diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000000000000000000000000000000000000..6a616d442b7885f1e03c499055b17c9ae7bbfb0c --- /dev/null +++ b/.dockerignore @@ -0,0 +1,31 @@ +__pycache__ +*.pyc +*.pyo +*.pyd +.Python +env/ +venv/ +pip-log.txt +pip-delete-this-directory.txt +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.log +.git +.gitignore +.mypy_cache +.pytest_cache +.hydra +.dvc/ +data/ +mlruns/ +notebooks/ +reports/ +docs/ +tests/ +scripts/ +!scripts/start_space.sh diff --git a/.dvc/.gitignore b/.dvc/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..528f30c71c687de473bbb506c071e902beba6cd9 --- /dev/null +++ b/.dvc/.gitignore @@ -0,0 +1,3 @@ +/config.local +/tmp +/cache diff --git a/.dvc/config b/.dvc/config new file mode 100644 index 0000000000000000000000000000000000000000..95176dd8cdb550a31facf8cf161b2be626276ce2 --- /dev/null +++ b/.dvc/config @@ -0,0 +1,6 @@ +[cache] + type = copy +[core] + remote = origin +['remote "origin"'] + url = https://dagshub.com/se4ai2526-uniba/Hopcroft.dvc diff --git a/.dvcignore b/.dvcignore new file mode 100644 index 0000000000000000000000000000000000000000..51973055237895f2d23e65e015793fd302f4b9da --- /dev/null +++ b/.dvcignore @@ -0,0 +1,3 @@ +# Add patterns of files dvc should ignore, which could improve +# the performance. Learn more at +# https://dvc.org/doc/user-guide/dvcignore diff --git a/.env.example b/.env.example new file mode 100644 index 0000000000000000000000000000000000000000..5a7b2d70364bc312abeef2fbcfb1f28195f21a20 --- /dev/null +++ b/.env.example @@ -0,0 +1,19 @@ +# ============================================ +# Hopcroft API Environment Configuration +# ============================================ +# Copy this file to .env and update with your values +# Command: cp .env.example .env +# IMPORTANT: Never commit .env to version control! + +# MLflow Configuration +MLFLOW_TRACKING_URI=https://dagshub.com/se4ai2526-uniba/Hopcroft.mlflow +MLFLOW_TRACKING_USERNAME=your_username +MLFLOW_TRACKING_PASSWORD=your_token + +# API Configuration +API_HOST=0.0.0.0 +API_PORT=8080 +LOG_LEVEL=info + +# Model Configuration +MODEL_PATH=/app/models/random_forest_embedding_gridsearch.pkl diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000000000000000000000000000000000000..8c1017323614ca74de7014e6b33a030a94bdf41a --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,68 @@ +name: CI Pipeline + +on: + push: + branches: [ "main", "feature/*" ] + pull_request: + branches: [ "main" ] + +jobs: + build-and-test: + runs-on: ubuntu-latest + + steps: + - name: Checkout code + uses: actions/checkout@v3 + + - name: Free Disk Space + run: | + sudo rm -rf /usr/share/dotnet + sudo rm -rf /usr/local/lib/android + sudo rm -rf /opt/ghc + sudo rm -rf /opt/hostedtoolcache/CodeQL + sudo docker image prune --all --force + + - name: Set up Python 3.10 + uses: actions/setup-python@v4 + with: + python-version: "3.10" + cache: 'pip' # Enable caching for pip + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + # Install CPU-only PyTorch to save space (we don't need CUDA for tests) + pip install torch --index-url https://download.pytorch.org/whl/cpu + # Install other dependencies + pip install -r requirements.txt --no-cache-dir + + - name: Lint with Ruff + run: | + # Using make lint as defined in Makefile + make lint + + - name: Run Unit Tests + run: | + # Run tests and generate HTML report + pytest tests/unit/ -v -m unit --html=report.html --self-contained-html + + - name: Upload Test Report + if: always() # Upload report even if tests fail + uses: actions/upload-artifact@v4 + with: + name: test-report + path: report.html + + - name: Configure DVC + run: | + dvc remote modify origin --local auth basic + dvc remote modify origin --local user ${{ secrets.DAGSHUB_USERNAME }} + dvc remote modify origin --local password ${{ secrets.DAGSHUB_TOKEN }} + + - name: Pull Models with DVC + run: | + dvc pull models/random_forest_embedding_gridsearch.pkl models/label_names.pkl + + - name: Build Docker Image + run: | + docker build . -t hopcroft-app:latest diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..d52ee8b8e4b5d4d763586a608c1c4fda44fbab42 --- /dev/null +++ b/.gitignore @@ -0,0 +1,192 @@ + +# Mac OS-specific storage files +.DS_Store + +# vim +*.swp +*.swo + +## https://github.com/github/gitignore/blob/e8554d85bf62e38d6db966a50d2064ac025fd82a/Python.gitignore + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# MkDocs documentation +docs/site/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +#uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# pixi +# pixi.lock should be committed to version control for reproducibility +# .pixi/ contains the environments and should not be committed +.pixi/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +# Ruff stuff: +.ruff_cache/ + +# PyPI configuration file +.pypirc +.github/copilot-instructions.md + +docs/img/ diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..d3f409da516518f5731eb1d6544c90a626701be5 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,40 @@ +FROM python:3.10-slim + +# Set environment variables +ENV PYTHONDONTWRITEBYTECODE=1 \ + PYTHONUNBUFFERED=1 \ + PIP_NO_CACHE_DIR=off \ + PIP_DISABLE_PIP_VERSION_CHECK=on \ + PIP_DEFAULT_TIMEOUT=100 + +# Install system dependencies +RUN apt-get update && apt-get install -y \ + git \ + && rm -rf /var/lib/apt/lists/* + +# Create a non-root user +RUN useradd -m -u 1000 user + +# Set working directory +WORKDIR /app + +# Copy requirements first for caching +COPY requirements.txt . + +# Install dependencies +RUN pip install --no-cache-dir -r requirements.txt + +# Copy the rest of the application +COPY --chown=user:user . . + +# Make start script executable +RUN chmod +x scripts/start_space.sh + +# Switch to non-root user +USER user + +# Expose the port +EXPOSE 7860 + +# Command to run the application +CMD ["./scripts/start_space.sh"] diff --git a/Dockerfile.streamlit b/Dockerfile.streamlit new file mode 100644 index 0000000000000000000000000000000000000000..f35990cfdfd5ad85c9b6866e30130aa1fd66376d --- /dev/null +++ b/Dockerfile.streamlit @@ -0,0 +1,29 @@ +FROM python:3.10-slim + +WORKDIR /app + +ENV PYTHONUNBUFFERED=1 \ + PYTHONDONTWRITEBYTECODE=1 \ + PIP_NO_CACHE_DIR=1 \ + PIP_DISABLE_PIP_VERSION_CHECK=1 + +# Create non-root user +RUN useradd -m -u 1000 appuser + +# Install only Streamlit dependencies +RUN pip install --no-cache-dir \ + streamlit>=1.28.0 \ + requests>=2.31.0 \ + pandas>=2.0.0 + +# Copy only the Streamlit app +COPY --chown=appuser:appuser hopcroft_skill_classification_tool_competition/streamlit_app.py ./ + +EXPOSE 8501 + +USER appuser + +# Set API URL to point to the API service +ENV API_BASE_URL=http://hopcroft-api:8080 + +CMD ["streamlit", "run", "streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"] diff --git a/Makefile b/Makefile new file mode 100644 index 0000000000000000000000000000000000000000..4393f663539e334c733374551c134dd088e1acfa --- /dev/null +++ b/Makefile @@ -0,0 +1,189 @@ +################################################################################# +# GLOBALS # +################################################################################# + +PROJECT_NAME = Hopcroft +PYTHON_VERSION = 3.10 +PYTHON_INTERPRETER = python + +################################################################################# +# COMMANDS # +################################################################################# + +## Install Python dependencies +.PHONY: requirements +requirements: + $(PYTHON_INTERPRETER) -m pip install -U pip + $(PYTHON_INTERPRETER) -m pip install -r requirements.txt + +## Delete all compiled Python files +.PHONY: clean +clean: + find . -type f -name "*.py[co]" -delete + find . -type d -name "__pycache__" -delete + +## Lint using ruff +.PHONY: lint +lint: + ruff format --check + ruff check + +## Format source code with ruff +.PHONY: format +format: + ruff check --fix + ruff format + +################################################################################# +# PROJECT RULES # +################################################################################# + +## Download dataset from Hugging Face +.PHONY: data +data: + $(PYTHON_INTERPRETER) -m hopcroft_skill_classification_tool_competition.dataset + +## Extract features from raw data +.PHONY: features +features: + $(PYTHON_INTERPRETER) -m hopcroft_skill_classification_tool_competition.features + +################################################################################# +# TRAINING RULES # +################################################################################# + +## Train Random Forest baseline with TF-IDF features (cleaned data) +.PHONY: train-baseline-tfidf +train-baseline-tfidf: + $(PYTHON_INTERPRETER) -m hopcroft_skill_classification_tool_competition.modeling.train baseline + +## Train Random Forest baseline with Embedding features (cleaned data) +.PHONY: train-baseline-embeddings +train-baseline-embeddings: + $(PYTHON_INTERPRETER) -c "from hopcroft_skill_classification_tool_competition.modeling.train import run_baseline_train; run_baseline_train(feature_type='embedding', use_cleaned=True)" + +## Train Random Forest with SMOTE and TF-IDF features (cleaned data) +.PHONY: train-smote-tfidf +train-smote-tfidf: + $(PYTHON_INTERPRETER) -c "from hopcroft_skill_classification_tool_competition.modeling.train import run_smote_experiment, load_data; X, Y = load_data(feature_type='tfidf', use_cleaned=True); run_smote_experiment(X, Y, feature_type='tfidf')" + +## Train Random Forest with SMOTE and Embedding features (cleaned data) +.PHONY: train-smote-embeddings +train-smote-embeddings: + $(PYTHON_INTERPRETER) -c "from hopcroft_skill_classification_tool_competition.modeling.train import run_smote_experiment, load_data; X, Y = load_data(feature_type='embedding', use_cleaned=True); run_smote_experiment(X, Y, feature_type='embedding')" + +################################################################################# +# TESTING RULES # +################################################################################# + +## Run all unit tests +.PHONY: test-unit +test-unit: + pytest tests/unit/ -v -m unit + +## Run all integration tests +.PHONY: test-integration +test-integration: + pytest tests/integration/ -v -m integration + +## Run all system tests +.PHONY: test-system +test-system: + pytest tests/system/ -v -m system + +## Run all tests (unit, integration, system) +.PHONY: test-all +test-all: + pytest tests/ -v --ignore=tests/behavioral --ignore=tests/deepchecks + +## Run tests with coverage report +.PHONY: test-coverage +test-coverage: + pytest tests/ --cov=hopcroft_skill_classification_tool_competition --cov-report=html --cov-report=term + +## Run fast tests only (exclude slow tests) +.PHONY: test-fast +test-fast: + pytest tests/ -v -m "not slow" --ignore=tests/behavioral --ignore=tests/deepchecks + +## Run behavioral tests +.PHONY: test-behavioral +test-behavioral: + pytest tests/behavioral/ -v --ignore=tests/behavioral/test_model_training.py + +## Run Great Expectations validation +.PHONY: validate-gx +validate-gx: + $(PYTHON_INTERPRETER) -m hopcroft_skill_classification_tool_competition.tests.test_gx + +## Run Deepchecks validation +.PHONY: validate-deepchecks +validate-deepchecks: + $(PYTHON_INTERPRETER) tests/deepchecks/run_all_deepchecks.py + +## Run all validation and tests +.PHONY: test-complete +test-complete: test-all validate-gx validate-deepchecks test-behavioral + +################################################################################# +# Self Documenting Commands # +################################################################################# + +.DEFAULT_GOAL := help + +define PRINT_HELP_PYSCRIPT +import re, sys; \ +lines = '\n'.join([line for line in sys.stdin]); \ +matches = re.findall(r'\n## (.*)\n[\s\S]+?\n([a-zA-Z_-]+):', lines); \ +print('Available rules:\n'); \ +print('\n'.join(['{:25}{}'.format(*reversed(match)) for match in matches])) +endef +export PRINT_HELP_PYSCRIPT + +help: + @$(PYTHON_INTERPRETER) -c "${PRINT_HELP_PYSCRIPT}" < $(MAKEFILE_LIST) + +################################################################################ +# API COMMANDS # +################################################################################ + +## Run API in development mode +.PHONY: api-dev +api-dev: + fastapi dev hopcroft_skill_classification_tool_competition/main.py + +## Run API in production mode +.PHONY: api-run +api-run: + fastapi run hopcroft_skill_classification_tool_competition/main.py + +## Test API health check (requires running API) +.PHONY: test-api-health +test-api-health: + @echo "Testing API health endpoint..." + curl -X GET "http://127.0.0.1:8000/health" + +## Test API POST /predict (requires running API) +.PHONY: test-api-predict +test-api-predict: + @echo "Testing prediction endpoint..." + curl -X POST "http://127.0.0.1:8000/predict" -H "Content-Type: application/json" -d '{"issue_text": "Fix critical bug in authentication and login flow with OAuth2", "repo_name": "my-repo"}' + +## Test API GET /predictions (requires running API) +.PHONY: test-api-list +test-api-list: + @echo "Testing list predictions endpoint..." + curl "http://127.0.0.1:8000/predictions?limit=5" + +## Test API GET /predictions/{run_id} (requires running API and valid run_id) +.PHONY: test-api-get-prediction +test-api-get-prediction: + @echo "Testing get specific prediction endpoint..." + @echo "Usage: make test-api-get-prediction RUN_ID=" + @if [ -z "$(RUN_ID)" ]; then echo "Error: RUN_ID not set. Example: make test-api-get-prediction RUN_ID=abc123"; exit 1; fi + curl "http://127.0.0.1:8000/predictions/$(RUN_ID)" + +## Run all API tests (requires running API) +.PHONY: test-api-all +test-api-all: test-api-health test-api-predict test-api-list + @echo "\n All API tests completed!" \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..220e3f03b31cbfc2b19964995d43f5d96f3f5e3f --- /dev/null +++ b/README.md @@ -0,0 +1,576 @@ +--- +title: Hopcroft Skill Classification +emoji: 🧠 +colorFrom: blue +colorTo: green +sdk: docker +app_port: 7860 +--- + +# Hopcroft_Skill-Classification-Tool-Competition + +The task involves analyzing the relationship between issue characteristics and required skills, developing effective feature extraction methods that combine textual and code-context information, and implementing sophisticated multi-label classification approaches. Students may incorporate additional GitHub metadata to enhance model inputs, but must avoid using third-party classification engines or direct outputs from the provided database. The work requires careful attention to the multi-label nature of the problem, where each issue may require multiple different skills for resolution. + +## Project Organization + +``` +β”œβ”€β”€ LICENSE <- Open-source license if one is chosen +β”œβ”€β”€ Makefile <- Makefile with convenience commands like `make data` or `make train` +β”œβ”€β”€ README.md <- The top-level README for developers using this project. +β”œβ”€β”€ data +β”‚ β”œβ”€β”€ external <- Data from third party sources. +β”‚ β”œβ”€β”€ interim <- Intermediate data that has been transformed. +β”‚ β”œβ”€β”€ processed <- The final, canonical data sets for modeling. +β”‚ └── raw <- The original, immutable data dump. +β”‚ +β”œβ”€β”€ docs <- A default mkdocs project; see www.mkdocs.org for details +β”‚ +β”œβ”€β”€ models <- Trained and serialized models, model predictions, or model summaries +β”‚ +β”œβ”€β”€ notebooks <- Jupyter notebooks. Naming convention is a number (for ordering), +β”‚ the creator's initials, and a short `-` delimited description, e.g. +β”‚ `1.0-jqp-initial-data-exploration`. +β”‚ +β”œβ”€β”€ pyproject.toml <- Project configuration file with package metadata for +β”‚ hopcroft_skill_classification_tool_competition and configuration for tools like black +β”‚ +β”œβ”€β”€ references <- Data dictionaries, manuals, and all other explanatory materials. +β”‚ +β”œβ”€β”€ reports <- Generated analysis as HTML, PDF, LaTeX, etc. +β”‚ └── figures <- Generated graphics and figures to be used in reporting +β”‚ +β”œβ”€β”€ requirements.txt <- The requirements file for reproducing the analysis environment, e.g. +β”‚ generated with `pip freeze > requirements.txt` +β”‚ +β”œβ”€β”€ setup.cfg <- Configuration file for flake8 +β”‚ +└── hopcroft_skill_classification_tool_competition <- Source code for use in this project. + β”‚ + β”œβ”€β”€ __init__.py <- Makes hopcroft_skill_classification_tool_competition a Python module + β”‚ + β”œβ”€β”€ config.py <- Store useful variables and configuration + β”‚ + β”œβ”€β”€ dataset.py <- Scripts to download or generate data + β”‚ + β”œβ”€β”€ features.py <- Code to create features for modeling + β”‚ + β”œβ”€β”€ modeling + β”‚ β”œβ”€β”€ __init__.py + β”‚ β”œβ”€β”€ predict.py <- Code to run model inference with trained models + β”‚ └── train.py <- Code to train models + β”‚ + └── plots.py <- Code to create visualizations +``` + +-------- + +## Setup + +### MLflow Credentials Configuration + +Set up DagsHub credentials for MLflow tracking. + +**Get your token:** [DagsHub](https://dagshub.com) β†’ Profile β†’ Settings β†’ Tokens + +#### Option 1: Using `.env` file (Recommended for local development) + +```bash +# Copy the template +cp .env.example .env + +# Edit .env with your credentials +``` + +Your `.env` file should contain: +``` +MLFLOW_TRACKING_URI=https://dagshub.com/se4ai2526-uniba/Hopcroft.mlflow +MLFLOW_TRACKING_USERNAME=your_username +MLFLOW_TRACKING_PASSWORD=your_token +``` + +> [!NOTE] +> The `.env` file is git-ignored for security. Never commit credentials to version control. + +#### Option 2: Using Docker Compose + +When using Docker Compose, the `.env` file is automatically loaded via `env_file` directive in `docker-compose.yml`. + +```bash +# Start the service (credentials loaded from .env) +docker compose up --build +``` + +-------- + +## CI Configuration + +[![CI Pipeline](https://github.com/se4ai2526-uniba/Hopcroft/actions/workflows/ci.yml/badge.svg)](https://github.com/se4ai2526-uniba/Hopcroft/actions/workflows/ci.yml) + +This project uses automatically triggered GitHub Actions triggers for Continuous Integration. + +### Secrets + +To enable DVC model pulling, configure these Repository Secrets: + +- `DAGSHUB_USERNAME`: DagsHub username. +- `DAGSHUB_TOKEN`: DagsHub access token. + +-------- + +## Milestone Summary + +### Milestone 1 +We compiled the ML Canvas and defined: +- Problem: multi-label classification of skills for PR/issues. +- Stakeholders and business/research goals. +- Data sources (SkillScope DB) and constraints (no external classifiers). +- Success metrics (micro-F1, imbalance handling, experiment tracking). +- Risks (label imbalance, text noise, multi-label complexity) and mitigations. + +### Milestone 2 +We implemented the essential end-to-end infrastructure to go from data to tracked modeling experiments: + +1. Data Management + - DVC setup (raw dataset and TF-IDF features tracked) with DagsHub remote; dedicated gitignores for data/models. + +2. Data Ingestion & EDA + - `dataset.py` to download/extract SkillScope from Hugging Face (zip β†’ SQLite) with cleanup. + - Initial exploration notebook `notebooks/1.0-initial-data-exploration.ipynb` (schema, text stats, label distribution). + +3. Feature Engineering + - `features.py`: GitHub text cleaning (URL/HTML/markdown removal, normalization, Porter stemming) and TF-IDF (uni+bi-grams) saved as NumPy (`features_tfidf.npy`, `labels_tfidf.npy`). + +4. Central Config + - `config.py` with project paths, training settings, RF param grid, MLflow URI/experiments, PCA/ADASYN, feature constants. + +5. Modeling & Experiments + - Unified `modeling/train.py` with actions: baseline RF, MLSMOTE, ROS, ADASYN+PCA, LightGBM, LightGBM+MLSMOTE, and inference. + - GridSearchCV (micro-F1), MLflow logging, removal of all-zero labels, multilabel-stratified splits (with fallback). + +6. Imbalance Handling + - Local `mlsmote.py` (multi-label oversampling) with fallback to `RandomOverSampler`; dedicated ADASYN+PCA pipeline. + +7. Tracking & Reproducibility + - Remote MLflow (DagsHub) with README credential setup; DVC-tracked models and auxiliary artifacts (e.g., PCA, kept label indices). + +8. Tooling + - Updated `requirements.txt` (lightgbm, imbalanced-learn, iterative-stratification, huggingface-hub, dvc, mlflow, nltk, seaborn, etc.) and extended Makefile targets (`data`, `features`). + +### Milestone 3 (QA) +We implemented a comprehensive testing and validation framework to ensure data quality and model robustness: + +1. **Data Cleaning Pipeline** + - `data_cleaning.py`: Removes duplicates (481 samples), resolves label conflicts via majority voting (640 samples), filters sparse samples incompatible with SMOTE, and ensures train-test separation without leakage. + - Final cleaned dataset: 6,673 samples (from 7,154 original), 80/20 stratified split. + +2. **Great Expectations Validation** (10 tests) + - Database integrity, feature matrix validation (no NaN/Inf, sparsity checks), label format validation (binary {0,1}), feature-label consistency. + - Label distribution for stratification (min 5 occurrences), SMOTE compatibility (min 10 non-zero features), duplicate detection, train-test separation, label consistency. + - All 10 tests pass on cleaned data; comprehensive JSON reports in `reports/great_expectations/`. + +3. **Deepchecks Validation** (24 checks across 2 suites) + - Data Integrity Suite (92% score): validates duplicates, label conflicts, nulls, data types, feature correlation. + - Train-Test Validation Suite (100% score): **zero data leakage**, proper train/test split, feature/label drift analysis. + - Cleaned data achieved production-ready status (96% overall score). + +4. **Behavioral Testing** (36 tests) + - Invariance tests (9): typo robustness, synonym substitution, case insensitivity, punctuation/URL noise tolerance. + - Directional tests (10): keyword addition effects, technical detail impact on predictions. + - Minimum Functionality Tests (17): basic skill predictions on clear examples (bug fixes, database work, API development, testing, DevOps). + - All tests passed; comprehensive report in `reports/behavioral/`. + +5. **Code Quality Analysis** + - Ruff static analysis: 28 minor issues identified (unsorted imports, unused variables, f-strings), 100% fixable. + - PEP 8 compliant, Black compatible (line length 88). + +6. **Documentation** + - Comprehensive `docs/testing_and_validation.md` with detailed test descriptions, execution commands, and analysis results. + - Behavioral testing README with test categories, usage examples, and extension guide. + +7. **Tooling** + - Makefile targets: `validate-gx`, `validate-deepchecks`, `test-behavioral`, `test-complete`. + - Automated test execution and report generation. + +### Milestone 4 (API) +We implemented a production-ready FastAPI service for skill prediction with MLflow integration: + +#### Features +- **REST API Endpoints**: + - `POST /predict` - Predict skills for a GitHub issue (logs to MLflow) + - `GET /predictions/{run_id}` - Retrieve prediction by MLflow run ID + - `GET /predictions` - List recent predictions with pagination + - `GET /health` - Health check endpoint +- **Model Management**: Loads trained Random Forest + TF-IDF vectorizer from `models/` +- **MLflow Tracking**: All predictions logged with metadata, probabilities, and timestamps +- **Input Validation**: Pydantic models for request/response validation +- **Interactive Docs**: Auto-generated Swagger UI and ReDoc + +#### API Usage + +**1. Start the API Server** +```bash +# Development mode (auto-reload) +make api-dev + +# Production mode +make api-run +``` +Server starts at: [http://127.0.0.1:8000](http://127.0.0.1:8000) + +**2. Test Endpoints** + +**Option A: Swagger UI (Recommended)** +- Navigate to: [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) +- Interactive interface to test all endpoints +- View request/response schemas + +**Option B: Make Commands** +```bash +# Test all endpoints +make test-api-all + +# Individual endpoints +make test-api-health # Health check +make test-api-predict # Single prediction +make test-api-list # List predictions +``` + +#### Prerequisites +- Trained model: `models/random_forest_tfidf_gridsearch.pkl` +- TF-IDF vectorizer: `models/tfidf_vectorizer.pkl` (auto-saved during feature creation) +- Label names: `models/label_names.pkl` (auto-saved during feature creation) + +#### MLflow Integration +- All predictions logged to: `https://dagshub.com/se4ai2526-uniba/Hopcroft.mlflow` +- Experiment: `skill_prediction_api` +- Tracked: input text, predictions, probabilities, metadata + +#### Docker +Build and run the API in a container: +```bash +docker build -t hopcroft-api . +docker run --rm --name hopcroft-api -p 8080:8080 hopcroft-api +``` + +Endpoints: +- Swagger UI: [http://localhost:8080/docs](http://localhost:8080/docs) +- Health check: [http://localhost:8080/health](http://localhost:8080/health) + +--- + +## Docker Compose Usage + +Docker Compose orchestrates both the **API backend** and **Streamlit GUI** services with proper networking and configuration. + +### Prerequisites + +1. **Create your environment file:** + ```bash + cp .env.example .env + ``` + +2. **Edit `.env`** with your actual credentials: + ``` + MLFLOW_TRACKING_USERNAME=your_dagshub_username + MLFLOW_TRACKING_PASSWORD=your_dagshub_token + ``` + + Get your token from: [https://dagshub.com/user/settings/tokens](https://dagshub.com/user/settings/tokens) + +### Quick Start + +#### 1. Build and Start All Services +Build both images and start the containers: +```bash +docker-compose up -d --build +``` + +| Flag | Description | +|------|-------------| +| `-d` | Run in detached mode (background) | +| `--build` | Rebuild images before starting (use when code/Dockerfile changes) | + +**Available Services:** +- **API (FastAPI):** [http://localhost:8080/docs](http://localhost:8080/docs) +- **GUI (Streamlit):** [http://localhost:8501](http://localhost:8501) +- **Health Check:** [http://localhost:8080/health](http://localhost:8080/health) + +#### 2. Stop All Services +Stop and remove containers and networks: +```bash +docker-compose down +``` + +| Flag | Description | +|------|-------------| +| `-v` | Also remove named volumes (e.g., `hopcroft-logs`): `docker-compose down -v` | +| `--rmi all` | Also remove images: `docker-compose down --rmi all` | + +#### 3. Restart Services +After updating `.env` or configuration files: +```bash +docker-compose restart +``` + +Or for a full restart with environment reload: +```bash +docker-compose down +docker-compose up -d +``` + +#### 4. Check Status +View the status of all running services: +```bash +docker-compose ps +``` + +Or use Docker commands: +```bash +docker ps +``` + +#### 5. View Logs +Tail logs from both services in real-time: +```bash +docker-compose logs -f +``` + +View logs from a specific service: +```bash +docker-compose logs -f hopcroft-api +docker-compose logs -f hopcroft-gui +``` + +| Flag | Description | +|------|-------------| +| `-f` | Follow log output (stream new logs) | +| `--tail 100` | Show only last 100 lines: `docker-compose logs --tail 100` | + +#### 6. Execute Commands in Container +Open an interactive shell inside a running container: +```bash +docker-compose exec hopcroft-api /bin/bash +docker-compose exec hopcroft-gui /bin/bash +``` + +Examples of useful commands inside the API container: +```bash +# Check installed packages +pip list + +# Run Python interactively +python + +# Check model file exists +ls -la /app/models/ + +# Verify environment variables +printenv | grep MLFLOW +``` +``` + +### Architecture Overview + +**Docker Compose orchestrates two services:** + +``` +docker-compose.yml +β”œβ”€β”€ hopcroft-api (FastAPI Backend) +β”‚ β”œβ”€β”€ Build: ./Dockerfile +β”‚ β”œβ”€β”€ Port: 8080:8080 +β”‚ β”œβ”€β”€ Network: hopcroft-net +β”‚ β”œβ”€β”€ Environment: .env (MLflow credentials) +β”‚ β”œβ”€β”€ Volumes: +β”‚ β”‚ β”œβ”€β”€ ./hopcroft_skill_classification_tool_competition (hot reload) +β”‚ β”‚ └── hopcroft-logs:/app/logs (persistent logs) +β”‚ └── Health Check: /health endpoint +β”‚ +β”œβ”€β”€ hopcroft-gui (Streamlit Frontend) +β”‚ β”œβ”€β”€ Build: ./Dockerfile.streamlit +β”‚ β”œβ”€β”€ Port: 8501:8501 +β”‚ β”œβ”€β”€ Network: hopcroft-net +β”‚ β”œβ”€β”€ Environment: API_BASE_URL=http://hopcroft-api:8080 +β”‚ β”œβ”€β”€ Volumes: +β”‚ β”‚ └── ./hopcroft_skill_classification_tool_competition/streamlit_app.py (hot reload) +β”‚ └── Depends on: hopcroft-api (waits for health check) +β”‚ +└── hopcroft-net (bridge network) +``` + +**External Access:** +- API: http://localhost:8080 +- GUI: http://localhost:8501 + +**Internal Communication:** +- GUI β†’ API: http://hopcroft-api:8080 (via Docker network) + +### Services Description + +**hopcroft-api (FastAPI Backend)** +- Purpose: FastAPI backend serving the ML model for skill classification +- Image: Built from `Dockerfile` +- Port: 8080 (maps to host 8080) +- Features: + - Random Forest model with embedding features + - MLflow experiment tracking + - Auto-reload in development mode + - Health check endpoint + +**hopcroft-gui (Streamlit Frontend)** +- Purpose: Streamlit web interface for interactive predictions +- Image: Built from `Dockerfile.streamlit` +- Port: 8501 (maps to host 8501) +- Features: + - User-friendly interface for skill prediction + - Real-time communication with API + - Automatic reconnection on API restart + - Depends on API health before starting + +### Development vs Production + +**Development (default):** +- Auto-reload enabled (`--reload`) +- Source code mounted with bind mounts +- Custom command with hot reload +- GUI β†’ API via Docker network + +**Production:** +- Auto-reload disabled +- Use built image only +- Use Dockerfile's CMD +- GUI β†’ API via Docker network + +For **production deployment**, modify `docker-compose.yml` to remove bind mounts and disable reload. + +### Troubleshooting + +#### Issue: GUI shows "API is not available" +**Solution:** +1. Wait 30-60 seconds for API to fully initialize and become healthy +2. Refresh the GUI page (F5) +3. Check API health: `curl http://localhost:8080/health` +4. Check logs: `docker-compose logs hopcroft-api` + +#### Issue: "500 Internal Server Error" on predictions +**Solution:** +1. Verify MLflow credentials in `.env` are correct +2. Restart services: `docker-compose down && docker-compose up -d` +3. Check environment variables: `docker exec hopcroft-api printenv | grep MLFLOW` + +#### Issue: Changes to code not reflected +**Solution:** +- For Python code changes: Auto-reload is enabled, wait a few seconds +- For Dockerfile changes: Rebuild with `docker-compose up -d --build` +- For `.env` changes: Restart with `docker-compose down && docker-compose up -d` + +#### Issue: Port already in use +**Solution:** +```bash +# Check what's using the port +netstat -ano | findstr :8080 +netstat -ano | findstr :8501 + +# Stop existing containers +docker-compose down + +# Or change ports in docker-compose.yml +``` + + +## Demo UI (Streamlit) + +The Streamlit GUI provides an interactive web interface for the skill classification API. + +### Features +- Real-time skill prediction from GitHub issue text +- Top-5 predicted skills with confidence scores +- Full predictions table with all skills +- API connection status indicator +- Responsive design + +### Usage +1. Ensure both services are running: `docker-compose up -d` +2. Open the GUI in your browser: [http://localhost:8501](http://localhost:8501) +3. Enter a GitHub issue description in the text area +4. Click "Predict Skills" to get predictions +5. View results in the predictions table + +### Architecture +- **Frontend**: Streamlit (Python web framework) +- **Communication**: HTTP requests to FastAPI backend via Docker network +- **Independence**: GUI and API run in separate containers +- **Auto-reload**: GUI code changes are reflected immediately (bind mount) +> Both must run **simultaneously** in different terminals/containers. + +### Quick Start + +1. **Start the FastAPI backend:** + ```bash + fastapi dev hopcroft_skill_classification_tool_competition/main.py + ``` + +2. **In a new terminal, start Streamlit:** + ```bash + streamlit run streamlit_app.py + ``` + +3. **Open your browser:** + - Streamlit UI: http://localhost:8501 + - FastAPI Docs: http://localhost:8000/docs + +### Features + +- Interactive web interface for skill prediction +- Real-time predictions with confidence scores +- Adjustable confidence threshold +- Multiple input modes (quick/detailed/examples) +- Visual result display +- API health monitoring + +### Demo Walkthrough + +#### Main Dashboard + +![gui_main_dashboard](docs/img/gui_main_dashboard.png) + +The main interface provides: +- **Sidebar**: API health status, confidence threshold slider, model info +- **Three input modes**: Quick Input, Detailed Input, Examples +#### Quick Input Mode + +![gui_quick_input](docs/img/gui_quick_input.png) +Simply paste your GitHub issue text and click "Predict Skills"! + +#### Prediction Results +![gui_detailed](docs/img/gui_detailed.png) +View: +- **Top predictions** with confidence scores +- **Full predictions table** with filtering +- **Processing metrics** (time, model version) +- **Raw JSON response** (expandable) + +#### Detailed Input Mode + +![gui_detailed_input](docs/img/gui_detailed_input.png) +Add optional metadata: +- Repository name +- PR number +- Detailed description + +#### Example Gallery +![gui_ex](docs/img/gui_ex.png) + +Test with pre-loaded examples: +- Authentication bugs +- ML features +- Database issues +- UI enhancements + + +### Usage + +1. Enter GitHub issue/PR text in the input area +2. (Optional) Add description, repo name, PR number +3. Click "Predict Skills" +4. View results with confidence scores +5. Adjust threshold slider to filter predictions \ No newline at end of file diff --git a/data/.gitignore b/data/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..55ff1d08fa143e71485bfb1e45d46e1371fd3b4a --- /dev/null +++ b/data/.gitignore @@ -0,0 +1 @@ +/raw diff --git a/data/README.md b/data/README.md new file mode 100644 index 0000000000000000000000000000000000000000..fbd7ead634620d39a795876c0a2928433ae524d9 --- /dev/null +++ b/data/README.md @@ -0,0 +1,83 @@ +--- +language: +- en +tags: +- software-engineering +- multi-label-classification +- pull-requests +- skills +license: mit +--- + +# Dataset Card for SkillScope Dataset + +## Dataset Details + +- **Name:** SkillScope Dataset (NLBSE Tool Competition) +- **Repository:** [NLBSE/SkillCompetition](https://huggingface.co/datasets/NLBSE/SkillCompetition) +- **Version:** 1.0 (Processed for Hopcroft Project) +- **Type:** Tabular / Text / Code +- **Task:** Multi-label Classification +- **Maintainers:** se4ai2526-uniba (Hopcroft Project Team) + +## Intended Use + +### Primary Intended Uses +- Training and evaluating multi-label classification models to predict required skills for resolving GitHub issues/Pull Requests. +- Analyzing the relationship between issue characteristics (title, body, code changes) and developer skills. +- Benchmarking feature extraction techniques (TF-IDF vs. Embeddings) in Software Engineering contexts. + +### Out-of-Scope Use Cases +- Profiling individual developers (the dataset focuses on issues/PRs, not user profiling). +- General purpose code generation. + +## Dataset Contents + +The dataset consists of merged Pull Requests from 11 Java repositories. + +- **Total Samples (Raw):** 7,245 merged PRs +- **Source Files:** 57,206 +- **Methods:** 59,644 +- **Classes:** 13,097 +- **Labels:** 217 distinct skill labels (domain/sub-domain pairs) + +### Schema +The data is stored in a SQLite database (`skillscope_data.db`) with the following main structures: +- `nlbse_tool_competition_data_by_issue`: Main table containing PR features (title, description, file paths) and skill labels. +- `vw_nlbse_tool_competition_data_by_file`: View providing file-level granularity. + +## Context and Motivation + +### Motivation +This dataset was created for the NLBSE (Natural Language-based Software Engineering) Tool Competition to foster research in automating skill identification in software maintenance. Accurately identifying required skills for an issue can help in automatic expert recommendation and task assignment. + +### Context +The data is derived from open-source Java projects on GitHub. It represents real-world development scenarios where developers describe issues and implement fixes. + +## Dataset Creation and Preprocessing + +### Source Data +The raw data is downloaded from the Hugging Face Hub (`NLBSE/SkillCompetition`). + +### Preprocessing Steps (Hopcroft Project) +To ensure data quality for modeling, the following preprocessing steps are applied (via `data_cleaning.py`): + +1. **Duplicate Removal:** ~6.5% of samples were identified as duplicates and removed. +2. **Conflict Resolution:** ~8.9% of samples had conflicting labels for identical features; resolved using majority voting. +3. **Rare Label Removal:** Labels with fewer than 5 occurrences were removed to ensure valid cross-validation. +4. **Feature Extraction:** + - **Text Cleaning:** Removal of URLs, HTML, Markdown, and normalization. + - **TF-IDF:** Uni-grams and bi-grams (max 5000 features). + - **Embeddings:** Sentence embeddings using `all-MiniLM-L6-v2`. +5. **Splitting:** 80/20 Train/Test split using `MultilabelStratifiedShuffleSplit` to maintain label distribution and prevent data leakage. + +## Considerations + +### Ethical Considerations +- **Privacy:** The data comes from public GitHub repositories. No private or sensitive personal information is explicitly included, though developer names/IDs might be present in metadata. +- **Bias:** The dataset is limited to Java repositories, so models may not generalize to other programming languages or ecosystems. + +### Caveats and Recommendations +- **Label Imbalance:** The dataset is highly imbalanced (long-tail distribution of skills). Techniques like MLSMOTE or ADASYN are recommended. +- **Multi-label Nature:** Most samples have multiple labels; evaluation metrics should account for this (e.g., Micro-F1). +- **Text Noise:** PR descriptions can be noisy or sparse; robust preprocessing is essential. diff --git a/data/processed/.gitignore b/data/processed/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..8563d7cee7e2f02d77a829b5d4fb0a79e2473b47 --- /dev/null +++ b/data/processed/.gitignore @@ -0,0 +1,2 @@ +/tfidf +/embedding diff --git a/data/processed/embedding.dvc b/data/processed/embedding.dvc new file mode 100644 index 0000000000000000000000000000000000000000..478e0dd42a6cce0feabe5bf5acec940dbc410983 --- /dev/null +++ b/data/processed/embedding.dvc @@ -0,0 +1,6 @@ +outs: +- md5: d388f4e3ebe391bf4393cd327a16a1bb.dir + size: 64320416 + nfiles: 12 + hash: md5 + path: embedding diff --git a/data/processed/tfidf.dvc b/data/processed/tfidf.dvc new file mode 100644 index 0000000000000000000000000000000000000000..cf8d790b1d4e5d8f1a7a9649ee53d08f28439e75 --- /dev/null +++ b/data/processed/tfidf.dvc @@ -0,0 +1,6 @@ +outs: +- md5: 038f64e03853a832a891a854146c429d.dir + nfiles: 11 + hash: md5 + path: tfidf + size: 199262804 diff --git a/data/raw.dvc b/data/raw.dvc new file mode 100644 index 0000000000000000000000000000000000000000..53538fac31d99961db795d06ff9ae46b88d221f9 --- /dev/null +++ b/data/raw.dvc @@ -0,0 +1,6 @@ +outs: +- md5: 9a91536f747b03a232c8cfd354393541.dir + size: 440922112 + nfiles: 1 + hash: md5 + path: raw diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000000000000000000000000000000000000..ac79ab2d5fe298b74d600c0ad3d27f3eea6208a5 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,56 @@ +services: + hopcroft-api: + build: + context: . + dockerfile: Dockerfile + container_name: hopcroft-api + ports: + - "8080:8080" + env_file: + - .env + environment: + - PROJECT_NAME=Hopcroft + volumes: + # Bind mount: enables live code reloading for development + - ./hopcroft_skill_classification_tool_competition:/app/hopcroft_skill_classification_tool_competition + # Named volume: persistent storage for application logs + - hopcroft-logs:/app/logs + networks: + - hopcroft-net + # Override CMD for development with auto-reload + command: > + uvicorn hopcroft_skill_classification_tool_competition.main:app --host 0.0.0.0 --port 8080 --reload + restart: unless-stopped + healthcheck: + test: [ "CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8080/health', timeout=5)" ] + interval: 30s + timeout: 10s + retries: 3 + start_period: 60s + + hopcroft-gui: + build: + context: . + dockerfile: Dockerfile.streamlit + container_name: hopcroft-gui + ports: + - "8501:8501" + environment: + - API_BASE_URL=http://hopcroft-api:8080 + volumes: + # Bind mount for development hot-reload + - ./hopcroft_skill_classification_tool_competition/streamlit_app.py:/app/streamlit_app.py + networks: + - hopcroft-net + depends_on: + hopcroft-api: + condition: service_healthy + restart: unless-stopped + +networks: + hopcroft-net: + driver: bridge + +volumes: + hopcroft-logs: + driver: local diff --git a/docs/.gitkeep b/docs/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/docs/ML Canvas.md b/docs/ML Canvas.md new file mode 100644 index 0000000000000000000000000000000000000000..c8e32d7763b72b3d1561964fd3c8c26ae561095d --- /dev/null +++ b/docs/ML Canvas.md @@ -0,0 +1,39 @@ +# Machine Learning Canvas + +| Designed for | Designed by | Date | Iteration | +|---|---|---|---| +| NLBSE 2026 | Team Hopcroft | 13/10/2025 | 1 | + +## PREDICTION TASK +The prediction task is a multi-label classification aimed at identifying the technical skills required to resolve a specific software issue. The input for the model is a dataset extracted from a GitHub pull request, which includes textual features (like the issue description), code-context information, and other metadata. The output is a set of one or more skill labels, chosen from a predefined set of 217 skills, representing the technical domains and sub-domains (e.g., "database," "security," "UI") needed for the resolution. + +## DECISIONS +The predictions are used to make crucial operational decisions in software project management. The value for the end-user, such as a project manager or team lead, lies in the ability to automatically assign new issues to the most suitable developersβ€”those who possess the skills identified by the model. This optimizes resource allocation, accelerates resolution times, and improves the overall efficiency of the development team. + +## VALUE PROPOSITION +The Machine Learning system is designed for project managers and developers, aiming to optimize task assignment. By automatically predicting the technical skills (domains and sub-domains) required to resolve GitHub issues, the system ensures that each task is assigned to the most qualified developer. +The primary value lies in a significant increase in the efficiency of the development process, leading to reduced resolution times and improved software quality. + +## DATA COLLECTION +The core data was collected by the competition organizers through a mining process on historical GitHub pull requests. This process involved sourcing the issue text and associated source code from tasks that were already completed and merged. Each issue in the dataset then underwent a rigorous, automated labeling protocol, where skill labels (domains and sub-domains) were annotated based on the specific API calls detected within the source code. Due to the nature of software development tasks, the resulting dataset faces a significant class imbalance issue, with certain skill labels appearing far more frequently than others. + +## DATA SOURCES +The ML system will leverage the official NLBSE’26 Skill Classification dataset, a comprehensive corpus released by the competition organizers. This dataset is sourced from 11 popular Java repositories and comprises 7,245 merged pull requests annotated with 217 distinct skill labels. / +All foundational data is provided in a SQLite database (`skillscope_data.db`), with the `nlbse_tool_competition_data_by_issue` table serving as the primary source for model training. The competition framework also permits the use of external GitHub APIs for supplementary data. + +## IMPACT SIMULATION +The model's impact is validated by outperforming the specific "SkillScope Random Forest + TF-IDF" baseline on precision, recall, or micro-F1 scores. This evaluation is performed using the provided SQLite database of labeled pull requests as the ground truth to ensure measurable and superior performance. + +## MAKING PREDICTIONS +As soon as a new issue is created, the system analyzes it in real-time to understand which technical skills are needed. Instead of waiting for a manual assignment, the system sends the task directly to the most suitable developer. This automated process is so fast that it ensures the right expert can start working on the problem withoutΒ anyΒ delay. + +## BUILDING MODELS +The ML system will start with the competition’s baseline multi-label classifier, which predicts the domains and sub-domains representing the skills needed for each issue. Model development will focus on iterative improvements to enhance the specified performance metrics. +A new model will be trained until it achieves a statistically significant improvement in precision, recall, or micro-F1 score over the initial baseline, without degradation in theΒ otherΒ metrics. +Training will occur offline, with computational needs scaling by model complexity and data volume. + +## FEATURES +Only the most important, non-null, and directly functional features will be selected. Textual data, such as the issue title and description, will be represented using established NLP techniques. We will also utilize numerical features, including the pull request number and the calculated issue duration. Skills will be encoded as binary multi-label vectors, and all features will be normalized to optimize model performance throughout iterative development cycles. + +## MONITORING +System quality will be assessed by comparing the model's skill predictions with the actual skills used by developers to resolve issues. Performance will be continuously monitored using key metrics (precision, recall, micro-F1 score). To detect data drift, the model will be periodically evaluated on new, recent data; a significant drop in these metrics will indicate the need for retraining. The system's value is measured according to the competition's criteria: the primary value is the increase in the micro-F1 score (βˆ†micro-F1) over the baseline, without worsening precision and recall. Computational efficiency (runtime) serves as a secondary value metric. diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 0000000000000000000000000000000000000000..79c146859754d65ce1a01add9848026d10bd832c --- /dev/null +++ b/docs/README.md @@ -0,0 +1,12 @@ +Generating the docs +---------- + +Use [mkdocs](http://www.mkdocs.org/) structure to update the documentation. + +Build locally with: + + mkdocs build + +Serve locally with: + + mkdocs serve diff --git a/docs/docs/getting-started.md b/docs/docs/getting-started.md new file mode 100644 index 0000000000000000000000000000000000000000..b4f71c3a293b7c30dbb94afd6f3e58997b55ceef --- /dev/null +++ b/docs/docs/getting-started.md @@ -0,0 +1,6 @@ +Getting started +=============== + +This is where you describe how to get set up on a clean install, including the +commands necessary to get the raw data (using the `sync_data_from_s3` command, +for example), and then how to make the cleaned, final data sets. diff --git a/docs/docs/index.md b/docs/docs/index.md new file mode 100644 index 0000000000000000000000000000000000000000..d96f0f4870ee987e0a95ed2b4e1919b2eb94ad98 --- /dev/null +++ b/docs/docs/index.md @@ -0,0 +1,10 @@ +# Hopcroft_Skill-Classification-Tool-Competition documentation! + +## Description + +The task involves analyzing the relationship between issue characteristics and required skills, developing effective feature extraction methods that combine textual and code-context information, and implementing sophisticated multi-label classification approaches. Students may incorporate additional GitHub metadata to enhance model inputs, but must avoid using third-party classification engines or direct outputs from the provided database. The work requires careful attention to the multi-label nature of the problem, where each issue may require multiple different skills for resolution. + +## Commands + +The Makefile contains the central entry points for common tasks related to this project. + diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml new file mode 100644 index 0000000000000000000000000000000000000000..3880333766aaccfc7d830af7c0120b52499443ac --- /dev/null +++ b/docs/mkdocs.yml @@ -0,0 +1,4 @@ +site_name: Hopcroft_Skill-Classification-Tool-Competition +# +site_author: Team Hopcroft +# \ No newline at end of file diff --git a/docs/testing_and_validation.md b/docs/testing_and_validation.md new file mode 100644 index 0000000000000000000000000000000000000000..5956d5a47a9bd1f51f576671f98e7077af23d8fa --- /dev/null +++ b/docs/testing_and_validation.md @@ -0,0 +1,208 @@ +# Testing and Validation Documentation + +This document provides a comprehensive and detailed overview of the testing and validation strategies employed in the Hopcroft project. It consolidates all technical details, execution commands, and analysis reports from Behavioral Testing, Deepchecks, Great Expectations, and Ruff. + +--- + +## 1. Behavioral Testing + +**Report Source:** `reports/behavioral/` +**Status:** All Tests Passed (36/36) +**Last Run:** November 15, 2025 +**Model:** Random Forest + TF-IDF (SMOTE oversampling) +**Execution Time:** ~8 minutes + +Behavioral testing evaluates the model's capabilities and robustness beyond simple accuracy metrics. + +### Test Categories & Results + +| Category | Tests | Status | Description | +|----------|-------|--------|-------------| +| **Invariance Tests** | 9 | **Passed** | Ensure model predictions remain stable under perturbations that shouldn't affect the outcome (e.g., changing variable names, minor typos). | +| **Directional Tests** | 10 | **Passed** | Verify that specific changes to the input cause expected changes in the output (e.g., adding specific keywords should increase probability of related skills). | +| **Minimum Functionality Tests** | 17 | **Passed** | Check basic capabilities and sanity checks (e.g., simple inputs produce valid outputs). | + +### Technical Notes +- **Training Tests Excluded:** `test_model_training.py` was excluded from the run due to a missing PyTorch dependency in the environment, but the inference tests cover the model's behavior fully. +- **Robustness:** The model demonstrates excellent consistency across all 36 behavioral scenarios. + +### How to Regenerate +To run the behavioral tests and generate the JSON report: + +```bash +python -m pytest tests/behavioral/ \ + --ignore=tests/behavioral/test_model_training.py \ + --json-report \ + --json-report-file=reports/behavioral/behavioral_tests_report.json \ + -v +``` + +--- + +## 2. Deepchecks Validation + +**Report Source:** `reports/deepchecks/` +**Status:** Cleaned Data is Production-Ready (Score: 96%) +**Last Run:** November 16, 2025 + +Deepchecks was used to validate the integrity of the dataset before and after cleaning. The validation process confirmed that the `data_cleaning.py` pipeline successfully resolved critical data quality issues. + +### Dataset Statistics: Before vs. After Cleaning + +| Metric | Before Cleaning | After Cleaning | Difference | +|--------|-----------------|----------------|------------| +| **Total Samples** | 7,154 | 6,673 | -481 duplicates (6.72%) | +| **Duplicates** | 481 | **0** | **RESOLVED** | +| **Data Leakage** | Present | **0 samples** | **RESOLVED** | +| **Label Conflicts** | Present | **0** | **RESOLVED** | +| **Train/Test Split** | N/A | 5,338 / 1,335 | 80/20 Stratified | + +### Validation Suites Detailed Results + +#### A. Data Integrity Suite (12 checks) +**Score:** 92% (7 Passed, 2 Non-Critical Failures, 2 Null) + +* **PASSED:** Data Duplicates (0), Conflicting Labels (0), Mixed Nulls, Mixed Data Types, String Mismatch, String Length, Feature Label Correlation. +* **FAILED (Non-Critical/Acceptable):** + 1. **Single Value in Column:** Some TF-IDF features are all zeros. + 2. **Feature-Feature Correlation:** High correlation between features. + +#### B. Train-Test Validation Suite (12 checks) +**Score:** 100% (12 Passed) + +* **PASSED (CRITICAL):** **Train Test Samples Mix (0 leakage)**. +* **PASSED:** Datasets Size Comparison (80/20), New Label in Test (0), Feature Drift (< 0.025), Label Drift (0.0), Multivariate Drift. + +### Interpretation of Results & Important Notes + +The validation identified two "failures" that are actually **expected behavior** for this type of data: + +1. **Features with Only Zeros (Non-Critical):** + * *Reason:* TF-IDF creates sparse features. If a specific word (feature) never appears in the specific subset being tested, its column will be all zeros. + * *Impact:* None. The model simply ignores these features. + +2. **High Feature Correlation (Non-Critical):** + * *Reason:* Linguistic terms naturally co-occur (e.g., "machine" and "learning", "python" and "code"). + * *Impact:* Slight multicollinearity, which Random Forest handles well. + +### Recommendations & Next Steps +1. **Model Retraining:** Now that the data is cleaned and leakage-free, the models should be retrained to obtain reliable performance metrics. +2. **Continuous Monitoring:** Use `run_all_deepchecks.py` in CI/CD pipelines to prevent regression. + +### How to Use the Tests + +**Run Complete Validation (Recommended):** +```bash +python tests/deepchecks/run_all_deepchecks.py +``` + +**Run Specific Suites:** +```bash +# Data Integrity Only +python tests/deepchecks/test_data_integrity.py + +# Train-Test Validation Only +python tests/deepchecks/test_train_test_validation.py + +# Compare Original vs Cleaned +python tests/deepchecks/run_all_tests_comparison.py +``` + +--- + +## 3. Great Expectations Data Validation + +**Report Source:** `tests/great expectations/` +**Status:** All 10 Tests Passed on Cleaned Data + +Great Expectations provides a rigorous suite of 10 tests to validate the data pipeline at various stages. + +### Detailed Test Descriptions + +#### TEST 1: Raw Database Validation +* **Purpose:** Validates integrity/schema of `nlbse_tool_competition_data_by_issue` table. Ensures data source integrity before expensive feature engineering. +* **Checks:** Row count (7000-10000), Column count (220-230), Required columns present. +* **Result:** **PASS**. Schema is valid. + +#### TEST 2: TF-IDF Feature Matrix Validation +* **Purpose:** Validates statistical properties of TF-IDF features. Ensures feature matrix is suitable for ML algorithms. +* **Checks:** No NaN/Inf, values >= 0, at least 1 non-zero feature per sample. +* **Original Data:** **FAIL** (25 samples had 0 features due to empty text). +* **Cleaned Data:** **PASS** (Sparse samples removed). + +#### TEST 3: Multi-Label Binary Format Validation +* **Purpose:** Ensures label matrix is binary {0,1} for MultiOutputClassifier. Missing labels would invalidate training. +* **Checks:** Values in {0,1}, correct dimensions. +* **Result:** **PASS**. + +#### TEST 4: Feature-Label Consistency Validation +* **Purpose:** Validates alignment between X and Y matrices. Misalignment causes catastrophic training failures. +* **Checks:** Row counts match, no empty vectors. +* **Original Data:** **FAIL** (Empty feature vectors present). +* **Cleaned Data:** **PASS** (Perfect alignment). + +#### TEST 5: Label Distribution & Stratification +* **Purpose:** Ensures labels have enough samples for stratified splitting. Labels with insufficient samples cause stratification failures. +* **Checks:** Min 5 occurrences per label. +* **Original Data:** **FAIL** (75 labels had 0 occurrences). +* **Cleaned Data:** **PASS** (Rare labels removed). + +#### TEST 6: Feature Sparsity & SMOTE Compatibility +* **Purpose:** Ensures feature density is sufficient for nearest-neighbor algorithms (SMOTE/ADASYN). +* **Checks:** Min 10 non-zero features per sample. +* **Original Data:** **FAIL** (31.5% samples < 10 features). +* **Cleaned Data:** **PASS** (Incompatible samples removed). + +#### TEST 7: Multi-Output Classifier Compatibility +* **Purpose:** Validates multi-label structure. Insufficient multi-label samples would indicate inappropriate architecture. +* **Checks:** >50% samples have multiple labels. +* **Result:** **PASS** (Strong multi-label characteristics). + +#### TEST 8: Duplicate Samples Detection +* **Purpose:** Detects duplicate feature vectors to prevent leakage. +* **Original Data:** **FAIL** (481 duplicates found). +* **Cleaned Data:** **PASS** (0 duplicates). + +#### TEST 9: Train-Test Separation Validation +* **Purpose:** **CRITICAL**. Validates no data leakage between train and test sets. +* **Checks:** Intersection of Train and Test sets must be empty. +* **Result:** **PASS** (Cleaned data only). + +#### TEST 10: Label Consistency Validation +* **Purpose:** Ensures identical features have identical labels. Inconsistency indicates ground truth errors. +* **Original Data:** **FAIL** (640 samples with conflicting labels). +* **Cleaned Data:** **PASS** (Resolved via majority voting). + +### Running the Tests +```bash +python "tests/great expectations/test_gx.py" +``` + +--- + +## 4. Ruff Code Quality Analysis + +**Report Source:** `reports/ruff/` +**Status:** All Issues Resolvable +**Last Analysis:** November 17, 2025 +**Total Issues:** 28 + +Static code analysis was performed using Ruff to ensure code quality and adherence to PEP 8 standards. + +### Issue Breakdown by File + +| File | Issues | Severity | Key Findings | +|------|--------|----------|--------------| +| `data_cleaning.py` | 16 | Low/Med | Unsorted imports (I001), Unused imports (F401), f-strings without placeholders (F541), Comparison to False (E712). | +| `modeling/train.py` | 7 | Low/Med | Unused `SMOTE` import, Unused variable `n_labels`, f-strings. | +| `features.py` | 2 | Low | Unused `nltk` import. | +| `dataset.py` | 2 | Low | Unused `DB_PATH` import. | +| `mlsmote.py` | 1 | Low | Unsorted imports. | + +### Configuration & Compliance +* **Command:** `ruff check . --output-format json --output-file reports/ruff/ruff_report.json` +* **Standards:** PEP 8 (Pass), Black compatible (line length 88), isort (Pass). +* **Fixability:** 100% of issues can be fixed (26 automatically, 2 manually). + +### Conclusion +The project code quality is high, with only minor style and import issues that do not affect functionality but should be cleaned up for maintainability. diff --git a/hopcroft_skill_classification_tool_competition/__init__.py b/hopcroft_skill_classification_tool_competition/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/hopcroft_skill_classification_tool_competition/api_models.py b/hopcroft_skill_classification_tool_competition/api_models.py new file mode 100644 index 0000000000000000000000000000000000000000..bc9723ed5e4a4a6475eb4afaa7c1c9aff3b7a926 --- /dev/null +++ b/hopcroft_skill_classification_tool_competition/api_models.py @@ -0,0 +1,221 @@ +""" +Pydantic models for API data validation. + +Defines request and response schemas with validation rules. +""" + +from datetime import datetime +from typing import Optional + +from pydantic import BaseModel, ConfigDict, Field, field_serializer, field_validator + + +class IssueInput(BaseModel): + """Input model for GitHub issue or pull request classification.""" + + issue_text: str = Field( + ..., + min_length=1, + description="Issue title text", + examples=["Fix bug in authentication module"], + ) + issue_description: Optional[str] = Field( + default=None, + description="Issue body text", + examples=["The authentication module fails when handling expired tokens"], + ) + repo_name: Optional[str] = Field( + default=None, description="Repository name", examples=["user/repo-name"] + ) + pr_number: Optional[int] = Field( + default=None, ge=1, description="Pull request number", examples=[123] + ) + created_at: Optional[datetime] = Field( + default=None, description="Issue creation timestamp", examples=["2024-01-15T10:30:00Z"] + ) + author_name: Optional[str] = Field( + default=None, description="Issue author username", examples=["johndoe"] + ) + + @field_validator("issue_text", "issue_description") + @classmethod + def clean_text(cls, v: Optional[str]) -> Optional[str]: + """Validate and clean text fields.""" + if v is None: + return v + v = v.strip() + if not v: + raise ValueError("Text cannot be empty or whitespace only") + return v + + model_config = ConfigDict( + json_schema_extra={ + "example": { + "issue_text": "Add support for OAuth authentication", + "issue_description": "Implement OAuth 2.0 flow for third-party providers", + "repo_name": "myorg/myproject", + "pr_number": 456, + "author_name": "developer123", + } + } + ) + + +class SkillPrediction(BaseModel): + """Single skill prediction with confidence score.""" + + skill_name: str = Field( + ..., + description="Name of the predicted skill (domain/subdomain)", + examples=["Language/Java", "DevOps/CI-CD"], + ) + confidence: float = Field( + ..., ge=0.0, le=1.0, description="Confidence score (0.0 to 1.0)", examples=[0.85] + ) + + model_config = ConfigDict( + json_schema_extra={"example": {"skill_name": "Language/Java", "confidence": 0.92}} + ) + + +class PredictionResponse(BaseModel): + """Response model for skill classification predictions.""" + + predictions: list[SkillPrediction] = Field( + default_factory=list, description="List of predicted skills with confidence scores" + ) + num_predictions: int = Field( + ..., ge=0, description="Total number of predicted skills", examples=[5] + ) + model_version: str = Field(default="1.0.0", description="Model version", examples=["1.0.0"]) + processing_time_ms: Optional[float] = Field( + default=None, ge=0.0, description="Processing time in milliseconds", examples=[125.5] + ) + + model_config = ConfigDict( + json_schema_extra={ + "example": { + "predictions": [ + {"skill_name": "Language/Java", "confidence": 0.92}, + {"skill_name": "DevOps/CI-CD", "confidence": 0.78}, + ], + "num_predictions": 2, + "model_version": "1.0.0", + "processing_time_ms": 125.5, + } + } + ) + + +class BatchIssueInput(BaseModel): + """Input model for batch prediction.""" + + issues: list[IssueInput] = Field( + ..., + min_length=1, + max_length=100, + description="Issues to classify (max 100)", + ) + + model_config = ConfigDict( + json_schema_extra={ + "example": { + "issues": [ + { + "issue_text": "Fix authentication bug", + "issue_description": "Users cannot login with OAuth", + }, + { + "issue_text": "Add database migration", + "issue_description": "Create migration for new user table", + }, + ] + } + } + ) + + +class BatchPredictionResponse(BaseModel): + """Response model for batch predictions.""" + + results: list[PredictionResponse] = Field( + default_factory=list, description="Prediction results, one per issue" + ) + total_issues: int = Field(..., ge=0, description="Number of issues processed", examples=[2]) + total_processing_time_ms: Optional[float] = Field( + default=None, ge=0.0, description="Processing time in milliseconds", examples=[250.0] + ) + + model_config = ConfigDict( + json_schema_extra={ + "example": { + "results": [ + { + "predictions": [{"skill_name": "Language/Java", "confidence": 0.92}], + "num_predictions": 1, + "model_version": "1.0.0", + } + ], + "total_issues": 2, + "total_processing_time_ms": 250.0, + } + } + ) + + +class ErrorResponse(BaseModel): + """Error response model.""" + + error: str = Field(..., description="Error message", examples=["Invalid input"]) + detail: Optional[str] = Field( + default=None, description="Detailed error", examples=["Field 'issue_text' is required"] + ) + timestamp: datetime = Field(default_factory=datetime.now, description="Error timestamp") + + @field_serializer("timestamp") + def serialize_timestamp(self, value: datetime) -> str: + return value.isoformat() + + model_config = ConfigDict( + json_schema_extra={ + "example": { + "error": "Validation Error", + "detail": "issue_text: field required", + "timestamp": "2024-01-15T10:30:00Z", + } + } + ) + + +class HealthCheckResponse(BaseModel): + """Health check response model.""" + + status: str = Field(default="healthy", description="Service status", examples=["healthy"]) + model_loaded: bool = Field(..., description="Model ready status", examples=[True]) + version: str = Field(default="1.0.0", description="API version", examples=["1.0.0"]) + timestamp: datetime = Field(default_factory=datetime.now, description="Timestamp") + + +class PredictionRecord(PredictionResponse): + """Extended prediction model with metadata from MLflow.""" + + run_id: str = Field(..., description="MLflow Run ID") + timestamp: datetime = Field(..., description="Prediction timestamp") + input_text: Optional[str] = Field(default="", description="Input text classified") + + model_config = ConfigDict( + json_schema_extra={ + "example": { + "predictions": [ + {"skill_name": "Language/Java", "confidence": 0.92}, + {"skill_name": "DevOps/CI-CD", "confidence": 0.78}, + ], + "num_predictions": 2, + "model_version": "1.0.0", + "processing_time_ms": 125.5, + "run_id": "a1b2c3d4e5f6", + "timestamp": "2024-01-15T10:30:00Z", + "input_text": "Fix bug in authentication module", + } + } + ) diff --git a/hopcroft_skill_classification_tool_competition/config.py b/hopcroft_skill_classification_tool_competition/config.py new file mode 100644 index 0000000000000000000000000000000000000000..7fd104520702a65f632b3b6fdfdc70f73b1c6d70 --- /dev/null +++ b/hopcroft_skill_classification_tool_competition/config.py @@ -0,0 +1,137 @@ +"""Configuration and constants for the project""" + +from pathlib import Path + +# Project paths +PROJECT_DIR = Path(__file__).resolve().parents[1] +DATA_DIR = PROJECT_DIR / "data" +RAW_DATA_DIR = DATA_DIR / "raw" +PROCESSED_DATA_DIR = DATA_DIR / "processed" +MODELS_DIR = PROJECT_DIR / "models" +REPORTS_DIR = PROJECT_DIR / "reports" + +# Dataset paths +DB_PATH = RAW_DATA_DIR / "skillscope_data.db" + +# Data paths configuration for training +# Updated to use cleaned data (duplicates removed, no data leakage) +# Now pointing to TF-IDF features for API compatibility +DATA_PATHS = { + "features": str(PROCESSED_DATA_DIR / "tfidf" / "features_tfidf.npy"), + "labels": str(PROCESSED_DATA_DIR / "tfidf" / "labels_tfidf.npy"), + "features_original": str(PROCESSED_DATA_DIR / "tfidf" / "features_tfidf.npy"), + "labels_original": str(PROCESSED_DATA_DIR / "tfidf" / "labels_tfidf.npy"), + "models_dir": str(MODELS_DIR), +} + +# Embedding configuration +EMBEDDING_MODEL_NAME = "all-MiniLM-L6-v2" + +# API Configuration - which model to use for predictions +API_CONFIG = { + # Model file to load (without path, just filename) + "model_name": "random_forest_embedding_gridsearch.pkl", + # Feature type: "tfidf" or "embedding" + # This determines how text is transformed before prediction + "feature_type": "embedding", +} + +# Training configuration +TRAINING_CONFIG = { + "random_state": 42, + "test_size": 0.2, + "val_size": 0.1, + "cv_folds": 5, +} + +# Model configuration (Random Forest) +MODEL_CONFIG = { + "param_grid": { + "estimator__n_estimators": [50, 100, 200], + "estimator__max_depth": [10, 20, 30], + "estimator__min_samples_split": [2, 5], + } +} + +# ADASYN configuration +ADASYN_CONFIG = { + "n_neighbors": 5, + "sampling_strategy": "auto", +} + +# PCA configuration +PCA_CONFIG = { + "variance_retained": 0.95, +} + +# MLflow configuration +MLFLOW_CONFIG = { + "uri": "https://dagshub.com/se4ai2526-uniba/Hopcroft.mlflow", + "experiments": { + "baseline": "hopcroft_random_forest_baseline", + "smote": "hopcroft_random_forest_smote", + "ros": "hopcroft_random_forest_ros", + "adasyn_pca": "hopcroft_random_forest_adasyn_pca", + "lightgbm": "hopcroft_lightgbm", + "lightgbm_smote": "hopcroft_lightgbm_smote", + }, +} + +# Model parameters (legacy - kept for compatibility) +RANDOM_STATE = 42 +TEST_SIZE = 0.2 +VAL_SIZE = 0.1 + +# Feature engineering +MAX_TFIDF_FEATURES = 5000 +NGRAM_RANGE = (1, 2) + +# Model training (legacy) +N_ESTIMATORS = 100 +MAX_DEPTH = 20 + +# Hugging Face dataset +HF_REPO_ID = "NLBSE/SkillCompetition" +HF_FILENAME = "skillscope_data.zip" + + +def get_feature_paths(feature_type: str = "embedding", use_cleaned: bool = True) -> dict: + """ + Get data paths for specified feature type. + + This function allows easy switching between TF-IDF and Embedding features + for baseline reproduction (TF-IDF) vs improved model (Embeddings). + + Args: + feature_type: Type of features - 'tfidf' or 'embedding' + use_cleaned: If True, use cleaned data (duplicates removed, no leakage). + If False, use original processed data. + + Returns: + Dictionary with paths to features, labels, and models directory + + Example: + # For baseline (paper reproduction) + paths = get_feature_paths(feature_type='tfidf', use_cleaned=True) + + # For improved model + paths = get_feature_paths(feature_type='embedding', use_cleaned=True) + """ + if feature_type not in ["tfidf", "embedding"]: + raise ValueError(f"Invalid feature_type: {feature_type}. Must be 'tfidf' or 'embedding'") + + feature_dir = PROCESSED_DATA_DIR / feature_type + + if use_cleaned: + suffix = "_clean" + else: + suffix = "" + + return { + "features": str(feature_dir / f"features_{feature_type}{suffix}.npy"), + "labels": str(feature_dir / f"labels_{feature_type}{suffix}.npy"), + "features_test": str(feature_dir / f"X_test_{feature_type}{suffix}.npy"), + "labels_test": str(feature_dir / f"Y_test_{feature_type}{suffix}.npy"), + "models_dir": str(MODELS_DIR), + "feature_type": feature_type, + } diff --git a/hopcroft_skill_classification_tool_competition/data_cleaning.py b/hopcroft_skill_classification_tool_competition/data_cleaning.py new file mode 100644 index 0000000000000000000000000000000000000000..3b8adec720fbe6ffac8726f4a2ed7b35145b9253 --- /dev/null +++ b/hopcroft_skill_classification_tool_competition/data_cleaning.py @@ -0,0 +1,559 @@ +""" +Data Cleaning and Quality Assurance Module + +This module addresses data quality issues identified by Deepchecks validation: +1. Removes duplicate samples (6.5% duplicates detected) +2. Resolves conflicting labels (8.9% samples with conflicts) +3. Ensures proper train/test split without data leakage +4. Removes highly correlated features + +This script should be run BEFORE training to ensure data quality. +It regenerates the processed data files with cleaned data. + +Usage: + python -m hopcroft_skill_classification_tool_competition.data_cleaning + +Output: + - data/processed/tfidf/features_tfidf_clean.npy (cleaned training features) + - data/processed/tfidf/labels_tfidf_clean.npy (cleaned training labels) + - data/processed/tfidf/X_test_clean.npy (cleaned test features) + - data/processed/tfidf/Y_test_clean.npy (cleaned test labels) +""" + +from datetime import datetime +from pathlib import Path +from typing import Dict, Optional, Tuple + +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split + +from hopcroft_skill_classification_tool_competition.config import PROCESSED_DATA_DIR + + +def remove_duplicates(X: np.ndarray, y: np.ndarray) -> Tuple[np.ndarray, np.ndarray, Dict]: + """ + Remove duplicate samples from the dataset. + + Duplicates are identified by identical feature vectors. + When duplicates are found with different labels, we keep the first occurrence. + + Args: + X: Feature matrix (samples x features) + y: Label matrix (samples x labels) + + Returns: + Tuple of (cleaned_X, cleaned_y, stats_dict) + """ + print("\n" + "=" * 80) + print("STEP 1: REMOVING DUPLICATES") + print("=" * 80) + + initial_samples = X.shape[0] + + # Convert to DataFrame for easier duplicate detection + # Use feature hash to identify duplicates (more memory efficient than full comparison) + df_features = pd.DataFrame(X) + + # Find duplicates based on all features + duplicates_mask = df_features.duplicated(keep="first") + n_duplicates = duplicates_mask.sum() + + print(f"Initial samples: {initial_samples:,}") + print(f"Duplicates found: {n_duplicates:,} ({n_duplicates / initial_samples * 100:.2f}%)") + + if n_duplicates > 0: + # Keep only non-duplicate rows + X_clean = X[~duplicates_mask] + y_clean = y[~duplicates_mask] + + print(f"Samples after removing duplicates: {X_clean.shape[0]:,}") + print(f"Removed: {n_duplicates:,} duplicate samples") + else: + X_clean = X + y_clean = y + print("No duplicates found") + + stats = { + "initial_samples": int(initial_samples), + "duplicates_found": int(n_duplicates), + "duplicates_percentage": float(n_duplicates / initial_samples * 100), + "final_samples": int(X_clean.shape[0]), + } + + return X_clean, y_clean, stats + + +def resolve_conflicting_labels( + X: np.ndarray, y: np.ndarray +) -> Tuple[np.ndarray, np.ndarray, Dict]: + """ + Resolve samples with conflicting labels. + + Conflicting labels occur when identical feature vectors have different labels. + Resolution strategy: Use majority voting for each label across duplicates. + + Args: + X: Feature matrix (samples x features) + y: Label matrix (samples x labels) + + Returns: + Tuple of (cleaned_X, cleaned_y, stats_dict) + """ + print("\n" + "=" * 80) + print("STEP 2: RESOLVING CONFLICTING LABELS") + print("=" * 80) + + initial_samples = X.shape[0] + + # Create a combined DataFrame + df_X = pd.DataFrame(X) + df_y = pd.DataFrame(y) + + # Add a unique identifier based on features (use hash for efficiency) + # Create a string representation of each row + feature_hashes = pd.util.hash_pandas_object(df_X, index=False) + + # Group by feature hash + groups = df_y.groupby(feature_hashes) + + # Count conflicts: groups with size > 1 + conflicts = groups.size() + n_conflict_groups = (conflicts > 1).sum() + n_conflict_samples = (conflicts[conflicts > 1]).sum() + + print(f"Initial samples: {initial_samples:,}") + print(f"Duplicate feature groups: {n_conflict_groups:,}") + print( + f"Samples in conflict groups: {n_conflict_samples:,} ({n_conflict_samples / initial_samples * 100:.2f}%)" + ) + + if n_conflict_groups > 0: + # Resolve conflicts using majority voting + # For each group of duplicates, use the most common label value + resolved_labels = groups.apply( + lambda x: x.mode(axis=0).iloc[0] if len(x) > 1 else x.iloc[0] + ) + + # Keep only one sample per unique feature vector + unique_indices = ~df_X.duplicated(keep="first") + X_clean = X[unique_indices] + + # Map resolved labels back to unique samples + unique_hashes = feature_hashes[unique_indices] + y_clean = np.array([resolved_labels.loc[h].values for h in unique_hashes]) + + print(f"Samples after conflict resolution: {X_clean.shape[0]:,}") + print("Conflicts resolved using majority voting") + else: + X_clean = X + y_clean = y + print("No conflicting labels found") + + stats = { + "initial_samples": int(initial_samples), + "conflict_groups": int(n_conflict_groups), + "conflict_samples": int(n_conflict_samples), + "conflict_percentage": float(n_conflict_samples / initial_samples * 100), + "final_samples": int(X_clean.shape[0]), + } + + return X_clean, y_clean, stats + + +def remove_sparse_samples( + X: np.ndarray, y: np.ndarray, min_nnz: int = 10 +) -> Tuple[np.ndarray, np.ndarray, Dict]: + """ + Remove samples with too few non-zero features (incompatible with SMOTE). + + Args: + X: Feature matrix + y: Label matrix + min_nnz: Minimum number of non-zero features required + + Returns: + Tuple of (X_filtered, y_filtered, statistics_dict) + """ + print("\n" + "=" * 80) + print(f"STEP 3: REMOVING SPARSE SAMPLES (min_nnz={min_nnz})") + print("=" * 80) + + n_initial = X.shape[0] + print(f"Initial samples: {n_initial:,}") + + nnz_counts = (X != 0).sum(axis=1) + valid_mask = nnz_counts >= min_nnz + + X_filtered = X[valid_mask] + y_filtered = y[valid_mask] + + n_removed = n_initial - X_filtered.shape[0] + removal_pct = (n_removed / n_initial * 100) if n_initial > 0 else 0 + + print(f"Sparse samples (< {min_nnz} features): {n_removed:,} ({removal_pct:.2f}%)") + print(f"Samples after filtering: {X_filtered.shape[0]:,}") + + stats = { + "initial_samples": int(n_initial), + "min_nnz_threshold": min_nnz, + "sparse_samples_removed": int(n_removed), + "removal_percentage": float(removal_pct), + "final_samples": int(X_filtered.shape[0]), + } + + return X_filtered, y_filtered, stats + + +def remove_empty_labels( + X: np.ndarray, y: np.ndarray, min_count: int = 5 +) -> Tuple[np.ndarray, np.ndarray, Dict]: + """ + Remove labels with too few occurrences (cannot be stratified). + + Args: + X: Feature matrix + y: Label matrix + min_count: Minimum number of occurrences required per label + + Returns: + Tuple of (X_same, y_filtered, statistics_dict) + """ + print("\n" + "=" * 80) + print(f"STEP 4: REMOVING RARE LABELS (min_count={min_count})") + print("=" * 80) + + n_initial_labels = y.shape[1] + print(f"Initial labels: {n_initial_labels:,}") + + label_counts = y.sum(axis=0) + valid_labels = label_counts >= min_count + + y_filtered = y[:, valid_labels] + + n_removed = n_initial_labels - y_filtered.shape[1] + removal_pct = (n_removed / n_initial_labels * 100) if n_initial_labels > 0 else 0 + + print(f"Rare labels (< {min_count} occurrences): {n_removed:,} ({removal_pct:.2f}%)") + print(f"Labels after filtering: {y_filtered.shape[1]:,}") + + stats = { + "initial_labels": int(n_initial_labels), + "min_count_threshold": min_count, + "rare_labels_removed": int(n_removed), + "removal_percentage": float(removal_pct), + "final_labels": int(y_filtered.shape[1]), + } + + return X, y_filtered, stats + + +def create_clean_train_test_split( + X: np.ndarray, y: np.ndarray, test_size: float = 0.2, random_state: int = 42 +) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, Dict]: + """ + Create train/test split with verification of no data leakage. + Uses MultilabelStratifiedShuffleSplit if available. + + Args: + X: Feature matrix + y: Label matrix + test_size: Proportion of test set (default: 0.2 = 20%) + random_state: Random seed for reproducibility + + Returns: + Tuple of (X_train, X_test, y_train, y_test, stats_dict) + """ + print("\n" + "=" * 80) + print("STEP 5: CREATING CLEAN TRAIN/TEST SPLIT") + print("=" * 80) + + print(f"Total samples: {X.shape[0]:,}") + print(f"Test size: {test_size * 100:.1f}%") + print(f"Random state: {random_state}") + + # Try to use iterative-stratification for better multi-label splits + try: + from iterstrat.ml_stratifiers import MultilabelStratifiedShuffleSplit + + has_iterstrat = True + print("Using MultilabelStratifiedShuffleSplit (iterative-stratification)") + except ImportError: + has_iterstrat = False + print( + "WARNING: iterative-stratification not installed. Using standard stratification (suboptimal for multi-label)." + ) + + if has_iterstrat: + msss = MultilabelStratifiedShuffleSplit( + n_splits=1, test_size=test_size, random_state=random_state + ) + train_index, test_index = next(msss.split(X, y)) + X_train, X_test = X[train_index], X[test_index] + y_train, y_test = y[train_index], y[test_index] + else: + # Fallback: Perform stratified split based on first label column (approximate stratification) + stratify_column = y[:, 0] if y.ndim > 1 else y + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=test_size, random_state=random_state, stratify=stratify_column + ) + + # Verify no data leakage: check for overlapping samples + print("\nVerifying no data leakage...") + + # Convert to sets of row hashes for efficient comparison + train_hashes = set(pd.util.hash_pandas_object(pd.DataFrame(X_train), index=False)) + test_hashes = set(pd.util.hash_pandas_object(pd.DataFrame(X_test), index=False)) + + overlap = train_hashes & test_hashes + + if len(overlap) > 0: + raise ValueError( + f"DATA LEAKAGE DETECTED: {len(overlap)} samples appear in both train and test!" + ) + + print("No data leakage detected") + print(f"Train samples: {X_train.shape[0]:,} ({X_train.shape[0] / X.shape[0] * 100:.1f}%)") + print(f"Test samples: {X_test.shape[0]:,} ({X_test.shape[0] / X.shape[0] * 100:.1f}%)") + + # Verify feature dimensions match + if X_train.shape[1] != X_test.shape[1]: + raise ValueError( + f"Feature dimensions don't match: train={X_train.shape[1]}, test={X_test.shape[1]}" + ) + + print(f"Feature dimensions match: {X_train.shape[1]:,}") + + stats = { + "total_samples": int(X.shape[0]), + "train_samples": int(X_train.shape[0]), + "test_samples": int(X_test.shape[0]), + "train_percentage": float(X_train.shape[0] / X.shape[0] * 100), + "test_percentage": float(X_test.shape[0] / X.shape[0] * 100), + "features": int(X_train.shape[1]), + "labels": int(y_train.shape[1]) if y_train.ndim > 1 else 1, + "data_leakage": False, + "overlap_samples": 0, + "stratification_method": "MultilabelStratifiedShuffleSplit" + if has_iterstrat + else "Standard StratifiedShuffleSplit", + } + + return X_train, X_test, y_train, y_test, stats + + +def save_cleaned_data( + X_train: np.ndarray, + X_test: np.ndarray, + y_train: np.ndarray, + y_test: np.ndarray, + stats: Dict, + output_dir: Optional[Path] = None, + feature_type: str = "tfidf", +) -> None: + """ + Save cleaned train/test split to disk. + + Args: + X_train: Training features + X_test: Test features + y_train: Training labels + y_test: Test labels + stats: Dictionary with cleaning statistics + output_dir: Output directory (default: data/processed/{feature_type}/) + feature_type: Type of features ('tfidf' or 'embedding') + """ + print("\n" + "=" * 80) + print("STEP 6: SAVING CLEANED DATA") + print("=" * 80) + + if output_dir is None: + output_dir = PROCESSED_DATA_DIR / feature_type + + output_dir.mkdir(parents=True, exist_ok=True) + + # Save cleaned data with "_clean" suffix + files = { + "features_train": output_dir / f"features_{feature_type}_clean.npy", + "labels_train": output_dir / f"labels_{feature_type}_clean.npy", + "features_test": output_dir / f"X_test_{feature_type}_clean.npy", + "labels_test": output_dir / f"Y_test_{feature_type}_clean.npy", + } + + np.save(files["features_train"], X_train) + np.save(files["labels_train"], y_train) + np.save(files["features_test"], X_test) + np.save(files["labels_test"], y_test) + + print(f"\nSaved cleaned data to: {output_dir}") + for name, path in files.items(): + print(f" - {path.name}") + + +def clean_and_split_data( + test_size: float = 0.2, + random_state: int = 42, + regenerate_features: bool = True, + feature_type: str = "embedding", # 'tfidf' or 'embedding' + model_name: str = "all-MiniLM-L6-v2", + max_features: int = 2000, # Only for TF-IDF (must match features.py default) +) -> Dict: + """ + Main function to clean data and create proper train/test split. + + This function: + 1. Loads or regenerates features (TF-IDF or Embeddings) + 2. Removes duplicate samples + 3. Resolves conflicting labels + 4. Creates clean train/test split + 5. Verifies no data leakage + 6. Saves cleaned data + + Args: + test_size: Proportion of test set (default: 0.2) + random_state: Random seed for reproducibility (default: 42) + regenerate_features: If True, regenerate features from database (default: True) + feature_type: Type of features to extract ('tfidf' or 'embedding') + model_name: Model name for embeddings + max_features: Maximum number of TF-IDF features (default: 1000) + + Returns: + Dictionary with all cleaning statistics + """ + print("=" * 80) + print("DATA CLEANING AND QUALITY ASSURANCE PIPELINE") + print("=" * 80) + print(f"Timestamp: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") + print(f"Test size: {test_size * 100:.1f}%") + print(f"Random state: {random_state}") + print(f"Regenerate features: {regenerate_features}") + print(f"Feature type: {feature_type}") + if feature_type == "embedding": + print(f"Model name: {model_name}") + else: + print(f"Max features: {max_features}") + + # Step 0: Load or generate features + if regenerate_features: + print("\nRegenerating features from database...") + # Load data and extract features + from hopcroft_skill_classification_tool_competition.features import create_feature_dataset + + # Use the unified create_feature_dataset function + features, labels, _, _ = create_feature_dataset( + save_processed=False, # Don't save intermediate raw features, just return them + feature_type=feature_type, + model_name=model_name, + ) + + X = features + y = labels.values + else: + print(f"\nLoading existing features ({feature_type})...") + data_dir = PROCESSED_DATA_DIR / feature_type + X = np.load(data_dir / f"features_{feature_type}.npy") + y = np.load(data_dir / f"labels_{feature_type}.npy") + + print("\nInitial data shape:") + print(f" Features: {X.shape}") + print(f" Labels: {y.shape}") + + # Step 1: Remove duplicates + X_no_dup, y_no_dup, dup_stats = remove_duplicates(X, y) + + # Step 2: Resolve conflicting labels + X_no_conf, y_no_conf, conflict_stats = resolve_conflicting_labels(X_no_dup, y_no_dup) + + # Step 3: Remove sparse samples + # For embeddings, we don't have "sparse" features in the same way as TF-IDF (zeros). + # But we can check for near-zero vectors if needed. + # For now, we skip sparse check for embeddings or keep it if it checks for all-zeros. + if feature_type == "tfidf": + X_no_sparse, y_no_sparse, sparse_stats = remove_sparse_samples( + X_no_conf, y_no_conf, min_nnz=10 + ) + else: + # Skip sparse check for embeddings as they are dense + X_no_sparse, y_no_sparse = X_no_conf, y_no_conf + sparse_stats = {"sparse_samples_removed": 0, "removal_percentage": 0.0} + print("\nSkipping sparse sample removal for dense embeddings.") + + # Step 4: Remove rare labels + X_clean, y_clean, rare_stats = remove_empty_labels(X_no_sparse, y_no_sparse, min_count=5) + + # Step 5: Create clean train/test split + X_train, X_test, y_train, y_test, split_stats = create_clean_train_test_split( + X_clean, y_clean, test_size=test_size, random_state=random_state + ) + + # Step 6: Save cleaned data + all_stats = { + "duplicates": dup_stats, + "conflicts": conflict_stats, + "sparse_samples": sparse_stats, + "rare_labels": rare_stats, + "split": split_stats, + "feature_type": feature_type, + } + + # Save to specific directory based on feature type + output_dir = PROCESSED_DATA_DIR / feature_type + save_cleaned_data( + X_train, + X_test, + y_train, + y_test, + all_stats, + output_dir=output_dir, + feature_type=feature_type, + ) + + # Print final summary + print("\n" + "=" * 80) + print("CLEANING PIPELINE COMPLETED SUCCESSFULLY") + print("=" * 80) + print("\nSummary:") + print(f" Original samples: {X.shape[0]:,}") + print(f" Original labels: {y.shape[1]:,}") + print( + f" Duplicates removed: {dup_stats['duplicates_found']:,} ({dup_stats['duplicates_percentage']:.2f}%)" + ) + print( + f" Conflicts resolved: {conflict_stats['conflict_samples']:,} ({conflict_stats['conflict_percentage']:.2f}%)" + ) + print( + f" Sparse samples removed: {sparse_stats['sparse_samples_removed']:,} ({sparse_stats['removal_percentage']:.2f}%)" + ) + print( + f" Rare labels removed: {rare_stats['rare_labels_removed']:,} ({rare_stats['removal_percentage']:.2f}%)" + ) + print(f" Final clean samples: {split_stats['total_samples']:,}") + print(f" Final clean labels: {y_clean.shape[1]:,}") + print( + f" Train samples: {split_stats['train_samples']:,} ({split_stats['train_percentage']:.1f}%)" + ) + print( + f" Test samples: {split_stats['test_samples']:,} ({split_stats['test_percentage']:.1f}%)" + ) + print("\nData quality issues resolved:") + print(" - Duplicates removed") + print(" - Label conflicts resolved") + if feature_type == "tfidf": + print(" - Sparse samples removed") + print(" - Rare labels removed") + print(" - Clean train/test split created") + print(" - No data leakage verified") + print("=" * 80) + + return all_stats + + +if __name__ == "__main__": + # Run the cleaning pipeline + stats = clean_and_split_data( + test_size=0.2, # 80/20 split + random_state=42, + regenerate_features=True, + feature_type="embedding", + model_name="all-MiniLM-L6-v2", + ) diff --git a/hopcroft_skill_classification_tool_competition/dataset.py b/hopcroft_skill_classification_tool_competition/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..42d50f52ff8b2b43d6bc5838bae2af87af60fece --- /dev/null +++ b/hopcroft_skill_classification_tool_competition/dataset.py @@ -0,0 +1,99 @@ +"""Scripts to download or generate data""" + +from pathlib import Path +import shutil +import zipfile + +from huggingface_hub import hf_hub_download + +from hopcroft_skill_classification_tool_competition.config import ( + HF_FILENAME, + HF_REPO_ID, + RAW_DATA_DIR, +) + + +def download_skillscope_dataset(output_dir: Path = None) -> Path: + """ + Download and extract SkillScope dataset from Hugging Face Hub. + + The dataset contains a SQLite database (skillscope_data.db) with: + - nlbse_tool_competition_data_by_issue: Main table with PR features and skill labels + - vw_nlbse_tool_competition_data_by_file: View with file-level labels + + Dataset details: + - 7,245 merged pull requests from 11 Java repositories + - 57,206 source files; 59,644 methods; 13,097 classes + - 217 skill labels (domain/sub-domain pairs) + + Args: + output_dir: Directory where to save the dataset (default: data/raw) + + Returns: + Path to the extracted database file + """ + if output_dir is None: + output_dir = RAW_DATA_DIR + + output_dir.mkdir(parents=True, exist_ok=True) + db_path = output_dir / "skillscope_data.db" + + if db_path.exists(): + print(f"Database already exists at: {db_path}") + return db_path + + print("Downloading SkillScope dataset from Hugging Face...") + + # Download without using cache - use local_dir to avoid .cache folder + zip_path = hf_hub_download( + repo_id=HF_REPO_ID, + filename=HF_FILENAME, + repo_type="dataset", + local_dir=output_dir, + local_dir_use_symlinks=False, # Don't create symlinks, copy directly + ) + + print(f"Downloaded to: {zip_path}") + print("Extracting database...") + + with zipfile.ZipFile(zip_path, "r") as zip_ref: + zip_ref.extractall(output_dir) + + if not db_path.exists(): + raise FileNotFoundError(f"Database file not found at: {db_path}") + + print(f"Database extracted to: {db_path}") + + # Clean up: remove zip file + print("Cleaning up temporary files...") + Path(zip_path).unlink() + + # Clean up: remove .cache folder if exists + cache_dir = output_dir / ".cache" + if cache_dir.exists(): + shutil.rmtree(cache_dir) + print("Removed .cache folder") + + # Clean up: remove download folder if exists + download_dir = output_dir / "download" + if download_dir.exists(): + shutil.rmtree(download_dir) + print("Removed download folder") + + print("Cleanup completed") + + print("\nDataset info:") + print(" - Table: nlbse_tool_competition_data_by_issue") + print(" - View: vw_nlbse_tool_competition_data_by_file") + + return db_path + + +if __name__ == "__main__": + print("=" * 80) + print("SKILLSCOPE DATASET DOWNLOAD") + print("=" * 80) + download_skillscope_dataset() + print("=" * 80) + print("DOWNLOAD COMPLETED") + print("=" * 80) diff --git a/hopcroft_skill_classification_tool_competition/features.py b/hopcroft_skill_classification_tool_competition/features.py new file mode 100644 index 0000000000000000000000000000000000000000..f1e3c8c2877c6b9ddc43084ad7b08cc3ca478ee3 --- /dev/null +++ b/hopcroft_skill_classification_tool_competition/features.py @@ -0,0 +1,492 @@ +""" +Feature extraction module for skill classification. + +This module provides functions to extract features from the SkillScope dataset, +starting with TF-IDF vectorization of textual data from pull request issues. + +Dataset Information (from nlbse_tool_competition_data_by_issue): +- 7,154 issues from 11 Java repositories + - 226 total columns: + - 2 text columns: 'issue text' (title) and 'issue description' (body) + - metadata and other columns containing PR/file/context information + - 217 label columns: domain/subdomain skill labels (142 active labels in this DB) + +Label Characteristics: +- Multi-label classification problem +- Average 32.9 labels per issue (median: 31) +- Highly imbalanced: some labels appear in all issues, others in very few +- Top labels: Language, Data Structure, DevOps, Error Handling +""" + +from pathlib import Path +import re +import sqlite3 +from typing import Optional, Tuple + +import joblib + +# Import per lo Stemming +from nltk.stem import PorterStemmer +import numpy as np +import pandas as pd +from sklearn.feature_extraction.text import TfidfVectorizer + +from hopcroft_skill_classification_tool_competition.config import ( + MODELS_DIR, + PROCESSED_DATA_DIR, + RAW_DATA_DIR, +) + +# Inizializza lo stemmer una volta per efficienza +stemmer = PorterStemmer() + + +def clean_github_text(text: str, use_stemming: bool = True) -> str: + """ + Clean GitHub issue text as per SkillScope paper (Aracena et al. process). + Removes emojis, URLs, HTML tags, and other noise commonly found in GitHub text. + Optionally applies stemming. + + Args: + text: Raw text from GitHub issue + use_stemming: If True, apply Porter stemming (recommended for TF-IDF). + If False, keep original words (recommended for Embeddings/LLMs). + + Returns: + Cleaned text string (stemmed if use_stemming=True) + """ + if pd.isna(text) or text is None: + return "" + + text = str(text) + + # Remove URLs (http/httpss/www) + text = re.sub(r"http\S+|www\.\S+", "", text) + + # Remove HTML tags + text = re.sub(r"<[^>]+>", "", text) + + # Remove markdown code blocks + text = re.sub(r"```[\s\S]*?```", "", text) + + # Remove inline code + text = re.sub(r"`[^`]*`", "", text) + + # Remove emojis and non-ASCII characters + text = text.encode("ascii", "ignore").decode("ascii") + + # Remove extra whitespace + text = re.sub(r"\s+", " ", text) + + text = text.strip() + + # Stemming condizionale: solo per TF-IDF, non per Embeddings + if use_stemming: + try: + tokens = text.split() + stemmed_tokens = [stemmer.stem(token) for token in tokens] + text = " ".join(stemmed_tokens) + except Exception as e: + print(f"Warning: Stemming failed for text snippet '{text[:50]}...'. Error: {e}") + # Ritorna il testo pulito ma non stemmato in caso di errore + return text.strip() + + return text + + +def get_dataset_info(df: pd.DataFrame) -> dict: + """ + Get summary information about the dataset. + + Args: + df: Input dataframe + + Returns: + Dictionary containing dataset statistics + """ + text_cols = get_text_columns(df) + label_cols = get_label_columns(df) + + # Convert to binary labels + binary_labels = (df[label_cols] > 0).astype(int) + labels_per_issue = binary_labels.sum(axis=1) + issues_per_label = binary_labels.sum(axis=0) + + info = { + "total_issues": len(df), + "total_columns": len(df.columns), + "text_columns": text_cols, + "num_text_columns": len(text_cols), + "label_columns": label_cols, + "num_labels": len(label_cols), + "avg_labels_per_issue": labels_per_issue.mean(), + "median_labels_per_issue": labels_per_issue.median(), + "max_labels_per_issue": labels_per_issue.max(), + "min_labels_per_issue": labels_per_issue.min(), + "avg_issues_per_label": issues_per_label.mean(), + "labels_with_no_issues": (issues_per_label == 0).sum(), + } + + return info + + +def load_data_from_db(db_path: Optional[Path] = None) -> pd.DataFrame: + """ + Load data from the SQLite database. + + Args: + db_path: Path to the SQLite database file. + If None, uses default path in data/raw/skillscope_data.db + + Returns: + DataFrame containing the nlbse_tool_competition_data_by_issue table + """ + if db_path is None: + db_path = RAW_DATA_DIR / "skillscope_data.db" + + conn = sqlite3.connect(db_path) + + # Load the main table + query = "SELECT * FROM nlbse_tool_competition_data_by_issue" + df = pd.read_sql_query(query, conn) + + conn.close() + + print(f"Loaded {len(df)} records from database") + return df + + +def get_text_columns(df: pd.DataFrame) -> list: + """ + Identify text columns in the dataframe (typically issue title, body, etc.). + + Args: + df: Input dataframe + + Returns: + List of column names containing textual data + """ + # Text columns from SkillScope database schema + # Based on exploration: issue text (title) and issue description (body) + text_cols = ["issue text", "issue description"] + + return [col for col in text_cols if col in df.columns] + + +def get_label_columns(df: pd.DataFrame) -> list: + """ + Identify label columns (domains/subdomains with API counts). + + Args: + df: Input dataframe + + Returns: + List of column names containing labels + """ + # Metadata columns to exclude from labels + # Based on exploration: these are not skill labels + exclude_cols = [ + "Repo Name", + "PR #", + "issue text", + "issue description", + "created_at", + "author_name", + ] + + # Label columns are numeric but not metadata. Use pandas is_numeric_dtype + # to be robust to dtype representations. + from pandas.api.types import is_numeric_dtype + + label_cols = [ + col for col in df.columns if col not in exclude_cols and is_numeric_dtype(df[col]) + ] + + return label_cols + + +def combine_text_fields( + df: pd.DataFrame, text_columns: list, use_stemming: bool = True +) -> pd.Series: + """ + Combine multiple text fields into a single text representation. + Applies text cleaning as per SkillScope paper. + + Args: + df: Input dataframe + text_columns: List of column names to combine + use_stemming: If True, apply stemming (for TF-IDF). If False, keep original words (for Embeddings). + + Returns: + Series containing cleaned and combined text for each row + """ + # Apply cleaning to each text column and then combine + combined_text = ( + df[text_columns] + .fillna("") + .astype(str) + .apply( + lambda x: " ".join( + x.map(lambda text: clean_github_text(text, use_stemming=use_stemming)) + ), + axis=1, + ) + ) + return combined_text + + +def extract_tfidf_features( + df: pd.DataFrame, + text_columns: Optional[list] = None, + max_features: Optional[int] = 2000, + min_df: int = 2, + max_df: float = 0.95, + ngram_range: Tuple[int, int] = (1, 2), +) -> Tuple[np.ndarray, TfidfVectorizer]: + """ + Extract TF-IDF features from textual data. + + Args: + df: Input dataframe + text_columns: List of text columns to use. If None, auto-detect. + max_features: Maximum number of features to extract (default: 2000 for balanced sparsity) + min_df: Minimum document frequency for a term to be included + max_df: Maximum document frequency (ignore terms appearing in >max_df of docs) + ngram_range: Range of n-grams to consider (e.g., (1,2) for unigrams and bigrams) + + Returns: + Tuple of (feature matrix, fitted vectorizer) + """ + if text_columns is None: + text_columns = get_text_columns(df) + + if not text_columns: + raise ValueError("No text columns found in dataframe") + + # Combine text fields (with stemming for TF-IDF) + print(f"Combining text from columns: {text_columns}") + combined_text = combine_text_fields(df, text_columns, use_stemming=True) + + # Initialize TF-IDF vectorizer + vectorizer = TfidfVectorizer( + max_features=max_features, + min_df=min_df, + max_df=max_df, + ngram_range=ngram_range, + stop_words="english", + lowercase=True, + strip_accents="unicode", + ) + + # Fit and transform + print( + f"Extracting TF-IDF features with max_features={max_features if max_features else 'All'}, " + f"ngram_range={ngram_range}" + ) + tfidf_matrix = vectorizer.fit_transform(combined_text) + + print( + f"Extracted {tfidf_matrix.shape[1]} TF-IDF features from {tfidf_matrix.shape[0]} samples" + ) + + return tfidf_matrix.toarray(), vectorizer + + +def extract_embedding_features( + df: pd.DataFrame, + text_columns: Optional[list] = None, + model_name: str = "all-MiniLM-L6-v2", + batch_size: int = 32, +) -> Tuple[np.ndarray, object]: + """ + Extract LLM embeddings from textual data using Sentence Transformers. + + Args: + df: Input dataframe + text_columns: List of text columns to use. If None, auto-detect. + model_name: Name of the pre-trained model to use + batch_size: Batch size for encoding + + Returns: + Tuple of (feature matrix, model object) + """ + try: + from sentence_transformers import SentenceTransformer + except ImportError as e: + raise ImportError( + f"sentence-transformers import failed: {e}. Try running: pip install sentence-transformers" + ) from e + + if text_columns is None: + text_columns = get_text_columns(df) + + if not text_columns: + raise ValueError("No text columns found in dataframe") + + # Combine text fields (without stemming for embeddings - LLMs need full words) + print(f"Combining text from columns: {text_columns}") + combined_text = combine_text_fields(df, text_columns, use_stemming=False) + + # Load model + print(f"Loading embedding model: {model_name}") + model = SentenceTransformer(model_name) + + # Encode + print(f"Extracting embeddings for {len(combined_text)} samples...") + embeddings = model.encode( + combined_text.tolist(), + batch_size=batch_size, + show_progress_bar=True, + convert_to_numpy=True, + ) + + print(f"Extracted embeddings shape: {embeddings.shape}") + + return embeddings, model + + +def prepare_labels(df: pd.DataFrame, label_columns: Optional[list] = None) -> pd.DataFrame: + """ + Prepare multi-label binary matrix from label columns. + + Args: + df: Input dataframe + label_columns: List of label columns. If None, auto-detect. + + Returns: + DataFrame with binary labels (1 if label present, 0 otherwise) + """ + if label_columns is None: + label_columns = get_label_columns(df) + + # Convert to binary: any value > 0 means label is present + labels = (df[label_columns] > 0).astype(int) + + print(f"Prepared {len(label_columns)} labels") + print(f"Label distribution:\n{labels.sum().describe()}") + + return labels + + +def create_feature_dataset( + db_path: Optional[Path] = None, + save_processed: bool = True, + feature_type: str = "tfidf", # 'tfidf' or 'embedding' + model_name: str = "all-MiniLM-L6-v2", +) -> Tuple[np.ndarray, pd.DataFrame, list, list]: + """ + Main function to create the complete feature dataset. + + Args: + db_path: Path to SQLite database + save_processed: Whether to save processed data to disk + feature_type: Type of features to extract ('tfidf' or 'embedding') + model_name: Model name for embeddings (ignored if feature_type='tfidf') + + Returns: + Tuple of (features, labels, feature_names, label_names) + """ + # Load data + df = load_data_from_db(db_path) + + # Get dataset info + info = get_dataset_info(df) + print("\n=== Dataset Information ===") + print(f"Total issues: {info['total_issues']:,}") + print(f"Text columns: {info['text_columns']}") + print(f"Number of labels: {info['num_labels']}") + print(f"Avg labels per issue: {info['avg_labels_per_issue']:.2f}") + print(f"Labels with no issues: {info['labels_with_no_issues']}") + + # Extract features + text_columns = get_text_columns(df) + label_columns = get_label_columns(df) + + feature_names = [] + + vectorizer = None + + if feature_type == "tfidf": + features, vectorizer = extract_tfidf_features(df, text_columns=text_columns) + feature_names = vectorizer.get_feature_names_out() + elif feature_type == "embedding": + features, _ = extract_embedding_features( + df, text_columns=text_columns, model_name=model_name + ) + feature_names = [f"emb_{i}" for i in range(features.shape[1])] + else: + raise ValueError(f"Unknown feature_type: {feature_type}") + + # Prepare labels + labels = prepare_labels(df, label_columns) + + # Save processed data + if save_processed: + # Path: processed/{feature_type}/ + output_dir = PROCESSED_DATA_DIR / feature_type + output_dir.mkdir(parents=True, exist_ok=True) + + features_path = output_dir / f"features_{feature_type}.npy" + labels_path = output_dir / f"labels_{feature_type}.npy" + + np.save(features_path, features) + np.save(labels_path, labels.values) + + print(f"\nSaved processed data to {output_dir}") + print(f" - {features_path.name}: {features.shape}") + print(f" - {labels_path.name}: {labels.shape}") + + # Save vectorizer and label names to models/ directory for inference + MODELS_DIR.mkdir(parents=True, exist_ok=True) + + if feature_type == "tfidf" and vectorizer is not None: + vectorizer_path = MODELS_DIR / "tfidf_vectorizer.pkl" + joblib.dump(vectorizer, vectorizer_path) + print(f" - Saved TF-IDF vectorizer to: {vectorizer_path}") + + # Always save label names (needed for both tfidf and embedding inference) + label_names_path = MODELS_DIR / "label_names.pkl" + joblib.dump(label_columns, label_names_path) + print(f" - Saved {len(label_columns)} label names to: {label_names_path}") + + return features, labels, feature_names, label_columns + + +def load_processed_data( + feature_name: str = "tfidf", data_dir: Optional[Path] = None +) -> Tuple[np.ndarray, np.ndarray]: + """ + Load processed features and labels from disk. + + Args: + feature_name: Name prefix of the features to load (e.g., 'tfidf', 'bow', 'embeddings') + data_dir: Path to processed data directory. If None, uses default. + + Returns: + Tuple of (features, labels) + """ + if data_dir is None: + data_dir = PROCESSED_DATA_DIR + + features_path = data_dir / f"features_{feature_name}.npy" + labels_path = data_dir / f"labels_{feature_name}.npy" + + features = np.load(features_path) + labels = np.load(labels_path) + + print(f"Loaded processed data from {data_dir}") + print(f" - Feature type: {feature_name}") + print(f" - Features shape: {features.shape}") + print(f" - Labels shape: {labels.shape}") + + return features, labels + + +if __name__ == "__main__": + features, labels, feature_names, label_names = create_feature_dataset(feature_type="embedding") + + print("\n=== Feature Extraction Summary ===") + print(f"Features shape: {features.shape}") + print(f"Labels shape: {labels.shape}") + print(f"Number of feature names: {len(feature_names)}") + print(f"Number of labels: {len(label_names)}") diff --git a/hopcroft_skill_classification_tool_competition/main.py b/hopcroft_skill_classification_tool_competition/main.py new file mode 100644 index 0000000000000000000000000000000000000000..acc2b6fc43869b57dd4a12e198d4bf602a638ad4 --- /dev/null +++ b/hopcroft_skill_classification_tool_competition/main.py @@ -0,0 +1,434 @@ +""" +FastAPI application for skill classification service. + +Provides REST API endpoints for classifying GitHub issues and pull requests +into skill categories using machine learning models. + +Usage: + Development: fastapi dev hopcroft_skill_classification_tool_competition/main.py + Production: fastapi run hopcroft_skill_classification_tool_competition/main.py + +Endpoints: + GET / - API information + GET /health - Health check + POST /predict - Single issue classification + POST /predict/batch - Batch classification +""" + +from contextlib import asynccontextmanager +from datetime import datetime +import json +import os +import time +from typing import List + +from fastapi import FastAPI, HTTPException, status +from fastapi.responses import JSONResponse, RedirectResponse +import mlflow +from pydantic import ValidationError + +from hopcroft_skill_classification_tool_competition.api_models import ( + BatchIssueInput, + BatchPredictionResponse, + ErrorResponse, + HealthCheckResponse, + IssueInput, + PredictionRecord, + PredictionResponse, + SkillPrediction, +) +from hopcroft_skill_classification_tool_competition.config import MLFLOW_CONFIG +from hopcroft_skill_classification_tool_competition.modeling.predict import SkillPredictor + +predictor = None +model_version = "1.0.0" + + +@asynccontextmanager +async def lifespan(app: FastAPI): + """Manage application startup and shutdown.""" + global predictor, model_version + + print("=" * 80) + print("Starting Skill Classification API") + print("=" * 80) + + # Configure MLflow + mlflow.set_tracking_uri(MLFLOW_CONFIG["uri"]) + print(f"MLflow tracking URI set to: {MLFLOW_CONFIG['uri']}") + + try: + model_name = os.getenv("MODEL_NAME", "random_forest_tfidf_gridsearch.pkl") + print(f"Loading model: {model_name}") + predictor = SkillPredictor(model_name=model_name) + print("Model and artifacts loaded successfully") + except Exception as e: + print(f"Failed to load model: {e}") + print("WARNING: API starting in degraded mode (prediction will fail)") + + print(f"Model version {model_version} initialized") + print("API ready") + print("=" * 80) + + yield + + print("Shutting down API") + + +app = FastAPI( + title="Skill Classification API", + description="API for classifying GitHub issues and pull requests into skill categories", + version="1.0.0", + docs_url="/docs", + redoc_url="/redoc", + lifespan=lifespan, +) + + +@app.get("/", tags=["Root"]) +async def root(): + """Return basic API information.""" + return { + "message": "Skill Classification API", + "version": "1.0.0", + "documentation": "/docs", + "demo": "/demo", + "health": "/health", + } + + +@app.get("/health", response_model=HealthCheckResponse, tags=["Health"]) +async def health_check(): + """Check API and model status.""" + return HealthCheckResponse( + status="healthy", + model_loaded=predictor is not None, + version="1.0.0", + timestamp=datetime.now(), + ) + + +@app.get("/demo") +async def redirect_to_demo(): + """Redirect to Streamlit demo.""" + return RedirectResponse(url="http://localhost:8501") + + +@app.post( + "/predict", + response_model=PredictionRecord, + status_code=status.HTTP_201_CREATED, + tags=["Prediction"], + summary="Classify a single issue", + response_description="Skill predictions with confidence scores", +) +async def predict_skills(issue: IssueInput) -> PredictionRecord: + """ + Classify a single GitHub issue or pull request into skill categories. + + Args: + issue: IssueInput containing issue text and optional metadata + + Returns: + PredictionRecord with list of predicted skills, confidence scores, and run_id + + Raises: + HTTPException: If prediction fails + """ + start_time = time.time() + + try: + if predictor is None: + raise HTTPException(status_code=503, detail="Model not loaded") + + # Combine text fields if needed, or just use issue_text + # The predictor expects a single string + full_text = f"{issue.issue_text} {issue.issue_description or ''} {issue.repo_name or ''}" + + predictions_data = predictor.predict(full_text) + + # Convert to Pydantic models + predictions = [ + SkillPrediction(skill_name=p["skill_name"], confidence=p["confidence"]) + for p in predictions_data + ] + + processing_time = (time.time() - start_time) * 1000 + + # Log to MLflow + run_id = "local" + timestamp = datetime.now() + + try: + experiment_name = MLFLOW_CONFIG["experiments"]["baseline"] + mlflow.set_experiment(experiment_name) + + with mlflow.start_run() as run: + run_id = run.info.run_id + # Log inputs + mlflow.log_param("issue_text", issue.issue_text) + if issue.repo_name: + mlflow.log_param("repo_name", issue.repo_name) + + # Log outputs (as metrics or params/tags for retrieval) + # For simple retrieval, we'll store the main prediction as a tag/param + if predictions: + mlflow.log_param("top_skill", predictions[0].skill_name) + mlflow.log_metric("top_confidence", predictions[0].confidence) + + # Store full predictions as a JSON artifact or tag + predictions_json = json.dumps([p.model_dump() for p in predictions]) + mlflow.set_tag("predictions_json", predictions_json) + mlflow.set_tag("model_version", model_version) + except Exception as e: + print(f"MLflow logging failed: {e}") + + return PredictionRecord( + predictions=predictions, + num_predictions=len(predictions), + model_version=model_version, + processing_time_ms=round(processing_time, 2), + run_id=run_id, + timestamp=timestamp, + input_text=issue.issue_text, + ) + + except Exception as e: + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=f"Prediction failed: {str(e)}", + ) + + +@app.post( + "/predict/batch", + response_model=BatchPredictionResponse, + status_code=status.HTTP_200_OK, + tags=["Prediction"], + summary="Classify multiple issues", + response_description="Batch skill predictions", +) +async def predict_skills_batch(batch: BatchIssueInput) -> BatchPredictionResponse: + """ + Classify multiple GitHub issues or pull requests in batch. + + Args: + batch: BatchIssueInput containing list of issues (max 100) + + Returns: + BatchPredictionResponse with prediction results for each issue + + Raises: + HTTPException: If batch prediction fails + """ + start_time = time.time() + + try: + results = [] + + if predictor is None: + raise HTTPException(status_code=503, detail="Model not loaded") + + for issue in batch.issues: + full_text = ( + f"{issue.issue_text} {issue.issue_description or ''} {issue.repo_name or ''}" + ) + predictions_data = predictor.predict(full_text) + + predictions = [ + SkillPrediction(skill_name=p["skill_name"], confidence=p["confidence"]) + for p in predictions_data + ] + + results.append( + PredictionResponse( + predictions=predictions, + num_predictions=len(predictions), + model_version=model_version, + ) + ) + + total_processing_time = (time.time() - start_time) * 1000 + + return BatchPredictionResponse( + results=results, + total_issues=len(batch.issues), + total_processing_time_ms=round(total_processing_time, 2), + ) + + except Exception as e: + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=f"Batch prediction failed: {str(e)}", + ) + + +@app.get( + "/predictions/{run_id}", + response_model=PredictionRecord, + status_code=status.HTTP_200_OK, + tags=["Prediction"], + summary="Get a prediction by ID", + response_description="Prediction details", +) +async def get_prediction(run_id: str) -> PredictionRecord: + """ + Retrieve a specific prediction by its MLflow Run ID. + + Args: + run_id: The MLflow Run ID + + Returns: + PredictionRecord containing the prediction details + + Raises: + HTTPException: If run not found or error occurs + """ + try: + run = mlflow.get_run(run_id) + data = run.data + + # Reconstruct predictions from tags + predictions_json = data.tags.get("predictions_json", "[]") + predictions_data = json.loads(predictions_json) + predictions = [SkillPrediction(**p) for p in predictions_data] + + # Get timestamp (start_time is in ms) + timestamp = datetime.fromtimestamp(run.info.start_time / 1000.0) + + return PredictionRecord( + predictions=predictions, + num_predictions=len(predictions), + model_version=data.tags.get("model_version", "unknown"), + processing_time_ms=None, # Not stored in standard tags, could be added + run_id=run.info.run_id, + timestamp=timestamp, + input_text=data.params.get("issue_text", ""), + ) + + except mlflow.exceptions.MlflowException: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, detail=f"Prediction with ID {run_id} not found" + ) + except Exception as e: + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=f"Failed to retrieve prediction: {str(e)}", + ) + + +@app.get( + "/predictions", + response_model=List[PredictionRecord], + status_code=status.HTTP_200_OK, + tags=["Prediction"], + summary="List predictions", + response_description="List of recent predictions", +) +async def list_predictions(skip: int = 0, limit: int = 10) -> List[PredictionRecord]: + """ + Retrieve a list of recent predictions. + + Args: + skip: Number of records to skip (not fully supported by MLflow search, handled client-side) + limit: Maximum number of records to return + + Returns: + List of PredictionRecord + """ + try: + experiment_name = MLFLOW_CONFIG["experiments"]["baseline"] + experiment = mlflow.get_experiment_by_name(experiment_name) + + if not experiment: + return [] + + # Search runs + runs = mlflow.search_runs( + experiment_ids=[experiment.experiment_id], + max_results=limit + skip, + order_by=["start_time DESC"], + ) + + results = [] + # Convert pandas DataFrame to list of dicts if needed, or iterate + # mlflow.search_runs returns a pandas DataFrame + + # We need to iterate through the DataFrame + if runs.empty: + return [] + + # Apply skip + runs = runs.iloc[skip:] + + for _, row in runs.iterrows(): + run_id = row.run_id + + # Extract data from columns (flattened) + # Tags are prefixed with 'tags.', Params with 'params.' + + # Helper to safely get value + def get_val(row, prefix, key, default=None): + col = f"{prefix}.{key}" + return row[col] if col in row else default + + predictions_json = get_val(row, "tags", "predictions_json", "[]") + try: + predictions_data = json.loads(predictions_json) + predictions = [SkillPrediction(**p) for p in predictions_data] + except Exception: + predictions = [] + + timestamp = row.start_time # This is usually a datetime object in the DF + + # Get model_version with fallback to "unknown" or inherited default + model_version = get_val(row, "tags", "model_version") + if model_version is None or model_version == "": + model_version = "unknown" + + # Get input_text with fallback to empty string + input_text = get_val(row, "params", "issue_text") + if input_text is None: + input_text = "" + + results.append( + PredictionRecord( + predictions=predictions, + num_predictions=len(predictions), + model_version=model_version, + processing_time_ms=None, + run_id=run_id, + timestamp=timestamp, + input_text=input_text, + ) + ) + + return results + + except Exception as e: + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=f"Failed to list predictions: {str(e)}", + ) + + +@app.exception_handler(ValidationError) +async def validation_exception_handler(request, exc: ValidationError): + """Handle Pydantic validation errors.""" + return JSONResponse( + status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, + content=ErrorResponse( + error="Validation Error", detail=str(exc), timestamp=datetime.now() + ).model_dump(), + ) + + +@app.exception_handler(HTTPException) +async def http_exception_handler(request, exc: HTTPException): + """Handle HTTP exceptions.""" + return JSONResponse( + status_code=exc.status_code, + content=ErrorResponse( + error=exc.detail, detail=None, timestamp=datetime.now() + ).model_dump(), + ) diff --git a/hopcroft_skill_classification_tool_competition/mlsmote.py b/hopcroft_skill_classification_tool_competition/mlsmote.py new file mode 100644 index 0000000000000000000000000000000000000000..f0218f4de8ff3d6ea2ec3573be884005d7ac4257 --- /dev/null +++ b/hopcroft_skill_classification_tool_competition/mlsmote.py @@ -0,0 +1,157 @@ +# github: https://github.com/niteshsukhwani/MLSMOTE.git +# -*- coding: utf-8 -*- +# Importing required Library +import random + +import numpy as np +import pandas as pd +from sklearn.datasets import make_classification +from sklearn.neighbors import NearestNeighbors + + +def create_dataset(n_sample=1000): + """ + Create a unevenly distributed sample data set multilabel + classification using make_classification function + + args + nsample: int, Number of sample to be created + + return + X: pandas.DataFrame, feature vector dataframe with 10 features + y: pandas.DataFrame, target vector dataframe with 5 labels + """ + X, y = make_classification( + n_classes=5, + class_sep=2, + weights=[0.1, 0.025, 0.205, 0.008, 0.9], + n_informative=3, + n_redundant=1, + flip_y=0, + n_features=10, + n_clusters_per_class=1, + n_samples=1000, + random_state=10, + ) + y = pd.get_dummies(y, prefix="class") + return pd.DataFrame(X), y + + +def get_tail_label(df): + """ + Give tail label colums of the given target dataframe + + args + df: pandas.DataFrame, target label df whose tail label has to identified + + return + tail_label: list, a list containing column name of all the tail label + """ + columns = df.columns + n = len(columns) + irpl = np.zeros(n) + for column in range(n): + irpl[column] = df[columns[column]].value_counts()[1] + irpl = max(irpl) / irpl + mir = np.average(irpl) + tail_label = [] + for i in range(n): + if irpl[i] > mir: + tail_label.append(columns[i]) + return tail_label + + +def get_index(df): + """ + give the index of all tail_label rows + args + df: pandas.DataFrame, target label df from which index for tail label has to identified + + return + index: list, a list containing index number of all the tail label + """ + tail_labels = get_tail_label(df) + index = set() + for tail_label in tail_labels: + sub_index = set(df[df[tail_label] == 1].index) + index = index.union(sub_index) + return list(index) + + +def get_minority_instace(X, y): + """ + Give minority dataframe containing all the tail labels + + args + X: pandas.DataFrame, the feature vector dataframe + y: pandas.DataFrame, the target vector dataframe + + return + X_sub: pandas.DataFrame, the feature vector minority dataframe + y_sub: pandas.DataFrame, the target vector minority dataframe + """ + index = get_index(y) + X_sub = X[X.index.isin(index)].reset_index(drop=True) + y_sub = y[y.index.isin(index)].reset_index(drop=True) + return X_sub, y_sub + + +def nearest_neighbour(X): + """ + Give index of 5 nearest neighbor of all the instance + + args + X: np.array, array whose nearest neighbor has to find + + return + indices: list of list, index of 5 NN of each element in X + """ + nbs = NearestNeighbors(n_neighbors=5, metric="euclidean", algorithm="kd_tree").fit(X) + euclidean, indices = nbs.kneighbors(X) + return indices + + +def MLSMOTE(X, y, n_sample): + """ + Give the augmented data using MLSMOTE algorithm + + args + X: pandas.DataFrame, input vector DataFrame + y: pandas.DataFrame, feature vector dataframe + n_sample: int, number of newly generated sample + + return + new_X: pandas.DataFrame, augmented feature vector data + target: pandas.DataFrame, augmented target vector data + """ + indices2 = nearest_neighbour(X) + n = len(indices2) + new_X = np.zeros((n_sample, X.shape[1])) + target = np.zeros((n_sample, y.shape[1])) + for i in range(n_sample): + reference = random.randint(0, n - 1) + neighbour = random.choice(indices2[reference, 1:]) + all_point = indices2[reference] + nn_df = y[y.index.isin(all_point)] + ser = nn_df.sum(axis=0, skipna=True) + target[i] = np.array([1 if val > 2 else 0 for val in ser]) + ratio = random.random() + gap = X.loc[reference, :] - X.loc[neighbour, :] + new_X[i] = np.array(X.loc[reference, :] + ratio * gap) + new_X = pd.DataFrame(new_X, columns=X.columns) + target = pd.DataFrame(target, columns=y.columns) + new_X = pd.concat([X, new_X], axis=0) + target = pd.concat([y, target], axis=0) + return new_X, target + + +# Keep original MLSMOTE function name for direct use + + +if __name__ == "__main__": + """ + main function to use the MLSMOTE + """ + X, y = create_dataset() # Creating a Dataframe + X_sub, y_sub = get_minority_instace(X, y) # Getting minority instance of that datframe + X_res, y_res = MLSMOTE(X_sub, y_sub, 100) # Applying MLSMOTE to augment the dataframe diff --git a/hopcroft_skill_classification_tool_competition/modeling/predict.py b/hopcroft_skill_classification_tool_competition/modeling/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..5e5a9e730b754c2fc44de55264d81c47b16ce933 --- /dev/null +++ b/hopcroft_skill_classification_tool_competition/modeling/predict.py @@ -0,0 +1,198 @@ +from pathlib import Path +from typing import Any, Dict, List, Optional + +import joblib +import numpy as np + +from hopcroft_skill_classification_tool_competition.config import ( + API_CONFIG, + DATA_PATHS, + EMBEDDING_MODEL_NAME, + MODELS_DIR, +) +from hopcroft_skill_classification_tool_competition.features import clean_github_text + + +class SkillPredictor: + """ + Skill prediction class that supports both TF-IDF and Embedding-based models. + + The feature_type determines how text is transformed: + - "tfidf": Uses saved TfidfVectorizer + - "embedding": Uses SentenceTransformer to generate embeddings + """ + + def __init__(self, model_name: Optional[str] = None, feature_type: Optional[str] = None): + """ + Initialize the SkillPredictor. + + Args: + model_name: Name of the model file. If None, uses API_CONFIG["model_name"] + feature_type: "tfidf" or "embedding". If None, uses API_CONFIG["feature_type"] + """ + # Use config defaults if not specified + self.model_name = model_name or API_CONFIG["model_name"] + self.feature_type = feature_type or API_CONFIG["feature_type"] + + self.model_path = MODELS_DIR / self.model_name + self.labels_path = MODELS_DIR / "label_names.pkl" + + # Paths for kept indices (may be in different locations) + self.kept_indices_path_models = MODELS_DIR / "kept_label_indices.npy" + self.kept_indices_path_tfidf = ( + Path(DATA_PATHS["features"]).parent.parent / "tfidf" / "kept_label_indices.npy" + ) + self.kept_indices_path_emb = ( + Path(DATA_PATHS["features"]).parent.parent / "embedding" / "kept_label_indices.npy" + ) + + self.model = None + self.vectorizer = None # TF-IDF vectorizer or SentenceTransformer + self.label_names = None + self.kept_indices = None + + self._load_artifacts() + + def _load_artifacts(self): + """Load model and required artifacts based on feature_type.""" + print(f"Loading model from {self.model_path}...") + if not self.model_path.exists(): + raise FileNotFoundError(f"Model not found at {self.model_path}") + self.model = joblib.load(self.model_path) + + # Load vectorizer/encoder based on feature type + if self.feature_type == "tfidf": + self._load_tfidf_vectorizer() + elif self.feature_type == "embedding": + self._load_embedding_model() + else: + raise ValueError( + f"Unknown feature_type: {self.feature_type}. Must be 'tfidf' or 'embedding'" + ) + + # Load label names + print(f"Loading label names from {self.labels_path}...") + if not self.labels_path.exists(): + raise FileNotFoundError(f"Label names not found at {self.labels_path}") + self.label_names = joblib.load(self.labels_path) + + # Load kept indices if available + if self.kept_indices_path_models.exists(): + print(f"Loading kept indices from {self.kept_indices_path_models}") + self.kept_indices = np.load(self.kept_indices_path_models) + elif self.kept_indices_path_emb.exists(): + print(f"Loading kept indices from {self.kept_indices_path_emb}") + self.kept_indices = np.load(self.kept_indices_path_emb) + elif self.kept_indices_path_tfidf.exists(): + print(f"Loading kept indices from {self.kept_indices_path_tfidf}") + self.kept_indices = np.load(self.kept_indices_path_tfidf) + else: + print("No kept_label_indices.npy found. Assuming all labels are used.") + self.kept_indices = None + + def _load_tfidf_vectorizer(self): + """Load the TF-IDF vectorizer.""" + vectorizer_path = MODELS_DIR / "tfidf_vectorizer.pkl" + print(f"Loading TF-IDF vectorizer from {vectorizer_path}...") + if not vectorizer_path.exists(): + raise FileNotFoundError( + f"TF-IDF vectorizer not found at {vectorizer_path}. " + "Run feature extraction first: python -m hopcroft_skill_classification_tool_competition.features" + ) + self.vectorizer = joblib.load(vectorizer_path) + + def _load_embedding_model(self): + """Load the SentenceTransformer model for embeddings.""" + try: + from sentence_transformers import SentenceTransformer + except ImportError as e: + raise ImportError( + f"sentence-transformers is required for embedding-based models. " + f"Install with: pip install sentence-transformers. Error: {e}" + ) from e + + print(f"Loading SentenceTransformer model: {EMBEDDING_MODEL_NAME}...") + self.vectorizer = SentenceTransformer(EMBEDDING_MODEL_NAME) + + def _transform_text(self, text: str) -> np.ndarray: + """ + Transform text to features based on feature_type. + + Args: + text: Cleaned input text + + Returns: + Feature array ready for model prediction + """ + if self.feature_type == "tfidf": + # TF-IDF: use stemming, return sparse matrix converted to array + cleaned = clean_github_text(text, use_stemming=True) + features = self.vectorizer.transform([cleaned]) + return features + else: + # Embedding: no stemming (LLMs need full words) + cleaned = clean_github_text(text, use_stemming=False) + features = self.vectorizer.encode([cleaned], convert_to_numpy=True) + return features + + def predict(self, text: str, threshold: float = 0.5) -> List[Dict[str, Any]]: + """ + Predict skills for a given text. + + Args: + text: Input text (issue title + body) + threshold: Confidence threshold for binary classification + + Returns: + List of dicts with 'skill_name' and 'confidence' + """ + # Transform text to features + features = self._transform_text(text) + + # Predict + # MultiOutputClassifier predict_proba returns a list of arrays (one per class) + # Each array is (n_samples, 2) -> [prob_0, prob_1] + probas_list = self.model.predict_proba(features) + + # Extract positive class probabilities + confidence_scores = [] + for i, prob in enumerate(probas_list): + if prob.shape[1] >= 2: + confidence_scores.append(prob[0][1]) + else: + # Only one class present + try: + estimator = self.model.estimators_[i] + classes = estimator.classes_ + if len(classes) == 1 and classes[0] == 1: + confidence_scores.append(1.0) + else: + confidence_scores.append(0.0) + except Exception: + confidence_scores.append(0.0) + + confidence_scores = np.array(confidence_scores) + + # Filter by threshold and map to label names + predictions = [] + + for i, score in enumerate(confidence_scores): + if score >= threshold: + if self.kept_indices is not None: + if i < len(self.kept_indices): + original_idx = self.kept_indices[i] + skill_name = self.label_names[original_idx] + else: + continue + else: + if i < len(self.label_names): + skill_name = self.label_names[i] + else: + skill_name = f"Unknown_Skill_{i}" + + predictions.append({"skill_name": skill_name, "confidence": float(score)}) + + # Sort by confidence descending + predictions.sort(key=lambda x: x["confidence"], reverse=True) + + return predictions diff --git a/hopcroft_skill_classification_tool_competition/modeling/train.py b/hopcroft_skill_classification_tool_competition/modeling/train.py new file mode 100644 index 0000000000000000000000000000000000000000..c63ae57919f66e22c4d40949b9789d434816d90b --- /dev/null +++ b/hopcroft_skill_classification_tool_competition/modeling/train.py @@ -0,0 +1,858 @@ +import argparse +import os +from pathlib import Path + +from imblearn.over_sampling import ADASYN, RandomOverSampler +import joblib +import lightgbm as lgb +import mlflow +import mlflow.sklearn +import numpy as np +from sklearn.decomposition import PCA +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import f1_score, precision_score, recall_score +from sklearn.model_selection import GridSearchCV, KFold, train_test_split +from sklearn.multioutput import MultiOutputClassifier + +from hopcroft_skill_classification_tool_competition.config import ( + ADASYN_CONFIG, + DATA_PATHS, + MLFLOW_CONFIG, + MODEL_CONFIG, + PCA_CONFIG, + TRAINING_CONFIG, + get_feature_paths, +) + +# Local MLSMOTE implementation (lightweight multi-label oversampling) +try: + import pandas as pd + + from hopcroft_skill_classification_tool_competition.mlsmote import MLSMOTE as mlsmote_function + from hopcroft_skill_classification_tool_competition.mlsmote import get_minority_instace + + _HAS_LOCAL_MLSMOTE = True +except Exception: + mlsmote_function = None + get_minority_instace = None + _HAS_LOCAL_MLSMOTE = False + print("[warning] Local MLSMOTE not available. Check mlsmote.py exists.") + + +# Prefer multilabel stratified splits for imbalanced multi-label data. +# Use `iterative-stratification` package when available. +try: + from iterstrat.ml_stratifiers import MultilabelStratifiedShuffleSplit + + _HAS_MLSTRAT = True +except Exception: + MultilabelStratifiedShuffleSplit = None + _HAS_MLSTRAT = False + + +# ------------------------------- +# MLflow authentication and setup +# Load environment variables from .env file (for local dev) +# In Docker, env vars are set via docker-compose env_file +# ------------------------------- +from dotenv import load_dotenv + +load_dotenv() + +_mlflow_env_uri = os.getenv("MLFLOW_TRACKING_URI") +_configured_uri = MLFLOW_CONFIG.get("uri", "https://dagshub.com/se4ai2526-uniba/Hopcroft.mlflow") + +if _mlflow_env_uri: + mlflow_uri = _mlflow_env_uri +else: + mlflow_uri = _configured_uri + +# If targeting DagsHub, require username/password; otherwise proceed. +if "dagshub.com" in mlflow_uri: + _username = os.getenv("MLFLOW_TRACKING_USERNAME") + _password = os.getenv("MLFLOW_TRACKING_PASSWORD") + if not _username or not _password: + raise ValueError( + "Set the environment variables MLFLOW_TRACKING_USERNAME and MLFLOW_TRACKING_PASSWORD for remote tracking" + ) + +mlflow.set_tracking_uri(mlflow_uri) + + +# ===================================================== +# Common utilities (merged from train_experiments.py) +# ===================================================== +def load_data(feature_type="tfidf", use_cleaned=True): + """Load features and labels using get_feature_paths. + + Args: + feature_type: 'tfidf' or 'embedding' + use_cleaned: whether to use cleaned data + + Returns: + X, Y: feature matrix and label matrix + """ + paths = get_feature_paths(feature_type=feature_type, use_cleaned=use_cleaned) + X = np.load(paths["features"]) + Y = np.load(paths["labels"]) + + print(f"Dataset loaded successfully: {X.shape} samples, {Y.shape} labels") + print(f"Using feature type: {feature_type}{'_clean' if use_cleaned else ''}") + return X, Y + + +def stratified_train_test_split(X, Y, test_size=None, random_state=None, fallback=True): + """Split X, Y using multilabel stratified shuffle split when possible. + + Args: + X: np.ndarray features + Y: np.ndarray multi-label binary matrix (n_samples, n_labels) + test_size: float or int, forwarded to splitter + random_state: int + fallback: if True and multilabel splitter unavailable, use sklearn.train_test_split + + Returns: + X_train, X_test, Y_train, Y_test + """ + if _HAS_MLSTRAT: + if isinstance(test_size, float): + tst = test_size + else: + # default to TRAINING_CONFIG if not provided + tst = TRAINING_CONFIG.get("test_size", 0.2) + + msss = MultilabelStratifiedShuffleSplit( + n_splits=1, test_size=tst, random_state=random_state + ) + train_idx, test_idx = next(msss.split(X, Y)) + return X[train_idx], X[test_idx], Y[train_idx], Y[test_idx] + + if fallback: + print( + "[warning] iterative-stratification not available; using standard train_test_split (no multilabel stratification). To enable stratified multilabel splitting install 'iterative-stratification'." + ) + return train_test_split(X, Y, test_size=test_size, random_state=random_state, shuffle=True) + + raise RuntimeError( + "iterative-stratification is required for multilabel stratified splitting but not installed." + ) + + +def stratified_train_val_test_split( + X, Y, test_size=0.2, val_size=0.1, random_state=None, fallback=True +): + """Split X, Y into train, val, test with multilabel stratification when possible. + + Args: + X, Y: arrays + test_size: proportion for final test set + val_size: proportion for validation set (relative to whole dataset) + random_state: seed + fallback: if True, falls back to sklearn splits + + Returns: + X_train, X_val, X_test, Y_train, Y_val, Y_test + """ + if not (0.0 < test_size < 1.0 and 0.0 <= val_size < 1.0 and val_size + test_size < 1.0): + raise ValueError("test_size and val_size must be fractions in (0,1) and sum < 1") + + # First split off the final test set + X_rem, X_test, Y_rem, Y_test = stratified_train_test_split( + X, Y, test_size=test_size, random_state=random_state, fallback=fallback + ) + + # Compute validation size relative to the remaining data + rel_val = 0.0 + if (1.0 - test_size) > 0: + rel_val = val_size / (1.0 - test_size) + else: + rel_val = 0.0 + + if rel_val <= 0: + # No validation requested + return X_rem, np.empty((0, X.shape[1])), X_test, Y_rem, np.empty((0, Y.shape[1])), Y_test + + X_train, X_val, Y_train, Y_val = stratified_train_test_split( + X_rem, Y_rem, test_size=rel_val, random_state=random_state, fallback=fallback + ) + + return X_train, X_val, X_test, Y_train, Y_val, Y_test + + +def _check_label_coverage(Y_train: np.ndarray, Y_val: np.ndarray, min_train: int = 1): + """Check that each label appears at least `min_train` times in train and + at least once in train+val. Prints a warning if some labels are scarce in + train, and raises an error if some labels are missing entirely from + train+val (which would make learning impossible for those labels). + + Args: + Y_train: (n_train, n_labels) binary matrix + Y_val: (n_val, n_labels) binary matrix (may be empty) + min_train: minimum occurrences in train to be considered "covered" + """ + # Defensive: handle empty val + if Y_val is None: + Y_val = np.empty((0, Y_train.shape[1])) + + counts_train = np.sum(Y_train, axis=0) + counts_train_val = counts_train + np.sum(Y_val, axis=0) + + missing_in_train = np.where(counts_train < min_train)[0] + missing_in_train_val = np.where(counts_train_val == 0)[0] + + if missing_in_train.size > 0: + # Small, actionable warning for debugging + preview = missing_in_train[:10].tolist() + print( + f"[warning] {missing_in_train.size} label(s) have <{min_train} occurrences in TRAIN. Example label indices: {preview}." + ) + + if missing_in_train_val.size > 0: + preview = missing_in_train_val[:10].tolist() + raise ValueError( + f"{missing_in_train_val.size} label(s) have 0 occurrences in TRAIN+VAL (indices example: {preview}). " + "Reduce test/val size, aggregate labels, or ensure these labels exist in the source DB." + ) + + +def evaluate_and_log(model, X_test, Y_test, best_params, cv_score, exp_name, extra_params=None): + Y_pred = model.predict(X_test) + precision = precision_score(Y_test, Y_pred, average="micro", zero_division=0) + recall = recall_score(Y_test, Y_pred, average="micro", zero_division=0) + f1 = f1_score(Y_test, Y_pred, average="micro", zero_division=0) + + mlflow.log_metrics( + { + "cv_best_f1_micro": cv_score, + "test_precision_micro": precision, + "test_recall_micro": recall, + "test_f1_micro": f1, + } + ) + + for k, v in best_params.items(): + mlflow.log_param(k, v) + if extra_params: + for k, v in extra_params.items(): + mlflow.log_param(k, v) + + os.makedirs(DATA_PATHS["models_dir"], exist_ok=True) + model_path = Path(DATA_PATHS["models_dir"]) / f"{exp_name}.pkl" + joblib.dump(model, model_path) + mlflow.log_artifact(str(model_path), artifact_path=f"model_{exp_name}") + print(f"Model saved to {model_path}") + print(f"{exp_name} completed and logged successfully.\n") + + +def run_grid_search(X, Y): + base_rf = RandomForestClassifier(random_state=TRAINING_CONFIG["random_state"], n_jobs=-1) + multi = MultiOutputClassifier(base_rf) + cv = KFold( + n_splits=TRAINING_CONFIG["cv_folds"], + shuffle=True, + random_state=TRAINING_CONFIG["random_state"], + ) + grid = GridSearchCV( + estimator=multi, + param_grid=MODEL_CONFIG["param_grid"], + scoring="f1_micro", + cv=cv, + n_jobs=-1, + verbose=2, + refit=True, + ) + return grid + + +def run_grid_search_lgb(X, Y): + base_lgb = lgb.LGBMClassifier( + random_state=TRAINING_CONFIG["random_state"], n_jobs=1, force_row_wise=True, verbose=-1 + ) + multi = MultiOutputClassifier(base_lgb, n_jobs=-1) + cv = KFold( + n_splits=TRAINING_CONFIG["cv_folds"], + shuffle=True, + random_state=TRAINING_CONFIG["random_state"], + ) + lgb_param_grid = { + "estimator__n_estimators": [50, 100, 200], + "estimator__max_depth": [3, 5, 7], + "estimator__learning_rate": [0.1], + "estimator__num_leaves": [15], + } + grid = GridSearchCV( + estimator=multi, + param_grid=lgb_param_grid, + scoring="f1_micro", + cv=cv, + n_jobs=-1, + verbose=2, + refit=True, + ) + return grid + + +# ===================================================== +# Experiments (merged) +# ===================================================== +def run_smote_experiment(X, Y, feature_type="tfidf"): + mlflow.set_experiment(MLFLOW_CONFIG["experiments"]["smote"]) + + # Split into train / val / test + X_train, X_val, X_test, Y_train, Y_val, Y_test = stratified_train_val_test_split( + X, + Y, + test_size=TRAINING_CONFIG.get("test_size", 0.2), + val_size=TRAINING_CONFIG.get("val_size", 0.1), + random_state=TRAINING_CONFIG["random_state"], + ) + # Check label coverage and fail early if labels are missing from train+val + _check_label_coverage(Y_train, Y_val) + + # Apply MLSMOTE (Multi-Label SMOTE) as per paper + # MLSMOTE handles multi-label classification natively by considering label correlations + print("Applying MLSMOTE (Multi-Label SMOTE) as per SkillScope paper...") + print(f" Original training set: {X_train.shape[0]} samples, {Y_train.shape[1]} labels") + + # Use local MLSMOTE implementation directly (function-based) + if _HAS_LOCAL_MLSMOTE: + try: + # Set random seed + if TRAINING_CONFIG["random_state"] is not None: + np.random.seed(TRAINING_CONFIG["random_state"]) + import random + + random.seed(TRAINING_CONFIG["random_state"]) + + # Convert to DataFrame (MLSMOTE function expects DataFrames) + X_train_df = pd.DataFrame(X_train) + Y_train_df = pd.DataFrame(Y_train) + + # Get minority instances + X_min, Y_min = get_minority_instace(X_train_df, Y_train_df) + + if len(X_min) == 0: + print("No minority instances found, using original dataset") + X_res, Y_res = X_train, Y_train + oversampling_method = "None (no minority instances)" + n_new = 0 + else: + # Calculate number of synthetic samples + label_counts = Y_train_df.sum(axis=0) + mean_count = int(label_counts.mean()) + min_count = int(label_counts.min()) + n_synthetic = max(100, int(mean_count - min_count)) + n_synthetic = min(n_synthetic, len(X_min) * 3) + + print( + f"Generating {n_synthetic} synthetic samples from {len(X_min)} minority instances" + ) + + # Apply MLSMOTE function directly + X_res_df, Y_res_df = mlsmote_function(X_min, Y_min, n_synthetic) + + # Convert back to numpy + X_res = X_res_df.values + Y_res = Y_res_df.values.astype(int) + + oversampling_method = "MLSMOTE (local implementation)" + n_new = len(X_res) - len(X_train) + print( + f"MLSMOTE completed: {n_new} synthetic samples generated. Total: {len(X_res)} samples" + ) + except Exception as e: + print(f"MLSMOTE failed ({e}); falling back to RandomOverSampler") + Y_train_str = ["".join(map(str, y)) for y in Y_train] + ros = RandomOverSampler(random_state=TRAINING_CONFIG["random_state"]) + X_res, Y_res_str = ros.fit_resample(X_train, Y_train_str) + Y_res = np.array([[int(c) for c in s] for s in Y_res_str]) + oversampling_method = "RandomOverSampler (MLSMOTE fallback)" + n_new = len(X_res) - len(X_train) + else: + print("Local MLSMOTE not available; falling back to RandomOverSampler") + Y_train_str = ["".join(map(str, y)) for y in Y_train] + ros = RandomOverSampler(random_state=TRAINING_CONFIG["random_state"]) + X_res, Y_res_str = ros.fit_resample(X_train, Y_train_str) + Y_res = np.array([[int(c) for c in s] for s in Y_res_str]) + oversampling_method = "RandomOverSampler (no MLSMOTE)" + n_new = len(X_res) - len(X_train) + + grid = run_grid_search(X_res, Y_res) + with mlflow.start_run(run_name="random_forest_with_smote"): + grid.fit(X_res, Y_res) + + # Refit final model on train + val (use original non-oversampled data for final fit) + best_params = grid.best_params_ + best_cv = grid.best_score_ + final_model = grid.best_estimator_ + X_comb = np.vstack([X_train, X_val]) if X_val.size else X_train + Y_comb = np.vstack([Y_train, Y_val]) if Y_val.size else Y_train + final_model.fit(X_comb, Y_comb) + + evaluate_and_log( + final_model, + X_test, + Y_test, + best_params, + best_cv, + f"random_forest_{feature_type}_gridsearch_smote", + { + "oversampling": oversampling_method, + "synthetic_samples": n_new, + "n_labels": Y_train.shape[1], + }, + ) + + +def run_ros_experiment(X, Y): + mlflow.set_experiment(MLFLOW_CONFIG["experiments"]["ros"]) + + # Split into train / val / test + X_train, X_val, X_test, Y_train, Y_val, Y_test = stratified_train_val_test_split( + X, + Y, + test_size=TRAINING_CONFIG.get("test_size", 0.2), + val_size=TRAINING_CONFIG.get("val_size", 0.1), + random_state=TRAINING_CONFIG["random_state"], + ) + + Y_train_str = ["".join(map(str, y)) for y in Y_train] + ros = RandomOverSampler(random_state=TRAINING_CONFIG["random_state"]) + X_res, Y_res_str = ros.fit_resample(X_train, Y_train_str) + + Y.shape[1] + Y_res = np.array([[int(c) for c in s] for s in Y_res_str]) + + grid = run_grid_search(X_res, Y_res) + with mlflow.start_run(run_name="random_forest_with_ros"): + grid.fit(X_res, Y_res) + + best_params = grid.best_params_ + best_cv = grid.best_score_ + final_model = grid.best_estimator_ + X_comb = np.vstack([X_train, X_val]) if X_val.size else X_train + Y_comb = np.vstack([Y_train, Y_val]) if Y_val.size else Y_train + final_model.fit(X_comb, Y_comb) + + evaluate_and_log( + final_model, + X_test, + Y_test, + best_params, + best_cv, + "random_forest_tfidf_gridsearch_ros", + {"oversampling": "RandomOverSampler"}, + ) + + +def run_adasyn_pca_experiment(X, Y): + mlflow.set_experiment(MLFLOW_CONFIG["experiments"]["adasyn_pca"]) + + # Split into train / val / test + X_train, X_val, X_test, Y_train, Y_val, Y_test = stratified_train_val_test_split( + X, + Y, + test_size=TRAINING_CONFIG.get("test_size", 0.2), + val_size=TRAINING_CONFIG.get("val_size", 0.1), + random_state=TRAINING_CONFIG["random_state"], + ) + + print("Applying PCA before ADASYN...") + pca = PCA( + n_components=PCA_CONFIG["variance_retained"], random_state=TRAINING_CONFIG["random_state"] + ) + X_train_pca = pca.fit_transform(X_train) + + adasyn = ADASYN( + random_state=TRAINING_CONFIG["random_state"], + n_neighbors=ADASYN_CONFIG["n_neighbors"], + sampling_strategy=ADASYN_CONFIG["sampling_strategy"], + ) + + valid_label_idx = next( + (i for i in range(Y_train.shape[1]) if len(np.unique(Y_train[:, i])) > 1), None + ) + + if valid_label_idx is None: + X_res, Y_res = X_train, Y_train + n_new = 0 + else: + X_res_pca, _ = adasyn.fit_resample(X_train_pca, Y_train[:, valid_label_idx]) + X_res = pca.inverse_transform(X_res_pca) + n_new = len(X_res) - len(X_train) + Y_res = np.vstack([Y_train, Y_train[np.random.randint(0, len(Y_train), n_new)]]) + + grid = run_grid_search(X_res, Y_res) + with mlflow.start_run(run_name="random_forest_with_adasyn_pca"): + grid.fit(X_res, Y_res) + + best_params = grid.best_params_ + best_cv = grid.best_score_ + final_model = grid.best_estimator_ + X_comb = np.vstack([X_train, X_val]) if X_val.size else X_train + Y_comb = np.vstack([Y_train, Y_val]) if Y_val.size else Y_train + final_model.fit(X_comb, Y_comb) + + evaluate_and_log( + final_model, + X_test, + Y_test, + best_params, + best_cv, + "random_forest_tfidf_gridsearch_adasyn_pca", + { + "oversampling": "ADASYN + PCA", + "pca_variance": PCA_CONFIG["variance_retained"], + "synthetic_samples": n_new, + }, + ) + pca_path = Path(DATA_PATHS["models_dir"]) / "pca_tfidf_adasyn.pkl" + joblib.dump(pca, pca_path) + mlflow.log_artifact(str(pca_path), artifact_path="model_adasyn_pca") + + +def run_lightgbm(X, Y): + mlflow.set_experiment(MLFLOW_CONFIG["experiments"].get("lightgbm", "LightGBM")) + + # Split into train / val / test + X_train, X_val, X_test, Y_train, Y_val, Y_test = stratified_train_val_test_split( + X, + Y, + test_size=TRAINING_CONFIG.get("test_size", 0.2), + val_size=TRAINING_CONFIG.get("val_size", 0.1), + random_state=TRAINING_CONFIG["random_state"], + ) + + print("\nTraining LightGBM with GridSearchCV...") + grid = run_grid_search_lgb(X_train, Y_train) + + with mlflow.start_run(run_name="lightgbm"): + grid.fit(X_train, Y_train) + + best_params = grid.best_params_ + best_cv = grid.best_score_ + final_model = grid.best_estimator_ + X_comb = np.vstack([X_train, X_val]) if X_val.size else X_train + Y_comb = np.vstack([Y_train, Y_val]) if Y_val.size else Y_train + final_model.fit(X_comb, Y_comb) + + evaluate_and_log( + final_model, + X_test, + Y_test, + best_params, + best_cv, + "lightgbm_tfidf_gridsearch", + {"oversampling": "None", "model": "LightGBM"}, + ) + + +def run_lightgbm_smote_experiment(X, Y): + mlflow.set_experiment(MLFLOW_CONFIG["experiments"].get("lightgbm_smote", "LightGBM_SMOTE")) + + # Split into train / val / test + X_train, X_val, X_test, Y_train, Y_val, Y_test = stratified_train_val_test_split( + X, + Y, + test_size=TRAINING_CONFIG.get("test_size", 0.2), + val_size=TRAINING_CONFIG.get("val_size", 0.1), + random_state=TRAINING_CONFIG["random_state"], + ) + + # Apply MLSMOTE (Multi-Label SMOTE) as per paper + print(" Applying MLSMOTE for LightGBM...") + print(f" Original training set: {X_train.shape[0]} samples, {Y_train.shape[1]} labels") + + # Use local MLSMOTE implementation directly (function-based) + if _HAS_LOCAL_MLSMOTE: + try: + # Set random seed + if TRAINING_CONFIG["random_state"] is not None: + np.random.seed(TRAINING_CONFIG["random_state"]) + import random + + random.seed(TRAINING_CONFIG["random_state"]) + + # Convert to DataFrame (MLSMOTE function expects DataFrames) + X_train_df = pd.DataFrame(X_train) + Y_train_df = pd.DataFrame(Y_train) + + # Get minority instances + X_min, Y_min = get_minority_instace(X_train_df, Y_train_df) + + if len(X_min) == 0: + print("No minority instances found, using original dataset") + X_res, Y_res = X_train, Y_train + oversampling_method = "None (no minority instances)" + n_new = 0 + else: + # Calculate number of synthetic samples + label_counts = Y_train_df.sum(axis=0) + mean_count = int(label_counts.mean()) + min_count = int(label_counts.min()) + n_synthetic = max(100, int(mean_count - min_count)) + n_synthetic = min(n_synthetic, len(X_min) * 3) + + print( + f"Generating {n_synthetic} synthetic samples from {len(X_min)} minority instances" + ) + + # Apply MLSMOTE function directly + X_res_df, Y_res_df = mlsmote_function(X_min, Y_min, n_synthetic) + + # Convert back to numpy + X_res = X_res_df.values + Y_res = Y_res_df.values.astype(int) + + oversampling_method = "MLSMOTE (local implementation)" + n_new = len(X_res) - len(X_train) + print( + f"MLSMOTE completed: {n_new} synthetic samples generated. Total: {len(X_res)} samples" + ) + except Exception as e: + print(f"MLSMOTE failed ({e}); falling back to RandomOverSampler") + Y_train_str = ["".join(map(str, y)) for y in Y_train] + ros = RandomOverSampler(random_state=TRAINING_CONFIG["random_state"]) + X_res, Y_res_str = ros.fit_resample(X_train, Y_train_str) + Y_res = np.array([[int(c) for c in s] for s in Y_res_str]) + oversampling_method = "RandomOverSampler (MLSMOTE fallback)" + n_new = len(X_res) - len(X_train) + else: + print(" Local MLSMOTE not available; falling back to RandomOverSampler") + Y_train_str = ["".join(map(str, y)) for y in Y_train] + ros = RandomOverSampler(random_state=TRAINING_CONFIG["random_state"]) + X_res, Y_res_str = ros.fit_resample(X_train, Y_train_str) + Y_res = np.array([[int(c) for c in s] for s in Y_res_str]) + oversampling_method = "RandomOverSampler (no MLSMOTE)" + n_new = len(X_res) - len(X_train) + + print(f"\n Training LightGBM with {oversampling_method} ({n_new} synthetic samples)...") + grid = run_grid_search_lgb(X_res, Y_res) + + with mlflow.start_run(run_name="lightgbm_with_smote"): + grid.fit(X_res, Y_res) + + best_params = grid.best_params_ + best_cv = grid.best_score_ + final_model = grid.best_estimator_ + X_comb = np.vstack([X_train, X_val]) if X_val.size else X_train + Y_comb = np.vstack([Y_train, Y_val]) if Y_val.size else Y_train + final_model.fit(X_comb, Y_comb) + + evaluate_and_log( + final_model, + X_test, + Y_test, + best_params, + best_cv, + "lightgbm_tfidf_gridsearch_smote", + { + "oversampling": oversampling_method, + "synthetic_samples": n_new, + "n_labels": Y_train.shape[1], + "model": "LightGBM", + }, + ) + + +# ===================================================== +# Baseline training (original train.py behavior) +# ===================================================== +def run_baseline_train(feature_type="tfidf", use_cleaned=True): + """Run baseline training with configurable feature type. + + Args: + feature_type: 'tfidf' or 'embedding' + use_cleaned: whether to use cleaned data + """ + mlflow.set_experiment( + MLFLOW_CONFIG.get("experiments", {}).get("baseline", "hopcroft_random_forest_baseline") + ) + + X, Y = load_data(feature_type=feature_type, use_cleaned=use_cleaned) + + # Use 80/20 split as per SkillScope paper (no validation set for baseline) + print(" Using 80/20 train/test split as per paper...") + X_train, X_test, Y_train, Y_test = stratified_train_test_split( + X, + Y, + test_size=TRAINING_CONFIG.get("test_size", 0.2), + random_state=TRAINING_CONFIG.get("random_state", 42), + ) + + # Remove labels that have 0 occurrences in training set (after split) + train_counts = np.sum(Y_train, axis=0).astype(int) + zero_in_train = np.where(train_counts == 0)[0] + + if zero_in_train.size > 0: + kept_idx = np.where(train_counts > 0)[0] + print( + f"[warning] Removing {zero_in_train.size} label(s) with 0 occurrences in TRAIN set. Example removed indices: {zero_in_train[:10].tolist()}" + ) + Y_train = Y_train[:, kept_idx] + Y_test = Y_test[:, kept_idx] + + # Save kept indices for inference + paths = get_feature_paths(feature_type=feature_type, use_cleaned=use_cleaned) + kept_indices_path = Path(paths["features"]).parent / "kept_label_indices.npy" + np.save(kept_indices_path, kept_idx) + print(f"Saved kept label indices to {kept_indices_path}") + + # Now check label coverage (should pass since we removed zero-occurrence labels) + _check_label_coverage(Y_train, np.empty((0, Y_train.shape[1]))) + + base_rf = RandomForestClassifier( + random_state=TRAINING_CONFIG.get("random_state", 42), n_jobs=-1 + ) + multi = MultiOutputClassifier(base_rf) + + # Use full param_grid from MODEL_CONFIG for optimal results as per paper + param_grid = MODEL_CONFIG.get( + "param_grid", + { + "estimator__n_estimators": [50, 100, 200], + "estimator__max_depth": [10, 20, 30], + "estimator__min_samples_split": [2, 5], + }, + ) + + cv = KFold( + n_splits=TRAINING_CONFIG.get("cv_folds", 5), + shuffle=True, + random_state=TRAINING_CONFIG.get("random_state", 42), + ) + + print( + f" GridSearch with {cv.n_splits} folds and {len(param_grid['estimator__n_estimators']) * len(param_grid['estimator__max_depth']) * len(param_grid['estimator__min_samples_split'])} combinations..." + ) + + grid = GridSearchCV( + estimator=multi, + param_grid=param_grid, + scoring="f1_micro", + cv=cv, + n_jobs=-1, + verbose=2, + refit=True, + ) + + with mlflow.start_run(run_name="random_forest_tfidf_gridsearch"): + grid.fit(X_train, Y_train) + + best = grid.best_estimator_ + best_params = grid.best_params_ + best_cv_score = grid.best_score_ + + # No need to refit on combined train+val since we don't have a val set + # Model is already fitted on full training data + + Y_pred_test = best.predict(X_test) + + precision = precision_score(Y_test, Y_pred_test, average="micro", zero_division=0) + recall = recall_score(Y_test, Y_pred_test, average="micro", zero_division=0) + f1 = f1_score(Y_test, Y_pred_test, average="micro", zero_division=0) + + mlflow.log_param("model_type", "RandomForest + MultiOutput") + for k, v in best_params.items(): + mlflow.log_param(k, v) + mlflow.log_metric("cv_best_f1_micro", best_cv_score) + + mlflow.log_metric("test_precision_micro", precision) + mlflow.log_metric("test_recall_micro", recall) + mlflow.log_metric("test_f1_micro", f1) + mlflow.log_param("feature_type", feature_type) + mlflow.log_param("use_cleaned", use_cleaned) + + print("\n=== Training Results ===") + print(f"Test Precision (Micro): {precision:.4f}") + print(f"Test Recall (Micro): {recall:.4f}") + print(f"Test F1 Score (Micro): {f1:.4f}") + print("========================\n") + + paths = get_feature_paths(feature_type=feature_type, use_cleaned=use_cleaned) + os.makedirs(paths["models_dir"], exist_ok=True) + + model_path = Path(paths["models_dir"]) / f"random_forest_{feature_type}_gridsearch.pkl" + joblib.dump(best, model_path) + + np.save(Path(paths["features"]).parent / "X_test.npy", X_test) + np.save(Path(paths["labels"]).parent / "Y_test.npy", Y_test) + + mlflow.sklearn.log_model(best, "model") + + print("Grid search training completed and logged successfully.") + + +# ===================================================== +# Inference utility (merged from predict.py) +# ===================================================== +def run_inference(model_path: str = None): + mlflow.set_experiment( + MLFLOW_CONFIG.get("experiments", {}).get("inference", "hopcroft_random_forest_inference") + ) + + if model_path is None: + model_path = Path(DATA_PATHS["models_dir"]) / "random_forest_tfidf_gridsearch.pkl" + else: + model_path = Path(model_path) + + model = joblib.load(str(model_path)) + + X_test = np.load(Path(DATA_PATHS["features"]).parent / "X_test.npy") + Y_test = np.load(Path(DATA_PATHS["labels"]).parent / "Y_test.npy") + + with mlflow.start_run(run_name="random_forest_tfidf_inference"): + Y_pred = model.predict(X_test) + + precision = precision_score(Y_test, Y_pred, average="micro", zero_division=0) + recall = recall_score(Y_test, Y_pred, average="micro", zero_division=0) + f1 = f1_score(Y_test, Y_pred, average="micro", zero_division=0) + + mlflow.log_metric("test_precision_micro", precision) + mlflow.log_metric("test_recall_micro", recall) + mlflow.log_metric("test_f1_micro", f1) + + print(f"Inference completed β€” Precision: {precision:.4f}, Recall: {recall:.4f}, F1: {f1:.4f}") + + +def _parse_args(): + p = argparse.ArgumentParser(description="Unified training & experiments script") + p.add_argument( + "action", + choices=[ + "baseline", + "smote", + "ros", + "adasyn_pca", + "lightgbm", + "lightgbm_smote", + "predict", + ], + help="Action to run", + ) + p.add_argument("--model-path", help="Custom model path for inference") + return p.parse_args() + + +if __name__ == "__main__": + args = _parse_args() + + # Baseline has its own load_data logic (removes rare labels after split) + if args.action == "baseline": + run_baseline_train(feature_type="tfidf", use_cleaned=True) + else: + # Other experiments use the original load_data() logic + X, Y = load_data(feature_type="tfidf", use_cleaned=True) + + if args.action == "smote": + run_smote_experiment(X, Y) + elif args.action == "ros": + run_ros_experiment(X, Y) + elif args.action == "adasyn_pca": + run_adasyn_pca_experiment(X, Y) + elif args.action == "lightgbm": + run_lightgbm(X, Y) + elif args.action == "lightgbm_smote": + run_lightgbm_smote_experiment(X, Y) + elif args.action == "predict": + run_inference(args.model_path) diff --git a/hopcroft_skill_classification_tool_competition/streamlit_app.py b/hopcroft_skill_classification_tool_competition/streamlit_app.py new file mode 100644 index 0000000000000000000000000000000000000000..b6f9bc88425ea12a3b56f57f7a001c176d0dd389 --- /dev/null +++ b/hopcroft_skill_classification_tool_competition/streamlit_app.py @@ -0,0 +1,322 @@ +import os +from typing import Dict, List + +import pandas as pd +import requests +import streamlit as st + +API_BASE_URL = os.getenv("API_BASE_URL", "http://localhost:8000") + +# Page config +st.set_page_config( + page_title="GitHub Skill Classifier", layout="wide", initial_sidebar_state="expanded" +) + +st.markdown( + """ + +""", + unsafe_allow_html=True, +) + + +def check_api_health() -> bool: + """Check if the API is running and healthy.""" + try: + response = requests.get(f"{API_BASE_URL}/health", timeout=2) + return response.status_code == 200 + except Exception: + return False + + +def predict_skills( + issue_text: str, issue_description: str = None, repo_name: str = None, pr_number: int = None +) -> Dict: + """Call the prediction API.""" + payload = {"issue_text": issue_text} + + if issue_description: + payload["issue_description"] = issue_description + if repo_name: + payload["repo_name"] = repo_name + if pr_number: + payload["pr_number"] = pr_number + + try: + response = requests.post(f"{API_BASE_URL}/predict", json=payload, timeout=30) + response.raise_for_status() + return response.json() + except requests.exceptions.RequestException as e: + st.error(f"API Error: {str(e)}") + return None + + +def display_predictions(predictions: List[Dict], threshold: float = 0.5): + """Display predictions with visual formatting.""" + + # Filter by threshold + filtered = [p for p in predictions if p["confidence"] >= threshold] + + if not filtered: + st.warning(f"No predictions above confidence threshold {threshold:.2f}") + return + + st.success(f"Found {len(filtered)} skills above threshold {threshold:.2f}") + + # Create DataFrame for table view + df = pd.DataFrame(filtered) + df["confidence"] = df["confidence"].apply(lambda x: f"{x:.2%}") + + col1, col2 = st.columns([2, 1]) + + with col1: + st.subheader("Predictions Table") + st.dataframe( + df, + use_container_width=True, + hide_index=True, + column_config={ + "skill_name": st.column_config.TextColumn("Skill", width="large"), + "confidence": st.column_config.TextColumn("Confidence", width="medium"), + }, + ) + + with col2: + st.subheader("Top 5 Skills") + for i, pred in enumerate(filtered[:5], 1): + confidence = pred["confidence"] + + if confidence >= 0.8: + conf_class = "confidence-high" + elif confidence >= 0.5: + conf_class = "confidence-medium" + else: + conf_class = "confidence-low" + + st.markdown( + f""" +
+ #{i} {pred["skill_name"]}
+ {confidence:.2%} +
+ """, + unsafe_allow_html=True, + ) + + +def main(): + """Main Streamlit app.""" + + if "example_text" not in st.session_state: + st.session_state.example_text = "" + + # Header + st.markdown('

GitHub Skill Classifier

', unsafe_allow_html=True) + + st.markdown(""" + This tool uses machine learning to predict the skills required for GitHub issues and pull requests. + Enter the issue text below to get started! + """) + + # Sidebar + with st.sidebar: + st.header("Settings") + + # API Status + st.subheader("API Status") + if check_api_health(): + st.success(" API is running") + else: + st.error(" API is not available") + st.info(f"Make sure FastAPI is running at {API_BASE_URL}") + st.code("fastapi dev hopcroft_skill_classification_tool_competition/main.py") + + # Confidence threshold + threshold = st.slider( + "Confidence Threshold", + min_value=0.0, + max_value=1.0, + value=0.5, + step=0.05, + help="Only show predictions above this confidence level", + ) + + # Model info + st.subheader("Model Info") + try: + health = requests.get(f"{API_BASE_URL}/health", timeout=2).json() + st.metric("Version", health.get("version", "N/A")) + st.metric("Model Loaded", "" if health.get("model_loaded") else "") + except Exception: + st.info("API not available") + + # Main + st.header("Input") + + # Tabs for different input modes + tab1, tab2, tab3 = st.tabs(["Quick Input", "Detailed Input", "Examples"]) + + with tab1: + issue_text = st.text_area( + "Issue/PR Text", + height=150, + placeholder="Enter the issue or pull request text here...", + help="Required: The main text of the GitHub issue or PR", + value=st.session_state.example_text, + ) + + if st.button("Predict Skills", type="primary", use_container_width=True): + if not issue_text.strip(): + st.error("Please enter some text!") + else: + st.session_state.example_text = "" + with st.spinner("Analyzing issue..."): + result = predict_skills(issue_text) + + if result: + st.header("Results") + + # Metadata + col1, col2, col3 = st.columns(3) + with col1: + st.metric("Total Predictions", result.get("num_predictions", 0)) + with col2: + st.metric( + "Processing Time", f"{result.get('processing_time_ms', 0):.2f} ms" + ) + with col3: + st.metric("Model Version", result.get("model_version", "N/A")) + + # Predictions + st.divider() + display_predictions(result.get("predictions", []), threshold) + + # Raw JSON + with st.expander("πŸ” View Raw Response"): + st.json(result) + + with tab2: + col1, col2 = st.columns(2) + + with col1: + issue_text_detailed = st.text_area( + "Issue Title/Text*", + height=100, + placeholder="e.g., Fix authentication bug in login module", + key="issue_text_detailed", + ) + + issue_description = st.text_area( + "Issue Description", + height=100, + placeholder="Optional: Detailed description of the issue", + key="issue_description", + ) + + with col2: + repo_name = st.text_input( + "Repository Name", + placeholder="e.g., owner/repository", + help="Optional: GitHub repository name", + ) + + pr_number = st.number_input( + "PR Number", + min_value=0, + value=0, + help="Optional: Pull request number (0 = not a PR)", + ) + + if st.button("Predict Skills (Detailed)", type="primary", use_container_width=True): + if not issue_text_detailed.strip(): + st.error("Issue text is required!") + else: + with st.spinner("Analyzing issue..."): + result = predict_skills( + issue_text_detailed, + issue_description if issue_description else None, + repo_name if repo_name else None, + pr_number if pr_number > 0 else None, + ) + + if result: + st.header("Results") + + # Metadata + col1, col2, col3 = st.columns(3) + with col1: + st.metric("Total Predictions", result.get("num_predictions", 0)) + with col2: + st.metric( + "Processing Time", f"{result.get('processing_time_ms', 0):.2f} ms" + ) + with col3: + st.metric("Model Version", result.get("model_version", "N/A")) + + st.divider() + display_predictions(result.get("predictions", []), threshold) + + with st.expander("πŸ” View Raw Response"): + st.json(result) + + with tab3: + st.markdown("### Example Issues") + + examples = [ + { + "title": "Authentication Bug", + "text": "Fix authentication bug in login module. Users cannot login with OAuth providers.", + }, + { + "title": "Machine Learning Feature", + "text": "Implement transfer learning with transformers for text classification using PyTorch and TensorFlow.", + }, + { + "title": "Database Issue", + "text": "Fix database connection pooling issue causing memory leaks in production environment.", + }, + { + "title": "UI Enhancement", + "text": "Add responsive design support for mobile devices with CSS media queries and flexbox layout.", + }, + ] + + for i, example in enumerate(examples): + if st.button(example["title"], use_container_width=True, key=f"example_btn_{i}"): + st.session_state.example_text = example["text"] + st.rerun() + + if st.session_state.example_text: + st.success(" Example loaded! Switch to 'Quick Input' tab to use it.") + with st.expander("Preview"): + st.code(st.session_state.example_text) + + +if __name__ == "__main__": + main() diff --git a/hopcroft_skill_classification_tool_competition/threshold_optimization.py b/hopcroft_skill_classification_tool_competition/threshold_optimization.py new file mode 100644 index 0000000000000000000000000000000000000000..972dbe2677c644b015763d0d64b9e8e03f631df6 --- /dev/null +++ b/hopcroft_skill_classification_tool_competition/threshold_optimization.py @@ -0,0 +1,295 @@ +""" +Threshold Optimization for Multi-Label Classification + +This module provides functions to optimize decision thresholds for multi-label +classification tasks to maximize F1-score (or other metrics). + +In multi-label classification, the default threshold of 0.5 for converting +probabilities to binary predictions is often suboptimal, especially for +imbalanced classes. This module finds optimal thresholds per-class or globally. + +Designed to work with Random Forest (baseline and improved models). + +Usage: + from threshold_optimization import optimize_thresholds, apply_thresholds + from sklearn.ensemble import RandomForestClassifier + + # Train Random Forest + model = RandomForestClassifier(n_estimators=100) + model.fit(X_train, y_train) + + # Get probability predictions + y_proba = model.predict_proba(X_val) + + # Find optimal thresholds on validation set + thresholds = optimize_thresholds(y_val, y_proba, method='per_class') + + # Apply thresholds to test set + y_pred = apply_thresholds(model.predict_proba(X_test), thresholds) +""" + +from typing import Dict, Tuple, Union +import warnings + +import numpy as np +from sklearn.metrics import f1_score + + +def optimize_thresholds( + y_true: np.ndarray, + y_proba: np.ndarray, + method: str = "per_class", + metric: str = "f1_weighted", + search_range: Tuple[float, float] = (0.1, 0.9), + n_steps: int = 50, +) -> Union[float, np.ndarray]: + """ + Optimize decision thresholds to maximize a given metric. + + This function searches for optimal thresholds that convert probability + predictions to binary predictions (0/1) in a way that maximizes the + specified metric (default: weighted F1-score). + + Args: + y_true: True binary labels, shape (n_samples, n_labels) + y_proba: Predicted probabilities, shape (n_samples, n_labels) + method: Threshold optimization method: + - 'global': Single threshold for all classes + - 'per_class': One threshold per class (default, recommended) + metric: Metric to optimize ('f1_weighted', 'f1_macro', 'f1_micro') + search_range: Range of thresholds to search (min, max) + n_steps: Number of threshold values to try + + Returns: + - If method='global': Single float threshold + - If method='per_class': Array of thresholds, one per class + + Example: + >>> y_true = np.array([[1, 0, 1], [0, 1, 0], [1, 1, 0]]) + >>> y_proba = np.array([[0.9, 0.3, 0.7], [0.2, 0.8, 0.4], [0.85, 0.6, 0.3]]) + >>> thresholds = optimize_thresholds(y_true, y_proba, method='per_class') + >>> print(thresholds) # Array of 3 thresholds, one per class + """ + if y_true.shape != y_proba.shape: + raise ValueError(f"Shape mismatch: y_true {y_true.shape} vs y_proba {y_proba.shape}") + + if method == "global": + return _optimize_global_threshold(y_true, y_proba, metric, search_range, n_steps) + elif method == "per_class": + return _optimize_per_class_thresholds(y_true, y_proba, metric, search_range, n_steps) + else: + raise ValueError(f"Invalid method: {method}. Must be 'global' or 'per_class'") + + +def _optimize_global_threshold( + y_true: np.ndarray, + y_proba: np.ndarray, + metric: str, + search_range: Tuple[float, float], + n_steps: int, +) -> float: + """ + Find single optimal threshold for all classes. + + This approach is faster but less flexible than per-class optimization. + Useful when classes have similar distributions. + """ + thresholds_to_try = np.linspace(search_range[0], search_range[1], n_steps) + best_threshold = 0.5 + best_score = -np.inf + + for threshold in thresholds_to_try: + y_pred = (y_proba >= threshold).astype(int) + score = _compute_score(y_true, y_pred, metric) + + if score > best_score: + best_score = score + best_threshold = threshold + + print(f"Optimal global threshold: {best_threshold:.3f} (score: {best_score:.4f})") + return best_threshold + + +def _optimize_per_class_thresholds( + y_true: np.ndarray, + y_proba: np.ndarray, + metric: str, + search_range: Tuple[float, float], + n_steps: int, +) -> np.ndarray: + """ + Find optimal threshold for each class independently. + + This approach is more flexible and typically yields better results + for imbalanced multi-label problems, but is slower. + """ + n_classes = y_true.shape[1] + optimal_thresholds = np.zeros(n_classes) + thresholds_to_try = np.linspace(search_range[0], search_range[1], n_steps) + + print(f"Optimizing thresholds for {n_classes} classes...") + + for class_idx in range(n_classes): + y_true_class = y_true[:, class_idx] + y_proba_class = y_proba[:, class_idx] + + # Skip classes with no positive samples + if y_true_class.sum() == 0: + optimal_thresholds[class_idx] = 0.5 + warnings.warn( + f"Class {class_idx} has no positive samples, using default threshold 0.5" + ) + continue + + best_threshold = 0.5 + best_score = -np.inf + + for threshold in thresholds_to_try: + y_pred_class = (y_proba_class >= threshold).astype(int) + + # Compute binary F1 for this class + try: + score = f1_score(y_true_class, y_pred_class, average="binary", zero_division=0) + except Exception: + continue + + if score > best_score: + best_score = score + best_threshold = threshold + + optimal_thresholds[class_idx] = best_threshold + + print( + f"Threshold statistics: min={optimal_thresholds.min():.3f}, " + f"max={optimal_thresholds.max():.3f}, mean={optimal_thresholds.mean():.3f}" + ) + + return optimal_thresholds + + +def _compute_score(y_true: np.ndarray, y_pred: np.ndarray, metric: str) -> float: + """Compute the specified metric.""" + if metric == "f1_weighted": + return f1_score(y_true, y_pred, average="weighted", zero_division=0) + elif metric == "f1_macro": + return f1_score(y_true, y_pred, average="macro", zero_division=0) + elif metric == "f1_micro": + return f1_score(y_true, y_pred, average="micro", zero_division=0) + else: + raise ValueError(f"Unsupported metric: {metric}") + + +def apply_thresholds(y_proba: np.ndarray, thresholds: Union[float, np.ndarray]) -> np.ndarray: + """ + Apply thresholds to probability predictions to get binary predictions. + + Args: + y_proba: Predicted probabilities, shape (n_samples, n_labels) + thresholds: Threshold(s) to apply: + - Single float: same threshold for all classes + - Array: one threshold per class + + Returns: + Binary predictions, shape (n_samples, n_labels) + + Example: + >>> y_proba = np.array([[0.9, 0.3, 0.7], [0.2, 0.8, 0.4]]) + >>> thresholds = np.array([0.5, 0.4, 0.6]) + >>> y_pred = apply_thresholds(y_proba, thresholds) + >>> print(y_pred) + [[1 0 1] + [0 1 0]] + """ + if isinstance(thresholds, float): + # Global threshold + return (y_proba >= thresholds).astype(int) + else: + # Per-class thresholds + if len(thresholds) != y_proba.shape[1]: + raise ValueError( + f"Number of thresholds ({len(thresholds)}) must match " + f"number of classes ({y_proba.shape[1]})" + ) + + # Broadcasting: compare each column with its threshold + return (y_proba >= thresholds[np.newaxis, :]).astype(int) + + +def evaluate_with_thresholds( + model, + X_val: np.ndarray, + y_val: np.ndarray, + X_test: np.ndarray, + y_test: np.ndarray, + method: str = "per_class", +) -> Dict: + """ + Complete workflow: optimize thresholds on validation set and evaluate on test set. + + This function encapsulates the entire threshold optimization pipeline: + 1. Get probability predictions on validation set + 2. Optimize thresholds using validation data + 3. Apply optimized thresholds to test set + 4. Compare with default threshold (0.5) + + Args: + model: Trained model with predict_proba method + X_val: Validation features + y_val: Validation labels (binary) + X_test: Test features + y_test: Test labels (binary) + method: 'global' or 'per_class' + + Returns: + Dictionary with results: + - 'thresholds': Optimized thresholds + - 'f1_default': F1-score with default threshold (0.5) + - 'f1_optimized': F1-score with optimized thresholds + - 'improvement': Absolute improvement in F1-score + + Example: + >>> results = evaluate_with_thresholds(model, X_val, y_val, X_test, y_test) + >>> print(f"F1 improvement: {results['improvement']:.4f}") + """ + # Get probability predictions + print("Getting probability predictions on validation set...") + y_val_proba = model.predict_proba(X_val) + + # Handle MultiOutputClassifier (returns list of arrays) + if isinstance(y_val_proba, list): + y_val_proba = np.column_stack([proba[:, 1] for proba in y_val_proba]) + + # Optimize thresholds + print(f"Optimizing thresholds ({method})...") + thresholds = optimize_thresholds(y_val, y_val_proba, method=method) + + # Evaluate on test set + print("Evaluating on test set...") + y_test_proba = model.predict_proba(X_test) + + # Handle MultiOutputClassifier + if isinstance(y_test_proba, list): + y_test_proba = np.column_stack([proba[:, 1] for proba in y_test_proba]) + + # Default predictions (threshold=0.5) + y_test_pred_default = (y_test_proba >= 0.5).astype(int) + f1_default = f1_score(y_test, y_test_pred_default, average="weighted", zero_division=0) + + # Optimized predictions + y_test_pred_optimized = apply_thresholds(y_test_proba, thresholds) + f1_optimized = f1_score(y_test, y_test_pred_optimized, average="weighted", zero_division=0) + + improvement = f1_optimized - f1_default + + print("\nResults:") + print(f" F1-score (default threshold=0.5): {f1_default:.4f}") + print(f" F1-score (optimized thresholds): {f1_optimized:.4f}") + print(f" Improvement: {improvement:+.4f} ({improvement / f1_default * 100:+.2f}%)") + + return { + "thresholds": thresholds, + "f1_default": f1_default, + "f1_optimized": f1_optimized, + "improvement": improvement, + "y_pred_optimized": y_test_pred_optimized, + } diff --git a/models/.gitignore b/models/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..76350fc0de64a2bfa24563dcc34a6ed7723924c4 --- /dev/null +++ b/models/.gitignore @@ -0,0 +1,11 @@ +/random_forest_tfidf_gridsearch.pkl +/random_forest_tfidf_gridsearch_adasyn_pca.pkl +/random_forest_tfidf_gridsearch_ros.pkl +/random_forest_tfidf_gridsearch_smote.pkl +/lightgbm_tfidf_gridsearch.pkl +/lightgbm_tfidf_gridsearch_smote.pkl +/pca_tfidf_adasyn.pkl +/label_names.pkl +/tfidf_vectorizer.pkl +/random_forest_embedding_gridsearch.pkl +/random_forest_embedding_gridsearch_smote.pkl diff --git a/models/.gitkeep b/models/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/README.md b/models/README.md new file mode 100644 index 0000000000000000000000000000000000000000..71791c9b10d96ed2d95b3e54eb6813b33ea7e78d --- /dev/null +++ b/models/README.md @@ -0,0 +1,206 @@ +--- +language: en +license: mit +tags: +- multi-label-classification +- tfidf +- embeddings +- random-forest +- oversampling +- mlsmote +- software-engineering +datasets: +- NLBSE/SkillCompetition +model-index: +- name: random_forest_tfidf_gridsearch + results: + - status: success + metrics: + cv_best_f1_micro: 0.595038375202279 + test_precision_micro: 0.690371373744215 + test_recall_micro: 0.5287455692919513 + test_f1_micro: 0.5988446098110252 + params: + estimator__max_depth: '10' + estimator__min_samples_split: '2' + estimator__n_estimators: '200' + feature_type: embedding + model_type: RandomForest + MultiOutput + use_cleaned: 'True' + oversampling: 'False' + dvc: + path: random_forest_tfidf_gridsearch.pkl +- name: random_forest_tfidf_gridsearch_smote + results: + - status: success + metrics: + cv_best_f1_micro: 0.59092598557871 + test_precision_micro: 0.6923300238053766 + test_recall_micro: 0.5154318319356791 + test_f1_micro: 0.59092598557871 + params: + feature_type: tfidf + oversampling: 'MLSMOTE (RandomOverSampler fallback)' + dvc: + path: random_forest_tfidf_gridsearch_smote.pkl +- name: random_forest_embedding_gridsearch + results: + - status: success + metrics: + cv_best_f1_micro: 0.6012826418169578 + test_precision_micro: 0.703060266254212 + test_recall_micro: 0.5252460640075934 + test_f1_micro: 0.6012826418169578 + params: + feature_type: embedding + oversampling: 'False' + dvc: + path: random_forest_embedding_gridsearch.pkl +- name: random_forest_embedding_gridsearch_smote + results: + - status: success + metrics: + cv_best_f1_micro: 0.5962084744755453 + test_precision_micro: 0.7031004709576139 + test_recall_micro: 0.5175288364319172 + test_f1_micro: 0.5962084744755453 + params: + feature_type: embedding + oversampling: 'MLSMOTE (RandomOverSampler fallback)' + dvc: + path: random_forest_embedding_gridsearch_smote.pkl +--- + + +Model cards for committed models + +Overview +- This file documents four trained model artifacts available in the repository: two TF‑IDF based Random Forest models (baseline and with oversampling) and two embedding‑based Random Forest models (baseline and with oversampling). +- For dataset provenance and preprocessing details see `data/README.md`. + +1) random_forest_tfidf_gridsearch + +Model details +- Name: `random_forest_tfidf_gridsearch` +- Organization: Hopcroft (se4ai2526-uniba) +- Model type: `RandomForestClassifier` wrapped in `MultiOutputClassifier` for multi-label outputs +- Branch: `Milestone-4` + +Intended use +- Suitable for research and benchmarking on multi-label skill prediction for GitHub PRs/issues. Not intended for automated high‑stakes decisions or profiling individuals without further validation. + +Training data and preprocessing +- Dataset: Processed SkillScope Dataset (NLBSE/SkillCompetition) as prepared for this project. +- Features: TF‑IDF (unigrams and bigrams), up to `MAX_TFIDF_FEATURES=5000`. +- Feature and label files are referenced via `get_feature_paths(feature_type='tfidf', use_cleaned=True)` in `config.py`. + +Evaluation +- Reported metrics include micro‑precision, micro‑recall and micro‑F1 on a held‑out test split. +- Protocol: 80/20 multilabel‑stratified split; hyperparameters selected via 5‑fold cross‑validation optimizing `f1_micro`. +- MLflow run: `random_forest_tfidf_gridsearch` (see `hopcroft_skill_classification_tool_competition/config.py`). + +Limitations and recommendations +- Trained on Java repositories; generalization to other languages is not ensured. +- Label imbalance affects rare labels; apply per‑label thresholds or further sampling strategies if required. + +Usage +- Artifact path: `models/random_forest_tfidf_gridsearch.pkl`. +- Example: + ```python + import joblib + model = joblib.load('models/random_forest_tfidf_gridsearch.pkl') + y = model.predict(X_tfidf) + ``` + +2) random_forest_tfidf_gridsearch_smote + +Model details +- Name: `random_forest_tfidf_gridsearch_smote` +- Model type: `RandomForestClassifier` inside `MultiOutputClassifier` trained with multi‑label oversampling + +Intended use +- Intended to improve recall for under‑represented labels by applying MLSMOTE (or RandomOverSampler fallback) during training. + +Training and preprocessing +- Features: TF‑IDF (same configuration as the baseline). +- Oversampling: local MLSMOTE implementation when available; otherwise `RandomOverSampler`. Oversampling metadata (method and synthetic sample counts) are logged to MLflow. +- Training script: `hopcroft_skill_classification_tool_competition/modeling/train.py` (action `smote`). + +Evaluation +- MLflow run: `random_forest_tfidf_gridsearch_smote`. + +Limitations and recommendations +- Synthetic samples may introduce distributional artifacts; validate synthetic examples and per‑label metrics before deployment. + +Usage +- Artifact path: `models/random_forest_tfidf_gridsearch_smote.pkl`. + +3) random_forest_embedding_gridsearch + +Model details +- Name: `random_forest_embedding_gridsearch` +- Features: sentence embeddings produced by `all-MiniLM-L6-v2` (see `config.EMBEDDING_MODEL_NAME`). + +Intended use +- Uses semantic embeddings to capture contextual information from PR text; suitable for research and prototyping. + +Training and preprocessing +- Embeddings generated and stored via `get_feature_paths(feature_type='embedding', use_cleaned=True)`. +- Training script: see `hopcroft_skill_classification_tool_competition/modeling/train.py`. + +Evaluation +- MLflow run: `random_forest_embedding_gridsearch`. + +Limitations and recommendations +- Embeddings encode dataset biases; verify performance when transferring to other repositories or languages. + +Usage +- Artifact path: `models/random_forest_embedding_gridsearch.pkl`. +- Example: + ```python + model.predict(X_embeddings) + ``` + +4) random_forest_embedding_gridsearch_smote + +Model details +- Name: `random_forest_embedding_gridsearch_smote` +- Combines embedding features with multi‑label oversampling to address rare labels. + +Training and evaluation +- Oversampling: MLSMOTE preferred; `RandomOverSampler` fallback if MLSMOTE is unavailable. +- MLflow run: `random_forest_embedding_gridsearch_smote`. + +Limitations and recommendations +- Review synthetic examples and re‑evaluate on target data prior to deployment. + +Usage +- Artifact path: `models/random_forest_embedding_gridsearch_smote.pkl`. + +Publishing guidance for Hugging Face Hub +- The YAML front‑matter enables rendering on the Hugging Face Hub. Recommended repository contents for publishing: + - `README.md` (this file) + - model artifact(s) (`*.pkl`) + - vectorizer(s) and label map (e.g. `tfidf_vectorizer.pkl`, `label_names.pkl`) + - a minimal inference example or notebook + + Evaluation Data and Protocol + - Evaluation split: an 80/20 multilabel‑stratified train/test split was used for final evaluation. + - Cross-validation: hyperparameters were selected via 5‑fold cross‑validation optimizing `f1_micro`. + - Test metrics reported: micro precision, micro recall, micro F1 (reported in the YAML `model-index` for each model). + + Quantitative Analyses + - Reported unitary results: micro‑precision, micro‑recall and micro‑F1 on the held‑out test split for each model. + - Where available, `cv_best_f1_micro` is the best cross‑validation f1_micro recorded during training; when a CV value was not present in tracking, the test F1 is used as a proxy and noted in the README. + - Notes on comparability: TF‑IDF and embedding models are evaluated on the same held‑out splits (features differ); reported metrics are comparable for broad benchmarking but not for per‑label fairness analyses. + + How Metrics Were Computed + - Metrics were computed using scikit‑learn's `precision_score`, `recall_score`, and `f1_score` with `average='micro'` and `zero_division=0` on the held‑out test labels and model predictions. + - Test feature and label files used are available under `data/processed/tfidf/` and `data/processed/embedding/` (paths referenced from `hopcroft_skill_classification_tool_competition.config.get_feature_paths`). + + Ethical Considerations and Caveats + - The dataset contains examples from Java repositories; model generalization to other languages or domains is not guaranteed. + - Label imbalance is present; oversampling (MLSMOTE or RandomOverSampler fallback) was used in two variants to improve recall for rare labels β€” inspect per‑label metrics before deploying. + - The models and README are intended for research and benchmarking. They are not validated for safety‑critical or high‑stakes automated decisioning. + + diff --git a/models/kept_label_indices.npy b/models/kept_label_indices.npy new file mode 100644 index 0000000000000000000000000000000000000000..4f7d6bd115af16caab5f9e62530976a3e1926add Binary files /dev/null and b/models/kept_label_indices.npy differ diff --git a/models/label_names.pkl.dvc b/models/label_names.pkl.dvc new file mode 100644 index 0000000000000000000000000000000000000000..3969903e8dc76a0082da09341cccc7732a47c868 --- /dev/null +++ b/models/label_names.pkl.dvc @@ -0,0 +1,5 @@ +outs: +- md5: bd94d38e415f8dc2aaee3f60b6776483 + size: 6708 + hash: md5 + path: label_names.pkl diff --git a/models/random_forest_embedding_gridsearch.pkl.dvc b/models/random_forest_embedding_gridsearch.pkl.dvc new file mode 100644 index 0000000000000000000000000000000000000000..5c780520bbcf79589fbbea1397294cf7d5274bbb --- /dev/null +++ b/models/random_forest_embedding_gridsearch.pkl.dvc @@ -0,0 +1,5 @@ +outs: +- md5: e1c1c0290e0c6036ee798275fdbad61c + size: 346568353 + hash: md5 + path: random_forest_embedding_gridsearch.pkl diff --git a/models/random_forest_embedding_gridsearch_smote.pkl.dvc b/models/random_forest_embedding_gridsearch_smote.pkl.dvc new file mode 100644 index 0000000000000000000000000000000000000000..9badaa3555738f4808aaae3de173e17135b4437a --- /dev/null +++ b/models/random_forest_embedding_gridsearch_smote.pkl.dvc @@ -0,0 +1,5 @@ +outs: +- md5: 4d5379a3847341f8de423778b94537b0 + size: 1035016993 + hash: md5 + path: random_forest_embedding_gridsearch_smote.pkl diff --git a/models/random_forest_tfidf_gridsearch.pkl.dvc b/models/random_forest_tfidf_gridsearch.pkl.dvc new file mode 100644 index 0000000000000000000000000000000000000000..2dd2c7cb88eb3b0f1e8545ed38ca634985141df7 --- /dev/null +++ b/models/random_forest_tfidf_gridsearch.pkl.dvc @@ -0,0 +1,5 @@ +outs: +- md5: 39165e064d60e4bd0688e6c7aa94258c + size: 137359137 + hash: md5 + path: random_forest_tfidf_gridsearch.pkl diff --git a/models/random_forest_tfidf_gridsearch_adasyn_pca.pkl.dvc b/models/random_forest_tfidf_gridsearch_adasyn_pca.pkl.dvc new file mode 100644 index 0000000000000000000000000000000000000000..377d9ce96cd34bc120f05f83d755554b97305517 --- /dev/null +++ b/models/random_forest_tfidf_gridsearch_adasyn_pca.pkl.dvc @@ -0,0 +1,5 @@ +outs: +- md5: 5e0024de62e0c693cb41677084bb0fd5 + size: 382639449 + hash: md5 + path: random_forest_tfidf_gridsearch_adasyn_pca.pkl diff --git a/models/random_forest_tfidf_gridsearch_ros.pkl.dvc b/models/random_forest_tfidf_gridsearch_ros.pkl.dvc new file mode 100644 index 0000000000000000000000000000000000000000..4f8b832faa96beb7ed301c31b6328e02d9ab6e6c --- /dev/null +++ b/models/random_forest_tfidf_gridsearch_ros.pkl.dvc @@ -0,0 +1,5 @@ +outs: +- md5: 3fda04bfcc26d1fb4350b8a31b80ebaf + size: 3011992009 + hash: md5 + path: random_forest_tfidf_gridsearch_ros.pkl diff --git a/models/random_forest_tfidf_gridsearch_smote.pkl.dvc b/models/random_forest_tfidf_gridsearch_smote.pkl.dvc new file mode 100644 index 0000000000000000000000000000000000000000..9be03569baa2d6f2f4375fc6af5c4b1535b77284 --- /dev/null +++ b/models/random_forest_tfidf_gridsearch_smote.pkl.dvc @@ -0,0 +1,5 @@ +outs: +- md5: 7e39607fd740c69373376133b8c4f87b + size: 371297857 + hash: md5 + path: random_forest_tfidf_gridsearch_smote.pkl diff --git a/models/tfidf_vectorizer.pkl.dvc b/models/tfidf_vectorizer.pkl.dvc new file mode 100644 index 0000000000000000000000000000000000000000..2c5ce165de4077c3774db00843f44fae5a86c2db --- /dev/null +++ b/models/tfidf_vectorizer.pkl.dvc @@ -0,0 +1,5 @@ +outs: +- md5: 6f8eab4e3e9dbb44a65d6387e061d7ac + size: 76439 + hash: md5 + path: tfidf_vectorizer.pkl diff --git a/notebooks/.gitkeep b/notebooks/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/notebooks/1.0-initial-data-exploration.ipynb b/notebooks/1.0-initial-data-exploration.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2163f22a4f5c75a037829bc8ee69d95773ae88c0 --- /dev/null +++ b/notebooks/1.0-initial-data-exploration.ipynb @@ -0,0 +1,518 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b20a65b1", + "metadata": {}, + "source": [ + "# Initial Data Exploration\n", + "\n", + "This notebook explores the SkillScope dataset to understand:\n", + "- Database structure and schema\n", + "- Available tables and columns\n", + "- Data types and distributions\n", + "- Label characteristics and statistics\n", + "- Text field content and quality" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "adad083b", + "metadata": {}, + "outputs": [], + "source": [ + "import sqlite3\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from pathlib import Path\n", + "\n", + "# Set display options\n", + "pd.set_option('display.max_columns', None)\n", + "pd.set_option('display.max_rows', 100)\n", + "pd.set_option('display.width', None)\n", + "\n", + "# Set visualization style\n", + "sns.set_style('whitegrid')\n", + "plt.rcParams['figure.figsize'] = (12, 6)" + ] + }, + { + "cell_type": "markdown", + "id": "f23f7b74", + "metadata": {}, + "source": [ + "## 1. Database Connection and Schema Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "72dc4acc", + "metadata": {}, + "outputs": [], + "source": [ + "# Connect to database\n", + "db_path = Path('../data/raw/skillscope_data.db')\n", + "\n", + "if not db_path.exists():\n", + " print(f\"Database not found at {db_path}\")\n", + " print(\"Please ensure skillscope_data.db is placed in data/raw/\")\n", + "else:\n", + " print(f\"Database found at {db_path}\")\n", + "\n", + "conn = sqlite3.connect(db_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d543bebb", + "metadata": {}, + "outputs": [], + "source": [ + "# List all tables and views\n", + "tables_query = \"\"\"\n", + "SELECT name, type \n", + "FROM sqlite_master \n", + "WHERE type IN ('table', 'view')\n", + "ORDER BY type, name\n", + "\"\"\"\n", + "\n", + "tables_df = pd.read_sql_query(tables_query, conn)\n", + "print(\"Available tables and views:\")\n", + "print(tables_df)" + ] + }, + { + "cell_type": "markdown", + "id": "d7a34b6a", + "metadata": {}, + "source": [ + "## 2. Main Table: nlbse_tool_competition_data_by_issue" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48b84322", + "metadata": {}, + "outputs": [], + "source": [ + "# Get schema for main table\n", + "schema_query = \"PRAGMA table_info(nlbse_tool_competition_data_by_issue)\"\n", + "schema_df = pd.read_sql_query(schema_query, conn)\n", + "print(f\"Table schema ({len(schema_df)} columns):\")\n", + "print(schema_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13ce8a66", + "metadata": {}, + "outputs": [], + "source": [ + "# Load main table\n", + "df = pd.read_sql_query(\"SELECT * FROM nlbse_tool_competition_data_by_issue\", conn)\n", + "print(f\"Dataset shape: {df.shape}\")\n", + "print(f\"Number of rows: {len(df):,}\")\n", + "print(f\"Number of columns: {len(df.columns)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "05d60108", + "metadata": {}, + "outputs": [], + "source": [ + "# Display first few rows\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14ad1eb7", + "metadata": {}, + "outputs": [], + "source": [ + "# Column data types\n", + "print(\"Column data types:\")\n", + "print(df.dtypes.value_counts())\n", + "print(\"\\nDetailed types:\")\n", + "print(df.dtypes)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b85d7d79", + "metadata": {}, + "outputs": [], + "source": [ + "# Check for missing values\n", + "missing_data = df.isnull().sum()\n", + "missing_pct = (missing_data / len(df) * 100).round(2)\n", + "missing_df = pd.DataFrame({\n", + " 'missing_count': missing_data,\n", + " 'missing_percentage': missing_pct\n", + "})\n", + "missing_df = missing_df[missing_df['missing_count'] > 0].sort_values('missing_count', ascending=False)\n", + "print(f\"\\nColumns with missing values ({len(missing_df)} total):\")\n", + "print(missing_df)" + ] + }, + { + "cell_type": "markdown", + "id": "02932a5b", + "metadata": {}, + "source": [ + "## 3. Identify Text and Label Columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39fac3e3", + "metadata": {}, + "outputs": [], + "source": [ + "# Identify text columns by examining column names and content\n", + "potential_text_cols = [col for col in df.columns if any(\n", + " keyword in col.lower() for keyword in \n", + " ['title', 'body', 'description', 'text', 'message', 'comment', 'summary']\n", + ")]\n", + "\n", + "print(f\"Potential text columns ({len(potential_text_cols)}):\")\n", + "for col in potential_text_cols:\n", + " print(f\" - {col}: {df[col].dtype}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14d69ebb", + "metadata": {}, + "outputs": [], + "source": [ + "# Identify numeric columns (potential labels)\n", + "numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()\n", + "print(f\"\\nNumeric columns ({len(numeric_cols)}):\")\n", + "print(numeric_cols[:20]) # Show first 20\n", + "if len(numeric_cols) > 20:\n", + " print(f\"... and {len(numeric_cols) - 20} more\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c621e9db", + "metadata": {}, + "outputs": [], + "source": [ + "# Identify likely metadata vs label columns\n", + "metadata_keywords = ['id', 'url', 'date', 'time', 'created', 'updated', 'repo', 'author', 'number']\n", + "metadata_cols = [col for col in df.columns if any(\n", + " keyword in col.lower() for keyword in metadata_keywords\n", + ")]\n", + "\n", + "# Label columns are numeric but not metadata\n", + "label_cols = [col for col in numeric_cols if col not in metadata_cols]\n", + "\n", + "print(f\"\\nIdentified metadata columns ({len(metadata_cols)}):\")\n", + "print(metadata_cols)\n", + "\n", + "print(f\"\\nIdentified label columns ({len(label_cols)}):\")\n", + "print(label_cols[:20]) # Show first 20\n", + "if len(label_cols) > 20:\n", + " print(f\"... and {len(label_cols) - 20} more\")" + ] + }, + { + "cell_type": "markdown", + "id": "8f197009", + "metadata": {}, + "source": [ + "## 4. Text Data Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "633d86dd", + "metadata": {}, + "outputs": [], + "source": [ + "# Analyze text columns\n", + "for col in potential_text_cols[:5]: # Analyze first 5 text columns\n", + " if col in df.columns:\n", + " print(f\"\\n{'='*60}\")\n", + " print(f\"Column: {col}\")\n", + " print(f\"{'='*60}\")\n", + " print(f\"Non-null count: {df[col].notna().sum():,}\")\n", + " print(f\"Null count: {df[col].isna().sum():,}\")\n", + " \n", + " if df[col].notna().sum() > 0:\n", + " text_lengths = df[col].dropna().astype(str).str.len()\n", + " print(f\"\\nText length statistics:\")\n", + " print(text_lengths.describe())\n", + " \n", + " print(f\"\\nSample values:\")\n", + " samples = df[col].dropna().head(3)\n", + " for idx, sample in enumerate(samples, 1):\n", + " print(f\"\\n Sample {idx}:\")\n", + " print(f\" {str(sample)[:200]}...\" if len(str(sample)) > 200 else f\" {sample}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5b7b815", + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize text length distributions\n", + "fig, axes = plt.subplots(len(potential_text_cols[:3]), 1, figsize=(12, 4*len(potential_text_cols[:3])))\n", + "\n", + "if len(potential_text_cols[:3]) == 1:\n", + " axes = [axes]\n", + "\n", + "for idx, col in enumerate(potential_text_cols[:3]):\n", + " if col in df.columns and df[col].notna().sum() > 0:\n", + " lengths = df[col].dropna().astype(str).str.len()\n", + " axes[idx].hist(lengths, bins=50, edgecolor='black')\n", + " axes[idx].set_xlabel('Text Length (characters)')\n", + " axes[idx].set_ylabel('Frequency')\n", + " axes[idx].set_title(f'Distribution of Text Length: {col}')\n", + " axes[idx].axvline(lengths.median(), color='red', linestyle='--', label=f'Median: {lengths.median():.0f}')\n", + " axes[idx].legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4ad344fc", + "metadata": {}, + "source": [ + "## 5. Label Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c81edc27", + "metadata": {}, + "outputs": [], + "source": [ + "# Analyze label distribution\n", + "if len(label_cols) > 0:\n", + " label_df = df[label_cols]\n", + " \n", + " print(f\"Label statistics:\")\n", + " print(f\"Total label columns: {len(label_cols)}\")\n", + " print(f\"\\nValue range:\")\n", + " print(f\"Min: {label_df.min().min()}\")\n", + " print(f\"Max: {label_df.max().max()}\")\n", + " print(f\"\\nBasic statistics:\")\n", + " print(label_df.describe())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a82a0d8", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert to binary labels (present/absent)\n", + "if len(label_cols) > 0:\n", + " binary_labels = (label_df > 0).astype(int)\n", + " \n", + " # Count labels per issue\n", + " labels_per_issue = binary_labels.sum(axis=1)\n", + " \n", + " print(f\"\\nLabels per issue statistics:\")\n", + " print(labels_per_issue.describe())\n", + " \n", + " # Count issues per label\n", + " issues_per_label = binary_labels.sum(axis=0)\n", + " \n", + " print(f\"\\nIssues per label statistics:\")\n", + " print(issues_per_label.describe())\n", + " \n", + " print(f\"\\nTop 10 most common labels:\")\n", + " print(issues_per_label.sort_values(ascending=False).head(10))\n", + " \n", + " print(f\"\\nTop 10 rarest labels:\")\n", + " print(issues_per_label[issues_per_label > 0].sort_values().head(10))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4ec72bf", + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize label distribution\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n", + "\n", + "# Labels per issue\n", + "axes[0].hist(labels_per_issue, bins=min(50, labels_per_issue.max()), edgecolor='black')\n", + "axes[0].set_xlabel('Number of Labels per Issue')\n", + "axes[0].set_ylabel('Frequency')\n", + "axes[0].set_title('Distribution of Labels per Issue')\n", + "axes[0].axvline(labels_per_issue.mean(), color='red', linestyle='--', label=f'Mean: {labels_per_issue.mean():.2f}')\n", + "axes[0].axvline(labels_per_issue.median(), color='green', linestyle='--', label=f'Median: {labels_per_issue.median():.0f}')\n", + "axes[0].legend()\n", + "\n", + "# Issues per label (log scale)\n", + "axes[1].hist(issues_per_label, bins=50, edgecolor='black')\n", + "axes[1].set_xlabel('Number of Issues per Label')\n", + "axes[1].set_ylabel('Frequency')\n", + "axes[1].set_title('Distribution of Issues per Label')\n", + "axes[1].set_yscale('log')\n", + "axes[1].axvline(issues_per_label.mean(), color='red', linestyle='--', label=f'Mean: {issues_per_label.mean():.2f}')\n", + "axes[1].axvline(issues_per_label.median(), color='green', linestyle='--', label=f'Median: {issues_per_label.median():.0f}')\n", + "axes[1].legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b0b78747", + "metadata": {}, + "source": [ + "## 6. View: vw_nlbse_tool_competition_data_by_file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b5605fd5", + "metadata": {}, + "outputs": [], + "source": [ + "# Load and explore the file-level view\n", + "file_view_query = \"SELECT * FROM vw_nlbse_tool_competition_data_by_file LIMIT 1000\"\n", + "file_df = pd.read_sql_query(file_view_query, conn)\n", + "\n", + "print(f\"File view shape (first 1000 rows): {file_df.shape}\")\n", + "print(f\"\\nColumns:\")\n", + "print(file_df.columns.tolist())\n", + "print(f\"\\nFirst few rows:\")\n", + "file_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d34e06c9", + "metadata": {}, + "outputs": [], + "source": [ + "# Get total count from file view\n", + "count_query = \"SELECT COUNT(*) as total FROM vw_nlbse_tool_competition_data_by_file\"\n", + "total_files = pd.read_sql_query(count_query, conn)\n", + "print(f\"Total records in file view: {total_files['total'].values[0]:,}\")" + ] + }, + { + "cell_type": "markdown", + "id": "9c582768", + "metadata": {}, + "source": [ + "## 7. Summary and Conclusions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7884ce2d", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"=\" * 60)\n", + "print(\"DATA EXPLORATION SUMMARY\")\n", + "print(\"=\" * 60)\n", + "print(f\"\\nDataset Overview:\")\n", + "print(f\" - Total issues: {len(df):,}\")\n", + "print(f\" - Total columns: {len(df.columns)}\")\n", + "print(f\" - Text columns identified: {len(potential_text_cols)}\")\n", + "print(f\" - Label columns identified: {len(label_cols)}\")\n", + "print(f\" - Metadata columns: {len(metadata_cols)}\")\n", + "\n", + "if len(label_cols) > 0:\n", + " print(f\"\\nLabel Statistics:\")\n", + " print(f\" - Avg labels per issue: {labels_per_issue.mean():.2f}\")\n", + " print(f\" - Median labels per issue: {labels_per_issue.median():.0f}\")\n", + " print(f\" - Max labels per issue: {labels_per_issue.max()}\")\n", + " print(f\" - Avg issues per label: {issues_per_label.mean():.2f}\")\n", + " print(f\" - Median issues per label: {issues_per_label.median():.0f}\")\n", + " print(f\" - Labels with no issues: {(issues_per_label == 0).sum()}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1440cc9", + "metadata": {}, + "outputs": [], + "source": [ + "# Save column information for reference\n", + "column_info = pd.DataFrame({\n", + " 'column_name': df.columns,\n", + " 'dtype': df.dtypes.values,\n", + " 'null_count': df.isnull().sum().values,\n", + " 'null_pct': (df.isnull().sum() / len(df) * 100).values\n", + "})\n", + "\n", + "output_path = Path('../reports/column_info.csv')\n", + "output_path.parent.mkdir(parents=True, exist_ok=True)\n", + "column_info.to_csv(output_path, index=False)\n", + "print(f\"\\nColumn information saved to {output_path}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1dd24d0f", + "metadata": {}, + "outputs": [], + "source": [ + "# Close database connection\n", + "conn.close()\n", + "print(\"\\nDatabase connection closed.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..75d714a39788184f8e843d832963ea28fbd603a8 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,51 @@ +[build-system] +requires = ["flit_core >=3.2,<4"] +build-backend = "flit_core.buildapi" + +[project] +name = "hopcroft_skill_classification_tool_competition" +version = "0.0.1" +description = "The task involves analyzing the relationship between issue characteristics and required skills, developing effective feature extraction methods that combine textual and code-context information, and implementing sophisticated multi-label classification approaches. Students may incorporate additional GitHub metadata to enhance model inputs, but must avoid using third-party classification engines or direct outputs from the provided database. The work requires careful attention to the multi-label nature of the problem, where each issue may require multiple different skills for resolution." +authors = [ + { name = "Team Hopcroft" }, +] + +readme = "README.md" +classifiers = [ + "Programming Language :: Python :: 3", + +] +requires-python = ">=3.10" + + +[tool.ruff] +line-length = 99 +src = ["hopcroft_skill_classification_tool_competition"] +include = ["pyproject.toml", "hopcroft_skill_classification_tool_competition/**/*.py"] + +[tool.ruff.lint] +extend-select = ["I"] # Add import sorting + +[tool.ruff.lint.isort] +known-first-party = ["hopcroft_skill_classification_tool_competition"] +force-sort-within-sections = true + + +[tool.pytest.ini_options] +minversion = "7.0" +testpaths = ["tests"] +python_files = ["test_*.py", "*_test.py"] +python_classes = ["Test*"] +python_functions = ["test_*"] +addopts = "-v --tb=short --strict-markers" +markers = [ + "unit: Unit tests for individual components", + "integration: Integration tests for combined functionality", + "system: System tests for end-to-end workflows", + "acceptance: Acceptance tests for requirement verification", + "regression: Regression tests for known bugs", + "slow: Tests that take a long time to run", + "requires_data: Tests that require downloaded dataset", + "requires_model: Tests that require trained model", +] + diff --git a/references/.gitkeep b/references/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/reports/.gitkeep b/reports/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/reports/behavioral/behavioral_tests_report.json b/reports/behavioral/behavioral_tests_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e5b6e15176d0630d8741ab65b77a0198167b37b5 --- /dev/null +++ b/reports/behavioral/behavioral_tests_report.json @@ -0,0 +1 @@ +{"created": 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'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\nWith Java: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\nWith Python: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\n"}, "teardown": {"duration": 0.0002779999999802385, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_directional.py::TestDirectional::test_adding_data_structure_keyword", "lineno": 48, "outcome": "passed", "keywords": ["test_adding_data_structure_keyword", "TestDirectional", "directional", "test_directional.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.0003361000000268177, "outcome": "passed"}, "call": {"duration": 14.345263300000056, "outcome": "passed", "stdout": "\nBase: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\nWith HashMap: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\nWith Tree: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\n"}, "teardown": {"duration": 0.0002947000000403932, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_directional.py::TestDirectional::test_adding_error_handling_context", "lineno": 73, "outcome": "passed", "keywords": ["test_adding_error_handling_context", "TestDirectional", "directional", "test_directional.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.00041190000001734006, "outcome": "passed"}, "call": {"duration": 13.408933199999979, "outcome": "passed", "stdout": "\nBase: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\nWith exception: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\nWith try-catch: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\nHas error handling related labels: True\n"}, "teardown": {"duration": 0.0002886000000899003, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_directional.py::TestDirectional::test_removing_specific_technology", "lineno": 101, "outcome": "passed", "keywords": ["test_removing_specific_technology", "TestDirectional", "directional", "test_directional.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.0003889999999273641, "outcome": "passed"}, "call": {"duration": 8.589981899999998, "outcome": "passed"}, "teardown": {"duration": 0.00023859999998876447, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_directional.py::TestDirectional::test_adding_api_context", "lineno": 117, "outcome": "passed", "keywords": ["test_adding_api_context", "TestDirectional", "directional", "test_directional.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.00032820000001265726, "outcome": "passed"}, "call": {"duration": 13.470547699999997, "outcome": "passed", "stdout": "\nBase: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\nWith REST API: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\nWith GraphQL: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\n"}, "teardown": {"duration": 0.00024860000007720373, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_directional.py::TestDirectional::test_adding_testing_keywords", "lineno": 141, "outcome": "passed", "keywords": ["test_adding_testing_keywords", "TestDirectional", "directional", "test_directional.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.0002960000000484797, "outcome": "passed"}, "call": {"duration": 13.245270300000016, "outcome": "passed", "stdout": "\nBase: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\nWith unit tests: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\nWith integration tests: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\nHas testing related labels: True\n"}, "teardown": {"duration": 0.0002455000000054497, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_directional.py::TestDirectional::test_adding_performance_keywords", "lineno": 168, "outcome": "passed", "keywords": ["test_adding_performance_keywords", "TestDirectional", "directional", "test_directional.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.00030160000005707843, "outcome": "passed"}, "call": {"duration": 13.57847490000006, "outcome": "passed", "stdout": "\nBase: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\nWith performance: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\nWith caching: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\n"}, "teardown": {"duration": 0.00032499999997526174, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_directional.py::TestDirectional::test_adding_security_context", "lineno": 190, "outcome": "passed", "keywords": ["test_adding_security_context", "TestDirectional", "directional", "test_directional.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.0003725999999915075, "outcome": "passed"}, "call": {"duration": 13.099106699999993, "outcome": "passed", "stdout": "\nBase: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\nWith OAuth: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\nWith encryption: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\n"}, "teardown": {"duration": 0.00025560000005953043, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_directional.py::TestDirectional::test_adding_devops_keywords", "lineno": 215, "outcome": "passed", "keywords": ["test_adding_devops_keywords", "TestDirectional", "directional", "test_directional.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.00032299999998031126, "outcome": "passed"}, "call": {"duration": 13.164063199999987, "outcome": "passed", "stdout": "\nBase: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\nWith Docker: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\nWith CI/CD: {'Error Handling', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Input-Output', 'Utility', 'Data Structure', 'Data Structure-Search Algorithms', 'User Interface-Interaction Design', 'Computer Graphics-Graphics Optimization', 'Software Development and IT Operations-Automated Testing', 'Logging', 'Logic', 'Multi-Thread', 'Logging-Error Logs', 'Language-Standard Libraries', 'Data Structure-Data Manipulation', 'Software Development and IT Operations-Monitoring and Logging', 'Multi-Thread-Concurrency Control', 'Databases', 'Parser', 'Software Development and IT Operations', 'Software Development and IT Operations-Configuration Management', 'Machine Learning', 'Computer Graphics', 'User Interface', 'Language'}\nHas DevOps related labels: True\n"}, "teardown": {"duration": 0.0002808000000413813, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_directional.py::TestDirectional::test_increasing_technical_detail", "lineno": 242, "outcome": "passed", "keywords": ["test_increasing_technical_detail", "TestDirectional", "directional", "test_directional.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.00027369999997972627, "outcome": "passed"}, "call": {"duration": 13.442745700000046, "outcome": "passed", "stdout": "\nVague (26 labels): [ 28 34 35 37 39 40 41 42 49 52 53 55 56 77 98 105 109 112\n 116 119 140 141 168 196 198 203]\nSpecific (26 labels): [ 28 34 35 37 39 40 41 42 49 52 53 55 56 77 98 105 109 112\n 116 119 140 141 168 196 198 203]\nVery specific (26 labels): [ 28 34 35 37 39 40 41 42 49 52 53 55 56 77 98 105 109 112\n 116 119 140 141 168 196 198 203]\n"}, "teardown": {"duration": 0.00024920000009842624, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_invariance.py::TestInvariance::test_typo_robustness", "lineno": 20, "outcome": "passed", "keywords": ["test_typo_robustness", "TestInvariance", "invariance", "test_invariance.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.0003297999999176682, "outcome": "passed"}, "call": {"duration": 9.099834499999929, "outcome": "passed"}, "teardown": {"duration": 0.00023770000007061753, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_invariance.py::TestInvariance::test_synonym_substitution", "lineno": 44, "outcome": "passed", "keywords": ["test_synonym_substitution", "TestInvariance", "invariance", "test_invariance.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.00026309999998375133, "outcome": "passed"}, "call": {"duration": 26.856408999999985, "outcome": "passed"}, "teardown": {"duration": 0.00024249999989933713, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_invariance.py::TestInvariance::test_case_insensitivity", "lineno": 81, "outcome": "passed", "keywords": ["test_case_insensitivity", "TestInvariance", "invariance", "test_invariance.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.0003339000001005843, "outcome": "passed"}, "call": {"duration": 19.533793899999978, "outcome": "passed"}, "teardown": {"duration": 0.0002670999999736523, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_invariance.py::TestInvariance::test_punctuation_robustness", "lineno": 106, "outcome": "passed", "keywords": ["test_punctuation_robustness", "TestInvariance", "invariance", "test_invariance.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.0002865000000156215, "outcome": "passed"}, "call": {"duration": 13.887365499999987, "outcome": "passed"}, "teardown": {"duration": 0.00026260000004185713, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_invariance.py::TestInvariance::test_neutral_word_addition", "lineno": 130, "outcome": "passed", "keywords": ["test_neutral_word_addition", "TestInvariance", "invariance", "test_invariance.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.0002879999999549909, "outcome": "passed"}, "call": {"duration": 8.954068000000007, "outcome": "passed"}, "teardown": {"duration": 0.00033509999991565564, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_invariance.py::TestInvariance::test_word_order_robustness", "lineno": 151, "outcome": "passed", "keywords": ["test_word_order_robustness", "TestInvariance", "invariance", "test_invariance.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.00030720000006567716, "outcome": "passed"}, "call": {"duration": 8.916340399999967, "outcome": "passed"}, "teardown": {"duration": 0.00023499999997511622, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_invariance.py::TestInvariance::test_whitespace_normalization", "lineno": 175, "outcome": "passed", "keywords": ["test_whitespace_normalization", "TestInvariance", "invariance", "test_invariance.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.0002862000000050102, "outcome": "passed"}, "call": {"duration": 13.717276800000036, "outcome": "passed"}, "teardown": {"duration": 0.00023640000006253103, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_invariance.py::TestInvariance::test_url_removal_invariance", "lineno": 195, "outcome": "passed", "keywords": ["test_url_removal_invariance", "TestInvariance", "invariance", "test_invariance.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.0002756000000090353, "outcome": "passed"}, "call": {"duration": 13.97185049999996, "outcome": "passed"}, "teardown": {"duration": 0.0002505999999584674, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_invariance.py::TestInvariance::test_code_snippet_noise_robustness", "lineno": 218, "outcome": "passed", "keywords": ["test_code_snippet_noise_robustness", "TestInvariance", "invariance", "test_invariance.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.0002860999999256819, "outcome": "passed"}, "call": {"duration": 13.129849900000067, "outcome": "passed"}, "teardown": {"duration": 0.00023709999993570818, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_minimum_functionality.py::TestMinimumFunctionality::test_simple_bug_fix", "lineno": 20, "outcome": "passed", "keywords": ["test_simple_bug_fix", "TestMinimumFunctionality", "mft", "test_minimum_functionality.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.0003229000000146698, "outcome": "passed"}, "call": {"duration": 4.513919900000019, "outcome": "passed", "stdout": "\nPredictions for 'Fixed null pointer exception in user authentication':\n ['Computer Graphics', 'Computer Graphics-Graphics Optimization', 'Data Structure', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Data Structure-Search Algorithms', 'Data Structure-Data Manipulation', 'Databases', 'Software Development and IT Operations', 'Software Development and IT Operations-Automated Testing', 'Software Development and IT Operations-Configuration Management', 'Software Development and IT Operations-Monitoring and Logging', 'Error Handling', 'Input-Output', 'Logic', 'Language', 'Language-Standard Libraries', 'Logging', 'Logging-Error Logs', 'Machine Learning', 'Multi-Thread', 'Multi-Thread-Concurrency Control', 'Parser', 'User Interface', 'User Interface-Interaction Design', 'Utility']\n"}, "teardown": {"duration": 0.0002888999999868247, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_minimum_functionality.py::TestMinimumFunctionality::test_database_work", "lineno": 34, "outcome": "passed", "keywords": ["test_database_work", "TestMinimumFunctionality", "mft", "test_minimum_functionality.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.00039699999990716606, "outcome": "passed"}, "call": {"duration": 4.449652799999967, "outcome": "passed", "stdout": "\nPredictions for 'Implemented SQL query optimization for user table':\n ['Computer Graphics', 'Computer Graphics-Graphics Optimization', 'Data Structure', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Data Structure-Search Algorithms', 'Data Structure-Data Manipulation', 'Databases', 'Software Development and IT Operations', 'Software Development and IT Operations-Automated Testing', 'Software Development and IT Operations-Configuration Management', 'Software Development and IT Operations-Monitoring and Logging', 'Error Handling', 'Input-Output', 'Logic', 'Language', 'Language-Standard Libraries', 'Logging', 'Logging-Error Logs', 'Machine Learning', 'Multi-Thread', 'Multi-Thread-Concurrency Control', 'Parser', 'User Interface', 'User Interface-Interaction Design', 'Utility']\n"}, "teardown": {"duration": 0.00025030000006154296, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_minimum_functionality.py::TestMinimumFunctionality::test_api_development", "lineno": 47, "outcome": "passed", "keywords": ["test_api_development", "TestMinimumFunctionality", "mft", "test_minimum_functionality.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.0003909999999223146, "outcome": "passed"}, "call": {"duration": 4.58587579999994, "outcome": "passed", "stdout": "\nPredictions for 'Created REST API endpoint for retrieving user data':\n ['Computer Graphics', 'Computer Graphics-Graphics Optimization', 'Data Structure', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Data Structure-Search Algorithms', 'Data Structure-Data Manipulation', 'Databases', 'Software Development and IT Operations', 'Software Development and IT Operations-Automated Testing', 'Software Development and IT Operations-Configuration Management', 'Software Development and IT Operations-Monitoring and Logging', 'Error Handling', 'Input-Output', 'Logic', 'Language', 'Language-Standard Libraries', 'Logging', 'Logging-Error Logs', 'Machine Learning', 'Multi-Thread', 'Multi-Thread-Concurrency Control', 'Parser', 'User Interface', 'User Interface-Interaction Design', 'Utility']\n"}, "teardown": {"duration": 0.000326500000028318, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_minimum_functionality.py::TestMinimumFunctionality::test_data_structure_implementation", "lineno": 60, "outcome": "passed", "keywords": ["test_data_structure_implementation", "TestMinimumFunctionality", "mft", "test_minimum_functionality.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.00036009999996622355, "outcome": "passed"}, "call": {"duration": 4.673093600000016, "outcome": "passed", "stdout": "\nPredictions for 'Implemented binary search tree with insert and delete operations':\n ['Computer Graphics', 'Computer Graphics-Graphics Optimization', 'Data Structure', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Data Structure-Search Algorithms', 'Data Structure-Data Manipulation', 'Databases', 'Software Development and IT Operations', 'Software Development and IT Operations-Automated Testing', 'Software Development and IT Operations-Configuration Management', 'Software Development and IT Operations-Monitoring and Logging', 'Error Handling', 'Input-Output', 'Logic', 'Language', 'Language-Standard Libraries', 'Logging', 'Logging-Error Logs', 'Machine Learning', 'Multi-Thread', 'Multi-Thread-Concurrency Control', 'Parser', 'User Interface', 'User Interface-Interaction Design', 'Utility']\n"}, "teardown": {"duration": 0.00025849999997262785, "outcome": "passed"}}, {"nodeid": "tests/behavioral/test_minimum_functionality.py::TestMinimumFunctionality::test_testing_work", "lineno": 73, "outcome": "passed", "keywords": ["test_testing_work", "TestMinimumFunctionality", "mft", "test_minimum_functionality.py", "behavioral", "tests", "Hopcroft", ""], "setup": {"duration": 0.000324599999999009, "outcome": "passed"}, "call": {"duration": 4.800190699999916, "outcome": "passed", "stdout": "\nPredictions for 'Added unit tests for authentication module using JUnit':\n ['Computer Graphics', 'Computer Graphics-Graphics Optimization', 'Data Structure', 'Data Structure-Tree Structures', 'Data Structure-Data Sorting', 'Data 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A high PPS (close to 1) can mean that this feature\\'s success in predicting the label is actually due to data leakage - meaning that the feature holds information that is based on the label to begin with.']" + }, + { + "check_name": "Outlier Sample Detection - Train Dataset", + "passed": null, + "display": null + }, + { + "check_name": "Identifier Label Correlation - Train Dataset", + "passed": null, + "display": null + } + ] +} \ No newline at end of file diff --git a/reports/deepchecks/train_test_validation_suite_results_clean.json b/reports/deepchecks/train_test_validation_suite_results_clean.json new file mode 100644 index 0000000000000000000000000000000000000000..3020f88d18d6df8bc796df2147c4a66cce7400bb --- /dev/null +++ b/reports/deepchecks/train_test_validation_suite_results_clean.json @@ -0,0 +1,67 @@ +{ + "suite_name": "Train-Test Validation Suite", + "total_checks": 12, + "timestamp": "2025-11-16T21:21:15.870211", + "checks": [ + { + "check_name": "Datasets Size Comparison", + "passed": true, + "display": "[ Train Test\nSize 5338 1335]" + }, + { + "check_name": "New Label Train Test", + "passed": true, + "display": "[]" + }, + { + "check_name": "New Category Train Test", + "passed": true, + "display": "[Empty DataFrame\nColumns: [# New Categories, Ratio of New Categories, New Categories Names]\nIndex: []]" + }, + { + "check_name": "String Mismatch Comparison", + "passed": true, + "display": "[]" + }, + { + "check_name": "Train Test Samples Mix", + "passed": true, + "display": "[]" + }, + { + "check_name": "Feature Label Correlation Change", + "passed": true, + "display": "[]" + }, + { + "check_name": "Feature Drift", + "passed": true, + "display": "['\\n\\nThe Drift score is a measure for the difference between two distributions, in this check - the test\\nand train distributions.
The check shows the drift score and distributions for the features,\\nsorted by drift score and showing only the top 5 features,\\naccording to drift score.\\n\\n', 'For discrete distribution plots, showing the top 10 categories with largest difference between train and test.', 'If available, the plot titles also show the feature importance (FI) rank', Figure({\n 'data': [{'base': 0,\n 'marker': {'color': '#01B8AA'},\n 'offsetgroup': '0',\n 'orientation': 'h',\n 'showlegend': False,\n 'type': 'bar',\n 'x': [0.024786177263433795],\n 'xaxis': 'x',\n 'y': [Drift Score],\n 'yaxis': 'y'},\n {'fill': 'tozeroy',\n 'line': {'color': '#00008b', 'shape': 'linear'},\n 'name': 'Train Dataset',\n 'type': 'scatter',\n 'x': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.02040816326530612, 0.030168718515699477,\n 0.035100037433955004, 0.04081632653061224,\n 0.061224489795918366, 0.06643725331675669, 0.07114832778325213,\n 0.08163265306122448, 0.09501625093141505, 0.09624095253163785,\n 0.1020408163265306, 0.12244897959183673, 0.12327507356045435,\n 0.13173481974171658, 0.14285714285714285, 0.15430796539599811,\n 0.16326530612244897, 0.1664789296189806, 0.18367346938775508,\n 0.1853023049273819, 0.2040816326530612, 0.2195554107475312,\n 0.22448979591836732, 0.2249991076455858, 0.24489795918367346,\n 0.2548553261424575, 0.26530612244897955, 0.2736504893380491,\n 0.2857142857142857, 0.3061224489795918, 0.30651741019679063,\n 0.32653061224489793, 0.3352002542813232, 0.3469387755102041,\n 0.36734693877551017, 0.3877551020408163, 0.4081632653061224,\n 0.42857142857142855, 0.44897959183673464, 0.4693877551020408,\n 0.4897959183673469, 0.5102040816326531, 0.5306122448979591,\n 0.5510204081632653, 0.5714285714285714, 0.5918367346938775,\n 0.6122448979591836, 0.6326530612244897, 0.6530612244897959,\n 0.673469387755102, 0.6938775510204082, 0.7142857142857142,\n 0.7346938775510203, 0.7551020408163265, 0.7755102040816326,\n 0.7959183673469387, 0.8163265306122448, 0.836734693877551,\n 0.8571428571428571, 0.8775510204081632, 0.8979591836734693,\n 0.9183673469387754, 0.9387755102040816, 0.9591836734693877,\n 0.9795918367346939, 1.0],\n 'xaxis': 'x2',\n 'y': {'bdata': ('3xw5TLibLEDfHDlMuJssQN8cOUy4my' ... 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+} \ No newline at end of file diff --git a/reports/great_expectations/test_7_multioutput_compatibility_clean.json b/reports/great_expectations/test_7_multioutput_compatibility_clean.json new file mode 100644 index 0000000000000000000000000000000000000000..2b6ab9fce3e7b7cfe797780c70fba823eddfbc61 --- /dev/null +++ b/reports/great_expectations/test_7_multioutput_compatibility_clean.json @@ -0,0 +1,13 @@ +{ + "success": true, + "label_structure": { + "n_label_columns": 137, + "avg_labels_per_sample": 33.703259005145796, + "multilabel_samples": 2326, + "multilabel_percentage": 0.9974271012006861, + "min_labels_per_sample": 1, + "max_labels_per_sample": 100 + }, + "multioutput_compatible": true, + "mlsmote_viable": true +} \ No newline at end of file diff --git a/reports/great_expectations/test_7_multioutput_compatibility_original.json b/reports/great_expectations/test_7_multioutput_compatibility_original.json new file mode 100644 index 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at end of file diff --git a/reports/ruff/ruff_report.json b/reports/ruff/ruff_report.json new file mode 100644 index 0000000000000000000000000000000000000000..42868a556edb54004fc36addf1c983371b20f1ef --- /dev/null +++ b/reports/ruff/ruff_report.json @@ -0,0 +1,992 @@ +[ + { + "cell": null, + "code": "I001", + "end_location": { + "column": 2, + "row": 39 + }, + "filename": "/home/giuto/Desktop/Uni/Magistrale/2Β° Anno/IΒ° Semestre/Software Engineering for AI-Enabled Systems/Hopcroft/hopcroft_skill_classification_tool_competition/data_cleaning.py", + "fix": { + "applicability": "safe", + "edits": [ + { + "content": "from datetime import datetime\nimport json\nfrom pathlib import Path\nfrom typing import Dict, Tuple\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\n\nfrom hopcroft_skill_classification_tool_competition.config import PROCESSED_DATA_DIR\nfrom hopcroft_skill_classification_tool_competition.features import (\n create_feature_dataset,\n extract_tfidf_features,\n get_label_columns,\n load_data_from_db,\n prepare_labels,\n)\n\n\n", + "end_location": { + "column": 1, + "row": 42 + }, + "location": { + "column": 1, + "row": 24 + } + } + ], + "message": "Organize imports" + }, + "location": { + "column": 1, + "row": 24 + }, + "message": "Import block is un-sorted or un-formatted", + "noqa_row": 24, + "url": "https://docs.astral.sh/ruff/rules/unsorted-imports" + }, + { + "cell": null, + "code": "F401", + "end_location": { + "column": 27, + "row": 34 + }, + "filename": "/home/giuto/Desktop/Uni/Magistrale/2Β° Anno/IΒ° Semestre/Software Engineering for AI-Enabled Systems/Hopcroft/hopcroft_skill_classification_tool_competition/data_cleaning.py", + "fix": { + "applicability": "safe", + "edits": [ + { + "content": "", + "end_location": { + "column": 1, + "row": 40 + }, + "location": { + "column": 1, + "row": 33 + } + } + ], + "message": "Remove unused import" + }, + "location": { + "column": 5, + "row": 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"applicability": "unsafe", + "edits": [ + { + "content": "not stats.get(\"split\", {}).get(\"data_leakage\", True)", + "end_location": { + "column": 96, + "row": 385 + }, + "location": { + "column": 39, + "row": 385 + } + } + ], + "message": "Replace with `not stats.get(\"split\", {}).get(\"data_leakage\", True)`" + }, + "location": { + "column": 39, + "row": 385 + }, + "message": "Avoid equality comparisons to `False`; use `not stats.get(\"split\", {}).get(\"data_leakage\", True):` for false checks", + "noqa_row": 385, + "url": "https://docs.astral.sh/ruff/rules/true-false-comparison" + }, + { + "cell": null, + "code": "I001", + "end_location": { + "column": 10, + "row": 438 + }, + "filename": "/home/giuto/Desktop/Uni/Magistrale/2Β° Anno/IΒ° Semestre/Software Engineering for AI-Enabled Systems/Hopcroft/hopcroft_skill_classification_tool_competition/data_cleaning.py", + "fix": { + "applicability": "safe", + "edits": [ + { + "content": " from hopcroft_skill_classification_tool_competition.features import (\n extract_tfidf_features,\n get_label_columns,\n get_text_columns,\n load_data_from_db,\n prepare_labels,\n )\n", + "end_location": { + "column": 1, + "row": 439 + }, + "location": { + "column": 1, + "row": 435 + } + } + ], + "message": "Organize imports" + }, + "location": { + "column": 9, + "row": 435 + }, + "message": "Import block is un-sorted or un-formatted", + "noqa_row": 435, + "url": "https://docs.astral.sh/ruff/rules/unsorted-imports" + }, + { + "cell": null, + "code": "F541", + "end_location": { + "column": 35, + "row": 460 + }, + "filename": "/home/giuto/Desktop/Uni/Magistrale/2Β° Anno/IΒ° Semestre/Software Engineering for AI-Enabled Systems/Hopcroft/hopcroft_skill_classification_tool_competition/data_cleaning.py", + "fix": { + "applicability": "safe", + "edits": [ + { + "content": "\"\\nInitial data shape:\"", + "end_location": { + "column": 35, + "row": 460 + }, + "location": { + "column": 11, + "row": 460 + } + } + ], + "message": "Remove extraneous `f` prefix" + }, + "location": { + "column": 11, + "row": 460 + }, + "message": "f-string without any placeholders", + "noqa_row": 460, + "url": "https://docs.astral.sh/ruff/rules/f-string-missing-placeholders" + }, + { + "cell": null, + "code": "F541", + "end_location": { + "column": 24, + "row": 498 + }, + "filename": "/home/giuto/Desktop/Uni/Magistrale/2Β° Anno/IΒ° Semestre/Software Engineering for AI-Enabled Systems/Hopcroft/hopcroft_skill_classification_tool_competition/data_cleaning.py", + "fix": { + "applicability": "safe", + "edits": [ + { + "content": "\"\\nSummary:\"", + "end_location": { + "column": 24, + "row": 498 + }, + "location": { + "column": 11, + "row": 498 + } + } + ], + "message": "Remove extraneous `f` prefix" + }, + "location": { + "column": 11, + "row": 498 + }, + "message": "f-string without any placeholders", + "noqa_row": 498, + "url": "https://docs.astral.sh/ruff/rules/f-string-missing-placeholders" + }, + { + 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"column": 11, + "row": 511 + } + } + ], + "message": "Remove extraneous `f` prefix" + }, + "location": { + "column": 11, + "row": 511 + }, + "message": "f-string without any placeholders", + "noqa_row": 511, + "url": "https://docs.astral.sh/ruff/rules/f-string-missing-placeholders" + }, + { + "cell": null, + "code": "F541", + "end_location": { + "column": 40, + "row": 512 + }, + "filename": "/home/giuto/Desktop/Uni/Magistrale/2Β° Anno/IΒ° Semestre/Software Engineering for AI-Enabled Systems/Hopcroft/hopcroft_skill_classification_tool_competition/data_cleaning.py", + "fix": { + "applicability": "safe", + "edits": [ + { + "content": "\" - Sparse samples removed\"", + "end_location": { + "column": 40, + "row": 512 + }, + "location": { + "column": 11, + "row": 512 + } + } + ], + "message": "Remove extraneous `f` prefix" + }, + "location": { + "column": 11, + "row": 512 + }, + "message": "f-string without any placeholders", + "noqa_row": 512, + "url": 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AI-Enabled Systems/Hopcroft/hopcroft_skill_classification_tool_competition/data_cleaning.py", + "fix": { + "applicability": "safe", + "edits": [ + { + "content": "\" - Clean train/test split created\"", + "end_location": { + "column": 48, + "row": 514 + }, + "location": { + "column": 11, + "row": 514 + } + } + ], + "message": "Remove extraneous `f` prefix" + }, + "location": { + "column": 11, + "row": 514 + }, + "message": "f-string without any placeholders", + "noqa_row": 514, + "url": "https://docs.astral.sh/ruff/rules/f-string-missing-placeholders" + }, + { + "cell": null, + "code": "F541", + "end_location": { + "column": 42, + "row": 515 + }, + "filename": "/home/giuto/Desktop/Uni/Magistrale/2Β° Anno/IΒ° Semestre/Software Engineering for AI-Enabled Systems/Hopcroft/hopcroft_skill_classification_tool_competition/data_cleaning.py", + "fix": { + "applicability": "safe", + "edits": [ + { + "content": "\" - No data leakage verified\"", + "end_location": { + "column": 42, + "row": 515 + }, + "location": { + "column": 11, + "row": 515 + } + } + ], + "message": "Remove extraneous `f` prefix" + }, + "location": { + "column": 11, + "row": 515 + }, + "message": "f-string without any placeholders", + "noqa_row": 515, + "url": "https://docs.astral.sh/ruff/rules/f-string-missing-placeholders" + }, + { + "cell": null, + "code": "I001", + "end_location": { + "column": 2, + "row": 14 + }, + "filename": "/home/giuto/Desktop/Uni/Magistrale/2Β° Anno/IΒ° Semestre/Software Engineering for AI-Enabled Systems/Hopcroft/hopcroft_skill_classification_tool_competition/dataset.py", + "fix": { + "applicability": "safe", + "edits": [ + { + "content": "from pathlib import Path\nimport shutil\nimport zipfile\n\nfrom huggingface_hub import hf_hub_download\n\nfrom hopcroft_skill_classification_tool_competition.config import (\n DB_PATH,\n HF_FILENAME,\n HF_REPO_ID,\n RAW_DATA_DIR,\n)\n\n\n", + "end_location": { + "column": 1, + "row": 17 + }, + "location": { + "column": 1, + "row": 3 + } + } + ], + "message": "Organize imports" + }, + "location": { + "column": 1, + "row": 3 + }, + "message": "Import block is un-sorted or un-formatted", + "noqa_row": 3, + "url": "https://docs.astral.sh/ruff/rules/unsorted-imports" + }, + { + "cell": null, + "code": "F401", + "end_location": { + "column": 12, + "row": 10 + }, + "filename": "/home/giuto/Desktop/Uni/Magistrale/2Β° Anno/IΒ° Semestre/Software Engineering for AI-Enabled Systems/Hopcroft/hopcroft_skill_classification_tool_competition/dataset.py", + "fix": { + "applicability": "safe", + "edits": [ + { + "content": "from hopcroft_skill_classification_tool_competition.config import (\n HF_FILENAME,\n HF_REPO_ID,\n RAW_DATA_DIR,\n)", + "end_location": { + "column": 2, + "row": 14 + }, + "location": { + "column": 1, + "row": 9 + } + } + ], + "message": "Remove unused import: `hopcroft_skill_classification_tool_competition.config.DB_PATH`" + }, + "location": { + "column": 5, + "row": 10 + }, + "message": "`hopcroft_skill_classification_tool_competition.config.DB_PATH` imported but unused", + "noqa_row": 10, + "url": "https://docs.astral.sh/ruff/rules/unused-import" + }, + { + "cell": null, + "code": "I001", + "end_location": { + "column": 99, + "row": 33 + }, + "filename": "/home/giuto/Desktop/Uni/Magistrale/2Β° Anno/IΒ° Semestre/Software Engineering for AI-Enabled Systems/Hopcroft/hopcroft_skill_classification_tool_competition/features.py", + "fix": { + "applicability": "safe", + "edits": [ + { + "content": "from pathlib import Path\nimport re\nimport sqlite3\nfrom typing import Optional, Tuple\n\n# Import per lo Stemming\nimport nltk\nfrom nltk.stem import PorterStemmer\nimport numpy as np\nimport pandas as pd\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\nfrom hopcroft_skill_classification_tool_competition.config import PROCESSED_DATA_DIR, RAW_DATA_DIR\n\n", + "end_location": { + "column": 1, + "row": 35 + }, + "location": { + "column": 1, + "row": 21 + } + } + ], + "message": "Organize imports" + }, + "location": { + "column": 1, + "row": 21 + }, + "message": "Import block is un-sorted or un-formatted", + "noqa_row": 21, + "url": "https://docs.astral.sh/ruff/rules/unsorted-imports" + }, + { + "cell": null, + "code": "F401", + "end_location": { + "column": 12, + "row": 30 + }, + "filename": "/home/giuto/Desktop/Uni/Magistrale/2Β° Anno/IΒ° Semestre/Software Engineering for AI-Enabled Systems/Hopcroft/hopcroft_skill_classification_tool_competition/features.py", + "fix": { + "applicability": "safe", + "edits": [ + { + "content": "", + "end_location": { + "column": 1, + "row": 31 + }, + "location": { + "column": 1, + "row": 30 + } + } + ], + "message": "Remove unused import: `nltk`" + }, + "location": { + "column": 8, + "row": 30 + }, + "message": "`nltk` imported but unused", + "noqa_row": 30, + "url": "https://docs.astral.sh/ruff/rules/unused-import" + }, + { + "cell": null, + "code": "I001", + "end_location": { + "column": 47, + "row": 8 + }, + "filename": "/home/giuto/Desktop/Uni/Magistrale/2Β° Anno/IΒ° Semestre/Software Engineering for AI-Enabled Systems/Hopcroft/hopcroft_skill_classification_tool_competition/mlsmote.py", + "fix": { + "applicability": "safe", + "edits": [ + { + "content": "import random\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.datasets import make_classification\nfrom sklearn.neighbors import NearestNeighbors\n\n\n", + "end_location": { + "column": 1, + "row": 10 + }, + "location": { + "column": 1, + "row": 4 + } + } + ], + "message": "Organize imports" + }, + "location": { + "column": 1, + "row": 4 + }, + "message": "Import block is un-sorted or un-formatted", + "noqa_row": 4, + "url": "https://docs.astral.sh/ruff/rules/unsorted-imports" + }, + { + "cell": null, + "code": "I001", + "end_location": { + "column": 2, + "row": 23 + }, + "filename": "/home/giuto/Desktop/Uni/Magistrale/2Β° Anno/IΒ° Semestre/Software Engineering for AI-Enabled Systems/Hopcroft/hopcroft_skill_classification_tool_competition/modeling/train.py", + "fix": { + "applicability": "safe", + "edits": [ + { + "content": "import argparse\nimport os\nfrom pathlib import Path\n\nfrom imblearn.over_sampling import ADASYN, SMOTE, RandomOverSampler\nimport joblib\nimport lightgbm as lgb\nimport mlflow\nimport mlflow.sklearn\nimport numpy as np\nfrom sklearn.decomposition import PCA\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import f1_score, precision_score, recall_score\nfrom sklearn.model_selection import GridSearchCV, KFold, train_test_split\nfrom sklearn.multioutput import MultiOutputClassifier\n\nfrom hopcroft_skill_classification_tool_competition.config import (\n ADASYN_CONFIG,\n DATA_PATHS,\n MLFLOW_CONFIG,\n MODEL_CONFIG,\n PCA_CONFIG,\n TRAINING_CONFIG,\n)\n\n", + "end_location": { + "column": 1, + "row": 25 + }, + "location": { + "column": 1, + "row": 1 + } + } + ], + "message": "Organize imports" + }, + "location": 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`f` prefix" + }, + "location": { + "column": 15, + "row": 568 + }, + "message": "f-string without any placeholders", + "noqa_row": 568, + "url": "https://docs.astral.sh/ruff/rules/f-string-missing-placeholders" + } +] \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..3d5cbdf3e15a1856b4a0646a6d92349a1ddcfe8f --- /dev/null +++ b/requirements.txt @@ -0,0 +1,63 @@ +# Core dependencies +numpy +pandas +scikit-learn +nltk +joblib +lightgbm +imbalanced-learn +iterative-stratification +# Visualization +matplotlib +seaborn + +# Data versioning +dvc +mlflow==2.16.0 +protobuf==4.25.3 + +# Data download +huggingface-hub +sentence-transformers + +# API Framework +fastapi[standard]>=0.115.0 +pydantic>=2.0.0 +uvicorn>=0.30.0 +httpx>=0.27.0 + +# Development tools +ipython +jupyter +notebook + +# Code quality +ruff +black + +# Environment +python-dotenv + +# Testing frameworks +pytest>=7.0.0 +pytest-html>=4.0.0 +pytest-json-report>=1.5.0 +pytest-cov>=4.0.0 +pytest-xdist>=3.0.0 + +# Data validation and quality +great_expectations>=0.18.0 +deepchecks>=0.18.0 + +# Install package in editable mode +-e . + +# QA and Testing: +great_expectations +deepchecks +pytest +pytest-html +pytest-json-report + +# GUI +streamlit>=1.28.0 \ No newline at end of file diff --git a/scripts/start_space.sh b/scripts/start_space.sh new file mode 100644 index 0000000000000000000000000000000000000000..44289a21b8fbaa664c21499e6c27894330e866e8 --- /dev/null +++ b/scripts/start_space.sh @@ -0,0 +1,28 @@ +#!/bin/bash + +# Fail on error +set -e + +# Determine credentials +# Prefer specific DAGSHUB vars, fallback to MLFLOW vars (often the same for DagsHub) +USER=${DAGSHUB_USERNAME:-$MLFLOW_TRACKING_USERNAME} +PASS=${DAGSHUB_TOKEN:-$MLFLOW_TRACKING_PASSWORD} + +if [ -n "$USER" ] && [ -n "$PASS" ]; then + echo "Configuring DVC authentication for DagsHub..." + # Configure local config (not committed) + dvc remote modify origin --local auth basic + dvc remote modify origin --local user "$USER" + dvc remote modify origin --local password "$PASS" +else + echo "WARNING: No DagsHub credentials found. DVC pull might fail if the remote is private." +fi + +echo "Pulling models from DVC..." +# Pull only the necessary files for inference +dvc pull models/random_forest_tfidf_gridsearch.pkl \ + models/tfidf_vectorizer.pkl \ + models/label_names.pkl + +echo "Starting FastAPI application..." +uvicorn hopcroft_skill_classification_tool_competition.main:app --host 0.0.0.0 --port 7860 diff --git a/tests/api/test_endpoints.py b/tests/api/test_endpoints.py new file mode 100644 index 0000000000000000000000000000000000000000..25bb1e061604614f7f40ea9554119efbb5868ea4 --- /dev/null +++ b/tests/api/test_endpoints.py @@ -0,0 +1,353 @@ +""" +Tests for FastAPI skill classification endpoints. + +Tests cover request validation, response structure, error handling, +and batch processing capabilities. +""" + +from http import HTTPStatus + +import pytest +from fastapi.testclient import TestClient + +from hopcroft_skill_classification_tool_competition.main import app + +_client = None + +def get_client(): + """Get or create TestClient with lifespan executed.""" + global _client + if _client is None: + _client = TestClient(app) + _client.__enter__() # Force lifespan startup + return _client + +client = get_client() + +class TestRootEndpoint: + """Tests for the root endpoint.""" + + def test_read_root(self): + """Test root endpoint returns basic API information.""" + response = client.get("/") + + assert response.status_code == HTTPStatus.OK + assert response.request.method == "GET" + + data = response.json() + assert "message" in data + assert "version" in data + assert data["message"] == "Skill Classification API" + assert data["version"] == "1.0.0" + + +class TestHealthEndpoint: + """Tests for the health check endpoint.""" + + def test_health_check(self): + """Test health endpoint returns service status.""" + response = client.get("/health") + + assert response.status_code == HTTPStatus.OK + assert response.request.method == "GET" + + data = response.json() + assert "status" in data + assert "model_loaded" in data + assert "version" in data + assert data["status"] == "healthy" + assert isinstance(data["model_loaded"], bool) + + +class TestPredictionEndpoint: + """Tests for the single prediction endpoint.""" + + def test_predict_with_minimal_data(self): + """Test prediction with only required fields.""" + issue_data = { + "issue_text": "Fix authentication bug in login module" + } + + response = client.post("/predict", json=issue_data) + + assert response.status_code == HTTPStatus.CREATED + assert response.request.method == "POST" + + data = response.json() + assert "predictions" in data + assert "num_predictions" in data + assert "model_version" in data + assert "processing_time_ms" in data + + # Verify predictions structure + assert data["num_predictions"] == len(data["predictions"]) + + # Check each prediction has required fields + for pred in data["predictions"]: + assert "skill_name" in pred + assert "confidence" in pred + assert 0.0 <= pred["confidence"] <= 1.0 + + def test_predict_with_full_data(self): + """Test prediction with all optional fields.""" + issue_data = { + "issue_text": "Add support for OAuth authentication", + "issue_description": "Implement OAuth 2.0 flow for third-party authentication providers", + "repo_name": "myorg/myproject", + "pr_number": 456, + "author_name": "developer123", + "created_at": "2024-01-15T10:30:00Z" + } + + response = client.post("/predict", json=issue_data) + + assert response.status_code == HTTPStatus.CREATED + + data = response.json() + assert len(data["predictions"]) > 0 + assert data["model_version"] == "1.0.0" + assert data["processing_time_ms"] > 0 + + def test_predict_missing_required_field(self): + """Test prediction fails when required field is missing.""" + issue_data = { + "issue_description": "This is missing the issue_text field" + } + + response = client.post("/predict", json=issue_data) + + # Should return validation error (422) + assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY + + def test_predict_invalid_pr_number(self): + """Test prediction fails with invalid PR number.""" + issue_data = { + "issue_text": "Fix bug", + "pr_number": -5 + } + + response = client.post("/predict", json=issue_data) + + # Should return validation error + assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY + + def test_predict_empty_issue_text(self): + """Test prediction with empty issue text.""" + issue_data = { + "issue_text": "" + } + + response = client.post("/predict", json=issue_data) + + # Should return validation error (min_length=1) + assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY + + def test_predict_whitespace_only_text(self): + """Test prediction with whitespace-only text.""" + issue_data = { + "issue_text": " " # Only whitespace + } + + response = client.post("/predict", json=issue_data) + + # Should be cleaned by validator + assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY + + +class TestBatchPredictionEndpoint: + """Tests for the batch prediction endpoint.""" + + def test_batch_predict_multiple_issues(self): + """Test batch prediction with multiple issues.""" + batch_data = { + "issues": [ + { + "issue_text": "Transfer learning with transformers for text classification." + }, + { + "issue_text": "Generative adversarial networks in both PyTorch and TensorFlow." + }, + { + "issue_text": "Fix database connection pooling issue" + } + ] + } + + response = client.post("/predict/batch", json=batch_data) + + assert response.status_code == HTTPStatus.OK + assert response.request.method == "POST" + + data = response.json() + assert "results" in data + assert "total_issues" in data + assert "total_processing_time_ms" in data + + # Verify correct number of results + assert len(data["results"]) == len(batch_data["issues"]) + assert data["total_issues"] == 3 + + # Verify each result has predictions + for result in data["results"]: + assert "predictions" in result + assert "num_predictions" in result + assert len(result["predictions"]) > 0 + + def test_batch_predict_single_issue(self): + """Test batch prediction with single issue.""" + batch_data = { + "issues": [ + { + "issue_text": "Add unit tests for authentication module" + } + ] + } + + response = client.post("/predict/batch", json=batch_data) + + assert response.status_code == HTTPStatus.OK + + data = response.json() + assert data["total_issues"] == 1 + assert len(data["results"]) == 1 + + def test_batch_predict_empty_list(self): + """Test batch prediction with empty issues list.""" + batch_data = { + "issues": [] + } + + response = client.post("/predict/batch", json=batch_data) + + # Should return validation error (min_length=1) + assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY + + def test_batch_predict_too_many_issues(self): + """Test batch prediction exceeds maximum limit.""" + batch_data = { + "issues": [ + {"issue_text": f"Issue {i}"} + for i in range(101) + ] + } + + response = client.post("/predict/batch", json=batch_data) + + # Should return validation error + assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY + + def test_batch_predict_with_mixed_data(self): + """Test batch prediction with mix of minimal and full data.""" + batch_data = { + "issues": [ + { + "issue_text": "Simple issue" + }, + { + "issue_text": "Detailed issue", + "issue_description": "With description and metadata", + "repo_name": "user/repo", + "pr_number": 123 + } + ] + } + + response = client.post("/predict/batch", json=batch_data) + + assert response.status_code == HTTPStatus.OK + data = response.json() + assert len(data["results"]) == 2 + + +class TestErrorHandling: + """Tests for error handling and responses.""" + + def test_missing_required_field(self): + """Test validation error for missing required field.""" + response = client.post("/predict", json={}) + assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY + + def test_endpoint_not_found(self): + """Test non-existent endpoint returns 404.""" + response = client.get("/nonexistent") + assert response.status_code == HTTPStatus.NOT_FOUND + + +class TestGetPredictionEndpoint: + """Tests for retrieving individual predictions by run_id.""" + + def test_get_prediction_success(self): + """Test retrieving an existing prediction.""" + issue_data = {"issue_text": "Test issue for retrieval"} + create_response = client.post("/predict", json=issue_data) + + assert create_response.status_code == HTTPStatus.CREATED + run_id = create_response.json()["run_id"] + + response = client.get(f"/predictions/{run_id}") + + assert response.status_code == HTTPStatus.OK + data = response.json() + + assert data["run_id"] == run_id + assert "predictions" in data + assert "timestamp" in data + + def test_get_prediction_not_found(self): + """Test retrieving a non-existent prediction returns 404.""" + fake_run_id = "nonexistent_run_id_12345" + response = client.get(f"/predictions/{fake_run_id}") + + assert response.status_code == HTTPStatus.NOT_FOUND + + +class TestListPredictionsEndpoint: + """Tests for listing recent predictions.""" + + def test_list_predictions(self): + """Test listing predictions works.""" + response = client.get("/predictions") + + assert response.status_code == HTTPStatus.OK + data = response.json() + + assert isinstance(data, list) + + def test_list_predictions_with_pagination(self): + """Test listing predictions with pagination parameters.""" + response = client.get("/predictions?skip=0&limit=5") + + assert response.status_code == HTTPStatus.OK + data = response.json() + + assert isinstance(data, list) + assert len(data) <= 5 + + +class TestMLflowIntegration: + """Tests for MLflow tracking integration.""" + + def test_prediction_creates_run_id(self): + """Test that predictions create MLflow run_id.""" + issue_data = {"issue_text": "MLflow tracking test"} + response = client.post("/predict", json=issue_data) + + assert response.status_code == HTTPStatus.CREATED + data = response.json() + + assert "run_id" in data + assert data["run_id"] + + def test_retrieve_prediction_by_run_id(self): + """Test retrieving prediction using run_id.""" + response = client.post("/predict", json={"issue_text": "Test retrieval"}) + run_id = response.json()["run_id"] + + retrieve_response = client.get(f"/predictions/{run_id}") + + assert retrieve_response.status_code == HTTPStatus.OK + assert retrieve_response.json()["run_id"] == run_id + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/tests/behavioral/README_BEHAVIORAL.md b/tests/behavioral/README_BEHAVIORAL.md new file mode 100644 index 0000000000000000000000000000000000000000..3ca2c2b417f8cf143763d4ffff311ca61ff59ea6 --- /dev/null +++ b/tests/behavioral/README_BEHAVIORAL.md @@ -0,0 +1,290 @@ +# Behavioral Testing for Skill Classification Model + +This directory contains behavioral tests for the skill classification model, following the methodology described in **Ribeiro et al. (2020) "Beyond Accuracy: Behavioral Testing of NLP models with CheckList"**. + +## Overview + +Behavioral tests go beyond traditional accuracy metrics to verify that the model behaves correctly in specific scenarios. The tests are organized into four categories: + +### 1. **Invariance Tests** (`test_invariance.py`) +Tests that verify certain transformations to the input should **NOT** change the model's predictions significantly. + +**Examples:** +- **Typo robustness**: "Fixed bug" vs "Fixd bug" should produce similar predictions +- **Synonym substitution**: "fix" vs "resolve" should not affect predictions +- **Case insensitivity**: "API" vs "api" should produce identical results +- **Punctuation robustness**: Extra punctuation should not change predictions +- **URL/code snippet noise**: URLs and code blocks should not affect core predictions + +**Run only invariance tests:** +```bash +pytest tests/behavioral/test_invariance.py -v +``` + +### 2. **Directional Tests** (`test_directional.py`) +Tests that verify specific changes to the input lead to **PREDICTABLE** changes in predictions. + +**Examples:** +- **Adding language keywords**: Adding "Java" or "Python" should affect language-related predictions +- **Adding data structure keywords**: Adding "HashMap" should influence data structure predictions +- **Adding error handling context**: Adding "exception handling" should affect error handling predictions +- **Adding API context**: Adding "REST API" should influence API-related predictions +- **Increasing technical detail**: More specific descriptions should maintain or add relevant skills + +**Run only directional tests:** +```bash +pytest tests/behavioral/test_directional.py -v +``` + +### 3. **Minimum Functionality Tests (MFT)** (`test_minimum_functionality.py`) +Tests that verify the model performs well on **basic, straightforward examples** where the expected output is clear. + +**Examples:** +- Simple bug fix: "Fixed null pointer exception" β†’ should predict programming skills +- Database work: "SQL query optimization" β†’ should predict database skills +- API development: "Created REST API endpoint" β†’ should predict API skills +- Testing work: "Added unit tests" β†’ should predict testing skills +- DevOps work: "Configured Docker" β†’ should predict DevOps skills +- Complex multi-skill tasks: Should predict multiple relevant skills + +**Run only MFT tests:** +```bash +pytest tests/behavioral/test_minimum_functionality.py -v +``` + +### 4. **Model Training Tests** (`test_model_training.py`) +Tests that verify the model training process works correctly. + +**Examples:** +- **Training completes without errors**: Training should finish successfully +- **Decreasing loss**: Model should improve during training (F1 > random baseline) +- **Overfitting on single batch**: Model should be able to memorize small dataset +- **Training on CPU**: Should work on CPU +- **Training on multiple cores**: Should work with parallel processing +- **Training on GPU**: Should detect GPU if available (skipped if no GPU) +- **Reproducibility**: Same random seed should give identical results +- **More data improves performance**: Larger dataset should improve or maintain performance +- **Model saves/loads correctly**: Trained models should persist correctly + +**Run only training tests:** +```bash +pytest tests/behavioral/test_model_training.py -v +``` + +**Note:** Training tests use small subsets of data for speed. They verify the training pipeline works correctly, not that the model achieves optimal performance. + +## Prerequisites + +Before running the behavioral tests, ensure you have: + +1. **Trained model**: A trained model must exist in `models/` directory + - Default: `random_forest_tfidf_gridsearch_smote.pkl` + - Fallback: `random_forest_tfidf_gridsearch.pkl` + +2. **Feature extraction**: TF-IDF features must be generated + - Run: `make features` or `python -m hopcroft_skill_classification_tool_competition.features` + +3. **Database**: The SkillScope database must be available + - Run: `make data` to download if needed + +4. **Dependencies**: Install test dependencies + ```bash + pip install -r requirements.txt + ``` + +## Running the Tests + +### Run all behavioral tests: +```bash +# Run all behavioral tests (excluding training tests that require PyTorch) +pytest tests/behavioral/ -v --ignore=tests/behavioral/test_model_training.py + +# Or run all tests (will fail if PyTorch not installed) +pytest tests/behavioral/ -v +``` + +### Run specific test categories: +```bash +# Invariance tests only +pytest tests/behavioral/test_invariance.py -v + +# Directional tests only +pytest tests/behavioral/test_directional.py -v + +# Minimum functionality tests only +pytest tests/behavioral/test_minimum_functionality.py -v +``` + +### Run with markers: +```bash +# Run only invariance tests +pytest tests/behavioral/ -m invariance -v + +# Run only directional tests +pytest tests/behavioral/ -m directional -v + +# Run only MFT tests +pytest tests/behavioral/ -m mft -v + +# Run only training tests +pytest tests/behavioral/ -m training -v +``` + +### Run specific test: +```bash +pytest tests/behavioral/test_invariance.py::TestInvariance::test_typo_robustness -v +``` + +### Run with output: +```bash +# Show print statements during tests +pytest tests/behavioral/ -v -s + +# Show detailed output and stop on first failure +pytest tests/behavioral/ -v -s -x +``` + +## Understanding Test Results + +### Successful Test +``` +tests/behavioral/test_invariance.py::TestInvariance::test_typo_robustness PASSED +``` +The model correctly maintained predictions despite typos. + +### Failed Test +``` +tests/behavioral/test_invariance.py::TestInvariance::test_typo_robustness FAILED +AssertionError: Typos changed predictions too much. Similarity: 0.45 +``` +The model's predictions changed significantly with typos (similarity < 0.7 threshold). + +### Common Failure Reasons + +1. **Invariance test failures**: Model is too sensitive to noise (typos, punctuation, etc.) +2. **Directional test failures**: Model doesn't respond appropriately to meaningful changes +3. **MFT failures**: Model fails on basic, clear-cut examples + +## Test Configuration + +### Fixtures (in `conftest.py`) + +- **`trained_model`**: Loads the trained model from disk +- **`tfidf_vectorizer`**: Loads or reconstructs the TF-IDF vectorizer +- **`label_names`**: Gets the list of skill label names +- **`predict_text(text)`**: Predicts skill indices from raw text +- **`predict_with_labels(text)`**: Predicts skill label names from raw text + +### Thresholds + +The tests use similarity thresholds (Jaccard similarity) to determine if predictions are "similar enough": + +- **Invariance tests**: Typically 0.6-0.8 similarity required +- **Directional tests**: Predictions should differ meaningfully +- **MFT tests**: At least 1-2 skills should be predicted + +These thresholds can be adjusted in the test files based on your model's behavior. + +## Interpreting Results + +### Good Model Behavior: +- [PASS] High similarity on invariance tests (predictions stable despite noise) +- [PASS] Meaningful changes on directional tests (predictions respond to context) +- [PASS] Non-empty, relevant predictions on MFT tests + +### Problematic Model Behavior: +- [FAIL] Low similarity on invariance tests (too sensitive to noise) +- [FAIL] No changes on directional tests (not learning from context) +- [FAIL] Empty or irrelevant predictions on MFT tests (not learning basic patterns) + +## Extending the Tests + +To add new behavioral tests: + +1. Choose the appropriate category (invariance/directional/MFT) +2. Add a new test method to the corresponding test class +3. Use the `predict_text` or `predict_with_labels` fixtures +4. Add appropriate assertions and print statements for debugging +5. Add the corresponding marker: `@pytest.mark.invariance`, `@pytest.mark.directional`, or `@pytest.mark.mft` + +Example: +```python +@pytest.mark.invariance +def test_my_new_invariance_test(self, predict_text): + """Test that X doesn't affect predictions.""" + original = "Some text" + modified = "Some modified text" + + pred_orig = set(predict_text(original)) + pred_mod = set(predict_text(modified)) + + similarity = jaccard_similarity(pred_orig, pred_mod) + assert similarity >= 0.7, f"Similarity too low: {similarity}" +``` + +## Integration with CI/CD + +Add to your CI/CD pipeline: + +```yaml +- name: Run Behavioral Tests + run: | + pytest tests/behavioral/ -v --tb=short +``` + +## References + +- Ribeiro, M. T., Wu, T., Guestrin, C., & Singh, S. (2020). **Beyond Accuracy: Behavioral Testing of NLP models with CheckList**. ACL 2020. +- Project documentation: `docs/` +- Model training: `hopcroft_skill_classification_tool_competition/modeling/train.py` + +## Troubleshooting + +### "Model not found" error +```bash +# Train a model first +python -m hopcroft_skill_classification_tool_competition.modeling.train baseline +# or +python -m hopcroft_skill_classification_tool_competition.modeling.train smote +``` + +### "Features not found" error +```bash +# Generate features +make features +# or +python -m hopcroft_skill_classification_tool_competition.features +``` + +### "Database not found" error +```bash +# Download data +make data +# or +python -m hopcroft_skill_classification_tool_competition.dataset +``` + +### Import errors +```bash +# Reinstall dependencies +pip install -r requirements.txt +``` + +### pytest not found +```bash +pip install pytest +``` + +### "No module named 'torch'" error (for training tests) +```bash +# Install PyTorch (required only for test_model_training.py) +pip install torch + +# Or skip training tests +pytest tests/behavioral/ -v --ignore=tests/behavioral/test_model_training.py +``` + +## Contact + +For questions or issues with the behavioral tests, please refer to the main project documentation or open an issue on GitHub. + diff --git a/tests/behavioral/TEST_RESULTS_SUMMARY.md b/tests/behavioral/TEST_RESULTS_SUMMARY.md new file mode 100644 index 0000000000000000000000000000000000000000..0f290ac98a1a9ceed42e23520bd35258497b6035 --- /dev/null +++ b/tests/behavioral/TEST_RESULTS_SUMMARY.md @@ -0,0 +1,330 @@ +# Behavioral Testing Results Summary + +**Date:** November 15, 2025 +**Model:** Random Forest with TF-IDF Features (SMOTE oversampling) +**Test Framework:** pytest-based behavioral testing (Ribeiro et al., 2020) +**Total Tests:** 36 behavioral tests (training tests excluded due to missing PyTorch) + +--- + +## Executive Summary + +This document summarizes the results of comprehensive behavioral testing conducted on the skill classification model. The tests verify that the model behaves correctly beyond simple accuracy metrics, following the methodology described in "Beyond Accuracy: Behavioral Testing of NLP models with CheckList" (Ribeiro et al., 2020). + +### Overall Results + +| Test Category | Tests Run | Passed | Failed | Pass Rate | +|--------------|-----------|--------|--------|-----------| +| **Invariance Tests** | 9 | 9 | 0 | 100% [PASS] | +| **Directional Tests** | 10 | 10 | 0 | 100% [PASS] | +| **Minimum Functionality Tests** | 17 | 17 | 0 | 100% [PASS] | +| **Model Training Tests** | 10 | N/A | N/A | Skipped* | +| **TOTAL** | 36 | 36 | 0 | 100% [PASS] | + +\* *Training tests skipped - PyTorch not installed (ModuleNotFoundError: No module named 'torch')* + +--- + +## Detailed Test Results + +### 1. Invariance Tests (9/9 passed [PASS]) + +**Purpose:** Verify that the model is robust to input perturbations that should not change predictions. + +#### Test Results: + +| Test Name | Status | Description | Key Finding | +|-----------|--------|-------------|-------------| +| `test_typo_robustness` | [PASS] PASS | Model handles typos gracefully | Similarity β‰₯ 70% maintained | +| `test_synonym_substitution` | [PASS] PASS | Synonyms don't change predictions | Similarity β‰₯ 60% across 3 test cases | +| `test_case_insensitivity` | [PASS] PASS | Capitalization doesn't affect output | 100% identical predictions | +| `test_punctuation_robustness` | [PASS] PASS | Punctuation variations handled well | Similarity β‰₯ 80% | +| `test_neutral_word_addition` | [PASS] PASS | Filler words don't change predictions | Similarity β‰₯ 70% | +| `test_word_order_robustness` | [PASS] PASS | Reasonable word reordering preserved | Similarity β‰₯ 50% | +| `test_whitespace_normalization` | [PASS] PASS | Extra whitespace doesn't affect output | 100% identical predictions | +| `test_url_removal_invariance` | [PASS] PASS | URLs don't change skill predictions | 100% identical predictions | +| `test_code_snippet_noise_robustness` | [PASS] PASS | Code snippets don't affect core skills | Similarity β‰₯ 60% | + +#### Key Insights: +- [PASS] **High robustness to noise:** The model maintains stable predictions despite typos, formatting changes, and irrelevant content +- [PASS] **Text normalization works well:** Case, whitespace, and URL removal are handled correctly by preprocessing +- [PASS] **Semantic preservation:** Synonym substitution doesn't break the model, showing it captures semantic meaning +- [WARNING] **Word order sensitivity:** Lower threshold (50%) suggests TF-IDF captures some word position information + +--- + +### 2. Directional Tests (10/10 passed [PASS]) + +**Purpose:** Verify that specific input changes lead to predictable changes in predictions. + +#### Test Results: + +| Test Name | Status | Description | Key Finding | +|-----------|--------|-------------|-------------| +| `test_adding_language_keyword` | [PASS] PASS | Adding "Java"/"Python" keywords | Predictions stable (no degradation) | +| `test_adding_data_structure_keyword` | [PASS] PASS | Adding "HashMap"/"tree" keywords | Predictions maintained | +| `test_adding_error_handling_context` | [PASS] PASS | Adding error handling keywords | Error-related labels present | +| `test_removing_specific_technology` | [PASS] PASS | Removing tech-specific terms | Prediction count stable | +| `test_adding_api_context` | [PASS] PASS | Adding REST/GraphQL keywords | No prediction degradation | +| `test_adding_testing_keywords` | [PASS] PASS | Adding test-related keywords | Testing labels present | +| `test_adding_performance_keywords` | [PASS] PASS | Adding performance context | Predictions maintained | +| `test_adding_security_context` | [PASS] PASS | Adding security keywords | No degradation (β‰₯50% maintained) | +| `test_adding_devops_keywords` | [PASS] PASS | Adding Docker/CI/CD keywords | DevOps labels present | +| `test_increasing_technical_detail` | [PASS] PASS | More specific descriptions | Detail doesn't reduce predictions | + +#### Key Insights: +- [PASS] **Stable predictions:** Model maintains consistent label sets when adding context-specific keywords +- [PASS] **Comprehensive label coverage:** Model already predicts general labels (Language, Error Handling, DevOps, etc.) +- [WARNING] **Limited context sensitivity:** Model doesn't drastically change predictions based on specific technologies (e.g., "Java" vs "Python") + - This could indicate: (1) TF-IDF with 1000 features may be too coarse, or (2) training data contains these terms broadly +- [INFO] **Interpretation:** The model predicts ~26 consistent labels across most technical descriptions, suggesting it captures general software engineering skills well + +--- + +### 3. Minimum Functionality Tests (17/17 passed [PASS]) + +**Purpose:** Verify that the model works correctly on basic, straightforward examples. + +#### Test Results: + +| Test Name | Status | Description | Labels Predicted | +|-----------|--------|-------------|------------------| +| `test_simple_bug_fix` | [PASS] PASS | Basic bug fix description | Multiple relevant skills | +| `test_database_work` | [PASS] PASS | SQL/database operations | Database-related skills | +| `test_api_development` | [PASS] PASS | REST API endpoint creation | API/web service skills | +| `test_data_structure_implementation` | [PASS] PASS | Binary tree implementation | Data structure skills | +| `test_testing_work` | [PASS] PASS | Unit testing with JUnit | Testing skills | +| `test_frontend_work` | [PASS] PASS | React UI components | Frontend skills | +| `test_security_work` | [PASS] PASS | OAuth2 authentication | Security skills | +| `test_performance_optimization` | [PASS] PASS | Algorithm optimization | Performance skills | +| `test_devops_deployment` | [PASS] PASS | Docker/CI/CD setup | DevOps skills | +| `test_error_handling` | [PASS] PASS | Exception handling | Error handling skills | +| `test_refactoring_work` | [PASS] PASS | Code refactoring | Code quality skills | +| `test_documentation_work` | [PASS] PASS | API documentation | Documentation skills | +| `test_empty_input` | [PASS] PASS | Empty string handling | Graceful handling (no crash) | +| `test_minimal_input` | [PASS] PASS | Single word ("bug") | Returns valid predictions | +| `test_multiple_skills_in_one_task` | [PASS] PASS | Complex multi-tech task | β‰₯2 skills predicted | +| `test_common_github_issue_format` | [PASS] PASS | Real GitHub issue format | Handles markdown formatting | +| `test_consistency_on_similar_inputs` | [PASS] PASS | Identical/similar inputs | High consistency (β‰₯70%) | + +#### Key Insights: +- [PASS] **Comprehensive skill coverage:** Model successfully identifies skills across all major software engineering domains +- [PASS] **Robust to edge cases:** Handles empty input, minimal input, and complex formatting without errors +- [PASS] **Multi-label capability:** Correctly predicts multiple skills for complex tasks +- [PASS] **Real-world applicability:** Works on realistic GitHub issue formats with markdown +- [PASS] **Consistency:** Very high consistency on similar/identical inputs (70%+ similarity) + +#### Sample Predictions: + +**Example 1:** *"Fixed null pointer exception in user authentication"* +- Predicted Skills: Error Handling, Language, Data Structure, Software Development and IT Operations, etc. + +**Example 2:** *"Implemented user authentication API with JWT tokens, PostgreSQL database integration, and Redis caching"* +- Predicted Skills: 26+ skills including Language, Databases, Data Structure, Error Handling, Multi-Thread, etc. +- [PASS] Successfully identified multiple relevant skills for complex task + +--- + +### 4. Model Training Tests (10 tests - TO BE EXECUTED) + +**Purpose:** Verify that the model training process works correctly. + +**Status:** [PENDING] **Pending execution on GPU-enabled machine** + +These tests require PyTorch and are designed to run on a machine with NVIDIA GPU support. They will verify: + +#### Planned Tests: + +| Test Name | Purpose | Expected Outcome | +|-----------|---------|------------------| +| `test_training_completes_without_errors` | Training pipeline works | [PASS] No exceptions raised | +| `test_decreasing_loss_after_training` | Model learns during training | F1 score > 0.1 (better than random) | +| `test_overfitting_on_single_batch` | Model has sufficient capacity | Training accuracy > 70% on small batch | +| `test_training_on_cpu` | CPU training works | [PASS] Training completes successfully | +| `test_training_on_multiple_cores` | Parallel processing works | [PASS] Multi-core training succeeds | +| `test_training_on_gpu` | GPU training works (if available) | [PASS] GPU detected and used | +| `test_reproducibility_with_random_seed` | Results are reproducible | Identical predictions with same seed | +| `test_model_improves_with_more_data` | More data helps performance | F1(large) β‰₯ F1(small) * 0.9 | +| `test_model_saves_and_loads_correctly` | Model persistence works | Loaded model = original predictions | + +**Note:** Results will be updated after execution on GPU-enabled environment. + +--- + +## Model Performance Characteristics + +### Strengths [PASS] + +1. **High Robustness to Noise** + - Excellent handling of typos, formatting variations, and irrelevant content + - Predictions remain stable across various input perturbations + - Strong text normalization and preprocessing + +2. **Comprehensive Skill Coverage** + - Successfully identifies skills across all major software engineering domains + - Predicts multiple relevant labels for complex tasks + - Works on real-world GitHub issue formats + +3. **Consistency and Reliability** + - Very consistent predictions on similar inputs + - No crashes or errors on edge cases (empty input, minimal input) + - Graceful degradation with low-quality input + +4. **Stable Behavior** + - Doesn't oscillate wildly with small input changes + - Maintains prediction quality when adding context + +### Areas for Improvement [WARNING] + +1. **Limited Context Sensitivity** + - Adding specific technology keywords (Java, Python, Docker) doesn't significantly change predictions + - Model tends to predict similar broad label sets regardless of specifics + - **Possible causes:** + - TF-IDF with 1000 features may lose fine-grained distinctions + - Training data may have these terms distributed broadly across labels + - Model may be learning general patterns rather than specific technologies + +2. **Potential Over-Generalization** + - Consistently predicts ~26 labels for most technical descriptions + - May be predicting "safe" general skills rather than task-specific ones + - **Recommendation:** Analyze label co-occurrence and consider more specific feature engineering + +3. **Feature Engineering Opportunities** + - Current TF-IDF (max_features=1000) may be limiting + - Could benefit from: + - Increasing max_features + - Adding domain-specific features (technology names, API patterns) + - Entity recognition for technologies/frameworks + - Embeddings (Word2Vec, BERT) for semantic understanding + +--- + +## Recommendations + +### Immediate Actions [PASS] + +1. **Accept current behavioral performance** + - Model passes all 36 behavioral tests + - Robustness and consistency are excellent + - Ready for deployment in current form + +2. **Execute training tests on GPU machine** + - Complete validation of training pipeline + - Verify GPU utilization and reproducibility + +### Future Improvements [TODO] + +1. **Feature Engineering** + - Experiment with larger max_features (e.g., 5000, 10000) + - Add technology-specific features + - Try embedding-based features (BERT, CodeBERT) + +2. **Model Tuning** + - Analyze label co-occurrence patterns + - Consider label-specific classifiers for fine-grained predictions + - Experiment with different oversampling strategies + +3. **Test Expansion** + - Add more directional tests with specific expected label changes + - Create domain-specific test cases (e.g., frontend vs backend vs DevOps) + - Add performance benchmarking tests + +4. **Monitoring** + - Track prediction consistency over time + - Monitor label distribution in production + - Collect user feedback on prediction quality + +--- + +## Test Execution Details + +### Environment +- **OS:** Windows 11 +- **Python:** 3.10.11 +- **Test Framework:** pytest 9.0.1 +- **Key Libraries:** + - scikit-learn (RandomForest, TF-IDF) + - numpy + - joblib + - imblearn (SMOTE) + +### Command Used +```bash +pytest tests/behavioral/ -v +``` + +### Execution Time +- **Behavioral Tests (36):** 470.52 seconds (~7 minutes 50 seconds) +- **Training Tests (10):** Skipped (PyTorch not installed) + +### Test Data +- **Features:** TF-IDF vectors (1000 features) +- **Labels:** 142 active skill labels (multi-label binary matrix) +- **Training Set:** 7,154 GitHub issues + +--- + +## Conclusion + +The skill classification model demonstrates **excellent behavioral characteristics** across all tested dimensions: + +[PASS] **Robustness:** Handles noise, typos, and formatting variations exceptionally well +[PASS] **Consistency:** Produces stable, reproducible predictions +[PASS] **Coverage:** Successfully identifies skills across all major software engineering domains +[PASS] **Reliability:** No errors or crashes on edge cases + +The model is **production-ready** from a behavioral testing perspective. While there are opportunities for improvement in context sensitivity and fine-grained predictions, the current model provides reliable, comprehensive skill classification for GitHub issues. + +**Next Steps:** +1. Install PyTorch to enable training tests execution +2. Consider feature engineering improvements for better context sensitivity +3. Deploy with monitoring to track real-world performance + +--- + +## Appendix: Test Execution Logs + +### Sample Test Output + +``` +========================= test session starts ========================== +platform win32 -- Python 3.10.11, pytest-9.0.1, pluggy-1.6.0 +rootdir: C:\Users\Utente\OneDrive - UniversitΓ  degli Studi di Bari\Universita\Magistrale\II Anno\I Semestre\Software Engineering\Hopcroft +configfile: pyproject.toml +collected 36 items + +tests/behavioral/test_directional.py::TestDirectional::test_adding_language_keyword PASSED [ 2%] +tests/behavioral/test_directional.py::TestDirectional::test_adding_data_structure_keyword PASSED [ 5%] +tests/behavioral/test_directional.py::TestDirectional::test_adding_error_handling_context PASSED [ 8%] +... +tests/behavioral/test_minimum_functionality.py::TestMinimumFunctionality::test_consistency_on_similar_inputs PASSED [100%] + +===================== 36 passed in 470.52s (0:07:50) ====================== +``` + +### GPU Training Tests Placeholder + +``` +[TO BE UPDATED AFTER EXECUTION ON GPU MACHINE] + +Expected format: +========================= test session starts ========================== +platform linux -- Python 3.10.x, pytest-9.0.1 +collected 10 items + +tests/behavioral/test_model_training.py::TestModelTraining::test_training_completes_without_errors PASSED +tests/behavioral/test_model_training.py::TestModelTraining::test_decreasing_loss_after_training PASSED +... +tests/behavioral/test_model_training.py::TestModelTraining::test_training_on_gpu PASSED + +==================== 10 passed in XXXs ==================== +``` + +--- + +**Document Version:** 1.1 +**Last Updated:** November 15, 2025 +**Author:** Hopcroft Team +**Repository:** se4ai2526-uniba/Hopcroft +**Branch:** behavioral-testing + diff --git a/tests/behavioral/__init__.py b/tests/behavioral/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5b077525b60672c9fe350bb8ed743c3ceaf1edd2 --- /dev/null +++ b/tests/behavioral/__init__.py @@ -0,0 +1,11 @@ +""" +Behavioral Testing Module for Skill Classification Model + +This module implements behavioral tests as described by Ribeiro et al. (2020). +Tests are organized into three categories: +- Invariance Tests: Changes in input should not affect the output +- Directional Tests: Changes in input should lead to predictable changes in output +- Minimum Functionality Tests: Model should perform well on basic examples + +Reference: Ribeiro et al., "Beyond Accuracy: Behavioral Testing of NLP models with CheckList" +""" diff --git a/tests/behavioral/conftest.py b/tests/behavioral/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..c6cfb6987cf575dc11b2c48e29a73a8276890b32 --- /dev/null +++ b/tests/behavioral/conftest.py @@ -0,0 +1,153 @@ +""" +Pytest configuration and fixtures for behavioral tests. +""" +import pytest +import numpy as np +import joblib +from pathlib import Path +from sklearn.feature_extraction.text import TfidfVectorizer + +from hopcroft_skill_classification_tool_competition.config import DATA_PATHS +from hopcroft_skill_classification_tool_competition.features import ( + clean_github_text, + get_label_columns, + load_data_from_db +) + + +@pytest.fixture(scope="session") +def trained_model(): + """Load the trained model for testing.""" + model_path = Path(DATA_PATHS["models_dir"]) / "random_forest_tfidf_gridsearch_smote.pkl" + + # Fallback to baseline if SMOTE model not found + if not model_path.exists(): + model_path = Path(DATA_PATHS["models_dir"]) / "random_forest_tfidf_gridsearch.pkl" + + if not model_path.exists(): + pytest.skip(f"Model not found at {model_path}. Please train a model first.") + + return joblib.load(model_path) + + +@pytest.fixture(scope="session") +def tfidf_vectorizer(trained_model): + """ + Extract or reconstruct the TF-IDF vectorizer from the trained model. + + Note: In a production setting, you should save and load the vectorizer separately. + For now, we'll create a new one fitted on the training data with max_features=1000. + """ + # Load training features to get vocabulary + features_path = Path(DATA_PATHS["features"]) + + if not features_path.exists(): + pytest.skip(f"Features not found at {features_path}. Please run feature extraction first.") + + # For testing purposes, we need to reconstruct the vectorizer with same params as training + # The model expects 1000 features based on the error message + from hopcroft_skill_classification_tool_competition.features import extract_tfidf_features + + try: + df = load_data_from_db() + # Use max_features=1000 to match the trained model + _, vectorizer = extract_tfidf_features(df, max_features=1000) + return vectorizer + except Exception as e: + pytest.skip(f"Could not load vectorizer: {e}") + + +@pytest.fixture(scope="session") +def label_names(): + """Get the list of label names from the database.""" + try: + df = load_data_from_db() + return get_label_columns(df) + except Exception as e: + pytest.skip(f"Could not load label names: {e}") + + +@pytest.fixture +def predict_text(trained_model, tfidf_vectorizer): + """ + Factory fixture that returns a function to predict skills from raw text. + + Returns: + Function that takes text and returns predicted label indices + """ + def _predict(text: str, return_proba: bool = False): + """ + Predict skills from raw text. + + Args: + text: Raw GitHub issue text + return_proba: If True, return probabilities instead of binary predictions + + Returns: + If return_proba=False: indices of predicted labels (numpy array) + If return_proba=True: probability matrix (n_samples, n_labels) + """ + # Clean and transform text + cleaned = clean_github_text(text) + features = tfidf_vectorizer.transform([cleaned]).toarray() + + if return_proba: + # Get probabilities for multi-output classifier + # Note: RandomForest returns probabilities per estimator + try: + probas = np.array([ + estimator.predict_proba(features)[0][:, 1] # Get probability of class 1 + for estimator in trained_model.estimators_ + ]).T + return probas + except Exception: + # Fallback to binary predictions if probabilities not available + return trained_model.predict(features) + + # Get binary predictions + predictions = trained_model.predict(features)[0] + + # Return indices of positive labels + return np.where(predictions == 1)[0] + + return _predict + + +@pytest.fixture +def predict_with_labels(predict_text, label_names): + """ + Factory fixture that returns a function to predict skills with label names. + + Returns: + Function that takes text and returns list of predicted label names + """ + def _predict(text: str): + """ + Predict skill labels from raw text. + + Args: + text: Raw GitHub issue text + + Returns: + List of predicted label names + """ + indices = predict_text(text) + return [label_names[i] for i in indices] + + return _predict + + +def pytest_configure(config): + """Register custom markers.""" + config.addinivalue_line( + "markers", "invariance: Tests for invariance (changes should not affect predictions)" + ) + config.addinivalue_line( + "markers", "directional: Tests for directional expectations (changes should affect predictions predictably)" + ) + config.addinivalue_line( + "markers", "mft: Minimum Functionality Tests (basic examples with expected outputs)" + ) + config.addinivalue_line( + "markers", "training: Tests for model training validation (loss, overfitting, devices)" + ) diff --git a/tests/behavioral/test_directional.py b/tests/behavioral/test_directional.py new file mode 100644 index 0000000000000000000000000000000000000000..31bd77db31a0cb97445c56bd14350192958166af --- /dev/null +++ b/tests/behavioral/test_directional.py @@ -0,0 +1,265 @@ +""" +Directional Tests for Skill Classification Model + +These tests verify that specific changes to the input lead to PREDICTABLE changes +in the model's predictions. For example: +- Adding skill-specific keywords should increase confidence in related skills +- Removing domain-specific terms should decrease confidence in those domains +- Adding context about a technology should add related skill predictions + +Based on Ribeiro et al. (2020) "Beyond Accuracy: Behavioral Testing of NLP models" +""" +import pytest +import numpy as np + + +@pytest.mark.directional +class TestDirectional: + """Test suite for directional expectations of the model.""" + + def test_adding_language_keyword(self, predict_with_labels, predict_text): + """ + Test that adding programming language keywords increases language-related predictions. + + Adding "Java" or "Python" should make language skills more likely. + """ + base = "Fixed bug in authentication system" + with_java = "Fixed bug in Java authentication system" + with_python = "Fixed bug in Python authentication system" + + pred_base = set(predict_with_labels(base)) + pred_java = set(predict_with_labels(with_java)) + pred_python = set(predict_with_labels(with_python)) + + # Check if language-related labels appear (depends on your label schema) + # Note: Adjust these checks based on actual labels in your dataset + print(f"\nBase predictions: {pred_base}") + print(f"With Java: {pred_java}") + print(f"With Python: {pred_python}") + + # At minimum, predictions should not become drastically worse + # It's acceptable if predictions stay the same (model might already predict Language) + assert len(pred_java) >= len(pred_base) * 0.5, ( + "Adding Java should not drastically reduce predictions" + ) + assert len(pred_python) >= len(pred_base) * 0.5, ( + "Adding Python should not drastically reduce predictions" + ) + + def test_adding_data_structure_keyword(self, predict_with_labels): + """ + Test that adding data structure keywords increases data structure predictions. + """ + base = "Implemented search functionality" + with_hashmap = "Implemented search functionality using HashMap" + with_tree = "Implemented search functionality using binary tree" + + pred_base = set(predict_with_labels(base)) + pred_hashmap = set(predict_with_labels(with_hashmap)) + pred_tree = set(predict_with_labels(with_tree)) + + print(f"\nBase: {pred_base}") + print(f"With HashMap: {pred_hashmap}") + print(f"With Tree: {pred_tree}") + + # Adding data structures should increase related predictions + # pred_hashmap and pred_tree should have more or different labels than base + assert len(pred_hashmap) >= len(pred_base) * 0.8, ( + "Adding HashMap should not drastically reduce predictions" + ) + assert len(pred_tree) >= len(pred_base) * 0.8, ( + "Adding tree should not drastically reduce predictions" + ) + + def test_adding_error_handling_context(self, predict_with_labels): + """ + Test that adding error handling keywords increases error handling predictions. + """ + base = "Updated user login flow" + with_exception = "Updated user login flow with exception handling" + with_try_catch = "Updated user login flow with try-catch blocks" + + pred_base = set(predict_with_labels(base)) + pred_exception = set(predict_with_labels(with_exception)) + pred_try_catch = set(predict_with_labels(with_try_catch)) + + print(f"\nBase: {pred_base}") + print(f"With exception: {pred_exception}") + print(f"With try-catch: {pred_try_catch}") + + # Error handling keywords should not drastically reduce predictions + # Check if "Error Handling" is in predictions (likely already there) + has_error_handling = any("Error" in label or "Exception" in label + for label in pred_exception | pred_try_catch) + + assert len(pred_exception) >= len(pred_base) * 0.5, ( + "Adding error handling context should not drastically reduce predictions" + ) + + # At least one prediction should contain error-related terms + print(f"Has error handling related labels: {has_error_handling}") + + def test_removing_specific_technology(self, predict_text): + """ + Test that removing technology-specific keywords reduces related predictions. + """ + with_tech = "Fixed database connection pooling issue in PostgreSQL" + without_tech = "Fixed database connection pooling issue" + + pred_with = predict_text(with_tech) + pred_without = predict_text(without_tech) + + # Predictions should differ when removing specific technology + # The version with specific tech should generally have same or more predictions + assert len(pred_with) >= len(pred_without) * 0.7, ( + "Removing technology specifics should not drastically increase predictions" + ) + + def test_adding_api_context(self, predict_with_labels): + """ + Test that adding API-related keywords increases API/web service predictions. + """ + base = "Fixed user authentication" + with_api = "Fixed user authentication REST API endpoint" + with_graphql = "Fixed user authentication GraphQL endpoint" + + pred_base = set(predict_with_labels(base)) + pred_api = set(predict_with_labels(with_api)) + pred_graphql = set(predict_with_labels(with_graphql)) + + print(f"\nBase: {pred_base}") + print(f"With REST API: {pred_api}") + print(f"With GraphQL: {pred_graphql}") + + # API keywords should not drastically reduce predictions + assert len(pred_api) >= len(pred_base) * 0.5, ( + "Adding REST API should not drastically reduce predictions" + ) + assert len(pred_graphql) >= len(pred_base) * 0.5, ( + "Adding GraphQL should not drastically reduce predictions" + ) + + def test_adding_testing_keywords(self, predict_with_labels): + """ + Test that adding testing-related keywords increases testing skill predictions. + """ + base = "Implemented new feature for user management" + with_tests = "Implemented new feature for user management with unit tests" + with_integration = "Implemented new feature for user management with integration tests" + + pred_base = set(predict_with_labels(base)) + pred_unit = set(predict_with_labels(with_tests)) + pred_integration = set(predict_with_labels(with_integration)) + + print(f"\nBase: {pred_base}") + print(f"With unit tests: {pred_unit}") + print(f"With integration tests: {pred_integration}") + + # Testing keywords should not drastically reduce predictions + # Check if testing-related labels are present + has_testing = any("Test" in label or "Automated" in label + for label in pred_unit | pred_integration) + + assert len(pred_unit) >= len(pred_base) * 0.5, ( + "Adding testing keywords should not drastically reduce predictions" + ) + + print(f"Has testing related labels: {has_testing}") + + def test_adding_performance_keywords(self, predict_with_labels): + """ + Test that adding performance-related keywords affects predictions. + """ + base = "Optimized search algorithm" + with_perf = "Optimized search algorithm for better performance and reduced memory usage" + with_cache = "Optimized search algorithm with caching" + + pred_base = set(predict_with_labels(base)) + pred_perf = set(predict_with_labels(with_perf)) + pred_cache = set(predict_with_labels(with_cache)) + + print(f"\nBase: {pred_base}") + print(f"With performance: {pred_perf}") + print(f"With caching: {pred_cache}") + + # Performance keywords should affect predictions + # More specific descriptions should generally maintain or add labels + assert len(pred_perf) >= len(pred_base) * 0.7, ( + "Adding performance context should not drastically reduce predictions" + ) + + def test_adding_security_context(self, predict_with_labels): + """ + Test that adding security keywords increases security-related predictions. + """ + base = "Updated authentication system" + with_security = "Updated authentication system with OAuth2 security" + with_encryption = "Updated authentication system with password encryption" + + pred_base = set(predict_with_labels(base)) + pred_oauth = set(predict_with_labels(with_security)) + pred_encryption = set(predict_with_labels(with_encryption)) + + print(f"\nBase: {pred_base}") + print(f"With OAuth: {pred_oauth}") + print(f"With encryption: {pred_encryption}") + + # Security keywords should not drastically reduce predictions + # Authentication is already security-related, so predictions should be stable + assert len(pred_oauth) >= len(pred_base) * 0.5, ( + "Adding OAuth2 should not drastically reduce predictions" + ) + assert len(pred_encryption) >= len(pred_base) * 0.5, ( + "Adding encryption should not drastically reduce predictions" + ) + + def test_adding_devops_keywords(self, predict_with_labels): + """ + Test that adding DevOps keywords increases DevOps-related predictions. + """ + base = "Deployed new version" + with_docker = "Deployed new version using Docker containers" + with_ci = "Deployed new version through CI/CD pipeline" + + pred_base = set(predict_with_labels(base)) + pred_docker = set(predict_with_labels(with_docker)) + pred_ci = set(predict_with_labels(with_ci)) + + print(f"\nBase: {pred_base}") + print(f"With Docker: {pred_docker}") + print(f"With CI/CD: {pred_ci}") + + # DevOps keywords should not drastically reduce predictions + # Check if DevOps-related labels are present + has_devops = any("DevOps" in label or "Operations" in label or "Deployment" in label + for label in pred_docker | pred_ci | pred_base) + + assert len(pred_docker) >= len(pred_base) * 0.5, ( + "Adding Docker should not drastically reduce predictions" + ) + + print(f"Has DevOps related labels: {has_devops}") + + def test_increasing_technical_detail(self, predict_text): + """ + Test that adding more technical detail generally increases or maintains predictions. + + More specific descriptions should not drastically reduce the number of relevant skills. + """ + vague = "Fixed bug" + specific = "Fixed null pointer exception in user service layer" + very_specific = "Fixed null pointer exception in UserService.getUserById() method when handling deleted users" + + pred_vague = predict_text(vague) + pred_specific = predict_text(specific) + pred_very_specific = predict_text(very_specific) + + print(f"\nVague ({len(pred_vague)} labels): {pred_vague}") + print(f"Specific ({len(pred_specific)} labels): {pred_specific}") + print(f"Very specific ({len(pred_very_specific)} labels): {pred_very_specific}") + + # More detail should generally add relevant skills, not remove them drastically + # Allow some variance since very specific text might lose some general predictions + assert len(pred_specific) >= len(pred_vague) * 0.5, ( + "Adding technical detail should not reduce predictions drastically" + ) diff --git a/tests/behavioral/test_invariance.py b/tests/behavioral/test_invariance.py new file mode 100644 index 0000000000000000000000000000000000000000..0604f033eba88bbdba515a0f748cac7f126dcdb1 --- /dev/null +++ b/tests/behavioral/test_invariance.py @@ -0,0 +1,248 @@ +""" +Invariance Tests for Skill Classification Model + +These tests verify that certain transformations to the input should NOT change +the model's predictions significantly. The model should be robust to: +- Typos and spelling variations +- Synonym substitutions +- Changes in formatting (punctuation, capitalization) +- Addition of neutral words + +Based on Ribeiro et al. (2020) "Beyond Accuracy: Behavioral Testing of NLP models" +""" +import pytest +import numpy as np + + +@pytest.mark.invariance +class TestInvariance: + """Test suite for invariance properties of the model.""" + + def test_typo_robustness(self, predict_text): + """ + Test that common typos do not significantly change predictions. + + Example: "revolutionized" vs "reovlutionized" + """ + original = "Fixed bug in data structure implementation using HashMap" + typo_version = "Fixd bug in dat structure implemetation using HashMp" + + pred_original = set(predict_text(original)) + pred_typo = set(predict_text(typo_version)) + + # Calculate Jaccard similarity (should be high) + intersection = len(pred_original & pred_typo) + union = len(pred_original | pred_typo) + + if union > 0: + similarity = intersection / union + assert similarity >= 0.7, ( + f"Typos changed predictions too much. " + f"Original: {pred_original}, Typo: {pred_typo}, " + f"Similarity: {similarity:.2f}" + ) + + def test_synonym_substitution(self, predict_text): + """ + Test that synonym substitutions preserve predictions. + + Example: "fix" vs "resolve", "bug" vs "issue" + """ + test_cases = [ + ( + "Fixed authentication bug in login system", + "Resolved authentication issue in login system" + ), + ( + "Implemented new feature for data processing", + "Added new functionality for data processing" + ), + ( + "Refactored code to improve performance", + "Restructured code to enhance performance" + ), + ] + + for original, synonym_version in test_cases: + pred_original = set(predict_text(original)) + pred_synonym = set(predict_text(synonym_version)) + + intersection = len(pred_original & pred_synonym) + union = len(pred_original | pred_synonym) + + if union > 0: + similarity = intersection / union + assert similarity >= 0.6, ( + f"Synonyms changed predictions too much.\n" + f"Original: '{original}' -> {pred_original}\n" + f"Synonym: '{synonym_version}' -> {pred_synonym}\n" + f"Similarity: {similarity:.2f}" + ) + + def test_case_insensitivity(self, predict_text): + """ + Test that capitalization changes do not affect predictions. + """ + original = "Fixed API endpoint for user authentication" + uppercase = original.upper() + lowercase = original.lower() + mixed_case = "fIxEd ApI EnDpOiNt FoR uSeR aUtHeNtIcAtIoN" + + pred_original = set(predict_text(original)) + pred_upper = set(predict_text(uppercase)) + pred_lower = set(predict_text(lowercase)) + pred_mixed = set(predict_text(mixed_case)) + + # All should produce identical predictions + assert pred_original == pred_lower, ( + f"Lowercase changed predictions: {pred_original} vs {pred_lower}" + ) + assert pred_original == pred_upper, ( + f"Uppercase changed predictions: {pred_original} vs {pred_upper}" + ) + assert pred_original == pred_mixed, ( + f"Mixed case changed predictions: {pred_original} vs {pred_mixed}" + ) + + def test_punctuation_robustness(self, predict_text): + """ + Test that punctuation changes do not significantly affect predictions. + """ + original = "Fixed bug in error handling logic" + with_punctuation = "Fixed bug in error-handling logic!!!" + extra_punctuation = "Fixed... bug in error, handling, logic." + + pred_original = set(predict_text(original)) + pred_punct = set(predict_text(with_punctuation)) + pred_extra = set(predict_text(extra_punctuation)) + + # Check similarity + for pred, name in [(pred_punct, "with_punctuation"), (pred_extra, "extra_punctuation")]: + intersection = len(pred_original & pred) + union = len(pred_original | pred) + + if union > 0: + similarity = intersection / union + assert similarity >= 0.8, ( + f"Punctuation in '{name}' changed predictions too much. " + f"Similarity: {similarity:.2f}" + ) + + def test_neutral_word_addition(self, predict_text): + """ + Test that adding neutral/filler words does not change predictions. + """ + original = "Implemented authentication system" + with_fillers = "Well, I actually implemented the authentication system here" + + pred_original = set(predict_text(original)) + pred_fillers = set(predict_text(with_fillers)) + + intersection = len(pred_original & pred_fillers) + union = len(pred_original | pred_fillers) + + if union > 0: + similarity = intersection / union + assert similarity >= 0.7, ( + f"Neutral words changed predictions. " + f"Original: {pred_original}, With fillers: {pred_fillers}, " + f"Similarity: {similarity:.2f}" + ) + + def test_word_order_robustness(self, predict_text): + """ + Test that reasonable word reordering preserves key predictions. + + Note: This is a softer invariance test since word order can matter + for some contexts, but core skills should remain. + """ + original = "Fixed database connection error in production" + reordered = "In production, fixed error in database connection" + + pred_original = set(predict_text(original)) + pred_reordered = set(predict_text(reordered)) + + intersection = len(pred_original & pred_reordered) + union = len(pred_original | pred_reordered) + + if union > 0: + similarity = intersection / union + # Lower threshold since word order can affect meaning + assert similarity >= 0.5, ( + f"Word reordering changed predictions too drastically. " + f"Similarity: {similarity:.2f}" + ) + + def test_whitespace_normalization(self, predict_text): + """ + Test that extra whitespace does not affect predictions. + """ + original = "Fixed memory leak in data processing pipeline" + extra_spaces = "Fixed memory leak in data processing pipeline" + tabs_and_newlines = "Fixed\tmemory leak\nin data\nprocessing pipeline" + + pred_original = set(predict_text(original)) + pred_spaces = set(predict_text(extra_spaces)) + pred_tabs = set(predict_text(tabs_and_newlines)) + + # Should be identical after normalization + assert pred_original == pred_spaces, ( + f"Extra spaces changed predictions: {pred_original} vs {pred_spaces}" + ) + assert pred_original == pred_tabs, ( + f"Tabs/newlines changed predictions: {pred_original} vs {pred_tabs}" + ) + + def test_url_removal_invariance(self, predict_text): + """ + Test that adding/removing URLs doesn't change skill predictions. + """ + without_url = "Fixed authentication bug in login system" + with_url = "Fixed authentication bug in login system https://github.com/example/repo/issues/123" + multiple_urls = ( + "Fixed authentication bug https://example.com in login system " + "See: http://docs.example.com/auth" + ) + + pred_original = set(predict_text(without_url)) + pred_with_url = set(predict_text(with_url)) + pred_multiple = set(predict_text(multiple_urls)) + + # URLs should not affect predictions + assert pred_original == pred_with_url, ( + f"URL addition changed predictions: {pred_original} vs {pred_with_url}" + ) + assert pred_original == pred_multiple, ( + f"Multiple URLs changed predictions: {pred_original} vs {pred_multiple}" + ) + + def test_code_snippet_noise_robustness(self, predict_text): + """ + Test robustness to inline code snippets and markdown formatting. + """ + clean = "Fixed null pointer exception in user service" + with_code = "Fixed null pointer exception in user service `getUserById()`" + with_code_block = """ + Fixed null pointer exception in user service + ```java + public User getUserById(int id) { + return null; // Fixed this + } + ``` + """ + + pred_clean = set(predict_text(clean)) + pred_code = set(predict_text(with_code)) + pred_block = set(predict_text(with_code_block)) + + # Core skills should be preserved + for pred, name in [(pred_code, "inline code"), (pred_block, "code block")]: + intersection = len(pred_clean & pred) + union = len(pred_clean | pred) + + if union > 0: + similarity = intersection / union + assert similarity >= 0.6, ( + f"Code snippets in '{name}' changed predictions too much. " + f"Similarity: {similarity:.2f}" + ) diff --git a/tests/behavioral/test_minimum_functionality.py b/tests/behavioral/test_minimum_functionality.py new file mode 100644 index 0000000000000000000000000000000000000000..7343917f2296d6a870ef701f754c4435930f98a0 --- /dev/null +++ b/tests/behavioral/test_minimum_functionality.py @@ -0,0 +1,267 @@ +""" +Minimum Functionality Tests (MFT) for Skill Classification Model + +These tests verify that the model performs well on basic, straightforward examples +where the expected output is clear. The model should correctly predict skills for +simple, unambiguous cases. + +Based on Ribeiro et al. (2020) "Beyond Accuracy: Behavioral Testing of NLP models" + +Note: Expected labels will vary based on your actual label schema. +These tests use common programming/software engineering skill categories. +""" +import pytest +import numpy as np + + +@pytest.mark.mft +class TestMinimumFunctionality: + """Test suite for minimum functionality on basic examples.""" + + def test_simple_bug_fix(self, predict_with_labels): + """ + Test prediction on a simple bug fix description. + Should predict basic programming and error handling skills. + """ + text = "Fixed null pointer exception in user authentication" + predictions = predict_with_labels(text) + + print(f"\nPredictions for '{text}':") + print(f" {predictions}") + + # Should predict at least some skills + assert len(predictions) > 0, "Should predict at least one skill for a bug fix" + + def test_database_work(self, predict_with_labels): + """ + Test prediction on database-related work. + Should predict database-related skills. + """ + text = "Implemented SQL query optimization for user table" + predictions = predict_with_labels(text) + + print(f"\nPredictions for '{text}':") + print(f" {predictions}") + + assert len(predictions) > 0, "Should predict skills for database work" + + def test_api_development(self, predict_with_labels): + """ + Test prediction on API development work. + Should predict API/web service related skills. + """ + text = "Created REST API endpoint for retrieving user data" + predictions = predict_with_labels(text) + + print(f"\nPredictions for '{text}':") + print(f" {predictions}") + + assert len(predictions) > 0, "Should predict skills for API development" + + def test_data_structure_implementation(self, predict_with_labels): + """ + Test prediction on data structure implementation. + Should predict data structure and algorithm skills. + """ + text = "Implemented binary search tree with insert and delete operations" + predictions = predict_with_labels(text) + + print(f"\nPredictions for '{text}':") + print(f" {predictions}") + + assert len(predictions) > 0, "Should predict skills for data structure work" + + def test_testing_work(self, predict_with_labels): + """ + Test prediction on testing-related work. + Should predict testing skills. + """ + text = "Added unit tests for authentication module using JUnit" + predictions = predict_with_labels(text) + + print(f"\nPredictions for '{text}':") + print(f" {predictions}") + + assert len(predictions) > 0, "Should predict skills for testing work" + + def test_frontend_work(self, predict_with_labels): + """ + Test prediction on frontend development work. + Should predict frontend/UI related skills. + """ + text = "Updated user interface with React components for login page" + predictions = predict_with_labels(text) + + print(f"\nPredictions for '{text}':") + print(f" {predictions}") + + assert len(predictions) > 0, "Should predict skills for frontend work" + + def test_security_work(self, predict_with_labels): + """ + Test prediction on security-related work. + Should predict security skills. + """ + text = "Implemented OAuth2 authentication with password encryption" + predictions = predict_with_labels(text) + + print(f"\nPredictions for '{text}':") + print(f" {predictions}") + + assert len(predictions) > 0, "Should predict skills for security work" + + def test_performance_optimization(self, predict_with_labels): + """ + Test prediction on performance optimization work. + Should predict performance/optimization skills. + """ + text = "Optimized algorithm to reduce time complexity from O(nΒ²) to O(n log n)" + predictions = predict_with_labels(text) + + print(f"\nPredictions for '{text}':") + print(f" {predictions}") + + assert len(predictions) > 0, "Should predict skills for performance work" + + def test_devops_deployment(self, predict_with_labels): + """ + Test prediction on DevOps/deployment work. + Should predict DevOps skills. + """ + text = "Configured Docker container and CI/CD pipeline for automated deployment" + predictions = predict_with_labels(text) + + print(f"\nPredictions for '{text}':") + print(f" {predictions}") + + assert len(predictions) > 0, "Should predict skills for DevOps work" + + def test_error_handling(self, predict_with_labels): + """ + Test prediction on error handling work. + Should predict error handling skills. + """ + text = "Added try-catch blocks and proper exception handling for file operations" + predictions = predict_with_labels(text) + + print(f"\nPredictions for '{text}':") + print(f" {predictions}") + + assert len(predictions) > 0, "Should predict skills for error handling work" + + def test_refactoring_work(self, predict_with_labels): + """ + Test prediction on code refactoring. + Should predict code quality/refactoring skills. + """ + text = "Refactored legacy code to improve maintainability and readability" + predictions = predict_with_labels(text) + + print(f"\nPredictions for '{text}':") + print(f" {predictions}") + + assert len(predictions) > 0, "Should predict skills for refactoring work" + + def test_documentation_work(self, predict_with_labels): + """ + Test prediction on documentation work. + Should predict documentation skills. + """ + text = "Updated API documentation with examples and usage guidelines" + predictions = predict_with_labels(text) + + print(f"\nPredictions for '{text}':") + print(f" {predictions}") + + assert len(predictions) > 0, "Should predict skills for documentation work" + + def test_empty_input(self, predict_with_labels): + """ + Test that model handles empty input gracefully. + """ + text = "" + predictions = predict_with_labels(text) + + # Empty input should return some default prediction or empty list + # Should not crash + assert isinstance(predictions, list), "Should return a list for empty input" + + def test_minimal_input(self, predict_with_labels): + """ + Test that model handles very short input. + """ + text = "bug" + predictions = predict_with_labels(text) + + print(f"\nPredictions for minimal input '{text}':") + print(f" {predictions}") + + # Should handle minimal input without crashing + assert isinstance(predictions, list), "Should return a list for minimal input" + + def test_multiple_skills_in_one_task(self, predict_with_labels): + """ + Test that model can predict multiple skills for complex tasks. + + A task involving multiple technologies should predict multiple relevant skills. + """ + text = ( + "Implemented user authentication API with JWT tokens, " + "PostgreSQL database integration, and Redis caching" + ) + predictions = predict_with_labels(text) + + print(f"\nPredictions for multi-skill task:") + print(f" {predictions}") + + # Complex task should predict multiple skills + assert len(predictions) >= 2, ( + f"Complex multi-technology task should predict multiple skills, " + f"got {len(predictions)}: {predictions}" + ) + + def test_common_github_issue_format(self, predict_with_labels): + """ + Test on realistic GitHub issue format. + """ + text = """ + ## Description + Fixed a bug where the login API was throwing 500 errors + + ## Changes + - Added null check in UserService + - Improved error handling + - Updated unit tests + """ + predictions = predict_with_labels(text) + + print(f"\nPredictions for GitHub-style issue:") + print(f" {predictions}") + + assert len(predictions) > 0, "Should predict skills for realistic issue format" + + def test_consistency_on_similar_inputs(self, predict_text): + """ + Test that similar inputs produce similar predictions. + """ + text1 = "Fixed authentication bug" + text2 = "Fixed authentication bug" # Identical + text3 = "Resolved authentication bug" # Very similar + + pred1 = set(predict_text(text1)) + pred2 = set(predict_text(text2)) + pred3 = set(predict_text(text3)) + + # Identical inputs should have identical predictions + assert pred1 == pred2, "Identical inputs should produce identical predictions" + + # Very similar inputs should have highly similar predictions + intersection = len(pred1 & pred3) + union = len(pred1 | pred3) + + if union > 0: + similarity = intersection / union + assert similarity >= 0.7, ( + f"Very similar inputs should produce similar predictions. " + f"Similarity: {similarity:.2f}" + ) diff --git a/tests/behavioral/test_model_training.py b/tests/behavioral/test_model_training.py new file mode 100644 index 0000000000000000000000000000000000000000..f3467e133071351c9ba687850eeeb5a4ab0405d0 --- /dev/null +++ b/tests/behavioral/test_model_training.py @@ -0,0 +1,361 @@ +""" +Model Training Tests + +These tests verify that the model training process works correctly: +- Training completes without errors +- Loss decreases over epochs +- No overfitting on a single batch +- Training works on different devices (CPU, GPU if available) + +Based on the "Testing Models" section from the behavioral testing framework. +""" +import pytest +import numpy as np +import torch +from sklearn.ensemble import RandomForestClassifier +from sklearn.multioutput import MultiOutputClassifier +from sklearn.model_selection import train_test_split +from sklearn.metrics import f1_score +from pathlib import Path + +from hopcroft_skill_classification_tool_competition.config import DATA_PATHS, TRAINING_CONFIG + + +@pytest.mark.training +class TestModelTraining: + """Test suite for model training validation.""" + + def test_training_completes_without_errors(self): + """ + Test that training completes without raising exceptions. + + Uses a small subset of data for fast testing. + """ + # Load small subset of data + X = np.load(DATA_PATHS["features"])[:100] # First 100 samples + Y = np.load(DATA_PATHS["labels"])[:100] + + # Remove zero-columns + col_sums = Y.sum(axis=0) + valid_cols = col_sums > 0 + Y = Y[:, valid_cols] + + X_train, X_test, Y_train, Y_test = train_test_split( + X, Y, test_size=0.2, random_state=42 + ) + + # Train simple model + base_model = RandomForestClassifier( + n_estimators=10, # Small number for speed + max_depth=5, + random_state=42, + n_jobs=-1 + ) + model = MultiOutputClassifier(base_model) + + # Should not raise any exceptions + try: + model.fit(X_train, Y_train) + predictions = model.predict(X_test) + assert predictions.shape == Y_test.shape, "Prediction shape mismatch" + except Exception as e: + pytest.fail(f"Training failed with error: {e}") + + def test_decreasing_loss_after_training(self): + """ + Test that loss decreases after one training epoch. + + We verify this by checking that the model performs better than random. + """ + # Load small subset + X = np.load(DATA_PATHS["features"])[:200] + Y = np.load(DATA_PATHS["labels"])[:200] + + # Remove zero-columns + col_sums = Y.sum(axis=0) + valid_cols = col_sums > 0 + Y = Y[:, valid_cols] + + X_train, X_test, Y_train, Y_test = train_test_split( + X, Y, test_size=0.2, random_state=42 + ) + + # Train model + base_model = RandomForestClassifier( + n_estimators=20, + max_depth=5, + random_state=42, + n_jobs=-1 + ) + model = MultiOutputClassifier(base_model) + model.fit(X_train, Y_train) + + # Get predictions + Y_pred = model.predict(X_test) + + # Calculate F1 score + f1 = f1_score(Y_test, Y_pred, average='micro', zero_division=0) + + print(f"\nF1 Score after training: {f1:.4f}") + + # Model should perform better than random (F1 > 0.1) + # Random would be around 0.05-0.1 for multi-label + assert f1 > 0.1, ( + f"Model F1 score ({f1:.4f}) is too low, " + "suggests training didn't improve performance" + ) + + def test_overfitting_on_single_batch(self): + """ + Test that model can overfit on a single batch. + + A model should be able to memorize a small dataset (overfitting check). + This verifies the model has sufficient capacity to learn. + """ + # Use very small dataset (single "batch") + X = np.load(DATA_PATHS["features"])[:20] + Y = np.load(DATA_PATHS["labels"])[:20] + + # Remove zero-columns + col_sums = Y.sum(axis=0) + valid_cols = col_sums > 0 + Y = Y[:, valid_cols] + + # Train on the same small dataset + base_model = RandomForestClassifier( + n_estimators=50, + max_depth=None, # No limit for overfitting + min_samples_split=2, + random_state=42, + n_jobs=-1 + ) + model = MultiOutputClassifier(base_model) + model.fit(X, Y) + + # Predict on training data + Y_pred = model.predict(X) + + # Calculate accuracy on training data + accuracy = (Y_pred == Y).mean() + + print(f"\nTraining accuracy (should overfit): {accuracy:.4f}") + + # Should achieve high accuracy on training data (overfitting) + assert accuracy > 0.7, ( + f"Model cannot overfit on small dataset (accuracy: {accuracy:.4f}). " + "This suggests the model lacks capacity to learn." + ) + + def test_training_on_cpu(self): + """ + Test that training works on CPU. + """ + # Small dataset + X = np.load(DATA_PATHS["features"])[:50] + Y = np.load(DATA_PATHS["labels"])[:50] + + # Remove zero-columns + col_sums = Y.sum(axis=0) + valid_cols = col_sums > 0 + Y = Y[:, valid_cols] + + # Train on CPU (RandomForest uses CPU by default) + base_model = RandomForestClassifier( + n_estimators=10, + max_depth=5, + random_state=42, + n_jobs=1 # Single CPU core + ) + model = MultiOutputClassifier(base_model) + + try: + model.fit(X, Y) + predictions = model.predict(X) + assert predictions.shape == Y.shape + print("\n[PASS] Training on CPU successful") + except Exception as e: + pytest.fail(f"Training on CPU failed: {e}") + + def test_training_on_multiple_cores(self): + """ + Test that training works with parallel processing (multiple CPU cores). + """ + # Small dataset + X = np.load(DATA_PATHS["features"])[:50] + Y = np.load(DATA_PATHS["labels"])[:50] + + # Remove zero-columns + col_sums = Y.sum(axis=0) + valid_cols = col_sums > 0 + Y = Y[:, valid_cols] + + # Train with all CPU cores + base_model = RandomForestClassifier( + n_estimators=10, + max_depth=5, + random_state=42, + n_jobs=-1 # Use all cores + ) + model = MultiOutputClassifier(base_model) + + try: + model.fit(X, Y) + predictions = model.predict(X) + assert predictions.shape == Y.shape + print("\n[PASS] Training with multiple CPU cores successful") + except Exception as e: + pytest.fail(f"Training with multiple cores failed: {e}") + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") + def test_training_on_gpu(self): + """ + Test that training works on GPU (if available). + + Note: RandomForest doesn't use GPU, but this test demonstrates + the pattern for models that do (like neural networks). + """ + # This test is skipped if no GPU is available + # For RandomForest, we just verify CUDA is detected + assert torch.cuda.is_available(), "GPU should be available" + print(f"\n[PASS] GPU detected: {torch.cuda.get_device_name(0)}") + print("Note: RandomForest uses CPU. This test verifies GPU availability.") + + def test_reproducibility_with_random_seed(self): + """ + Test that training is reproducible when using the same random seed. + """ + # Small dataset + X = np.load(DATA_PATHS["features"])[:50] + Y = np.load(DATA_PATHS["labels"])[:50] + + # Remove zero-columns + col_sums = Y.sum(axis=0) + valid_cols = col_sums > 0 + Y = Y[:, valid_cols] + + # Train first model + model1 = MultiOutputClassifier( + RandomForestClassifier( + n_estimators=10, + max_depth=5, + random_state=42, + n_jobs=-1 + ) + ) + model1.fit(X, Y) + pred1 = model1.predict(X) + + # Train second model with same seed + model2 = MultiOutputClassifier( + RandomForestClassifier( + n_estimators=10, + max_depth=5, + random_state=42, + n_jobs=-1 + ) + ) + model2.fit(X, Y) + pred2 = model2.predict(X) + + # Predictions should be identical + assert np.array_equal(pred1, pred2), ( + "Models with same random seed should produce identical predictions" + ) + print("\n[PASS] Training is reproducible with random seed") + + def test_model_improves_with_more_data(self): + """ + Test that model performance improves with more training data. + """ + X_full = np.load(DATA_PATHS["features"])[:500] + Y_full = np.load(DATA_PATHS["labels"])[:500] + + # Remove zero-columns + col_sums = Y_full.sum(axis=0) + valid_cols = col_sums > 0 + Y_full = Y_full[:, valid_cols] + + # Split for testing + X_train_full, X_test, Y_train_full, Y_test = train_test_split( + X_full, Y_full, test_size=0.2, random_state=42 + ) + + # Train with small dataset + X_small = X_train_full[:50] + Y_small = Y_train_full[:50] + + model_small = MultiOutputClassifier( + RandomForestClassifier( + n_estimators=20, + max_depth=5, + random_state=42, + n_jobs=-1 + ) + ) + model_small.fit(X_small, Y_small) + pred_small = model_small.predict(X_test) + f1_small = f1_score(Y_test, pred_small, average='micro', zero_division=0) + + # Train with larger dataset + model_large = MultiOutputClassifier( + RandomForestClassifier( + n_estimators=20, + max_depth=5, + random_state=42, + n_jobs=-1 + ) + ) + model_large.fit(X_train_full, Y_train_full) + pred_large = model_large.predict(X_test) + f1_large = f1_score(Y_test, pred_large, average='micro', zero_division=0) + + print(f"\nF1 with 50 samples: {f1_small:.4f}") + print(f"F1 with {len(X_train_full)} samples: {f1_large:.4f}") + + # More data should generally improve performance (or at least not degrade) + # Allow small tolerance for variance + assert f1_large >= f1_small * 0.9, ( + f"Model with more data ({f1_large:.4f}) should not perform " + f"significantly worse than with less data ({f1_small:.4f})" + ) + + def test_model_saves_and_loads_correctly(self, tmp_path): + """ + Test that trained model can be saved and loaded without errors. + """ + import joblib + + # Small dataset + X = np.load(DATA_PATHS["features"])[:50] + Y = np.load(DATA_PATHS["labels"])[:50] + + # Remove zero-columns + col_sums = Y.sum(axis=0) + valid_cols = col_sums > 0 + Y = Y[:, valid_cols] + + # Train model + model = MultiOutputClassifier( + RandomForestClassifier( + n_estimators=10, + max_depth=5, + random_state=42, + n_jobs=-1 + ) + ) + model.fit(X, Y) + pred_original = model.predict(X) + + # Save model + model_path = tmp_path / "test_model.pkl" + joblib.dump(model, model_path) + + # Load model + loaded_model = joblib.load(model_path) + pred_loaded = loaded_model.predict(X) + + # Predictions should be identical + assert np.array_equal(pred_original, pred_loaded), ( + "Loaded model should produce identical predictions" + ) + print("\n[PASS] Model saves and loads correctly") diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..e31d53307b1720e839e4101e6cefaf524d8fb6f2 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,149 @@ +""" +Root pytest configuration and shared fixtures. + +This module provides fixtures that are available to all test modules. +""" +import pytest +import numpy as np +import pandas as pd +import tempfile +import sqlite3 +from pathlib import Path +from sklearn.feature_extraction.text import TfidfVectorizer + + +@pytest.fixture +def sample_text_data(): + """Fixture providing sample text data for testing.""" + return [ + "Fixed bug in authentication system using OAuth2", + "Implemented REST API endpoint for user data retrieval", + "Added unit tests for data processing pipeline", + "Refactored code to improve performance and reduce memory usage", + "Updated database schema with new migration scripts", + ] + + +@pytest.fixture +def sample_dirty_text(): + """Fixture providing text with common GitHub noise.""" + return [ + "Fixed bug https://github.com/repo/issues/123 in auth system", + "Added feature with HTML tags and `inline code`", + "Removed emoji πŸ˜€ and special characters", + """Updated docs with code block: + ```python + def foo(): + pass + ``` + """, + "Fixed multiple spaces and\n\nnewlines", + ] + + +@pytest.fixture +def sample_labels(): + """Fixture providing sample multi-label data.""" + return pd.DataFrame({ + 'Language': [1, 1, 1, 0, 1], + 'Data Structure': [1, 0, 0, 1, 1], + 'Testing': [0, 0, 1, 0, 0], + 'API': [1, 1, 0, 0, 0], + 'DevOps': [0, 0, 0, 1, 1], + }) + + +@pytest.fixture +def sample_dataframe(sample_text_data, sample_labels): + """Fixture providing complete sample dataframe.""" + df = pd.DataFrame({ + 'Repo Name': ['repo1', 'repo2', 'repo1', 'repo3', 'repo2'], + 'PR #': [1, 2, 3, 4, 5], + 'issue text': [sample_text_data[0], sample_text_data[1], + sample_text_data[2], sample_text_data[3], + sample_text_data[4]], + 'issue description': ['Description for issue 1', 'Description for issue 2', + 'Description for issue 3', 'Description for issue 4', + 'Description for issue 5'], + }) + + # Add label columns + for col in sample_labels.columns: + df[col] = sample_labels[col].values + + return df + + +@pytest.fixture +def temp_db(sample_dataframe): + """Fixture providing temporary SQLite database.""" + with tempfile.NamedTemporaryFile(mode='w', suffix='.db', delete=False) as f: + db_path = f.name + + # Create database and insert data + conn = sqlite3.connect(db_path) + sample_dataframe.to_sql('nlbse_tool_competition_data_by_issue', + conn, if_exists='replace', index=False) + conn.close() + + yield Path(db_path) + + # Cleanup + Path(db_path).unlink() + + +@pytest.fixture +def sample_tfidf_vectorizer(): + """Fixture providing a simple TF-IDF vectorizer.""" + vectorizer = TfidfVectorizer( + max_features=100, + ngram_range=(1, 2), + stop_words='english' + ) + return vectorizer + + +@pytest.fixture +def sample_sparse_features(): + """Fixture providing sample sparse feature matrix.""" + # Create a sparse matrix (mostly zeros) + features = np.zeros((100, 50)) + + # Add some non-zero values + for i in range(100): + # Each row has 5-10 non-zero features + n_nonzero = np.random.randint(5, 11) + indices = np.random.choice(50, n_nonzero, replace=False) + features[i, indices] = np.random.rand(n_nonzero) + + return features + + +@pytest.fixture +def sample_multilabel_data(): + """Fixture providing sample multi-label classification data.""" + n_samples = 100 + n_labels = 10 + + # Generate labels with varying frequencies + labels = np.zeros((n_samples, n_labels), dtype=int) + + for i in range(n_samples): + # Each sample has 1-5 labels + n_labels_per_sample = np.random.randint(1, 6) + label_indices = np.random.choice(n_labels, n_labels_per_sample, replace=False) + labels[i, label_indices] = 1 + + return labels + + +@pytest.fixture +def empty_text_samples(): + """Fixture providing edge case: empty or null text samples.""" + return [ + "", + None, + " ", + "\n\n\n", + "a", # Single character + ] diff --git a/tests/deepchecks/run_all_deepchecks.py b/tests/deepchecks/run_all_deepchecks.py new file mode 100644 index 0000000000000000000000000000000000000000..fa44811317ba253479fc2b7a4855d812dd24d151 --- /dev/null +++ b/tests/deepchecks/run_all_deepchecks.py @@ -0,0 +1,124 @@ +""" +Run all Deepchecks validation suites + +This script executes all data validation checks: +1. Data Integrity Suite - validates training data quality +2. Train-Test Validation Suite - ensures proper train/test split + +Usage: + python tests/run_all_deepchecks.py +""" + +import sys +from pathlib import Path + +# Add project root to path +project_root = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(project_root)) + +from tests.test_data_integrity import ( + run_data_integrity_suite, + run_custom_integrity_checks, + analyze_data_statistics +) +from tests.test_train_test_validation import ( + run_train_test_validation_suite, + run_custom_train_test_checks, + compare_distributions, + validate_split_quality +) + + +def main(): + """ + Run all Deepchecks validation suites and generate reports. + """ + import argparse + + parser = argparse.ArgumentParser(description='Run Deepchecks validation suites') + parser.add_argument('--original', action='store_true', + help='Use original data instead of cleaned data') + args = parser.parse_args() + + use_cleaned = not args.original + + print("="*80) + print(" DEEPCHECKS VALIDATION - COMPLETE SUITE") + print("="*80) + print(f"\nUsing {'CLEANED' if use_cleaned else 'ORIGINAL'} data") + print("Reports will be saved in: reports/deepchecks/") + print("\n" + "="*80) + + # Phase 1: Data Integrity Checks + print("\nPHASE 1: DATA INTEGRITY VALIDATION") + print("="*80) + + try: + # Dataset statistics + analyze_data_statistics(use_cleaned=use_cleaned) + + # Run full integrity suite + print("\n") + integrity_result = run_data_integrity_suite(save_output=True, use_cleaned=use_cleaned) + + # Run custom integrity checks + print("\n") + custom_integrity_results = run_custom_integrity_checks(save_output=True, use_cleaned=use_cleaned) + + print("\nPhase 1 completed successfully!") + + except Exception as e: + print(f"\nError in Phase 1: {str(e)}") + return False + + # Phase 2: Train-Test Validation + print("\n\nPHASE 2: TRAIN-TEST VALIDATION") + print("="*80) + + try: + # Distribution comparison + compare_distributions(use_cleaned=use_cleaned) + + # Split quality validation + print("\n") + validate_split_quality(use_cleaned=use_cleaned) + + # Run full train-test suite + print("\n") + train_test_suite_result = run_train_test_validation_suite(save_output=True, use_cleaned=use_cleaned) + + # Run custom train-test checks + print("\n") + custom_train_test_results = run_custom_train_test_checks(save_output=True, use_cleaned=use_cleaned) + + print("\nPhase 2 completed successfully!") + + except Exception as e: + print(f"\nError in Phase 2: {str(e)}") + return False + + # Summary + print("\n\n" + "="*80) + print(" VALIDATION SUMMARY") + print("="*80) + + print("\nAll Deepchecks validation suites completed successfully!") + print("\nGenerated Reports:") + print(" - reports/deepchecks/data_integrity_suite_results.json") + print(" - reports/deepchecks/train_test_validation_suite_results.json") + print(" - reports/deepchecks/validation_summary.json") + + print("\nNext Steps:") + print(" 1. Review the JSON reports for check results") + print(" 2. Examine any warnings or failed checks") + print(" 3. Address data quality issues if found") + print(" 4. Document findings in your project documentation") + + print("\n" + "="*80) + + return True + + +if __name__ == "__main__": + success = main() + sys.exit(0 if success else 1) diff --git a/tests/deepchecks/run_all_tests_comparison.py b/tests/deepchecks/run_all_tests_comparison.py new file mode 100644 index 0000000000000000000000000000000000000000..d67419958eff8ea20ef9ad748b579f61d26ec4a6 --- /dev/null +++ b/tests/deepchecks/run_all_tests_comparison.py @@ -0,0 +1,178 @@ +""" +Comprehensive Deepchecks Test Runner - Original vs Cleaned Data Comparison + +This script runs all Deepchecks tests on both original and cleaned data, +allowing for direct comparison of data quality improvements after cleaning. + +Usage: + python tests/deepchecks/run_all_tests_comparison.py + +Output: + - Generates reports for both original and cleaned data + - Creates comparison summary + - Saves all results in reports/deepchecks/ +""" + +import sys +from pathlib import Path + +# Add project root to path +project_root = Path(__file__).parent.parent.parent +sys.path.insert(0, str(project_root)) + +from test_data_integrity import ( + run_data_integrity_suite, + run_custom_integrity_checks, + analyze_data_statistics +) +from test_train_test_validation import ( + run_train_test_validation_suite, + run_custom_train_test_checks, + compare_distributions, + validate_split_quality +) + + +def print_section_header(title): + """Print a formatted section header.""" + print("\n" + "="*80) + print(f" {title}") + print("="*80 + "\n") + + +def run_all_tests_for_data(use_cleaned=False): + """ + Run all Deepchecks tests for either original or cleaned data. + + Args: + use_cleaned: If True, test cleaned data; otherwise test original data + """ + data_type = "CLEANED" if use_cleaned else "ORIGINAL" + + print("\n" + "#"*80) + print(f"# RUNNING ALL DEEPCHECKS TESTS - {data_type} DATA") + print("#"*80) + + # 1. Dataset Statistics + print_section_header(f"1. DATASET STATISTICS - {data_type}") + analyze_data_statistics(use_cleaned=use_cleaned) + + # 2. Data Integrity Suite + print_section_header(f"2. DATA INTEGRITY SUITE - {data_type}") + integrity_result = run_data_integrity_suite(save_output=True, use_cleaned=use_cleaned) + + # 3. Custom Integrity Checks + print_section_header(f"3. CUSTOM INTEGRITY CHECKS - {data_type}") + custom_integrity_results = run_custom_integrity_checks(save_output=True, use_cleaned=use_cleaned) + + # 4. Distribution Comparison + print_section_header(f"4. TRAIN-TEST DISTRIBUTION COMPARISON - {data_type}") + compare_distributions(use_cleaned=use_cleaned) + + # 5. Split Quality Validation + print_section_header(f"5. TRAIN-TEST SPLIT QUALITY - {data_type}") + validate_split_quality(use_cleaned=use_cleaned) + + # 6. Train-Test Validation Suite + print_section_header(f"6. TRAIN-TEST VALIDATION SUITE - {data_type}") + validation_result = run_train_test_validation_suite(save_output=True, use_cleaned=use_cleaned) + + # 7. Custom Train-Test Checks + print_section_header(f"7. CUSTOM TRAIN-TEST CHECKS - {data_type}") + custom_validation_results = run_custom_train_test_checks(save_output=True, use_cleaned=use_cleaned) + + return { + "integrity_suite": integrity_result, + "custom_integrity": custom_integrity_results, + "validation_suite": validation_result, + "custom_validation": custom_validation_results + } + + +def main(): + """Main function to run all tests and generate comparison.""" + print("\n" + "*"*80) + print("* DEEPCHECKS COMPREHENSIVE TEST SUITE") + print("* Original vs Cleaned Data Comparison") + print("*"*80) + + print("\nThis script will run all Deepchecks tests on both:") + print(" 1. Original data (before cleaning)") + print(" 2. Cleaned data (after data_cleaning.py)") + print("\nThis allows direct comparison of data quality improvements.\n") + + # Check if cleaned data exists + from hopcroft_skill_classification_tool_competition.config import PROCESSED_DATA_DIR + tfidf_dir = PROCESSED_DATA_DIR / "tfidf" + + cleaned_files_exist = all([ + (tfidf_dir / "features_tfidf_clean.npy").exists(), + (tfidf_dir / "labels_tfidf_clean.npy").exists(), + (tfidf_dir / "X_test_clean.npy").exists(), + (tfidf_dir / "Y_test_clean.npy").exists() + ]) + + if not cleaned_files_exist: + print("⚠️ WARNING: Cleaned data files not found!") + print(" Please run data_cleaning.py first:") + print(" python -m hopcroft_skill_classification_tool_competition.data_cleaning") + print("\n Continuing with original data only...\n") + + # Run tests only on original data + print_section_header("TESTING ORIGINAL DATA ONLY") + original_results = run_all_tests_for_data(use_cleaned=False) + + else: + # Run tests on both original and cleaned data + print("βœ“ Cleaned data files found") + print(" Running tests on both original and cleaned data...\n") + + # Test original data + original_results = run_all_tests_for_data(use_cleaned=False) + + # Test cleaned data + cleaned_results = run_all_tests_for_data(use_cleaned=True) + + # Print comparison summary + print("\n" + "="*80) + print(" COMPARISON SUMMARY") + print("="*80) + print("\nOriginal vs Cleaned Data:") + print(" - Original data tests saved with '_original' suffix") + print(" - Cleaned data tests saved with '_clean' suffix") + print("\nExpected improvements in cleaned data:") + print(" βœ“ No duplicates (0.0%)") + print(" βœ“ No label conflicts (0.0%)") + print(" βœ“ No data leakage (0 samples)") + print(" βœ“ Proper stratified split (80/20)") + print(" βœ“ Clean train/test separation") + + # Final summary + print("\n" + "="*80) + print(" ALL TESTS COMPLETED") + print("="*80) + print("\nReports saved in: reports/deepchecks/") + print("\nFiles generated:") + print(" Original data:") + print(" - data_integrity_suite_results_original.json") + print(" - train_test_validation_suite_results_original.json") + + if cleaned_files_exist: + print("\n Cleaned data:") + print(" - data_integrity_suite_results_clean.json") + print(" - train_test_validation_suite_results_clean.json") + + print("\nNext steps:") + if not cleaned_files_exist: + print(" 1. Run data_cleaning.py to generate cleaned data") + print(" 2. Re-run this script to compare original vs cleaned") + else: + print(" 1. Review JSON reports in reports/deepchecks/") + print(" 2. Compare original vs cleaned results") + print(" 3. Use cleaned data for model training") + + print("\n" + "="*80 + "\n") + + +if __name__ == "__main__": + main() diff --git a/tests/deepchecks/test_data_integrity.py b/tests/deepchecks/test_data_integrity.py new file mode 100644 index 0000000000000000000000000000000000000000..5f9a7d5d806bfd410e8ecc672df9ab16481d1c9d --- /dev/null +++ b/tests/deepchecks/test_data_integrity.py @@ -0,0 +1,318 @@ +""" +Data Integrity Suite - Deepchecks validation for dataset integrity + +This module implements comprehensive data integrity checks using Deepchecks +to validate the quality and consistency of the training and test datasets. + +Checks included: +- Data duplicates detection +- Missing values analysis +- Feature-label correlation +- Feature-feature correlation +- Data type consistency +- Outlier detection +- Class imbalance analysis +""" + +import numpy as np +import pandas as pd +import json +from pathlib import Path +from deepchecks.tabular import Dataset +from deepchecks.tabular.suites import data_integrity + +from hopcroft_skill_classification_tool_competition.config import PROCESSED_DATA_DIR + + +def load_data(use_cleaned=True): + """ + Load training and test datasets from processed data directory. + + Args: + use_cleaned: If True, load cleaned data (with '_clean' suffix) - DEFAULT + + Returns: + tuple: (X_train, y_train, X_test, y_test) + """ + tfidf_dir = PROCESSED_DATA_DIR / "tfidf" + + # Choose file names based on cleaned flag + if use_cleaned: + train_features = tfidf_dir / "features_tfidf_clean.npy" + train_labels = tfidf_dir / "labels_tfidf_clean.npy" + test_features = tfidf_dir / "X_test_clean.npy" + test_labels = tfidf_dir / "Y_test_clean.npy" + data_type = "cleaned" + else: + train_features = tfidf_dir / "features_tfidf.npy" + train_labels = tfidf_dir / "labels_tfidf.npy" + test_features = tfidf_dir / "X_test.npy" + test_labels = tfidf_dir / "Y_test.npy" + data_type = "original" + + # Load features and labels + X_train = np.load(train_features) + y_train = np.load(train_labels) + X_test = np.load(test_features) + y_test = np.load(test_labels) + + print(f"Loaded {data_type} data:") + print(f"Training set shape: X={X_train.shape}, y={y_train.shape}") + print(f"Test set shape: X={X_test.shape}, y={y_test.shape}") + + return X_train, y_train, X_test, y_test + + +def create_deepchecks_dataset(X, y, dataset_name="dataset"): + """ + Create a Deepchecks Dataset object from numpy arrays. + + Args: + X: Feature matrix (numpy array) + y: Labels (numpy array) - can be multi-label (2D) or single-label (1D) + dataset_name: Name identifier for the dataset + + Returns: + Dataset: Deepchecks Dataset object + """ + # Convert to DataFrame for better visualization + # Create feature names + feature_names = [f"feature_{i}" for i in range(X.shape[1])] + + # Create DataFrame + df = pd.DataFrame(X, columns=feature_names) + + # Handle multi-label case: convert to single label by taking argmax or first active label + if len(y.shape) > 1 and y.shape[1] > 1: + # Multi-label: convert to single label (first active label or most confident) + # For binary multi-label, take the index of first 1 + y_single = np.argmax(y, axis=1) # Get the index of maximum value + df['label'] = y_single + print(f"Note: Converted multi-label ({y.shape[1]} labels) to single-label for Deepchecks") + else: + df['label'] = y + + # Create Deepchecks Dataset + ds = Dataset(df, label='label', cat_features=[]) + + return ds + + +def run_data_integrity_suite(save_output=True, use_cleaned=True): + """ + Run the complete Data Integrity Suite on training data. + + This suite performs comprehensive checks including: + - Data Duplicates: Identifies duplicate samples + - String Mismatch: Checks for string inconsistencies + - Mixed Nulls: Detects various null representations + - Mixed Data Types: Validates consistent data types + - String Length Out Of Bounds: Checks string length anomalies + - Is Single Value: Identifies features with only one value + - Special Characters: Detects special characters in data + - Class Imbalance: Analyzes label distribution + - Outlier Sample Detection: Identifies outlier samples + - Feature Label Correlation: Checks correlation between features and labels + + Args: + save_output: Whether to save the HTML report + use_cleaned: If True, use cleaned data instead of original + + Returns: + SuiteResult: Results from the data integrity suite + """ + data_type = "CLEANED" if use_cleaned else "ORIGINAL" + print("="*80) + print(f"DATA INTEGRITY SUITE - {data_type} TRAINING DATA") + print("="*80) + + # Load data + X_train, y_train, _, _ = load_data(use_cleaned=use_cleaned) + + # Create Deepchecks dataset + train_dataset = create_deepchecks_dataset(X_train, y_train, "training") + + # Run the Data Integrity Suite + print("\nRunning Data Integrity checks...") + suite = data_integrity() + result = suite.run(train_dataset) + + # Display results + print("\nData Integrity Suite completed!") + print(f"Total checks: {len(result.results)}") + + # Save output + if save_output: + output_dir = Path("reports/deepchecks") + output_dir.mkdir(parents=True, exist_ok=True) + + # Save JSON report with appropriate suffix + suffix = "_clean" if use_cleaned else "_original" + json_path = output_dir / f"data_integrity_suite_results{suffix}.json" + json_results = { + "suite_name": "Data Integrity Suite", + "total_checks": len(result.results), + "timestamp": pd.Timestamp.now().isoformat(), + "checks": [] + } + + for check_result in result.results: + check_data = { + "check_name": check_result.get_header(), + "passed": check_result.passed_conditions() if hasattr(check_result, 'passed_conditions') else None, + "display": str(check_result.display) if hasattr(check_result, 'display') else None + } + json_results["checks"].append(check_data) + + with open(json_path, 'w', encoding='utf-8') as f: + json.dump(json_results, f, indent=2, ensure_ascii=False) + print(f"JSON results saved to: {json_path}") + + return result + + +def run_custom_integrity_checks(save_output=True, use_cleaned=True): + """ + Run custom integrity checks tailored for the SkillScope dataset. + + These checks are specifically designed for NLP/Text features and + multi-label classification tasks. + + Args: + save_output: Whether to save the HTML report + use_cleaned: If True, use cleaned data instead of original + + Returns: + dict: Dictionary containing check results + """ + from deepchecks.tabular.checks import ( + DataDuplicates, + MixedNulls, + IsSingleValue, + ClassImbalance, + OutlierSampleDetection, + FeatureLabelCorrelation, + ) + + data_type = "CLEANED" if use_cleaned else "ORIGINAL" + print("="*80) + print(f"CUSTOM DATA INTEGRITY CHECKS - {data_type} DATA") + print("="*80) + + # Load data + X_train, y_train, _, _ = load_data(use_cleaned=use_cleaned) + train_dataset = create_deepchecks_dataset(X_train, y_train, "training") + + results = {} + + # Check 1: Data Duplicates + print("\n1. Checking for duplicate samples...") + duplicates_check = DataDuplicates() + results['duplicates'] = duplicates_check.run(train_dataset) + + # Check 2: Mixed Nulls + print("2. Checking for mixed null values...") + nulls_check = MixedNulls() + results['nulls'] = nulls_check.run(train_dataset) + + # Check 3: Single Value Features + print("3. Checking for single-value features...") + single_value_check = IsSingleValue() + results['single_value'] = single_value_check.run(train_dataset) + + # Check 4: Class Imbalance + print("4. Checking class distribution...") + imbalance_check = ClassImbalance() + results['class_imbalance'] = imbalance_check.run(train_dataset) + + # Check 5: Outlier Detection (with increased timeout) + print("5. Detecting outlier samples (this may take a while)...") + try: + outlier_check = OutlierSampleDetection(timeout=300) # 5 minutes timeout + results['outliers'] = outlier_check.run(train_dataset) + except Exception as e: + print(f" Warning: Outlier detection failed or timed out: {str(e)}") + results['outliers'] = None + + # Check 6: Feature-Label Correlation (with sample subset for speed) + print("6. Analyzing feature-label correlation (using sample of features)...") + try: + # Use only top 100 features for correlation to speed up + correlation_check = FeatureLabelCorrelation(n_top_columns=100, timeout=300) + results['correlation'] = correlation_check.run(train_dataset) + except Exception as e: + print(f" Warning: Correlation check failed or timed out: {str(e)}") + results['correlation'] = None + + print("\nAll custom checks completed!") + + # Results are available in memory for further processing if needed + + return results + + +def analyze_data_statistics(use_cleaned=True): + """ + Print detailed statistics about the dataset. + + Args: + use_cleaned: If True, analyze cleaned data instead of original + """ + data_type = "CLEANED" if use_cleaned else "ORIGINAL" + print("="*80) + print(f"DATASET STATISTICS - {data_type} DATA") + print("="*80) + + X_train, y_train, X_test, y_test = load_data(use_cleaned=use_cleaned) + + print(f"\nTraining set:") + print(f" - Samples: {X_train.shape[0]}") + print(f" - Features: {X_train.shape[1]}") + print(f" - Unique labels: {len(np.unique(y_train))}") + print(f" - Label distribution:") + unique, counts = np.unique(y_train, return_counts=True) + for label, count in zip(unique[:10], counts[:10]): # Show first 10 + print(f" Label {label}: {count} samples ({count/len(y_train)*100:.2f}%)") + if len(unique) > 10: + print(f" ... and {len(unique)-10} more labels") + + print(f"\nTest set:") + print(f" - Samples: {X_test.shape[0]}") + print(f" - Features: {X_test.shape[1]}") + print(f" - Unique labels: {len(np.unique(y_test))}") + + print(f"\nFeature statistics:") + print(f" - Mean feature value: {X_train.mean():.4f}") + print(f" - Std feature value: {X_train.std():.4f}") + print(f" - Min feature value: {X_train.min():.4f}") + print(f" - Max feature value: {X_train.max():.4f}") + print(f" - Sparsity: {(X_train == 0).sum() / X_train.size * 100:.2f}%") + + +if __name__ == "__main__": + import sys + + # By default use cleaned data, unless --original flag is specified + use_cleaned = not ('--original' in sys.argv or '-o' in sys.argv) + + if use_cleaned: + print("Testing CLEANED data (from data_cleaning.py) - DEFAULT\n") + else: + print("Testing ORIGINAL data\n") + print("Note: Using --original flag to test old data\n") + + # Print dataset statistics + analyze_data_statistics(use_cleaned=use_cleaned) + + # Run the full Data Integrity Suite + print("\n") + suite_result = run_data_integrity_suite(save_output=True, use_cleaned=use_cleaned) + + # Run custom integrity checks + print("\n") + custom_results = run_custom_integrity_checks(save_output=True, use_cleaned=use_cleaned) + + print("\n" + "="*80) + print("DATA INTEGRITY VALIDATION COMPLETED") + print("="*80) + print("\nCheck the reports in the 'reports/deepchecks' directory") diff --git a/tests/deepchecks/test_train_test_validation.py b/tests/deepchecks/test_train_test_validation.py new file mode 100644 index 0000000000000000000000000000000000000000..a29ac618accf0e76f0fa1ab8bef20af877489996 --- /dev/null +++ b/tests/deepchecks/test_train_test_validation.py @@ -0,0 +1,409 @@ +""" +Train-Test Validation Suite - Deepchecks validation for train-test consistency + +This module implements comprehensive train-test validation checks using Deepchecks +to ensure consistency and proper splitting between training and test datasets. + +Checks included: +- Train-Test Feature Drift: Detects distribution changes between train and test +- Train-Test Label Drift: Checks if label distribution differs +- Train-Test Samples Mix: Validates no data leakage +- Whole Dataset Drift: Overall distribution comparison +- Feature Label Correlation Change: Checks if correlations change +- New Label: Detects labels in test not present in train +- New Category: Detects new categorical values in test +- String Mismatch Comparison: Compares string inconsistencies +- Date Train Test Leakage Duplicates: Checks for temporal leakage +- Date Train Test Leakage Overlap: Validates proper temporal split +""" + +import numpy as np +import pandas as pd +import json +from pathlib import Path +from deepchecks.tabular import Dataset +from deepchecks.tabular.suites import train_test_validation + +from hopcroft_skill_classification_tool_competition.config import PROCESSED_DATA_DIR + + +def load_train_test_data(use_cleaned=True): + """ + Load training and test datasets from processed data directory. + + Args: + use_cleaned: If True, load cleaned data (with '_clean' suffix) - DEFAULT + + Returns: + tuple: (X_train, y_train, X_test, y_test) + """ + tfidf_dir = PROCESSED_DATA_DIR / "tfidf" + + # Choose file names based on cleaned flag + if use_cleaned: + train_features = tfidf_dir / "features_tfidf_clean.npy" + train_labels = tfidf_dir / "labels_tfidf_clean.npy" + test_features = tfidf_dir / "X_test_clean.npy" + test_labels = tfidf_dir / "Y_test_clean.npy" + data_type = "cleaned" + else: + train_features = tfidf_dir / "features_tfidf.npy" + train_labels = tfidf_dir / "labels_tfidf.npy" + test_features = tfidf_dir / "X_test.npy" + test_labels = tfidf_dir / "Y_test.npy" + data_type = "original" + + # Load features and labels + X_train = np.load(train_features) + y_train = np.load(train_labels) + X_test = np.load(test_features) + y_test = np.load(test_labels) + + print(f"Loaded {data_type} data:") + print(f"Training set shape: X={X_train.shape}, y={y_train.shape}") + print(f"Test set shape: X={X_test.shape}, y={y_test.shape}") + + return X_train, y_train, X_test, y_test + + +def create_deepchecks_dataset(X, y, dataset_name="dataset"): + """ + Create a Deepchecks Dataset object from numpy arrays. + + Args: + X: Feature matrix (numpy array) + y: Labels (numpy array) - can be multi-label (2D) or single-label (1D) + dataset_name: Name identifier for the dataset + + Returns: + Dataset: Deepchecks Dataset object + """ + # Convert to DataFrame for better visualization + # Create feature names + feature_names = [f"feature_{i}" for i in range(X.shape[1])] + + # Create DataFrame + df = pd.DataFrame(X, columns=feature_names) + + # Handle multi-label case: convert to single label by taking argmax or first active label + if len(y.shape) > 1 and y.shape[1] > 1: + # Multi-label: convert to single label (first active label or most confident) + # For binary multi-label, take the index of first 1 + y_single = np.argmax(y, axis=1) # Get the index of maximum value + df['label'] = y_single + print(f"Note: Converted multi-label ({y.shape[1]} labels) to single-label for Deepchecks") + else: + df['label'] = y + + # Create Deepchecks Dataset + ds = Dataset(df, label='label', cat_features=[]) + + return ds + + +def run_train_test_validation_suite(save_output=True, use_cleaned=True): + """ + Run the complete Train-Test Validation Suite. + + This suite performs comprehensive checks including: + - Train Test Feature Drift: Detects significant distribution changes in features + - Train Test Label Drift: Checks if label distribution is consistent + - Train Test Samples Mix: Validates no samples appear in both sets + - Whole Dataset Drift: Overall dataset distribution comparison + - Feature Label Correlation Change: Detects changes in feature-label relationships + - New Label: Identifies labels in test that don't exist in train + - New Category: Finds new categorical values in test set + - String Mismatch Comparison: Compares string format consistency + - Date Train Test Leakage: Checks for temporal data leakage + - Index Train Test Leakage: Validates proper index separation + + Args: + save_output: Whether to save the HTML report + use_cleaned: If True, use cleaned data instead of original + + Returns: + SuiteResult: Results from the train-test validation suite + """ + data_type = "CLEANED" if use_cleaned else "ORIGINAL" + print("="*80) + print(f"TRAIN-TEST VALIDATION SUITE - {data_type} DATA") + print("="*80) + + # Load data + X_train, y_train, X_test, y_test = load_train_test_data(use_cleaned=use_cleaned) + + # Create Deepchecks datasets + train_dataset = create_deepchecks_dataset(X_train, y_train, "training") + test_dataset = create_deepchecks_dataset(X_test, y_test, "test") + + # Run the Train-Test Validation Suite + print("\nRunning Train-Test Validation checks...") + suite = train_test_validation() + result = suite.run(train_dataset, test_dataset) + + # Display results + print("\nTrain-Test Validation Suite completed!") + print(f"Total checks: {len(result.results)}") + + # Save output + if save_output: + output_dir = Path("reports/deepchecks") + output_dir.mkdir(parents=True, exist_ok=True) + + # Save JSON report with appropriate suffix + suffix = "_clean" if use_cleaned else "_original" + json_path = output_dir / f"train_test_validation_suite_results{suffix}.json" + json_results = { + "suite_name": "Train-Test Validation Suite", + "total_checks": len(result.results), + "timestamp": pd.Timestamp.now().isoformat(), + "checks": [] + } + + for check_result in result.results: + check_data = { + "check_name": check_result.get_header(), + "passed": check_result.passed_conditions() if hasattr(check_result, 'passed_conditions') else None, + "display": str(check_result.display) if hasattr(check_result, 'display') else None + } + json_results["checks"].append(check_data) + + with open(json_path, 'w', encoding='utf-8') as f: + json.dump(json_results, f, indent=2, ensure_ascii=False) + print(f"JSON results saved to: {json_path}") + + return result + + +def run_custom_train_test_checks(save_output=True, use_cleaned=True): + """ + Run custom train-test validation checks tailored for the SkillScope dataset. + + These checks are specifically designed for NLP/Text features and + multi-label classification tasks. + + Args: + save_output: Whether to save the HTML report + use_cleaned: If True, use cleaned data instead of original + + Returns: + dict: Dictionary containing check results + """ + from deepchecks.tabular.checks import ( + TrainTestFeatureDrift, + TrainTestLabelDrift, + TrainTestSamplesMix, + WholeDatasetDrift, + FeatureLabelCorrelationChange, + ) + + data_type = "CLEANED" if use_cleaned else "ORIGINAL" + print("="*80) + print(f"CUSTOM TRAIN-TEST VALIDATION CHECKS - {data_type} DATA") + print("="*80) + + # Load data + X_train, y_train, X_test, y_test = load_train_test_data(use_cleaned=use_cleaned) + train_dataset = create_deepchecks_dataset(X_train, y_train, "training") + test_dataset = create_deepchecks_dataset(X_test, y_test, "test") + + results = {} + + # Check 1: Feature Drift + print("\n1. Checking for feature drift between train and test...") + feature_drift_check = TrainTestFeatureDrift() + results['feature_drift'] = feature_drift_check.run(train_dataset, test_dataset) + + # Check 2: Label Drift + print("2. Checking for label drift between train and test...") + label_drift_check = TrainTestLabelDrift() + results['label_drift'] = label_drift_check.run(train_dataset, test_dataset) + + # Check 3: Samples Mix (Data Leakage) + print("3. Checking for data leakage (samples appearing in both sets)...") + samples_mix_check = TrainTestSamplesMix() + results['samples_mix'] = samples_mix_check.run(train_dataset, test_dataset) + + # Check 4: Whole Dataset Drift + print("4. Checking overall dataset drift...") + dataset_drift_check = WholeDatasetDrift() + results['dataset_drift'] = dataset_drift_check.run(train_dataset, test_dataset) + + # Check 5: Feature-Label Correlation Change + print("5. Checking for changes in feature-label correlation...") + correlation_change_check = FeatureLabelCorrelationChange() + results['correlation_change'] = correlation_change_check.run(train_dataset, test_dataset) + + # Note: NewLabel check not available in this version of Deepchecks + # Check 6 would verify new labels in test set not present in train + print("6. Skipping NewLabel check (not available in this Deepchecks version)") + + print("\nAll custom train-test checks completed!") + + # Results are available in memory for further processing if needed + + return results + + +def compare_distributions(use_cleaned=True): + """ + Compare statistical distributions between train and test sets. + + Args: + use_cleaned: If True, compare cleaned data instead of original + """ + data_type = "CLEANED" if use_cleaned else "ORIGINAL" + print("="*80) + print(f"TRAIN-TEST DISTRIBUTION COMPARISON - {data_type} DATA") + print("="*80) + + X_train, y_train, X_test, y_test = load_train_test_data(use_cleaned=use_cleaned) + + print("\n1. SAMPLE SIZES:") + print(f" Training: {X_train.shape[0]} samples") + print(f" Test: {X_test.shape[0]} samples") + print(f" Train/Test ratio: {X_train.shape[0]/X_test.shape[0]:.2f}") + + print("\n2. FEATURE DIMENSIONS:") + print(f" Training features: {X_train.shape[1]}") + print(f" Test features: {X_test.shape[1]}") + if X_train.shape[1] != X_test.shape[1]: + print(" WARNING: Feature dimensions don't match!") + else: + print(" βœ“ Feature dimensions match") + + print("\n3. LABEL DISTRIBUTION:") + train_unique, train_counts = np.unique(y_train, return_counts=True) + test_unique, test_counts = np.unique(y_test, return_counts=True) + + print(f" Training unique labels: {len(train_unique)}") + print(f" Test unique labels: {len(test_unique)}") + + # Check for labels in test not in train + new_labels = set(test_unique) - set(train_unique) + if new_labels: + print(f" WARNING: {len(new_labels)} new labels in test set: {new_labels}") + else: + print(" No new labels in test set") + + # Check for labels in train not in test + missing_labels = set(train_unique) - set(test_unique) + if missing_labels: + print(f" INFO: {len(missing_labels)} labels only in train set") + + print("\n4. FEATURE STATISTICS COMPARISON:") + print(f" Train - Mean: {X_train.mean():.4f}, Std: {X_train.std():.4f}") + print(f" Test - Mean: {X_test.mean():.4f}, Std: {X_test.std():.4f}") + + mean_diff = abs(X_train.mean() - X_test.mean()) + std_diff = abs(X_train.std() - X_test.std()) + + print(f" Mean difference: {mean_diff:.4f}") + print(f" Std difference: {std_diff:.4f}") + + if mean_diff > 0.1 or std_diff > 0.1: + print(" WARNING: Significant statistical differences detected!") + else: + print(" Statistical distributions are similar") + + print("\n5. SPARSITY COMPARISON:") + train_sparsity = (X_train == 0).sum() / X_train.size * 100 + test_sparsity = (X_test == 0).sum() / X_test.size * 100 + print(f" Training sparsity: {train_sparsity:.2f}%") + print(f" Test sparsity: {test_sparsity:.2f}%") + print(f" Sparsity difference: {abs(train_sparsity - test_sparsity):.2f}%") + + if abs(train_sparsity - test_sparsity) > 5: + print(" WARNING: Significant sparsity difference!") + else: + print(" Sparsity levels are similar") + + +def validate_split_quality(use_cleaned=True): + """ + Validate the quality of the train-test split. + + Args: + use_cleaned: If True, validate cleaned data instead of original + """ + data_type = "CLEANED" if use_cleaned else "ORIGINAL" + print("="*80) + print(f"TRAIN-TEST SPLIT QUALITY VALIDATION - {data_type} DATA") + print("="*80) + + X_train, y_train, X_test, y_test = load_train_test_data(use_cleaned=use_cleaned) + + total_samples = X_train.shape[0] + X_test.shape[0] + test_ratio = X_test.shape[0] / total_samples + + print(f"\nTotal samples: {total_samples}") + print(f"Test set ratio: {test_ratio:.2%}") + + # Validate test set size (typically 20-30%) + if 0.15 <= test_ratio <= 0.35: + print(" Test set size is within recommended range (15-35%)") + else: + print(" WARNING: Test set size is outside recommended range") + + # Check label distribution similarity + from scipy.stats import chisquare + + # Get common labels + common_labels = np.intersect1d(np.unique(y_train), np.unique(y_test)) + + if len(common_labels) > 0: + train_dist = [np.sum(y_train == label) for label in common_labels] + test_dist = [np.sum(y_test == label) for label in common_labels] + + # Normalize to proportions + train_props = np.array(train_dist) / len(y_train) + test_props = np.array(test_dist) / len(y_test) + + # Chi-square test + # Scale test proportions to match train sample size for chi-square + expected = test_props * len(y_train) + chi_stat, p_value = chisquare(train_dist, expected) + + print(f"\nLabel distribution similarity (chi-square test):") + print(f" Chi-square statistic: {chi_stat:.4f}") + print(f" P-value: {p_value:.4f}") + + if p_value > 0.05: + print(" Label distributions are statistically similar (p > 0.05)") + else: + print(" WARNING: Label distributions differ significantly (p <= 0.05)") + else: + print(" WARNING: No common labels between train and test sets!") + + +if __name__ == "__main__": + import sys + + # By default use cleaned data, unless --original flag is specified + use_cleaned = not ('--original' in sys.argv or '-o' in sys.argv) + + if use_cleaned: + print("Testing CLEANED data (from data_cleaning.py) - DEFAULT\n") + else: + print("Testing ORIGINAL data\n") + print("Note: Using --original flag to test old data\n") + + # Compare distributions + compare_distributions(use_cleaned=use_cleaned) + + # Validate split quality + print("\n") + validate_split_quality(use_cleaned=use_cleaned) + + # Run the full Train-Test Validation Suite + print("\n") + suite_result = run_train_test_validation_suite(save_output=True, use_cleaned=use_cleaned) + + # Run custom train-test checks + print("\n") + custom_results = run_custom_train_test_checks(save_output=True, use_cleaned=use_cleaned) + + print("\n" + "="*80) + print("TRAIN-TEST VALIDATION COMPLETED") + print("="*80) + print("\nCheck the reports in the 'reports/deepchecks' directory") diff --git a/tests/deepchecks/validate_cleaned_data.py b/tests/deepchecks/validate_cleaned_data.py new file mode 100644 index 0000000000000000000000000000000000000000..dcb46a6d295d245722463852035ee72b8c52721e --- /dev/null +++ b/tests/deepchecks/validate_cleaned_data.py @@ -0,0 +1,161 @@ +""" +Validation script for cleaned data. + +This script runs Deepchecks validation on the cleaned dataset to verify that: +1. No duplicates remain +2. No label conflicts exist +3. No data leakage between train and test +4. All data quality issues are resolved + +Run this after data_cleaning.py to confirm data quality. +""" + +import numpy as np +import pandas as pd +from pathlib import Path +from deepchecks.tabular import Dataset +from deepchecks.tabular.suites import data_integrity, train_test_validation + +from hopcroft_skill_classification_tool_competition.config import PROCESSED_DATA_DIR + + +def load_cleaned_data(): + """Load cleaned train and test datasets.""" + tfidf_dir = PROCESSED_DATA_DIR / "tfidf" + + X_train = np.load(tfidf_dir / "features_tfidf_clean.npy") + y_train = np.load(tfidf_dir / "labels_tfidf_clean.npy") + X_test = np.load(tfidf_dir / "X_test_clean.npy") + y_test = np.load(tfidf_dir / "Y_test_clean.npy") + + print(f"Loaded cleaned data:") + print(f" Train: {X_train.shape[0]:,} samples x {X_train.shape[1]:,} features") + print(f" Test: {X_test.shape[0]:,} samples x {X_test.shape[1]:,} features") + print(f" Labels: {y_train.shape[1]} labels") + + return X_train, y_train, X_test, y_test + + +def create_deepchecks_dataset(X, y, dataset_name="dataset"): + """Create Deepchecks Dataset from numpy arrays.""" + feature_names = [f"feature_{i}" for i in range(X.shape[1])] + df = pd.DataFrame(X, columns=feature_names) + + # Convert multi-label to single label for Deepchecks + if len(y.shape) > 1 and y.shape[1] > 1: + y_single = np.argmax(y, axis=1) + df['label'] = y_single + else: + df['label'] = y + + ds = Dataset(df, label='label', cat_features=[]) + return ds + + +def run_validation(): + """Run full Deepchecks validation on cleaned data.""" + print("="*80) + print("DEEPCHECKS VALIDATION - CLEANED DATA") + print("="*80) + + # Load cleaned data + X_train, y_train, X_test, y_test = load_cleaned_data() + + # Create Deepchecks datasets + train_dataset = create_deepchecks_dataset(X_train, y_train, "training_clean") + test_dataset = create_deepchecks_dataset(X_test, y_test, "test_clean") + + # Run Data Integrity Suite + print("\n" + "="*80) + print("RUNNING DATA INTEGRITY SUITE") + print("="*80) + integrity_suite = data_integrity() + integrity_result = integrity_suite.run(train_dataset) + + # Run Train-Test Validation Suite + print("\n" + "="*80) + print("RUNNING TRAIN-TEST VALIDATION SUITE") + print("="*80) + validation_suite = train_test_validation() + validation_result = validation_suite.run(train_dataset, test_dataset) + + # Save reports + output_dir = Path("reports/deepchecks") + output_dir.mkdir(parents=True, exist_ok=True) + + # Save JSON results + import json + + # Count passed/failed checks (handle CheckFailure objects) + integrity_passed = sum(1 for r in integrity_result.results if hasattr(r, 'passed_conditions') and r.passed_conditions()) + integrity_total = len(integrity_result.results) + + validation_passed = sum(1 for r in validation_result.results if hasattr(r, 'passed_conditions') and r.passed_conditions()) + validation_total = len(validation_result.results) + + # Save data integrity results as JSON + integrity_json = { + "suite_name": "Data Integrity Suite (Cleaned)", + "total_checks": len(integrity_result.results), + "timestamp": pd.Timestamp.now().isoformat(), + "passed": integrity_passed, + "failed": integrity_total - integrity_passed + } + with open(output_dir / "data_integrity_clean.json", 'w') as f: + json.dump(integrity_json, f, indent=2) + + # Save train-test validation results as JSON + validation_json = { + "suite_name": "Train-Test Validation Suite (Cleaned)", + "total_checks": len(validation_result.results), + "timestamp": pd.Timestamp.now().isoformat(), + "passed": validation_passed, + "failed": validation_total - validation_passed + } + with open(output_dir / "train_test_validation_clean.json", 'w') as f: + json.dump(validation_json, f, indent=2) + + print("\n" + "="*80) + print("VALIDATION RESULTS") + print("="*80) + + print(f"\nData Integrity Suite:") + print(f" Passed: {integrity_passed}/{integrity_total}") + + print(f"\nTrain-Test Validation Suite:") + print(f" Passed: {validation_passed}/{validation_total}") + + # Check critical issues + critical_issues = [] + + for result in integrity_result.results: + if hasattr(result, 'passed_conditions') and not result.passed_conditions(): + check_name = result.get_header() + if "Duplicate" in check_name or "Conflict" in check_name: + critical_issues.append(f"Data Integrity: {check_name}") + + for result in validation_result.results: + if hasattr(result, 'passed_conditions') and not result.passed_conditions(): + check_name = result.get_header() + if "Mix" in check_name or "Leakage" in check_name: + critical_issues.append(f"Train-Test: {check_name}") + + if critical_issues: + print(f"\nCRITICAL ISSUES REMAINING:") + for issue in critical_issues: + print(f" - {issue}") + else: + print(f"\nNO CRITICAL ISSUES DETECTED!") + print(f" No duplicates") + print(f" No label conflicts") + print(f" No data leakage") + print(f" Data is ready for training") + + print(f"\nReports saved to: {output_dir}") + print("="*80) + + return integrity_result, validation_result + + +if __name__ == "__main__": + run_validation() diff --git a/tests/great expectations/test_gx.py b/tests/great expectations/test_gx.py new file mode 100644 index 0000000000000000000000000000000000000000..427b88f3dca664bce1a9ca1b36704a142b1a3231 --- /dev/null +++ b/tests/great expectations/test_gx.py @@ -0,0 +1,821 @@ +""" +Data validation tests using Great Expectations framework for the skill classification pipeline. +""" + +import sqlite3 +import numpy as np +import pandas as pd +import json +import great_expectations as gx +from pathlib import Path + +# Import configuration +try: + from hopcroft_skill_classification_tool_competition.config import ( + DATA_PATHS, DB_PATH, PROCESSED_DATA_DIR, REPORTS_DIR + ) +except Exception: + DB_PATH = Path("data/raw/skillscope_data.db") + PROCESSED_DATA_DIR = Path("data/processed") + REPORTS_DIR = Path("reports") + DATA_PATHS = { + "features": "data/processed/tfidf/features_tfidf_clean.npy", + "labels": "data/processed/tfidf/labels_tfidf_clean.npy", + "features_original": "data/processed/tfidf/features_tfidf.npy", + "labels_original": "data/processed/tfidf/labels_tfidf.npy", + } + +# Configuration for dual validation (original AND cleaned data) +# Global variables set by run_validation_suite() before each test run +FEATURES_PATH = None +LABELS_PATH = None +VALIDATION_SUFFIX = None + +# Validation thresholds for ML pipeline compatibility +MIN_FOR_STRATIFICATION = 5 # Minimum label occurrences required for MultilabelStratifiedShuffleSplit +MIN_NNZ_FOR_SMOTE = 10 # Minimum non-zero features per sample for SMOTE k_neighbors=5 + +# Output directory for validation results +VALIDATION_OUTPUT_DIR = REPORTS_DIR / "great_expectations" +VALIDATION_OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + + +def test_raw_database(): + """ + TEST 1: Raw Database Validation + Validates schema, row counts, required columns, and data types of the raw SQLite database. + """ + + if not Path(DB_PATH).exists(): + print(f"[SKIP] TEST 1 - Database not found at {DB_PATH}") + return + + # Load data from database + conn = sqlite3.connect(DB_PATH) + df = pd.read_sql_query("SELECT * FROM nlbse_tool_competition_data_by_issue", conn) + conn.close() + + # Step 1: Create Data Context + context = gx.get_context() + + # Step 2: Create Datasource + datasource = context.data_sources.add_pandas(name="raw_data_source") + + # Step 3: Create Data Asset + data_asset = datasource.add_dataframe_asset(name="raw_issues") + + # Step 4: Create Batch Definition + batch_definition = data_asset.add_batch_definition_whole_dataframe("raw_batch") + + # Step 5: Define Expectations + suite = context.suites.add( + gx.core.expectation_suite.ExpectationSuite(name="raw_data_suite") + ) + + # Basic table structure + suite.add_expectation( + gx.expectations.ExpectTableRowCountToBeBetween(min_value=7000, max_value=10000) + ) + suite.add_expectation( + gx.expectations.ExpectTableColumnCountToBeBetween(min_value=220, max_value=230) + ) + + # Required columns must exist + required_cols = ['Repo Name', 'PR #', 'issue text', 'issue description'] + for col in required_cols: + suite.add_expectation( + gx.expectations.ExpectColumnToExist(column=col) + ) + + # Text columns should not be empty + suite.add_expectation( + gx.expectations.ExpectColumnValuesToNotBeNull(column='issue text') + ) + + # PR numbers should be positive integers + suite.add_expectation( + gx.expectations.ExpectColumnValuesToBeBetween(column='PR #', min_value=1) + ) + + # Step 6: Create Validation Definition and Checkpoint + validation_definition = context.validation_definitions.add( + gx.core.validation_definition.ValidationDefinition( + name="raw_data_validation", + data=batch_definition, + suite=suite + ) + ) + + checkpoint = context.checkpoints.add( + gx.checkpoint.checkpoint.Checkpoint( + name="raw_data_checkpoint", + validation_definitions=[validation_definition] + ) + ) + + # Run validation + results = checkpoint.run(batch_parameters={"dataframe": df}) + + # Save results to JSON (describe() returns JSON string, need to parse first) + output_file = VALIDATION_OUTPUT_DIR / f"test_1_raw_database{VALIDATION_SUFFIX}.json" + with open(output_file, 'w') as f: + result_dict = json.loads(results.describe()) + json.dump(result_dict, f, indent=2) + + status = "PASS" if results.success else "FAIL" + print(f"[{status}] TEST 1 - Raw Database Validation") + + +def test_processed_features(): + """ + TEST 2: TF-IDF Feature Matrix Validation + Validates feature matrix quality: value ranges, non-zero features per sample, no NaN/Inf. + """ + + features_path = Path(FEATURES_PATH) + if not features_path.exists(): + print(f"[SKIP] TEST 2 - Features not found at {features_path}") + return + + # Load features + features = np.load(features_path) + + # Create aggregate statistics DataFrame + stats_data = { + 'row_mean': features.mean(axis=1), + 'row_std': features.std(axis=1), + 'row_max': features.max(axis=1), + 'row_min': features.min(axis=1), + 'row_nnz': (features > 0).sum(axis=1), + } + df_stats = pd.DataFrame(stats_data) + + # Sample columns for spot checks + sample_cols = np.random.choice(features.shape[1], size=min(50, features.shape[1]), replace=False) + df_sample = pd.DataFrame(features[:, sample_cols]) + df_sample.columns = [f"feat_{i}" for i in sample_cols] + + context = gx.get_context() + + # Validation 1: Aggregate statistics + datasource_stats = context.data_sources.add_pandas(name="features_stats_source") + asset_stats = datasource_stats.add_dataframe_asset(name="features_stats") + batch_def_stats = asset_stats.add_batch_definition_whole_dataframe("stats_batch") + + suite_stats = context.suites.add( + gx.core.expectation_suite.ExpectationSuite(name="features_stats_suite") + ) + + # Range adjusted for cleaned data: original ~7000, cleaned ~2000-7000 (after deduplication and filtering) + min_samples = 2000 if VALIDATION_SUFFIX == "_clean" else 7000 + suite_stats.add_expectation( + gx.expectations.ExpectTableRowCountToBeBetween(min_value=min_samples, max_value=10000) + ) + + suite_stats.add_expectation( + gx.expectations.ExpectColumnValuesToBeBetween(column='row_nnz', min_value=1) + ) + + suite_stats.add_expectation( + gx.expectations.ExpectColumnValuesToBeBetween(column='row_mean', min_value=0.0, max_value=1.0) + ) + + suite_stats.add_expectation( + gx.expectations.ExpectColumnValuesToBeBetween(column='row_min', min_value=0.0) + ) + + suite_stats.add_expectation( + gx.expectations.ExpectColumnValuesToBeBetween(column='row_max', min_value=0.0, max_value=10.0) + ) + + # Validation 2: Sample columns + datasource_sample = context.data_sources.add_pandas(name="features_sample_source") + asset_sample = datasource_sample.add_dataframe_asset(name="features_sample") + batch_def_sample = asset_sample.add_batch_definition_whole_dataframe("sample_batch") + + suite_sample = context.suites.add( + gx.core.expectation_suite.ExpectationSuite(name="features_sample_suite") + ) + + for col in df_sample.columns[:10]: + suite_sample.add_expectation( + gx.expectations.ExpectColumnValuesToNotBeNull(column=col) + ) + suite_sample.add_expectation( + gx.expectations.ExpectColumnValuesToBeBetween(column=col, min_value=0.0) + ) + + # Run validations + vd_stats = context.validation_definitions.add( + gx.core.validation_definition.ValidationDefinition( + name="features_stats_validation", + data=batch_def_stats, + suite=suite_stats + ) + ) + + cp_stats = context.checkpoints.add( + gx.checkpoint.checkpoint.Checkpoint( + name="features_stats_checkpoint", + validation_definitions=[vd_stats] + ) + ) + result_stats = cp_stats.run(batch_parameters={"dataframe": df_stats}) + + # Save results to JSON (parse describe() string first) + output_file = VALIDATION_OUTPUT_DIR / f"test_2_processed_features{VALIDATION_SUFFIX}.json" + with open(output_file, 'w') as f: + result_dict = json.loads(result_stats.describe()) + json.dump(result_dict, f, indent=2) + + status = "PASS" if result_stats.success else "FAIL" + print(f"[{status}] TEST 2 - TF-IDF Feature Validation") + + +def test_labels(): + """ + TEST 3: Multi-Label Binary Format Validation + Validates label matrix conforms to binary {0,1} format required for multi-label classification. + """ + + labels_path = Path(LABELS_PATH) + if not labels_path.exists(): + print(f"[SKIP] TEST 3 - Labels not found at {labels_path}") + return + + labels = np.load(labels_path) + + df_labels = pd.DataFrame(labels) + df_labels.columns = [f"label_{i}" for i in range(labels.shape[1])] + + context = gx.get_context() + datasource = context.data_sources.add_pandas(name="labels_source") + asset = datasource.add_dataframe_asset(name="labels_asset") + batch_def = asset.add_batch_definition_whole_dataframe("labels_batch") + + suite = context.suites.add( + gx.core.expectation_suite.ExpectationSuite(name="labels_suite") + ) + + # Range adjusted for cleaned data: original ~7000 samples, cleaned ~2000-7000 + min_samples = 2000 if VALIDATION_SUFFIX == "_clean" else 7000 + # Label count adjusted: original 217, cleaned ~100-217 (after removing empty labels) + min_labels = 100 if VALIDATION_SUFFIX == "_clean" else 140 + suite.add_expectation( + gx.expectations.ExpectTableRowCountToBeBetween(min_value=min_samples, max_value=10000) + ) + suite.add_expectation( + gx.expectations.ExpectTableColumnCountToBeBetween(min_value=min_labels, max_value=220) + ) + + # Sample columns for binary format check + sample_label_cols = np.random.choice(df_labels.columns, size=min(20, len(df_labels.columns)), replace=False) + for col in sample_label_cols: + suite.add_expectation( + gx.expectations.ExpectColumnValuesToBeInSet(column=col, value_set=[0, 1, 0.0, 1.0]) + ) + + vd = context.validation_definitions.add( + gx.core.validation_definition.ValidationDefinition( + name="labels_validation", + data=batch_def, + suite=suite + ) + ) + + checkpoint = context.checkpoints.add( + gx.checkpoint.checkpoint.Checkpoint( + name="labels_checkpoint", + validation_definitions=[vd] + ) + ) + + result = checkpoint.run(batch_parameters={"dataframe": df_labels}) + + # Save results to JSON (parse describe() string first) + output_file = VALIDATION_OUTPUT_DIR / f"test_3_labels{VALIDATION_SUFFIX}.json" + with open(output_file, 'w') as f: + result_dict = json.loads(result.describe()) + json.dump(result_dict, f, indent=2) + + status = "PASS" if result.success else "FAIL" + print(f"[{status}] TEST 3 - Label Format Validation") + + +def test_feature_label_consistency(): + """ + TEST 4: Feature-Label Consistency Validation + Validates alignment between feature matrix (X) and label matrix (Y) - same number of samples. + """ + + features_path = Path(FEATURES_PATH) + labels_path = Path(LABELS_PATH) + + if not features_path.exists() or not labels_path.exists(): + print(f"[SKIP] TEST 4 - Required files not found") + return + + features = np.load(features_path) + labels = np.load(labels_path) + + # Check row counts match + alignment_pass = bool(features.shape[0] == labels.shape[0]) + + # Check for empty samples + empty_features = np.sum(features, axis=1) == 0 + n_empty = int(np.sum(empty_features)) + + # Create result summary + result = { + "success": alignment_pass and n_empty == 0, + "alignment_check": { + "success": alignment_pass, + "n_samples_features": int(features.shape[0]), + "n_samples_labels": int(labels.shape[0]) + }, + "empty_vectors_check": { + "success": n_empty == 0, + "n_empty_samples": int(n_empty), + "empty_sample_indices": np.where(empty_features)[0][:50].tolist() + } + } + + # Save results to JSON + output_file = VALIDATION_OUTPUT_DIR / f"test_4_feature_label_consistency{VALIDATION_SUFFIX}.json" + with open(output_file, 'w') as f: + json.dump(result, f, indent=2) + + status = "PASS" if result["success"] else "FAIL" + print(f"[{status}] TEST 4 - Feature-Label Consistency") + + +def test_label_imbalance(): + """ + TEST 5: Label Distribution and Stratification Compatibility + Validates label distribution is suitable for stratified train/test splitting. + """ + + labels_path = Path(LABELS_PATH) + if not labels_path.exists(): + print(f"[SKIP] TEST 5 - Labels not found") + return + + labels = np.load(labels_path) + + # Calculate label statistics + label_counts = labels.sum(axis=0) + + df_label_stats = pd.DataFrame({ + 'label_id': range(len(label_counts)), + 'count': label_counts, + 'percentage': (label_counts / len(labels) * 100) + }) + + context = gx.get_context() + datasource = context.data_sources.add_pandas(name="label_stats_source") + asset = datasource.add_dataframe_asset(name="label_stats_asset") + batch_def = asset.add_batch_definition_whole_dataframe("label_stats_batch") + + suite = context.suites.add( + gx.core.expectation_suite.ExpectationSuite(name="label_imbalance_suite") + ) + + suite.add_expectation( + gx.expectations.ExpectColumnValuesToBeBetween( + column='count', + min_value=float(MIN_FOR_STRATIFICATION), + mostly=0.95 + ) + ) + + suite.add_expectation( + gx.expectations.ExpectColumnValuesToBeBetween( + column='count', + min_value=1.0 + ) + ) + + vd = context.validation_definitions.add( + gx.core.validation_definition.ValidationDefinition( + name="label_imbalance_validation", + data=batch_def, + suite=suite + ) + ) + + checkpoint = context.checkpoints.add( + gx.checkpoint.checkpoint.Checkpoint( + name="label_imbalance_checkpoint", + validation_definitions=[vd] + ) + ) + + results = checkpoint.run(batch_parameters={"dataframe": df_label_stats}) + + # Save results to JSON (parse describe() string first) + output_file = VALIDATION_OUTPUT_DIR / f"test_5_label_imbalance{VALIDATION_SUFFIX}.json" + with open(output_file, 'w') as f: + result_dict = json.loads(results.describe()) + json.dump(result_dict, f, indent=2) + + status = "PASS" if results.success else "FAIL" + print(f"[{status}] TEST 5 - Label Imbalance & Stratification") + + +def test_sparsity_and_oversampling_compatibility(): + """ + TEST 6: Feature Sparsity and Oversampling Algorithm Compatibility + Validates TF-IDF features have appropriate sparsity for SMOTE/ADASYN/MLSMOTE algorithms. + """ + + features_path = Path(FEATURES_PATH) + if not features_path.exists(): + print(f"[SKIP] TEST 6 - Features not found") + return + + features = np.load(features_path) + + # Calculate sparsity metrics + nnz_counts = (features > 0).sum(axis=1) + row_sparsity = 1.0 - (nnz_counts / features.shape[1]) + + df_sparsity = pd.DataFrame({ + 'row_id': range(len(features)), + 'nnz_count': nnz_counts, + 'sparsity': row_sparsity + }) + + context = gx.get_context() + datasource = context.data_sources.add_pandas(name="sparsity_source") + asset = datasource.add_dataframe_asset(name="sparsity_asset") + batch_def = asset.add_batch_definition_whole_dataframe("sparsity_batch") + + suite = context.suites.add( + gx.core.expectation_suite.ExpectationSuite(name="sparsity_suite") + ) + + suite.add_expectation( + gx.expectations.ExpectColumnValuesToBeBetween( + column='nnz_count', + min_value=float(MIN_NNZ_FOR_SMOTE), + mostly=0.95 + ) + ) + + suite.add_expectation( + gx.expectations.ExpectColumnValuesToBeBetween( + column='sparsity', + min_value=0.5, + max_value=0.999 + ) + ) + + vd = context.validation_definitions.add( + gx.core.validation_definition.ValidationDefinition( + name="sparsity_validation", + data=batch_def, + suite=suite + ) + ) + + checkpoint = context.checkpoints.add( + gx.checkpoint.checkpoint.Checkpoint( + name="sparsity_checkpoint", + validation_definitions=[vd] + ) + ) + + results = checkpoint.run(batch_parameters={"dataframe": df_sparsity}) + + # Save results to JSON (parse describe() string first) + output_file = VALIDATION_OUTPUT_DIR / f"test_6_sparsity{VALIDATION_SUFFIX}.json" + with open(output_file, 'w') as f: + result_dict = json.loads(results.describe()) + json.dump(result_dict, f, indent=2) + + status = "PASS" if results.success else "FAIL" + print(f"[{status}] TEST 6 - Feature Sparsity & Oversampling") + + +def test_multioutput_compatibility(): + """ + TEST 7: Multi-Output Classifier Compatibility Validation + Validates label structure is compatible with MultiOutputClassifier and MLSMOTE architectures. + """ + + labels_path = Path(LABELS_PATH) + if not labels_path.exists(): + print(f"[SKIP] TEST 7 - Labels not found") + return + + labels = np.load(labels_path) + + # Additional multi-label analysis + labels_per_sample = labels.sum(axis=1) + avg_labels = labels_per_sample.mean() + multilabel_samples = np.sum(labels_per_sample > 1) + multilabel_pct = multilabel_samples / len(labels) + + # Validate multi-label characteristics + multiple_columns = bool(labels.shape[1] > 1) + sufficient_cooccurrence = bool(multilabel_pct > 0.5) + + result = { + "success": multiple_columns and sufficient_cooccurrence, + "label_structure": { + "n_label_columns": int(labels.shape[1]), + "avg_labels_per_sample": float(avg_labels), + "multilabel_samples": int(multilabel_samples), + "multilabel_percentage": float(multilabel_pct), + "min_labels_per_sample": int(labels_per_sample.min()), + "max_labels_per_sample": int(labels_per_sample.max()) + }, + "multioutput_compatible": multiple_columns, + "mlsmote_viable": sufficient_cooccurrence + } + + # Save results to JSON + output_file = VALIDATION_OUTPUT_DIR / f"test_7_multioutput_compatibility{VALIDATION_SUFFIX}.json" + with open(output_file, 'w') as f: + json.dump(result, f, indent=2) + + status = "PASS" if result["success"] else "FAIL" + print(f"[{status}] TEST 7 - MultiOutput Classifier Compatibility") + + +def test_duplicates(): + """ + TEST 8: Duplicate Samples Detection + Validates absence of duplicate samples in the feature matrix. + """ + + features_path = Path(FEATURES_PATH) + if not features_path.exists(): + print(f"[SKIP] TEST 8 - Features not found at {features_path}") + return + + features = np.load(features_path) + + # Find duplicate rows + unique_features, unique_indices = np.unique(features, axis=0, return_index=True) + n_samples = features.shape[0] + n_unique = unique_features.shape[0] + n_duplicates = n_samples - n_unique + duplicate_percentage = (n_duplicates / n_samples) * 100 + + # Get indices of duplicate samples + all_indices = set(range(n_samples)) + unique_indices_set = set(unique_indices) + duplicate_indices = sorted(list(all_indices - unique_indices_set)) + + result = { + "success": n_duplicates == 0, + "duplicate_check": { + "n_total_samples": int(n_samples), + "n_unique_samples": int(n_unique), + "n_duplicates": int(n_duplicates), + "duplicate_percentage": float(duplicate_percentage), + "duplicate_indices": duplicate_indices[:100] # First 100 duplicate indices + } + } + + # Save results to JSON + output_file = VALIDATION_OUTPUT_DIR / f"test_8_duplicates{VALIDATION_SUFFIX}.json" + with open(output_file, 'w') as f: + json.dump(result, f, indent=2) + + status = "PASS" if result["success"] else "FAIL" + print(f"[{status}] TEST 8 - Duplicate Samples ({n_duplicates} duplicates, {duplicate_percentage:.2f}%)") + + +def test_train_test_separation(): + """ + TEST 9: Train-Test Separation Validation + Validates no data leakage between training and test sets. + """ + + features_path = Path(FEATURES_PATH) + + # Try to load train and test sets if they exist + train_file = features_path.parent / f"X_train{VALIDATION_SUFFIX.replace('_original', '').replace('_clean', '_clean')}.npy" + test_file = features_path.parent / f"X_test{VALIDATION_SUFFIX.replace('_original', '').replace('_clean', '_clean')}.npy" + + if not train_file.exists() or not test_file.exists(): + print(f"[SKIP] TEST 9 - Train/test split files not found") + return + + X_train = np.load(train_file) + X_test = np.load(test_file) + + # Find overlapping samples + overlap_count = 0 + overlap_indices = [] + + for i, test_sample in enumerate(X_test): + for j, train_sample in enumerate(X_train): + if np.array_equal(test_sample, train_sample): + overlap_count += 1 + overlap_indices.append({"test_idx": int(i), "train_idx": int(j)}) + if len(overlap_indices) >= 100: # Limit stored indices + break + if len(overlap_indices) >= 100: + break + + total_samples = X_train.shape[0] + X_test.shape[0] + leakage_percentage = (overlap_count / X_test.shape[0]) * 100 if X_test.shape[0] > 0 else 0 + + result = { + "success": overlap_count == 0, + "train_test_separation": { + "n_train_samples": int(X_train.shape[0]), + "n_test_samples": int(X_test.shape[0]), + "n_overlapping_samples": int(overlap_count), + "leakage_percentage": float(leakage_percentage), + "overlapping_indices": overlap_indices + } + } + + # Save results to JSON + output_file = VALIDATION_OUTPUT_DIR / f"test_9_train_test_separation{VALIDATION_SUFFIX}.json" + with open(output_file, 'w') as f: + json.dump(result, f, indent=2) + + status = "PASS" if result["success"] else "FAIL" + print(f"[{status}] TEST 9 - Train-Test Separation ({overlap_count} overlapping samples)") + + +def test_label_consistency(): + """ + TEST 10: Label Consistency Validation + Validates that identical feature vectors have identical labels. + """ + + features_path = Path(FEATURES_PATH) + labels_path = Path(LABELS_PATH) + + if not features_path.exists() or not labels_path.exists(): + print(f"[SKIP] TEST 10 - Required files not found") + return + + features = np.load(features_path) + labels = np.load(labels_path) + + # Find duplicate feature vectors and check if their labels match + conflicts = [] + n_conflicts = 0 + + # Create a dictionary to store feature hash -> indices + from collections import defaultdict + feature_dict = defaultdict(list) + + for idx, feature_vec in enumerate(features): + # Use hash of feature vector as key + feature_hash = hash(feature_vec.tobytes()) + feature_dict[feature_hash].append(idx) + + # Check for conflicts in duplicate groups + for feature_hash, indices in feature_dict.items(): + if len(indices) > 1: + # Check if all labels are identical for these duplicate features + first_label = labels[indices[0]] + for idx in indices[1:]: + if not np.array_equal(first_label, labels[idx]): + n_conflicts += 1 + if len(conflicts) < 100: # Limit stored conflicts + conflicts.append({ + "indices": [int(i) for i in indices], + "n_duplicates": len(indices) + }) + break + + result = { + "success": n_conflicts == 0, + "label_consistency": { + "n_duplicate_groups_checked": len([v for v in feature_dict.values() if len(v) > 1]), + "n_conflicting_groups": int(n_conflicts), + "conflicting_groups": conflicts + } + } + + # Save results to JSON + output_file = VALIDATION_OUTPUT_DIR / f"test_10_label_consistency{VALIDATION_SUFFIX}.json" + with open(output_file, 'w') as f: + json.dump(result, f, indent=2) + + status = "PASS" if result["success"] else "FAIL" + print(f"[{status}] TEST 10 - Label Consistency ({n_conflicts} conflicts)") + + +def run_validation_suite(validate_original: bool): + """ + Run all validation tests on either original or cleaned data. + + Args: + validate_original: If True, validates original data; if False, validates cleaned data + """ + global FEATURES_PATH, LABELS_PATH, VALIDATION_SUFFIX + + # Set global variables for this validation run + if validate_original: + FEATURES_PATH = DATA_PATHS.get("features_original", DATA_PATHS["features"]) + LABELS_PATH = DATA_PATHS.get("labels_original", DATA_PATHS["labels"]) + VALIDATION_SUFFIX = "_original" + dataset_name = "ORIGINAL (pre-cleaning)" + else: + FEATURES_PATH = DATA_PATHS["features"] + LABELS_PATH = DATA_PATHS["labels"] + VALIDATION_SUFFIX = "_clean" + dataset_name = "CLEANED (post-cleaning)" + + print("\n" + "="*80) + print(f"Validating: {dataset_name}") + print("="*80 + "\n") + + try: + test_raw_database() + except Exception as e: + print(f"[ERROR] TEST 1 - {e}") + + try: + test_processed_features() + except Exception as e: + print(f"[ERROR] TEST 2 - {e}") + + try: + test_labels() + except Exception as e: + print(f"[ERROR] TEST 3 - {e}") + + try: + test_feature_label_consistency() + except Exception as e: + print(f"[ERROR] TEST 4 - {e}") + + try: + test_label_imbalance() + except Exception as e: + print(f"[ERROR] TEST 5 - {e}") + + try: + test_sparsity_and_oversampling_compatibility() + except Exception as e: + print(f"[ERROR] TEST 6 - {e}") + + try: + test_multioutput_compatibility() + except Exception as e: + print(f"[ERROR] TEST 7 - {e}") + + try: + test_duplicates() + except Exception as e: + print(f"[ERROR] TEST 8 - {e}") + + # TEST 9 is only applicable to cleaned data (train/test split files) + if not validate_original: + try: + test_train_test_separation() + except Exception as e: + print(f"[ERROR] TEST 9 - {e}") + + try: + test_label_consistency() + except Exception as e: + print(f"[ERROR] TEST 10 - {e}") + + +if __name__ == "__main__": + """ + Execute all data validation tests on BOTH original and cleaned datasets. + + Tests are independent and handle failures gracefully. + Test suite completion is guaranteed even if individual tests fail. + """ + print("\n" + "="*80) + print("GREAT EXPECTATIONS DATA VALIDATION SUITE") + print("Hopcroft Skill Classification Project") + print("="*80 + "\n") + print("This suite validates BOTH original and cleaned datasets:") + print(" 1. ORIGINAL data - documents baseline data quality issues") + print(" 2. CLEANED data - verifies data is ready for training") + + # Validate ORIGINAL data first (if exists) + print("\n" + "-"*80) + print("PHASE 1: Validating ORIGINAL processed data") + print("-"*80) + run_validation_suite(validate_original=True) + + # Validate CLEANED data (if exists) + print("\n" + "-"*80) + print("PHASE 2: Validating CLEANED processed data") + print("-"*80) + run_validation_suite(validate_original=False) + + # Summary + print("\n" + "="*80) + print("VALIDATION COMPLETE") + print("="*80) + print(f"\nResults saved to: {VALIDATION_OUTPUT_DIR.absolute()}") + print("\nGenerated files:") + for json_file in sorted(VALIDATION_OUTPUT_DIR.glob("test_*.json")): + print(f" - {json_file.name}") diff --git a/tests/integration/test_feature_pipeline.py b/tests/integration/test_feature_pipeline.py new file mode 100644 index 0000000000000000000000000000000000000000..a54e1abc7d7af90a743d62d811b63592f8a05e73 --- /dev/null +++ b/tests/integration/test_feature_pipeline.py @@ -0,0 +1,311 @@ +""" +Integration tests for the feature extraction pipeline. + +Tests the combined functionality of dataset loading, text processing, +and feature extraction working together. +""" +import pytest +import numpy as np +import pandas as pd +import tempfile +import sqlite3 +from pathlib import Path + +from hopcroft_skill_classification_tool_competition.features import ( + load_data_from_db, + create_feature_dataset, + extract_tfidf_features, + prepare_labels, + get_text_columns, + get_label_columns, +) + + +@pytest.mark.integration +class TestFeatureExtractionPipeline: + """Integration tests for complete feature extraction pipeline.""" + + def test_full_pipeline_from_dataframe_to_features(self, sample_dataframe): + """Test complete pipeline from DataFrame to features and labels.""" + # Extract features + features, vectorizer = extract_tfidf_features(sample_dataframe, max_features=50) + + # Prepare labels + labels = prepare_labels(sample_dataframe) + + # Verify alignment + assert features.shape[0] == len(labels) + assert features.shape[0] == len(sample_dataframe) + + # Verify data types + assert isinstance(features, np.ndarray) + assert isinstance(labels, pd.DataFrame) + + # Verify no NaN or Inf values + assert not np.any(np.isnan(features)) + assert not np.any(np.isinf(features)) + assert not labels.isnull().any().any() + + def test_pipeline_with_database_to_features(self, temp_db): + """Test pipeline from database loading to feature extraction.""" + # Load from database + df = load_data_from_db(temp_db) + + # Extract features + features, vectorizer = extract_tfidf_features(df, max_features=50) + + # Prepare labels + labels = prepare_labels(df) + + # Verify complete pipeline + assert features.shape[0] == len(df) + assert labels.shape[0] == len(df) + assert features.shape[0] == labels.shape[0] + + def test_create_feature_dataset_integration(self, temp_db): + """Test the complete create_feature_dataset function.""" + features, labels, feature_names, label_names = create_feature_dataset( + db_path=temp_db, + save_processed=False + ) + + # Verify outputs + assert isinstance(features, np.ndarray) + assert isinstance(labels, pd.DataFrame) + assert isinstance(feature_names, np.ndarray) # sklearn returns ndarray + assert isinstance(label_names, list) + + # Verify shapes match + assert features.shape[0] == labels.shape[0] + assert features.shape[1] == len(feature_names) + assert labels.shape[1] == len(label_names) + + def test_pipeline_preserves_sample_count(self, sample_dataframe): + """Test that no samples are lost during pipeline.""" + initial_count = len(sample_dataframe) + + features, _ = extract_tfidf_features(sample_dataframe, max_features=50) + labels = prepare_labels(sample_dataframe) + + assert features.shape[0] == initial_count + assert labels.shape[0] == initial_count + + def test_pipeline_with_various_text_lengths(self): + """Test pipeline with documents of varying lengths.""" + df = pd.DataFrame({ + 'issue text': [ + 'short', + 'This is a medium length text with several words', + 'This is a very long text ' * 50, # Very long + ], + 'issue description': ['desc1', 'desc2', 'desc3'], + 'Label1': [1, 0, 1], + 'Label2': [0, 1, 1], + }) + + features, _ = extract_tfidf_features(df, max_features=50) + labels = prepare_labels(df) + + # All documents should be processed + assert features.shape[0] == 3 + assert labels.shape[0] == 3 + + # Features should have reasonable values + assert not np.all(features == 0) + + +@pytest.mark.integration +class TestDataFlowConsistency: + """Integration tests for data consistency through the pipeline.""" + + def test_text_cleaning_affects_features(self, sample_dataframe): + """Test that text cleaning impacts feature extraction.""" + # Add dirty text + dirty_df = sample_dataframe.copy() + dirty_df['issue text'] = [ + "Bug https://example.com with HTML", + "Feature with ```code block```", + "Update with extra spaces", + "Test with πŸ˜€ emoji", + "Normal clean text", + ] + + features_dirty, _ = extract_tfidf_features(dirty_df, max_features=50) + + # Clean version + clean_df = sample_dataframe.copy() + clean_df['issue text'] = [ + "Bug with HTML", + "Feature with", + "Update with extra spaces", + "Test with emoji", + "Normal clean text", + ] + + features_clean, _ = extract_tfidf_features(clean_df, max_features=50) + + # Features should be similar (cleaning is applied to both) + # But not necessarily identical due to stemming + assert features_dirty.shape == features_clean.shape + + def test_label_binarization_consistency(self): + """Test that label binarization is consistent.""" + df = pd.DataFrame({ + 'issue text': ['text1', 'text2', 'text3'], + 'issue description': ['desc1', 'desc2', 'desc3'], + 'Label1': [0, 5, 10], # Different counts + 'Label2': [1, 0, 100], + }) + + labels = prepare_labels(df) + + # All values should be 0 or 1 + assert set(labels.values.flatten()).issubset({0, 1}) + + # Specific checks + assert labels.loc[0, 'Label1'] == 0 + assert labels.loc[1, 'Label1'] == 1 + assert labels.loc[2, 'Label1'] == 1 + assert labels.loc[0, 'Label2'] == 1 + assert labels.loc[1, 'Label2'] == 0 + assert labels.loc[2, 'Label2'] == 1 + + def test_feature_label_alignment(self, sample_dataframe): + """Test that features and labels remain aligned.""" + features, _ = extract_tfidf_features(sample_dataframe, max_features=50) + labels = prepare_labels(sample_dataframe) + + # Check alignment by comparing indices + for i in range(len(sample_dataframe)): + # Each row should correspond to the same sample + assert features[i].shape[0] > 0 # Has features + assert labels.iloc[i].shape[0] > 0 # Has labels + + +@pytest.mark.integration +@pytest.mark.slow +class TestLargeDatasetHandling: + """Integration tests with larger datasets (marked as slow).""" + + def test_pipeline_with_large_dataset(self): + """Test pipeline with a larger number of samples.""" + # Create larger dataset + n_samples = 1000 + df = pd.DataFrame({ + 'issue text': [f'Issue number {i} with some text' for i in range(n_samples)], + 'issue description': [f'Description for issue {i}' for i in range(n_samples)], + 'Label1': np.random.randint(0, 2, n_samples), + 'Label2': np.random.randint(0, 2, n_samples), + 'Label3': np.random.randint(0, 2, n_samples), + }) + + features, _ = extract_tfidf_features(df, max_features=500) + labels = prepare_labels(df) + + assert features.shape[0] == n_samples + assert labels.shape[0] == n_samples + assert features.shape[1] <= 500 + + def test_pipeline_with_many_labels(self): + """Test pipeline with many label columns.""" + n_labels = 50 + df = pd.DataFrame({ + 'issue text': ['text1', 'text2', 'text3'], + 'issue description': ['desc1', 'desc2', 'desc3'], + }) + + # Add many label columns + for i in range(n_labels): + df[f'Label_{i}'] = np.random.randint(0, 2, 3) + + labels = prepare_labels(df) + + assert labels.shape[1] == n_labels + assert set(labels.values.flatten()).issubset({0, 1}) + + +@pytest.mark.integration +class TestSaveAndLoadIntegration: + """Integration tests for saving and loading processed data.""" + + def test_save_and_load_features(self, temp_db): + """Test saving features and labels then loading them back.""" + with tempfile.TemporaryDirectory() as tmpdir: + from hopcroft_skill_classification_tool_competition.features import ( + create_feature_dataset, + load_processed_data + ) + + # Mock the PROCESSED_DATA_DIR + with pytest.MonkeyPatch.context() as m: + tmpdir_path = Path(tmpdir) + tfidf_dir = tmpdir_path / "tfidf" + tfidf_dir.mkdir(parents=True) + + # Create and save + features_orig, labels_orig, _, _ = create_feature_dataset( + db_path=temp_db, + save_processed=True + ) + + # Save manually since we're mocking + np.save(tfidf_dir / "features_tfidf.npy", features_orig) + np.save(tfidf_dir / "labels_tfidf.npy", labels_orig.values) + + # Load back + features_loaded = np.load(tfidf_dir / "features_tfidf.npy") + labels_loaded = np.load(tfidf_dir / "labels_tfidf.npy") + + # Verify they match + np.testing.assert_array_equal(features_orig, features_loaded) + np.testing.assert_array_equal(labels_orig.values, labels_loaded) + + +@pytest.mark.integration +class TestErrorHandlingInPipeline: + """Integration tests for error handling throughout pipeline.""" + + def test_pipeline_with_missing_columns(self): + """Test pipeline behavior with missing expected columns.""" + df = pd.DataFrame({ + 'wrong_col_1': ['text1', 'text2'], + 'wrong_col_2': ['desc1', 'desc2'], + 'Label1': [1, 0], + }) + + # Should handle missing text columns gracefully + text_cols = get_text_columns(df) + assert len(text_cols) == 0 + + # Should still work with explicit column specification + # (though results may not be meaningful) + with pytest.raises(ValueError, match="No text columns found"): + extract_tfidf_features(df) + + def test_pipeline_with_all_nan_text(self): + """Test pipeline with all NaN text values.""" + df = pd.DataFrame({ + 'issue text': [None, None, None], + 'issue description': [None, None, None], + 'Label1': [1, 0, 1], + }) + + # Should handle NaN values without crashing + features, _ = extract_tfidf_features(df, max_features=50) + + # May result in zero features for all samples + assert features.shape[0] == 3 + assert not np.any(np.isnan(features)) + + def test_pipeline_with_empty_labels(self): + """Test pipeline when no labels are present.""" + df = pd.DataFrame({ + 'issue text': ['text1', 'text2'], + 'issue description': ['desc1', 'desc2'], + # No label columns + }) + + label_cols = get_label_columns(df) + + # Should return empty list + assert len(label_cols) == 0 diff --git a/tests/system/test_training_inference.py b/tests/system/test_training_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..ca9e77586a654182c07ff20d615838447dc0e120 --- /dev/null +++ b/tests/system/test_training_inference.py @@ -0,0 +1,507 @@ +""" +System tests for end-to-end workflows. + +Tests the complete system including training and inference pipelines. +""" +import pytest +import numpy as np +import pandas as pd +import tempfile +from pathlib import Path +from unittest.mock import patch, MagicMock +import joblib + +from sklearn.ensemble import RandomForestClassifier +from sklearn.multioutput import MultiOutputClassifier + + +@pytest.mark.system +@pytest.mark.slow +class TestTrainingPipeline: + """System tests for model training pipeline.""" + + def test_complete_training_workflow(self, sample_dataframe): + """Test complete training workflow from data to model.""" + from hopcroft_skill_classification_tool_competition.features import ( + extract_tfidf_features, + prepare_labels, + ) + from sklearn.model_selection import train_test_split + + # Extract features + features, vectorizer = extract_tfidf_features(sample_dataframe, max_features=50) + labels = prepare_labels(sample_dataframe) + + # Split data + X_train, X_test, y_train, y_test = train_test_split( + features, labels.values, test_size=0.2, random_state=42 + ) + + # Train model + rf = RandomForestClassifier(n_estimators=10, random_state=42) + model = MultiOutputClassifier(rf) + model.fit(X_train, y_train) + + # Predict + predictions = model.predict(X_test) + + # Verify + assert predictions.shape[0] == X_test.shape[0] + assert predictions.shape[1] == y_test.shape[1] + assert np.all((predictions == 0) | (predictions == 1)) + + def test_training_with_oversampling(self, sample_dataframe): + """Test training pipeline with oversampling.""" + from hopcroft_skill_classification_tool_competition.features import ( + extract_tfidf_features, + prepare_labels, + ) + from imblearn.over_sampling import RandomOverSampler + from sklearn.model_selection import train_test_split + + # Prepare data + features, _ = extract_tfidf_features(sample_dataframe, max_features=50) + labels = prepare_labels(sample_dataframe) + + # Use only one label column for oversampling + y_single = labels.iloc[:, 0].values + + # Split + X_train, X_test, y_train, y_test = train_test_split( + features, y_single, test_size=0.2, random_state=42 + ) + + # Oversample + ros = RandomOverSampler(random_state=42) + X_resampled, y_resampled = ros.fit_resample(X_train, y_train) + + # Train + rf = RandomForestClassifier(n_estimators=10, random_state=42) + rf.fit(X_resampled, y_resampled) + + # Predict + predictions = rf.predict(X_test) + + # Verify + assert len(predictions) == len(X_test) + assert np.all((predictions == 0) | (predictions == 1)) + + def test_model_serialization(self, sample_dataframe): + """Test model can be saved and loaded.""" + from hopcroft_skill_classification_tool_competition.features import ( + extract_tfidf_features, + prepare_labels, + ) + + # Train simple model + features, _ = extract_tfidf_features(sample_dataframe, max_features=50) + labels = prepare_labels(sample_dataframe) + + rf = RandomForestClassifier(n_estimators=5, random_state=42) + model = MultiOutputClassifier(rf) + model.fit(features, labels.values) + + # Save and load + with tempfile.NamedTemporaryFile(suffix='.pkl', delete=False) as f: + model_path = f.name + + try: + joblib.dump(model, model_path) + loaded_model = joblib.load(model_path) + + # Verify predictions match + pred_original = model.predict(features) + pred_loaded = loaded_model.predict(features) + + np.testing.assert_array_equal(pred_original, pred_loaded) + finally: + Path(model_path).unlink() + + +@pytest.mark.system +class TestInferencePipeline: + """System tests for inference pipeline.""" + + def test_inference_on_new_text(self, sample_dataframe): + """Test inference pipeline on new unseen text.""" + from hopcroft_skill_classification_tool_competition.features import ( + extract_tfidf_features, + prepare_labels, + clean_github_text, + ) + + # Train model + features, vectorizer = extract_tfidf_features(sample_dataframe, max_features=50) + labels = prepare_labels(sample_dataframe) + + rf = RandomForestClassifier(n_estimators=5, random_state=42) + model = MultiOutputClassifier(rf) + model.fit(features, labels.values) + + # New text + new_texts = [ + "Fixed critical bug in authentication module", + "Added new REST API endpoint for users", + ] + + # Process new text + cleaned_texts = [clean_github_text(text) for text in new_texts] + new_features = vectorizer.transform(cleaned_texts).toarray() + + # Predict + predictions = model.predict(new_features) + + # Verify + assert predictions.shape[0] == len(new_texts) + assert predictions.shape[1] == labels.shape[1] + assert np.all((predictions == 0) | (predictions == 1)) + + def test_inference_with_empty_input(self, sample_dataframe): + """Test inference handles empty input gracefully.""" + from hopcroft_skill_classification_tool_competition.features import ( + extract_tfidf_features, + prepare_labels, + clean_github_text, + ) + + # Train model + features, vectorizer = extract_tfidf_features(sample_dataframe, max_features=50) + labels = prepare_labels(sample_dataframe) + + rf = RandomForestClassifier(n_estimators=5, random_state=42) + model = MultiOutputClassifier(rf) + model.fit(features, labels.values) + + # Empty text + empty_text = "" + cleaned = clean_github_text(empty_text) + new_features = vectorizer.transform([cleaned]).toarray() + + # Should not crash + predictions = model.predict(new_features) + + assert predictions.shape[0] == 1 + assert predictions.shape[1] == labels.shape[1] + + def test_batch_inference(self, sample_dataframe): + """Test inference on batch of samples.""" + from hopcroft_skill_classification_tool_competition.features import ( + extract_tfidf_features, + prepare_labels, + ) + + # Train model + features, vectorizer = extract_tfidf_features(sample_dataframe, max_features=50) + labels = prepare_labels(sample_dataframe) + + rf = RandomForestClassifier(n_estimators=5, random_state=42) + model = MultiOutputClassifier(rf) + model.fit(features, labels.values) + + # Batch prediction + predictions = model.predict(features) + + assert predictions.shape == labels.shape + assert np.all((predictions == 0) | (predictions == 1)) + + +@pytest.mark.system +@pytest.mark.requires_data +class TestEndToEndDataFlow: + """System tests for complete data flow from raw to predictions.""" + + def test_full_pipeline_database_to_predictions(self, temp_db): + """Test complete pipeline from database to predictions.""" + from hopcroft_skill_classification_tool_competition.features import ( + load_data_from_db, + extract_tfidf_features, + prepare_labels, + ) + from sklearn.model_selection import train_test_split + + # Load data + df = load_data_from_db(temp_db) + + # Extract features + features, vectorizer = extract_tfidf_features(df, max_features=50) + labels = prepare_labels(df) + + # Split + X_train, X_test, y_train, y_test = train_test_split( + features, labels.values, test_size=0.4, random_state=42 + ) + + # Train + rf = RandomForestClassifier(n_estimators=5, random_state=42) + model = MultiOutputClassifier(rf) + model.fit(X_train, y_train) + + # Predict + predictions = model.predict(X_test) + + # Evaluate (simple check) + from sklearn.metrics import accuracy_score + + # Per-label accuracy + accuracies = [] + for i in range(y_test.shape[1]): + acc = accuracy_score(y_test[:, i], predictions[:, i]) + accuracies.append(acc) + + # Should have some predictive power (better than random for at least one label) + assert np.mean(accuracies) > 0.4 # Very lenient threshold for small test data + + +@pytest.mark.system +class TestModelValidation: + """System tests for model validation workflows.""" + + def test_cross_validation_workflow(self, sample_dataframe): + """Test cross-validation workflow.""" + from hopcroft_skill_classification_tool_competition.features import ( + extract_tfidf_features, + prepare_labels, + ) + from sklearn.model_selection import cross_val_score + + # Prepare data + features, _ = extract_tfidf_features(sample_dataframe, max_features=50) + labels = prepare_labels(sample_dataframe) + + # Use single label for CV + y_single = labels.iloc[:, 0].values + + # Cross-validation + rf = RandomForestClassifier(n_estimators=5, random_state=42) + + # Should not crash (though scores may be poor with small data) + scores = cross_val_score(rf, features, y_single, cv=2, scoring='accuracy') + + assert len(scores) == 2 + assert all(0 <= score <= 1 for score in scores) + + def test_grid_search_workflow(self, sample_dataframe): + """Test grid search workflow.""" + from hopcroft_skill_classification_tool_competition.features import ( + extract_tfidf_features, + prepare_labels, + ) + from sklearn.model_selection import GridSearchCV + + # Prepare data + features, _ = extract_tfidf_features(sample_dataframe, max_features=50) + labels = prepare_labels(sample_dataframe) + + # Use single label + y_single = labels.iloc[:, 0].values + + # Small grid search + param_grid = { + 'n_estimators': [5, 10], + 'max_depth': [5, 10], + } + + rf = RandomForestClassifier(random_state=42) + grid_search = GridSearchCV(rf, param_grid, cv=2, scoring='accuracy') + grid_search.fit(features, y_single) + + # Verify + assert hasattr(grid_search, 'best_params_') + assert hasattr(grid_search, 'best_score_') + assert grid_search.best_score_ >= 0 + + +@pytest.mark.system +@pytest.mark.regression +class TestRegressionScenarios: + """Regression tests for known issues and edge cases.""" + + def test_empty_feature_vectors_handling(self): + """ + Regression test: Ensure empty feature vectors don't crash training. + + This was identified in Great Expectations TEST 2 - 25 samples with + zero features after TF-IDF extraction. + """ + from sklearn.ensemble import RandomForestClassifier + from sklearn.multioutput import MultiOutputClassifier + + # Create data with some zero vectors + X = np.array([ + [0.1, 0.2, 0.3], + [0.0, 0.0, 0.0], # Empty vector + [0.4, 0.5, 0.6], + [0.0, 0.0, 0.0], # Another empty vector + ]) + + y = np.array([ + [1, 0], + [0, 1], + [1, 1], + [0, 0], + ]) + + # Should not crash + rf = RandomForestClassifier(n_estimators=5, random_state=42) + model = MultiOutputClassifier(rf) + model.fit(X, y) + + predictions = model.predict(X) + assert predictions.shape == y.shape + + def test_zero_occurrence_labels_handling(self): + """ + Regression test: Handle labels with zero occurrences. + + This was identified in Great Expectations TEST 5 - 75 labels with + zero occurrences in the dataset. + """ + from hopcroft_skill_classification_tool_competition.features import get_label_columns + + # Create dataframe with some zero-occurrence labels + df = pd.DataFrame({ + 'issue text': ['text1', 'text2', 'text3'], + 'Label1': [1, 1, 0], # Has occurrences + 'Label2': [0, 0, 0], # Zero occurrences + 'Label3': [1, 0, 1], # Has occurrences + }) + + label_cols = get_label_columns(df) + + # Should include all labels + assert 'Label1' in label_cols + assert 'Label2' in label_cols + assert 'Label3' in label_cols + + # Training code should filter these out before stratification + # This test just verifies detection works + + def test_high_sparsity_features(self): + """ + Regression test: Handle very sparse features (>99% zeros). + + This was identified in Great Expectations TEST 6 - 99.88% sparsity. + """ + from sklearn.ensemble import RandomForestClassifier + + # Create highly sparse feature matrix + X = np.zeros((100, 1000)) + + # Only 0.12% non-zero values (very sparse) + for i in range(100): + indices = np.random.choice(1000, size=1, replace=False) + X[i, indices] = np.random.rand(1) + + y = np.random.randint(0, 2, size=100) + + # Should handle high sparsity without crashing + rf = RandomForestClassifier(n_estimators=5, random_state=42) + rf.fit(X, y) + + predictions = rf.predict(X) + assert len(predictions) == len(y) + + def test_duplicate_samples_detection(self): + """ + Regression test: Detect duplicate samples. + + This was identified in Deepchecks validation - 481 duplicates (6.72%). + """ + df = pd.DataFrame({ + 'issue text': ['duplicate', 'duplicate', 'unique'], + 'issue description': ['desc', 'desc', 'different'], + 'Label1': [1, 1, 0], + }) + + # Check for duplicates + duplicates = df[['issue text', 'issue description']].duplicated() + + assert duplicates.sum() == 1 # One duplicate found + + # Removal should be done in data cleaning pipeline + df_cleaned = df.drop_duplicates(subset=['issue text', 'issue description']) + assert len(df_cleaned) == 2 + + +@pytest.mark.system +@pytest.mark.acceptance +class TestAcceptanceCriteria: + """Acceptance tests verifying requirements are met.""" + + def test_multi_label_classification_support(self, sample_dataframe): + """ + Acceptance test: System supports multi-label classification. + + Requirement: Each issue can have multiple skill labels. + """ + from hopcroft_skill_classification_tool_competition.features import ( + extract_tfidf_features, + prepare_labels, + ) + + features, _ = extract_tfidf_features(sample_dataframe, max_features=50) + labels = prepare_labels(sample_dataframe) + + # Train multi-output model + rf = RandomForestClassifier(n_estimators=5, random_state=42) + model = MultiOutputClassifier(rf) + model.fit(features, labels.values) + + # Predict multiple labels + predictions = model.predict(features) + + # Verify multiple labels can be predicted + labels_per_sample = predictions.sum(axis=1) + assert np.any(labels_per_sample > 1), "System should support multiple labels per sample" + + def test_handles_github_text_format(self): + """ + Acceptance test: System handles GitHub issue text format. + + Requirement: Process text from GitHub issues with URLs, code, etc. + """ + from hopcroft_skill_classification_tool_competition.features import clean_github_text + + github_text = """ + Fixed bug in authentication #123 + + See: https://github.com/repo/issues/123 + + ```python + def login(user): + return authenticate(user) + ``` + + Related to security improvements πŸ”’ + """ + + cleaned = clean_github_text(github_text) + + # Should remove noise but keep meaningful content + assert "https://" not in cleaned + assert "```" not in cleaned + assert "" not in cleaned + assert len(cleaned) > 0 + + def test_produces_binary_predictions(self, sample_dataframe): + """ + Acceptance test: System produces binary predictions (0 or 1). + + Requirement: Clear yes/no predictions for each skill. + """ + from hopcroft_skill_classification_tool_competition.features import ( + extract_tfidf_features, + prepare_labels, + ) + + features, _ = extract_tfidf_features(sample_dataframe, max_features=50) + labels = prepare_labels(sample_dataframe) + + rf = RandomForestClassifier(n_estimators=5, random_state=42) + model = MultiOutputClassifier(rf) + model.fit(features, labels.values) + + predictions = model.predict(features) + + # All predictions should be 0 or 1 + assert np.all((predictions == 0) | (predictions == 1)) diff --git a/tests/unit/test_dataset.py b/tests/unit/test_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..64ee81b2116ff6264159ef62fac2fb7dac2a29e5 --- /dev/null +++ b/tests/unit/test_dataset.py @@ -0,0 +1,252 @@ +""" +Unit tests for dataset.py module. + +Tests functions for downloading and extracting the SkillScope dataset. +""" +import pytest +from pathlib import Path +import tempfile +import zipfile +import sqlite3 +from unittest.mock import patch, MagicMock + +from hopcroft_skill_classification_tool_competition.dataset import ( + download_skillscope_dataset, +) + + +@pytest.mark.unit +class TestDatasetDownload: + """Unit tests for dataset download functionality.""" + + def test_download_returns_path(self): + """Test that download function returns a Path object.""" + with tempfile.TemporaryDirectory() as tmpdir: + output_dir = Path(tmpdir) + + # Mock the actual download to avoid network calls + with patch('hopcroft_skill_classification_tool_competition.dataset.hf_hub_download') as mock_download: + # Create a mock zip file + zip_path = output_dir / "skillscope_data.zip" + db_path = output_dir / "skillscope_data.db" + + # Create a dummy database + conn = sqlite3.connect(db_path) + conn.execute("CREATE TABLE test (id INTEGER)") + conn.close() + + # Create a zip of the database + with zipfile.ZipFile(zip_path, 'w') as zf: + zf.write(db_path, arcname='skillscope_data.db') + + # Remove the original database + db_path.unlink() + + # Mock download to return the zip path + mock_download.return_value = str(zip_path) + + result = download_skillscope_dataset(output_dir) + + assert isinstance(result, Path) + assert result.exists() + assert result.name == "skillscope_data.db" + + def test_download_creates_directory(self): + """Test that download creates output directory if it doesn't exist.""" + with tempfile.TemporaryDirectory() as tmpdir: + output_dir = Path(tmpdir) / "nonexistent" / "nested" / "dir" + + assert not output_dir.exists() + + with patch('hopcroft_skill_classification_tool_competition.dataset.hf_hub_download') as mock_download: + # Create mock database + temp_db = Path(tmpdir) / "skillscope_data.db" + conn = sqlite3.connect(temp_db) + conn.execute("CREATE TABLE test (id INTEGER)") + conn.close() + + # Create zip + zip_path = Path(tmpdir) / "skillscope_data.zip" + with zipfile.ZipFile(zip_path, 'w') as zf: + zf.write(temp_db, arcname='skillscope_data.db') + + mock_download.return_value = str(zip_path) + + download_skillscope_dataset(output_dir) + + assert output_dir.exists() + + def test_download_skips_if_exists(self): + """Test that download is skipped if database already exists.""" + with tempfile.TemporaryDirectory() as tmpdir: + output_dir = Path(tmpdir) + db_path = output_dir / "skillscope_data.db" + + # Create existing database + conn = sqlite3.connect(db_path) + conn.execute("CREATE TABLE test (id INTEGER)") + conn.close() + + with patch('hopcroft_skill_classification_tool_competition.dataset.hf_hub_download') as mock_download: + result = download_skillscope_dataset(output_dir) + + # Should not call download if file exists + mock_download.assert_not_called() + assert result == db_path + + def test_download_extracts_zip(self): + """Test that zip file is properly extracted.""" + with tempfile.TemporaryDirectory() as tmpdir: + output_dir = Path(tmpdir) + + with patch('hopcroft_skill_classification_tool_competition.dataset.hf_hub_download') as mock_download: + # Create database and zip it + temp_db = Path(tmpdir) / "temp_skillscope_data.db" + conn = sqlite3.connect(temp_db) + conn.execute("CREATE TABLE nlbse_tool_competition_data_by_issue (id INTEGER)") + conn.close() + + zip_path = output_dir / "skillscope_data.zip" + with zipfile.ZipFile(zip_path, 'w') as zf: + zf.write(temp_db, arcname='skillscope_data.db') + + temp_db.unlink() + mock_download.return_value = str(zip_path) + + result = download_skillscope_dataset(output_dir) + + # Check database was extracted + assert result.exists() + + # Verify it's a valid SQLite database + conn = sqlite3.connect(result) + cursor = conn.cursor() + cursor.execute("SELECT name FROM sqlite_master WHERE type='table'") + tables = cursor.fetchall() + conn.close() + + assert len(tables) > 0 + + def test_download_cleans_up_zip(self): + """Test that zip file is deleted after extraction.""" + with tempfile.TemporaryDirectory() as tmpdir: + output_dir = Path(tmpdir) + + with patch('hopcroft_skill_classification_tool_competition.dataset.hf_hub_download') as mock_download: + # Create database and zip + temp_db = Path(tmpdir) / "temp_db.db" + conn = sqlite3.connect(temp_db) + conn.execute("CREATE TABLE test (id INTEGER)") + conn.close() + + zip_path = output_dir / "skillscope_data.zip" + with zipfile.ZipFile(zip_path, 'w') as zf: + zf.write(temp_db, arcname='skillscope_data.db') + + temp_db.unlink() + mock_download.return_value = str(zip_path) + + download_skillscope_dataset(output_dir) + + # Zip should be deleted + assert not zip_path.exists() + + def test_download_raises_on_missing_database(self): + """Test that error is raised if database not in zip.""" + with tempfile.TemporaryDirectory() as tmpdir: + output_dir = Path(tmpdir) + + with patch('hopcroft_skill_classification_tool_competition.dataset.hf_hub_download') as mock_download: + # Create zip without database file + zip_path = output_dir / "skillscope_data.zip" + with zipfile.ZipFile(zip_path, 'w') as zf: + zf.writestr('dummy.txt', 'dummy content') + + mock_download.return_value = str(zip_path) + + with pytest.raises(FileNotFoundError): + download_skillscope_dataset(output_dir) + + +@pytest.mark.unit +class TestDatasetEdgeCases: + """Unit tests for edge cases in dataset handling.""" + + def test_download_with_none_output_dir(self): + """Test download with None as output directory (should use default).""" + with patch('hopcroft_skill_classification_tool_competition.dataset.hf_hub_download') as mock_download: + with patch('hopcroft_skill_classification_tool_competition.dataset.RAW_DATA_DIR') as mock_raw_dir: + # Mock the default directory + with tempfile.TemporaryDirectory() as tmpdir: + mock_raw_dir.__truediv__ = MagicMock(return_value=Path(tmpdir) / "skillscope_data.db") + + # Create existing database to skip download + db_path = Path(tmpdir) / "skillscope_data.db" + db_path.parent.mkdir(parents=True, exist_ok=True) + conn = sqlite3.connect(db_path) + conn.execute("CREATE TABLE test (id INTEGER)") + conn.close() + + # This should use default RAW_DATA_DIR + result = download_skillscope_dataset(None) + + assert isinstance(result, Path) + + def test_download_handles_permission_error(self): + """Test handling of permission errors during file operations.""" + with tempfile.TemporaryDirectory() as tmpdir: + output_dir = Path(tmpdir) + + with patch('hopcroft_skill_classification_tool_competition.dataset.hf_hub_download') as mock_download: + # This test is platform-dependent, so we'll just verify it doesn't crash + # with invalid permissions + pass # Placeholder for permission tests + + +@pytest.mark.unit +class TestDatasetIntegration: + """Integration-like tests for dataset module (still unit-scoped).""" + + def test_download_produces_valid_sqlite_database(self): + """Test that downloaded file is a valid SQLite database.""" + with tempfile.TemporaryDirectory() as tmpdir: + output_dir = Path(tmpdir) + + with patch('hopcroft_skill_classification_tool_competition.dataset.hf_hub_download') as mock_download: + # Create a proper database structure + temp_db = Path(tmpdir) / "temp.db" + conn = sqlite3.connect(temp_db) + conn.execute(""" + CREATE TABLE nlbse_tool_competition_data_by_issue ( + id INTEGER PRIMARY KEY, + repo_name TEXT, + pr_number INTEGER + ) + """) + conn.execute(""" + INSERT INTO nlbse_tool_competition_data_by_issue + VALUES (1, 'test_repo', 123) + """) + conn.commit() + conn.close() + + # Create zip + zip_path = output_dir / "skillscope_data.zip" + with zipfile.ZipFile(zip_path, 'w') as zf: + zf.write(temp_db, arcname='skillscope_data.db') + + temp_db.unlink() + mock_download.return_value = str(zip_path) + + result = download_skillscope_dataset(output_dir) + + # Verify database is valid and queryable + conn = sqlite3.connect(result) + cursor = conn.cursor() + cursor.execute("SELECT * FROM nlbse_tool_competition_data_by_issue") + rows = cursor.fetchall() + conn.close() + + assert len(rows) == 1 + assert rows[0][1] == 'test_repo' + assert rows[0][2] == 123 diff --git a/tests/unit/test_features.py b/tests/unit/test_features.py new file mode 100644 index 0000000000000000000000000000000000000000..2839b4f9c9e84d8798fba6a35c18e56c5e5f7062 --- /dev/null +++ b/tests/unit/test_features.py @@ -0,0 +1,463 @@ +""" +Unit tests for features.py module. + +Tests individual functions for text cleaning, feature extraction, +and label preparation. +""" +import pytest +import numpy as np +import pandas as pd +from sklearn.feature_extraction.text import TfidfVectorizer + +from hopcroft_skill_classification_tool_competition.features import ( + clean_github_text, + get_text_columns, + get_label_columns, + combine_text_fields, + extract_tfidf_features, + prepare_labels, + get_dataset_info, + load_data_from_db, +) + + +@pytest.mark.unit +class TestTextCleaning: + """Unit tests for text cleaning functionality.""" + + def test_clean_github_text_removes_urls(self): + """Test that URLs are removed from text.""" + text = "Fixed bug https://github.com/repo/issues/123 in authentication" + cleaned = clean_github_text(text) + + assert "https://" not in cleaned + assert "github.com" not in cleaned + assert "fix" in cleaned.lower() # Stemmed version of "fixed" + assert "authent" in cleaned.lower() # Stemmed version + + def test_clean_github_text_removes_html(self): + """Test that HTML tags are removed.""" + text = "Added bold feature with italic text" + cleaned = clean_github_text(text) + + assert "" not in cleaned + assert "" not in cleaned + assert "bold" in cleaned.lower() + # After stemming, "italic" becomes "ital" + assert "ital" in cleaned.lower() + + def test_clean_github_text_removes_code_blocks(self): + """Test that markdown code blocks are removed.""" + text = """Fixed bug in code: + ```python + def foo(): + pass + ``` + """ + cleaned = clean_github_text(text) + + assert "```" not in cleaned + assert "python" not in cleaned + assert "def" not in cleaned + assert "fix" in cleaned.lower() + + def test_clean_github_text_removes_inline_code(self): + """Test that inline code markers are removed.""" + text = "Updated `getUserById()` method implementation" + cleaned = clean_github_text(text) + + assert "`" not in cleaned + assert "method" in cleaned.lower() + + def test_clean_github_text_normalizes_whitespace(self): + """Test that extra whitespace is normalized.""" + text = "Fixed multiple spaces and\n\n\nnewlines" + cleaned = clean_github_text(text) + + assert " " not in cleaned + assert "\n\n" not in cleaned + # Should be single spaces + words = cleaned.split() + assert len(words) == len([w for w in words if w]) # No empty strings + + @pytest.mark.parametrize("text,expected_empty", [ + ("", True), + (None, True), + (" ", True), + ("\n\n", True), + ("a", False), + ]) + def test_clean_github_text_empty_inputs(self, text, expected_empty): + """Test handling of empty or null inputs.""" + cleaned = clean_github_text(text) + assert isinstance(cleaned, str) + + if expected_empty: + assert cleaned == "" or cleaned.isspace() + else: + assert len(cleaned) > 0 + + def test_clean_github_text_applies_stemming(self): + """Test that stemming is applied to words.""" + text = "running walked swimming" + cleaned = clean_github_text(text) + + # Porter stemmer should convert to stems + assert "run" in cleaned.lower() # running -> run + assert "walk" in cleaned.lower() # walked -> walk + assert "swim" in cleaned.lower() # swimming -> swim + + def test_clean_github_text_removes_emojis(self): + """Test that emojis and non-ASCII characters are removed.""" + text = "Fixed bug πŸ˜€ with special chars" + cleaned = clean_github_text(text) + + # Should only contain ASCII + assert cleaned.isascii() + assert "fix" in cleaned.lower() + + +@pytest.mark.unit +class TestColumnIdentification: + """Unit tests for column identification functions.""" + + def test_get_text_columns_identifies_correctly(self, sample_dataframe): + """Test that text columns are correctly identified.""" + text_cols = get_text_columns(sample_dataframe) + + assert 'issue text' in text_cols + assert 'issue description' in text_cols + assert len(text_cols) == 2 + + def test_get_text_columns_handles_missing_columns(self): + """Test handling when text columns are missing.""" + df = pd.DataFrame({'col1': [1, 2, 3], 'col2': ['a', 'b', 'c']}) + text_cols = get_text_columns(df) + + assert isinstance(text_cols, list) + assert len(text_cols) == 0 # No standard text columns found + + def test_get_label_columns_identifies_correctly(self, sample_dataframe): + """Test that label columns are correctly identified.""" + label_cols = get_label_columns(sample_dataframe) + + # Should exclude metadata columns + assert 'Repo Name' not in label_cols + assert 'PR #' not in label_cols + assert 'issue text' not in label_cols + assert 'issue description' not in label_cols + + # Should include label columns + assert 'Language' in label_cols + assert 'Data Structure' in label_cols + assert 'Testing' in label_cols + + def test_get_label_columns_only_numeric(self, sample_dataframe): + """Test that only numeric columns are identified as labels.""" + label_cols = get_label_columns(sample_dataframe) + + # All label columns should be numeric + for col in label_cols: + assert pd.api.types.is_numeric_dtype(sample_dataframe[col]) + + +@pytest.mark.unit +class TestTextCombination: + """Unit tests for text combination functionality.""" + + def test_combine_text_fields_combines_correctly(self, sample_dataframe): + """Test that multiple text fields are combined.""" + text_cols = ['issue text', 'issue description'] + combined = combine_text_fields(sample_dataframe, text_cols) + + assert len(combined) == len(sample_dataframe) + assert isinstance(combined, pd.Series) + + # Check that both columns are present + for i, text in enumerate(combined): + assert isinstance(text, str) + # Should contain content from both columns (stemmed) + assert len(text) > 0 + + def test_combine_text_fields_applies_cleaning(self, sample_dataframe): + """Test that cleaning is applied during combination.""" + # Add dirty text + sample_dataframe['issue text'] = [ + "Fixed https://example.com bug", + "Added feature", + "Updated docs", + "Refactored code", + "Improved tests" + ] + + text_cols = ['issue text'] + combined = combine_text_fields(sample_dataframe, text_cols) + + # URLs should be removed + for text in combined: + assert "https://" not in text + assert "example.com" not in text + + def test_combine_text_fields_handles_nulls(self): + """Test handling of null values in text fields.""" + df = pd.DataFrame({ + 'text1': ['hello', None, 'world'], + 'text2': [None, 'foo', 'bar'] + }) + + combined = combine_text_fields(df, ['text1', 'text2']) + + assert len(combined) == 3 + # Should not raise error and should handle nulls gracefully + for text in combined: + assert isinstance(text, str) + + +@pytest.mark.unit +class TestTfidfExtraction: + """Unit tests for TF-IDF feature extraction.""" + + def test_extract_tfidf_features_returns_correct_shape(self, sample_dataframe): + """Test that TF-IDF extraction returns correct shape.""" + features, vectorizer = extract_tfidf_features( + sample_dataframe, + max_features=50 + ) + + assert features.shape[0] == len(sample_dataframe) + assert features.shape[1] <= 50 # May be less if vocabulary is small + assert isinstance(vectorizer, TfidfVectorizer) + + def test_extract_tfidf_features_returns_numpy_array(self, sample_dataframe): + """Test that features are returned as numpy array.""" + features, _ = extract_tfidf_features(sample_dataframe) + + assert isinstance(features, np.ndarray) + assert features.dtype == np.float64 or features.dtype == np.float32 + + @pytest.mark.parametrize("max_features", [10, 50, 100, None]) + def test_extract_tfidf_features_respects_max_features( + self, sample_dataframe, max_features + ): + """Test that max_features parameter is respected.""" + features, _ = extract_tfidf_features( + sample_dataframe, + max_features=max_features + ) + + if max_features is not None: + assert features.shape[1] <= max_features + + @pytest.mark.parametrize("ngram_range", [(1, 1), (1, 2), (1, 3)]) + def test_extract_tfidf_features_ngram_range( + self, sample_dataframe, ngram_range + ): + """Test different n-gram ranges.""" + features, vectorizer = extract_tfidf_features( + sample_dataframe, + ngram_range=ngram_range, + max_features=50 + ) + + assert features.shape[0] == len(sample_dataframe) + vocab = vectorizer.get_feature_names_out() + + # Check that n-grams are present if range includes them + if ngram_range[1] > 1: + # Should have some bigrams (words with space) + bigrams = [term for term in vocab if ' ' in term] + assert len(bigrams) > 0 or len(vocab) < 50 # May not have bigrams if vocab is small + + def test_extract_tfidf_features_handles_empty_text(self): + """Test handling of documents with empty text.""" + df = pd.DataFrame({ + 'issue text': ['', 'valid text', ' '], + 'issue description': ['desc', '', 'another desc'] + }) + + features, vectorizer = extract_tfidf_features(df, max_features=50) + + # Should not raise error + assert features.shape[0] == 3 + assert not np.any(np.isnan(features)) + assert not np.any(np.isinf(features)) + + +@pytest.mark.unit +class TestLabelPreparation: + """Unit tests for label preparation.""" + + def test_prepare_labels_returns_binary(self, sample_dataframe): + """Test that labels are converted to binary format.""" + labels = prepare_labels(sample_dataframe) + + # Should only contain 0 and 1 + unique_values = np.unique(labels.values) + assert set(unique_values).issubset({0, 1}) + + def test_prepare_labels_correct_shape(self, sample_dataframe): + """Test that label matrix has correct shape.""" + label_cols = get_label_columns(sample_dataframe) + labels = prepare_labels(sample_dataframe) + + assert labels.shape[0] == len(sample_dataframe) + assert labels.shape[1] == len(label_cols) + + def test_prepare_labels_converts_counts_to_binary(self): + """Test that label counts > 0 are converted to 1.""" + df = pd.DataFrame({ + 'Repo Name': ['repo1', 'repo2'], + 'issue text': ['text1', 'text2'], + 'Label1': [0, 5], # 5 should become 1 + 'Label2': [3, 0], # 3 should become 1 + 'Label3': [0, 0], + }) + + labels = prepare_labels(df) + + assert labels.loc[0, 'Label1'] == 0 + assert labels.loc[0, 'Label2'] == 1 + assert labels.loc[1, 'Label1'] == 1 + assert labels.loc[1, 'Label2'] == 0 + + def test_prepare_labels_preserves_column_names(self, sample_dataframe): + """Test that label column names are preserved.""" + label_cols = get_label_columns(sample_dataframe) + labels = prepare_labels(sample_dataframe) + + assert list(labels.columns) == label_cols + + +@pytest.mark.unit +class TestDatasetInfo: + """Unit tests for dataset information extraction.""" + + def test_get_dataset_info_returns_dict(self, sample_dataframe): + """Test that dataset info returns a dictionary.""" + info = get_dataset_info(sample_dataframe) + + assert isinstance(info, dict) + + def test_get_dataset_info_contains_required_keys(self, sample_dataframe): + """Test that all required keys are present.""" + info = get_dataset_info(sample_dataframe) + + required_keys = [ + 'total_issues', 'total_columns', 'text_columns', + 'num_text_columns', 'label_columns', 'num_labels', + 'avg_labels_per_issue', 'median_labels_per_issue' + ] + + for key in required_keys: + assert key in info + + def test_get_dataset_info_correct_counts(self, sample_dataframe): + """Test that counts are calculated correctly.""" + info = get_dataset_info(sample_dataframe) + + assert info['total_issues'] == len(sample_dataframe) + assert info['total_columns'] == len(sample_dataframe.columns) + assert info['num_text_columns'] == 2 # issue text and description + + def test_get_dataset_info_label_statistics(self, sample_dataframe): + """Test label statistics are reasonable.""" + info = get_dataset_info(sample_dataframe) + + assert info['avg_labels_per_issue'] >= 0 + assert info['median_labels_per_issue'] >= 0 + assert info['avg_labels_per_issue'] <= info['num_labels'] + + +@pytest.mark.unit +@pytest.mark.requires_data +class TestDatabaseLoading: + """Unit tests for database loading (requires temp DB).""" + + def test_load_data_from_db_returns_dataframe(self, temp_db): + """Test that loading from DB returns a DataFrame.""" + df = load_data_from_db(temp_db) + + assert isinstance(df, pd.DataFrame) + assert len(df) > 0 + + def test_load_data_from_db_contains_expected_columns(self, temp_db): + """Test that loaded data has expected columns.""" + df = load_data_from_db(temp_db) + + assert 'issue text' in df.columns + assert 'issue description' in df.columns + assert 'Repo Name' in df.columns + assert 'PR #' in df.columns + + def test_load_data_from_db_nonexistent_file(self): + """Test handling of nonexistent database file.""" + from pathlib import Path + + with pytest.raises(Exception): # Could be FileNotFoundError or sqlite3 error + load_data_from_db(Path("/nonexistent/path/to/db.db")) + + +@pytest.mark.unit +class TestEdgeCases: + """Unit tests for edge cases and error handling.""" + + def test_extract_tfidf_with_single_document(self): + """Test TF-IDF extraction with only one document.""" + df = pd.DataFrame({ + 'issue text': ['Single document for testing'], + 'issue description': ['Description'], + 'Label1': [1] + }) + + # Must set min_df=1 for single document + features, vectorizer = extract_tfidf_features( + df, + max_features=50, + min_df=1, + max_df=1.0 + ) + + assert features.shape[0] == 1 + assert features.shape[1] > 0 + + def test_extract_tfidf_with_identical_documents(self): + """Test TF-IDF with identical documents.""" + df = pd.DataFrame({ + 'issue text': ['Same text'] * 3, + 'issue description': ['Same description'] * 3, + 'Label1': [1, 0, 1] + }) + + # Must set max_df=1.0 because all docs are identical (100% frequency) + # Must set min_df=1 to ensure terms are kept even if they appear in all docs + features, _ = extract_tfidf_features( + df, + max_features=50, + min_df=1, + max_df=1.0 + ) + + # All documents should have similar (but not necessarily identical) features + assert features.shape[0] == 3 + assert not np.all(features == 0) + + def test_prepare_labels_with_all_zeros(self): + """Test label preparation when a label has all zeros.""" + df = pd.DataFrame({ + 'issue text': ['text1', 'text2'], + 'Label1': [0, 0], # All zeros + 'Label2': [1, 1], + }) + + labels = prepare_labels(df) + + assert labels['Label1'].sum() == 0 + assert labels['Label2'].sum() == 2 + + def test_clean_text_with_only_special_characters(self): + """Test cleaning text that contains only special characters.""" + text = "!@#$%^&*()" + cleaned = clean_github_text(text) + + # Should handle gracefully (may be empty or contain only ASCII equivalents) + assert isinstance(cleaned, str)