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 @@
+importnumpyasnp
+importload_ml100k
+importregression
+importcorrneighbours
+fromsklearnimportmetrics
+importnorm
+
+defpredict(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,
+ ])
+returnstack.mean(0)
+
+
+defmain():
+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()