multioutput regression (#292)

* make AutoML inherit sklearn.base.BaseEstimator such that it can be wrapped in sklearn.multioutput.MultiOutputRegressor for multi-output regression.

* moved and simplified preprocessing code in AutoML.predictI() to _preprocess()

test/automl/test_regression.py

@@ -195,5 +195,26 @@ class TestRegression(unittest.TestCase):
         print(automl_experiment.best_config_train_time)
 
 
+def test_multioutput():
+    from sklearn.datasets import make_regression
+    from sklearn.model_selection import train_test_split
+    from sklearn.multioutput import MultiOutputRegressor
+
+    # create regression data
+    X, y = make_regression(n_targets=3)
+
+    # split into train and test data
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.30, random_state=42
+    )
+
+    # train the model
+    model = MultiOutputRegressor(AutoML(task="regression", time_budget=1))
+    model.fit(X_train, y_train)
+
+    # predict
+    print(model.predict(X_test))
+
+
 if __name__ == "__main__":
     unittest.main()