[FIX] clone test of dummy estimator (#1129)

* [FIX] clone test of dummy estimator

* [FIX] flake8

test/test_automl/test_automl.py

@@ -12,7 +12,6 @@
 import pandas as pd
 import pytest
 import sklearn.datasets
-from sklearn.base import clone
 from smac.scenario.scenario import Scenario
 from smac.facade.roar_facade import ROAR
 
@@ -427,16 +426,6 @@ def test_do_dummy_prediction(backend, dask_client, datasets):
         'predictions_ensemble_1_1_0.0.npy')
     )
 
-    model_path = os.path.join(backend.temporary_directory, '.auto-sklearn',
-                              'runs', '1_1_0.0',
-                              '1.1.0.0.model')
-
-    # Make sure the dummy model complies with scikit learn
-    # get/set params
-    assert os.path.exists(model_path)
-    with open(model_path, 'rb') as model_handler:
-        clone(pickle.load(model_handler))
-
     auto._clean_logger()
 
     del auto

test/test_evaluation/test_dummy_pipelines.py

@@ -0,0 +1,31 @@
+import numpy as np
+
+import pytest
+
+from sklearn.base import clone
+from sklearn.datasets import make_classification, make_regression
+from sklearn.utils.validation import check_is_fitted
+
+from autosklearn.evaluation.abstract_evaluator import MyDummyClassifier, MyDummyRegressor
+
+
+@pytest.mark.parametrize("task_type", ['classification', 'regression'])
+def test_dummy_pipeline(task_type):
+    if task_type == 'classification':
+        estimator_class = MyDummyClassifier
+        data_maker = make_classification
+    elif task_type == 'regression':
+        estimator_class = MyDummyRegressor
+        data_maker = make_regression
+    else:
+        pytest.fail(task_type)
+
+    estimator = estimator_class(config=1, random_state=np.random.RandomState(42))
+    X, y = data_maker()
+    estimator.fit(X, y)
+    check_is_fitted(estimator)
+
+    assert np.shape(X)[0] == np.shape(estimator.predict(X))[0]
+
+    # make sure we comply with scikit-learn estimator API
+    clone(estimator)