69 lines
1.7 KiB
Python
69 lines
1.7 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 pylab as pl
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from sklearn import linear_model, decomposition, datasets
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logistic = linear_model.LogisticRegression()
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pca = decomposition.PCA()
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from sklearn.pipeline import Pipeline
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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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###############################################################################
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# Plot the PCA spectrum
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pca.fit(X_digits)
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pl.figure(1, figsize=(4, 3))
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pl.clf()
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pl.axes([.2, .2, .7, .7])
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pl.plot(pca.explained_variance_, linewidth=2)
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pl.axis('tight')
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pl.xlabel('n_components')
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pl.ylabel('explained_variance_')
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###############################################################################
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# Prediction
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from sklearn.grid_search import GridSearchCV
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n_components = [20, 40, 64]
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Cs = np.logspace(-4, 4, 3)
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#Parameters of pipelines can be set using ‘__’ separated parameter names:
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estimator = GridSearchCV(pipe,
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dict(pca__n_components=n_components,
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logistic__C=Cs))
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estimator.fit(X_digits, y_digits)
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pl.axvline(estimator.best_estimator_.named_steps['pca'].n_components,
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linestyle=':', label='n_components chosen')
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pl.legend(prop=dict(size=12))
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pl.show()
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