condprob.py

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

from sklearn.svm import SVC

classifier = SVC(gamma=2, C=1, random_state=42, probability=True)
dataset = make_moons(noise=0.3, random_state=0)

figure = plt.figure()

X, y = dataset
X_train, X_test, y_train, y_test = train_test_split(
  X, y, test_size=0.4, random_state=42
)

x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5
y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5

clf = make_pipeline(StandardScaler(), classifier)
clf.fit(X_train, y_train)

grid_resolution = 300
xx, yy = np.meshgrid(
    np.linspace(x_min, x_max, grid_resolution),
    np.linspace(y_min, y_max, grid_resolution),
)

X_grid = np.vstack((xx.flatten(), yy.flatten())).T

yhat = clf.predict(X_grid)
conf = clf.predict_proba(X_grid)

color1 = conf[:,1].reshape(*xx.shape) * (yhat == 0)
color2 = conf[:,0].reshape(*xx.shape) * (yhat == 1)

alpha1 = 0.8 * (yhat == 0)
alpha2 = 0.8 * (yhat == 1)

fig, ax = plt.subplots()

ext = [x_min, x_max, y_min, y_max]

ax.imshow(color1, cmap='Reds_r', origin='lower', 
  extent=ext, alpha = alpha1)
ax.imshow(color2, cmap='Blues_r', origin='lower', 
  extent=ext, alpha = alpha2)

# Plot the training points
ax.scatter(
    X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k", alpha=0.5,
)
# Plot the testing points
ax.scatter(
    X_test[:, 0], X_test[:, 1], c=y_test,
    cmap=cm_bright, edgecolors="w"
)

ax.set_xlim(x_min, x_max)
ax.set_ylim(y_min, y_max)
ax.set_xticks(())
ax.set_yticks(())

ax.axis('off')
fig.tight_layout()