initial commit

utils/__init__.py

@@ -0,0 +1,3 @@
+from .helper import plot_data, plot_centroids, plot_decision_boundaries, plot_svc_decision_boundary, make_xor, plot_predictions, plot_dataset
+
+__all__ = ('plot_data', 'plot_centroids', 'plot_decision_boundaries', 'plot_svc_decision_boundary', 'make_xor', 'plot_predictions', 'plot_dataset')

utils/helper.py

@@ -0,0 +1,103 @@
+# Python ≥3.5 is required
+import sys
+assert sys.version_info >= (3, 5)
+
+# Common imports
+import numpy as np
+import os
+
+# To plot pretty figures
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+mpl.rc('axes', labelsize=14)
+mpl.rc('xtick', labelsize=12)
+mpl.rc('ytick', labelsize=12)
+
+# Where to save the figures
+PROJECT_ROOT_DIR = "."
+CHAPTER_ID = "svm"
+IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, "images", CHAPTER_ID)
+os.makedirs(IMAGES_PATH, exist_ok=True)
+
+
+def plot_data(X):
+    plt.plot(X[:, 0], X[:, 1], 'k.', markersize=2)
+
+
+def plot_centroids(centroids, weights=None, circle_color='w', cross_color='k'):
+    if weights is not None:
+        centroids = centroids[weights > weights.max() / 10]
+    plt.scatter(centroids[:, 0], centroids[:, 1],
+                marker='o', s=30, linewidths=8,
+                color=circle_color, zorder=10, alpha=0.9)
+    plt.scatter(centroids[:, 0], centroids[:, 1],
+                marker='x', s=50, linewidths=50,
+                color=cross_color, zorder=11, alpha=1)
+
+
+def plot_decision_boundaries(clusterer, X, resolution=1000, show_centroids=True,
+                             show_xlabels=True, show_ylabels=True):
+    mins = X.min(axis=0) - 0.1
+    maxs = X.max(axis=0) + 0.1
+    xx, yy = np.meshgrid(np.linspace(mins[0], maxs[0], resolution),
+                         np.linspace(mins[1], maxs[1], resolution))
+    Z = clusterer.predict(np.c_[xx.ravel(), yy.ravel()])
+    Z = Z.reshape(xx.shape)
+
+    plt.contourf(Z, extent=(mins[0], maxs[0], mins[1], maxs[1]),
+                 cmap="Pastel2")
+    plt.contour(Z, extent=(mins[0], maxs[0], mins[1], maxs[1]),
+                linewidths=1, colors='k')
+    plot_data(X)
+    if show_centroids:
+        plot_centroids(clusterer.cluster_centers_)
+
+    if show_xlabels:
+        plt.xlabel("$x_1$", fontsize=14)
+    else:
+        plt.tick_params(labelbottom=False)
+    if show_ylabels:
+        plt.ylabel("$x_2$", fontsize=14, rotation=0)
+    else:
+        plt.tick_params(labelleft=False)
+
+
+def plot_svc_decision_boundary(svm_clf, xmin, xmax):
+    w = svm_clf.coef_[0]
+    b = svm_clf.intercept_[0]
+
+    # At the decision boundary, w0*x0 + w1*x1 + b = 0
+    # => x1 = -w0/w1 * x0 - b/w1
+    x0 = np.linspace(xmin, xmax, 200)
+    decision_boundary = -w[0] / w[1] * x0 - b / w[1]
+
+    margin = 1 / w[1]
+    gutter_up = decision_boundary + margin
+    gutter_down = decision_boundary - margin
+
+    svs = svm_clf.support_vectors_
+    plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#FFAAAA')
+    plt.plot(x0, decision_boundary, "k-", linewidth=2)
+    plt.plot(x0, gutter_up, "k--", linewidth=2)
+    plt.plot(x0, gutter_down, "k--", linewidth=2)
+
+def plot_predictions(clf, axes):
+    x0s = np.linspace(axes[0], axes[1], 100)
+    x1s = np.linspace(axes[2], axes[3], 100)
+    x0, x1 = np.meshgrid(x0s, x1s)
+    X = np.c_[x0.ravel(), x1.ravel()]
+    y_pred = clf.predict(X).reshape(x0.shape)
+    y_decision = clf.decision_function(X).reshape(x0.shape)
+    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)
+    plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)
+
+def plot_dataset(X, y):
+    plt.plot(X[:, 0][y == 0], X[:, 1][y == 0], "bs")
+    plt.plot(X[:, 0][y == 1], X[:, 1][y == 1], "g^")
+    # plt.axis(axes)
+    plt.grid(True, which='both')
+    plt.xlabel(r"$x_1$", fontsize=20)
+    plt.ylabel(r"$x_2$", fontsize=20, rotation=0)
+
+def make_xor():
+    pass