DecisionRegions.py

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap


def plot_decision_regions(X, y, classifier, title=None, test_idx=None, resolution=0.02):
    markers = ('s', 'x', 'o', '^', 'v')
    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
    cmap = ListedColormap(colors[:len(np.unique(y))])
    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max()+1
    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max()+1
    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
                           np.arange(x2_min, x2_max, resolution))
    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
    Z[Z> .5] = 1
    Z[Z<=.5] = 0
    Z = Z.reshape(xx1.shape)
    plt.figure(figsize=(8, 8))
    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
    plt.xlim(xx1.min(), xx1.max())
    plt.ylim(xx2.min(), xx2.max())
    if title:
        plt.title(title)
    for idx, cl in enumerate(np.unique(y)):
        plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1], alpha=0.8,
                    c=cmap(idx), marker=markers[idx], label=cl)
    if test_idx:
        X_t, y_t = X[test_idx, :], y[test_idx]
        plt.scatter(X_t[:, 0], X_t[:, 1], c='black', alpha=1.0,
                    linewidths=2, marker='|', s=55, label='test set')