thurstone/.github/workflows/ci.yml at 8fed0a63b74b66e9b55fe9aac69015e19716a115 · microprediction/thurstone · GitHub

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name: CI

on:
  push:
    branches: [ main ]
  pull_request:
    branches: [ main ]

jobs:
  test:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        python-version: ["3.12"]

    steps:
    - uses: actions/checkout@v4

    - name: Set up Python ${{ matrix.python-version }}
      uses: actions/setup-python@v4
      with:
        python-version: ${{ matrix.python-version }}

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -e ".[test]"

    - name: Run pytest
      run: |
        pytest --tb=short

    - name: Run integration tests
      run: |
        python test_integration.py

  lint:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v4

    - name: Set up Python
      uses: actions/setup-python@v4
      with:
        python-version: "3.11"

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install flake8 black isort
        pip install -e .

    - name: Check code formatting with black
      run: |
        black --check --diff .

    - name: Check import sorting with isort
      run: |
        isort --check-only --diff .

    - name: Lint with flake8
      run: |
        # stop the build if there are Python syntax errors or undefined names
        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
        # treat warnings as errors for consistent code quality
        flake8 . --count --max-complexity=10 --max-line-length=127 --statistics

  diffeomorphism-tests:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v4

    - name: Set up Python
      uses: actions/setup-python@v4
      with:
        python-version: "3.11"

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -e ".[test]"

    - name: Test enhanced diffeomorphism framework
      run: |
        python -c "
        import sys
        sys.path.insert(0, '.')

        print('Testing enhanced diffeomorphism framework...')

        # Test imports
        from thurstone import SigmoidParams, comprehensive_quality_assessment
        from thurstone.enhanced_cube_to_simplex import EnhancedCubeToSimplexMapping
        from thurstone.adaptive_special_horse import AdaptiveSpecialHorse, SpecialHorseConfig, DistributionType
        from thurstone.enhanced_optimization import enhanced_optimize_diffeomorphism
        from thurstone.pure_optimizers import pure_optimize

        print('✅ All imports successful')

        # Test basic functionality
        sigmoid_params = [SigmoidParams(alpha=1.2, beta=4.0, gamma=0.5) for _ in range(2)]
        config = SpecialHorseConfig(distribution=DistributionType.NORMAL, base_ability=0.0)
        special_horse = AdaptiveSpecialHorse(config)
        mapping = EnhancedCubeToSimplexMapping(sigmoid_params=sigmoid_params, special_horse=special_horse)

        # Test mapping
        import numpy as np
        test_point = np.array([0.3, 0.7])
        result = mapping(test_point)
        assert abs(np.sum(result) - 1.0) < 1e-10, f'Simplex constraint violated: sum = {np.sum(result)}'

        print('✅ Basic mapping test passed')

        # Test quality assessment (minimal samples for speed)
        metrics = comprehensive_quality_assessment(mapping,
                                                 symmetry_samples=20,
                                                 volume_samples=5,
                                                 smoothness_samples=5,
                                                 coverage_samples=20,
                                                 invertibility_samples=3,
                                                 random_seed=42)

        assert metrics.symmetry_score is not None, 'Symmetry assessment failed'
        print('✅ Quality assessment test passed')

        # Test pure optimization (very minimal)
        def simple_objective(x):
            return np.sum((x - 0.5)**2)

        best_val, best_x = pure_optimize(simple_objective, 'HarmonySearch', n_trials=5, n_dim=2)
        assert best_val is not None, 'Optimization failed'

        print('✅ All enhanced framework tests passed!')
        "