Updated dev requirements

spotify · Apr 1, 2022 · 42d0f8b · 42d0f8b
1 parent c6e464a
commit 42d0f8b
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Showing 4 changed files with 17 additions and 21 deletions.
diff --git a/requirements_dev.txt b/requirements_dev.txt
@@ -8,11 +8,11 @@ flake8==3.7.7
 tox==3.21.0
 Sphinx==1.4.8
 pytest-runner==2.11.1
-jupyter==1.0.0
+jupyterlab==3.2.9
 pylint==1.7.4
 coverage==4.5.1
 pytest==7.0.1
 pytest-cov==2.5.1
 ipywidgets>=7.1.0
-black==21.9b0
+black==22.3.0
 scipy>=1.4.1,<1.8.0
diff --git a/spotify_confidence/analysis/frequentist/confidence_computers/generic_computer.py b/spotify_confidence/analysis/frequentist/confidence_computers/generic_computer.py
@@ -926,18 +926,14 @@ def _compute_sequential_adjusted_alpha(df: DataFrame, method_column: str, arg_di
     def _add_ci(df: DataFrame, arg_dict: Dict) -> DataFrame:
         lower, upper = confidence_computers[df[arg_dict[METHOD]].values[0]].ci(df, ALPHA, arg_dict)
 
-        if (
-            arg_dict[CORRECTION_METHOD]
-            in [
-                HOLM,
-                HOMMEL,
-                SIMES_HOCHBERG,
-                SPOT_1_HOLM,
-                SPOT_1_HOMMEL,
-                SPOT_1_SIMES_HOCHBERG,
-            ]
-            and all(df[PREFERENCE_TEST] != TWO_SIDED)
-        ):
+        if arg_dict[CORRECTION_METHOD] in [
+            HOLM,
+            HOMMEL,
+            SIMES_HOCHBERG,
+            SPOT_1_HOLM,
+            SPOT_1_HOMMEL,
+            SPOT_1_SIMES_HOCHBERG,
+        ] and all(df[PREFERENCE_TEST] != TWO_SIDED):
             if all(df[arg_dict[METHOD]] == "z-test"):
                 adjusted_lower, adjusted_upper = confidence_computers["z-test"].ci_for_multiple_comparison_methods(
                     df, arg_dict[CORRECTION_METHOD], alpha=1 - arg_dict[INTERVAL_SIZE]

diff --git a/tests/frequentist/test_ttest.py b/tests/frequentist/test_ttest.py
@@ -91,7 +91,7 @@ def test_standard_error_diff_mean_from_sums(self):
         sigma2 = 2
         x2 = np.random.normal(mu2, sigma2, n2)
 
-        std_diff = np.sqrt(sigma1 ** 2 / n1 + sigma2 ** 2 / n2)
+        std_diff = np.sqrt(sigma1**2 / n1 + sigma2**2 / n2)
 
         arg_dict = {DENOMINATOR: "n"}
         diff_se = computer.std_err(

diff --git a/tests/frequentist/test_ztest_linreg.py b/tests/frequentist/test_ztest_linreg.py
@@ -15,8 +15,8 @@ def setup(self):
         data = pd.DataFrame({"variation_name": list(map(str, d)), "y": y, "x": x})
         data = (
             data.assign(xy=y * x)
-            .assign(x2=x ** 2)
-            .assign(y2=y ** 2)
+            .assign(x2=x**2)
+            .assign(y2=y**2)
             .groupby(["variation_name"])
             .agg({"y": ["sum", "count"], "y2": "sum", "x": "sum", "x2": "sum", "xy": "sum"})
             .reset_index()
@@ -83,8 +83,8 @@ def setup(self):
         data = pd.DataFrame({"variation_name": list(map(str, d)), "metric_name": m, "y": y, "x": x})
         data = (
             data.assign(xy=y * x)
-            .assign(x2=x ** 2)
-            .assign(y2=y ** 2)
+            .assign(x2=x**2)
+            .assign(y2=y**2)
             .groupby(["variation_name", "metric_name"])
             .agg({"y": ["sum", "count"], "y2": "sum", "x": "sum", "x2": "sum", "xy": "sum"})
             .reset_index()
@@ -226,7 +226,7 @@ def setup(self):
         y = 0.5 * d + 0.5 * x1 + 0.5 * x2 + np.random.standard_normal(size=n)
         df = pd.DataFrame({"variation_name": list(map(str, d)), "y": y, "x1": x1, "x2": x2})
 
-        data = df.assign(y2=y ** 2).groupby(["variation_name"]).agg({"y": ["sum", "count"], "y2": "sum"}).reset_index()
+        data = df.assign(y2=y**2).groupby(["variation_name"]).agg({"y": ["sum", "count"], "y2": "sum"}).reset_index()
 
         data.columns = data.columns.map("_".join).str.strip("_")
 
@@ -332,7 +332,7 @@ def setup(self):
         )
 
         data = (
-            df.assign(y2=y ** 2)
+            df.assign(y2=y**2)
             .groupby(["variation_name", "metric_name"])
             .agg({"y": ["sum", "count"], "y2": "sum"})
             .reset_index()