87 lines
2.6 KiB
Python
87 lines
2.6 KiB
Python
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"""
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=========================================================
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Pipelining: chaining a PCA and a logistic regression
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=========================================================
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The PCA does an unsupervised dimensionality reduction, while the logistic
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regression does the prediction.
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We use a GridSearchCV to set the dimensionality of the PCA
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"""
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# Code source: Gaël Varoquaux
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# Modified for documentation by Jaques Grobler
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# License: BSD 3 clause
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import matplotlib.pyplot as plt
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import numpy as np
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import polars as pl
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from sklearn import datasets
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from sklearn.decomposition import PCA
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from sklearn.linear_model import LogisticRegression
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from sklearn.model_selection import GridSearchCV
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from sklearn.pipeline import Pipeline
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from sklearn.preprocessing import StandardScaler
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# Define a pipeline to search for the best combination of PCA truncation
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# and classifier regularization.
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pca = PCA()
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# Define a Standard Scaler to normalize inputs
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scaler = StandardScaler()
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# set the tolerance to a large value to make the example faster
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logistic = LogisticRegression(max_iter=10000, tol=0.1)
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pipe = Pipeline(steps=[("scaler", scaler), ("pca", pca), ("logistic", logistic)])
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X_digits, y_digits = datasets.load_digits(return_X_y=True)
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# Parameters of pipelines can be set using '__' separated parameter names:
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param_grid = {
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"pca__n_components": [5, 15, 30, 45, 60],
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"logistic__C": np.logspace(-4, 4, 4),
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}
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search = GridSearchCV(pipe, param_grid, n_jobs=2)
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search.fit(X_digits, y_digits)
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print("Best parameter (CV score=%0.3f):" % search.best_score_)
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print(search.best_params_)
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# Plot the PCA spectrum
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pca.fit(X_digits)
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fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True, figsize=(6, 6))
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ax0.plot(
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np.arange(1, pca.n_components_ + 1), pca.explained_variance_ratio_, "+", linewidth=2
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)
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ax0.set_ylabel("PCA explained variance ratio")
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ax0.axvline(
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search.best_estimator_.named_steps["pca"].n_components,
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linestyle=":",
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label="n_components chosen",
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)
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ax0.legend(prop=dict(size=12))
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# For each number of components, find the best classifier results
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components_col = "param_pca__n_components"
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is_max_test_score = pl.col("mean_test_score") == pl.col("mean_test_score").max()
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best_clfs = (
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pl.LazyFrame(search.cv_results_)
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.filter(is_max_test_score.over(components_col))
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.unique(components_col)
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.sort(components_col)
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.collect()
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)
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ax1.errorbar(
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best_clfs[components_col],
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best_clfs["mean_test_score"],
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yerr=best_clfs["std_test_score"],
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)
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ax1.set_ylabel("Classification accuracy (val)")
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ax1.set_xlabel("n_components")
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plt.xlim(-1, 70)
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plt.tight_layout()
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plt.show()
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