Adding release 3.0.4

HISTORY.rst

@@ -2,6 +2,11 @@
 History
 =======
 
+3.0.4 (2023-10-12)
+------------------
+* Fixing so that other multiple correction methods than Bonferroni are applied correctly.
+
+
 3.0.3 (2023-10-12)
 ------------------
 * Relaxing version requirements for scipy and pandas to allow versions 2.x

README.md

@@ -2,7 +2,7 @@ Spotify Confidence
 ========
 
 ![Status](https://img.shields.io/badge/Status-Beta-blue.svg)
-![Latest release](https://img.shields.io/badge/release-3.0.3-green.svg "Latest release: 3.0.3")
+![Latest release](https://img.shields.io/badge/release-3.0.3-green.svg "Latest release: 3.0.4")
 ![Python](https://img.shields.io/badge/Python-3.7-blue.svg "Python")
 ![Python](https://img.shields.io/badge/Python-3.8-blue.svg "Python")
 ![Python](https://img.shields.io/badge/Python-3.9-blue.svg "Python")

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = spotify-confidence
-version = 3.0.3
+version = 3.0.4
 author = Per Sillren
 author_email = pers@spotify.com
 description = Package for calculating and visualising confidence intervals, e.g. for A/B test analysis.

spotify_confidence/analysis/frequentist/chartify_grapher.py

@@ -278,12 +278,14 @@ def _categorical_difference_chart(
                 df[~df[NIM].isna()]
                 .assign(
                     color_column=lambda df: df.apply(
-                        lambda row: "red"
-                        if row[LOWER] < row[NULL_HYPOTHESIS]
-                        and row[PREFERENCE] == "increase"
-                        or row[NULL_HYPOTHESIS] < row[UPPER]
-                        and row[PREFERENCE] == "decrease"
-                        else "green",
+                        lambda row: (
+                            "red"
+                            if row[LOWER] < row[NULL_HYPOTHESIS]
+                            and row[PREFERENCE] == "increase"
+                            or row[NULL_HYPOTHESIS] < row[UPPER]
+                            and row[PREFERENCE] == "decrease"
+                            else "green"
+                        ),
                         axis=1,
                     )
                 )

spotify_confidence/analysis/frequentist/confidence_computers/confidence_computer.py

@@ -588,17 +588,13 @@ def _add_variance_reduction_rate(df: DataFrame, **kwargs: Dict) -> DataFrame:
 
 
 def _add_p_value(df: DataFrame, **kwargs: Dict) -> DataFrame:
-    return (
-        df.pipe(set_alpha_and_adjust_preference, **kwargs)
-        .assign(**{P_VALUE: lambda df: df.pipe(_p_value, **kwargs)})
+    return df.pipe(set_alpha_and_adjust_preference, **kwargs).assign(
+        **{P_VALUE: lambda df: df.pipe(_p_value, **kwargs)}
     )
 
 
 def _add_ci_and_adjust_if_absolute(df: DataFrame, **kwargs: Dict) -> DataFrame:
-    return (
-        df.pipe(add_ci, **kwargs)
-        .pipe(_adjust_if_absolute, absolute=kwargs[ABSOLUTE])
-    )
+    return df.pipe(add_ci, **kwargs).pipe(_adjust_if_absolute, absolute=kwargs[ABSOLUTE])
 
 
 def _adjust_if_absolute(df: DataFrame, absolute: bool) -> DataFrame:

spotify_confidence/analysis/frequentist/confidence_computers/z_test_computer.py

@@ -141,9 +141,11 @@ def adjusted_alphas_for_group(grp: DataFrame) -> Series:
             .assign(
                 **{
                     ADJUSTED_ALPHA: lambda df: df.apply(
-                        lambda row: 2 * (1 - st.norm.cdf(row["zb"]))
-                        if (grp[PREFERENCE_TEST] == TWO_SIDED).all()
-                        else 1 - st.norm.cdf(row["zb"]),
+                        lambda row: (
+                            2 * (1 - st.norm.cdf(row["zb"]))
+                            if (grp[PREFERENCE_TEST] == TWO_SIDED).all()
+                            else 1 - st.norm.cdf(row["zb"])
+                        ),
                         axis=1,
                     )
                 }

spotify_confidence/analysis/frequentist/multiple_comparison.py

@@ -4,9 +4,7 @@
 from pandas import DataFrame
 from statsmodels.stats.multitest import multipletests
 
-from spotify_confidence.analysis.confidence_utils import (
-    groupbyApplyParallel
-)
+from spotify_confidence.analysis.confidence_utils import groupbyApplyParallel
 from spotify_confidence.analysis.constants import (
     BONFERRONI,
     BONFERRONI_ONLY_COUNT_TWOSIDED,
@@ -154,13 +152,10 @@ def add_adjusted_p_and_is_significant(df: DataFrame, **kwargs: Dict) -> DataFram
             for column in df.index.names
             if kwargs[ORDINAL_GROUP_COLUMN] is not None
             and column is not None
-            and (column != kwargs[ORDINAL_GROUP_COLUMN]
-                 or kwargs[FINAL_EXPECTED_SAMPLE_SIZE] is None)
+            and (column != kwargs[ORDINAL_GROUP_COLUMN] or kwargs[FINAL_EXPECTED_SAMPLE_SIZE] is None)
         ]
         df = groupbyApplyParallel(
-            df.groupby(
-                groups_except_ordinal + [kwargs[METHOD], "level_1", "level_2"], as_index=False, sort=False
-            ),
+            df.groupby(groups_except_ordinal + [kwargs[METHOD], "level_1", "level_2"], as_index=False, sort=False),
             lambda df: compute_sequential_adjusted_alpha(df, **kwargs),
         )
     elif kwargs[CORRECTION_METHOD] in [
@@ -273,9 +268,9 @@ def set_alpha_and_adjust_preference(df: DataFrame, **kwargs: Dict) -> DataFrame:
     return df.assign(
         **{
             ALPHA: df.apply(
-                lambda row: 2 * alpha_0
-                if kwargs[CORRECTION_METHOD] == SPOT_1 and row[PREFERENCE] != TWO_SIDED
-                else alpha_0,
+                lambda row: (
+                    2 * alpha_0 if kwargs[CORRECTION_METHOD] == SPOT_1 and row[PREFERENCE] != TWO_SIDED else alpha_0
+                ),
                 axis=1,
             )
         }

spotify_confidence/analysis/frequentist/nims_and_mdes.py

@@ -29,9 +29,11 @@ def add_nim_input_columns_from_tuple_or_dict(df, nims: NIM_TYPE, mde_column: str
     elif nims is None or not nims:
         return df.assign(**{NIM_COLUMN_DEFAULT: None}).assign(
             **{
-                PREFERRED_DIRECTION_COLUMN_DEFAULT: None
-                if PREFERRED_DIRECTION_COLUMN_DEFAULT not in df or mde_column is None
-                else df[PREFERRED_DIRECTION_COLUMN_DEFAULT]
+                PREFERRED_DIRECTION_COLUMN_DEFAULT: (
+                    None
+                    if PREFERRED_DIRECTION_COLUMN_DEFAULT not in df or mde_column is None
+                    else df[PREFERRED_DIRECTION_COLUMN_DEFAULT]
+                )
             }
         )
     else:

tests/frequentist/test_ztest.py

@@ -1541,7 +1541,7 @@ def test_multiple_difference(self, correction_method):
             BONFERRONI,
             BONFERRONI_ONLY_COUNT_TWOSIDED,
             BONFERRONI_DO_NOT_COUNT_NON_INFERIORITY,
-            SPOT_1
+            SPOT_1,
         ]:
             difference_df = self.test.multiple_difference(
                 level=("control", 1), groupby="country", level_as_reference=True
@@ -1584,7 +1584,7 @@ def test_multiple_difference(self, correction_method):
             _, adjusted_p, _, _ = multipletests(
                 pvals=difference_df["p-value"],
                 alpha=1 - self.test._confidence_computer._interval_size,
-                method=corr_method
+                method=corr_method,
             )
 
             assert np.allclose(
@@ -1600,7 +1600,7 @@ def test_multiple_difference_groupby(self, correction_method):
             BONFERRONI,
             BONFERRONI_ONLY_COUNT_TWOSIDED,
             BONFERRONI_DO_NOT_COUNT_NON_INFERIORITY,
-            SPOT_1
+            SPOT_1,
         ]:
             difference_df = self.test.multiple_difference(
                 level="control", groupby=["days_since_reg", "country"], level_as_reference=True
@@ -1640,7 +1640,7 @@ def test_multiple_difference_groupby(self, correction_method):
             _, adjusted_p, _, _ = multipletests(
                 pvals=difference_df["p-value"],
                 alpha=1 - self.test._confidence_computer._interval_size,
-                method=corr_method
+                method=corr_method,
             )
 
             assert np.allclose(