79 lines
2.3 KiB
Python
79 lines
2.3 KiB
Python
#!/usr/bin/python
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# -*- coding: utf-8 -*-
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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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print(__doc__)
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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 numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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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 SGDClassifier
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from sklearn.pipeline import Pipeline
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from sklearn.model_selection import GridSearchCV
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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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logistic = SGDClassifier(loss='log', penalty='l2', early_stopping=True,
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max_iter=10000, tol=1e-5, random_state=0)
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pca = PCA()
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pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)])
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digits = datasets.load_digits()
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X_digits = digits.data
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y_digits = digits.target
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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, 20, 30, 40, 50, 64],
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'logistic__alpha': np.logspace(-4, 4, 5),
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}
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search = GridSearchCV(pipe, param_grid, cv=5)
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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(pca.explained_variance_ratio_, linewidth=2)
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ax0.set_ylabel('PCA explained variance')
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ax0.axvline(search.best_estimator_.named_steps['pca'].n_components,
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linestyle=':', label='n_components chosen')
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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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results = pd.DataFrame(search.cv_results_)
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components_col = 'param_pca__n_components'
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best_clfs = results.groupby(components_col).apply(
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lambda g: g.nlargest(1, 'mean_test_score'))
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best_clfs.plot(x=components_col, y='mean_test_score', yerr='std_test_score',
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legend=False, ax=ax1)
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ax1.set_ylabel('Classification accuracy (val)')
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ax1.set_xlabel('n_components')
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plt.tight_layout()
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plt.show()
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