Fix bindings

CHANGES.rst

@@ -17,6 +17,7 @@ Unreleased
     - Implemented the benford distribution feature (#689)
     - Reworked the notebooks (#701, #704)
     - Speed up the result pivoting (#705)
+    - Add a test for the dask bindings (#719)
 - Bugfixes:
     - Fixed readthedocs (#695, #696)
     - Fix spark and dask after #705 and for non-id named id columns (#712)

tests/integrations/test_bindings.py

@@ -0,0 +1,33 @@
+from unittest import TestCase
+
+from tsfresh.convenience.bindings import dask_feature_extraction_on_chunk
+from tsfresh.feature_extraction.settings import MinimalFCParameters
+
+from dask import dataframe as dd
+import pandas as pd
+
+
+class DaskBindingsTestCase(TestCase):
+    def test_feature_extraction(self):
+        df = pd.DataFrame({"my_id": [1, 1, 1, 2, 2, 2], "my_kind": ["a"]*6,
+                           "my_value": [1, 2, 3, 4, 5, 6]})
+
+        df = dd.from_pandas(df, chunksize=3)
+
+        df_grouped = df.groupby(["my_id", "my_kind"])
+
+        features = dask_feature_extraction_on_chunk(df_grouped, column_id="my_id",
+                                                    column_kind="my_kind",
+                                                    column_value="my_value",
+                                                    column_sort=None,
+                                                    default_fc_parameters=MinimalFCParameters())
+
+        features = features.categorize(columns=["variable"])
+        features = features.reset_index(drop=True)
+
+        feature_table = features.pivot_table(index="my_id", columns="variable", values="value", aggfunc="sum")
+
+        feature_table = feature_table.compute()
+
+        self.assertEqual(len(feature_table.columns), len(MinimalFCParameters()))
+        self.assertEqual(len(feature_table), 2)

tsfresh/convenience/bindings.py

@@ -21,7 +21,10 @@ def _feature_extraction_on_chunk_helper(df, column_id, column_kind,
     elif default_fc_parameters is None and kind_to_fc_parameters is not None:
         default_fc_parameters = {}
 
-    chunk = df[column_id].iloc[0], df[column_kind].iloc[0], df.sort_values(column_sort)[column_value]
+    if column_sort is not None:
+        df = df.sort_values(column_sort)
+
+    chunk = df[column_id].iloc[0], df[column_kind].iloc[0], df[column_value]
     features = _do_extraction_on_chunk(chunk, default_fc_parameters=default_fc_parameters,
                                        kind_to_fc_parameters=kind_to_fc_parameters)
     features = pd.DataFrame(features, columns=[column_id, "variable", "value"])
@@ -31,7 +34,7 @@ def _feature_extraction_on_chunk_helper(df, column_id, column_kind,
 
 
 def dask_feature_extraction_on_chunk(df, column_id, column_kind,
-                                     column_sort, column_value,
+                                     column_value, column_sort=None,
                                      default_fc_parameters=None, kind_to_fc_parameters=None):
     """
     Extract features on a grouped dask dataframe given the column names and the extraction settings.
@@ -99,7 +102,7 @@ def dask_feature_extraction_on_chunk(df, column_id, column_kind,
     :type column_id: str
 
     :param column_sort: The name of the sort column.
-    :type column_sort: str
+    :type column_sort: str or None
 
     :param column_kind: The name of the column keeping record on the kind of the value.
     :type column_kind: str
@@ -121,8 +124,8 @@ def dask_feature_extraction_on_chunk(df, column_id, column_kind,
 
 
 def spark_feature_extraction_on_chunk(df, column_id, column_kind,
-                                      column_sort, column_value,
-                                      default_fc_parameters, kind_to_fc_parameters=None):
+                                      column_value, column_sort=None,
+                                      default_fc_parameters=None, kind_to_fc_parameters=None):
     """
     Extract features on a grouped spark dataframe given the column names and the extraction settings.
     This wrapper function should only be used if you have a spark dataframe as input.
@@ -184,7 +187,7 @@ def spark_feature_extraction_on_chunk(df, column_id, column_kind,
     :type column_id: str
 
     :param column_sort: The name of the sort column.
-    :type column_sort: str
+    :type column_sort: str or None
 
     :param column_kind: The name of the column keeping record on the kind of the value.
     :type column_kind: str