swapped function names for percentage of reoccuring (#725)

* swapped function names for percentage of reoccuring

* fix  comments in reoccurring_values functions

* working on reoccurring_values comments

Co-authored-by: Emanuele Fumagalli <efumagal@synamedia.com>

tests/units/feature_extraction/test_feature_calculations.py

@@ -516,19 +516,19 @@ def test_first_location_of_minimum(self):
 
     def test_percentage_of_doubled_datapoints(self):
         self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_datapoints_to_all_datapoints, [1, 1, 2, 3, 4],
-                                              0.25)
+                                              0.4)
         self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_datapoints_to_all_datapoints, [1, 1.5, 2, 3], 0)
         self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_datapoints_to_all_datapoints, [1], 0)
         self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_datapoints_to_all_datapoints,
-                                              [1.111, -2.45, 1.111, 2.45], 1.0 / 3.0)
+                                              [1.111, -2.45, 1.111, 2.45], 0.5)
         self.assertIsNanOnAllArrayTypes(percentage_of_reoccurring_datapoints_to_all_datapoints, [])
 
     def test_ratio_of_doubled_values(self):
-        self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_values_to_all_values, [1, 1, 2, 3, 4], 0.4)
+        self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_values_to_all_values, [1, 1, 2, 3, 4], 0.25)
         self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_values_to_all_values, [1, 1.5, 2, 3], 0)
         self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_values_to_all_values, [1], 0)
         self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_values_to_all_values,
-                                              [1.111, -2.45, 1.111, 2.45], 0.5)
+                                              [1.111, -2.45, 1.111, 2.45], 1.0 / 3.0)
         self.assertIsNanOnAllArrayTypes(percentage_of_reoccurring_values_to_all_values, [])
 
     def test_sum_of_reoccurring_values(self):

tsfresh/feature_extraction/feature_calculators.py

@@ -863,15 +863,15 @@ def first_location_of_minimum(x):
 
 
 @set_property("fctype", "simple")
-def percentage_of_reoccurring_datapoints_to_all_datapoints(x):
+def percentage_of_reoccurring_values_to_all_values(x):
     """
-    Returns the percentage of unique values, that are present in the time series
+    Returns the percentage of values that are present in the time series
     more than once.
 
         len(different values occurring more than once) / len(different values)
 
     This means the percentage is normalized to the number of unique values,
-    in contrast to the percentage_of_reoccurring_values_to_all_values.
+    in contrast to the percentage_of_reoccurring_datapoints_to_all_datapoints.
 
     :param x: the time series to calculate the feature of
     :type x: numpy.ndarray
@@ -891,15 +891,15 @@ def percentage_of_reoccurring_datapoints_to_all_datapoints(x):
 
 @set_property("fctype", "simple")
 @set_property("input", "pd.Series")
-def percentage_of_reoccurring_values_to_all_values(x):
+def percentage_of_reoccurring_datapoints_to_all_datapoints(x):
     """
-    Returns the ratio of unique values, that are present in the time series
-    more than once.
+    Returns the percentage of non-unique data points. Non-unique means that they are
+    contained another time in the time series again.
 
         # of data points occurring more than once / # of all data points
 
     This means the ratio is normalized to the number of data points in the time series,
-    in contrast to the percentage_of_reoccurring_datapoints_to_all_datapoints.
+    in contrast to the percentage_of_reoccurring_values_to_all_values.
 
     :param x: the time series to calculate the feature of
     :type x: numpy.ndarray