openml · mfeurer · Jan 28, 2021 · Oct 29, 2020 · Oct 29, 2020 · Oct 29, 2020
diff --git a/examples/30_extended/run_setup_tutorial.py b/examples/30_extended/run_setup_tutorial.py
@@ -34,6 +34,8 @@
 
 import numpy as np
 import openml
+from openml.extensions.sklearn import cat, cont
+
 from sklearn.pipeline import make_pipeline, Pipeline
 from sklearn.compose import ColumnTransformer
 from sklearn.impute import SimpleImputer
@@ -57,15 +59,6 @@
 # easy as you want it to be
 
 
-# Helper functions to return required columns for ColumnTransformer
-def cont(X):
-    return X.dtypes != "category"
-
-
-def cat(X):
-    return X.dtypes == "category"
-
-
 cat_imp = make_pipeline(
     SimpleImputer(strategy="most_frequent"),
     OneHotEncoder(handle_unknown="ignore", sparse=False),

diff --git a/openml/datasets/functions.py b/openml/datasets/functions.py
@@ -183,7 +183,7 @@ def list_datasets(
     status: Optional[str] = None,
     tag: Optional[str] = None,
     output_format: str = "dict",
-    **kwargs
+    **kwargs,
 ) -> Union[Dict, pd.DataFrame]:
 
     """
@@ -251,7 +251,7 @@ def list_datasets(
         size=size,
         status=status,
         tag=tag,
-        **kwargs
+        **kwargs,
     )
 
 
@@ -334,8 +334,7 @@ def _load_features_from_file(features_file: str) -> Dict:
 
 
 def check_datasets_active(
-    dataset_ids: List[int],
-    raise_error_if_not_exist: bool = True,
+    dataset_ids: List[int], raise_error_if_not_exist: bool = True,
 ) -> Dict[int, bool]:
     """
     Check if the dataset ids provided are active.
@@ -363,7 +362,7 @@ def check_datasets_active(
         dataset = dataset_list.get(did, None)
         if dataset is None:
             if raise_error_if_not_exist:
-                raise ValueError(f'Could not find dataset {did} in OpenML dataset list.')
+                raise ValueError(f"Could not find dataset {did} in OpenML dataset list.")
         else:
             active[did] = dataset["status"] == "active"
 

diff --git a/openml/extensions/sklearn/__init__.py b/openml/extensions/sklearn/__init__.py
@@ -7,3 +7,31 @@
 __all__ = ["SklearnExtension"]
 
 register_extension(SklearnExtension)
+
+
+def cont(X):
+    """Returns True for all non-categorical columns, False for the rest.
+
+    This is a helper function for OpenML datasets encoded as DataFrames simplifying the handling
+    of mixed data types. To build sklearn models on mixed data types, a ColumnTransformer is
+    required to process each type of columns separately.
+    This function allows transformations meant for continuous/numeric columns to access the
+    continuous/numeric columns given the dataset as DataFrame.
+    """
+    if not hasattr(X, "dtypes"):
+        raise AttributeError("Not a Pandas DataFrame with 'dtypes' as attribute!")
+    return X.dtypes != "category"
+
+
+def cat(X):
+    """Returns True for all categorical columns, False for the rest.
+
+    This is a helper function for OpenML datasets encoded as DataFrames simplifying the handling
+    of mixed data types. To build sklearn models on mixed data types, a ColumnTransformer is
+    required to process each type of columns separately.
+    This function allows transformations meant for categorical columns to access the
+    categorical columns given the dataset as DataFrame.
+    """
+    if not hasattr(X, "dtypes"):
+        raise AttributeError("Not a Pandas DataFrame with 'dtypes' as attribute!")
+    return X.dtypes == "category"
diff --git a/openml/testing.py b/openml/testing.py
@@ -267,12 +267,4 @@ class CustomImputer(SimpleImputer):
     pass
 
 
-def cont(X):
-    return X.dtypes != "category"
-
-
-def cat(X):
-    return X.dtypes == "category"
-
-
-__all__ = ["TestBase", "SimpleImputer", "CustomImputer", "cat", "cont"]
+__all__ = ["TestBase", "SimpleImputer", "CustomImputer"]
diff --git a/tests/test_datasets/test_dataset_functions.py b/tests/test_datasets/test_dataset_functions.py
@@ -227,10 +227,7 @@ def test_list_datasets_empty(self):
     def test_check_datasets_active(self):
         # Have to test on live because there is no deactivated dataset on the test server.
         openml.config.server = self.production_server
-        active = openml.datasets.check_datasets_active(
-            [2, 17, 79],
-            raise_error_if_not_exist=False,
-        )
+        active = openml.datasets.check_datasets_active([2, 17, 79], raise_error_if_not_exist=False,)
         self.assertTrue(active[2])
         self.assertFalse(active[17])
         self.assertIsNone(active.get(79))

diff --git a/tests/test_extensions/test_sklearn_extension/test_sklearn_extension.py b/tests/test_extensions/test_sklearn_extension/test_sklearn_extension.py
@@ -40,7 +40,8 @@
 from openml.flows import OpenMLFlow
 from openml.flows.functions import assert_flows_equal
 from openml.runs.trace import OpenMLRunTrace
-from openml.testing import TestBase, SimpleImputer, CustomImputer, cat, cont
+from openml.testing import TestBase, SimpleImputer, CustomImputer
+from openml.extensions.sklearn import cat, cont
 
 
 this_directory = os.path.dirname(os.path.abspath(__file__))
@@ -2183,16 +2184,6 @@ def test_failed_serialization_of_custom_class(self):
             # for lower versions
             from sklearn.preprocessing import Imputer as SimpleImputer
 
-        class CustomImputer(SimpleImputer):
-            pass
-
-        def cont(X):
-            return X.dtypes != "category"
-
-        def cat(X):
-            return X.dtypes == "category"
-
-        import sklearn.metrics
         import sklearn.tree
         from sklearn.pipeline import Pipeline, make_pipeline
         from sklearn.compose import ColumnTransformer
@@ -2215,3 +2206,37 @@ def cat(X):
                 raise AttributeError(e)
             else:
                 raise Exception(e)
+
+    @unittest.skipIf(
+        LooseVersion(sklearn.__version__) < "0.20",
+        reason="columntransformer introduction in 0.20.0",
+    )
+    def test_setupid_with_column_transformer(self):
+        """Test to check if inclusion of ColumnTransformer in a pipleline is treated as a new
+        flow each time.
+        """
+        import sklearn.compose
+        from sklearn.svm import SVC
+
+        def column_transformer_pipe(task_id):
+            task = openml.tasks.get_task(task_id)
+            # make columntransformer
+            preprocessor = sklearn.compose.ColumnTransformer(
+                transformers=[
+                    ("num", StandardScaler(), cont),
+                    ("cat", OneHotEncoder(handle_unknown="ignore"), cat),
+                ]
+            )
+            # make pipeline
+            clf = SVC(gamma="scale", random_state=1)
+            pipe = make_pipeline(preprocessor, clf)
+            # run task
+            run = openml.runs.run_model_on_task(pipe, task, avoid_duplicate_runs=True)
+            run.publish()
+            new_run = openml.runs.get_run(run.run_id)
+            return new_run.setup_id
+
+        setup1 = column_transformer_pipe(11)  # only categorical
+        setup2 = column_transformer_pipe(23)  # only numeric
+
+        self.assertEqual(setup1, setup2)
diff --git a/tests/test_runs/test_run_functions.py b/tests/test_runs/test_run_functions.py
@@ -20,7 +20,8 @@
 import pandas as pd
 
 import openml.extensions.sklearn
-from openml.testing import TestBase, SimpleImputer, CustomImputer, cat, cont
+from openml.testing import TestBase, SimpleImputer, CustomImputer
+from openml.extensions.sklearn import cat, cont
 from openml.runs.functions import _run_task_get_arffcontent, run_exists, format_prediction
 from openml.runs.trace import OpenMLRunTrace
 from openml.tasks import TaskType

diff --git a/tests/test_study/test_study_examples.py b/tests/test_study/test_study_examples.py
@@ -1,6 +1,7 @@
 # License: BSD 3-Clause
 
-from openml.testing import TestBase, SimpleImputer, CustomImputer, cat, cont
+from openml.testing import TestBase, SimpleImputer, CustomImputer
+from openml.extensions.sklearn import cat, cont
 
 import sklearn
 import unittest