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utils.py

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import numpy as np

import matplotlib.pyplot as plt

data_colors = [(1, 0, 0), (0, 0, 1)]

def read_points_file(filename):

pts = []

with open(filename, "r") as f:

for pt in f:

pt = pt.strip("\n").split()

pts.append([float(pt[0]), float(pt[1])])

return pts

def read_data(class_0_file, class_1_file):

pts_0 = read_points_file(class_0_file)

pts_1 = read_points_file(class_1_file)

x = pts_0 + pts_1

labels = [0] * len(pts_0) + [1] * len(pts_1)

x = np.array(x)

return (x, labels)

def plot_data(X_train, y_train, X_test, y_test):

X = np.concatenate((X_train, X_test))

y = np.concatenate((y_train, y_test))

colors = get_colors(y)

colors_train = get_colors(y_train)

colors_test = get_colors(y_test)

plt.figure(figsize=(12, 4), dpi=150)

# Plot all data plot

plt.subplot(131)

plt.axis('equal')

plt.scatter(X[:, 0], X[:, 1], c = colors, s = 10, edgecolors=colors)

plt.title("Data (100%)")

# training data plot

plt.subplot(132)

plt.axis('equal')

#plt.axis('off')

plt.scatter(X_train[:, 0], X_train[:, 1], c = colors_train, s = 10, edgecolors=colors_train)

plt.title("Training Data (80%)")

# testing data plot

plt.subplot(133)

plt.axis('equal')

#plt.axis('off')

plt.scatter(X_test[:, 0], X_test[:, 1], c = colors_test, s = 10, edgecolors=colors_test)

plt.title("Test Data (20%)")

plt.tight_layout()

plt.show()

def get_colors(y):

return [data_colors[item] for item in y]

def plot_decision_function(X_train, y_train, X_test, y_test, clf):

plt.figure(figsize=(8, 4), dpi=150)

plt.subplot(121)

plt.title("Training data")

plot_decision_function_helper(X_train, y_train, clf)

plt.subplot(122)

plt.title("Test data")

plot_decision_function_helper(X_test, y_test, clf, True)

plt.show()

def plot_decision_function_helper(X, y, clf, show_only_decision_function = False):

colors = get_colors(y)

plt.axis('equal')

plt.tight_layout()

#plt.axis('off')

plt.scatter(X[:, 0], X[:, 1], c = colors, s = 10, edgecolors=colors)

ax = plt.gca()

xlim = ax.get_xlim()

ylim = ax.get_ylim()

# Create grid to evaluate model

xx = np.linspace(xlim[0], xlim[1], 30)

yy = np.linspace(ylim[0], ylim[1], 30)

YY, XX = np.meshgrid(yy, xx)

xy = np.vstack([XX.ravel(), YY.ravel()]).T

Z = clf.decision_function(xy).reshape(XX.shape)

if show_only_decision_function:

# Plot decision boundary

ax.contour(XX, YY, Z, colors='k', levels=[0], alpha=0.5,

linestyles=['-'])

else :

# Plot decision boundary and margins

ax.contour(XX, YY, Z, colors='k', levels=[-1, 0, 1], alpha=0.5,

linestyles=['--', '-', '--'])

# Plot support vectors

#ax.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s = 10,

# linewidth=1, facecolors='k', c = 'k', label='Support Vectors')

#plt.legend(fontsize='small')