ENH Use sklearn for KNN & cross-validation

This is the same processes that are written "by hand" before, but now
using sklearn (this is the end of the chapter).

Author: luispedro

ch02/seeds_knn_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
+
+# Basic imports
+import numpy as np
+from load import load_dataset
+
+
+# Import sklearn implementation of KNN
+from sklearn.neighbors import KNeighborsClassifier
+
+features, labels = load_dataset('seeds')
+classifier = KNeighborsClassifier(n_neighbors=4)
+
+
+n = len(features)
+correct = 0.0
+for ei in range(n):
+    training = np.ones(n, bool)
+    training[ei] = 0
+    testing = ~training
+    classifier.fit(features[training], labels[training])
+    pred = classifier.predict(features[ei])
+    correct += (pred == labels[ei])
+print(correct/n)
+
+# Import KFold object
+from sklearn.cross_validation import KFold
+
+# means will hold the mean for each fold
+means = []
+
+# kf is a generator of pairs (training,testing) so that each iteration
+# implements a separate fold.
+kf = KFold(len(features), n_folds=3, shuffle=True)
+for training,testing in kf:
+    # We learn a model for this fold with `fit` and then apply it to the
+    # testing data with `predict`:
+    classifier.fit(features[training], labels[training])
+    prediction = classifier.predict(features[testing])
+
+    # np.mean on an array of booleans returns the fraction of correct decisions
+    # for this fold:
+    curmean = np.mean(prediction == labels[testing])
+    means.append(curmean)
+print(means)
+
+# The function cross_val_score does the same thing as the loop above with a
+# single function call
+
+from sklearn.cross_validation import cross_val_score
+print(cross_val_score(classifier, features, labels))