Merge pull request #408 from Crunch-io/bug-on-scale-means-diff-188552693

Bug on scale means diff 188552693

Author: ernestoarbitrio

src/cr/cube/matrix/measure.py

@@ -2906,15 +2906,22 @@ def is_defined(self):
     @lazyproperty
     def _proportions(self):
         """List of 2 ndarray matrixes of the relevant proportion blocks"""
+        count_blocks = self._second_order_measures.weighted_counts.blocks
         if self.orientation == MO.ROWS:
             # --- Use *row* proportions ---
-            props = self._second_order_measures.row_proportions.blocks
+            weighted_base_blocks = self._second_order_measures.row_weighted_bases.blocks
             # --- Get base values & *row* subtotals
-            return [props[0][0], props[1][0]]
+            with np.errstate(divide="ignore", invalid="ignore"):
+                base_values = count_blocks[0][0] / weighted_base_blocks[0][0]
+                row_subtotals = count_blocks[1][0] / weighted_base_blocks[1][0]
+                return [base_values, row_subtotals]
         # --- Use *column* proportions ---
-        props = self._second_order_measures.column_proportions.blocks
-        # --- Get base values & *column* subtotals
-        return [props[0][0], props[0][1]]
+        weighted_base_blocks = self._second_order_measures.column_weighted_bases.blocks
+        with np.errstate(divide="ignore", invalid="ignore"):
+            base_values = count_blocks[0][0] / weighted_base_blocks[0][0]
+            col_subtotals = count_blocks[0][1] / weighted_base_blocks[0][1]
+            # --- Get base values & *column* subtotals
+            return [base_values, col_subtotals]
 
     @staticmethod
     def _weighted_mean(proportions, values):

tests/integration/test_headers_and_subtotals.py

@@ -2980,6 +2980,12 @@ def test_it_computes_diff_for_cat_x_cat_date_with_subdiffs_on_both(self):
             nan_ok=True,
             rel=1e-4,
         )
+        assert slice_.columns_scale_mean == pytest.approx(
+            np.array(
+                [np.nan, 2.26559356, 2.43811395, 2.08453608, 1.9968254, 1.81967213]
+            ),
+            nan_ok=True,
+        )
 
     def test_it_computes_diff_for_cat_date_x_cat_with_subdiffs_on_both(self):
         slice_ = Cube(
@@ -3083,6 +3089,10 @@ def test_it_computes_diff_for_cat_date_x_cat_with_subdiffs_on_both(self):
             ),
             nan_ok=True,
         )
+        assert slice_.rows_scale_mean == pytest.approx(
+            np.array([np.nan, 1.625, 1.4675835, 1.78818737, 1.68138801, 1.46994536]),
+            nan_ok=True,
+        )
 
     def test_it_computes_diff_for_cat_date_x_cat_with_subdiffs_on_rows(self):
         insertions = [

tests/unit/matrix/test_measure.py

@@ -2758,30 +2758,34 @@ def test_it_can_tell_if_it_is_defined(self, request, values, expected):
         assert mean.is_defined == expected
 
     def test_it_gets_the_right_proportions_for_rows(self, request):
-        row_proportions_ = instance_mock(
-            request, _RowProportions, blocks=[["a", "b"], ["c", "d"]]
-        )
-        second_order_measures_ = instance_mock(
-            request, SecondOrderMeasures, row_proportions=row_proportions_
+        second_order_measures_ = instance_mock(request, SecondOrderMeasures)
+        counts_ = instance_mock(request, _WeightedCounts, blocks=[[1, 2], [3, 4]])
+        bases_ = instance_mock(
+            request,
+            _RowWeightedBases,
+            blocks=[[11, 12], [13, 14]],
         )
+        second_order_measures_.weighted_counts = counts_
+        second_order_measures_.row_weighted_bases = bases_
 
         mean = _ScaleMean(None, second_order_measures_, None, MO.ROWS)
 
-        assert mean._proportions == ["a", "c"]
+        assert mean._proportions == [0.09090909090909091, 0.23076923076923078]
 
     def test_it_gets_the_right_counts_for_columns(self, request):
-        column_proportions_ = instance_mock(
-            request, _ColumnProportions, blocks=[["a", "b"], ["c", "d"]]
-        )
-        second_order_measures_ = instance_mock(
+        second_order_measures_ = instance_mock(request, SecondOrderMeasures)
+        counts_ = instance_mock(request, _WeightedCounts, blocks=[[1, 2], [3, 4]])
+        bases_ = instance_mock(
             request,
-            SecondOrderMeasures,
-            column_proportions=column_proportions_,
+            _ColumnWeightedBases,
+            blocks=[[11, 12], [13, 14]],
         )
+        second_order_measures_.weighted_counts = counts_
+        second_order_measures_.column_weighted_bases = bases_
 
         mean = _ScaleMean(None, second_order_measures_, None, MO.COLUMNS)
 
-        assert mean._proportions == ["a", "b"]
+        assert mean._proportions == [0.09090909090909091, 0.16666666666666666]
 
     @pytest.mark.parametrize(
         "proportions, values, expected",