[178390751]: fix rebase problems

Author: ernestoarbitrio

src/cr/cube/cubepart.py

@@ -1255,12 +1255,6 @@ def _only_larger(self):
         # matrix/measures.py
         return self._columns_dimension.pairwise_significance_spec.only_larger
 
-    @lazyproperty
-    def _row_variance(self):
-        """2D np.float64 ndarray of row-percentage variance for each cell."""
-        p = self.row_proportions
-        return p * (1 - p)
-
     @lazyproperty
     def _rows_dimension(self):
         return self._dimensions[0]

src/cr/cube/matrix/assembler.py

@@ -20,7 +20,6 @@
 )
 from cr.cube.enums import (
     COLLATION_METHOD as CM,
-    DIMENSION_TYPE as DT,
     MARGINAL,
     MARGINAL_ORIENTATION as MO,
     MEASURE as M,

src/cr/cube/matrix/measure.py

@@ -12,7 +12,6 @@
 from cr.cube.matrix.subtotals import (
     SumSubtotals,
     NanSubtotals,
-    NoneSubtotals,
 )
 from cr.cube.util import lazyproperty
 
@@ -1649,8 +1648,7 @@ class _PairwiseSigPvals(_PairwiseSigTstats):
     @lazyproperty
     def blocks(self):
         """2D array of the four 2D "blocks" making up this measure."""
-        col_idx = self._selected_column_idx
-        t_stats = self._second_order_measures.pairwise_t_stats(col_idx).blocks
+        t_stats = self._t_stats_blocks
         column_bases = self._second_order_measures.column_unweighted_bases.blocks
         body_selected_base = self._selected_columns_base(0)
         ins_selected_base = self._selected_columns_base(1)
@@ -1686,12 +1684,11 @@ def _selected_columns_base(self, table_index):
         intersections). By choosing between them, we get the bases in a shape that we
         don't have to broadcast.
         """
-        col_idx = self._selected_column_idx
         column_bases = self._second_order_measures.column_unweighted_bases.blocks
         return (
-            column_bases[table_index][1][:, [col_idx]]
-            if col_idx < 0
-            else column_bases[table_index][0][:, [col_idx]]
+            column_bases[table_index][1][:, [self._insertion_idx]]
+            if self._insertion_idx is not None
+            else column_bases[table_index][0][:, [self._col_idx]]
         )
 
     @lazyproperty

tests/integration/test_pairwise_significance.py

@@ -1190,9 +1190,11 @@ def test_mean_diff_significance_for_numeric_array_grouped_by_cat(self):
 
     def test_mean_diff_significance_for_numeric_array_grouped_by_cat_hs(self):
         transforms = {
-            "pairwise_indices": {"alpha": [0.5]},
             "columns_dimension": {
-                "pairwise_significance": {"column_significance": {"insertion_id": 1}},
+                "pairwise_significance": {
+                    "column_significance": {"insertion_id": 1},
+                    "pairwise_indices": {"alpha": [0.5]},
+                },
                 "insertions": [
                     {
                         "function": "subtotal",
@@ -1234,7 +1236,8 @@ def test_mean_diff_significance_for_numeric_array_grouped_by_cat_hs(self):
         )
 
         transforms["columns_dimension"]["pairwise_significance"] = {
-            "column_significance": {"column_idx": 4}
+            "column_significance": {"column_idx": 4},
+            "pairwise_indices": {"alpha": [0.5]},
         }
         slice_ = Cube(
             NA.NUM_ARR_MEANS_GROUPED_BY_CAT_HS, transforms=transforms
@@ -1324,7 +1327,7 @@ def test_mean_diff_significance_for_numeric_array_x_mr(self):
     def test_mean_diff_significance_indices_for_numeric_array_grouped_by_cat(self):
         transforms = {
             "columns_dimension": {
-                "pairwise_significance": {"pairwise_indices": {"alpha": [0.05, 0.08]}}
+                "pairwise_significance": {"pairwise_indices": {"alpha": [0.45, 0.333]}}
             }
         }
         slice_ = Cube(
@@ -1416,13 +1419,15 @@ def test_mean_diff_significance_for_cat_x_cat(self):
         slice_ = Cube(
             CR.MEAN_CAT_X_CAT,
             transforms={
-                "pairwise_indices": {"alpha": [0.1]},
                 "rows_dimension": {
                     "elements": {"1": {"hide": True}},
                     "prune": True,
                 },
                 "columns_dimension": {
-                    "pairwise_significance": {"column_significance": {"column_idx": 1}},
+                    "pairwise_significance": {
+                        "column_significance": {"column_idx": 1},
+                        "pairwise_indices": {"alpha": [0.1]},
+                    },
                     "elements": {"1": {"hide": True}},
                     "prune": True,
                     "order": {"type": "explicit", "element_ids": [4, 2, 5, 0]},
@@ -1456,7 +1461,7 @@ def test_mean_diff_significance_for_cat_x_cat(self):
     def test_mean_diff_significance_indices_for_cat_x_cat(self):
         transforms = {
             "columns_dimension": {
-                "pairwise_significance": {"pairwise_indices": {"alpha": [0.05, 0.01]}}
+                "pairwise_significance": {"pairwise_indices": {"alpha": [0.15, 0.05]}}
             }
         }
         slice_ = Cube(CR.MEAN_CAT_X_CAT, transforms=transforms).partitions[0]

tests/unit/matrix/test_assembler.py

@@ -43,7 +43,6 @@
     _Means,
     _PopulationProportions,
     _PopulationStandardError,
-    _MarginProportion,
     _PairwiseIndices,
     _PairwiseSubvarIndices,
     _PairwiseMeansIndices,

tests/unit/matrix/test_measure.py

@@ -700,7 +700,7 @@ def it_provides_the_df(self, request):
         cube_measures_ = instance_mock(request, CubeMeasures)
         cube_measures_.cube_stddev = cube_std_dev_
         cube_measures_.unweighted_cube_counts = unweighted_cube_counts_
-        pairwise_pvals = _PairwiseMeansSigPVals(None, None, cube_measures_, 0)
+        pairwise_pvals = _PairwiseMeansSigPVals(None, None, cube_measures_, None, 0)
 
         assert pairwise_pvals._df == pytest.approx(np.array([[44, 37.8554]]))
 
@@ -724,7 +724,7 @@ def it_provides_the_tstats(self, request):
         cube_measures_.cube_means = cube_means_
         cube_measures_.cube_stddev = cube_std_dev_
         cube_measures_.unweighted_cube_counts = unweighted_cube_counts_
-        pairwise_tstat = _PairwiseMeansSigTStats(None, None, cube_measures_, 0)
+        pairwise_tstat = _PairwiseMeansSigTStats(None, None, cube_measures_, None, 0)
 
         assert pairwise_tstat.t_stats == pytest.approx(np.array([[0, 2.310889]]))
 
@@ -737,7 +737,7 @@ def it_provides_a_blocks_interface(self, request):
         property_mock(request, _PairwiseSigTstats, "_subtotal_columns", return_value=2)
         property_mock(request, _PairwiseSigTstats, "_subtotal_rows", return_value=3)
         property_mock(request, _PairwiseSigTstats, "_intersections", return_value=4)
-        pairwise_tstat = _PairwiseSigTstats(None, None, None, None)
+        pairwise_tstat = _PairwiseSigTstats(None, None, None, None, None)
 
         assert pairwise_tstat.blocks == [[1, 2], [3, 4]]
 
@@ -748,7 +748,7 @@ def it_calculates_the_base_values_to_help(
         _proportions_prop_.return_value = [[0.5, 0.6], [0.7, 0.8]]
         _reference_values_.return_value = [9, 0]
         _calculate_t_stats_.return_value = "tstat"
-        pairwise_tstat = _PairwiseSigTstats(None, None, None, None)
+        pairwise_tstat = _PairwiseSigTstats(None, None, None, None, None)
 
         base_values = pairwise_tstat._base_values
 
@@ -758,12 +758,14 @@ def it_calculates_the_base_values_to_help(
 
     def it_provides_the_bases_to_help(self, second_order_measures_):
         second_order_measures_.column_unweighted_bases.blocks = [1, 2]
-        pairwise_tstat = _PairwiseSigTstats(None, second_order_measures_, None, None)
+        pairwise_tstat = _PairwiseSigTstats(
+            None, second_order_measures_, None, None, None
+        )
 
         assert pairwise_tstat._bases == [1, 2]
 
     def it_can_calculate_the_t_stat_to_help(self):
-        pairwise_tstat = _PairwiseSigTstats(None, None, None, None)
+        pairwise_tstat = _PairwiseSigTstats(None, None, None, None, None)
 
         actual = pairwise_tstat._calculate_t_stats(
             np.array([[0.5, 0.3, 0.1], [0.5, 0.7, 0.9]]),
@@ -786,7 +788,7 @@ def it_can_calculate_the_t_stat_to_help(self):
         )
 
     def but_tstat_calculation_is_ok_with_empty_inputs(self):
-        pairwise_tstat = _PairwiseSigTstats(None, None, None, None)
+        pairwise_tstat = _PairwiseSigTstats(None, None, None, None, None)
 
         actual = pairwise_tstat._calculate_t_stats(
             np.array([[]]),
@@ -804,7 +806,7 @@ def it_provides_the_intersections_to_help(
         _proportions_prop_.return_value = [[0.5, 0.6], [0.7, 0.8]]
         _reference_values_.return_value = [9, 0]
         _calculate_t_stats_.return_value = "tstat"
-        pairwise_tstat = _PairwiseSigTstats(None, None, None, None)
+        pairwise_tstat = _PairwiseSigTstats(None, None, None, None, None)
 
         base_values = pairwise_tstat._intersections
 
@@ -814,22 +816,30 @@ def it_provides_the_intersections_to_help(
 
     def it_provides_the_proportions_to_help(self, second_order_measures_):
         second_order_measures_.column_proportions.blocks = [1, 2]
-        pairwise_tstat = _PairwiseSigTstats(None, second_order_measures_, None, None)
+        pairwise_tstat = _PairwiseSigTstats(
+            None, second_order_measures_, None, None, None
+        )
 
         assert pairwise_tstat._proportions == [1, 2]
 
     @pytest.mark.parametrize(
-        "col_index, block_index, expected",
+        "insertion_index, col_index, block_index, expected",
         (
-            (0, 0, ("0", "2")),
-            (1, 0, ("1", "3")),
-            (1, 1, ("10", "12")),
-            (-1, 0, ("6", "8")),
-            (-2, 1, ("13", "15")),
+            (None, 0, 0, ("0", "2")),
+            (None, 1, 0, ("1", "3")),
+            (None, 1, 1, ("10", "12")),
+            (1, None, 0, ("6", "8")),
+            (0, None, 1, ("13", "15")),
         ),
     )
     def it_can_calculate_the_reference_values_to_help(
-        self, _bases_prop_, _proportions_prop_, col_index, block_index, expected
+        self,
+        _bases_prop_,
+        _proportions_prop_,
+        insertion_index,
+        col_index,
+        block_index,
+        expected,
     ):
         _bases_prop_.return_value = [
             [
@@ -851,7 +861,9 @@ def it_can_calculate_the_reference_values_to_help(
                 np.array([["p13", "p14"], ["p15", "p16"]]),
             ],
         ]
-        pairwise_tstat = _PairwiseSigTstats(None, None, None, col_index)
+        pairwise_tstat = _PairwiseSigTstats(
+            None, None, None, insertion_index, col_index
+        )
 
         actual = pairwise_tstat._reference_values(block_index)
 
@@ -865,7 +877,7 @@ def it_provides_the_subtotal_columns_to_help(
         _proportions_prop_.return_value = [[0.5, 0.6], [0.7, 0.8]]
         _reference_values_.return_value = [9, 0]
         _calculate_t_stats_.return_value = "tstat"
-        pairwise_tstat = _PairwiseSigTstats(None, None, None, None)
+        pairwise_tstat = _PairwiseSigTstats(None, None, None, None, None)
 
         base_values = pairwise_tstat._subtotal_columns
 
@@ -880,7 +892,7 @@ def it_provides_the_subtotal_rows_to_help(
         _proportions_prop_.return_value = [[0.5, 0.6], [0.7, 0.8]]
         _reference_values_.return_value = [9, 0]
         _calculate_t_stats_.return_value = "tstat"
-        pairwise_tstat = _PairwiseSigTstats(None, None, None, None)
+        pairwise_tstat = _PairwiseSigTstats(None, None, None, None, None)
 
         base_values = pairwise_tstat._subtotal_rows