51 lines
1.1 KiB
Python
51 lines
1.1 KiB
Python
#!/usr/bin/env python
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"""
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=================================
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Path with L1- Logistic Regression
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=================================
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Computes path on IRIS dataset.
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"""
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print __doc__
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# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
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# License: BSD Style.
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from datetime import datetime
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import numpy as np
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import pylab as pl
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from scikits.learn import linear_model
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from scikits.learn import datasets
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iris = datasets.load_iris()
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X = iris.data
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y = iris.target
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X = X[y != 2]
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y = y[y != 2]
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X -= np.mean(X, 0)
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################################################################################
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# Demo path functions
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alphas = np.logspace(2, -4, 100)
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print "Computing regularization path ..."
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start = datetime.now()
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clf = linear_model.LogisticRegression(C=1.0, penalty='l1', tol=1e-6)
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coefs_ = [clf.fit(X, y, C=1.0/alpha).coef_.ravel().copy() for alpha in alphas]
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print "This took ", datetime.now() - start
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coefs_ = np.array(coefs_)
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pl.plot(-np.log10(alphas), coefs_)
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ymin, ymax = pl.ylim()
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pl.xlabel('-log(alpha)')
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pl.ylabel('Coefficients')
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pl.title('Logistic Regression Path')
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pl.axis('tight')
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pl.show()
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