ENH Average methods (instead of stacked learning)

This is mentioned in the book, but the code is only shown here.

Author: luispedro

ch08/README.rst

@@ -25,7 +25,7 @@ regression.py
 stacked.py
     Stacked predictions
 averaged.py
-    Averaging of predictions
+    Averaging of predictions (mentioned in book, but code is not shown there).
 
 Association Rule Mining
 -----------------------

ch08/averaged.py

@@ -0,0 +1,33 @@
+import numpy as np
+import load_ml100k
+import regression
+import corrneighbours
+from sklearn import metrics
+import norm
+
+def predict(train):
+    predicted0 = regression.predict(train)
+    predicted1 = regression.predict(train.T).T
+    predicted2 = corrneighbours.predict(train)
+    predicted3 = corrneighbours.predict(train.T).T
+    predicted4 = norm.predict(train)
+    predicted5 = norm.predict(train.T).T
+    stack = np.array([
+        predicted0,
+        predicted1,
+        predicted2,
+        predicted3,
+        predicted4,
+        predicted5,
+        ])
+    return stack.mean(0)
+
+
+def main():
+    train,test = load_ml100k.get_train_test(random_state=12)
+    predicted = predict(train)
+    r2 = metrics.r2_score(test[test > 0], predicted[test > 0])
+    print('R2 averaged: {:.2%}'.format(r2))
+
+if __name__ == '__main__':
+    main()