ENH Repeat figure4_5 using sklearn

Author: luispedro

ch02/figure4_5_sklearn.py

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+# This code is supporting material for the book
+# Building Machine Learning Systems with Python
+# by Willi Richert and Luis Pedro Coelho
+# published by PACKT Publishing
+#
+# It is made available under the MIT License
+
+COLOUR_FIGURE = False
+
+from matplotlib import pyplot as plt
+from matplotlib.colors import ListedColormap
+from load import load_dataset
+import numpy as np
+from sklearn.neighbors import KNeighborsClassifier
+from knn import fit_model, predict
+
+feature_names = [
+    'area',
+    'perimeter',
+    'compactness',
+    'length of kernel',
+    'width of kernel',
+    'asymmetry coefficien',
+    'length of kernel groove',
+]
+
+
+def plot_decision(features, labels):
+    '''Plots decision boundary for KNN
+
+    Parameters
+    ----------
+    features : ndarray
+    labels : sequence
+
+    Returns
+    -------
+    fig : Matplotlib Figure
+    ax  : Matplotlib Axes
+    '''
+    y0, y1 = features[:, 2].min() * .9, features[:, 2].max() * 1.1
+    x0, x1 = features[:, 0].min() * .9, features[:, 0].max() * 1.1
+    X = np.linspace(x0, x1, 100)
+    Y = np.linspace(y0, y1, 100)
+    X, Y = np.meshgrid(X, Y)
+
+    model = KNeighborsClassifier(1)
+    model.fit(features[:, (0,2)], labels)
+    C = model.predict(np.vstack([X.ravel(), Y.ravel()]).T).reshape(X.shape)
+    if COLOUR_FIGURE:
+        cmap = ListedColormap([(1., .6, .6), (.6, 1., .6), (.6, .6, 1.)])
+    else:
+        cmap = ListedColormap([(1., 1., 1.), (.2, .2, .2), (.6, .6, .6)])
+    fig,ax = plt.subplots()
+    ax.set_xlim(x0, x1)
+    ax.set_ylim(y0, y1)
+    ax.set_xlabel(feature_names[0])
+    ax.set_ylabel(feature_names[2])
+    ax.pcolormesh(X, Y, C, cmap=cmap)
+    if COLOUR_FIGURE:
+        cmap = ListedColormap([(1., .0, .0), (.0, 1., .0), (.0, .0, 1.)])
+        ax.scatter(features[:, 0], features[:, 2], c=labels, cmap=cmap)
+    else:
+        for lab, ma in zip(range(3), "Do^"):
+            ax.plot(features[labels == lab, 0], features[
+                     labels == lab, 2], ma, c=(1., 1., 1.))
+    return fig,ax
+
+
+features, labels = load_dataset('seeds')
+names = sorted(set(labels))
+labels = np.array([names.index(ell) for ell in labels])
+
+fig,ax = plot_decision(features, labels)
+fig.savefig('figure4sklearn.png')
+
+features -= features.mean(0)
+features /= features.std(0)
+fig,ax = plot_decision(features, labels)
+fig.savefig('figure5sklearn.png')