Update dependencies, add PCA explained variance plotting functionalit…#127
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idanmoradarthas merged 3 commits intoMay 21, 2026
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…y, and enhance documentation - Updated `pyarrow`, `kaleido`, `ruff`, and `build` dependencies in `pyproject.toml`. - Introduced `plot_pca_explained_variance` function to visualize cumulative explained variance of PCA components, aiding in dimensionality reduction analysis. - Added usage examples and detailed documentation for the new PCA plotting function in `README.md` and `visualization.rst`. - Updated `SKILL.md` to include the new PCA function with parameters and common usage scenarios. - Bumped version to 1.10.0rc13 to reflect these changes.
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…A parameters - Updated the `plot_pca_explained_variance` function to allow passing additional keyword arguments (`pca_kwargs`) directly to the PCA initialization, enhancing flexibility in PCA configuration. - Modified the scaler parameter to default to `None`, using `StandardScaler` if no scaler is provided. - Enhanced documentation to reflect the new parameter options and usage examples in `SKILL.md`. - Added tests to validate the new functionality, ensuring correct handling of PCA parameters and Axes object passing.
idanmoradarthas
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85-add-plot_pca_explained_variance-function-to-preprocess-module
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May 21, 2026 03:23
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Description
This PR resolves #85. It enhances the
preprocessmodule by adding a new plotting function,plot_pca_explained_variance, which visualizes the cumulative explained variance ratio from Principal Component Analysis (PCA).This visualization will help users quickly determine how many principal components are required to capture a desired proportion of variance in their datasets.
Key Changes
plot_pca_explained_variancetods_utils/preprocess/visualization.py. It accepts numerical data, optionally scales it (defaulting toStandardScaler), and draws a line plot for cumulative variance with horizontal reference lines at 70% and 80%.tests/test_preprocess/test_plot_pca_explained_variance.pywith comprehensive unit tests for different parameters, handling invalid inputs, and rendering the plot. Usedsklearn.datasets.make_classificationto generate a realistic test dataset.pytest-mplbaseline images to test plot rendering.docs/source/preprocess/visualization.rstwith the function definition, code example, and the generated output image.README.mdcontaining a code snippet and the example image.skills/preprocess/SKILL.mdto document the new feature for agentic workflows.ruff.Type of change
Checklist:
pytest&pytest-mpl)ruff