Make sure X and y are sorted equally in select_features

CHANGES.rst

@@ -18,6 +18,7 @@ Unreleased
     - Reworked the notebooks (#701, #704)
     - Speed up the result pivoting (#705)
 - Bugfixes:
+    - Fixed a bug in the selection, that caused all regression tasks with un-ordered index to be wrong (#715)
     - Fixed readthedocs (#695, #696)
     - Fix spark and dask after #705 and for non-id named id columns (#712)
 

tests/integrations/examples/test_driftbif_simulation.py

@@ -7,6 +7,7 @@
 import pandas as pd
 
 from tsfresh.examples.driftbif_simulation import velocity, load_driftbif, sample_tau
+from tsfresh import extract_relevant_features
 
 
 class DriftBifSimlationTestCase(unittest.TestCase):
@@ -63,6 +64,18 @@ def test_dimensionality(self):
         self.assertEqual(v.shape, (Nt, 3),
                          'The returned vector should reflect the dimension of the initial condition.')
 
+    def test_relevant_feature_extraction(self):
+        df, y = load_driftbif(100, 10, classification=False, seed=42)
+
+        df['id'] = df['id'].astype('str')
+        y.index = y.index.astype('str')
+
+        X = extract_relevant_features(df, y,
+                                      column_id="id", column_sort="time",
+                                      column_kind="dimension", column_value="value")
+
+        self.assertGreater(len(X.columns), 10)
+
 
 class SampleTauTestCase(unittest.TestCase):
     def test_range(self):

tests/units/feature_selection/test_relevance.py

@@ -83,8 +83,14 @@ def test_target_real_calls_correct_tests(self, significance_test_feature_binary_
 
         assert 0.5 == relevance_table.loc['feature_binary'].p_value
         assert 0.7 == relevance_table.loc['feature_real'].p_value
-        significance_test_feature_binary_mock.assert_called_once_with(X['feature_binary'], y=y_real)
-        significance_test_feature_real_mock.assert_called_once_with(X['feature_real'], y=y_real)
+
+        assert significance_test_feature_binary_mock.call_count == 1
+        pd.testing.assert_series_equal(significance_test_feature_binary_mock.call_args[0][0], X["feature_binary"])
+        pd.testing.assert_series_equal(significance_test_feature_binary_mock.call_args[1]["y"], y_real)
+
+        assert significance_test_feature_real_mock.call_count == 1
+        pd.testing.assert_series_equal(significance_test_feature_real_mock.call_args[0][0], X["feature_real"])
+        pd.testing.assert_series_equal(significance_test_feature_real_mock.call_args[1]["y"], y_real)
 
     @mock.patch('tsfresh.feature_selection.relevance.target_real_feature_real_test')
     @mock.patch('tsfresh.feature_selection.relevance.target_real_feature_binary_test')

tsfresh/feature_selection/relevance.py

@@ -131,6 +131,13 @@ def calculate_relevance_table(X, y, ml_task='auto', n_jobs=defaults.N_PROCESSES,
              not relevant] for this feature)
     :rtype: pandas.DataFrame
     """
+
+    # Make sure X and y both have the exact same indices
+    y = y.sort_index()
+    X = X.sort_index()
+
+    assert list(y.index) == list(X.index), "The index of X and y need to be the same"
+
     if ml_task not in ['auto', 'classification', 'regression']:
         raise ValueError('ml_task must be one of: \'auto\', \'classification\', \'regression\'')
     elif ml_task == 'auto':

tsfresh/feature_selection/significance_tests.py

@@ -203,6 +203,8 @@ def __check_if_pandas_series(x, y):
         raise TypeError("x should be a pandas Series")
     if not isinstance(y, pd.Series):
         raise TypeError("y should be a pandas Series")
+    if not list(y.index) == list(x.index):
+        raise ValueError("X and y need to have the same index!")
 
 
 def __check_for_binary_target(y):