fix formatting per black

Author: wali-reheman

mlxtend/frequent_patterns/association_rules.py

@@ -143,12 +143,18 @@ def kulczynski_helper(sAC, sA, sC, disAC, disA, disC, dis_int, dis_int_):
         kulczynski = (conf_AC + conf_CA) / 2
         return kulczynski
 
-    def mutual_information_metric_helper(sAC, sA, sC, disAC, disA, disC, dis_int, dis_int_):
+    def mutual_information_metric_helper(
+        sAC, sA, sC, disAC, disA, disC, dis_int, dis_int_
+    ):
         with np.errstate(divide="ignore", invalid="ignore"):
             numerator = sAC
             denominator = sA * sC
             # MI = log2(s_AC / (s_A * s_C)); returns -inf if any support is 0
-            mi = np.where((numerator > 0) & (denominator > 0), np.log2(numerator / denominator), -np.inf)
+            mi = np.where(
+                (numerator > 0) & (denominator > 0),
+                np.log2(numerator / denominator),
+                -np.inf,
+            )
         return mi
 
     def conviction_helper(conf, sC):

mlxtend/frequent_patterns/tests/test_association_rules.py

@@ -544,17 +544,20 @@ def test_with_empty_dataframe():
 
 def test_mutual_information_metric():
     """Test mutual_information metric returns correct values."""
-    res_df = association_rules(df_freq_items, len(df), metric="mutual_information", min_threshold=-100)
+    res_df = association_rules(
+        df_freq_items, len(df), metric="mutual_information", min_threshold=-100
+    )
     assert "mutual_information" in res_df.columns
 
     # Find the rule (Eggs -> Kidney Beans): sAC=0.6, sA=0.8, sC=1.0
     # MI = log2(0.6 / (0.8 * 1.0)) = log2(0.75) approx -0.415
     rule = res_df[
-        res_df["antecedents"].apply(lambda x: x == frozenset({3})) &
-        res_df["consequents"].apply(lambda x: x == frozenset({5}))
+        res_df["antecedents"].apply(lambda x: x == frozenset({3}))
+        & res_df["consequents"].apply(lambda x: x == frozenset({5}))
     ]
     assert len(rule) == 1
     import math
+
     expected_mi = math.log2(0.6 / (0.8 * 1.0))
     assert abs(rule["mutual_information"].values[0] - expected_mi) < 1e-6