56 lines
1.1 KiB
Python
56 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 sklearn import linear_model
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from sklearn import datasets
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from sklearn.svm import l1_min_c
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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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cs = l1_min_c(X, y, loss='log') * np.logspace(0, 3)
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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_ = []
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for c in cs:
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clf.set_params(C=c)
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clf.fit(X, y)
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coefs_.append(clf.coef_.ravel().copy())
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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(cs), coefs_)
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ymin, ymax = pl.ylim()
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pl.xlabel('log(C)')
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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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