File Content: plot_logistic_multinomial.py """ ==================================================== Plot multinomial and One-vs-Rest Logistic Regression ==================================================== 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. """ print(__doc__) # Authors: Tom Dupre la Tour <tom.dupre-la-tour@m4x.org> # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import make_blobs from sklearn.linear_model import LogisticRegression # make 3-class dataset for classification centers = [[-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() Submit