Utilities file:
file name: utilities.py
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import numpy as np
import matplotlib.pyplot as plt
def visualize_classifier(classifier,
X, y):
# Define the minimum and maximum values for X and Y
# that will be used in the mesh grid
min_x, max_x = X[:, 0].min() - 1.0, X[:, 0].max() + 1.0
min_y, max_y = X[:, 1].min() -
1.0, X[:, 1].max() + 1.0
# Define the step size to use in plotting the mesh grid
mesh_step_size = 0.01
# Define the mesh grid of X and Y values
x_vals, y_vals = np.meshgrid(np.arange(min_x, max_x, mesh_step_size),
np.arange(min_y, max_y, mesh_step_size))
# Run the classifier on the mesh grid
output = classifier.predict(np.c_[x_vals.ravel(), y_vals.ravel()])
# Reshape the output array
output = output.reshape(x_vals.shape)
# Create a plot
plt.figure()
# Choose a color scheme for the plot
plt.pcolormesh(x_vals, y_vals, output, cmap=plt.cm.gray)
# Overlay the training points on the plot
plt.scatter(X[:, 0], X[:, 1], c=y, s=75, edgecolors='black',
linewidth=1, cmap=plt.cm.Paired)
# Specify the boundaries of the plot
plt.xlim(x_vals.min(), x_vals.max())
plt.ylim(y_vals.min(), y_vals.max())
# Specify the ticks on the X and Y axes
plt.xticks((np.arange(int(X[:, 0].min() - 1), int(X[:, 0].max() + 1),
1.0)))
plt.yticks((np.arange(int(X[:, 1].min() - 1), int(X[:, 1].max() + 1),
1.0)))
plt.show()
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