Add example multi-output regression

examples/20_basic/README.txt

@@ -4,4 +4,4 @@
 Basic Examples
 ==============
 
-Examples for basic classification, regression and multi-label classification datasets.
+Examples for basic classification, regression, multi-output regression, and multi-label classification datasets.

examples/20_basic/example_multioutput_regression.py

@@ -0,0 +1,57 @@
+# -*- encoding: utf-8 -*-
+"""
+=======================
+Multi-output Regression
+=======================
+
+The following example shows how to fit a multioutput regression model with
+*auto-sklearn*.
+"""
+import numpy as numpy
+
+from sklearn.datasets import make_regression
+from sklearn.metrics import r2_score
+from sklearn.model_selection import train_test_split
+
+from autosklearn.regression import AutoSklearnRegressor
+
+
+############################################################################
+# Data Loading
+# ============
+
+X, y = make_regression(n_samples=1000, n_features=10, n_informative=5, n_targets=3)
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
+
+############################################################################
+# Build and fit a regressor
+# =========================
+
+automl = AutoSklearnRegressor(
+    time_left_for_this_task=120,
+    per_run_time_limit=30,
+    tmp_folder='/tmp/autosklearn_regression_example_tmp',
+    output_folder='/tmp/autosklearn_regression_example_out',
+)
+automl.fit(X_train, y_train, dataset_name='synthetic')
+
+############################################################################
+# Print the final ensemble constructed by auto-sklearn
+# ====================================================
+
+print(automl.show_models())
+
+###########################################################################
+# Get the Score of the final ensemble
+# ===================================
+
+predictions = automl.predict(X_test)
+print("R2 score:", r2_score(y_test, predictions))
+
+###########################################################################
+# Get the configuration space
+# ===========================
+
+# The configuration space is reduced, i.e. no SVM.
+print(automl.get_configuration_space(X_train, y_train))