Plot decision surface of multinomial and One-vs-Rest Logistic Regression. The hyperplanes corresponding to the three One-vs-Rest (OVR) classifiers are represented by the dashed lines.
Out:
1 2 | training score : 0.995 (multinomial)training score : 0.976 (ovr) |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 | print(__doc__)# Authors: Tom Dupre la Tour <tom.dupre-la-tour@m4x.org># License: BSD 3 clauseimport numpy as npimport matplotlib.pyplot as pltfrom sklearn.datasets import make_blobsfrom sklearn.linear_model import LogisticRegression# make 3-class dataset for classificationcenters = [[-5, 0], [0, 1.5], [5, -1]]X, y = make_blobs(n_samples=1000, centers=centers, random_state=40)transformation = [[0.4, 0.2], [-0.4, 1.2]]X = np.dot(X, transformation)for multi_class in ('multinomial', 'ovr'): clf = LogisticRegression(solver='sag', max_iter=100, random_state=42, multi_class=multi_class).fit(X, y) # print the training scores print("training score : %.3f (%s)" % (clf.score(X, y), multi_class)) # create a mesh to plot in h = .02 # step size in the mesh x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) # Plot the decision boundary. For that, we will assign a color to each # point in the mesh [x_min, x_max]x[y_min, y_max]. Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) # Put the result into a color plot Z = Z.reshape(xx.shape) plt.figure() plt.contourf(xx, yy, Z, cmap=plt.cm.Paired) plt.title("Decision surface of LogisticRegression (%s)" % multi_class) plt.axis('tight') # Plot also the training points colors = "bry" for i, color in zip(clf.classes_, colors): idx = np.where(y == i) plt.scatter(X[idx, 0], X[idx, 1], c=color, cmap=plt.cm.Paired) # Plot the three one-against-all classifiers xmin, xmax = plt.xlim() ymin, ymax = plt.ylim() coef = clf.coef_ intercept = clf.intercept_ def plot_hyperplane(c, color): def line(x0): return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1] plt.plot([xmin, xmax], [line(xmin), line(xmax)], ls="--", color=color) for i, color in zip(clf.classes_, colors): plot_hyperplane(i, color)plt.show() |
Total running time of the script: (0 minutes 0.403 seconds)
Download Python source code:
plot_logistic_multinomial.py
Download IPython notebook:
plot_logistic_multinomial.ipynb
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