[#177824342]: invert tstats mean sign calculation to respect the corr…

…ect color shading in exporter
Crunch-io · Apr 20, 2021 · ea3a624 · ea3a624
1 parent 714fd31
commit ea3a624
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Showing 5 changed files with 33 additions and 34 deletions.
diff --git a/src/cr/cube/matrix/assembler.py b/src/cr/cube/matrix/assembler.py
@@ -196,7 +196,7 @@ def pairwise_indices(p_vals, t_stats):
             """1D ndarray of tuples of int pairwise indices of each column."""
             significance = p_vals < alpha
             if only_larger:
-                significance = np.logical_and(t_stats > 0, significance)
+                significance = np.logical_and(t_stats < 0, significance)
             col_signif = np.empty((len(significance),), dtype=object)
             col_signif[:] = [tuple(np.where(sig_row)[0]) for sig_row in significance]
             return col_signif

diff --git a/src/cr/cube/matrix/measure.py b/src/cr/cube/matrix/measure.py
@@ -639,7 +639,7 @@ def _t_stats(self):
                 t_stats.append(np.full(means.shape, np.nan))
             else:
                 combined_variance = variance[:, col - offset] + variance.T
-                diff = means[:, col - offset] - means.T
+                diff = means.T - means[:, col - offset]
                 n = col_bases[:, col - offset] + col_bases.T
                 t_stats.append(diff.T * np.sqrt(n.T / combined_variance.T))
         return t_stats

diff --git a/tests/expectations/num-arr-means-grouped-by-cat-hs-t-stats-col-7.py b/tests/expectations/num-arr-means-grouped-by-cat-hs-t-stats-col-7.py
@@ -1,46 +1,46 @@
 [
     [
         float("NaN"),
-        -1.70521377,
-        -2.07569522,
+        1.70521377,
+        2.07569522,
         float("NaN"),
-        -1.70521377,
+        1.70521377,
         float("NaN"),
-        -1.60530814,
-        0.0,
+        1.60530814,
+        -0.0,
         float("NaN"),
     ],
     [
         float("NaN"),
-        1.29347475,
-        0.0,
+        -1.29347475,
+        -0.0,
         float("NaN"),
-        1.4061553,
+        -1.4061553,
         float("NaN"),
-        1.29347475,
+        -1.29347475,
         float("NaN"),
         float("NaN"),
     ],
     [
         float("NaN"),
-        0.58756965,
-        -4.18330013,
+        -0.58756965,
+        4.18330013,
         float("NaN"),
-        0.27920779,
+        -0.27920779,
         float("NaN"),
-        -4.11298756,
-        0.0,
+        4.11298756,
+        -0.0,
         float("NaN"),
     ],
     [
         float("NaN"),
-        -1.32797346,
-        -0.6882472,
+        1.32797346,
+        0.6882472,
         float("NaN"),
-        -1.32797346,
+        1.32797346,
         float("NaN"),
-        -0.68972588,
-        0.0,
+        0.68972588,
+        -0.0,
         float("NaN"),
     ],
 ]
diff --git a/tests/expectations/num-arr-means-grouped-by-cat-t-stats-col-4.py b/tests/expectations/num-arr-means-grouped-by-cat-t-stats-col-4.py
@@ -1,6 +1,6 @@
 [
-    [-1.70521377, -2.07569522, -1.70521377, -1.60530814, 0.0],
-    [1.29347475, 0.0, 1.4061553, 1.29347475, float("NaN")],
-    [0.58756965, -4.18330013, 0.27920779, -4.11298756, 0.0],
-    [-1.32797346, -0.6882472, -1.32797346, -0.68972588, 0.0],
+    [1.70521377, 2.07569522, 1.70521377, 1.60530814, -0.0],
+    [-1.29347475, -0.0, -1.4061553, -1.29347475, float("NaN")],
+    [-0.58756965, 4.18330013, -0.27920779, 4.11298756, -0.0],
+    [1.32797346, 0.6882472, 1.32797346, 0.68972588, -0.0],
 ]
diff --git a/tests/integration/test_pairwise_significance.py b/tests/integration/test_pairwise_significance.py
@@ -629,13 +629,12 @@ def test_pairwise_significance_all_empty(self):
 class TestMeanDifferenceSignificance(object):
     def test_mean_diff_significance_for_numeric_array_grouped_by_cat(self):
         slice_ = Cube(NA.NUM_ARR_MULTI_NUMERIC_MEASURES_GROUPED_BY_CAT).partitions[0]
-
         assert slice_.pairwise_significance_means_t_stats(0) == pytest.approx(
             np.array(
                 [
-                    [0.0, 0.7482866, 4.61524529, 5.29463547],
-                    [0.0, 4.64371041, 7.14738681, 3.67511747],
-                    [0.0, -3.13387218, -6.35310465, -2.3932706],
+                    [-0.0, -0.7482866, -4.61524529, -5.29463547],
+                    [-0.0, -4.64371041, -7.14738681, -3.67511747],
+                    [-0.0, 3.13387218, 6.35310465, 2.3932706],
                 ]
             )
         )
@@ -732,10 +731,10 @@ def test_mean_diff_significance_for_numeric_array_x_mr(self):
         assert slice_.pairwise_significance_means_t_stats(0) == pytest.approx(
             np.array(
                 [
-                    [0.0, -1.3056921, 1.1842639],
-                    [0.0, -1.7117695, 1.6297401],
+                    [-0.0, 1.3056921, -1.1842639],
+                    [-0.0, 1.7117695, -1.6297401],
                     [np.nan, np.nan, np.nan],
-                    [np.nan, 5.8872721, 4.4442801],
+                    [np.nan, -5.8872721, -4.4442801],
                 ]
             ),
             nan_ok=True,
@@ -808,8 +807,8 @@ def test_mean_diff_significance_for_cat_x_cat(self):
             np.array(
                 [
                     [np.nan, np.nan, np.nan, np.nan],
-                    [-14.1421356, 0.0, -3.1066887, np.nan],
-                    [-1.75662013, 0.0, np.nan, np.nan],
+                    [14.1421356, -0.0, 3.1066887, np.nan],
+                    [1.75662013, -0.0, np.nan, np.nan],
                 ]
             ),
             nan_ok=True,