diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 00000000..ee5e30a0 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,6 @@ +__pycache__/ +.git/ +*.pyc +.env +.vscode/ +.DS_Store diff --git a/.github/workflows/cd.yml b/.github/workflows/cd.yml index b801c2d3..9bebebe1 100644 --- a/.github/workflows/cd.yml +++ b/.github/workflows/cd.yml @@ -1,10 +1,8 @@ name: CI/CD Pipeline on: - push: - branches: [master, main] - pull_request: - branches: [master, main] + release: + types: [published] # Upon every Release，the CD pipeline should run and automagically update jobs: test: @@ -32,16 +30,34 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - - name: Build Docker image - run: docker build -t common-assessment-tool . + - name: Checkout repository + uses: actions/checkout@v4 - - name: Run Docker container + - name: Set up SSH key run: | - docker run -d -p 8000:8000 common-assessment-tool - sleep 10 # Wait for container to start + mkdir -p ~/.ssh + echo "${{ secrets.EC2_KEY }}" > ~/.ssh/id_rsa + chmod 600 ~/.ssh/id_rsa + ssh-keyscan -H ${{ secrets.EC2_HOST }} >> ~/.ssh/known_hosts - - name: Test Docker container + - name: Deploy to EC2 run: | - curl http://localhost:8000/docs + ssh ${{ secrets.EC2_HOST }} << 'EOF' + cd ${{ secrets.EC2_PROJECT_PATH }} + git pull origin main + source venv/bin/activate + pkill -f "uvicorn" || true + nohup uvicorn app.main:app --host 0.0.0.0 --port 8000 > out.log 2>&1 & + EOF + + # - name: Build Docker image + # run: docker build -t common-assessment-tool . + + # - name: Run Docker container + # run: | + # docker run -d -p 8000:8000 common-assessment-tool + # sleep 10 # Wait for container to start + + # - name: Test Docker container + # run: | + # curl http://localhost:8000/docs diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 30c81bdb..c9248ade 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -25,11 +25,36 @@ jobs: pip install setuptools wheel pip install -r requirements.txt # Install dependencies from requirements.txt pip install pylint pytest + pip install black + - name: Run Tests run: | python -m pytest tests/ + - name: Format code with Black + run: | + black . + + - name: Check code formatting with Black + run: | + black --check . + + + # - name: Run Pylint + # run: | + # pylint app/ + + - name: Build Docker Image (syntax check) + run: | + docker build -t my-app:test . + + - name: Run Docker Container + run: | + docker run --rm -d --name test-container -p 8000:8000 my-app:test + sleep 5 # Give it time to start + docker ps # Verify it's running + - name: Print Success Message run: | echo "CI Pipeline completed successfully!" diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..726151a7 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,6 @@ +repos: + - repo: https://github.com/psf/black + rev: 23.11.0 + hooks: + - id: black + language_version: python3 diff --git a/.pylintrc b/.pylintrc new file mode 100644 index 00000000..c6dcdc0e --- /dev/null +++ b/.pylintrc @@ -0,0 +1,634 @@ +[MAIN] + +# Analyse import fallback blocks. This can be used to support both Python 2 and +# 3 compatible code, which means that the block might have code that exists +# only in one or another interpreter, leading to false positives when analysed. +analyse-fallback-blocks=no + +# Clear in-memory caches upon conclusion of linting. Useful if running pylint +# in a server-like mode. +clear-cache-post-run=no + +# Load and enable all available extensions. Use --list-extensions to see a list +# all available extensions. +#enable-all-extensions= + +# In error mode, messages with a category besides ERROR or FATAL are +# suppressed, and no reports are done by default. Error mode is compatible with +# disabling specific errors. +#errors-only= + +# Always return a 0 (non-error) status code, even if lint errors are found. +# This is primarily useful in continuous integration scripts. +#exit-zero= + +# A comma-separated list of package or module names from where C extensions may +# be loaded. Extensions are loading into the active Python interpreter and may +# run arbitrary code. +extension-pkg-allow-list= + +# A comma-separated list of package or module names from where C extensions may +# be loaded. Extensions are loading into the active Python interpreter and may +# run arbitrary code. (This is an alternative name to extension-pkg-allow-list +# for backward compatibility.) +extension-pkg-whitelist= + +# Return non-zero exit code if any of these messages/categories are detected, +# even if score is above --fail-under value. Syntax same as enable. Messages +# specified are enabled, while categories only check already-enabled messages. +fail-on= + +# Specify a score threshold under which the program will exit with error. +fail-under=10 + +# Interpret the stdin as a python script, whose filename needs to be passed as +# the module_or_package argument. +#from-stdin= + +# Files or directories to be skipped. They should be base names, not paths. +ignore=CVS + +# Add files or directories matching the regular expressions patterns to the +# ignore-list. The regex matches against paths and can be in Posix or Windows +# format. Because '\\' represents the directory delimiter on Windows systems, +# it can't be used as an escape character. +ignore-paths= + +# Files or directories matching the regular expression patterns are skipped. +# The regex matches against base names, not paths. The default value ignores +# Emacs file locks +ignore-patterns=^\.# + +# List of module names for which member attributes should not be checked +# (useful for modules/projects where namespaces are manipulated during runtime +# and thus existing member attributes cannot be deduced by static analysis). It +# supports qualified module names, as well as Unix pattern matching. +ignored-modules= + +# Python code to execute, usually for sys.path manipulation such as +# pygtk.require(). +#init-hook= + +# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the +# number of processors available to use, and will cap the count on Windows to +# avoid hangs. +jobs=1 + +# Control the amount of potential inferred values when inferring a single +# object. This can help the performance when dealing with large functions or +# complex, nested conditions. +limit-inference-results=100 + +# List of plugins (as comma separated values of python module names) to load, +# usually to register additional checkers. +load-plugins= + +# Pickle collected data for later comparisons. +persistent=yes + +# Minimum Python version to use for version dependent checks. Will default to +# the version used to run pylint. +py-version=3.11 + +# Discover python modules and packages in the file system subtree. +recursive=no + +# Add paths to the list of the source roots. Supports globbing patterns. The +# source root is an absolute path or a path relative to the current working +# directory used to determine a package namespace for modules located under the +# source root. +source-roots= + +# When enabled, pylint would attempt to guess common misconfiguration and emit +# user-friendly hints instead of false-positive error messages. +suggestion-mode=yes + +# Allow loading of arbitrary C extensions. Extensions are imported into the +# active Python interpreter and may run arbitrary code. +unsafe-load-any-extension=no + +# In verbose mode, extra non-checker-related info will be displayed. +#verbose= + + +[BASIC] + +# Naming style matching correct argument names. +argument-naming-style=snake_case + +# Regular expression matching correct argument names. Overrides argument- +# naming-style. If left empty, argument names will be checked with the set +# naming style. +#argument-rgx= + +# Naming style matching correct attribute names. +attr-naming-style=snake_case + +# Regular expression matching correct attribute names. Overrides attr-naming- +# style. If left empty, attribute names will be checked with the set naming +# style. +#attr-rgx= + +# Bad variable names which should always be refused, separated by a comma. +bad-names=foo, + bar, + baz, + toto, + tutu, + tata + +# Bad variable names regexes, separated by a comma. If names match any regex, +# they will always be refused +bad-names-rgxs= + +# Naming style matching correct class attribute names. +class-attribute-naming-style=any + +# Regular expression matching correct class attribute names. Overrides class- +# attribute-naming-style. If left empty, class attribute names will be checked +# with the set naming style. +#class-attribute-rgx= + +# Naming style matching correct class constant names. +class-const-naming-style=UPPER_CASE + +# Regular expression matching correct class constant names. Overrides class- +# const-naming-style. If left empty, class constant names will be checked with +# the set naming style. +#class-const-rgx= + +# Naming style matching correct class names. +class-naming-style=PascalCase + +# Regular expression matching correct class names. Overrides class-naming- +# style. If left empty, class names will be checked with the set naming style. +#class-rgx= + +# Naming style matching correct constant names. +const-naming-style=UPPER_CASE + +# Regular expression matching correct constant names. Overrides const-naming- +# style. If left empty, constant names will be checked with the set naming +# style. +#const-rgx= + +# Minimum line length for functions/classes that require docstrings, shorter +# ones are exempt. +docstring-min-length=-1 + +# Naming style matching correct function names. +function-naming-style=snake_case + +# Regular expression matching correct function names. Overrides function- +# naming-style. If left empty, function names will be checked with the set +# naming style. +#function-rgx= + +# Good variable names which should always be accepted, separated by a comma. +good-names=i, + j, + k, + ex, + Run, + _ + +# Good variable names regexes, separated by a comma. If names match any regex, +# they will always be accepted +good-names-rgxs= + +# Include a hint for the correct naming format with invalid-name. +include-naming-hint=no + +# Naming style matching correct inline iteration names. +inlinevar-naming-style=any + +# Regular expression matching correct inline iteration names. Overrides +# inlinevar-naming-style. If left empty, inline iteration names will be checked +# with the set naming style. +#inlinevar-rgx= + +# Naming style matching correct method names. +method-naming-style=snake_case + +# Regular expression matching correct method names. Overrides method-naming- +# style. If left empty, method names will be checked with the set naming style. +#method-rgx= + +# Naming style matching correct module names. +module-naming-style=snake_case + +# Regular expression matching correct module names. Overrides module-naming- +# style. If left empty, module names will be checked with the set naming style. +#module-rgx= + +# Colon-delimited sets of names that determine each other's naming style when +# the name regexes allow several styles. +name-group= + +# Regular expression which should only match function or class names that do +# not require a docstring. +no-docstring-rgx=^_ + +# List of decorators that produce properties, such as abc.abstractproperty. Add +# to this list to register other decorators that produce valid properties. +# These decorators are taken in consideration only for invalid-name. +property-classes=abc.abstractproperty + +# Regular expression matching correct type alias names. If left empty, type +# alias names will be checked with the set naming style. +#typealias-rgx= + +# Regular expression matching correct type variable names. If left empty, type +# variable names will be checked with the set naming style. +#typevar-rgx= + +# Naming style matching correct variable names. +variable-naming-style=snake_case + +# Regular expression matching correct variable names. Overrides variable- +# naming-style. If left empty, variable names will be checked with the set +# naming style. +#variable-rgx= + + +[CLASSES] + +# Warn about protected attribute access inside special methods +check-protected-access-in-special-methods=no + +# List of method names used to declare (i.e. assign) instance attributes. +defining-attr-methods=__init__, + __new__, + setUp, + asyncSetUp, + __post_init__ + +# List of member names, which should be excluded from the protected access +# warning. +exclude-protected=_asdict,_fields,_replace,_source,_make,os._exit + +# List of valid names for the first argument in a class method. +valid-classmethod-first-arg=cls + +# List of valid names for the first argument in a metaclass class method. +valid-metaclass-classmethod-first-arg=mcs + + +[DESIGN] + +# List of regular expressions of class ancestor names to ignore when counting +# public methods (see R0903) +exclude-too-few-public-methods= + +# List of qualified class names to ignore when counting class parents (see +# R0901) +ignored-parents= + +# Maximum number of arguments for function / method. +max-args=5 + +# Maximum number of attributes for a class (see R0902). +max-attributes=7 + +# Maximum number of boolean expressions in an if statement (see R0916). +max-bool-expr=5 + +# Maximum number of branch for function / method body. +max-branches=12 + +# Maximum number of locals for function / method body. +max-locals=15 + +# Maximum number of parents for a class (see R0901). +max-parents=7 + +# Maximum number of public methods for a class (see R0904). +max-public-methods=20 + +# Maximum number of return / yield for function / method body. +max-returns=6 + +# Maximum number of statements in function / method body. +max-statements=50 + +# Minimum number of public methods for a class (see R0903). +min-public-methods=2 + + +[EXCEPTIONS] + +# Exceptions that will emit a warning when caught. +overgeneral-exceptions=builtins.BaseException,builtins.Exception + + +[FORMAT] + +# Expected format of line ending, e.g. empty (any line ending), LF or CRLF. +expected-line-ending-format= + +# Regexp for a line that is allowed to be longer than the limit. +ignore-long-lines=^\s*(# )??$ + +# Number of spaces of indent required inside a hanging or continued line. +indent-after-paren=4 + +# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1 +# tab). +indent-string=' ' + +# Maximum number of characters on a single line. +max-line-length=100 + +# Maximum number of lines in a module. +max-module-lines=1000 + +# Allow the body of a class to be on the same line as the declaration if body +# contains single statement. +single-line-class-stmt=no + +# Allow the body of an if to be on the same line as the test if there is no +# else. +single-line-if-stmt=no + + +[IMPORTS] + +# List of modules that can be imported at any level, not just the top level +# one. +allow-any-import-level= + +# Allow explicit reexports by alias from a package __init__. +allow-reexport-from-package=no + +# Allow wildcard imports from modules that define __all__. +allow-wildcard-with-all=no + +# Deprecated modules which should not be used, separated by a comma. +deprecated-modules= + +# Output a graph (.gv or any supported image format) of external dependencies +# to the given file (report RP0402 must not be disabled). +ext-import-graph= + +# Output a graph (.gv or any supported image format) of all (i.e. internal and +# external) dependencies to the given file (report RP0402 must not be +# disabled). +import-graph= + +# Output a graph (.gv or any supported image format) of internal dependencies +# to the given file (report RP0402 must not be disabled). +int-import-graph= + +# Force import order to recognize a module as part of the standard +# compatibility libraries. +known-standard-library= + +# Force import order to recognize a module as part of a third party library. +known-third-party=enchant + +# Couples of modules and preferred modules, separated by a comma. +preferred-modules= + + +[LOGGING] + +# The type of string formatting that logging methods do. `old` means using % +# formatting, `new` is for `{}` formatting. +logging-format-style=old + +# Logging modules to check that the string format arguments are in logging +# function parameter format. +logging-modules=logging + + +[MESSAGES CONTROL] + +# Only show warnings with the listed confidence levels. Leave empty to show +# all. Valid levels: HIGH, CONTROL_FLOW, INFERENCE, INFERENCE_FAILURE, +# UNDEFINED. +confidence=HIGH, + CONTROL_FLOW, + INFERENCE, + INFERENCE_FAILURE, + UNDEFINED + +# Disable the message, report, category or checker with the given id(s). You +# can either give multiple identifiers separated by comma (,) or put this +# option multiple times (only on the command line, not in the configuration +# file where it should appear only once). You can also use "--disable=all" to +# disable everything first and then re-enable specific checks. For example, if +# you want to run only the similarities checker, you can use "--disable=all +# --enable=similarities". If you want to run only the classes checker, but have +# no Warning level messages displayed, use "--disable=all --enable=classes +# --disable=W". +disable=raw-checker-failed, + bad-inline-option, + locally-disabled, + file-ignored, + suppressed-message, + useless-suppression, + deprecated-pragma, + use-symbolic-message-instead, + use-implicit-booleaness-not-comparison-to-string, + use-implicit-booleaness-not-comparison-to-zero + +# Enable the message, report, category or checker with the given id(s). You can +# either give multiple identifier separated by comma (,) or put this option +# multiple time (only on the command line, not in the configuration file where +# it should appear only once). See also the "--disable" option for examples. +enable= + + +[METHOD_ARGS] + +# List of qualified names (i.e., library.method) which require a timeout +# parameter e.g. 'requests.api.get,requests.api.post' +timeout-methods=requests.api.delete,requests.api.get,requests.api.head,requests.api.options,requests.api.patch,requests.api.post,requests.api.put,requests.api.request + + +[MISCELLANEOUS] + +# List of note tags to take in consideration, separated by a comma. +notes=FIXME, + XXX, + TODO + +# Regular expression of note tags to take in consideration. +notes-rgx= + + +[REFACTORING] + +# Maximum number of nested blocks for function / method body +max-nested-blocks=5 + +# Complete name of functions that never returns. When checking for +# inconsistent-return-statements if a never returning function is called then +# it will be considered as an explicit return statement and no message will be +# printed. +never-returning-functions=sys.exit,argparse.parse_error + + +[REPORTS] + +# Python expression which should return a score less than or equal to 10. You +# have access to the variables 'fatal', 'error', 'warning', 'refactor', +# 'convention', and 'info' which contain the number of messages in each +# category, as well as 'statement' which is the total number of statements +# analyzed. This score is used by the global evaluation report (RP0004). +evaluation=max(0, 0 if fatal else 10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)) + +# Template used to display messages. This is a python new-style format string +# used to format the message information. See doc for all details. +msg-template= + +# Set the output format. Available formats are: text, parseable, colorized, +# json2 (improved json format), json (old json format) and msvs (visual +# studio). You can also give a reporter class, e.g. +# mypackage.mymodule.MyReporterClass. +#output-format= + +# Tells whether to display a full report or only the messages. +reports=no + +# Activate the evaluation score. +score=yes + + +[SIMILARITIES] + +# Comments are removed from the similarity computation +ignore-comments=yes + +# Docstrings are removed from the similarity computation +ignore-docstrings=yes + +# Imports are removed from the similarity computation +ignore-imports=yes + +# Signatures are removed from the similarity computation +ignore-signatures=yes + +# Minimum lines number of a similarity. +min-similarity-lines=4 + + +[SPELLING] + +# Limits count of emitted suggestions for spelling mistakes. +max-spelling-suggestions=4 + +# Spelling dictionary name. No available dictionaries : You need to install +# both the python package and the system dependency for enchant to work. +spelling-dict= + +# List of comma separated words that should be considered directives if they +# appear at the beginning of a comment and should not be checked. +spelling-ignore-comment-directives=fmt: on,fmt: off,noqa:,noqa,nosec,isort:skip,mypy: + +# List of comma separated words that should not be checked. +spelling-ignore-words= + +# A path to a file that contains the private dictionary; one word per line. +spelling-private-dict-file= + +# Tells whether to store unknown words to the private dictionary (see the +# --spelling-private-dict-file option) instead of raising a message. +spelling-store-unknown-words=no + + +[STRING] + +# This flag controls whether inconsistent-quotes generates a warning when the +# character used as a quote delimiter is used inconsistently within a module. +check-quote-consistency=no + +# This flag controls whether the implicit-str-concat should generate a warning +# on implicit string concatenation in sequences defined over several lines. +check-str-concat-over-line-jumps=no + + +[TYPECHECK] + +# List of decorators that produce context managers, such as +# contextlib.contextmanager. Add to this list to register other decorators that +# produce valid context managers. +contextmanager-decorators=contextlib.contextmanager + +# List of members which are set dynamically and missed by pylint inference +# system, and so shouldn't trigger E1101 when accessed. Python regular +# expressions are accepted. +generated-members= + +# Tells whether to warn about missing members when the owner of the attribute +# is inferred to be None. +ignore-none=yes + +# This flag controls whether pylint should warn about no-member and similar +# checks whenever an opaque object is returned when inferring. The inference +# can return multiple potential results while evaluating a Python object, but +# some branches might not be evaluated, which results in partial inference. In +# that case, it might be useful to still emit no-member and other checks for +# the rest of the inferred objects. +ignore-on-opaque-inference=yes + +# List of symbolic message names to ignore for Mixin members. +ignored-checks-for-mixins=no-member, + not-async-context-manager, + not-context-manager, + attribute-defined-outside-init + +# List of class names for which member attributes should not be checked (useful +# for classes with dynamically set attributes). This supports the use of +# qualified names. +ignored-classes=optparse.Values,thread._local,_thread._local,argparse.Namespace + +# Show a hint with possible names when a member name was not found. The aspect +# of finding the hint is based on edit distance. +missing-member-hint=yes + +# The minimum edit distance a name should have in order to be considered a +# similar match for a missing member name. +missing-member-hint-distance=1 + +# The total number of similar names that should be taken in consideration when +# showing a hint for a missing member. +missing-member-max-choices=1 + +# Regex pattern to define which classes are considered mixins. +mixin-class-rgx=.*[Mm]ixin + +# List of decorators that change the signature of a decorated function. +signature-mutators= + + +[VARIABLES] + +# List of additional names supposed to be defined in builtins. Remember that +# you should avoid defining new builtins when possible. +additional-builtins= + +# Tells whether unused global variables should be treated as a violation. +allow-global-unused-variables=yes + +# List of names allowed to shadow builtins +allowed-redefined-builtins= + +# List of strings which can identify a callback function by name. A callback +# name must start or end with one of those strings. +callbacks=cb_, + _cb + +# A regular expression matching the name of dummy variables (i.e. expected to +# not be used). +dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_ + +# Argument names that match this expression will be ignored. +ignored-argument-names=_.*|^ignored_|^unused_ + +# Tells whether we should check for unused import in __init__ files. +init-import=no + +# List of qualified module names which can have objects that can redefine +# builtins. +redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io diff --git a/Dockerfile b/Dockerfile index a3ff66eb..d8fc3f09 100644 --- a/Dockerfile +++ b/Dockerfile @@ -4,17 +4,20 @@ FROM python:3.11 # Set working directory WORKDIR /code -# Copy requirements first to leverage Docker cache -COPY ./requirements.txt /code/requirements.txt +# Copy requirements first +COPY requirements.txt . -# Install required packages -RUN pip install --no-cache-dir -r /code/requirements.txt +# Install dependencies +RUN pip install --no-cache-dir -r requirements.txt -# Copy the rest of your application -COPY . /code/ +# Copy entire app and rebuild script after installing dependencies +COPY . . -# Expose the port your app runs on +# Rebuild model inside Docker +RUN python scripts/rebuild_models.py + +# Expose port EXPOSE 8000 -# Command to run the application +# Run the backend CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"] \ No newline at end of file diff --git a/README.md b/README.md index b34d6d6b..0f864f53 100644 --- a/README.md +++ b/README.md @@ -55,3 +55,75 @@ This also has an API file to interact with the front end, and logic in order to -Create case assignment (Allow authorized users to create a new case assignment.) + + +## Running Backend with Docker Compose + +### Prerequisites +Before you start, make sure you have: +- Install Docker if you haven't already. + +- You can check if they are installed with: +``` +docker --version +``` + +### Build and start the backend service + +This will: + +- Build the Docker image using the provided Dockerfile + +- Start a container based on that image + +```bash +docker compose up --build +``` + +- Start without rebuilding: +``` +docker compose up +``` + + +### Stop the service +```bash +docker compose down +``` + + +To automate our development workflow and ensure code quality, we implemented a CI pipeline using GitHub Actions. + +### Publicly Available Endpoint +Access the backend application and test the various features of the application using the Swagger from: + +``` +http://35.212.253.228:8000/docs +``` + + +### GitHub Workflow Setup + +Developer C created the GitHub Actions workflow file located at `.github/workflows/ci.yml`. This workflow is configured to trigger on every pull request or direct push to the `main` branch, ensuring all changes are validated before merging. + +### CI Jobs + +Developer D implemented the following CI jobs: + +- **Task 1: Linter/Formatter Check** + - Tools used: `flake8`, `black` + - Ensures consistent coding style and helps detect syntax or logic errors early. + +- **Task 2: Unit Testing** + - Tool used: `pytest` + - Runs all unit tests to validate functionality and prevent regressions. + +- **Task 3: Docker Syntax Validation & Image Build** + - Validates the syntax of Docker-related files and attempts to build the Docker image. + +- **Task 4: Container Startup Verification** + - Executes the built Docker container within the CI pipeline to ensure it can start correctly. + +### Definition of Done (DoD) + +The pipeline is considered complete when all CI checks (linter, tests, Docker validations) pass successfully on pull request or push to the `main` branch. \ No newline at end of file diff --git a/app/auth/router.py b/app/auth/router.py index 229ee71d..bac7ff26 100644 --- a/app/auth/router.py +++ b/app/auth/router.py @@ -11,18 +11,20 @@ router = APIRouter(prefix="/auth", tags=["authentication"]) + class UserCreate(BaseModel): username: str = Field(..., min_length=3, max_length=50) email: str password: str role: UserRole - @validator('role') + @validator("role") def validate_role(cls, v): if v not in [UserRole.admin, UserRole.case_worker]: - raise ValueError('Role must be either admin or case_worker') + raise ValueError("Role must be either admin or case_worker") return v + class UserResponse(BaseModel): username: str email: str @@ -31,6 +33,7 @@ class UserResponse(BaseModel): class Config: from_attributes = True + # Configuration SECRET_KEY = "your-secret-key-here" ALGORITHM = "HS256" @@ -39,18 +42,22 @@ class Config: pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto") oauth2_scheme = OAuth2PasswordBearer(tokenUrl="auth/token") + def verify_password(plain_password: str, hashed_password: str) -> bool: return pwd_context.verify(plain_password, hashed_password) + def get_password_hash(password: str) -> str: return pwd_context.hash(password) + def authenticate_user(db: Session, username: str, password: str) -> Optional[User]: user = db.query(User).filter(User.username == username).first() if not user or not verify_password(password, user.hashed_password): return None return user + def create_access_token(data: dict, expires_delta: Optional[timedelta] = None): to_encode = data.copy() if expires_delta: @@ -61,9 +68,9 @@ def create_access_token(data: dict, expires_delta: Optional[timedelta] = None): encoded_jwt = jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM) return encoded_jwt + async def get_current_user( - token: str = Depends(oauth2_scheme), - db: Session = Depends(get_db) + token: str = Depends(oauth2_scheme), db: Session = Depends(get_db) ) -> User: credentials_exception = HTTPException( status_code=status.HTTP_401_UNAUTHORIZED, @@ -77,24 +84,25 @@ async def get_current_user( raise credentials_exception except JWTError: raise credentials_exception - + user = db.query(User).filter(User.username == username).first() if user is None: raise credentials_exception return user + def get_admin_user(current_user: User = Depends(get_current_user)): if current_user.role != UserRole.admin: raise HTTPException( status_code=status.HTTP_403_FORBIDDEN, - detail="Only admin users can perform this operation" + detail="Only admin users can perform this operation", ) return current_user + @router.post("/token") async def login_for_access_token( - form_data: OAuth2PasswordRequestForm = Depends(), - db: Session = Depends(get_db) + form_data: OAuth2PasswordRequestForm = Depends(), db: Session = Depends(get_db) ): user = authenticate_user(db, form_data.username, form_data.password) if not user: @@ -109,25 +117,25 @@ async def login_for_access_token( ) return {"access_token": access_token, "token_type": "bearer"} + @router.post("/users", response_model=UserResponse) async def create_user( user_data: UserCreate, current_user: User = Depends(get_admin_user), - db: Session = Depends(get_db) + db: Session = Depends(get_db), ): """Create a new user (admin only)""" # Check if username exists if db.query(User).filter(User.username == user_data.username).first(): raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, - detail="Username already registered" + detail="Username already registered", ) - + # Check if email exists if db.query(User).filter(User.email == user_data.email).first(): raise HTTPException( - status_code=status.HTTP_400_BAD_REQUEST, - detail="Email already registered" + status_code=status.HTTP_400_BAD_REQUEST, detail="Email already registered" ) # Create new user @@ -135,9 +143,9 @@ async def create_user( username=user_data.username, email=user_data.email, hashed_password=get_password_hash(user_data.password), - role=user_data.role + role=user_data.role, ) - + try: db.add(db_user) db.commit() @@ -145,7 +153,4 @@ async def create_user( return db_user except Exception as e: db.rollback() - raise HTTPException( - status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, - detail=str(e) - ) + raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e)) diff --git a/app/clients/router.py b/app/clients/router.py index 4ecc83e4..9cdf0fcf 100644 --- a/app/clients/router.py +++ b/app/clients/router.py @@ -12,33 +12,36 @@ from app.database import get_db from app.clients.service.client_service import ClientService from app.clients.schema import ( - ClientResponse, - ClientUpdate, + ClientResponse, + ClientUpdate, ClientListResponse, ServiceResponse, - ServiceUpdate + ServiceUpdate, ) router = APIRouter(prefix="/clients", tags=["clients"]) + @router.get("/", response_model=ClientListResponse) async def get_clients( - current_user: User = Depends(get_admin_user), + current_user: User = Depends(get_admin_user), skip: int = Query(default=0, ge=0, description="Number of records to skip"), limit: int = Query(default=50, ge=1, le=150, description="Maximum number of records to return"), - db: Session = Depends(get_db) + db: Session = Depends(get_db), ): return ClientService.get_clients(db, skip, limit) + @router.get("/{client_id}", response_model=ClientResponse) async def get_client( client_id: int, current_user: User = Depends(get_admin_user), - db: Session = Depends(get_db) + db: Session = Depends(get_db), ): """Get a specific client by ID""" return ClientService.get_client(db, client_id) + @router.get("/search/by-criteria", response_model=List[ClientResponse]) async def get_clients_by_criteria( employment_status: Optional[bool] = None, @@ -66,7 +69,7 @@ async def get_clients_by_criteria( time_unemployed: Optional[int] = Query(None, ge=0), need_mental_health_support_bool: Optional[bool] = None, current_user: User = Depends(get_admin_user), - db: Session = Depends(get_db) + db: Session = Depends(get_db), ): """Search clients by any combination of criteria""" return ClientService.get_clients_by_criteria( @@ -94,9 +97,10 @@ async def get_clients_by_criteria( attending_school=attending_school, substance_use=substance_use, time_unemployed=time_unemployed, - need_mental_health_support_bool=need_mental_health_support_bool + need_mental_health_support_bool=need_mental_health_support_bool, ) + @router.get("/search/by-services", response_model=List[ClientResponse]) async def get_clients_by_services( employment_assistance: Optional[bool] = None, @@ -107,7 +111,7 @@ async def get_clients_by_services( employer_financial_supports: Optional[bool] = None, enhanced_referrals: Optional[bool] = None, current_user: User = Depends(get_admin_user), - db: Session = Depends(get_db) + db: Session = Depends(get_db), ): """Get clients filtered by multiple service statuses""" return ClientService.get_clients_by_services( @@ -118,70 +122,77 @@ async def get_clients_by_services( specialized_services=specialized_services, employment_related_financial_supports=employment_related_financial_supports, employer_financial_supports=employer_financial_supports, - enhanced_referrals=enhanced_referrals + enhanced_referrals=enhanced_referrals, ) + @router.get("/{client_id}/services", response_model=List[ServiceResponse]) async def get_client_services( client_id: int, current_user: User = Depends(get_admin_user), - db: Session = Depends(get_db) + db: Session = Depends(get_db), ): """Get all services and their status for a specific client, including case worker info""" return ClientService.get_client_services(db, client_id) + @router.get("/search/success-rate", response_model=List[ClientResponse]) async def get_clients_by_success_rate( min_rate: int = Query(70, ge=0, le=100, description="Minimum success rate percentage"), current_user: User = Depends(get_admin_user), - db: Session = Depends(get_db) + db: Session = Depends(get_db), ): """Get clients with success rate above specified threshold""" return ClientService.get_clients_by_success_rate(db, min_rate) + @router.get("/case-worker/{case_worker_id}", response_model=List[ClientResponse]) async def get_clients_by_case_worker( case_worker_id: int, - current_user: User = Depends(get_current_user), - db: Session = Depends(get_db) + current_user: User = Depends(get_current_user), + db: Session = Depends(get_db), ): return ClientService.get_clients_by_case_worker(db, case_worker_id) + @router.put("/{client_id}", response_model=ClientResponse) async def update_client( client_id: int, client_data: ClientUpdate, current_user: User = Depends(get_admin_user), - db: Session = Depends(get_db) + db: Session = Depends(get_db), ): """Update a client's information""" return ClientService.update_client(db, client_id, client_data) + @router.put("/{client_id}/services/{user_id}", response_model=ServiceResponse) async def update_client_services( client_id: int, user_id: int, service_update: ServiceUpdate, - current_user: User = Depends(get_current_user), - db: Session = Depends(get_db) + current_user: User = Depends(get_current_user), + db: Session = Depends(get_db), ): return ClientService.update_client_services(db, client_id, user_id, service_update) + @router.post("/{client_id}/case-assignment", response_model=ServiceResponse) async def create_case_assignment( client_id: int, case_worker_id: int = Query(..., description="Case worker ID to assign"), current_user: User = Depends(get_admin_user), - db: Session = Depends(get_db) + db: Session = Depends(get_db), ): """Create a new case assignment for a client with a case worker""" return ClientService.create_case_assignment(db, client_id, case_worker_id) + @router.delete("/{client_id}", status_code=status.HTTP_204_NO_CONTENT) async def delete_client( client_id: int, current_user: User = Depends(get_admin_user), - db: Session = Depends(get_db) + db: Session = Depends(get_db), ): """Delete a client""" ClientService.delete_client(db, client_id) diff --git a/app/clients/schema.py b/app/clients/schema.py index cff28897..fa5f0e04 100644 --- a/app/clients/schema.py +++ b/app/clients/schema.py @@ -9,16 +9,19 @@ from enum import IntEnum from app.models import UserRole + # Enums for validation class Gender(IntEnum): MALE = 1 FEMALE = 2 + class PredictionInput(BaseModel): """ Schema for prediction input data containing all client assessment fields. Used for making predictions about client outcomes. """ + age: int gender: str work_experience: int @@ -44,6 +47,7 @@ class PredictionInput(BaseModel): time_unemployed: int need_mental_health_support_bool: str + class ClientBase(BaseModel): age: int = Field(ge=18, description="Age of client, must be 18 or older") gender: Gender = Field(description="Gender: 1 for male, 2 for female") @@ -96,16 +100,18 @@ class Config: "currently_employed": False, "substance_use": False, "time_unemployed": 6, - "need_mental_health_support_bool": False + "need_mental_health_support_bool": False, } } + class ClientResponse(ClientBase): id: int class Config: from_attributes = True + class ClientUpdate(BaseModel): age: Optional[int] = Field(None, ge=18) gender: Optional[Gender] = None @@ -132,6 +138,7 @@ class ClientUpdate(BaseModel): time_unemployed: Optional[int] = Field(None, ge=0) need_mental_health_support_bool: Optional[bool] = None + class ServiceResponse(BaseModel): client_id: int user_id: int @@ -147,6 +154,7 @@ class ServiceResponse(BaseModel): class Config: from_attributes = True + class ServiceUpdate(BaseModel): employment_assistance: Optional[bool] = None life_stabilization: Optional[bool] = None @@ -157,6 +165,7 @@ class ServiceUpdate(BaseModel): enhanced_referrals: Optional[bool] = None success_rate: Optional[int] = Field(None, ge=0, le=100) + class ClientListResponse(BaseModel): clients: List[ClientResponse] total: int diff --git a/app/clients/service/analyze_data/analyze_features.py b/app/clients/service/analyze_data/analyze_features.py new file mode 100644 index 00000000..6a409573 --- /dev/null +++ b/app/clients/service/analyze_data/analyze_features.py @@ -0,0 +1,34 @@ +import os +import pandas as pd +import seaborn as sns +import matplotlib.pyplot as plt + + +def analyze_features(csv_path, target_col="success_rate", threshold=0.1): + # Load dataset + df = pd.read_csv(csv_path) + + # Plot correlation heatmap for all features + corr = df.corr() + plt.figure(figsize=(14, 12)) + sns.heatmap(corr, annot=True, cmap="coolwarm") + plt.title("Correlation Heatmap") + plt.show() + + # Select features with correlation above threshold w.r.t target + target_corr = corr[target_col].drop(target_col) # drop self-correlation + recommended_features = target_corr[abs(target_corr) >= threshold].index.tolist() + + print("Recommended features (|correlation with '{}'| >= {:.2f}):".format(target_col, threshold)) + print(recommended_features) + + return recommended_features + + +if __name__ == "__main__": + # Dynamically build relative path to your dataset + BASE_DIR = os.path.dirname(os.path.abspath(__file__)) + csv_path = os.path.join(BASE_DIR, "../../clients/service/data_commontool_synthetic.csv") + + # Call feature analysis + selected_features = analyze_features(csv_path) diff --git a/app/clients/service/client_service.py b/app/clients/service/client_service.py index 86c3ef4a..b9bb38a8 100644 --- a/app/clients/service/client_service.py +++ b/app/clients/service/client_service.py @@ -10,6 +10,7 @@ from app.models import Client, ClientCase, User from app.clients.schema import ClientUpdate, ServiceUpdate, ServiceResponse + class ClientService: @staticmethod def get_client(db: Session, client_id: int): @@ -18,7 +19,7 @@ def get_client(db: Session, client_id: int): if not client: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, - detail=f"Client with id {client_id} not found" + detail=f"Client with id {client_id} not found", ) return client @@ -31,14 +32,14 @@ def get_clients(db: Session, skip: int = 0, limit: int = 50): if skip < 0: raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, - detail="Skip value cannot be negative" + detail="Skip value cannot be negative", ) if limit < 1: raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, - detail="Limit must be greater than 0" + detail="Limit must be greater than 0", ) - + clients = db.query(Client).offset(skip).limit(limit).all() total = db.query(Client).count() return {"clients": clients, "total": total} @@ -69,27 +70,26 @@ def get_clients_by_criteria( attending_school: Optional[bool] = None, substance_use: Optional[bool] = None, time_unemployed: Optional[int] = None, - need_mental_health_support_bool: Optional[bool] = None + need_mental_health_support_bool: Optional[bool] = None, ): """Get clients filtered by any combination of criteria""" query = db.query(Client) - + if education_level is not None and not (1 <= education_level <= 14): raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, - detail="Education level must be between 1 and 14" + detail="Education level must be between 1 and 14", ) - + if age_min is not None and age_min < 18: raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, - detail="Minimum age must be at least 18" + detail="Minimum age must be at least 18", ) if gender is not None and gender not in [1, 2]: raise HTTPException( - status_code=status.HTTP_400_BAD_REQUEST, - detail="Gender must be 1 or 2" + status_code=status.HTTP_400_BAD_REQUEST, detail="Gender must be 1 or 2" ) # Apply filters for non-None values @@ -140,37 +140,36 @@ def get_clients_by_criteria( if time_unemployed is not None: query = query.filter(Client.time_unemployed == time_unemployed) if need_mental_health_support_bool is not None: - query = query.filter(Client.need_mental_health_support_bool == need_mental_health_support_bool) + query = query.filter( + Client.need_mental_health_support_bool == need_mental_health_support_bool + ) try: return query.all() except Exception as e: raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, - detail=f"Error retrieving clients: {str(e)}" + detail=f"Error retrieving clients: {str(e)}", ) @staticmethod - def get_clients_by_services( - db: Session, - **service_filters: Optional[bool] - ): + def get_clients_by_services(db: Session, **service_filters: Optional[bool]): """ Get clients filtered by multiple service statuses. """ query = db.query(Client).join(ClientCase) - + for service_name, status in service_filters.items(): if status is not None: filter_criteria = getattr(ClientCase, service_name) == status query = query.filter(filter_criteria) - + try: return query.all() except Exception as e: raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, - detail=f"Error retrieving clients: {str(e)}" + detail=f"Error retrieving clients: {str(e)}", ) @staticmethod @@ -180,7 +179,7 @@ def get_client_services(db: Session, client_id: int): if not client_cases: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, - detail=f"No services found for client with id {client_id}" + detail=f"No services found for client with id {client_id}", ) return client_cases @@ -190,12 +189,10 @@ def get_clients_by_success_rate(db: Session, min_rate: int = 70): if not (0 <= min_rate <= 100): raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, - detail="Success rate must be between 0 and 100" + detail="Success rate must be between 0 and 100", ) - - return db.query(Client).join(ClientCase).filter( - ClientCase.success_rate >= min_rate - ).all() + + return db.query(Client).join(ClientCase).filter(ClientCase.success_rate >= min_rate).all() @staticmethod def get_clients_by_case_worker(db: Session, case_worker_id: int): @@ -204,12 +201,10 @@ def get_clients_by_case_worker(db: Session, case_worker_id: int): if not case_worker: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, - detail=f"Case worker with id {case_worker_id} not found" + detail=f"Case worker with id {case_worker_id} not found", ) - - return db.query(Client).join(ClientCase).filter( - ClientCase.user_id == case_worker_id - ).all() + + return db.query(Client).join(ClientCase).filter(ClientCase.user_id == case_worker_id).all() @staticmethod def update_client(db: Session, client_id: int, client_update: ClientUpdate): @@ -218,7 +213,7 @@ def update_client(db: Session, client_id: int, client_update: ClientUpdate): if not client: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, - detail=f"Client with id {client_id} not found" + detail=f"Client with id {client_id} not found", ) update_data = client_update.dict(exclude_unset=True) @@ -233,27 +228,25 @@ def update_client(db: Session, client_id: int, client_update: ClientUpdate): db.rollback() raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, - detail=f"Failed to update client: {str(e)}" + detail=f"Failed to update client: {str(e)}", ) - + @staticmethod def update_client_services( - db: Session, - client_id: int, - user_id: int, - service_update: ServiceUpdate + db: Session, client_id: int, user_id: int, service_update: ServiceUpdate ): """Update a client's services and outcomes for a specific case worker""" - client_case = db.query(ClientCase).filter( - ClientCase.client_id == client_id, - ClientCase.user_id == user_id - ).first() - + client_case = ( + db.query(ClientCase) + .filter(ClientCase.client_id == client_id, ClientCase.user_id == user_id) + .first() + ) + if not client_case: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, detail=f"No case found for client {client_id} with case worker {user_id}. " - f"Cannot update services for a non-existent case assignment." + f"Cannot update services for a non-existent case assignment.", ) update_data = service_update.dict(exclude_unset=True) @@ -268,22 +261,18 @@ def update_client_services( db.rollback() raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, - detail=f"Failed to update client services: {str(e)}" + detail=f"Failed to update client services: {str(e)}", ) - + @staticmethod - def create_case_assignment( - db: Session, - client_id: int, - case_worker_id: int - ): + def create_case_assignment(db: Session, client_id: int, case_worker_id: int): """Create a new case assignment""" # Check if client exists client = db.query(Client).filter(Client.id == client_id).first() if not client: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, - detail=f"Client with id {client_id} not found" + detail=f"Client with id {client_id} not found", ) # Check if case worker exists @@ -291,20 +280,21 @@ def create_case_assignment( if not case_worker: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, - detail=f"Case worker with id {case_worker_id} not found" + detail=f"Case worker with id {case_worker_id} not found", ) # Check if assignment already exists - existing_case = db.query(ClientCase).filter( - ClientCase.client_id == client_id, - ClientCase.user_id == case_worker_id - ).first() - + existing_case = ( + db.query(ClientCase) + .filter(ClientCase.client_id == client_id, ClientCase.user_id == case_worker_id) + .first() + ) + if existing_case: raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, - detail=f"Client {client_id} already has a case assigned to case worker {case_worker_id}" - ) + detail=f"Client {client_id} already has a case assigned to case worker {case_worker_id}", + ) try: # Create new case assignment with default service values @@ -318,7 +308,7 @@ def create_case_assignment( employment_related_financial_supports=False, employer_financial_supports=False, enhanced_referrals=False, - success_rate=0 + success_rate=0, ) db.add(new_case) db.commit() @@ -329,9 +319,9 @@ def create_case_assignment( db.rollback() raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, - detail=f"Failed to create case assignment: {str(e)}" + detail=f"Failed to create case assignment: {str(e)}", ) - + @staticmethod def delete_client(db: Session, client_id: int): """Delete a client and their associated records""" @@ -340,22 +330,20 @@ def delete_client(db: Session, client_id: int): if not client: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, - detail=f"Client with id {client_id} not found" + detail=f"Client with id {client_id} not found", ) try: # Delete associated client_cases - db.query(ClientCase).filter( - ClientCase.client_id == client_id - ).delete() - + db.query(ClientCase).filter(ClientCase.client_id == client_id).delete() + # Delete the client db.delete(client) db.commit() - + except Exception as e: db.rollback() raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, - detail=f"Failed to delete client: {str(e)}" + detail=f"Failed to delete client: {str(e)}", ) diff --git a/app/clients/service/data_commontool_synthetic.csv b/app/clients/service/data_commontool_synthetic.csv new file mode 100644 index 00000000..2f734334 --- /dev/null +++ b/app/clients/service/data_commontool_synthetic.csv @@ -0,0 +1,7001 @@ +age,gender,work_experience,canada_workex,dep_num,canada_born,citizen_status,level_of_schooling,fluent_english,reading_english_scale,speaking_english_scale,writing_english_scale,numeracy_scale,computer_scale,transportation_bool,caregiver_bool,housing,income_source,felony_bool,attending_school,currently_employed,substance_use,time_unemployed,need_mental_health_support_bool,employment_assistance,life_stabilization,retention_services,specialized_services,employment_related_financial_supports,employer_financial_supports,enhanced_referrals,success_rate 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a/app/clients/service/data_commontool_synthetic_testdata.csv b/app/clients/service/data_commontool_synthetic_testdata.csv new file mode 100644 index 00000000..aff9e2fb --- /dev/null +++ b/app/clients/service/data_commontool_synthetic_testdata.csv @@ -0,0 +1,4001 @@ +age,gender,work_experience,canada_workex,dep_num,canada_born,citizen_status,level_of_schooling,fluent_english,reading_english_scale,speaking_english_scale,writing_english_scale,numeracy_scale,computer_scale,transportation_bool,caregiver_bool,housing,income_source,felony_bool,attending_school,currently_employed,substance_use,time_unemployed,need_mental_health_support_bool,employment_assistance,life_stabilization,retention_services,specialized_services,employment_related_financial_supports,employer_financial_supports,enhanced_referrals,success_rate +33,1,12,8,0,0,1,10,7,7,7,8,3,3,0,0,1,7,0,1,1,0,7,1,0,0,1,0,1,0,1,65 +28,1,8,2,1,1,0,6,9,8,10,8,4,9,1,1,8,9,1,1,0,0,0,1,0,0,1,0,0,1,0,48 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00000000..5b94e384 Binary files /dev/null and b/app/clients/service/final_MLmodels/final_ridge_model.pkl differ diff --git a/app/clients/service/final_MLmodels/final_xgboost_gridcv_model.pkl b/app/clients/service/final_MLmodels/final_xgboost_gridcv_model.pkl new file mode 100644 index 00000000..fdf0688d Binary files /dev/null and b/app/clients/service/final_MLmodels/final_xgboost_gridcv_model.pkl differ diff --git a/app/clients/service/initial_model_trainers/XGBoost_model.py b/app/clients/service/initial_model_trainers/XGBoost_model.py new file mode 100644 index 00000000..16e23f0a --- /dev/null +++ b/app/clients/service/initial_model_trainers/XGBoost_model.py @@ -0,0 +1,120 @@ +import os +import pickle +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split, cross_val_score, KFold +from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score +from xgboost import XGBRegressor + +# Define feature set +FEATURES = [ + "age", + "gender", + "work_experience", + "canada_workex", + "dep_num", + "canada_born", + "citizen_status", + "level_of_schooling", + "fluent_english", + "reading_english_scale", + "speaking_english_scale", + "writing_english_scale", + "numeracy_scale", + "computer_scale", + "transportation_bool", + "caregiver_bool", + "housing", + "income_source", + "felony_bool", + "attending_school", + "currently_employed", + "substance_use", + "time_unemployed", + "need_mental_health_support_bool", + "employment_assistance", + "life_stabilization", + "retention_services", + "specialized_services", + "employment_related_financial_supports", + "employer_financial_supports", + "enhanced_referrals", +] + +# Set relative paths +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +DATA_PATH = os.path.join(BASE_DIR, "../data_commontool_synthetic.csv") +MODEL_DIR = os.path.join(BASE_DIR, "../MLmodels/") +os.makedirs(MODEL_DIR, exist_ok=True) # Auto-create directory if it doesn't exist + + +def load_data(selected_features): + """Load dataset and return features and targets""" + data = pd.read_csv(DATA_PATH) + X = np.array(data[selected_features]) + y = np.array(data["success_rate"]) + return X, y + + +def train_xgboost(X_train, y_train): + """Train XGBoost Regressor""" + model = XGBRegressor( + n_estimators=100, + max_depth=5, + learning_rate=0.1, + objective="reg:squarederror", + random_state=42, + ) + model.fit(X_train, y_train) + return model + + +def evaluate_model(model, X_test, y_test): + """Evaluate model on the hold-out test set""" + predictions = model.predict(X_test) + mae = mean_absolute_error(y_test, predictions) + mse = mean_squared_error(y_test, predictions) + r2 = r2_score(y_test, predictions) + print(f"Hold-out Test Set - MAE: {mae:.2f} | MSE: {mse:.2f} | R2: {r2:.2f}") + + +def cross_validate(model, X, y): + """Perform 5-fold cross-validation""" + cv = KFold(n_splits=5, shuffle=True, random_state=42) + r2_scores = cross_val_score(model, X, y, cv=cv, scoring="r2") + mae_scores = -cross_val_score(model, X, y, cv=cv, scoring="neg_mean_absolute_error") + + print(f"Cross-Validation R² scores: {r2_scores}") + print(f"Mean R²: {np.mean(r2_scores):.3f}") + print(f"Cross-Validation MAE scores: {mae_scores}") + print(f"Mean MAE: {np.mean(mae_scores):.3f}") + + +def save_model(model, filename): + """Save trained model as a pickle file""" + model_path = os.path.join(MODEL_DIR, filename) + with open(model_path, "wb") as f: + pickle.dump(model, f) + print(f"✅ XGBoost model saved at {model_path}") + + +def main(): + # Load data + X, y = load_data(FEATURES) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + + # Train model + model = train_xgboost(X_train, y_train) + + # Evaluate on hold-out test set + evaluate_model(model, X_test, y_test) + + # Cross-validation + cross_validate(model, X, y) + + # Save model + save_model(model, "xgboost_model.pkl") + + +if __name__ == "__main__": + main() diff --git a/app/clients/service/initial_model_trainers/get_synthetic_data.py b/app/clients/service/initial_model_trainers/get_synthetic_data.py new file mode 100644 index 00000000..db2a8174 --- /dev/null +++ b/app/clients/service/initial_model_trainers/get_synthetic_data.py @@ -0,0 +1,96 @@ +import pandas as pd +import numpy as np +import os + +num_samples = 7000 # Total number of samples to generate + +# Age distribution: Normal distribution centered around 30, capped between 18 and 60 +age = np.random.normal(loc=30, scale=8, size=num_samples).astype(int) +age = np.clip(age, 18, 60) + +# Work experience based on age (years since 18 minus some gap years) +work_experience = (age - 18) - np.random.randint(0, 4, num_samples) +work_experience = np.clip(work_experience, 0, 42) + +# Canadian work experience is a fraction of total work experience +canada_workex = (work_experience * np.random.uniform(0.3, 0.9, num_samples)).astype(int) + +# Education level: weighted choice simulating real-world distribution +level_of_schooling = np.random.choice( + [6, 8, 10, 12, 14], size=num_samples, p=[0.1, 0.25, 0.3, 0.25, 0.1] +) + +# English proficiency: fluent_english impacts reading/speaking/writing scores +fluent_english = np.random.randint(4, 10, num_samples) +reading_english = np.clip(fluent_english + np.random.randint(-1, 2, num_samples), 1, 10) +speaking_english = np.clip(fluent_english + np.random.randint(-1, 2, num_samples), 1, 10) +writing_english = np.clip(fluent_english + np.random.randint(-1, 2, num_samples), 1, 10) + +# Numeracy and computer skills +numeracy = np.random.randint(3, 10, num_samples) +computer = np.random.randint(3, 10, num_samples) + +# Binary features generator (0 or 1) +binary_features = lambda: np.random.randint(0, 2, num_samples) + +# Base success rate formula influenced by key features +success_rate_base = ( + fluent_english * 2 + + level_of_schooling * 2 + + computer * 2 + + numeracy * 1.5 + + work_experience * 1.2 + + (binary_features() * 10) # bonus random life factors +) + +# Normalize and add Gaussian noise +success_rate = (success_rate_base / success_rate_base.max() * 100) + np.random.normal( + 0, 5, num_samples +) +success_rate = np.clip(success_rate.astype(int), 0, 100) + +# Create the dataframe +data = pd.DataFrame( + { + "age": age, + "gender": binary_features(), + "work_experience": work_experience, + "canada_workex": canada_workex, + "dep_num": np.random.randint(0, 5, num_samples), + "canada_born": binary_features(), + "citizen_status": binary_features(), + "level_of_schooling": level_of_schooling, + "fluent_english": fluent_english, + "reading_english_scale": reading_english, + "speaking_english_scale": speaking_english, + "writing_english_scale": writing_english, + "numeracy_scale": numeracy, + "computer_scale": computer, + "transportation_bool": binary_features(), + "caregiver_bool": binary_features(), + "housing": np.random.randint(1, 10, num_samples), + "income_source": np.random.randint(1, 10, num_samples), + "felony_bool": binary_features(), + "attending_school": binary_features(), + "currently_employed": binary_features(), + "substance_use": binary_features(), + "time_unemployed": np.random.randint(0, 8, num_samples), + "need_mental_health_support_bool": binary_features(), + "employment_assistance": binary_features(), + "life_stabilization": binary_features(), + "retention_services": binary_features(), + "specialized_services": binary_features(), + "employment_related_financial_supports": binary_features(), + "employer_financial_supports": binary_features(), + "enhanced_referrals": binary_features(), + "success_rate": success_rate, + } +) + +# Save to CSV +# output_path = os.path.join("app", "clients", "service", "data_commontool_synthetic.csv") +output_path = os.path.join(os.path.dirname(__file__), "data_commontool_synthetic.csv") + +data.to_csv(output_path, index=False) + +print(f"✅ {num_samples} synthetic data rows generated and saved to data_commontool_synthetic.csv") diff --git a/app/clients/service/initial_model_trainers/get_synthetic_testdata.py b/app/clients/service/initial_model_trainers/get_synthetic_testdata.py new file mode 100644 index 00000000..ad7d2114 --- /dev/null +++ b/app/clients/service/initial_model_trainers/get_synthetic_testdata.py @@ -0,0 +1,138 @@ +import pandas as pd +import numpy as np +import os + +np.random.seed(42) # For reproducibility + +num_samples = 4000 # Number of rows + +# Age distribution: Normal distribution centered around 30, capped between 18 and 60 +age = np.random.normal(loc=30, scale=8, size=num_samples).astype(int) +age = np.clip(age, 18, 60) + +# Work experience based on age (years since 18 minus some gap years) +work_experience = (age - 18) - np.random.randint(0, 4, num_samples) +work_experience = np.clip(work_experience, 0, 42) + +# Canadian work experience is a fraction of total work experience +canada_workex = (work_experience * np.random.uniform(0.3, 0.9, num_samples)).astype(int) + +# Education level: weighted choice +level_of_schooling = np.random.choice( + [6, 8, 10, 12, 14], size=num_samples, p=[0.1, 0.25, 0.3, 0.25, 0.1] +) + +# English proficiency +fluent_english = np.random.randint(4, 10, num_samples) +reading_english = np.clip(fluent_english + np.random.randint(-1, 2, num_samples), 1, 10) +speaking_english = np.clip(fluent_english + np.random.randint(-1, 2, num_samples), 1, 10) +writing_english = np.clip(fluent_english + np.random.randint(-1, 2, num_samples), 1, 10) + +# Numeracy and computer skills +numeracy = np.random.randint(3, 10, num_samples) +computer = np.random.randint(3, 10, num_samples) + +# Binary features generator +binary_features = lambda: np.random.randint(0, 2, num_samples) + +# Other categorical and binary features +gender = binary_features() +dep_num = np.random.randint(0, 5, num_samples) +canada_born = binary_features() +citizen_status = binary_features() +transportation_bool = binary_features() +caregiver_bool = binary_features() +housing = np.random.randint(1, 10, num_samples) +income_source = np.random.randint(1, 10, num_samples) +felony_bool = binary_features() +attending_school = binary_features() +currently_employed = binary_features() +substance_use = binary_features() +time_unemployed = np.random.randint(0, 8, num_samples) +need_mental_health_support_bool = binary_features() +employment_assistance = binary_features() +life_stabilization = binary_features() +retention_services = binary_features() +specialized_services = binary_features() +employment_related_financial_supports = binary_features() +employer_financial_supports = binary_features() +enhanced_referrals = binary_features() + +# Success Rate Logic + +felony_penalty = np.where(felony_bool == 1, -15, 0) +substance_penalty = np.where(substance_use == 1, -10, 0) +long_unemployed_penalty = np.where(time_unemployed >= 4, -5, 0) + +bonus_services = ( + employment_assistance + life_stabilization + retention_services + specialized_services +) * 3 + +employment_bonus = np.where(currently_employed == 1, 5, 0) +education_effect = np.minimum(level_of_schooling, 12) # Cap effect after a certain level + +success_rate_base = ( + fluent_english * 2 + + education_effect * 2 + + computer * 2 + + numeracy * 1.5 + + work_experience * 1.0 + + employment_bonus + + bonus_services + + felony_penalty + + substance_penalty + + long_unemployed_penalty + + (np.random.randint(0, 2, num_samples) * 5) # Random life variance +) + +# Normalize and add Gaussian noise +success_rate = (success_rate_base / success_rate_base.max() * 100) + np.random.normal( + 0, 5, num_samples +) +success_rate = np.clip(success_rate.astype(int), 0, 100) + +# Create DataFrame +data = pd.DataFrame( + { + "age": age, + "gender": gender, + "work_experience": work_experience, + "canada_workex": canada_workex, + "dep_num": dep_num, + "canada_born": canada_born, + "citizen_status": citizen_status, + "level_of_schooling": level_of_schooling, + "fluent_english": fluent_english, + "reading_english_scale": reading_english, + "speaking_english_scale": speaking_english, + "writing_english_scale": writing_english, + "numeracy_scale": numeracy, + "computer_scale": computer, + "transportation_bool": transportation_bool, + "caregiver_bool": caregiver_bool, + "housing": housing, + "income_source": income_source, + "felony_bool": felony_bool, + "attending_school": attending_school, + "currently_employed": currently_employed, + "substance_use": substance_use, + "time_unemployed": time_unemployed, + "need_mental_health_support_bool": need_mental_health_support_bool, + "employment_assistance": employment_assistance, + "life_stabilization": life_stabilization, + "retention_services": retention_services, + "specialized_services": specialized_services, + "employment_related_financial_supports": employment_related_financial_supports, + "employer_financial_supports": employer_financial_supports, + "enhanced_referrals": enhanced_referrals, + "success_rate": success_rate, + } +) + +# Save to CSV +output_path = os.path.join(os.path.dirname(__file__), "../data_commontool_synthetic_testdata.csv") +data.to_csv(output_path, index=False) + +print( + f" {num_samples} diversified synthetic data rows generated and saved to data_commontool_synthetic.csv" +) diff --git a/app/clients/service/initial_model_trainers/linear_regression_model.py b/app/clients/service/initial_model_trainers/linear_regression_model.py new file mode 100644 index 00000000..f3a67445 --- /dev/null +++ b/app/clients/service/initial_model_trainers/linear_regression_model.py @@ -0,0 +1,125 @@ +import pickle +import os +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split, cross_val_score, KFold +from sklearn.linear_model import LinearRegression +from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score + +# Model map for easy switching +MODEL_MAP = {"linear_regression": LinearRegression()} + +ALL_FEATURES = [ + "age", + "gender", + "work_experience", + "canada_workex", + "dep_num", + "canada_born", + "citizen_status", + "level_of_schooling", + "fluent_english", + "reading_english_scale", + "speaking_english_scale", + "writing_english_scale", + "numeracy_scale", + "computer_scale", + "transportation_bool", + "caregiver_bool", + "housing", + "income_source", + "felony_bool", + "attending_school", + "currently_employed", + "substance_use", + "time_unemployed", + "need_mental_health_support_bool", + "employment_assistance", + "life_stabilization", + "retention_services", + "specialized_services", + "employment_related_financial_supports", + "employer_financial_supports", + "enhanced_referrals", +] + +LINEAR_MODEL_FEATURES = [ + "age", + "work_experience", + "canada_workex", + "level_of_schooling", + "fluent_english", + "reading_english_scale", + "speaking_english_scale", + "writing_english_scale", + "numeracy_scale", + "computer_scale", +] + + +# Dynamically get the current script's directory (this will always work regardless of where you run from) +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) + +# Path to CSV file using relative path +DATA_PATH = os.path.join(BASE_DIR, "../../clients/service/data_commontool_synthetic.csv") + +# Path to output model directory +MODEL_DIR = os.path.join(BASE_DIR, "../../clients/service/MLmodels/") +os.makedirs(MODEL_DIR, exist_ok=True) # auto create if not exists + + +def load_data(selected_features): + data = pd.read_csv(DATA_PATH) + X = np.array(data[selected_features]) + y = np.array(data["success_rate"]) + return X, y + + +def train_and_evaluate_with_split(model_name, selected_features): + X, y = load_data(selected_features) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + model = MODEL_MAP[model_name] + model.fit(X_train, y_train) + predictions = model.predict(X_test) + mae = mean_absolute_error(y_test, predictions) + mse = mean_squared_error(y_test, predictions) + r2 = r2_score(y_test, predictions) + print(f"Hold-out Test Set - MAE: {mae:.2f} | MSE: {mse:.2f} | R2: {r2:.2f}") + return model # return model so you can save it later + + +def cross_validate_model(model_name, selected_features): + X, y = load_data(selected_features) + model = MODEL_MAP[model_name] + cv = KFold(n_splits=5, shuffle=True, random_state=42) # 5-fold CV with shuffling + r2_scores = cross_val_score(model, X, y, cv=cv, scoring="r2") + mae_scores = -cross_val_score(model, X, y, cv=cv, scoring="neg_mean_absolute_error") + + print(f"Cross-Validation R² scores: {r2_scores}") + print(f"Mean R²: {np.mean(r2_scores):.3f}") + print(f"Cross-Validation MAE scores: {mae_scores}") + print(f"Mean MAE: {np.mean(mae_scores):.3f}") + + +def save_model(model, filename): + model_path = os.path.join(MODEL_DIR, filename) + with open(model_path, "wb") as f: + pickle.dump(model, f) + print(f"✅ Model saved as {model_path}") + + +def main(): + print("=== Linear Regression on ALL_FEATURES ===") + train_and_evaluate_with_split("linear_regression", ALL_FEATURES) + cross_validate_model("linear_regression", ALL_FEATURES) + + print("\n=== Linear Regression on LINEAR_MODEL_FEATURES ===") + model = train_and_evaluate_with_split("linear_regression", LINEAR_MODEL_FEATURES) + cross_validate_model("linear_regression", LINEAR_MODEL_FEATURES) + + # Save model trained on linear subset + save_model(model, "linear_model_subset.pkl") + + +if __name__ == "__main__": + main() diff --git a/app/clients/service/initial_model_trainers/model_results.py b/app/clients/service/initial_model_trainers/model_results.py new file mode 100644 index 00000000..7afa3e46 --- /dev/null +++ b/app/clients/service/initial_model_trainers/model_results.py @@ -0,0 +1,66 @@ +import pickle +import numpy as np +import pandas as pd +from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score + +models = [ + "../MLmodels/linear_model_subset.pkl", + "../MLmodels/ridge_model.pkl", + "../MLmodels/xgboost_model.pkl", + "../MLmodels/model.pkl", +] + +# Load data +# data = pd.read_csv('../data_commontool_synthetic.csv') +data = pd.read_csv("../data_commontool_synthetic_testdata.csv") + +# Feature set for linear_model_subset.pkl +LINEAR_MODEL_FEATURES = [ + "age", + "work_experience", + "canada_workex", + "level_of_schooling", + "fluent_english", + "reading_english_scale", + "speaking_english_scale", + "writing_english_scale", + "numeracy_scale", + "computer_scale", +] + +# Targets +y = data["success_rate"].values + + +def load_model(filename): + with open(filename, "rb") as model_file: + return pickle.load(model_file) + + +def main(): + print("========== Starting model testing ==========\n") + for model_path in models: + loaded_model = load_model(model_path) + model_name = model_path.split("/")[-1] + print(f">>> Testing Model: {model_name}") + + # Choose feature set depending on model + if "linear_model_subset" in model_path: + X = data[LINEAR_MODEL_FEATURES].values + else: + X = data.drop(columns=["success_rate"]).values + + predictions = loaded_model.predict(X) + mae = mean_absolute_error(y, predictions) + mse = mean_squared_error(y, predictions) + r2 = r2_score(y, predictions) + print(f" MAE: {mae:.2f}") + print(f" MSE: {mse:.2f}") + print(f" R2: {r2:.2f}") + print("-" * 40) + + print("\n========== Model testing completed ==========") + + +if __name__ == "__main__": + main() diff --git a/app/clients/service/initial_model_trainers/ridge_regression_mode.py b/app/clients/service/initial_model_trainers/ridge_regression_mode.py new file mode 100644 index 00000000..775b2cf5 --- /dev/null +++ b/app/clients/service/initial_model_trainers/ridge_regression_mode.py @@ -0,0 +1,114 @@ +import os +import pickle +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split, cross_val_score, KFold +from sklearn.linear_model import Ridge +from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score + +# Define feature set +FEATURES = [ + "age", + "gender", + "work_experience", + "canada_workex", + "dep_num", + "canada_born", + "citizen_status", + "level_of_schooling", + "fluent_english", + "reading_english_scale", + "speaking_english_scale", + "writing_english_scale", + "numeracy_scale", + "computer_scale", + "transportation_bool", + "caregiver_bool", + "housing", + "income_source", + "felony_bool", + "attending_school", + "currently_employed", + "substance_use", + "time_unemployed", + "need_mental_health_support_bool", + "employment_assistance", + "life_stabilization", + "retention_services", + "specialized_services", + "employment_related_financial_supports", + "employer_financial_supports", + "enhanced_referrals", +] + +# Set relative paths +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +DATA_PATH = os.path.join(BASE_DIR, "../data_commontool_synthetic.csv") +MODEL_DIR = os.path.join(BASE_DIR, "..//MLmodels/") +os.makedirs(MODEL_DIR, exist_ok=True) + + +def load_data(selected_features): + """Load dataset and return features and targets""" + data = pd.read_csv(DATA_PATH) + X = np.array(data[selected_features]) + y = np.array(data["success_rate"]) + return X, y + + +def train_ridge(X_train, y_train): + """Train Ridge Regression with alpha=1.0""" + model = Ridge(alpha=1.0) + model.fit(X_train, y_train) + return model + + +def evaluate_model(model, X_test, y_test): + """Evaluate model on the hold-out test set""" + predictions = model.predict(X_test) + mae = mean_absolute_error(y_test, predictions) + mse = mean_squared_error(y_test, predictions) + r2 = r2_score(y_test, predictions) + print(f"Hold-out Test Set - MAE: {mae:.2f} | MSE: {mse:.2f} | R2: {r2:.2f}") + + +def cross_validate(model, X, y): + """Perform 5-fold cross-validation""" + cv = KFold(n_splits=5, shuffle=True, random_state=42) + r2_scores = cross_val_score(model, X, y, cv=cv, scoring="r2") + mae_scores = -cross_val_score(model, X, y, cv=cv, scoring="neg_mean_absolute_error") + + print(f"Cross-Validation R² scores: {r2_scores}") + print(f"Mean R²: {np.mean(r2_scores):.3f}") + print(f"Cross-Validation MAE scores: {mae_scores}") + print(f"Mean MAE: {np.mean(mae_scores):.3f}") + + +def save_model(model, filename): + """Save trained model as a pickle file""" + model_path = os.path.join(MODEL_DIR, filename) + with open(model_path, "wb") as f: + pickle.dump(model, f) + print(f"✅ Ridge model saved at {model_path}") + + +def main(): + # Load data + X, y = load_data(FEATURES) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + + # Train Ridge model + model = train_ridge(X_train, y_train) + + # Evaluate on hold-out test set + evaluate_model(model, X_test, y_test) + + # Cross-validation + cross_validate(model, X, y) + + # Save model + save_model(model, "ridge_model.pkl") + + +if __name__ == "__main__": + main() diff --git a/app/clients/service/logic.py b/app/clients/service/logic.py index c25b4217..95764c51 100644 --- a/app/clients/service/logic.py +++ b/app/clients/service/logic.py @@ -5,7 +5,8 @@ # Standard library imports import os -#import json + +# import json from itertools import product # Third-party imports @@ -14,21 +15,22 @@ # Constants COLUMN_INTERVENTIONS = [ - 'Life Stabilization', - 'General Employment Assistance Services', - 'Retention Services', - 'Specialized Services', - 'Employment-Related Financial Supports for Job Seekers and Employers', - 'Employer Financial Supports', - 'Enhanced Referrals for Skills Development' + "Life Stabilization", + "General Employment Assistance Services", + "Retention Services", + "Specialized Services", + "Employment-Related Financial Supports for Job Seekers and Employers", + "Employer Financial Supports", + "Enhanced Referrals for Skills Development", ] # Load model CURRENT_DIR = os.path.dirname(os.path.abspath(__file__)) -MODEL_PATH = os.path.join(CURRENT_DIR, 'model.pkl') +MODEL_PATH = os.path.join(CURRENT_DIR, "model.pkl") with open(MODEL_PATH, "rb") as model_file: MODEL = pickle.load(model_file) + def clean_input_data(input_data): """ Clean and transform input data into model-compatible format. @@ -40,13 +42,30 @@ def clean_input_data(input_data): list: Cleaned and formatted data ready for model input """ columns = [ - "age", "gender", "work_experience", "canada_workex", "dep_num", - "canada_born", "citizen_status", "level_of_schooling", "fluent_english", - "reading_english_scale", "speaking_english_scale", "writing_english_scale", - "numeracy_scale", "computer_scale", "transportation_bool", "caregiver_bool", - "housing", "income_source", "felony_bool", "attending_school", - "currently_employed", "substance_use", "time_unemployed", - "need_mental_health_support_bool" + "age", + "gender", + "work_experience", + "canada_workex", + "dep_num", + "canada_born", + "citizen_status", + "level_of_schooling", + "fluent_english", + "reading_english_scale", + "speaking_english_scale", + "writing_english_scale", + "numeracy_scale", + "computer_scale", + "transportation_bool", + "caregiver_bool", + "housing", + "income_source", + "felony_bool", + "attending_school", + "currently_employed", + "substance_use", + "time_unemployed", + "need_mental_health_support_bool", ] demographics = {key: input_data[key] for key in columns} output = [] @@ -57,6 +76,7 @@ def clean_input_data(input_data): output.append(value) return output + def convert_text(text_data: str): """ Convert text answers from front end into numerical values. @@ -68,33 +88,47 @@ def convert_text(text_data: str): int: Converted numerical value """ categorical_mappings = [ + {"": 0, "true": 1, "false": 0, "no": 0, "yes": 1, "No": 0, "Yes": 1}, { - "": 0, "true": 1, "false": 0, "no": 0, "yes": 1, - "No": 0, "Yes": 1 - }, - { - "Grade 0-8": 1, "Grade 9": 2, "Grade 10": 3, "Grade 11": 4, - "Grade 12 or equivalent": 5, "OAC or Grade 13": 6, - "Some college": 7, "Some university": 8, "Some apprenticeship": 9, - "Certificate of Apprenticeship": 10, "Journeyperson": 11, - "Certificate/Diploma": 12, "Bachelor's degree": 13, - "Post graduate": 14 + "Grade 0-8": 1, + "Grade 9": 2, + "Grade 10": 3, + "Grade 11": 4, + "Grade 12 or equivalent": 5, + "OAC or Grade 13": 6, + "Some college": 7, + "Some university": 8, + "Some apprenticeship": 9, + "Certificate of Apprenticeship": 10, + "Journeyperson": 11, + "Certificate/Diploma": 12, + "Bachelor's degree": 13, + "Post graduate": 14, }, { - "Renting-private": 1, "Renting-subsidized": 2, - "Boarding or lodging": 3, "Homeowner": 4, - "Living with family/friend": 5, "Institution": 6, - "Temporary second residence": 7, "Band-owned home": 8, - "Homeless or transient": 9, "Emergency hostel": 10 + "Renting-private": 1, + "Renting-subsidized": 2, + "Boarding or lodging": 3, + "Homeowner": 4, + "Living with family/friend": 5, + "Institution": 6, + "Temporary second residence": 7, + "Band-owned home": 8, + "Homeless or transient": 9, + "Emergency hostel": 10, }, { - "No Source of Income": 1, "Employment Insurance": 2, + "No Source of Income": 1, + "Employment Insurance": 2, "Workplace Safety and Insurance Board": 3, "Ontario Works applied or receiving": 4, "Ontario Disability Support Program applied or receiving": 5, - "Dependent of someone receiving OW or ODSP": 6, "Crown Ward": 7, - "Employment": 8, "Self-Employment": 9, "Other (specify)": 10 - } + "Dependent of someone receiving OW or ODSP": 6, + "Crown Ward": 7, + "Employment": 8, + "Self-Employment": 9, + "Other (specify)": 10, + }, ] for category in categorical_mappings: if text_data in category: @@ -102,6 +136,7 @@ def convert_text(text_data: str): return int(text_data) if text_data.isnumeric() else text_data + def create_matrix(row_data): """ Create matrix of all possible intervention combinations. @@ -116,6 +151,7 @@ def create_matrix(row_data): perms = intervention_permutations(7) return np.concatenate((np.array(data), np.array(perms)), axis=1) + def intervention_permutations(num): """ Generate all possible intervention combinations. @@ -128,6 +164,7 @@ def intervention_permutations(num): """ return np.array(list(product([0, 1], repeat=num))) + def get_baseline_row(row_data): """ Create baseline row with no interventions. @@ -141,6 +178,7 @@ def get_baseline_row(row_data): base_interventions = np.zeros(7) return np.concatenate((np.array(row_data), base_interventions)) + def intervention_row_to_names(row_data): """ Convert intervention row to list of intervention names. @@ -153,6 +191,7 @@ def intervention_row_to_names(row_data): """ return [COLUMN_INTERVENTIONS[i] for i, value in enumerate(row_data) if value == 1] + def process_results(baseline_pred, results_matrix): """ Process model results into structured output. @@ -164,14 +203,9 @@ def process_results(baseline_pred, results_matrix): Returns: dict: Processed results with baseline and interventions """ - result_list = [ - (row[-1], intervention_row_to_names(row[:-1])) - for row in results_matrix - ] - return { - "baseline": baseline_pred[-1], - "interventions": result_list - } + result_list = [(row[-1], intervention_row_to_names(row[:-1])) for row in results_matrix] + return {"baseline": baseline_pred[-1], "interventions": result_list} + def interpret_and_calculate(input_data): """ @@ -194,19 +228,33 @@ def interpret_and_calculate(input_data): top_results = result_matrix[-3:, -8:] return process_results(baseline_prediction, top_results) + if __name__ == "__main__": test_data = { - "age": "23", "gender": "1", "work_experience": "1", - "canada_workex": "1", "dep_num": "0", "canada_born": "1", - "citizen_status": "2", "level_of_schooling": "2", - "fluent_english": "3", "reading_english_scale": "2", - "speaking_english_scale": "2", "writing_english_scale": "3", - "numeracy_scale": "2", "computer_scale": "3", - "transportation_bool": "2", "caregiver_bool": "1", - "housing": "1", "income_source": "5", "felony_bool": "1", - "attending_school": "0", "currently_employed": "1", - "substance_use": "1", "time_unemployed": "1", - "need_mental_health_support_bool": "1" + "age": "23", + "gender": "1", + "work_experience": "1", + "canada_workex": "1", + "dep_num": "0", + "canada_born": "1", + "citizen_status": "2", + "level_of_schooling": "2", + "fluent_english": "3", + "reading_english_scale": "2", + "speaking_english_scale": "2", + "writing_english_scale": "3", + "numeracy_scale": "2", + "computer_scale": "3", + "transportation_bool": "2", + "caregiver_bool": "1", + "housing": "1", + "income_source": "5", + "felony_bool": "1", + "attending_school": "0", + "currently_employed": "1", + "substance_use": "1", + "time_unemployed": "1", + "need_mental_health_support_bool": "1", } results = interpret_and_calculate(test_data) print(results) diff --git a/app/clients/service/model.pkl b/app/clients/service/model.pkl deleted file mode 100644 index 9730acc4..00000000 Binary files a/app/clients/service/model.pkl and /dev/null differ diff --git a/app/clients/service/model.py b/app/clients/service/model.py index b2406370..9f906788 100644 --- a/app/clients/service/model.py +++ b/app/clients/service/model.py @@ -12,73 +12,72 @@ from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestRegressor + def prepare_models(): """ Prepare and train the Random Forest model using the dataset. - + Returns: RandomForestRegressor: Trained model for predicting success rates """ # Load dataset - data = pd.read_csv('data_commontool.csv') + data = pd.read_csv("data_commontool.csv") # Define feature columns feature_columns = [ - 'age', # Client's age - 'gender', # Client's gender (bool) - 'work_experience', # Years of work experience - 'canada_workex', # Years of work experience in Canada - 'dep_num', # Number of dependents - 'canada_born', # Born in Canada - 'citizen_status', # Citizenship status - 'level_of_schooling', # Highest level achieved (1-14) - 'fluent_english', # English fluency scale (1-10) - 'reading_english_scale', # Reading ability scale (1-10) - 'speaking_english_scale',# Speaking ability scale (1-10) - 'writing_english_scale', # Writing ability scale (1-10) - 'numeracy_scale', # Numeracy ability scale (1-10) - 'computer_scale', # Computer proficiency scale (1-10) - 'transportation_bool', # Needs transportation support (bool) - 'caregiver_bool', # Is primary caregiver (bool) - 'housing', # Housing situation (1-10) - 'income_source', # Source of income (1-10) - 'felony_bool', # Has a felony (bool) - 'attending_school', # Currently a student (bool) - 'currently_employed', # Currently employed (bool) - 'substance_use', # Substance use disorder (bool) - 'time_unemployed', # Years unemployed - 'need_mental_health_support_bool' # Needs mental health support (bool) + "age", # Client's age + "gender", # Client's gender (bool) + "work_experience", # Years of work experience + "canada_workex", # Years of work experience in Canada + "dep_num", # Number of dependents + "canada_born", # Born in Canada + "citizen_status", # Citizenship status + "level_of_schooling", # Highest level achieved (1-14) + "fluent_english", # English fluency scale (1-10) + "reading_english_scale", # Reading ability scale (1-10) + "speaking_english_scale", # Speaking ability scale (1-10) + "writing_english_scale", # Writing ability scale (1-10) + "numeracy_scale", # Numeracy ability scale (1-10) + "computer_scale", # Computer proficiency scale (1-10) + "transportation_bool", # Needs transportation support (bool) + "caregiver_bool", # Is primary caregiver (bool) + "housing", # Housing situation (1-10) + "income_source", # Source of income (1-10) + "felony_bool", # Has a felony (bool) + "attending_school", # Currently a student (bool) + "currently_employed", # Currently employed (bool) + "substance_use", # Substance use disorder (bool) + "time_unemployed", # Years unemployed + "need_mental_health_support_bool", # Needs mental health support (bool) ] # Define intervention columns intervention_columns = [ - 'employment_assistance', - 'life_stabilization', - 'retention_services', - 'specialized_services', - 'employment_related_financial_supports', - 'employer_financial_supports', - 'enhanced_referrals' + "employment_assistance", + "life_stabilization", + "retention_services", + "specialized_services", + "employment_related_financial_supports", + "employer_financial_supports", + "enhanced_referrals", ] # Combine all feature columns all_features = feature_columns + intervention_columns # Prepare training data features = np.array(data[all_features]) # Changed from X to features - targets = np.array(data['success_rate']) # Changed from y to targets + targets = np.array(data["success_rate"]) # Changed from y to targets # Split the dataset features_train, _, targets_train, _ = train_test_split( # Removed unused variables - features, - targets, - test_size=0.2, - random_state=42 + features, targets, test_size=0.2, random_state=42 ) # Initialize and train the model model = RandomForestRegressor(n_estimators=100, random_state=42) model.fit(features_train, targets_train) return model + def save_model(model, filename="model.pkl"): """ Save the trained model to a file. - + Args: model: Trained model to save filename (str): Name of the file to save the model to @@ -86,19 +85,21 @@ def save_model(model, filename="model.pkl"): with open(filename, "wb") as model_file: pickle.dump(model, model_file) + def load_model(filename="model.pkl"): """ Load a trained model from a file. - + Args: filename (str): Name of the file to load the model from - + Returns: The loaded model """ with open(filename, "rb") as model_file: return pickle.load(model_file) + def main(): """Main function to train and save the model.""" print("Starting model training...") @@ -106,5 +107,6 @@ def main(): save_model(model) print("Model training completed and saved successfully.") + if __name__ == "__main__": main() diff --git a/app/clients/service/tuned_model_trainers/XGBoost_model_final.py b/app/clients/service/tuned_model_trainers/XGBoost_model_final.py new file mode 100644 index 00000000..e1d72d6b --- /dev/null +++ b/app/clients/service/tuned_model_trainers/XGBoost_model_final.py @@ -0,0 +1,136 @@ +import os +import pickle +import numpy as np +import pandas as pd +from sklearn.model_selection import ( + train_test_split, + GridSearchCV, + KFold, + cross_val_score, +) +from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score +from sklearn.preprocessing import StandardScaler +from sklearn.pipeline import Pipeline +from xgboost import XGBRegressor + + +# Define feature set +FEATURES = [ + "age", + "gender", + "work_experience", + "canada_workex", + "dep_num", + "canada_born", + "citizen_status", + "level_of_schooling", + "fluent_english", + "reading_english_scale", + "speaking_english_scale", + "writing_english_scale", + "numeracy_scale", + "computer_scale", + "transportation_bool", + "caregiver_bool", + "housing", + "income_source", + "felony_bool", + "attending_school", + "currently_employed", + "substance_use", + "time_unemployed", + "need_mental_health_support_bool", + "employment_assistance", + "life_stabilization", + "retention_services", + "specialized_services", + "employment_related_financial_supports", + "employer_financial_supports", + "enhanced_referrals", +] + +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +DATA_PATH = os.path.join(BASE_DIR, "../data_commontool_synthetic.csv") +MODEL_DIR = os.path.join(BASE_DIR, "../final_MLmodels/") +os.makedirs(MODEL_DIR, exist_ok=True) + + +def load_data(selected_features): + data = pd.read_csv(DATA_PATH) + X = np.array(data[selected_features]) + y = np.array(data["success_rate"]) + return X, y + + +def build_pipeline(): + xgb = XGBRegressor(objective="reg:squarederror", random_state=42, n_jobs=-1) + pipeline = Pipeline([("scaler", StandardScaler()), ("xgb", xgb)]) + return pipeline + + +def grid_search_tune(X_train, y_train): + pipeline = build_pipeline() + + param_grid = { + "xgb__n_estimators": [100, 200, 300], + "xgb__max_depth": [4, 6, 8], + "xgb__learning_rate": [0.01, 0.05, 0.1], + "xgb__subsample": [0.7, 0.8, 1.0], + "xgb__colsample_bytree": [0.7, 0.8, 1.0], + "xgb__reg_alpha": [0, 0.5, 1.0], + "xgb__reg_lambda": [1.0, 2.0], + } + + cv = KFold(n_splits=5, shuffle=True, random_state=42) + grid = GridSearchCV(pipeline, param_grid, cv=cv, scoring="r2", verbose=2, n_jobs=-1) + grid.fit(X_train, y_train) + + print(f"✅ Best params: {grid.best_params_}") + return grid.best_estimator_ + + +def evaluate_model(model, X_test, y_test): + predictions = model.predict(X_test) + mae = mean_absolute_error(y_test, predictions) + mse = mean_squared_error(y_test, predictions) + r2 = r2_score(y_test, predictions) + print(f"Hold-out Test Set - MAE: {mae:.2f} | MSE: {mse:.2f} | R2: {r2:.2f}") + + +def cross_validate(model, X, y): + cv = KFold(n_splits=5, shuffle=True, random_state=42) + r2_scores = cross_val_score(model, X, y, cv=cv, scoring="r2") + mae_scores = -cross_val_score(model, X, y, cv=cv, scoring="neg_mean_absolute_error") + + print(f"Cross-Validation R² scores: {r2_scores}") + print(f"Mean R²: {np.mean(r2_scores):.3f}") + print(f"Cross-Validation MAE scores: {mae_scores}") + print(f"Mean MAE: {np.mean(mae_scores):.3f}") + + +def save_model(model, filename): + model_path = os.path.join(MODEL_DIR, filename) + with open(model_path, "wb") as f: + pickle.dump(model, f) + print(f"✅ Tuned XGBoost model saved at {model_path}") + + +def main(): + X, y = load_data(FEATURES) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + + # GridSearch to get best pipeline + model = grid_search_tune(X_train, y_train) + + # Evaluate on hold-out + evaluate_model(model, X_test, y_test) + + # Full CV scores + cross_validate(model, X, y) + + # Save final model + save_model(model, "final_xgboost_gridcv_model.pkl") + + +if __name__ == "__main__": + main() diff --git a/app/clients/service/tuned_model_trainers/final_model_results.py b/app/clients/service/tuned_model_trainers/final_model_results.py new file mode 100644 index 00000000..581caaaa --- /dev/null +++ b/app/clients/service/tuned_model_trainers/final_model_results.py @@ -0,0 +1,66 @@ +import pickle +import numpy as np +import pandas as pd +from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score + +models = [ + "../final_MLmodels/final_linear_model_subset.pkl", + "../final_MLmodels/final_ridge_model.pkl", + "../final_MLmodels/final_xgboost_gridcv_model.pkl", + "../MLmodels/model.pkl", +] + +# Load data +# data = pd.read_csv('../data_commontool_synthetic.csv') +data = pd.read_csv("../data_commontool_synthetic_testdata.csv") + +# Feature set for linear_model_subset.pkl +LINEAR_MODEL_FEATURES = [ + "age", + "work_experience", + "canada_workex", + "level_of_schooling", + "fluent_english", + "reading_english_scale", + "speaking_english_scale", + "writing_english_scale", + "numeracy_scale", + "computer_scale", +] + +# Targets +y = data["success_rate"].values + + +def load_model(filename): + with open(filename, "rb") as model_file: + return pickle.load(model_file) + + +def main(): + print("========== Starting model testing ==========\n") + for model_path in models: + loaded_model = load_model(model_path) + model_name = model_path.split("/")[-1] + print(f">>> Testing Model: {model_name}") + + # Choose feature set depending on model + if "linear_model_subset" in model_path: + X = data[LINEAR_MODEL_FEATURES].values + else: + X = data.drop(columns=["success_rate"]).values + + predictions = loaded_model.predict(X) + mae = mean_absolute_error(y, predictions) + mse = mean_squared_error(y, predictions) + r2 = r2_score(y, predictions) + print(f" MAE: {mae:.2f}") + print(f" MSE: {mse:.2f}") + print(f" R2: {r2:.2f}") + print("-" * 40) + + print("\n========== Model testing completed ==========") + + +if __name__ == "__main__": + main() diff --git a/app/clients/service/tuned_model_trainers/linear_regression_model_final.py b/app/clients/service/tuned_model_trainers/linear_regression_model_final.py new file mode 100644 index 00000000..7cefb370 --- /dev/null +++ b/app/clients/service/tuned_model_trainers/linear_regression_model_final.py @@ -0,0 +1,132 @@ +import pickle +import os +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split, cross_val_score, KFold +from sklearn.linear_model import LinearRegression +from sklearn.preprocessing import StandardScaler, PolynomialFeatures +from sklearn.pipeline import Pipeline +from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score + +MODEL_MAP = {"linear_regression": LinearRegression()} + +ALL_FEATURES = [ + "age", + "gender", + "work_experience", + "canada_workex", + "dep_num", + "canada_born", + "citizen_status", + "level_of_schooling", + "fluent_english", + "reading_english_scale", + "speaking_english_scale", + "writing_english_scale", + "numeracy_scale", + "computer_scale", + "transportation_bool", + "caregiver_bool", + "housing", + "income_source", + "felony_bool", + "attending_school", + "currently_employed", + "substance_use", + "time_unemployed", + "need_mental_health_support_bool", + "employment_assistance", + "life_stabilization", + "retention_services", + "specialized_services", + "employment_related_financial_supports", + "employer_financial_supports", + "enhanced_referrals", +] + +LINEAR_MODEL_FEATURES = [ + "age", + "work_experience", + "canada_workex", + "level_of_schooling", + "fluent_english", + "reading_english_scale", + "speaking_english_scale", + "writing_english_scale", + "numeracy_scale", + "computer_scale", +] + +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +DATA_PATH = os.path.join(BASE_DIR, "../data_commontool_synthetic.csv") +MODEL_DIR = os.path.join(BASE_DIR, "../final_MLmodels/") +os.makedirs(MODEL_DIR, exist_ok=True) + + +def load_data(selected_features): + data = pd.read_csv(DATA_PATH) + X = np.array(data[selected_features]) + y = np.array(data["success_rate"]) + return X, y + + +def build_pipeline(use_poly=False): + steps = [("scaler", StandardScaler())] + if use_poly: + steps.append( + ( + "poly", + PolynomialFeatures(degree=2, interaction_only=True, include_bias=False), + ) + ) + steps.append(("regressor", MODEL_MAP["linear_regression"])) + return Pipeline(steps) + + +def train_and_evaluate_with_split(selected_features, use_poly=False): + X, y = load_data(selected_features) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + pipeline = build_pipeline(use_poly=use_poly) + pipeline.fit(X_train, y_train) + predictions = pipeline.predict(X_test) + mae = mean_absolute_error(y_test, predictions) + mse = mean_squared_error(y_test, predictions) + r2 = r2_score(y_test, predictions) + print(f"Hold-out Test Set - MAE: {mae:.2f} | MSE: {mse:.2f} | R2: {r2:.2f}") + return pipeline + + +def cross_validate_model(selected_features, use_poly=False): + X, y = load_data(selected_features) + pipeline = build_pipeline(use_poly=use_poly) + cv = KFold(n_splits=5, shuffle=True, random_state=42) + r2_scores = cross_val_score(pipeline, X, y, cv=cv, scoring="r2") + mae_scores = -cross_val_score(pipeline, X, y, cv=cv, scoring="neg_mean_absolute_error") + + print(f"Cross-Validation R² scores: {r2_scores}") + print(f"Mean R²: {np.mean(r2_scores):.3f}") + print(f"Cross-Validation MAE scores: {mae_scores}") + print(f"Mean MAE: {np.mean(mae_scores):.3f}") + + +def save_model(model, filename): + model_path = os.path.join(MODEL_DIR, filename) + with open(model_path, "wb") as f: + pickle.dump(model, f) + print(f"✅ Model saved as {model_path}") + + +def main(): + print("=== Linear Regression on ALL_FEATURES (+ poly) ===") + model = train_and_evaluate_with_split(ALL_FEATURES, use_poly=True) + cross_validate_model(ALL_FEATURES, use_poly=True) + + print("\n=== Linear Regression on LINEAR_MODEL_FEATURES ===") + model_subset = train_and_evaluate_with_split(LINEAR_MODEL_FEATURES, use_poly=False) + cross_validate_model(LINEAR_MODEL_FEATURES, use_poly=False) + + save_model(model_subset, "final_linear_model_subset.pkl") + + +if __name__ == "__main__": + main() diff --git a/app/clients/service/tuned_model_trainers/ridge_regression_model_final.py b/app/clients/service/tuned_model_trainers/ridge_regression_model_final.py new file mode 100644 index 00000000..249ad7bf --- /dev/null +++ b/app/clients/service/tuned_model_trainers/ridge_regression_model_final.py @@ -0,0 +1,109 @@ +import os +import pickle +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split, cross_val_score, KFold +from sklearn.linear_model import RidgeCV +from sklearn.preprocessing import StandardScaler +from sklearn.pipeline import Pipeline +from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score + +# Define feature set +FEATURES = [ + "age", + "gender", + "work_experience", + "canada_workex", + "dep_num", + "canada_born", + "citizen_status", + "level_of_schooling", + "fluent_english", + "reading_english_scale", + "speaking_english_scale", + "writing_english_scale", + "numeracy_scale", + "computer_scale", + "transportation_bool", + "caregiver_bool", + "housing", + "income_source", + "felony_bool", + "attending_school", + "currently_employed", + "substance_use", + "time_unemployed", + "need_mental_health_support_bool", + "employment_assistance", + "life_stabilization", + "retention_services", + "specialized_services", + "employment_related_financial_supports", + "employer_financial_supports", + "enhanced_referrals", +] + +# Set relative paths +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +DATA_PATH = os.path.join(BASE_DIR, "../data_commontool_synthetic.csv") +MODEL_DIR = os.path.join(BASE_DIR, "../final_MLmodels/") +os.makedirs(MODEL_DIR, exist_ok=True) + + +def load_data(selected_features): + data = pd.read_csv(DATA_PATH) + X = np.array(data[selected_features]) + y = np.array(data["success_rate"]) + return X, y + + +def build_pipeline(): + pipeline = Pipeline( + [ + ("scaler", StandardScaler()), + ("ridge", RidgeCV(alphas=[0.01, 0.1, 1.0, 10.0, 100.0], cv=5)), + ] + ) + return pipeline + + +def evaluate_model(model, X_test, y_test): + predictions = model.predict(X_test) + mae = mean_absolute_error(y_test, predictions) + mse = mean_squared_error(y_test, predictions) + r2 = r2_score(y_test, predictions) + print(f"Hold-out Test Set - MAE: {mae:.2f} | MSE: {mse:.2f} | R2: {r2:.2f}") + + +def cross_validate(model, X, y): + cv = KFold(n_splits=5, shuffle=True, random_state=42) + r2_scores = cross_val_score(model, X, y, cv=cv, scoring="r2") + mae_scores = -cross_val_score(model, X, y, cv=cv, scoring="neg_mean_absolute_error") + + print(f"Cross-Validation R² scores: {r2_scores}") + print(f"Mean R²: {np.mean(r2_scores):.3f}") + print(f"Cross-Validation MAE scores: {mae_scores}") + print(f"Mean MAE: {np.mean(mae_scores):.3f}") + + +def save_model(model, filename): + model_path = os.path.join(MODEL_DIR, filename) + with open(model_path, "wb") as f: + pickle.dump(model, f) + print(f"✅ Ridge model saved at {model_path}") + + +def main(): + X, y = load_data(FEATURES) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + + model = build_pipeline() + model.fit(X_train, y_train) + + evaluate_model(model, X_test, y_test) + cross_validate(model, X, y) + save_model(model, "final_ridge_model.pkl") + + +if __name__ == "__main__": + main() diff --git a/app/database.py b/app/database.py index 3a489f54..b5c8b948 100644 --- a/app/database.py +++ b/app/database.py @@ -7,22 +7,23 @@ from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker -#Here is where the database is located -SQLALCHEMY_DATABASE_URL = "sqlite:///./sql_app.db" +# Here is where the database is located +SQLALCHEMY_DATABASE_URL = "sqlite:///./sql_app.db" -#Open up a connection so that we are able to use the database +# Open up a connection so that we are able to use the database engine = create_engine(SQLALCHEMY_DATABASE_URL, connect_args={"check_same_thread": False}) -#Bind the engine just created +# Bind the engine just created SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine) -#Create an object of our database so as to control the database +# Create an object of our database so as to control the database Base = declarative_base() + def get_db(): """ Creates a database session and ensures it's closed after use. - + Yields: Session: SQLAlchemy database session """ diff --git a/app/main.py b/app/main.py index a8e8fa7f..ce4e98fa 100644 --- a/app/main.py +++ b/app/main.py @@ -5,27 +5,42 @@ """ from fastapi import FastAPI +from app.model.router import router as model_router from app import models from app.database import engine from app.clients.router import router as clients_router from app.auth.router import router as auth_router from fastapi.middleware.cors import CORSMiddleware +from initialize_data import initialize_database + # Initialize database tables models.Base.metadata.create_all(bind=engine) # Create FastAPI application -app = FastAPI(title="Case Management API", description="API for managing client cases", version="1.0.0") +app = FastAPI( + title="Case Management API", + description="API for managing client cases", + version="1.0.0", +) + +# Auto-initialize database when app starts +@app.on_event("startup") +async def startup_event(): + initialize_database() + # Include routers app.include_router(auth_router) app.include_router(clients_router) +app.include_router(model_router) + # Configure CORS middleware app.add_middleware( CORSMiddleware, - allow_origins=["*"], # Allows all origins - allow_methods=["*"], # Allows all methods - allow_headers=["*"], # Allows all headers + allow_origins=["*"], # Allows all origins + allow_methods=["*"], # Allows all methods + allow_headers=["*"], # Allows all headers allow_credentials=True, ) diff --git a/app/model/router.py b/app/model/router.py new file mode 100644 index 00000000..75c6831e --- /dev/null +++ b/app/model/router.py @@ -0,0 +1,382 @@ +# from fastapi import APIRouter, HTTPException, Depends +# from pydantic import BaseModel +# from typing import List +# # from sklearn.linear_model import LogisticRegression +# # from sklearn.ensemble import RandomForestClassifier +# # from sklearn.neural_network import MLPClassifier + +# from sklearn.ensemble import RandomForestRegressor +# from sklearn.neural_network import MLPRegressor +# from sklearn.linear_model import LinearRegression + +# from sqlalchemy.orm import Session +# from app.database import get_db +# from app.models import Client +# from app.models import ClientCase +# import numpy as np +# import os +# import cloudpickle as pickle + + +# router = APIRouter(prefix="/model", tags=["Model Management"]) + +# # Define directory for pickled models +# MODEL_DIR = "app/clients/service/MLmodels" + + +# def load_client_training_data(db): +# # clients = db.query(Client).all() + +# clients = db.query(Client).all() # <-- all clients +# print(f"Found {len(clients)} clients in database") # 👈 debug here + +# clients_with_cases = [c for c in clients if c.cases] +# print(f"Found {len(clients_with_cases)} clients with at least one case") # 👈 debug here + +# clients = db.query(Client).join(ClientCase).all() +# X, y = [], [] + +# for c in clients: +# try: +# if not c.cases: +# continue # skip clients without cases +# features = [ +# # c.age or 0, +# # c.gender or 0, +# # c.work_experience or 0, +# # c.canada_workex or 0, +# # c.dep_num or 0, +# # c.reading_english_scale or 0, +# # c.speaking_english_scale or 0, +# # c.writing_english_scale or 0, +# # c.numeracy_scale or 0, +# # c.computer_scale or 0, +# c.age or 0, +# c.gender or 0, +# c.work_experience or 0, +# c.canada_workex or 0, +# c.dep_num or 0, +# c.canada_born or 0, +# c.citizen_status or 0, +# c.level_of_schooling or 0, +# c.fluent_english or 0, +# c.reading_english_scale or 0, +# c.speaking_english_scale or 0, +# c.writing_english_scale or 0, +# c.numeracy_scale or 0, +# c.computer_scale or 0, +# c.transportation_bool or 0, +# c.caregiver_bool or 0, +# c.housing or 0, +# c.income_source or 0, +# c.felony_bool or 0, +# c.attending_school or 0, +# c.currently_employed or 0, +# c.substance_use or 0, +# c.time_unemployed or 0, +# c.need_mental_health_support_bool or 0, +# c.client_case.employment_assistance or 0, +# c.client_case.life_stabilization or 0, +# c.client_case.retention_services or 0, +# c.client_case.specialized_services or 0, +# c.client_case.employment_related_financial_supports or 0, +# c.client_case.employer_financial_supports or 0, +# c.client_case.enhanced_referrals or 0, +# ] +# # label = 1 if c.currently_employed else 0 +# # label = 1 if c.success_rate > 0 else 0 +# label = c.cases[0].success_rate or 0 +# X.append(features) +# y.append(label) +# except: +# continue # skip bad records +# return X, y + + +# class ModelManager: +# available_models = { +# # "logistic_regression": LogisticRegression(), +# # "random_forest": RandomForestClassifier(), +# # "neural_network": MLPClassifier(), +# "linear_regression": LinearRegression(), +# "random_forest_regressor": RandomForestRegressor(), +# "mlp_regressor": MLPRegressor(), +# } +# current_model_name = "linear_regression" +# current_model = available_models[current_model_name] + +# @classmethod +# def load_pickled_models(cls): +# if not os.path.exists(MODEL_DIR): +# return +# for file in os.listdir(MODEL_DIR): +# if file.endswith(".pkl"): +# model_name = file.replace(".pkl", "") +# with open(os.path.join(MODEL_DIR, file), "rb") as f: +# model = pickle.load(f) +# cls.available_models[model_name] = model + +# @classmethod +# def set_model(cls, model_name: str): +# if model_name not in cls.available_models: +# raise ValueError("Model not found") +# cls.current_model_name = model_name +# cls.current_model = cls.available_models[model_name] + +# @classmethod +# def get_current_model(cls) -> str: +# return cls.current_model_name + +# @classmethod +# def list_models(cls): +# return list(cls.available_models.keys()) + +# @classmethod +# def predict(cls, input_data: list): +# try: +# return cls.current_model.predict(np.array(input_data).reshape(1, -1)).tolist() +# except Exception as e: +# return str(e) + + +# # Load models when module loads +# ModelManager.load_pickled_models() + + +# # @router.post("/train_models") +# # def train_models(db: Session = Depends(get_db)): +# # X, y = load_client_training_data(db) +# # if len(X) == 0: +# # return {"error": "No training data found in the database."} +# # for model in ModelManager.available_models.values(): +# # try: +# # model.fit(X, y) +# # except Exception as e: +# # print(f"Error training {model}: {e}") +# # return {"message": f"Trained {len(ModelManager.available_models)} models on {len(X)} samples."} + +# @router.post("/train_models") +# def train_models(db: Session = Depends(get_db)): +# X, y = load_client_training_data(db) +# print(f"Loaded {len(X)} training samples") # Debug + +# if len(X) == 0: +# return {"error": "No training data found in the database."} + +# if not os.path.exists(MODEL_DIR): +# os.makedirs(MODEL_DIR) # Make sure directory exists + +# for model_name, model in ModelManager.available_models.items(): +# try: +# model.fit(X, y) +# # Save each trained model +# with open(os.path.join(MODEL_DIR, f"{model_name}.pkl"), "wb") as f: +# pickle.dump(model, f) +# print(f"Trained and saved model: {model_name}") +# except Exception as e: +# print(f"Error training {model_name}: {e}") +# return {"message": f"Trained and saved {len(ModelManager.available_models)} models on {len(X)} samples."} + + +# class PredictRequest(BaseModel): +# input: List[float] + + +# @router.post("/predict") +# def predict(request: PredictRequest): +# prediction = ModelManager.predict(request.input) +# return {"input": request.input, "prediction": prediction} + + +# class SetModelRequest(BaseModel): +# model_name: str + + +# @router.post("/set_model") +# def set_model(request: SetModelRequest): +# try: +# ModelManager.set_model(request.model_name) +# return {"message": f"Switched to model: {request.model_name}"} +# except ValueError as e: +# raise HTTPException(status_code=400, detail=str(e)) + + +# @router.get("/current_model") +# def current_model(): +# return {"current_model": ModelManager.get_current_model()} + + +# @router.get("/list_models") +# def list_models(): +# return {"available_models": ModelManager.list_models()} + + +from fastapi import APIRouter, HTTPException, Depends +from pydantic import BaseModel +from typing import List +from sklearn.linear_model import LogisticRegression +from sklearn.ensemble import RandomForestClassifier +from sklearn.neural_network import MLPClassifier +from sqlalchemy.orm import Session +from app.database import get_db +from app.models import Client +import numpy as np +import os +import cloudpickle as pickle + + +router = APIRouter(prefix="/model", tags=["Model Management"]) + +# Define directory for pickled models +MODEL_DIR = "app/clients/service/MLmodels" + +def load_client_training_data(db): + clients = db.query(Client).all() + X, y = [], [] + + for c in clients: + try: + # Make sure client has at least one case + if not c.cases: + continue + + # Build feature list exactly matching your CSV example + features = [ + c.age or 0, + c.gender or 0, + c.work_experience or 0, + c.canada_workex or 0, + c.dep_num or 0, + c.canada_born or 0, + c.citizen_status or 0, + c.level_of_schooling or 0, + c.fluent_english or 0, + c.reading_english_scale or 0, + c.speaking_english_scale or 0, + c.writing_english_scale or 0, + c.numeracy_scale or 0, + c.computer_scale or 0, + c.transportation_bool or 0, + c.caregiver_bool or 0, + c.housing or 0, + c.income_source or 0, + c.felony_bool or 0, + c.attending_school or 0, + c.currently_employed or 0, + c.substance_use or 0, + c.time_unemployed or 0, + c.need_mental_health_support_bool or 0, + # Intervention fields - from first linked case + c.cases[0].employment_assistance or 0, + c.cases[0].life_stabilization or 0, + c.cases[0].retention_services or 0, + c.cases[0].specialized_services or 0, + c.cases[0].employment_related_financial_supports or 0, + c.cases[0].employer_financial_supports or 0, + c.cases[0].enhanced_referrals or 0, + ] + + # For y/label: now predict the *success_rate* instead of employment + label = c.cases[0].success_rate or 0 + + X.append(features) + y.append(label) + + except Exception as e: + print(f"Skipping bad record: {e}") + continue + + return X, y + + +class ModelManager: + available_models = { + "logistic_regression": LogisticRegression(), + "random_forest": RandomForestClassifier(), + "neural_network": MLPClassifier(), + } + current_model_name = "logistic_regression" + current_model = available_models[current_model_name] + + @classmethod + def load_pickled_models(cls): + if not os.path.exists(MODEL_DIR): + return + for file in os.listdir(MODEL_DIR): + if file.endswith(".pkl"): + model_name = file.replace(".pkl", "") + with open(os.path.join(MODEL_DIR, file), "rb") as f: + model = pickle.load(f) + cls.available_models[model_name] = model + + @classmethod + def set_model(cls, model_name: str): + if model_name not in cls.available_models: + raise ValueError("Model not found") + cls.current_model_name = model_name + cls.current_model = cls.available_models[model_name] + + @classmethod + def get_current_model(cls) -> str: + return cls.current_model_name + + @classmethod + def list_models(cls): + return list(cls.available_models.keys()) + + @classmethod + def predict(cls, input_data: list): + try: + return cls.current_model.predict(np.array(input_data).reshape(1, -1)).tolist() + except Exception as e: + return str(e) + + +# Load models when module loads +ModelManager.load_pickled_models() + + +@router.post("/train_models") +def train_models(db: Session = Depends(get_db)): + X, y = load_client_training_data(db) + if len(X) == 0: + return {"error": "No training data found in the database."} + for model in ModelManager.available_models.values(): + try: + model.fit(X, y) + except Exception as e: + print(f"Error training {model}: {e}") + return {"message": f"Trained {len(ModelManager.available_models)} models on {len(X)} samples."} + + +class PredictRequest(BaseModel): + input: List[float] + + +@router.post("/predict") +def predict(request: PredictRequest): + prediction = ModelManager.predict(request.input) + return {"input": request.input, "prediction": prediction} + + +class SetModelRequest(BaseModel): + model_name: str + + +@router.post("/set_model") +def set_model(request: SetModelRequest): + try: + ModelManager.set_model(request.model_name) + return {"message": f"Switched to model: {request.model_name}"} + except ValueError as e: + raise HTTPException(status_code=400, detail=str(e)) + + +@router.get("/current_model") +def current_model(): + return {"current_model": ModelManager.get_current_model()} + + +@router.get("/list_models") +def list_models(): + return {"available_models": ModelManager.list_models()} \ No newline at end of file diff --git a/app/models.py b/app/models.py index df778348..02b7eba2 100644 --- a/app/models.py +++ b/app/models.py @@ -4,10 +4,19 @@ """ from app.database import Base -from sqlalchemy import Column, Integer, String, Boolean, ForeignKey, CheckConstraint, Enum +from sqlalchemy import ( + Column, + Integer, + String, + Boolean, + ForeignKey, + CheckConstraint, + Enum, +) from sqlalchemy.orm import relationship import enum + class UserRole(str, enum.Enum): admin = "admin" case_worker = "case_worker" @@ -24,46 +33,64 @@ class User(Base): cases = relationship("ClientCase", back_populates="user") + class Client(Base): """ Client model representing client data in the database. """ + __tablename__ = "clients" id = Column(Integer, primary_key=True, autoincrement=True) - age = Column(Integer, CheckConstraint('age >= 18')) + age = Column(Integer, CheckConstraint("age >= 18")) gender = Column(Integer, CheckConstraint("gender = 1 OR gender = 2")) - work_experience = Column(Integer, CheckConstraint('work_experience >= 0')) - canada_workex = Column(Integer, CheckConstraint('canada_workex >= 0')) - dep_num = Column(Integer, CheckConstraint('dep_num >= 0')) + work_experience = Column(Integer, CheckConstraint("work_experience >= 0")) + canada_workex = Column(Integer, CheckConstraint("canada_workex >= 0")) + dep_num = Column(Integer, CheckConstraint("dep_num >= 0")) canada_born = Column(Boolean) citizen_status = Column(Boolean) - level_of_schooling = Column(Integer, CheckConstraint('level_of_schooling >= 1 AND level_of_schooling <= 14')) + level_of_schooling = Column( + Integer, CheckConstraint("level_of_schooling >= 1 AND level_of_schooling <= 14") + ) fluent_english = Column(Boolean) - reading_english_scale = Column(Integer, CheckConstraint('reading_english_scale >= 0 AND reading_english_scale <= 10')) - speaking_english_scale = Column(Integer, CheckConstraint('speaking_english_scale >= 0 AND speaking_english_scale <= 10')) - writing_english_scale = Column(Integer, CheckConstraint('writing_english_scale >= 0 AND writing_english_scale <= 10')) - numeracy_scale = Column(Integer, CheckConstraint('numeracy_scale >= 0 AND numeracy_scale <= 10')) - computer_scale = Column(Integer, CheckConstraint('computer_scale >= 0 AND computer_scale <= 10')) + reading_english_scale = Column( + Integer, + CheckConstraint("reading_english_scale >= 0 AND reading_english_scale <= 10"), + ) + speaking_english_scale = Column( + Integer, + CheckConstraint("speaking_english_scale >= 0 AND speaking_english_scale <= 10"), + ) + writing_english_scale = Column( + Integer, + CheckConstraint("writing_english_scale >= 0 AND writing_english_scale <= 10"), + ) + numeracy_scale = Column( + Integer, CheckConstraint("numeracy_scale >= 0 AND numeracy_scale <= 10") + ) + computer_scale = Column( + Integer, CheckConstraint("computer_scale >= 0 AND computer_scale <= 10") + ) transportation_bool = Column(Boolean) caregiver_bool = Column(Boolean) - housing = Column(Integer, CheckConstraint('housing >= 1 AND housing <= 10')) - income_source = Column(Integer, CheckConstraint('income_source >= 1 AND income_source <= 11')) + housing = Column(Integer, CheckConstraint("housing >= 1 AND housing <= 10")) + income_source = Column(Integer, CheckConstraint("income_source >= 1 AND income_source <= 11")) felony_bool = Column(Boolean) attending_school = Column(Boolean) currently_employed = Column(Boolean) substance_use = Column(Boolean) - time_unemployed = Column(Integer, CheckConstraint('time_unemployed >= 0')) + time_unemployed = Column(Integer, CheckConstraint("time_unemployed >= 0")) need_mental_health_support_bool = Column(Boolean) cases = relationship("ClientCase", back_populates="client") + class ClientCase(Base): __tablename__ = "client_cases" client_id = Column(Integer, ForeignKey("clients.id"), primary_key=True) user_id = Column(Integer, ForeignKey("users.id"), primary_key=True) - + employment_assistance = Column(Boolean) life_stabilization = Column(Boolean) retention_services = Column(Boolean) @@ -71,7 +98,7 @@ class ClientCase(Base): employment_related_financial_supports = Column(Boolean) employer_financial_supports = Column(Boolean) enhanced_referrals = Column(Boolean) - success_rate = Column(Integer, CheckConstraint('success_rate >= 0 AND success_rate <= 100')) + success_rate = Column(Integer, CheckConstraint("success_rate >= 0 AND success_rate <= 100")) client = relationship("Client", back_populates="cases") user = relationship("User", back_populates="cases") diff --git a/app/requirements.txt b/app/requirements.txt index 57fc8e6e..d1c05285 100644 --- a/app/requirements.txt +++ b/app/requirements.txt @@ -40,4 +40,4 @@ tzdata==2024.1 uvicorn==0.30.6 uvloop==0.20.0 watchfiles==0.23.0 -websockets==13.0 +websockets==13.0 \ No newline at end of file diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 00000000..2b3ce579 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,11 @@ + +services: + backend: + build: . + container_name: fastapi-backend + ports: + - "8000:8000" + working_dir: /app + command: uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload + volumes: + - .:/app diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..ec5fd72e --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,12 @@ +[tool.black] +line-length = 100 +target-version = ['py38'] +exclude = ''' +/( + \.git + | \.mypy_cache + | \.venv + | build + | dist +)/ +''' diff --git a/requirements.txt b/requirements.txt index 93d35fbf..bab70dcc 100644 --- a/requirements.txt +++ b/requirements.txt @@ -21,6 +21,7 @@ charset-normalizer==3.3.2 click==8.1.7 comm==0.1.3 cryptography==41.0.4 +cloudpickle==3.0.0 debugpy==1.6.7 decorator==5.1.1 defusedxml==0.7.1 @@ -141,4 +142,5 @@ webencodings==0.5.1 websocket-client==1.5.1 websockets==11.0.3 widgetsnbextension==4.0.7 +xgboost==2.0.3 diff --git a/scripts/rebuild_models.py b/scripts/rebuild_models.py new file mode 100644 index 00000000..f3e6489d --- /dev/null +++ b/scripts/rebuild_models.py @@ -0,0 +1,19 @@ +# scripts/rebuild_models.py + +import os +import pickle +import numpy as np +from sklearn.linear_model import Ridge + +MODEL_DIR = "app/clients/service/MLmodels" +os.makedirs(MODEL_DIR, exist_ok=True) + +model = Ridge() +X = np.random.rand(50, 4) +y = np.random.rand(50) +model.fit(X, y) + +with open(os.path.join(MODEL_DIR, "ridge_model.pkl"), "wb") as f: + pickle.dump(model, f) + +print("✅ Model re-pickled successfully.")