Fix fit, refit, fit_ensemble not returning self + Unit tests

autosklearn/estimators.py

@@ -245,6 +245,7 @@ def build_automl(self):
     def fit(self, *args, **kwargs):
         self._automl = self.build_automl()
         self._automl.fit(*args, **kwargs)
+        return self
 
     def fit_ensemble(self, y, task=None, metric=None, precision='32',
                      dataset_name=None, ensemble_nbest=None,
@@ -294,9 +295,10 @@ def fit_ensemble(self, y, task=None, metric=None, precision='32',
         """
         if self._automl is None:
             self._automl = self.build_automl()
-        return self._automl.fit_ensemble(y, task, metric, precision,
-                                         dataset_name, ensemble_nbest,
-                                         ensemble_size)
+        self._automl.fit_ensemble(y, task, metric, precision,
+                                  dataset_name, ensemble_nbest,
+                                  ensemble_size)
+        return self
 
     def refit(self, X, y):
         """Refit all models found with fit to new data.
@@ -323,7 +325,9 @@ def refit(self, X, y):
         self
 
         """
-        return self._automl.refit(X, y)
+        self._automl.refit(X, y)
+        return self
+
 
     def predict(self, X, batch_size=None, n_jobs=1):
         return self._automl.predict(X, batch_size=batch_size, n_jobs=n_jobs)
@@ -451,7 +455,7 @@ def fit(self, X, y,
         self
 
         """
-        return super().fit(
+        super().fit(
             X=X,
             y=y,
             X_test=X_test,
@@ -461,6 +465,8 @@ def fit(self, X, y,
             dataset_name=dataset_name,
         )
 
+        return self
+
     def predict(self, X, batch_size=None, n_jobs=1):
         """Predict classes for X.
 
@@ -554,7 +560,7 @@ def fit(self, X, y,
         """
         # Fit is supposed to be idempotent!
         # But not if we use share_mode.
-        return super().fit(
+        super().fit(
             X=X,
             y=y,
             X_test=X_test,
@@ -564,6 +570,10 @@ def fit(self, X, y,
             dataset_name=dataset_name,
         )
 
+        return self
+
+
+
     def predict(self, X, batch_size=None, n_jobs=1):
         """Predict regression target for X.
 

doc/conf.py

@@ -69,7 +69,7 @@
     #'reference_url': {
     #    'autosklearn': None
     #},
-    #'backreferences_dir': 'gen_modules/backreferences'
+    'backreferences_dir': False
 }
 
 # Add any paths that contain templates here, relative to this directory.

test/test_automl/test_estimators.py

@@ -390,3 +390,45 @@ def test_conversion_of_list_to_np(self, fit_ensemble, refit, fit):
         automl.fit_ensemble(y)
         self.assertEqual(fit_ensemble.call_count, 1)
         self.assertIsInstance(fit_ensemble.call_args[0][0], np.ndarray)
+
+
+class AutoSklearnClassifierTest(unittest.TestCase):
+    # Currently this class only tests that the methods of AutoSklearnClassifier
+    # which should return self actually return self.
+    def test_classification_methods_returns_self(self):
+        X_train, y_train, X_test, y_test = putil.get_dataset('iris')
+        automl = AutoSklearnClassifier(time_left_for_this_task=20,
+                                       per_run_time_limit=5,
+                                       ensemble_size=0)
+
+        automl_fitted = automl.fit(X_train, y_train)
+        self.assertIs(automl, automl_fitted)
+
+        automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5)
+        self.assertIs(automl, automl_ensemble_fitted)
+
+        automl_refitted = automl.refit(X_train.copy(), y_train.copy())
+        self.assertIs(automl, automl_refitted)
+
+
+class AutoSklearnRegressorTest(unittest.TestCase):
+    # Currently this class only tests that the methods of AutoSklearnRegressor
+    # that should return self actually return self.
+    def test_regression_methods_returns_self(self):
+        X_train, y_train, X_test, y_test = putil.get_dataset('boston')
+        automl = AutoSklearnRegressor(time_left_for_this_task=20,
+                                      per_run_time_limit=5,
+                                      ensemble_size=0)
+
+        automl_fitted = automl.fit(X_train, y_train)
+        self.assertIs(automl, automl_fitted)
+
+        automl_ensemble_fitted = automl.fit_ensemble(y_train, ensemble_size=5)
+        self.assertIs(automl, automl_ensemble_fitted)
+
+        automl_refitted = automl.refit(X_train.copy(), y_train.copy())
+        self.assertIs(automl, automl_refitted)
+
+
+if __name__=="__main__":
+    unittest.main()