") - # document.to_parquet(f"{output_folder}/{file_name}{output_file_name}.parquet") + start_row = current_batch_id * BATCH_SIZE + end_row = start_row + BATCH_SIZE + + rows_to_save = document.iloc[start_row:end_row][[ + 'documents', + 'label', + 'corrected_spelling', + 'codable', + 'llm_soc_code', + 'llm_soc_candidates', + 'reasoning' + ]] + + + # document.to_csv(f"{output_folder}/{file_name}{output_file_name}.csv") + + rows_to_save.to_csv( + f"{output_folder}/{file_name}{output_file_name}.csv", + mode='a', + header=False, + index=False, + ) # pylint: disable=R0801 json_data = { - "completed_batches": current_batch_id, + "completed_batches": current_batch_id + 1, } with open( f"{output_folder}/{file_name}{output_file_name}.json", "w", encoding="utf8" @@ -220,14 +241,24 @@ async def split_in_batches(document: pd.DataFrame): json_file, ) - if current_batch_id + 1 == final_batch: - document.to_csv( - f"{knowledge_bucket}wip_data/{file_name}{output_file_name}.csv" - ) - print("SAVED TO BUCKET") + # if current_batch_id + 1 == final_batch: + # document.to_csv( + # f"{knowledge_bucket}wip_data/{file_name}{output_file_name}.csv" + # ) + # print("SAVED TO BUCKET") + + +if not os.path.exists(f"{output_folder}/{file_name}{output_file_name}.csv"): + all_columns = [ + 'documents', + 'label', + 'corrected_spelling', + 'codable', + 'llm_soc_code', + 'llm_soc_candidates', + 'reasoning' + ] + pd.DataFrame(columns=all_columns).to_csv(f"{output_folder}/{file_name}{output_file_name}.csv", index=False) -asyncio.run(split_in_batches(data)) -# asyncio.run(split_in_batches(data_sample)) -# print(data_sample) -# print(data_sample["codable"].value_counts()) +asyncio.run(split_in_batches(data)) \ No newline at end of file From 633187a0b3fe20b68cb6b7b28d85d4b66d2d6904 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Thu, 30 Apr 2026 13:58:10 +0000 Subject: [PATCH 16/61] prompt attempt2 --- .../llm/prompt.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/src/occupational_classification_utils/llm/prompt.py b/src/occupational_classification_utils/llm/prompt.py index 358a6b8..0e864d1 100644 --- a/src/occupational_classification_utils/llm/prompt.py +++ b/src/occupational_classification_utils/llm/prompt.py @@ -40,10 +40,14 @@ for the use in the UK official statistics. Respondent data may be in English or Welsh, but you always respond in British English.""" -_soc_template = """"Given the respondent job title your task is to determine -the UK SOC (Standard Occupational Classification) code for this job. +_soc_template = """"Given the respondent job title your task is to determine the UK SOC +(Standard Occupational Classification) code for this job strictly from the list of codes provided. If the code cannot be determined, identify the additional information -needed to determine it. Make sure to use the provided 2020 SOC index. +needed to determine it. + +You must return the subset list of possible soc codes (UK SOC 2020 codes provided). + +SOC codes must come from the list provided below. ===Respondent Data=== - Job Title: {job_title} @@ -52,7 +56,7 @@ ===Output Format=== {format_instructions} -===Relevant subset of UK SOC 2020=== +===UK SOC 2020 codes=== {soc_index} """ @@ -67,7 +71,7 @@ template=_core_prompt + _soc_template, partial_variables={ "format_instructions": parser.get_format_instructions(), - "soc_index": soc_index, + # "soc_index": soc_index, }, ) @@ -110,7 +114,7 @@ template=_core_prompt + _sa_soc_template_rag, partial_variables={ "format_instructions": parser.get_format_instructions(), - "soc_index": soc_index, + # "soc_index": soc_index, }, ) From c4aa376dacbd389a393ca616d239b5186cc74048 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Thu, 30 Apr 2026 14:01:01 +0000 Subject: [PATCH 17/61] prompt attempt3 --- src/occupational_classification_utils/llm/prompt.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/occupational_classification_utils/llm/prompt.py b/src/occupational_classification_utils/llm/prompt.py index 0e864d1..38262f5 100644 --- a/src/occupational_classification_utils/llm/prompt.py +++ b/src/occupational_classification_utils/llm/prompt.py @@ -40,14 +40,14 @@ for the use in the UK official statistics. Respondent data may be in English or Welsh, but you always respond in British English.""" -_soc_template = """"Given the respondent job title your task is to determine the UK SOC -(Standard Occupational Classification) code for this job strictly from the list of codes provided. -If the code cannot be determined, identify the additional information -needed to determine it. +_soc_template = """"Given the respondent job title your task is to determine code for this job, +selecting strictly from the list of codes provided, matching the job title with a description of the code. -You must return the subset list of possible soc codes (UK SOC 2020 codes provided). +If the code cannot be determined, identify the additional information needed to determine it. -SOC codes must come from the list provided below. +You must return the subset list of possible codes. + +Codes must come from the list provided below. ===Respondent Data=== - Job Title: {job_title} From 73d9ade40d03126a43846e3b1e6a83ef3a0ba9cf Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Thu, 30 Apr 2026 14:24:37 +0000 Subject: [PATCH 18/61] prompt attempt4 --- src/occupational_classification_utils/llm/prompt.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/occupational_classification_utils/llm/prompt.py b/src/occupational_classification_utils/llm/prompt.py index 38262f5..9056aed 100644 --- a/src/occupational_classification_utils/llm/prompt.py +++ b/src/occupational_classification_utils/llm/prompt.py @@ -41,7 +41,7 @@ but you always respond in British English.""" _soc_template = """"Given the respondent job title your task is to determine code for this job, -selecting strictly from the list of codes provided, matching the job title with a description of the code. +that have the same meaning as the job title provided. Select codes strictly from the list provided. If the code cannot be determined, identify the additional information needed to determine it. From 4f7fe85f7a977fab664af61533e7a757ed35765b Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Thu, 30 Apr 2026 14:34:32 +0000 Subject: [PATCH 19/61] prompt attempt5 --- src/occupational_classification_utils/llm/prompt.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/occupational_classification_utils/llm/prompt.py b/src/occupational_classification_utils/llm/prompt.py index 9056aed..8c27c5e 100644 --- a/src/occupational_classification_utils/llm/prompt.py +++ b/src/occupational_classification_utils/llm/prompt.py @@ -56,7 +56,7 @@ ===Output Format=== {format_instructions} -===UK SOC 2020 codes=== +===Codes with titles=== {soc_index} """ From 0e36e722311687b5892800adf3dd537bdcb98891 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Thu, 30 Apr 2026 15:21:26 +0000 Subject: [PATCH 20/61] prompt attempt5 --- .../llm/llm.py | 25 ++++---- .../llm/prompt.py | 59 +++++++++++++++++-- 2 files changed, 67 insertions(+), 17 deletions(-) diff --git a/src/occupational_classification_utils/llm/llm.py b/src/occupational_classification_utils/llm/llm.py index 455ffe9..8c23c65 100644 --- a/src/occupational_classification_utils/llm/llm.py +++ b/src/occupational_classification_utils/llm/llm.py @@ -113,9 +113,10 @@ def __init__( # noqa: PLR0913 async def get_soc_code( self, job_title: str, + # soc_codes: list, code_digits: int = 4, candidates_limit: int = 5, - short_list: Optional[list[dict[Any, Any]]] = None, + # short_list: Optional[list[dict[Any, Any]]] = None, ) -> SocResponse: """Generates a SOC classification based on respondent's data using the full SOC index embedded in the query (mirror SIC one-shot). @@ -138,28 +139,28 @@ async def get_soc_code( ValueError: If there is an error parsing the response from the LLM model. """ - if short_list is None: - raise ValueError( - "Short list is None - list provided from embedding search." - ) - soc_codes = self._prompt_candidate_list( - short_list, code_digits=code_digits, candidates_limit=candidates_limit - ) - - def prep_call_dict(job_title, soc_codes): + # if short_list is None: + # raise ValueError( + # "Short list is None - list provided from embedding search." + # ) + # soc_codes = self._prompt_candidate_list( + # short_list, code_digits=code_digits, candidates_limit=candidates_limit + # ) + + def prep_call_dict(job_title):#, soc_codes): # Helper function to prepare the call dictionary is_job_title_present = job_title is None or job_title in {"", " "} job_title = "Unknown" if is_job_title_present else job_title call_dict = { "job_title": job_title, - "soc_index": soc_codes, + # "soc_index": soc_codes, } return call_dict call_dict = prep_call_dict( job_title=job_title, - soc_codes=soc_codes, + # soc_codes=soc_codes, ) chain = self.soc_prompt | self.llm diff --git a/src/occupational_classification_utils/llm/prompt.py b/src/occupational_classification_utils/llm/prompt.py index 8c27c5e..56cbade 100644 --- a/src/occupational_classification_utils/llm/prompt.py +++ b/src/occupational_classification_utils/llm/prompt.py @@ -30,9 +30,13 @@ RagResponse, SocResponse, ) -from occupational_classification_utils.utils.soc_data_access import ( - load_soc_index, -) +# from occupational_classification_utils.utils.soc_data_access import ( +# load_soc_index, +# ) + +from occupational_classification.data_access.soc_data_access import combine_job_title +import dotenv +import pandas as pd config = get_config() @@ -61,7 +65,52 @@ """ # Load the full SOC index from the configuration (mirror SIC: full index into one-shot prompt) -soc_index = load_soc_index(config["lookups"]["soc_index"]) +# soc_index = load_soc_index(config["lookups"]["soc_index"]) + + + +def load_soc_framework(filepath: str) -> pd.DataFrame: + """Load SOC structure. + + Provides structure with all levels and names of the SOC 2020. + + Args: + filepath (str): A path to the file containing SOC Structure. + + Returns: + pd.DataFrame: A DataFrame containing group code, group title, + group description, typical entry routes and associated qualifications, + and list of tasks. + """ + soc_df = pd.read_excel( + filepath, + sheet_name="SOC2020 framework", + usecols=[ + "SOC2020 Unit Group", + "SOC2020 Group Title", + ], + dtype=str, + ) + soc_df.columns = [ + col.lower().replace(" ", "_").replace("__", "_").replace("\n", "") + for col in soc_df.columns + ] + soc_df = soc_df.rename( + columns={"soc2020_unit_group": "code", "soc2020_group_title": "title"} + ) + + for col in soc_df.columns: + soc_df[col] = soc_df[col].str.strip() + + return soc_df + +knowledge_bucket = dotenv.get_key(".env", "KNOWLEDGE_BUCKET") +soc_index = load_soc_framework( + f"{knowledge_bucket}soc2020volume2thecodingindexexcel03122025.xlsx" +) + + + parser = PydanticOutputParser( # type: ignore # Suspect langchain ver bug pydantic_object=SocResponse @@ -71,7 +120,7 @@ template=_core_prompt + _soc_template, partial_variables={ "format_instructions": parser.get_format_instructions(), - # "soc_index": soc_index, + "soc_index": soc_index, }, ) From 100edb4c35abd7391fab5a31f44a51f19110f92f Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Thu, 30 Apr 2026 15:41:04 +0000 Subject: [PATCH 21/61] prompt attempt6 --- .../llm/prompt.py | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) diff --git a/src/occupational_classification_utils/llm/prompt.py b/src/occupational_classification_utils/llm/prompt.py index 56cbade..f81c622 100644 --- a/src/occupational_classification_utils/llm/prompt.py +++ b/src/occupational_classification_utils/llm/prompt.py @@ -44,14 +44,10 @@ for the use in the UK official statistics. Respondent data may be in English or Welsh, but you always respond in British English.""" -_soc_template = """"Given the respondent job title your task is to determine code for this job, -that have the same meaning as the job title provided. Select codes strictly from the list provided. - -If the code cannot be determined, identify the additional information needed to determine it. - -You must return the subset list of possible codes. - -Codes must come from the list provided below. +_soc_template = """"Given the respondent job title your task is to determine +the UK SOC (Standard Occupational Classification) code for this job. +If the code cannot be determined, identify the additional information +needed to determine it. Make sure to use the provided 2020 SOC index. ===Respondent Data=== - Job Title: {job_title} @@ -60,7 +56,7 @@ ===Output Format=== {format_instructions} -===Codes with titles=== +===Relevant subset of UK SOC 2020=== {soc_index} """ From efc097808667052488e02a612b2cfb8953f3d163 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Thu, 30 Apr 2026 16:08:53 +0000 Subject: [PATCH 22/61] prompt attempt7 --- src/occupational_classification_utils/llm/prompt.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/occupational_classification_utils/llm/prompt.py b/src/occupational_classification_utils/llm/prompt.py index f81c622..d3ea40c 100644 --- a/src/occupational_classification_utils/llm/prompt.py +++ b/src/occupational_classification_utils/llm/prompt.py @@ -45,9 +45,9 @@ but you always respond in British English.""" _soc_template = """"Given the respondent job title your task is to determine -the UK SOC (Standard Occupational Classification) code for this job. +code for this job. If the code cannot be determined, identify the additional information -needed to determine it. Make sure to use the provided 2020 SOC index. +needed to determine it. Make sure to use the provided coding index. ===Respondent Data=== - Job Title: {job_title} @@ -56,7 +56,7 @@ ===Output Format=== {format_instructions} -===Relevant subset of UK SOC 2020=== +===Relevant coding index=== {soc_index} """ From 265dad2d2165de5628755ae643873dead18d8762 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Tue, 5 May 2026 10:46:23 +0000 Subject: [PATCH 23/61] prompt attempt8 --- src/occupational_classification_utils/llm/prompt.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/src/occupational_classification_utils/llm/prompt.py b/src/occupational_classification_utils/llm/prompt.py index d3ea40c..ec4bdaa 100644 --- a/src/occupational_classification_utils/llm/prompt.py +++ b/src/occupational_classification_utils/llm/prompt.py @@ -44,10 +44,11 @@ for the use in the UK official statistics. Respondent data may be in English or Welsh, but you always respond in British English.""" -_soc_template = """"Given the respondent job title your task is to determine -code for this job. +_soc_template = """""Given the respondent job title your task is to determine +the UK SOC (Standard Occupational Classification) code for this job. If the code cannot be determined, identify the additional information -needed to determine it. Make sure to use the provided coding index. +needed to determine it. +The codes you assign MUST appear in the provided 2020 SOC index. ===Respondent Data=== - Job Title: {job_title} @@ -56,7 +57,7 @@ ===Output Format=== {format_instructions} -===Relevant coding index=== +===2020 SOC index=== {soc_index} """ From 877c771a34ec2f9d5a6efe60151b439674315c93 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Tue, 5 May 2026 19:45:12 +0000 Subject: [PATCH 24/61] clean file to assigning soc code --- notebooks/assign_soc_code_2026_03.py | 83 +++++++++++++++------------- 1 file changed, 44 insertions(+), 39 deletions(-) diff --git a/notebooks/assign_soc_code_2026_03.py b/notebooks/assign_soc_code_2026_03.py index 6033407..5921e2e 100644 --- a/notebooks/assign_soc_code_2026_03.py +++ b/notebooks/assign_soc_code_2026_03.py @@ -6,21 +6,27 @@ import dotenv import pandas as pd - from occupational_classification.data_access.soc_data_access import combine_job_title + from occupational_classification_utils.llm.llm import ClassificationLLM ### Constants ### knowledge_bucket = dotenv.get_key(".env", "KNOWLEDGE_BUCKET") output_folder = "notebooks/soc_data" + # file_name = "ashe_in_soc_index" file_name = "ashe_correct_spelling" + input_file_name = "_2026_04_20" -output_file_name = "_llm_soc_codes_index_2026_04_29" +# input_file_name = "_clean" + +output_file_name = "_llm_soc_codes_index_2026_04_30_attempt8" + JOB_TITLE_COLUMN = "corrected_spelling" # JOB_TITLE_COLUMN = "documents" CODE_COLUMN = "label" + BATCH_SIZE = 10 ### Initiate llm connection ### @@ -28,7 +34,6 @@ ### Access data ### try: - # data = pd.read_csv(f"{output_folder}/checkpoint_spelling_llm{input_file_name}.csv") data = pd.read_csv(f"{output_folder}/{file_name}{input_file_name}.csv") print("Database loaded from local.") except FileNotFoundError: @@ -45,7 +50,7 @@ recent_batch_id = 0 print( - f"STARTING FROM {recent_batch_id} batch (row {recent_batch_id * BATCH_SIZE} out of {len(data)})." + f"STARTING FROM {recent_batch_id} batch (row {recent_batch_id * BATCH_SIZE} out of {len(data)})." # pylint: disable=C0301 ) @@ -96,6 +101,7 @@ def load_soc_framework(filepath: str) -> pd.DataFrame: group description, typical entry routes and associated qualifications, and list of tasks. """ + # pylint: disable=R0801 soc_df = pd.read_excel( filepath, sheet_name="SOC2020 framework", @@ -123,6 +129,10 @@ def load_soc_framework(filepath: str) -> pd.DataFrame: s_list = load_soc_framework( f"{knowledge_bucket}soc2020volume2thecodingindexexcel03122025.xlsx" ) +# s_list = load_soc_index( +# f"{knowledge_bucket}soc2020volume2thecodingindexexcel03122025.xlsx" +# ) + s_list = s_list[s_list["code"].notna()] if isinstance(s_list, pd.DataFrame): @@ -140,7 +150,7 @@ async def run_soc_code(jt: str): Returns: codable (bool) """ - response = await c_llm.get_soc_code(job_title=jt, short_list=s_list) + response = await c_llm.get_soc_code(job_title=jt) return response @@ -160,7 +170,7 @@ async def batching(job_titles_column: pd.Series, batch_id: int): return job_titles_column[start_id:end_id].copy() -async def split_in_batches(document: pd.DataFrame): +async def split_in_batches(document: pd.DataFrame): # pylint: disable=R0914 """Takes the whole dataset, splits in batches and uses LLM to determine whether the job title allows to provide a final SOC code. @@ -187,9 +197,7 @@ async def split_in_batches(document: pd.DataFrame): print(f"batch {current_batch_id}") - current_batch = await batching( - document[JOB_TITLE_COLUMN], current_batch_id - ) + current_batch = await batching(document[JOB_TITLE_COLUMN], current_batch_id) tasks = [run_soc_code(jt) for jt in current_batch] responses = await asyncio.gather(*tasks) @@ -207,37 +215,33 @@ async def split_in_batches(document: pd.DataFrame): document.at[current_row, "reasoning"] = reasoning start_row = current_batch_id * BATCH_SIZE - end_row = start_row + BATCH_SIZE - - rows_to_save = document.iloc[start_row:end_row][[ - 'documents', - 'label', - 'corrected_spelling', - 'codable', - 'llm_soc_code', - 'llm_soc_candidates', - 'reasoning' - ]] - - # document.to_csv(f"{output_folder}/{file_name}{output_file_name}.csv") + rows_to_save = document.iloc[start_row : start_row + BATCH_SIZE][ + [ + "documents", + "label", + "corrected_spelling", + "codable", + "llm_soc_code", + "llm_soc_candidates", + "reasoning", + ] + ] rows_to_save.to_csv( f"{output_folder}/{file_name}{output_file_name}.csv", - mode='a', + mode="a", header=False, index=False, ) - # pylint: disable=R0801 - json_data = { - "completed_batches": current_batch_id + 1, - } with open( f"{output_folder}/{file_name}{output_file_name}.json", "w", encoding="utf8" ) as json_file: json.dump( - json_data, + { + "completed_batches": current_batch_id + 1, + }, json_file, ) @@ -248,17 +252,18 @@ async def split_in_batches(document: pd.DataFrame): # print("SAVED TO BUCKET") - if not os.path.exists(f"{output_folder}/{file_name}{output_file_name}.csv"): all_columns = [ - 'documents', - 'label', - 'corrected_spelling', - 'codable', - 'llm_soc_code', - 'llm_soc_candidates', - 'reasoning' - ] - pd.DataFrame(columns=all_columns).to_csv(f"{output_folder}/{file_name}{output_file_name}.csv", index=False) + "documents", + "label", + "corrected_spelling", + "codable", + "llm_soc_code", + "llm_soc_candidates", + "reasoning", + ] + pd.DataFrame(columns=all_columns).to_csv( + f"{output_folder}/{file_name}{output_file_name}.csv", index=False + ) -asyncio.run(split_in_batches(data)) \ No newline at end of file +asyncio.run(split_in_batches(data)) From 36b09758339e27d80cf455be66769908c051224c Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Tue, 5 May 2026 19:45:59 +0000 Subject: [PATCH 25/61] clean commented out code from llm and promp files --- src/occupational_classification_utils/llm/llm.py | 15 ++------------- .../llm/prompt.py | 10 ++++------ 2 files changed, 6 insertions(+), 19 deletions(-) diff --git a/src/occupational_classification_utils/llm/llm.py b/src/occupational_classification_utils/llm/llm.py index 8c23c65..2e81faa 100644 --- a/src/occupational_classification_utils/llm/llm.py +++ b/src/occupational_classification_utils/llm/llm.py @@ -113,10 +113,6 @@ def __init__( # noqa: PLR0913 async def get_soc_code( self, job_title: str, - # soc_codes: list, - code_digits: int = 4, - candidates_limit: int = 5, - # short_list: Optional[list[dict[Any, Any]]] = None, ) -> SocResponse: """Generates a SOC classification based on respondent's data using the full SOC index embedded in the query (mirror SIC one-shot). @@ -139,15 +135,8 @@ async def get_soc_code( ValueError: If there is an error parsing the response from the LLM model. """ - # if short_list is None: - # raise ValueError( - # "Short list is None - list provided from embedding search." - # ) - # soc_codes = self._prompt_candidate_list( - # short_list, code_digits=code_digits, candidates_limit=candidates_limit - # ) - - def prep_call_dict(job_title):#, soc_codes): + + def prep_call_dict(job_title): # Helper function to prepare the call dictionary is_job_title_present = job_title is None or job_title in {"", " "} job_title = "Unknown" if is_job_title_present else job_title diff --git a/src/occupational_classification_utils/llm/prompt.py b/src/occupational_classification_utils/llm/prompt.py index ec4bdaa..2845a25 100644 --- a/src/occupational_classification_utils/llm/prompt.py +++ b/src/occupational_classification_utils/llm/prompt.py @@ -22,6 +22,8 @@ # pylint: disable=invalid-name # Need to clean up the code to remove this +import dotenv +import pandas as pd from langchain.output_parsers import PydanticOutputParser from langchain.prompts.prompt import PromptTemplate @@ -30,13 +32,11 @@ RagResponse, SocResponse, ) + # from occupational_classification_utils.utils.soc_data_access import ( # load_soc_index, # ) -from occupational_classification.data_access.soc_data_access import combine_job_title -import dotenv -import pandas as pd config = get_config() @@ -65,7 +65,6 @@ # soc_index = load_soc_index(config["lookups"]["soc_index"]) - def load_soc_framework(filepath: str) -> pd.DataFrame: """Load SOC structure. @@ -101,14 +100,13 @@ def load_soc_framework(filepath: str) -> pd.DataFrame: return soc_df + knowledge_bucket = dotenv.get_key(".env", "KNOWLEDGE_BUCKET") soc_index = load_soc_framework( f"{knowledge_bucket}soc2020volume2thecodingindexexcel03122025.xlsx" ) - - parser = PydanticOutputParser( # type: ignore # Suspect langchain ver bug pydantic_object=SocResponse ) From 5cbf18de0c3eecc3052f3eceab05a88730e45951 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Tue, 5 May 2026 21:00:16 +0000 Subject: [PATCH 26/61] faster saving --- notebooks/ashe_clean_2026_04.py | 75 ++++++++++++++++++++++----------- 1 file changed, 51 insertions(+), 24 deletions(-) diff --git a/notebooks/ashe_clean_2026_04.py b/notebooks/ashe_clean_2026_04.py index a3e1654..3fc6306 100644 --- a/notebooks/ashe_clean_2026_04.py +++ b/notebooks/ashe_clean_2026_04.py @@ -3,6 +3,7 @@ import asyncio import json import math +import os import dotenv import pandas as pd @@ -13,9 +14,11 @@ ### Constants ### knowledge_bucket = dotenv.get_key(".env", "KNOWLEDGE_BUCKET") -output_folder = "notebooks/ashe_data_cleaning" -file_prefix = "soc_index_spellcheck" -file_suffix = "_2026_04_28" +output_folder = "notebooks/soc_data" +file_prefix = "ashe_correct_spelling" +file_suffix = "_2026_05_05" + +BATCH_SIZE = 10 soc_coding_index_file = ( f"{knowledge_bucket}soc2020volume2thecodingindexexcel03122025.xlsx" @@ -25,22 +28,24 @@ ### Access data ### try: + data = pd.read_csv(f"{knowledge_bucket}ASHE_classifai_soc_kb.csv") + print("Database loaded from storage.") + + +except FileNotFoundError: + print("KNOWLEDGE_BUCKET not found in .env file. Please set it.") data = pd.read_csv(f"{output_folder}/{file_prefix}{file_suffix}.csv") + print("Database loaded from local.") +try: with open( - f"{output_folder}/checkpoint_{file_prefix}{file_suffix}.json", encoding="utf-8" + f"{output_folder}/{file_prefix}{file_suffix}.json", encoding="utf-8" ) as file: recent_batch_id = json.load(file)["completed_batches"] - - print("Database loaded from local.") - print(f"STARTING FROM {recent_batch_id} batch (row {recent_batch_id * 10}).") - except FileNotFoundError: - print("KNOWLEDGE_BUCKET not found in .env file. Please set it.") - - data = pd.read_csv(f"{knowledge_bucket}ASHE_classifai_soc_kb.csv") recent_batch_id = 0 - print("Database loaded from storage.") + +print(f"STARTING FROM {recent_batch_id} batch (row {recent_batch_id * BATCH_SIZE}).") ### Read the data ### @@ -130,8 +135,7 @@ def load_soc_abbreviations(filepath: str) -> dict: ) # those are titles that came from soc_list # save the subset of titles that are repeated from SOC index -in_list.to_csv(f"{output_folder}/{file_prefix}{file_suffix}.csv") - +in_list.to_csv(f"{output_folder}/ashe_in_soc_index{file_suffix}.csv") print("ASHE IN SOC SAVED.") @@ -146,8 +150,8 @@ async def batching(job_titles_column: pd.Series, batch_id: int): job_titles_column: snippet of the data provided of size 10. """ batch = batch_id - start_id = batch * 10 - end_id = batch * 10 + 10 + start_id = batch * BATCH_SIZE + end_id = batch * BATCH_SIZE + BATCH_SIZE return job_titles_column[start_id:end_id].copy() @@ -174,7 +178,7 @@ async def split_in_batches(df: pd.DataFrame): Args: df (pd.DataFrame): file. """ - final_batch = math.ceil(len(df) / 10) # get the amount of batches + final_batch = math.ceil(len(df) / BATCH_SIZE) # get the amount of batches for current_batch_id in range(recent_batch_id, final_batch): print(f"batch {current_batch_id}") @@ -185,21 +189,34 @@ async def split_in_batches(df: pd.DataFrame): responses = await asyncio.gather(*tasks) for k, llm_response in enumerate(responses): - current_row = 10 * current_batch_id + k + current_row = BATCH_SIZE * current_batch_id + k df.loc[current_row, "corrected_spelling"] = llm_response - df.to_csv(f"{output_folder}/{file_prefix}{file_suffix}.csv") + start_row = current_batch_id * BATCH_SIZE + + rows_to_save = df.iloc[start_row : start_row + BATCH_SIZE][ + ["documents", + "label", + "corrected_spelling", + ] + ] + + rows_to_save.to_csv( + f"{output_folder}/{file_prefix}{file_suffix}.csv", + mode="a", + header=False, + index=False, + ) - json_data = { - "completed_batches": current_batch_id, - } with open( - f"{output_folder}/checkpoint_{file_prefix}{file_suffix}.json", + f"{output_folder}/{file_prefix}{file_suffix}.json", "w", encoding="utf8", ) as json_file: json.dump( - json_data, + { + "completed_batches": current_batch_id, + }, json_file, ) @@ -215,5 +232,15 @@ async def split_in_batches(df: pd.DataFrame): # print("SAVED TO BUCKET") +if not os.path.exists(f"{output_folder}/{file_prefix}{file_suffix}.csv"): + all_columns = [ + "documents", + "label", + "corrected_spelling", + ] + pd.DataFrame(columns=all_columns).to_csv( + f"{output_folder}/{file_prefix}{file_suffix}.csv", index=False + ) + # asyncio.run(split_in_batches(not_in_list)) asyncio.run(split_in_batches(in_list)) From cc7f1481a8cccaad30569b2e8c4711cbfe1bec1c Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Tue, 5 May 2026 21:07:15 +0000 Subject: [PATCH 27/61] clean layout --- notebooks/ashe_clean_2026_04.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/notebooks/ashe_clean_2026_04.py b/notebooks/ashe_clean_2026_04.py index 3fc6306..e4eabb8 100644 --- a/notebooks/ashe_clean_2026_04.py +++ b/notebooks/ashe_clean_2026_04.py @@ -195,9 +195,10 @@ async def split_in_batches(df: pd.DataFrame): start_row = current_batch_id * BATCH_SIZE rows_to_save = df.iloc[start_row : start_row + BATCH_SIZE][ - ["documents", - "label", - "corrected_spelling", + [ + "documents", + "label", + "corrected_spelling", ] ] From a5d89a6f6aede556c8c2072180d3fea72d28b8ba Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Tue, 5 May 2026 21:47:43 +0000 Subject: [PATCH 28/61] create soc lookup - notebook --- notebooks/create_soc_lookup_2026_04.py | 372 +++++++++++++++++++++++++ 1 file changed, 372 insertions(+) create mode 100644 notebooks/create_soc_lookup_2026_04.py diff --git a/notebooks/create_soc_lookup_2026_04.py b/notebooks/create_soc_lookup_2026_04.py new file mode 100644 index 0000000..b20f9f8 --- /dev/null +++ b/notebooks/create_soc_lookup_2026_04.py @@ -0,0 +1,372 @@ +# pylint: disable=C0103, C0114 +# %% +import ast +import re + +import dotenv +import pandas as pd + +# %% +output_folder = "soc_data" +# output_folder = "sample" + +file_name = "ashe_in_soc_index_llm_soc_codes_" +index_file = "2026_04_22" + +file_name_not_in_index = "ashe_correct_spelling_llm_soc_codes_" +# file_name_not_in_index = "data_subset_llm_soc_codes_" +# framework_file = "index_2026_04_21" +framework_file = "index_2026_04_30" + + +# %% +data_not_in_index = pd.read_csv( + f"{output_folder}/{file_name_not_in_index}{framework_file}.csv" +) # not in index +data_in_index = pd.read_csv(f"{output_folder}/{file_name}{index_file}.csv") + +# %% +print(len(data_not_in_index)) +print(len(data_in_index)) + +# %% +data_not_in_index = data_not_in_index[ + [ + "documents", + "corrected_spelling", + "label", + "codable", + "llm_soc_code", + "llm_soc_candidates", + "reasoning", + ] +] +data_in_index = data_in_index[ + [ + "documents", + "corrected_spelling", + "label", + "codable", + "llm_soc_code", + "llm_soc_candidates", + "reasoning", + ] +] + + +# %% +def parse_string(text): + """Convert string to a list of dictionaries for SOC candidates.""" + if isinstance(text, str): + processed = text.replace("SocCandidate(", "dict(") + processed = re.sub(r"(\w+)=", r'"\1":', processed) + processed = processed.replace("dict(", "{").replace(")", "}") + return ast.literal_eval(processed) + return [] + + +# %% +data_not_in_index["llm_soc_candidates"] = data_not_in_index["llm_soc_candidates"].map( + parse_string +) +data_in_index["llm_soc_candidates"] = data_in_index["llm_soc_candidates"].map( + parse_string +) + +# %% +print(f"index: {data_not_in_index["codable"].value_counts()}") +print(f"framework: {data_in_index["codable"].value_counts()}") + + +# %% +def access_soc_code_from_candidate_list(row_values: list[dict]) -> list[str]: + """From list of potential SOC candidates, access SOC codes. + + Args: + row_values (list[dict]): list of dictionaries with SOC candidates. + + Return: + candidates (list[str]): list of 4-digit candidate codes. + """ + if isinstance(row_values, list): + candidates = [] + for row in row_values: + if len(row) < 1: + return None + candidates.append(row.get("soc_code")) + else: + return None + return candidates + + +# %% +def float_to_list_of_codes(row_values: float) -> str: + """Convert float to a string of codes (str). + + Args: + row_values (float): SOC code as a float. + + Return: + row_values (str): SOC code as a string. + """ + if isinstance(row_values, float): + codes_list = [f"{row_values:.0f}"] + return codes_list + return row_values + + +# %% +data_not_in_index["label"] = data_not_in_index["label"].astype(str) +data_in_index["label"] = data_in_index["label"].astype(str) + +# %% +msk_not_index = data_not_in_index[ + "llm_soc_code" +].isna() # take rows, where LLM didn't provide a code. +msk_index = data_in_index[ + "llm_soc_code" +].isna() # take rows, where LLM didn't provide a code. + +# %% +data_not_in_index.loc[~msk_not_index, "llm_soc_code"] = data_not_in_index.loc[ + ~msk_not_index, "llm_soc_code" +].apply(float_to_list_of_codes) +data_in_index.loc[~msk_index, "llm_soc_code"] = data_in_index.loc[ + ~msk_index, "llm_soc_code" +].apply(float_to_list_of_codes) + +# %% +data_not_in_index.loc[msk_not_index, "llm_soc_code"] = data_not_in_index.loc[ + msk_not_index, "llm_soc_candidates" +].apply(access_soc_code_from_candidate_list) +data_in_index.loc[msk_index, "llm_soc_code"] = data_in_index.loc[ + msk_index, "llm_soc_candidates" +].apply(access_soc_code_from_candidate_list) + + +# %% +def check_agreement(df: pd.DataFrame, df_source: str): + """Checks agreement between ASHE and LLM assigned codes. + + Args: + df (pd.DataFrame): dataframe containing columns 'label' and 'llm_soc_code' with codes. + df_source (str): String indicaitng the source of the dataframe (ASHE or soc index). + """ + agr, in_cand = 0, 0 + # check if 'label' is the same as 'llm_soc_code'. + # If LLM uncodable, check if 'label' in candidates. + for row in range(len(df)): + if len(df.iloc[row]["llm_soc_code"]) == 1: + agr += df.iloc[row]["label"] == df.iloc[row]["llm_soc_code"][0] + df.loc[row, "codable"] = True + elif len(df.iloc[row]["llm_soc_code"]) > 1: + in_cand += df.iloc[row]["label"] in df.iloc[row]["llm_soc_code"] + + print( + f"Argeement full {df_source}: {agr} ({round(agr/len(df), 2) * 100}% of all rows)" + ) + print( + f"Argeement (code in candidates) {df_source}: {in_cand} ({round(in_cand/len(df), 2) * 100}% of all rows)" # pylint: disable=C0301 + ) + print( + f"Argeement (label the same or within candidates) {df_source}: {agr + in_cand} ({round((agr + in_cand)/len(df), 2) * 100}% of all rows)" # pylint: disable=C0301 + ) + + +# %% +check_agreement(data_not_in_index, "ASHE - not in index") +check_agreement(data_in_index, "SOC index rows") + + +# %% +def check_code_count(df: pd.DataFrame, df_source: str): + """Check if the LLM assigned a sigle, multiple, or none codes when assessing SOC codes. + + Args: + df (pd.DataFrame): dataframe containing LLM assessment of SOC codes. + Requires 'llm_soc_code' column. + df_source (str): String indicaitng the source of the dataframe (ASHE or soc index). + """ + longer, shorter, one_code = 0, 0, 0 + for code in df["llm_soc_code"]: + if isinstance(code, list): + if len(code) > 1: + longer += 1 + if len(code) < 1: + shorter += 1 + if len(code) == 1: + one_code += 1 + else: + one_code += 1 + + print( + f"More than one code {df_source}: {longer} ({round(longer/len(df) * 100, 2)}%)" + ) + print( + f"No codes assigned {df_source}: {shorter} ({round(shorter/len(df) * 100, 2)}%)" + ) + print( + f"One code assigned {df_source}: {one_code} ({round(one_code/len(df) * 100, 2)}%)" + ) + + +# %% +check_code_count(data_not_in_index, "ASHE - not in index") +check_code_count(data_in_index, "SOC index rows") + +# %% +data_in_index["corrected_spelling"] = data_in_index["documents"] + +# %% +full_data_post_cleaning = pd.concat( + [data_not_in_index, data_in_index], ignore_index=True +) + +# %% +full_data_codable = full_data_post_cleaning[full_data_post_cleaning["codable"]] + +# %% +data_only_columns = full_data_post_cleaning[["corrected_spelling", "llm_soc_code"]] + +# %% +data_one_code = data_only_columns[data_only_columns["llm_soc_code"].str.len() == 1] + + +# %% +e = data_one_code["llm_soc_code"].str[0] + +# %% +numeric = pd.to_numeric(e, errors="coerce") + +# %% +data_one_code["llm_soc_code"] = numeric + +# %% +data_one_code = data_one_code.dropna(subset=["llm_soc_code"]) + +# %% +data_one_code["llm_soc_code"] = data_one_code["llm_soc_code"].astype(int) + +# %% +data_one_code = data_one_code.rename( + columns={"corrected_spelling": "documents", "llm_soc_code": "label"} +) + +# %% +data_one_code = data_one_code.drop_duplicates( + subset=["documents", "label"], keep="last", ignore_index=True +) + + +# %% +def load_soc_framework(filepath: str) -> pd.DataFrame: + """Load SOC structure. + + Provides structure with all levels and names of the SOC 2020. + + Args: + filepath (str): A path to the file containing SOC Structure. + + Returns: + pd.DataFrame: A DataFrame containing group code, group title, + group description, typical entry routes and associated qualifications, + and list of tasks. + """ + # pylint: disable=R0801 + soc_df = pd.read_excel( + filepath, + sheet_name="SOC2020 framework", + usecols=[ + "SOC2020 Unit Group", + "SOC2020 Group Title", + ], + dtype=str, + ) + soc_df.columns = [ + col.lower().replace(" ", "_").replace("__", "_").replace("\n", "") + for col in soc_df.columns + ] + soc_df = soc_df.rename( + columns={"soc2020_unit_group": "code", "soc2020_group_title": "title"} + ) + + for col in soc_df.columns: + soc_df[col] = soc_df[col].str.strip() + + return soc_df + + +# %% +knowledge_bucket = dotenv.get_key("../.env", "KNOWLEDGE_BUCKET") + +# %% +s_list = load_soc_framework( + f"{knowledge_bucket}soc2020volume2thecodingindexexcel03122025.xlsx" +) +s_list = s_list[s_list["code"].notna()] + +# %% +codes_from_framework_str = list(s_list["code"].value_counts().keys()) + +# %% +codes_from_framework_int = [] +for i in codes_from_framework_str: + codes_from_framework_int.append(int(i)) + +# %% +phantom_codes = ( + data_one_code[~data_one_code["label"].isin(codes_from_framework_int)]["label"] + .value_counts() + .keys() +) + +# %% +print(phantom_codes) + +# %% +data_one_code_no_phantoms = data_one_code[ + data_one_code["label"].isin(codes_from_framework_int) +] + +# %% +coded_all = len(data_one_code) + +# %% +coded_no_phantom = len(data_one_code_no_phantoms) + +# %% +diff = len(data_one_code) - len(data_one_code_no_phantoms) + +# %% +drop = diff / coded_all * 100 + +# %% +print( + f"with phantoms: {coded_all}\ncoded no phantoms: {coded_no_phantom}\ndiff: {diff}\ndrop(%): {drop:.2f}" # pylint: disable=C0301 +) + +# %% +print(data_one_code_no_phantoms) + +# %% +print( + data_one_code_no_phantoms[ + data_one_code_no_phantoms.duplicated(subset=["documents"]) + ] +) + +# %% +print( + data_one_code_no_phantoms[data_one_code_no_phantoms["documents"] == "BIKE MECHANIC"] +) + +# %% +data_one_code_no_phantoms = data_one_code_no_phantoms.drop_duplicates( + subset=["documents"], keep="last", ignore_index=True +) + +# %% +# data_one_code_no_phantoms.to_csv("soc_data/SOC_DIRECT_LOOKUP.csv") + +# %% +# data_one_code_no_phantoms.to_csv(f"{knowledge_bucket}SOC_DIRECT_LOOKUP.csv") From ad0d103f85cda9f273010d78355b1c307c2dcd69 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Thu, 14 May 2026 11:26:47 +0000 Subject: [PATCH 29/61] update poetery lock, add env template --- .env.template | 1 + poetry.lock | 27 +++------------------------ 2 files changed, 4 insertions(+), 24 deletions(-) create mode 100644 .env.template diff --git a/.env.template b/.env.template new file mode 100644 index 0000000..76c65fc --- /dev/null +++ b/.env.template @@ -0,0 +1 @@ +KNOWLEDGE_BUCKET= \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index c0d1e23..bef8e35 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.3.4 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.4.1 and should not be changed by hand. [[package]] name = "aiohappyeyeballs" @@ -2101,27 +2101,7 @@ proto-plus = [ {version = ">=1.22.2,<2.0.0", markers = "python_version >= \"3.11\""}, {version = ">=1.25.0,<2.0.0", markers = "python_version >= \"3.13\""}, ] -protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<7.0.0dev" - -[[package]] -name = "google-cloud-iam" -version = "2.19.1" -description = "Google Cloud Iam API client library" -optional = false -python-versions = ">=3.7" -groups = ["main"] -markers = "python_version >= \"3.14\"" -files = [ - {file = "google_cloud_iam-2.19.1-py3-none-any.whl", hash = "sha256:11b08b86d82510021f9dd9f0beb5a08219e070deab09e28d4c0ce49f8c70997d"}, - {file = "google_cloud_iam-2.19.1.tar.gz", hash = "sha256:f059c369ad98af6be3401f0f5d087775d775fb96833be1e9ab8048c422fb1bf4"}, -] - -[package.dependencies] -google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0", extras = ["grpc"]} -google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0" -grpc-google-iam-v1 = ">=0.12.4,<1.0.0" -proto-plus = {version = ">=1.25.0,<2.0.0", markers = "python_version >= \"3.13\""} -protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<7.0.0" +protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<7.0.0.dev0" [[package]] name = "google-cloud-iam" @@ -2469,7 +2449,6 @@ description = "HTTP/2-based RPC framework" optional = false python-versions = ">=3.9" groups = ["main", "dev"] -markers = "python_version < \"3.14\"" files = [ {file = "grpcio-1.78.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:7cc47943d524ee0096f973e1081cb8f4f17a4615f2116882a5f1416e4cfe92b5"}, {file = "grpcio-1.78.0-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:c3f293fdc675ccba4db5a561048cca627b5e7bd1c8a6973ffedabe7d116e22e2"}, @@ -2538,7 +2517,7 @@ files = [ typing-extensions = ">=4.12,<5.0" [package.extras] -protobuf = ["grpcio-tools (>=1.71.0)"] +protobuf = ["grpcio-tools (>=1.78.0)"] [[package]] name = "grpcio-status" From cdf362c86ca7ee0c2eab28d84a8a7153a48fedbb Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Thu, 14 May 2026 15:16:03 +0000 Subject: [PATCH 30/61] correct imports after new version --- notebooks/ashe_clean_2026_04.py | 2 +- src/occupational_classification_utils/llm/llm.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/ashe_clean_2026_04.py b/notebooks/ashe_clean_2026_04.py index e4eabb8..5af6845 100644 --- a/notebooks/ashe_clean_2026_04.py +++ b/notebooks/ashe_clean_2026_04.py @@ -7,7 +7,7 @@ import dotenv import pandas as pd -from occupational_classification.data_access.soc_data_access import combine_job_title +from occupational_classification.data_access.soc_data_access import _combine_soc_index_job_title as combine_job_title from occupational_classification_utils.llm.llm import ClassificationLLM diff --git a/src/occupational_classification_utils/llm/llm.py b/src/occupational_classification_utils/llm/llm.py index 0eac190..9222a81 100644 --- a/src/occupational_classification_utils/llm/llm.py +++ b/src/occupational_classification_utils/llm/llm.py @@ -42,7 +42,7 @@ SocResponse, Spelling, ) -from occupational_classification_utils.utils.soc_data_access import ( +from occupational_classification.data_access.soc_data_access import ( get_soc_meta, load_soc_hierarchy, ) From cc7ddbb2eccf723fd09c913c9ed762603c958df8 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Thu, 14 May 2026 16:11:02 +0000 Subject: [PATCH 31/61] add docstrings to scripts --- notebooks/ashe_clean_2026_04.py | 14 ++++++++++++-- notebooks/assign_soc_code_2026_03.py | 12 +++++++++++- 2 files changed, 23 insertions(+), 3 deletions(-) diff --git a/notebooks/ashe_clean_2026_04.py b/notebooks/ashe_clean_2026_04.py index 5af6845..0959d98 100644 --- a/notebooks/ashe_clean_2026_04.py +++ b/notebooks/ashe_clean_2026_04.py @@ -1,4 +1,12 @@ -# pylint: disable=C0103, C0114 +# pylint: disable=C0103 +"""This is not a notebook. Run as a script. + +Allows to use LLMS to correct spelling of provided text strings. +Recognises ABBREVIATIONS commonly used in SOC. + +To execute, run: + `python notebooks/ashe_clean_2026_04.py ` +""" import asyncio import json @@ -7,7 +15,9 @@ import dotenv import pandas as pd -from occupational_classification.data_access.soc_data_access import _combine_soc_index_job_title as combine_job_title +from occupational_classification.data_access.soc_data_access import ( + _combine_soc_index_job_title as combine_job_title, +) from occupational_classification_utils.llm.llm import ClassificationLLM diff --git a/notebooks/assign_soc_code_2026_03.py b/notebooks/assign_soc_code_2026_03.py index 5921e2e..4f3111b 100644 --- a/notebooks/assign_soc_code_2026_03.py +++ b/notebooks/assign_soc_code_2026_03.py @@ -1,4 +1,12 @@ # pylint: disable=C0103, C0114 +"""This is not a notebook. Run as a script. + +Allows to use LLM to assign SOC codes. + +To execute, run: + `python notebooks/assign_soc_code_2026_03.py ` +""" + import asyncio import json import math @@ -6,7 +14,9 @@ import dotenv import pandas as pd -from occupational_classification.data_access.soc_data_access import combine_job_title +from occupational_classification.data_access.soc_data_access import ( + _combine_soc_index_job_title as combine_job_title, +) from occupational_classification_utils.llm.llm import ClassificationLLM From 11725d7a07fe96a1a8545c8d1502cfa2018c0e18 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Thu, 14 May 2026 16:11:35 +0000 Subject: [PATCH 32/61] lint --- notebooks/assign_soc_code_2026_03.py | 2 +- src/occupational_classification_utils/llm/llm.py | 4 ---- src/occupational_classification_utils/llm/prompt.py | 1 - 3 files changed, 1 insertion(+), 6 deletions(-) diff --git a/notebooks/assign_soc_code_2026_03.py b/notebooks/assign_soc_code_2026_03.py index 4f3111b..fef101f 100644 --- a/notebooks/assign_soc_code_2026_03.py +++ b/notebooks/assign_soc_code_2026_03.py @@ -1,4 +1,4 @@ -# pylint: disable=C0103, C0114 +# pylint: disable=C0103 """This is not a notebook. Run as a script. Allows to use LLM to assign SOC codes. diff --git a/src/occupational_classification_utils/llm/llm.py b/src/occupational_classification_utils/llm/llm.py index 9222a81..a0adc7b 100644 --- a/src/occupational_classification_utils/llm/llm.py +++ b/src/occupational_classification_utils/llm/llm.py @@ -42,10 +42,6 @@ SocResponse, Spelling, ) -from occupational_classification.data_access.soc_data_access import ( - get_soc_meta, - load_soc_hierarchy, -) logger = get_logger(__name__) config = get_config() diff --git a/src/occupational_classification_utils/llm/prompt.py b/src/occupational_classification_utils/llm/prompt.py index c9c6b2a..2845a25 100644 --- a/src/occupational_classification_utils/llm/prompt.py +++ b/src/occupational_classification_utils/llm/prompt.py @@ -26,7 +26,6 @@ import pandas as pd from langchain.output_parsers import PydanticOutputParser from langchain.prompts.prompt import PromptTemplate -from occupational_classification.data_access.soc_data_access import load_soc_index from occupational_classification_utils.embed.embedding import get_config from occupational_classification_utils.models.response_model import ( From f47f50fb6a0874ce44acd7fa92e18ad6f1784b36 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Thu, 14 May 2026 18:18:04 +0000 Subject: [PATCH 33/61] Small changes: import logic, typehints, not saving index, replace None with original text --- notebooks/ashe_clean_2026_04.py | 29 ++++++++++++++++------------ notebooks/assign_soc_code_2026_03.py | 9 +++++---- notebooks/soc_kb_2026_04.py | 2 +- 3 files changed, 23 insertions(+), 17 deletions(-) diff --git a/notebooks/ashe_clean_2026_04.py b/notebooks/ashe_clean_2026_04.py index 0959d98..a949574 100644 --- a/notebooks/ashe_clean_2026_04.py +++ b/notebooks/ashe_clean_2026_04.py @@ -55,7 +55,7 @@ except FileNotFoundError: recent_batch_id = 0 -print(f"STARTING FROM {recent_batch_id} batch (row {recent_batch_id * BATCH_SIZE}).") +print(f"STARTING FROM {recent_batch_id} batch (row {recent_batch_id * BATCH_SIZE} out of {len(data)}).") ### Read the data ### @@ -79,11 +79,11 @@ def load_soc_index(filepath: str) -> pd.DataFrame: soc_index_df.columns = [col.lower() for col in soc_index_df.columns] soc_index_df = soc_index_df.rename( - columns={"indexocc-natural_word_order": "natural_word", "soc_2020": "code"} + columns={"indexocc-natural_word_order": "indexocc", "soc_2020": "code"} ) soc_index_df = soc_index_df[soc_index_df["code"] != "}}}}"] - soc_index_df = soc_index_df.dropna(subset=["code", "natural_word"]) + soc_index_df = soc_index_df.dropna(subset=["code", "indexocc"]) soc_index_df["title"] = soc_index_df.apply(combine_job_title, axis=1) soc_index_df = soc_index_df[["code", "title"]] soc_index_df["title"] = soc_index_df["title"].str.capitalize() @@ -94,7 +94,7 @@ def load_soc_index(filepath: str) -> pd.DataFrame: soc_list = load_soc_index(soc_coding_index_file) -def load_soc_abbreviations(filepath: str) -> dict: +def load_soc_abbreviations(filepath: str) -> pd.DataFrame: """Load abbreviations used in SOC. Provides a list of abbreviations recognised in SOC titles. @@ -102,7 +102,7 @@ def load_soc_abbreviations(filepath: str) -> dict: filepath (str): A path to the file containing SOC Abbreviations. Returns: - dict: A dictioary with abbreviations. + pd.DataFrame: A dictioary with abbreviations. """ soc_abbreviation = pd.read_excel( filepath, @@ -145,7 +145,7 @@ def load_soc_abbreviations(filepath: str) -> dict: ) # those are titles that came from soc_list # save the subset of titles that are repeated from SOC index -in_list.to_csv(f"{output_folder}/ashe_in_soc_index{file_suffix}.csv") +in_list.to_csv(f"{output_folder}/ashe_in_soc_index{file_suffix}.csv", index=False) print("ASHE IN SOC SAVED.") @@ -202,6 +202,9 @@ async def split_in_batches(df: pd.DataFrame): current_row = BATCH_SIZE * current_batch_id + k df.loc[current_row, "corrected_spelling"] = llm_response + if df.loc[current_row, "corrected_spelling"] in [None, "None"]: + df.loc[current_row, "corrected_spelling"] = df.loc[current_row, "label"] + start_row = current_batch_id * BATCH_SIZE rows_to_save = df.iloc[start_row : start_row + BATCH_SIZE][ @@ -233,13 +236,15 @@ async def split_in_batches(df: pd.DataFrame): if current_batch_id + 1 == final_batch: df = df.drop_duplicates( - subset=["corrected_spelling", "label"], keep="last" + subset=["corrected_spelling", "label"], keep="last", ignore_index=True ) # remove duplicates in the corrected spelling - df.to_csv(f"{output_folder}/{file_prefix}{file_suffix}.csv") - print("FILE SAVED TO LOCAL") + df = df[["documents","label","corrected_spelling"]].copy() + df.to_csv(f"{output_folder}/{file_prefix}{file_suffix}.csv", index=False) - # df.to_csv(f"{output_folder}/{file_prefix}{file_suffix}.csv") + print("FILE SAVED TO LOCAL") + + # df.to_csv(f"{knowledge_bucket}wip_data/{file_prefix}{file_suffix}.csv", index=False) # print("SAVED TO BUCKET") @@ -253,5 +258,5 @@ async def split_in_batches(df: pd.DataFrame): f"{output_folder}/{file_prefix}{file_suffix}.csv", index=False ) -# asyncio.run(split_in_batches(not_in_list)) -asyncio.run(split_in_batches(in_list)) +asyncio.run(split_in_batches(not_in_list)) +# asyncio.run(split_in_batches(in_list)) diff --git a/notebooks/assign_soc_code_2026_03.py b/notebooks/assign_soc_code_2026_03.py index fef101f..d899e3e 100644 --- a/notebooks/assign_soc_code_2026_03.py +++ b/notebooks/assign_soc_code_2026_03.py @@ -44,13 +44,14 @@ ### Access data ### try: - data = pd.read_csv(f"{output_folder}/{file_name}{input_file_name}.csv") - print("Database loaded from local.") -except FileNotFoundError: - print("KNOWLEDGE_BUCKET not found in .env file. Please set it.") data = pd.read_csv(f"{knowledge_bucket}{file_name}{input_file_name}.csv") print("Database loaded from storage.") +except FileNotFoundError: + print("KNOWLEDGE_BUCKET not found in .env file. Please set it.") + data = pd.read_csv(f"{output_folder}/{file_name}{input_file_name}.csv") + print("Database loaded from local.") + try: with open( f"{output_folder}/{file_name}{output_file_name}.json", encoding="utf-8" diff --git a/notebooks/soc_kb_2026_04.py b/notebooks/soc_kb_2026_04.py index 3b613e2..6f83e96 100644 --- a/notebooks/soc_kb_2026_04.py +++ b/notebooks/soc_kb_2026_04.py @@ -31,7 +31,7 @@ ) # %% -# soc_kb.to_csv(f"{knowledge_bucket}SOC_KB.csv") +# soc_kb.to_csv(f"{knowledge_bucket}SOC_KB.csv", index=False) # %% mask = soc_kb.groupby("text")["label"].transform("nunique") > 1 From 67d01f680f378bf74884abde35acdc807785720d Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Thu, 14 May 2026 18:51:24 +0000 Subject: [PATCH 34/61] remove async where not needed --- notebooks/ashe_clean_2026_04.py | 4 ++-- notebooks/assign_soc_code_2026_03.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/notebooks/ashe_clean_2026_04.py b/notebooks/ashe_clean_2026_04.py index a949574..dad27e8 100644 --- a/notebooks/ashe_clean_2026_04.py +++ b/notebooks/ashe_clean_2026_04.py @@ -149,7 +149,7 @@ def load_soc_abbreviations(filepath: str) -> pd.DataFrame: print("ASHE IN SOC SAVED.") -async def batching(job_titles_column: pd.Series, batch_id: int): +def batching(job_titles_column: pd.Series, batch_id: int): """Takes next batch from the dataset of size 10. Args: @@ -193,7 +193,7 @@ async def split_in_batches(df: pd.DataFrame): for current_batch_id in range(recent_batch_id, final_batch): print(f"batch {current_batch_id}") - current_batch = await batching(df["documents"], current_batch_id) + current_batch = batching(df["documents"], current_batch_id) tasks = [spelling(jt=jt, abb_dict=soc_abb_dict) for jt in current_batch] responses = await asyncio.gather(*tasks) diff --git a/notebooks/assign_soc_code_2026_03.py b/notebooks/assign_soc_code_2026_03.py index d899e3e..51b4393 100644 --- a/notebooks/assign_soc_code_2026_03.py +++ b/notebooks/assign_soc_code_2026_03.py @@ -165,7 +165,7 @@ async def run_soc_code(jt: str): return response -async def batching(job_titles_column: pd.Series, batch_id: int): +def batching(job_titles_column: pd.Series, batch_id: int): """Takes next batch from the dataset of specified size. Args: @@ -208,7 +208,7 @@ async def split_in_batches(document: pd.DataFrame): # pylint: disable=R0914 print(f"batch {current_batch_id}") - current_batch = await batching(document[JOB_TITLE_COLUMN], current_batch_id) + current_batch = batching(document[JOB_TITLE_COLUMN], current_batch_id) tasks = [run_soc_code(jt) for jt in current_batch] responses = await asyncio.gather(*tasks) From b239c589050f5931d64531175a69f3d5e5c0a972 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Thu, 14 May 2026 19:13:04 +0000 Subject: [PATCH 35/61] remove unused method, variable and import --- notebooks/assign_soc_code_2026_03.py | 87 ---------------------------- 1 file changed, 87 deletions(-) diff --git a/notebooks/assign_soc_code_2026_03.py b/notebooks/assign_soc_code_2026_03.py index 51b4393..6515fe4 100644 --- a/notebooks/assign_soc_code_2026_03.py +++ b/notebooks/assign_soc_code_2026_03.py @@ -14,9 +14,6 @@ import dotenv import pandas as pd -from occupational_classification.data_access.soc_data_access import ( - _combine_soc_index_job_title as combine_job_title, -) from occupational_classification_utils.llm.llm import ClassificationLLM @@ -65,90 +62,6 @@ ) -### Read the data ### -def load_soc_index(filepath: str) -> pd.DataFrame: - """Load SOC index. - Provides a list of over 32,000 titles associated with employment. - - Args: - filepath (str): A path to the file containing SOC Index. - - Returns: - pd.DataFrame: A DataFrame with transformed job titles. - """ - # pylint: disable=R0801 - soc_index_df = pd.read_excel( - filepath, - sheet_name="SOC2020 coding index", - usecols=["SOC_2020", "INDEXOCC-natural_word_order", "ADD", "IND"], - dtype=str, - ) - - soc_index_df.columns = [col.lower() for col in soc_index_df.columns] - - soc_index_df = soc_index_df.rename( - columns={"indexocc-natural_word_order": "natural_word", "soc_2020": "code"} - ) - - soc_index_df = soc_index_df[soc_index_df["code"] != "}}}}"] - soc_index_df = soc_index_df.dropna(subset=["code", "natural_word"]) - soc_index_df["title"] = soc_index_df.apply(combine_job_title, axis=1) - soc_index_df = soc_index_df[["code", "title"]] - soc_index_df["title"] = soc_index_df["title"].str.capitalize() - - return soc_index_df - - -def load_soc_framework(filepath: str) -> pd.DataFrame: - """Load SOC structure. - - Provides structure with all levels and names of the SOC 2020. - - Args: - filepath (str): A path to the file containing SOC Structure. - - Returns: - pd.DataFrame: A DataFrame containing group code, group title, - group description, typical entry routes and associated qualifications, - and list of tasks. - """ - # pylint: disable=R0801 - soc_df = pd.read_excel( - filepath, - sheet_name="SOC2020 framework", - usecols=[ - "SOC2020 Unit Group", - "SOC2020 Group Title", - ], - dtype=str, - ) - soc_df.columns = [ - col.lower().replace(" ", "_").replace("__", "_").replace("\n", "") - for col in soc_df.columns - ] - soc_df = soc_df.rename( - columns={"soc2020_unit_group": "code", "soc2020_group_title": "title"} - ) - - for col in soc_df.columns: - soc_df[col] = soc_df[col].str.strip() - - return soc_df - - -### Create a dictionary for short list ### -s_list = load_soc_framework( - f"{knowledge_bucket}soc2020volume2thecodingindexexcel03122025.xlsx" -) -# s_list = load_soc_index( -# f"{knowledge_bucket}soc2020volume2thecodingindexexcel03122025.xlsx" -# ) - -s_list = s_list[s_list["code"].notna()] - -if isinstance(s_list, pd.DataFrame): - s_list = s_list.to_dict(orient="records") - data = data.drop_duplicates(subset=[JOB_TITLE_COLUMN, CODE_COLUMN], keep="last") From 6b33a4583fc093bb25034780c403b098648c1e9d Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Mon, 18 May 2026 16:32:11 +0000 Subject: [PATCH 36/61] replace reasoning in the corrected_spelling with original label --- notebooks/ashe_clean_2026_04.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/notebooks/ashe_clean_2026_04.py b/notebooks/ashe_clean_2026_04.py index dad27e8..09a6d1a 100644 --- a/notebooks/ashe_clean_2026_04.py +++ b/notebooks/ashe_clean_2026_04.py @@ -26,7 +26,7 @@ output_folder = "notebooks/soc_data" file_prefix = "ashe_correct_spelling" -file_suffix = "_2026_05_05" +file_suffix = "_2026_05_18" BATCH_SIZE = 10 @@ -202,7 +202,9 @@ async def split_in_batches(df: pd.DataFrame): current_row = BATCH_SIZE * current_batch_id + k df.loc[current_row, "corrected_spelling"] = llm_response - if df.loc[current_row, "corrected_spelling"] in [None, "None"]: + if isinstance(df.loc[current_row, "corrected_spelling"], float): + df.loc[current_row, "corrected_spelling"] = df.loc[current_row, "label"] + elif "JOB TITLE" in df.loc[current_row, "corrected_spelling"]: df.loc[current_row, "corrected_spelling"] = df.loc[current_row, "label"] start_row = current_batch_id * BATCH_SIZE @@ -235,16 +237,17 @@ async def split_in_batches(df: pd.DataFrame): ) if current_batch_id + 1 == final_batch: - df = df.drop_duplicates( + + final_df = pd.read_csv(f"{output_folder}/{file_prefix}{file_suffix}.csv") + final_df = final_df.drop_duplicates( subset=["corrected_spelling", "label"], keep="last", ignore_index=True ) # remove duplicates in the corrected spelling - df = df[["documents","label","corrected_spelling"]].copy() - df.to_csv(f"{output_folder}/{file_prefix}{file_suffix}.csv", index=False) + final_df.to_csv(f"{output_folder}/{file_prefix}{file_suffix}.csv", index=False) print("FILE SAVED TO LOCAL") - # df.to_csv(f"{knowledge_bucket}wip_data/{file_prefix}{file_suffix}.csv", index=False) + # final_df.to_csv(f"{knowledge_bucket}wip_data/{file_prefix}{file_suffix}.csv", index=False) # print("SAVED TO BUCKET") @@ -258,5 +261,6 @@ async def split_in_batches(df: pd.DataFrame): f"{output_folder}/{file_prefix}{file_suffix}.csv", index=False ) + asyncio.run(split_in_batches(not_in_list)) # asyncio.run(split_in_batches(in_list)) From 95703a87d87e0b631bfc756d3fe33d1b821ae36b Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Mon, 18 May 2026 16:32:53 +0000 Subject: [PATCH 37/61] abbreviations in prompt --- .../llm/prompt.py | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/src/occupational_classification_utils/llm/prompt.py b/src/occupational_classification_utils/llm/prompt.py index 2845a25..6938582 100644 --- a/src/occupational_classification_utils/llm/prompt.py +++ b/src/occupational_classification_utils/llm/prompt.py @@ -209,13 +209,19 @@ def load_soc_framework(filepath: str) -> pd.DataFrame: CORRECT_SPELLING_PROMPT = PromptTemplate.from_template( - """Correct spelling of this job title: {job_title} + """Correct spelling of this job title: {job_title}. - Accepted abbreviations and their meaning in a dictionary format: {abbreviations} - Do not replace abbreviations. Use the dictionary only to correct the misspelled job titles. + Accepted abbreviations and their meaning: {abbreviations} + + Do NOT replace abbreviations if they appear in the provided list of abbreviations. + Use British English. + + Use provided list of abbreviations, only to correct spelling, keeping the abbreviations + when they appear in the job title provided. + If the original job title contains an abbreviation, check if the meaning is correct, - but do not replace the abbreviation. + and do NOT replace the abbreviation. - If there is no need to correct the spelling, return the original job title. + When the speling is correct, return the original job title. """ ) From 87fa56e9c1acaeaf80a74627a8725970b9940d7d Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Mon, 18 May 2026 16:33:05 +0000 Subject: [PATCH 38/61] poetry --- poetry.toml | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 poetry.toml diff --git a/poetry.toml b/poetry.toml new file mode 100644 index 0000000..ab1033b --- /dev/null +++ b/poetry.toml @@ -0,0 +1,2 @@ +[virtualenvs] +in-project = true From c2cceb789ba61f854c923240225135123e19b646 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Tue, 19 May 2026 10:02:16 +0000 Subject: [PATCH 39/61] typo in a prompt for correcting typos --- src/occupational_classification_utils/llm/prompt.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/occupational_classification_utils/llm/prompt.py b/src/occupational_classification_utils/llm/prompt.py index 6938582..0fe0b6b 100644 --- a/src/occupational_classification_utils/llm/prompt.py +++ b/src/occupational_classification_utils/llm/prompt.py @@ -222,6 +222,6 @@ def load_soc_framework(filepath: str) -> pd.DataFrame: If the original job title contains an abbreviation, check if the meaning is correct, and do NOT replace the abbreviation. - When the speling is correct, return the original job title. + When the spelling is correct, return the original job title. """ ) From 964506a03e8f81b285129118efa66ea045ebcfe4 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Wed, 27 May 2026 17:46:45 +0000 Subject: [PATCH 40/61] add template for .env file --- .env.template | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.env.template b/.env.template index 76c65fc..abbb8a0 100644 --- a/.env.template +++ b/.env.template @@ -1 +1,3 @@ -KNOWLEDGE_BUCKET= \ No newline at end of file +KNOWLEDGE_BUCKET= +SA_DEV= +SA_SANDBOX= \ No newline at end of file From 25186e0f1dba44322918887c090abb0eabc64a45 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Wed, 27 May 2026 17:47:40 +0000 Subject: [PATCH 41/61] reflect new requirements for the task - not using correcting spelling --- notebooks/ashe_clean_2026_04.py | 33 +- notebooks/assign_soc_code_2026_03.py | 37 +- notebooks/create_soc_lookup_2026_04.py | 478 ++++++++++++++++++++----- 3 files changed, 430 insertions(+), 118 deletions(-) diff --git a/notebooks/ashe_clean_2026_04.py b/notebooks/ashe_clean_2026_04.py index 09a6d1a..9b88d4e 100644 --- a/notebooks/ashe_clean_2026_04.py +++ b/notebooks/ashe_clean_2026_04.py @@ -1,4 +1,4 @@ -# pylint: disable=C0103 +# pylint: disable=C0103, C0301 """This is not a notebook. Run as a script. Allows to use LLMS to correct spelling of provided text strings. @@ -6,6 +6,8 @@ To execute, run: `python notebooks/ashe_clean_2026_04.py ` + +Diasbling line-too-long: commentary and discussion. """ import asyncio @@ -26,7 +28,7 @@ output_folder = "notebooks/soc_data" file_prefix = "ashe_correct_spelling" -file_suffix = "_2026_05_18" +file_suffix = "_2026_05_19_sample4" BATCH_SIZE = 10 @@ -55,7 +57,9 @@ except FileNotFoundError: recent_batch_id = 0 -print(f"STARTING FROM {recent_batch_id} batch (row {recent_batch_id * BATCH_SIZE} out of {len(data)}).") +print( + f"STARTING FROM {recent_batch_id} batch (row {recent_batch_id * BATCH_SIZE} out of {len(data)})." +) ### Read the data ### @@ -202,10 +206,13 @@ async def split_in_batches(df: pd.DataFrame): current_row = BATCH_SIZE * current_batch_id + k df.loc[current_row, "corrected_spelling"] = llm_response - if isinstance(df.loc[current_row, "corrected_spelling"], float): - df.loc[current_row, "corrected_spelling"] = df.loc[current_row, "label"] - elif "JOB TITLE" in df.loc[current_row, "corrected_spelling"]: - df.loc[current_row, "corrected_spelling"] = df.loc[current_row, "label"] + if ( + isinstance(df.loc[current_row, "corrected_spelling"], float) + or "JOB TITLE" in df.loc[current_row, "corrected_spelling"] + ): + df.loc[current_row, "corrected_spelling"] = df.loc[ + current_row, "documents" + ] start_row = current_batch_id * BATCH_SIZE @@ -237,16 +244,18 @@ async def split_in_batches(df: pd.DataFrame): ) if current_batch_id + 1 == final_batch: - + final_df = pd.read_csv(f"{output_folder}/{file_prefix}{file_suffix}.csv") final_df = final_df.drop_duplicates( subset=["corrected_spelling", "label"], keep="last", ignore_index=True ) # remove duplicates in the corrected spelling - final_df.to_csv(f"{output_folder}/{file_prefix}{file_suffix}.csv", index=False) + final_df.to_csv( + f"{output_folder}/{file_prefix}{file_suffix}.csv", index=False + ) print("FILE SAVED TO LOCAL") - + # final_df.to_csv(f"{knowledge_bucket}wip_data/{file_prefix}{file_suffix}.csv", index=False) # print("SAVED TO BUCKET") @@ -261,6 +270,8 @@ async def split_in_batches(df: pd.DataFrame): f"{output_folder}/{file_prefix}{file_suffix}.csv", index=False ) +s = not_in_list.head(1000) +asyncio.run(split_in_batches(s)) -asyncio.run(split_in_batches(not_in_list)) +# asyncio.run(split_in_batches(not_in_list)) # asyncio.run(split_in_batches(in_list)) diff --git a/notebooks/assign_soc_code_2026_03.py b/notebooks/assign_soc_code_2026_03.py index 6515fe4..dd6100c 100644 --- a/notebooks/assign_soc_code_2026_03.py +++ b/notebooks/assign_soc_code_2026_03.py @@ -1,10 +1,12 @@ -# pylint: disable=C0103 +# pylint: disable=C0103, R0801 """This is not a notebook. Run as a script. Allows to use LLM to assign SOC codes. To execute, run: `python notebooks/assign_soc_code_2026_03.py ` + +Diasbling duplicate code - methods needs to be changed in other repos to reflect the change in data. """ import asyncio @@ -20,18 +22,14 @@ ### Constants ### knowledge_bucket = dotenv.get_key(".env", "KNOWLEDGE_BUCKET") -output_folder = "notebooks/soc_data" - -# file_name = "ashe_in_soc_index" -file_name = "ashe_correct_spelling" +data_folder = "notebooks/soc_data" -input_file_name = "_2026_04_20" -# input_file_name = "_clean" +file_name = "ashe_llm_soc_codes" -output_file_name = "_llm_soc_codes_index_2026_04_30_attempt8" +output_file_name = "_2026_05_19" -JOB_TITLE_COLUMN = "corrected_spelling" -# JOB_TITLE_COLUMN = "documents" +# JOB_TITLE_COLUMN = "corrected_spelling" +JOB_TITLE_COLUMN = "documents" CODE_COLUMN = "label" BATCH_SIZE = 10 @@ -41,17 +39,17 @@ ### Access data ### try: - data = pd.read_csv(f"{knowledge_bucket}{file_name}{input_file_name}.csv") + data = pd.read_csv(f"{knowledge_bucket}ASHE_classifai_soc_kb.csv") print("Database loaded from storage.") except FileNotFoundError: print("KNOWLEDGE_BUCKET not found in .env file. Please set it.") - data = pd.read_csv(f"{output_folder}/{file_name}{input_file_name}.csv") + data = pd.read_csv(f"{data_folder}/ASHE_classifai_soc_kb.csv") print("Database loaded from local.") try: with open( - f"{output_folder}/{file_name}{output_file_name}.json", encoding="utf-8" + f"{data_folder}/{file_name}{output_file_name}.json", encoding="utf-8" ) as file: recent_batch_id = json.load(file)["completed_batches"] except FileNotFoundError: @@ -144,7 +142,7 @@ async def split_in_batches(document: pd.DataFrame): # pylint: disable=R0914 [ "documents", "label", - "corrected_spelling", + # "corrected_spelling", "codable", "llm_soc_code", "llm_soc_candidates", @@ -153,18 +151,19 @@ async def split_in_batches(document: pd.DataFrame): # pylint: disable=R0914 ] rows_to_save.to_csv( - f"{output_folder}/{file_name}{output_file_name}.csv", + f"{data_folder}/{file_name}{output_file_name}.csv", mode="a", header=False, index=False, ) with open( - f"{output_folder}/{file_name}{output_file_name}.json", "w", encoding="utf8" + f"{data_folder}/{file_name}{output_file_name}.json", "w", encoding="utf8" ) as json_file: json.dump( { "completed_batches": current_batch_id + 1, + "total batches": final_batch, }, json_file, ) @@ -176,18 +175,18 @@ async def split_in_batches(document: pd.DataFrame): # pylint: disable=R0914 # print("SAVED TO BUCKET") -if not os.path.exists(f"{output_folder}/{file_name}{output_file_name}.csv"): +if not os.path.exists(f"{data_folder}/{file_name}{output_file_name}.csv"): all_columns = [ "documents", "label", - "corrected_spelling", + # "corrected_spelling", "codable", "llm_soc_code", "llm_soc_candidates", "reasoning", ] pd.DataFrame(columns=all_columns).to_csv( - f"{output_folder}/{file_name}{output_file_name}.csv", index=False + f"{data_folder}/{file_name}{output_file_name}.csv", index=False ) asyncio.run(split_in_batches(data)) diff --git a/notebooks/create_soc_lookup_2026_04.py b/notebooks/create_soc_lookup_2026_04.py index b20f9f8..4de7543 100644 --- a/notebooks/create_soc_lookup_2026_04.py +++ b/notebooks/create_soc_lookup_2026_04.py @@ -1,4 +1,12 @@ -# pylint: disable=C0103, C0114 +# %% +# pylint: disable=C0103, C0114, C0301, R0801, W0105 + +"""Noetbook attempting to create a SOC DIRECT LOOKUP. + +Diasbling duplicate code - methods needs to be changed in other repos to reflect the change in data. +Diasbling line-too-long: commentary and discussion. +Disabling pointless-string-statement: comments to the code for reading clarity. +""" # %% import ast import re @@ -7,44 +15,27 @@ import pandas as pd # %% -output_folder = "soc_data" -# output_folder = "sample" - -file_name = "ashe_in_soc_index_llm_soc_codes_" -index_file = "2026_04_22" +from occupational_classification.data_access.soc_data_access import ( + _combine_soc_index_job_title as combine_job_title, +) -file_name_not_in_index = "ashe_correct_spelling_llm_soc_codes_" -# file_name_not_in_index = "data_subset_llm_soc_codes_" -# framework_file = "index_2026_04_21" -framework_file = "index_2026_04_30" +# %% +input_folder = "soc_data" +file_name = "ashe_llm_soc_codes" -# %% -data_not_in_index = pd.read_csv( - f"{output_folder}/{file_name_not_in_index}{framework_file}.csv" -) # not in index -data_in_index = pd.read_csv(f"{output_folder}/{file_name}{index_file}.csv") +file_suffix = "_2026_05_19" # %% -print(len(data_not_in_index)) -print(len(data_in_index)) +# read the data +data = pd.read_csv(f"notebooks/{input_folder}/{file_name}{file_suffix}.csv") # %% -data_not_in_index = data_not_in_index[ - [ - "documents", - "corrected_spelling", - "label", - "codable", - "llm_soc_code", - "llm_soc_candidates", - "reasoning", - ] -] -data_in_index = data_in_index[ +# use only columns needed +data = data[ [ "documents", - "corrected_spelling", + # "corrected_spelling", "label", "codable", "llm_soc_code", @@ -66,16 +57,12 @@ def parse_string(text): # %% -data_not_in_index["llm_soc_candidates"] = data_not_in_index["llm_soc_candidates"].map( - parse_string -) -data_in_index["llm_soc_candidates"] = data_in_index["llm_soc_candidates"].map( - parse_string -) +# string to list of dictionaries +data["llm_soc_candidates"] = data["llm_soc_candidates"].map(parse_string) + # %% -print(f"index: {data_not_in_index["codable"].value_counts()}") -print(f"framework: {data_in_index["codable"].value_counts()}") +print(f"llm {data["codable"].value_counts()}") # %% @@ -111,37 +98,26 @@ def float_to_list_of_codes(row_values: float) -> str: """ if isinstance(row_values, float): codes_list = [f"{row_values:.0f}"] + print(type(codes_list)) return codes_list - return row_values + return [row_values] # %% -data_not_in_index["label"] = data_not_in_index["label"].astype(str) -data_in_index["label"] = data_in_index["label"].astype(str) +data["label"] = data["label"].astype(str) # %% -msk_not_index = data_not_in_index[ - "llm_soc_code" -].isna() # take rows, where LLM didn't provide a code. -msk_index = data_in_index[ - "llm_soc_code" -].isna() # take rows, where LLM didn't provide a code. +msk = data["llm_soc_code"].isna() # take rows, where LLM didn't provide a code. # %% -data_not_in_index.loc[~msk_not_index, "llm_soc_code"] = data_not_in_index.loc[ - ~msk_not_index, "llm_soc_code" -].apply(float_to_list_of_codes) -data_in_index.loc[~msk_index, "llm_soc_code"] = data_in_index.loc[ - ~msk_index, "llm_soc_code" -].apply(float_to_list_of_codes) +data.loc[~msk, "llm_soc_code"] = data.loc[~msk, "llm_soc_code"].apply( + float_to_list_of_codes +) # %% -data_not_in_index.loc[msk_not_index, "llm_soc_code"] = data_not_in_index.loc[ - msk_not_index, "llm_soc_candidates" -].apply(access_soc_code_from_candidate_list) -data_in_index.loc[msk_index, "llm_soc_code"] = data_in_index.loc[ - msk_index, "llm_soc_candidates" -].apply(access_soc_code_from_candidate_list) +data.loc[msk, "llm_soc_code"] = data.loc[msk, "llm_soc_candidates"].apply( + access_soc_code_from_candidate_list +) # %% @@ -163,19 +139,21 @@ def check_agreement(df: pd.DataFrame, df_source: str): in_cand += df.iloc[row]["label"] in df.iloc[row]["llm_soc_code"] print( - f"Argeement full {df_source}: {agr} ({round(agr/len(df), 2) * 100}% of all rows)" + f"Agreement full {df_source}: {agr} ({round(agr/len(df), 2) * 100}% of all rows)" ) print( - f"Argeement (code in candidates) {df_source}: {in_cand} ({round(in_cand/len(df), 2) * 100}% of all rows)" # pylint: disable=C0301 + f"Agreement (code in candidates) {df_source}: {in_cand} ({round(in_cand/len(df), 2) * 100}% of all rows)" # pylint: disable=C0301 ) print( - f"Argeement (label the same or within candidates) {df_source}: {agr + in_cand} ({round((agr + in_cand)/len(df), 2) * 100}% of all rows)" # pylint: disable=C0301 + f"Agreement (label the same or within candidates) {df_source}: {agr + in_cand} ({round((agr + in_cand)/len(df), 2) * 100}% of all rows)" # pylint: disable=C0301 ) # %% -check_agreement(data_not_in_index, "ASHE - not in index") -check_agreement(data_in_index, "SOC index rows") +check_agreement(data, "ASHE and LLM") + +# %% +len(data) # %% @@ -211,27 +189,17 @@ def check_code_count(df: pd.DataFrame, df_source: str): # %% -check_code_count(data_not_in_index, "ASHE - not in index") -check_code_count(data_in_index, "SOC index rows") - -# %% -data_in_index["corrected_spelling"] = data_in_index["documents"] - -# %% -full_data_post_cleaning = pd.concat( - [data_not_in_index, data_in_index], ignore_index=True -) +check_code_count(data, "ASHE") # %% -full_data_codable = full_data_post_cleaning[full_data_post_cleaning["codable"]] +full_data_codable = data[data["codable"]] # %% -data_only_columns = full_data_post_cleaning[["corrected_spelling", "llm_soc_code"]] +data_only_columns = data[["documents", "llm_soc_code"]] # %% data_one_code = data_only_columns[data_only_columns["llm_soc_code"].str.len() == 1] - # %% e = data_one_code["llm_soc_code"].str[0] @@ -272,7 +240,6 @@ def load_soc_framework(filepath: str) -> pd.DataFrame: group description, typical entry routes and associated qualifications, and list of tasks. """ - # pylint: disable=R0801 soc_df = pd.read_excel( filepath, sheet_name="SOC2020 framework", @@ -297,7 +264,7 @@ def load_soc_framework(filepath: str) -> pd.DataFrame: # %% -knowledge_bucket = dotenv.get_key("../.env", "KNOWLEDGE_BUCKET") +knowledge_bucket = dotenv.get_key(".env", "KNOWLEDGE_BUCKET") # %% s_list = load_soc_framework( @@ -310,8 +277,8 @@ def load_soc_framework(filepath: str) -> pd.DataFrame: # %% codes_from_framework_int = [] -for i in codes_from_framework_str: - codes_from_framework_int.append(int(i)) +for k in codes_from_framework_str: + codes_from_framework_int.append(int(k)) # %% phantom_codes = ( @@ -321,7 +288,7 @@ def load_soc_framework(filepath: str) -> pd.DataFrame: ) # %% -print(phantom_codes) +print("codes that don't appear in the SOC codes list\n", phantom_codes) # %% data_one_code_no_phantoms = data_one_code[ @@ -350,14 +317,10 @@ def load_soc_framework(filepath: str) -> pd.DataFrame: # %% print( + "check if there is any duplicates\n", data_one_code_no_phantoms[ data_one_code_no_phantoms.duplicated(subset=["documents"]) - ] -) - -# %% -print( - data_one_code_no_phantoms[data_one_code_no_phantoms["documents"] == "BIKE MECHANIC"] + ], ) # %% @@ -365,8 +328,347 @@ def load_soc_framework(filepath: str) -> pd.DataFrame: subset=["documents"], keep="last", ignore_index=True ) +# %% +""" data_one_code_no_phantoms contains codes assigned by the LLM. Some of the codes were not present in the SOC codes list, and have been removed. +Those codes not neccessairly agree with codes initially assigned in ASHE dataset. +""" + # %% # data_one_code_no_phantoms.to_csv("soc_data/SOC_DIRECT_LOOKUP.csv") # %% # data_one_code_no_phantoms.to_csv(f"{knowledge_bucket}SOC_DIRECT_LOOKUP.csv") + +# %% [markdown] +# # AGREEMENT + +# %% +"""Select a subset of codes, where LLM and ASHE assign the same code for a given job title. +""" + +# %% +msk_codable = data["codable"] + +# %% +data_codable = data[msk_codable] + +# %% +len(data_codable[data_codable["llm_soc_code"].str.len() > 1]) + +# %% +len(data_codable[(data_codable["llm_soc_code"].str.len() == 1)]) + +# %% +len( + data_codable[data_codable["llm_soc_code"].str.len() < 1] +) # expect 0 - if is codable, there should be a code available + +# %% +print(data_codable[data_codable["llm_soc_code"].str.len() == 1]) + +# %% +one_code_subset = data_codable[data_codable["llm_soc_code"].str.len() == 1] + +# %% +codes_with_agreement = one_code_subset[ + one_code_subset.apply(lambda r: str(r["label"]) in str(r["llm_soc_code"]), axis=1) +].reset_index(drop=True) + +# %% +soc_lookup = codes_with_agreement[["documents", "label"]] + +# %% +# save this once all is finished + +# %% [markdown] +# # One code from LLM - why disagreement? + +# %% +full_data_one_code = data[data["llm_soc_code"].str.len() == 1] + +# %% +one_code_disagreement = full_data_one_code[ + full_data_one_code.apply( + lambda r: str(r["label"]) not in str(r["llm_soc_candidates"]), axis=1 + ) +].reset_index(drop=True) + +# %% +print(one_code_disagreement) + +# %% +"""Look at the cases, where: +- LLM claims is codable ('codable' == True) +- ASHE does not agree with LLM ('label' != 'llm_soc_code') +- ASHE is one of the candidates selected by LLM ('label' in 'llm_soc_candidates') +""" + + +# %% +def get_candidates_list(row: pd.Series) -> list: + """Get a list of candidates determined by LLM. + + Args: + row: pd.Series: row with LLM output + + Returns: + list: lsit of candidates. + """ + candidates = [] + for i in row["llm_soc_candidates"]: + candidates.append(i["soc_code"]) + return candidates + + +# %% +ashe_llm_disagreement_multi_candidate = one_code_disagreement[ + one_code_disagreement["llm_soc_candidates"].str.len() > 1 +].reset_index(drop=True) + +# %% +ashe_llm_disagreement_multi_candidate.loc[:, "candidate_list"] = ( + ashe_llm_disagreement_multi_candidate.apply(get_candidates_list, axis=1) +) + +# %% +ashe_in_canidates = ashe_llm_disagreement_multi_candidate[ + ashe_llm_disagreement_multi_candidate.apply( + lambda r: str(r["label"]) in str(r["candidate_list"]), axis=1 + ) +] + +# %% +print( + f"""There is {len(ashe_in_canidates)} rows, where code determined by ASHE appears in the cadnidates from LLM, when LLM assessed the job title is codable.""" +) + +# %% [markdown] +# # How many of the rows that are in SOC INDEX have agreement/don't have agreement with ASHE + +# %% +# Access SOC_INDEX data +soc_coding_index_file = ( + f"{knowledge_bucket}soc2020volume2thecodingindexexcel03122025.xlsx" +) + + +# %% +def load_soc_index(filepath: str) -> pd.DataFrame: + """Load SOC index. + Provides a list of over 32,000 titles associated with employment. + + Args: + filepath (str): A path to the file containing SOC Index. + + Returns: + pd.DataFrame: A DataFrame with transformed job titles. + """ + soc_index_df = pd.read_excel( + filepath, + sheet_name="SOC2020 coding index", + usecols=["SOC_2020", "INDEXOCC-natural_word_order", "ADD", "IND"], + dtype=str, + ) + + soc_index_df.columns = [col.lower() for col in soc_index_df.columns] + + soc_index_df = soc_index_df.rename( + columns={"indexocc-natural_word_order": "indexocc", "soc_2020": "code"} + ) + + soc_index_df = soc_index_df[soc_index_df["code"] != "}}}}"] + soc_index_df = soc_index_df.dropna(subset=["code", "indexocc"]) + soc_index_df["title"] = soc_index_df.apply(combine_job_title, axis=1) + soc_index_df = soc_index_df[["code", "title"]] + soc_index_df["title"] = soc_index_df["title"].str.capitalize() + + return soc_index_df + + +# %% +soc_list = load_soc_index(soc_coding_index_file) + +# %% +soc_list["title"] = soc_list["title"].str.upper() + +# %% +titles_list = soc_list["title"] +titles_list = titles_list.to_list() + +# %% +# get subset of the ASHE data that comes from soc_index +in_list = data[data["documents"].isin(titles_list)].reset_index(drop=True) + +# %% +in_list_codable = in_list[in_list["codable"]] + +# %% +in_list_codable_disagreement = in_list_codable[ + in_list_codable.apply( + lambda r: str(r["label"]) not in str(r["llm_soc_candidates"]), axis=1 + ) +].reset_index(drop=True) +in_list_codable_agreement = in_list_codable[ + in_list_codable.apply( + lambda r: str(r["label"]) in str(r["llm_soc_candidates"]), axis=1 + ) +].reset_index(drop=True) + +# %% +in_list_codable_disagreement_one_code = in_list_codable_disagreement[ + in_list_codable_disagreement["llm_soc_code"].str.len() == 1 +] +in_list_codable_agreement_one_code = in_list_codable_agreement[ + in_list_codable_agreement["llm_soc_code"].str.len() == 1 +] + +# %% +soc_lookup = ( + pd.concat([soc_lookup, in_list_codable_agreement_one_code[["documents", "label"]]]) + .drop_duplicates(subset=["documents", "label"]) + .reset_index(drop=True) +) + +# %% [markdown] +# # LLM candidates - high likelihood (0.9/0.7) + +# %% +data_multiple_candidates = data[data["llm_soc_candidates"].str.len() > 1].reset_index( + drop=True +) + + +# %% +def get_high_candidate(row: pd.Series) -> str: + """Get a most likely candidate with likelihood greater than 0.9 (assessed by the LLM), + where only one candidate got that score. + + Args: + row: pd.Series: row with LLM output + + Returns: + str: most likely candidate. + """ + high_likelihood = [] + for i in row["llm_soc_candidates"]: + if i["likelihood"] >= 0.9: # noqa: PLR2004 + high_likelihood.append(i) + if len(high_likelihood) != 1: + return None + return high_likelihood[0]["soc_code"] + + +# %% +def get_high_candidate_with_low_other(row: pd.Series) -> str: + """Get a most likely candidate with likelihood greater than 0.9 (assessed by the LLM), + where only one candidate got that score, and no other candidates got likelihood score above 0.7. + + Args: + row: pd.Series: row with LLM output + + Returns: + str: most likely candidate. + """ + high_likelihood, lower_likelihood = [], [] + + for i in row["llm_soc_candidates"]: + if i["likelihood"] >= 0.9: # noqa: PLR2004 + high_likelihood.append(i) + elif i["likelihood"] >= 0.7: # noqa: PLR2004 + lower_likelihood.append(i) + + if len(high_likelihood) != 1 or len(lower_likelihood) > 0: + return None + return high_likelihood[0]["soc_code"] + + +# %% +data_multiple_candidates.loc[:, "most_likely_candidate"] = ( + data_multiple_candidates.apply(get_high_candidate, axis=1) +) + +# %% +print(len(data_multiple_candidates)) + +# %% +data_high_likelihood = data_multiple_candidates[ + data_multiple_candidates["most_likely_candidate"].notna() +] + +# %% +data_high_likelihood_agreement = data_high_likelihood[ + data_high_likelihood.apply( + lambda r: str(r["label"]) == str(r["most_likely_candidate"]), axis=1 + ) +].reset_index(drop=True) + +# %% +print(data_high_likelihood_agreement.iloc[0]) + +# %% +misspelled = 0 +for k in data_high_likelihood_agreement["reasoning"]: + # print(k) + if "misspelling" in k or "misspelled" in k: + misspelled += 1 + + +# %% +print(misspelled) + +# %% +print( + f"""We looked at the rows, where the LLM decided thre is more than one possible SOC code candidate {len(data_multiple_candidates)} codes ({round((len(data_multiple_candidates)/len(data)) * 100, 2)}% of all codes). +To that subset of data, we added a new column 'most_likely_candidate'. +It was populated with codes, that were assessed to have a high (0.9) likelihood. +If the LLM assigned more than one code with high likelihood, those were disregarded, +as there is no way to determine which code is more likely, according to the LLM, meaning it is not unambiguous. + +Only one candidate with 0.9 likelihood was assigned for {len(data_high_likelihood)} rows. + +Next, we compared the agreement between the label assigned in the original data with the "most_likely_candidate", +which resulted in {len(data_high_likelihood_agreement)} cases. +""" +) + +# %% +data_high = data[data["llm_soc_candidates"].str.len() > 1].reset_index(drop=True) + +# %% +data_high.loc[:, "most_likely_candidate"] = data_high.apply( + get_high_candidate_with_low_other, axis=1 +) + +# %% +data_high_notna = data_high[data_high["most_likely_candidate"].notna()] + +# %% +data_high_notna_agreement = data_high_notna[ + data_high_notna.apply( + lambda r: str(r["label"]) == str(r["most_likely_candidate"]), axis=1 + ) +].reset_index(drop=True) + +# %% +soc_lookup = ( + pd.concat([soc_lookup, data_high_notna_agreement[["documents", "label"]]]) + .drop_duplicates(subset=["documents", "label"]) + .reset_index(drop=True) +) + +# %% +# soc_lookup.to_csv(f"{knowledge_bucket}wip_data/SOC_DIRECT_LOOKUP.csv") + +# %% +# data_one_code_no_phantoms +len(soc_lookup) + +# %% +soc_copy = ( + pd.concat([soc_lookup, data_one_code_no_phantoms[["documents", "label"]]]) + .drop_duplicates(subset=["documents"]) + .reset_index(drop=True) +) + +# %% +len(soc_copy) From d4de101a5b7c7bee7b6738eb6745b7a4760fa391 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Wed, 27 May 2026 17:47:59 +0000 Subject: [PATCH 42/61] change spelling prompt --- .../llm/prompt.py | 23 +++++++++++-------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/src/occupational_classification_utils/llm/prompt.py b/src/occupational_classification_utils/llm/prompt.py index 0fe0b6b..119b464 100644 --- a/src/occupational_classification_utils/llm/prompt.py +++ b/src/occupational_classification_utils/llm/prompt.py @@ -209,19 +209,22 @@ def load_soc_framework(filepath: str) -> pd.DataFrame: CORRECT_SPELLING_PROMPT = PromptTemplate.from_template( - """Correct spelling of this job title: {job_title}. + """Correct typos for this job title: {job_title}. - Accepted abbreviations and their meaning: {abbreviations} - - Do NOT replace abbreviations if they appear in the provided list of abbreviations. + Keep the abbreviations and acronyms. Use British English. - Use provided list of abbreviations, only to correct spelling, keeping the abbreviations - when they appear in the job title provided. - - If the original job title contains an abbreviation, check if the meaning is correct, - and do NOT replace the abbreviation. + If the spelling is correct, return the original job title. + Job titles often contain a grade, such as "(B)", "(I)", "3", "4". Those should be kept. + + Examples of abbreviations and acronyms that are acceptable: + - "(B)" + - "(I)" + - "3" + - {abbreviations} - When the spelling is correct, return the original job title. + Examples of typos that should be changed, when the context of the job title makes logical sense: + - "11" change to "1 to 1" + - "14016" change to "14-16" """ ) From 7711abb190ef6919ec111a975f828b694dc0bc4e Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Wed, 27 May 2026 17:49:11 +0000 Subject: [PATCH 43/61] temporary comment out arguments not present in the get_soc_code method, that were removed for the purpose of assessing ASHE data, that contains only job title and no other arguments --- tests/test_llm.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tests/test_llm.py b/tests/test_llm.py index 35b76fd..d6c356d 100644 --- a/tests/test_llm.py +++ b/tests/test_llm.py @@ -215,10 +215,10 @@ async def test_llm_response_mocked_get_soc_code(): ): result = await ClassificationLLM(model_name=MODEL_NAME).get_soc_code( job_title="teacher", - job_description="teach children", - level_of_education="degree", - manage_others=False, - industry_descr="school", + # job_description="teach children", + # level_of_education="degree", + # manage_others=False, + # industry_descr="school", ) assert isinstance(result, SocResponse) From d467658f26a475fe1c728b41db1d03a7895a40ea Mon Sep 17 00:00:00 2001 From: Thomas Owen Date: Thu, 7 May 2026 17:42:51 +0100 Subject: [PATCH 44/61] merge pyproject.toml --- poetry.lock | 49 ++++++++++++++++++++++++++----------------------- pyproject.toml | 2 +- 2 files changed, 27 insertions(+), 24 deletions(-) diff --git a/poetry.lock b/poetry.lock index bef8e35..fd8245f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.4.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.3.4 and should not be changed by hand. 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"tensorflow-text (<2.16)", "tf2onnx"] tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] tiktoken = ["blobfile", "tiktoken"] timm = ["timm (<=1.0.11)"] tokenizers = ["tokenizers (>=0.21,<0.22)"] -torch = ["accelerate (>=0.26.0)", "torch (>=2.0)"] +torch = ["accelerate (>=0.26.0)", "torch (>=2.1)"] torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"] -torchhub = ["filelock", "huggingface-hub (>=0.24.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.21,<0.22)", "torch (>=2.0)", "tqdm (>=4.27)"] -video = ["av (==9.2.0)"] +torchhub = ["filelock", "huggingface-hub (>=0.30.0,<1.0)", "importlib_metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.21,<0.22)", "torch (>=2.1)", "tqdm (>=4.27)"] +video = ["av"] vision = ["Pillow (>=10.0.1,<=15.0)"] [[package]] @@ -8748,4 +8751,4 @@ cffi = ["cffi (>=1.11)"] [metadata] lock-version = "2.1" python-versions = "^3.12" -content-hash = "590e027329cc8e1e118b2742db0e8cd8747f955155c5ea39a2eeb8b3a6e33e87" +content-hash = "71cf20225f7621da120a7f68f27af435b77d6b1047ef8b7d1c734b2f3b6447c6" diff --git a/pyproject.toml b/pyproject.toml index e32fab4..a326109 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,7 +28,7 @@ autocorrect = "^2.6.1" numpy = "^2.3.0" torch = "2.7.1" sentence-transformers = "4.1.0" -transformers = "4.48.0" +transformers = ">=4.52.4,<5.0.0" chromadb = "^1.0.12" openpyxl = "^3.1.5" langchain-openai = "0.3.21" From fc71991a3f08dfdfb77d27221e35a35e91d7b4b6 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Tue, 16 Jun 2026 08:35:49 +0000 Subject: [PATCH 45/61] create soc lookup --- notebooks/create_soc_lookup_2026_04.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/notebooks/create_soc_lookup_2026_04.py b/notebooks/create_soc_lookup_2026_04.py index 4de7543..3a716e3 100644 --- a/notebooks/create_soc_lookup_2026_04.py +++ b/notebooks/create_soc_lookup_2026_04.py @@ -7,6 +7,7 @@ Diasbling line-too-long: commentary and discussion. Disabling pointless-string-statement: comments to the code for reading clarity. """ + # %% import ast import re @@ -60,7 +61,6 @@ def parse_string(text): # string to list of dictionaries data["llm_soc_candidates"] = data["llm_soc_candidates"].map(parse_string) - # %% print(f"llm {data["codable"].value_counts()}") @@ -612,7 +612,6 @@ def get_high_candidate_with_low_other(row: pd.Series) -> str: if "misspelling" in k or "misspelled" in k: misspelled += 1 - # %% print(misspelled) From 5a34c96b915e749c2e868e5f89469ba56528d9e4 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Tue, 16 Jun 2026 09:37:25 +0000 Subject: [PATCH 46/61] knowledgebase --- notebooks/soc_kb_2026_04.py | 36 +++++++++++------------------------- 1 file changed, 11 insertions(+), 25 deletions(-) diff --git a/notebooks/soc_kb_2026_04.py b/notebooks/soc_kb_2026_04.py index 6f83e96..175c4d1 100644 --- a/notebooks/soc_kb_2026_04.py +++ b/notebooks/soc_kb_2026_04.py @@ -1,43 +1,29 @@ +# %% # pylint: disable=C0103, C0114 + # %% import dotenv import pandas as pd # %% -knowledge_bucket = dotenv.get_key("../.env", "KNOWLEDGE_BUCKET") - -# %% -rephrased = pd.read_csv("soc_data/ashe_correct_spelling_2026_04_20.csv") -in_index = pd.read_csv("soc_data/ashe_in_soc_index_2026_04_20.csv") - -# %% -in_index["corrected_spelling"] = in_index["documents"] +knowledge_bucket = dotenv.get_key(".env", "KNOWLEDGE_BUCKET") +sa_dev = dotenv.get_key(".env", "SA_DEV") +sandbox = dotenv.get_key(".env", "SA_SANDBOX") # %% -full_data = pd.concat([rephrased, in_index], ignore_index=True) +data = pd.read_csv(f"{knowledge_bucket}ASHE_classifai_soc_kb.csv") # %% -soc_kb = full_data[["corrected_spelling", "label"]] +data = data.rename(columns={"documents": "text"}) # %% -soc_kb = soc_kb.rename(columns={"corrected_spelling": "text"}) +data = data[['text','label']].copy() # %% -soc_kb["text"] = soc_kb["text"].str.capitalize() +# data.to_csv(f"{sa_dev}soc_vector_store_config/data/soc_kb_for_classifai.csv") +# data.to_csv(f"{sandbox}soc_vector_store_config/data/soc_kb_for_classifai.csv") # %% -soc_kb = soc_kb.drop_duplicates( - subset=["text", "label"], keep="last", ignore_index=True -) +data.to_csv(f"{knowledge_bucket}SOC_KB.csv", index=False) -# %% -# soc_kb.to_csv(f"{knowledge_bucket}SOC_KB.csv", index=False) - -# %% -mask = soc_kb.groupby("text")["label"].transform("nunique") > 1 -# %% -conflict_codes = soc_kb[mask].sort_values("text") - -# %% -print(conflict_codes) From d2d05963215151499e03aaf53a7aac5e3a1c25db Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Tue, 16 Jun 2026 13:23:07 +0000 Subject: [PATCH 47/61] soc_kb read codes as strings, save with no index, follow naming convention for soc data in storage --- notebooks/soc_kb_2026_04.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/notebooks/soc_kb_2026_04.py b/notebooks/soc_kb_2026_04.py index 175c4d1..7c083e5 100644 --- a/notebooks/soc_kb_2026_04.py +++ b/notebooks/soc_kb_2026_04.py @@ -11,7 +11,7 @@ sandbox = dotenv.get_key(".env", "SA_SANDBOX") # %% -data = pd.read_csv(f"{knowledge_bucket}ASHE_classifai_soc_kb.csv") +data = pd.read_csv(f"{knowledge_bucket}ASHE_classifai_soc_kb.csv", dtype={"label": str}) # %% data = data.rename(columns={"documents": "text"}) @@ -20,10 +20,10 @@ data = data[['text','label']].copy() # %% -# data.to_csv(f"{sa_dev}soc_vector_store_config/data/soc_kb_for_classifai.csv") -# data.to_csv(f"{sandbox}soc_vector_store_config/data/soc_kb_for_classifai.csv") +# data.to_csv(f"{sa_dev}soc_vector_store_config/data/soc_kb_for_classifai.csv", index=False) +# data.to_csv(f"{sandbox}soc_vector_store_config/data/soc_kb_for_classifai.csv", index=False) # %% -data.to_csv(f"{knowledge_bucket}SOC_KB.csv", index=False) +# data.to_csv(f"{knowledge_bucket}soc_kb_for_classifai.csv", index=False) From f9e278ff1e838ddd804cf3a780d7b039509319f7 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Tue, 16 Jun 2026 13:39:44 +0000 Subject: [PATCH 48/61] soc_kb remove whitespaces from job titles --- notebooks/soc_kb_2026_04.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/notebooks/soc_kb_2026_04.py b/notebooks/soc_kb_2026_04.py index 7c083e5..9f80805 100644 --- a/notebooks/soc_kb_2026_04.py +++ b/notebooks/soc_kb_2026_04.py @@ -19,6 +19,9 @@ # %% data = data[['text','label']].copy() +# %% +data['text'] = data['text'].str.strip() + # %% # data.to_csv(f"{sa_dev}soc_vector_store_config/data/soc_kb_for_classifai.csv", index=False) # data.to_csv(f"{sandbox}soc_vector_store_config/data/soc_kb_for_classifai.csv", index=False) From a7b6af9523143424c8c3b85e83908abbb5f6f2fa Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Tue, 16 Jun 2026 14:15:16 +0000 Subject: [PATCH 49/61] docstring --- notebooks/ashe_clean_2026_04.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/notebooks/ashe_clean_2026_04.py b/notebooks/ashe_clean_2026_04.py index 9b88d4e..f93bf05 100644 --- a/notebooks/ashe_clean_2026_04.py +++ b/notebooks/ashe_clean_2026_04.py @@ -1,5 +1,6 @@ # pylint: disable=C0103, C0301 -"""This is not a notebook. Run as a script. +"""Exploratory work, not longer used for the data cleaning. +This is not a notebook. Run as a script. Allows to use LLMS to correct spelling of provided text strings. Recognises ABBREVIATIONS commonly used in SOC. From c5b941dabc351126e8abb9b1f3d91c1b04f934a0 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Tue, 16 Jun 2026 14:17:13 +0000 Subject: [PATCH 50/61] correct error messages --- notebooks/ashe_clean_2026_04.py | 2 +- notebooks/assign_soc_code_2026_03.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/ashe_clean_2026_04.py b/notebooks/ashe_clean_2026_04.py index f93bf05..efe599a 100644 --- a/notebooks/ashe_clean_2026_04.py +++ b/notebooks/ashe_clean_2026_04.py @@ -46,7 +46,7 @@ except FileNotFoundError: - print("KNOWLEDGE_BUCKET not found in .env file. Please set it.") + print("File not found in the specified KNOWLEDGE_BUCKET.") data = pd.read_csv(f"{output_folder}/{file_prefix}{file_suffix}.csv") print("Database loaded from local.") diff --git a/notebooks/assign_soc_code_2026_03.py b/notebooks/assign_soc_code_2026_03.py index dd6100c..dc45159 100644 --- a/notebooks/assign_soc_code_2026_03.py +++ b/notebooks/assign_soc_code_2026_03.py @@ -43,7 +43,7 @@ print("Database loaded from storage.") except FileNotFoundError: - print("KNOWLEDGE_BUCKET not found in .env file. Please set it.") + print("File not found in the specified KNOWLEDGE_BUCKET.") data = pd.read_csv(f"{data_folder}/ASHE_classifai_soc_kb.csv") print("Database loaded from local.") From 46ee733b66a7b9ffbd6371e892e1b24b398b863a Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Tue, 16 Jun 2026 14:47:42 +0000 Subject: [PATCH 51/61] move location of local files --- notebooks/assign_soc_code_2026_03.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/notebooks/assign_soc_code_2026_03.py b/notebooks/assign_soc_code_2026_03.py index dc45159..78bc18c 100644 --- a/notebooks/assign_soc_code_2026_03.py +++ b/notebooks/assign_soc_code_2026_03.py @@ -22,13 +22,11 @@ ### Constants ### knowledge_bucket = dotenv.get_key(".env", "KNOWLEDGE_BUCKET") -data_folder = "notebooks/soc_data" - +# location and names for saving the output files +data_folder = "src/occupational_classification_utils/data/soc_data" file_name = "ashe_llm_soc_codes" - output_file_name = "_2026_05_19" -# JOB_TITLE_COLUMN = "corrected_spelling" JOB_TITLE_COLUMN = "documents" CODE_COLUMN = "label" From 0d8d1cf0e275857b80359658cf5890f7e66fe57a Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Tue, 16 Jun 2026 14:48:20 +0000 Subject: [PATCH 52/61] remove correct spelling from the output file --- notebooks/assign_soc_code_2026_03.py | 1 - 1 file changed, 1 deletion(-) diff --git a/notebooks/assign_soc_code_2026_03.py b/notebooks/assign_soc_code_2026_03.py index 78bc18c..cec493c 100644 --- a/notebooks/assign_soc_code_2026_03.py +++ b/notebooks/assign_soc_code_2026_03.py @@ -140,7 +140,6 @@ async def split_in_batches(document: pd.DataFrame): # pylint: disable=R0914 [ "documents", "label", - # "corrected_spelling", "codable", "llm_soc_code", "llm_soc_candidates", From 421b7cd0b4e5bd9b7e1b3d37cd1e1ba88600d072 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Tue, 16 Jun 2026 15:08:27 +0000 Subject: [PATCH 53/61] remove correct spelling from the output file creation --- notebooks/assign_soc_code_2026_03.py | 1 - 1 file changed, 1 deletion(-) diff --git a/notebooks/assign_soc_code_2026_03.py b/notebooks/assign_soc_code_2026_03.py index cec493c..4ab4f92 100644 --- a/notebooks/assign_soc_code_2026_03.py +++ b/notebooks/assign_soc_code_2026_03.py @@ -176,7 +176,6 @@ async def split_in_batches(document: pd.DataFrame): # pylint: disable=R0914 all_columns = [ "documents", "label", - # "corrected_spelling", "codable", "llm_soc_code", "llm_soc_candidates", From 24e4ce97347a62d6ab8c258d530dbb67755d4dc1 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Wed, 17 Jun 2026 13:37:07 +0000 Subject: [PATCH 54/61] read data from the storage or local (fallback) --- notebooks/create_soc_lookup_2026_04.py | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/notebooks/create_soc_lookup_2026_04.py b/notebooks/create_soc_lookup_2026_04.py index 3a716e3..3c5fb91 100644 --- a/notebooks/create_soc_lookup_2026_04.py +++ b/notebooks/create_soc_lookup_2026_04.py @@ -21,15 +21,24 @@ ) # %% -input_folder = "soc_data" - +input_folder = "src/occupational_classification_utils/data/soc_data" file_name = "ashe_llm_soc_codes" - file_suffix = "_2026_05_19" +knowledge_bucket = dotenv.get_key(".env", "KNOWLEDGE_BUCKET") + + # %% # read the data -data = pd.read_csv(f"notebooks/{input_folder}/{file_name}{file_suffix}.csv") + +try: + data = pd.read_csv(f"{knowledge_bucket}wip_data/{file_name}{file_suffix}.csv") + print("Database loaded from storage.") + +except FileNotFoundError: + print("File not found in the specified KNOWLEDGE_BUCKET.") + data = pd.read_csv(f"{input_folder}/{file_name}{file_suffix}.csv") + print("Database loaded from local.") # %% # use only columns needed From 1728b2e7a0c59fb6fad425fad882d85b2679304c Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Thu, 18 Jun 2026 07:11:19 +0000 Subject: [PATCH 55/61] correct file locations from bucket --- notebooks/assign_soc_code_2026_03.py | 2 +- notebooks/create_soc_lookup_2026_04.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/assign_soc_code_2026_03.py b/notebooks/assign_soc_code_2026_03.py index 4ab4f92..f4ebc25 100644 --- a/notebooks/assign_soc_code_2026_03.py +++ b/notebooks/assign_soc_code_2026_03.py @@ -25,7 +25,7 @@ # location and names for saving the output files data_folder = "src/occupational_classification_utils/data/soc_data" file_name = "ashe_llm_soc_codes" -output_file_name = "_2026_05_19" +output_file_name = "_2026_06_16" JOB_TITLE_COLUMN = "documents" CODE_COLUMN = "label" diff --git a/notebooks/create_soc_lookup_2026_04.py b/notebooks/create_soc_lookup_2026_04.py index 3c5fb91..f27b716 100644 --- a/notebooks/create_soc_lookup_2026_04.py +++ b/notebooks/create_soc_lookup_2026_04.py @@ -23,7 +23,7 @@ # %% input_folder = "src/occupational_classification_utils/data/soc_data" file_name = "ashe_llm_soc_codes" -file_suffix = "_2026_05_19" +file_suffix = "_2026_06_16" knowledge_bucket = dotenv.get_key(".env", "KNOWLEDGE_BUCKET") From 318d9cdc9bcb2402802d1cef4601eeae0cd6b678 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Thu, 18 Jun 2026 11:21:40 +0000 Subject: [PATCH 56/61] correct saving to the storage --- notebooks/assign_soc_code_2026_03.py | 16 +++++++++++----- notebooks/create_soc_lookup_2026_04.py | 9 ++++----- notebooks/soc_kb_2026_04.py | 6 ++---- 3 files changed, 17 insertions(+), 14 deletions(-) diff --git a/notebooks/assign_soc_code_2026_03.py b/notebooks/assign_soc_code_2026_03.py index f4ebc25..5fd0707 100644 --- a/notebooks/assign_soc_code_2026_03.py +++ b/notebooks/assign_soc_code_2026_03.py @@ -165,11 +165,17 @@ async def split_in_batches(document: pd.DataFrame): # pylint: disable=R0914 json_file, ) - # if current_batch_id + 1 == final_batch: - # document.to_csv( - # f"{knowledge_bucket}wip_data/{file_name}{output_file_name}.csv" - # ) - # print("SAVED TO BUCKET") + if current_batch_id + 1 == final_batch: + + final_df = pd.read_csv(f"{data_folder}/{file_name}{output_file_name}.csv") + final_df = final_df.drop_duplicates( + subset=["documents", "label"], keep="last", ignore_index=True + ) # remove duplicates + final_df.to_csv( + f"{knowledge_bucket}wip_data/{file_name}{output_file_name}.csv", + index=False, + ) + print("SAVED TO BUCKET") if not os.path.exists(f"{data_folder}/{file_name}{output_file_name}.csv"): diff --git a/notebooks/create_soc_lookup_2026_04.py b/notebooks/create_soc_lookup_2026_04.py index f27b716..6e0779a 100644 --- a/notebooks/create_soc_lookup_2026_04.py +++ b/notebooks/create_soc_lookup_2026_04.py @@ -24,10 +24,10 @@ input_folder = "src/occupational_classification_utils/data/soc_data" file_name = "ashe_llm_soc_codes" file_suffix = "_2026_06_16" +# file_suffix = "_2026_05_19" knowledge_bucket = dotenv.get_key(".env", "KNOWLEDGE_BUCKET") - # %% # read the data @@ -40,6 +40,9 @@ data = pd.read_csv(f"{input_folder}/{file_name}{file_suffix}.csv") print("Database loaded from local.") +# data = pd.read_csv(f"{input_folder}/{file_name}{file_suffix}.csv") + + # %% # use only columns needed data = data[ @@ -161,9 +164,6 @@ def check_agreement(df: pd.DataFrame, df_source: str): # %% check_agreement(data, "ASHE and LLM") -# %% -len(data) - # %% def check_code_count(df: pd.DataFrame, df_source: str): @@ -668,7 +668,6 @@ def get_high_candidate_with_low_other(row: pd.Series) -> str: # soc_lookup.to_csv(f"{knowledge_bucket}wip_data/SOC_DIRECT_LOOKUP.csv") # %% -# data_one_code_no_phantoms len(soc_lookup) # %% diff --git a/notebooks/soc_kb_2026_04.py b/notebooks/soc_kb_2026_04.py index 9f80805..553fd5e 100644 --- a/notebooks/soc_kb_2026_04.py +++ b/notebooks/soc_kb_2026_04.py @@ -17,10 +17,10 @@ data = data.rename(columns={"documents": "text"}) # %% -data = data[['text','label']].copy() +data = data[["text", "label"]].copy() # %% -data['text'] = data['text'].str.strip() +data["text"] = data["text"].str.strip() # %% # data.to_csv(f"{sa_dev}soc_vector_store_config/data/soc_kb_for_classifai.csv", index=False) @@ -28,5 +28,3 @@ # %% # data.to_csv(f"{knowledge_bucket}soc_kb_for_classifai.csv", index=False) - 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"jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] -type = ["pytest-mypy"] - [[package]] name = "zstandard" version = "0.23.0" @@ -8751,4 +9338,4 @@ cffi = ["cffi (>=1.11)"] [metadata] lock-version = "2.1" python-versions = "^3.12" -content-hash = "71cf20225f7621da120a7f68f27af435b77d6b1047ef8b7d1c734b2f3b6447c6" +content-hash = "1d2106feccdb8ea67df696735a51df6cba88688bc2fddb67e8d5ec710ae0a225" From 22caba14247fa0305a2067a3f86cd6da1ff12c4e Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Thu, 18 Jun 2026 13:27:45 +0000 Subject: [PATCH 58/61] move the local data folder --- notebooks/assign_soc_code_2026_03.py | 2 +- notebooks/create_soc_lookup_2026_04.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/assign_soc_code_2026_03.py b/notebooks/assign_soc_code_2026_03.py index 5fd0707..d7262e8 100644 --- a/notebooks/assign_soc_code_2026_03.py +++ b/notebooks/assign_soc_code_2026_03.py @@ -23,7 +23,7 @@ knowledge_bucket = dotenv.get_key(".env", "KNOWLEDGE_BUCKET") # location and names for saving the output files -data_folder = "src/occupational_classification_utils/data/soc_data" +data_folder = "data/soc_data" file_name = "ashe_llm_soc_codes" output_file_name = "_2026_06_16" diff --git a/notebooks/create_soc_lookup_2026_04.py b/notebooks/create_soc_lookup_2026_04.py index 6e0779a..85a64bf 100644 --- a/notebooks/create_soc_lookup_2026_04.py +++ b/notebooks/create_soc_lookup_2026_04.py @@ -21,7 +21,7 @@ ) # %% -input_folder = "src/occupational_classification_utils/data/soc_data" +input_folder = "data/soc_data" file_name = "ashe_llm_soc_codes" file_suffix = "_2026_06_16" # file_suffix = "_2026_05_19" From 0cf9dcf755f0000e07c6c93857c88bf803527bef Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Thu, 18 Jun 2026 14:31:29 +0000 Subject: [PATCH 59/61] open codes as string --- notebooks/ashe_clean_2026_04.py | 12 +++++++++--- notebooks/assign_soc_code_2026_03.py | 10 +++++++--- notebooks/create_soc_lookup_2026_04.py | 24 +++++++----------------- 3 files changed, 23 insertions(+), 23 deletions(-) diff --git a/notebooks/ashe_clean_2026_04.py b/notebooks/ashe_clean_2026_04.py index efe599a..1f3a895 100644 --- a/notebooks/ashe_clean_2026_04.py +++ b/notebooks/ashe_clean_2026_04.py @@ -41,13 +41,17 @@ ### Access data ### try: - data = pd.read_csv(f"{knowledge_bucket}ASHE_classifai_soc_kb.csv") + data = pd.read_csv( + f"{knowledge_bucket}ASHE_classifai_soc_kb.csv", dtype={"label": str} + ) print("Database loaded from storage.") except FileNotFoundError: print("File not found in the specified KNOWLEDGE_BUCKET.") - data = pd.read_csv(f"{output_folder}/{file_prefix}{file_suffix}.csv") + data = pd.read_csv( + f"{output_folder}/{file_prefix}{file_suffix}.csv", dtype={"label": str} + ) print("Database loaded from local.") try: @@ -246,7 +250,9 @@ async def split_in_batches(df: pd.DataFrame): if current_batch_id + 1 == final_batch: - final_df = pd.read_csv(f"{output_folder}/{file_prefix}{file_suffix}.csv") + final_df = pd.read_csv( + f"{output_folder}/{file_prefix}{file_suffix}.csv", dtype={"label": str} + ) final_df = final_df.drop_duplicates( subset=["corrected_spelling", "label"], keep="last", ignore_index=True ) # remove duplicates in the corrected spelling diff --git a/notebooks/assign_soc_code_2026_03.py b/notebooks/assign_soc_code_2026_03.py index d7262e8..219a02a 100644 --- a/notebooks/assign_soc_code_2026_03.py +++ b/notebooks/assign_soc_code_2026_03.py @@ -37,12 +37,14 @@ ### Access data ### try: - data = pd.read_csv(f"{knowledge_bucket}ASHE_classifai_soc_kb.csv") + data = pd.read_csv( + f"{knowledge_bucket}ASHE_classifai_soc_kb.csv", dtype={"label": str} + ) print("Database loaded from storage.") except FileNotFoundError: print("File not found in the specified KNOWLEDGE_BUCKET.") - data = pd.read_csv(f"{data_folder}/ASHE_classifai_soc_kb.csv") + data = pd.read_csv(f"{data_folder}/ASHE_classifai_soc_kb.csv", dtype={"label": str}) print("Database loaded from local.") try: @@ -167,7 +169,9 @@ async def split_in_batches(document: pd.DataFrame): # pylint: disable=R0914 if current_batch_id + 1 == final_batch: - final_df = pd.read_csv(f"{data_folder}/{file_name}{output_file_name}.csv") + final_df = pd.read_csv( + f"{data_folder}/{file_name}{output_file_name}.csv", dtype={"label": str} + ) final_df = final_df.drop_duplicates( subset=["documents", "label"], keep="last", ignore_index=True ) # remove duplicates diff --git a/notebooks/create_soc_lookup_2026_04.py b/notebooks/create_soc_lookup_2026_04.py index 85a64bf..7db01d6 100644 --- a/notebooks/create_soc_lookup_2026_04.py +++ b/notebooks/create_soc_lookup_2026_04.py @@ -27,22 +27,22 @@ # file_suffix = "_2026_05_19" knowledge_bucket = dotenv.get_key(".env", "KNOWLEDGE_BUCKET") - # %% # read the data try: - data = pd.read_csv(f"{knowledge_bucket}wip_data/{file_name}{file_suffix}.csv") + data = pd.read_csv( + f"{knowledge_bucket}wip_data/{file_name}{file_suffix}.csv", dtype={"label": str} + ) print("Database loaded from storage.") except FileNotFoundError: print("File not found in the specified KNOWLEDGE_BUCKET.") - data = pd.read_csv(f"{input_folder}/{file_name}{file_suffix}.csv") + data = pd.read_csv( + f"{input_folder}/{file_name}{file_suffix}.csv", dtype={"label": str} + ) print("Database loaded from local.") -# data = pd.read_csv(f"{input_folder}/{file_name}{file_suffix}.csv") - - # %% # use only columns needed data = data[ @@ -665,17 +665,7 @@ def get_high_candidate_with_low_other(row: pd.Series) -> str: ) # %% -# soc_lookup.to_csv(f"{knowledge_bucket}wip_data/SOC_DIRECT_LOOKUP.csv") +# soc_lookup.to_csv(f"{knowledge_bucket}wip_data/soc_kb_for_direct_lookup.csv") # %% len(soc_lookup) - -# %% -soc_copy = ( - pd.concat([soc_lookup, data_one_code_no_phantoms[["documents", "label"]]]) - .drop_duplicates(subset=["documents"]) - .reset_index(drop=True) -) - -# %% -len(soc_copy) From 4e37980e745c5c27ada3002820407c3165094b10 Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Thu, 18 Jun 2026 14:56:01 +0000 Subject: [PATCH 60/61] strip whitespaces from titles --- notebooks/create_soc_lookup_2026_04.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/notebooks/create_soc_lookup_2026_04.py b/notebooks/create_soc_lookup_2026_04.py index 7db01d6..4655a94 100644 --- a/notebooks/create_soc_lookup_2026_04.py +++ b/notebooks/create_soc_lookup_2026_04.py @@ -57,6 +57,8 @@ ] ] +data["documents"] = data["documents"].str.strip() + # %% def parse_string(text): From b8bef9f969593f6cfc6bb1b21bd1c706e2e2f51c Mon Sep 17 00:00:00 2001 From: peter-spencer-ons Date: Thu, 18 Jun 2026 15:10:01 +0000 Subject: [PATCH 61/61] correct column names --- notebooks/create_soc_lookup_2026_04.py | 44 +++++++------------------- 1 file changed, 11 insertions(+), 33 deletions(-) diff --git a/notebooks/create_soc_lookup_2026_04.py b/notebooks/create_soc_lookup_2026_04.py index 4655a94..f2ef416 100644 --- a/notebooks/create_soc_lookup_2026_04.py +++ b/notebooks/create_soc_lookup_2026_04.py @@ -31,16 +31,12 @@ # read the data try: - data = pd.read_csv( - f"{knowledge_bucket}wip_data/{file_name}{file_suffix}.csv", dtype={"label": str} - ) + data = pd.read_csv(f"{knowledge_bucket}wip_data/{file_name}{file_suffix}.csv", dtype={"label": str}) print("Database loaded from storage.") except FileNotFoundError: print("File not found in the specified KNOWLEDGE_BUCKET.") - data = pd.read_csv( - f"{input_folder}/{file_name}{file_suffix}.csv", dtype={"label": str} - ) + data = pd.read_csv(f"{input_folder}/{file_name}{file_suffix}.csv", dtype={"label": str}) print("Database loaded from local.") # %% @@ -57,9 +53,6 @@ ] ] -data["documents"] = data["documents"].str.strip() - - # %% def parse_string(text): """Convert string to a list of dictionaries for SOC candidates.""" @@ -70,7 +63,6 @@ def parse_string(text): return ast.literal_eval(processed) return [] - # %% # string to list of dictionaries data["llm_soc_candidates"] = data["llm_soc_candidates"].map(parse_string) @@ -78,7 +70,6 @@ def parse_string(text): # %% print(f"llm {data["codable"].value_counts()}") - # %% def access_soc_code_from_candidate_list(row_values: list[dict]) -> list[str]: """From list of potential SOC candidates, access SOC codes. @@ -99,7 +90,6 @@ def access_soc_code_from_candidate_list(row_values: list[dict]) -> list[str]: return None return candidates - # %% def float_to_list_of_codes(row_values: float) -> str: """Convert float to a string of codes (str). @@ -116,7 +106,6 @@ def float_to_list_of_codes(row_values: float) -> str: return codes_list return [row_values] - # %% data["label"] = data["label"].astype(str) @@ -133,7 +122,6 @@ def float_to_list_of_codes(row_values: float) -> str: access_soc_code_from_candidate_list ) - # %% def check_agreement(df: pd.DataFrame, df_source: str): """Checks agreement between ASHE and LLM assigned codes. @@ -162,11 +150,9 @@ def check_agreement(df: pd.DataFrame, df_source: str): f"Agreement (label the same or within candidates) {df_source}: {agr + in_cand} ({round((agr + in_cand)/len(df), 2) * 100}% of all rows)" # pylint: disable=C0301 ) - # %% check_agreement(data, "ASHE and LLM") - # %% def check_code_count(df: pd.DataFrame, df_source: str): """Check if the LLM assigned a sigle, multiple, or none codes when assessing SOC codes. @@ -198,7 +184,6 @@ def check_code_count(df: pd.DataFrame, df_source: str): f"One code assigned {df_source}: {one_code} ({round(one_code/len(df) * 100, 2)}%)" ) - # %% check_code_count(data, "ASHE") @@ -228,7 +213,7 @@ def check_code_count(df: pd.DataFrame, df_source: str): # %% data_one_code = data_one_code.rename( - columns={"corrected_spelling": "documents", "llm_soc_code": "label"} + columns={"llm_soc_code": "label"} ) # %% @@ -236,7 +221,6 @@ def check_code_count(df: pd.DataFrame, df_source: str): subset=["documents", "label"], keep="last", ignore_index=True ) - # %% def load_soc_framework(filepath: str) -> pd.DataFrame: """Load SOC structure. @@ -273,7 +257,6 @@ def load_soc_framework(filepath: str) -> pd.DataFrame: return soc_df - # %% knowledge_bucket = dotenv.get_key(".env", "KNOWLEDGE_BUCKET") @@ -344,12 +327,6 @@ def load_soc_framework(filepath: str) -> pd.DataFrame: Those codes not neccessairly agree with codes initially assigned in ASHE dataset. """ -# %% -# data_one_code_no_phantoms.to_csv("soc_data/SOC_DIRECT_LOOKUP.csv") - -# %% -# data_one_code_no_phantoms.to_csv(f"{knowledge_bucket}SOC_DIRECT_LOOKUP.csv") - # %% [markdown] # # AGREEMENT @@ -414,7 +391,6 @@ def load_soc_framework(filepath: str) -> pd.DataFrame: - ASHE is one of the candidates selected by LLM ('label' in 'llm_soc_candidates') """ - # %% def get_candidates_list(row: pd.Series) -> list: """Get a list of candidates determined by LLM. @@ -430,7 +406,6 @@ def get_candidates_list(row: pd.Series) -> list: candidates.append(i["soc_code"]) return candidates - # %% ashe_llm_disagreement_multi_candidate = one_code_disagreement[ one_code_disagreement["llm_soc_candidates"].str.len() > 1 @@ -462,7 +437,6 @@ def get_candidates_list(row: pd.Series) -> list: f"{knowledge_bucket}soc2020volume2thecodingindexexcel03122025.xlsx" ) - # %% def load_soc_index(filepath: str) -> pd.DataFrame: """Load SOC index. @@ -495,7 +469,6 @@ def load_soc_index(filepath: str) -> pd.DataFrame: return soc_index_df - # %% soc_list = load_soc_index(soc_coding_index_file) @@ -548,7 +521,6 @@ def load_soc_index(filepath: str) -> pd.DataFrame: drop=True ) - # %% def get_high_candidate(row: pd.Series) -> str: """Get a most likely candidate with likelihood greater than 0.9 (assessed by the LLM), @@ -568,7 +540,6 @@ def get_high_candidate(row: pd.Series) -> str: return None return high_likelihood[0]["soc_code"] - # %% def get_high_candidate_with_low_other(row: pd.Series) -> str: """Get a most likely candidate with likelihood greater than 0.9 (assessed by the LLM), @@ -592,7 +563,6 @@ def get_high_candidate_with_low_other(row: pd.Series) -> str: return None return high_likelihood[0]["soc_code"] - # %% data_multiple_candidates.loc[:, "most_likely_candidate"] = ( data_multiple_candidates.apply(get_high_candidate, axis=1) @@ -666,8 +636,16 @@ def get_high_candidate_with_low_other(row: pd.Series) -> str: .reset_index(drop=True) ) +# %% +soc_lookup = soc_lookup.rename(columns={"documents": "description"}) + +# %% +soc_lookup = soc_lookup[["label", "description"]] + # %% # soc_lookup.to_csv(f"{knowledge_bucket}wip_data/soc_kb_for_direct_lookup.csv") # %% len(soc_lookup) + +