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thinkallCopilot
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ci: enable code coverage reporting on GitHub
- Upgrade codecov-action to v5 with token support - Add coverage config to pyproject.toml (fail_under=85%) - Add term-missing to coverage report output - Upload coverage from Python 3.12 matrix - Add Tests and Codecov badges to README Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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.github/workflows/tests.yml

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@@ -27,13 +27,16 @@ jobs:
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python -m pip install --upgrade pip
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pip install -e ".[dev]"
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- name: Run tests
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- name: Run tests with coverage
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run: |
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pytest tests/ -v --cov=featcopilot --cov-report=xml
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pytest tests/ -v --cov=featcopilot --cov-report=xml --cov-report=term-missing
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- name: Upload coverage
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uses: codecov/codecov-action@v4
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if: matrix.python-version == '3.11'
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- name: Upload coverage to Codecov
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uses: codecov/codecov-action@v5
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if: matrix.python-version == '3.12'
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with:
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files: ./coverage.xml
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token: ${{ secrets.CODECOV_TOKEN }}
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fail_ci_if_error: false
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flags: unittests
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name: featcopilot-py${{ matrix.python-version }}

README.md

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**Next-Generation LLM-Powered Auto Feature Engineering Framework**
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[![Tests](https://github.com/thinkall/featcopilot/actions/workflows/tests.yml/badge.svg)](https://github.com/thinkall/featcopilot/actions/workflows/tests.yml)
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[![codecov](https://codecov.io/gh/thinkall/featcopilot/graph/badge.svg)](https://codecov.io/gh/thinkall/featcopilot)
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FeatCopilot automatically generates, selects, and explains predictive features using semantic understanding. It analyzes column meanings, applies domain-aware transformations, and provides human-readable explanations—turning raw data into ML-ready features in seconds.
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## 🎬 Introduction Video

pyproject.toml

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[tool.pytest.ini_options]
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testpaths = ["tests"]
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asyncio_mode = "auto"
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[tool.coverage.run]
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source = ["featcopilot"]
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omit = ["featcopilot/__pycache__/*"]
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[tool.coverage.report]
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show_missing = true
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fail_under = 85
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exclude_lines = [
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"pragma: no cover",
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"if __name__ == .__main__.",
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"pass",
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"raise NotImplementedError",
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]

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