Computes path on IRIS dataset.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | print(__doc__)# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr># License: BSD 3 clausefrom datetime import datetimeimport numpy as npimport matplotlib.pyplot as pltfrom sklearn import linear_modelfrom sklearn import datasetsfrom sklearn.svm import l1_min_ciris = datasets.load_iris()X = iris.datay = iris.targetX = X[y != 2]y = y[y != 2]X -= np.mean(X, 0) |
Demo path functions
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | cs = l1_min_c(X, y, loss='log') * np.logspace(0, 3)print("Computing regularization path ...")start = datetime.now()clf = linear_model.LogisticRegression(C=1.0, penalty='l1', tol=1e-6)coefs_ = []for c in cs: clf.set_params(C=c) clf.fit(X, y) coefs_.append(clf.coef_.ravel().copy())print("This took ", datetime.now() - start)coefs_ = np.array(coefs_)plt.plot(np.log10(cs), coefs_)ymin, ymax = plt.ylim()plt.xlabel('log(C)')plt.ylabel('Coefficients')plt.title('Logistic Regression Path')plt.axis('tight')plt.show() |
Out:
1 2 | Computing regularization path ...This took 0:00:00.049016 |
Total running time of the script: (0 minutes 0.107 seconds)
Download Python source code:
plot_logistic_path.py
Download IPython notebook:
plot_logistic_path.ipynb
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