pauls_multinomal_ovr_fitting.py

# 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"):                                      # solvers: newton
    for solver in ("newton-cg", "saga"):                     #"liblinear",
        for penalty in ("l2", "none"):                                       # "l1",  "elasticnet",
            for C in (0.001, 0.002):
                clf = LogisticRegression(
                    solver=solver, max_iter=100000, random_state=42, multi_class=multi_class, penalty=penalty, C=C
                ).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 = 0.02  # 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, edgecolor="black", s=20
                    )

                # 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()