updated

Author: marchboy

python_test/ml_stacking.py

@@ -16,18 +16,19 @@ def get_stacking(clf, x_train, y_train, x_test, n_folds=10):
     second_level_train_set, second_level_test_set = np.zeros((train_num, )), np.zeros((test_num, ))
     test_nfolds_sets = np.zeros((test_num, n_folds))
     print("-------------------------------------------")
-    print(second_level_train_set, second_level_test_set, test_nfolds_sets)
+    print(second_level_train_set.shape, second_level_test_set.shape, test_nfolds_sets.shape)
 
     kf = KFold(n_splits=n_folds)
 
     for i, (train_index, test_index) in enumerate(kf.split(x_train)):
+        print(i, train_index, test_index)
         x_tra, y_tra = x_train[train_index], y_train[train_index]
         x_tst, _ = x_train[test_index], y_train[test_index]
 
         clf.fit(x_tra, y_tra)
 
         second_level_train_set[test_index] = clf.predict(x_tst)
-        test_nfolds_sets[:, i] = clf.predict(x_tst)
+        test_nfolds_sets[:, i] = clf.predict(x_test)
 
     second_level_test_set[:] = test_nfolds_sets.mean(axis=1)
     return second_level_train_set, second_level_test_set