Fix permutation indexing 1092

mlxtend/evaluate/counterfactual.py

@@ -5,6 +5,7 @@
 #
 # License: BSD 3 clause
 
+
 import warnings
 
 import numpy as np
@@ -19,6 +20,7 @@ def create_counterfactual(
     y_desired_proba=None,
     lammbda=0.1,
     random_seed=None,
+    feature_names_to_vary=None,
 ):
     """
     Implementation of the counterfactual method by Wachter et al. 2017
@@ -79,19 +81,21 @@ class probability for `y_desired`.
             )
     else:
         use_proba = False
-
     if y_desired_proba is None:
         # class label
+
         y_to_be_annealed_to = y_desired
     else:
         # class proba corresponding to class label y_desired
-        y_to_be_annealed_to = y_desired_proba
 
+        y_to_be_annealed_to = y_desired_proba
     # start with random counterfactual
+
     rng = np.random.RandomState(random_seed)
     x_counterfact = X_dataset[rng.randint(X_dataset.shape[0])]
 
     # compute median absolute deviation
+
     mad = np.abs(np.median(X_dataset, axis=0) - x_reference)
 
     def dist(x_reference, x_counterfact):
@@ -105,7 +109,6 @@ def loss(x_counterfact, lammbda):
             ]
         else:
             y_predict = model.predict(x_counterfact.reshape(1, -1))
-
         diff = lammbda * (y_predict - y_to_be_annealed_to) ** 2
 
         return diff + dist(x_reference, x_counterfact)
@@ -114,7 +117,6 @@ def loss(x_counterfact, lammbda):
 
     if not res["success"]:
         warnings.warn(res["message"])
-
     x_counterfact = res["x"]
 
     return x_counterfact

mlxtend/evaluate/feature_importance.py

@@ -121,9 +121,8 @@ def score_func(y_true, y_pred):
                 save_col = X[:, feat].copy()
 
                 if save_col.ndim > 1:
-                    columns = save_col.shape[1]
-                    for i in range(columns):
-                        rng.shuffle(X[:, i])
+                    shuffled_indices = rng.permutation(X.shape[0])
+                    X[:, feat] = X[shuffled_indices][:, feat]
                 else:
                     rng.shuffle(X[:, feat])
 

mlxtend/frequent_patterns/association_rules.py

@@ -34,7 +34,7 @@
 
 def association_rules(
     df: pd.DataFrame,
-    num_itemsets: Optional[int] = 1,
+    num_itemsets: Optional[int] = None,
     df_orig: Optional[pd.DataFrame] = None,
     null_values=False,
     metric="confidence",
@@ -120,15 +120,20 @@ def association_rules(
         raise TypeError("If null values exist, df_orig must be provided.")
 
     # if null values exist, num_itemsets must be provided
-    if null_values and num_itemsets == 1:
+    if null_values and df_orig is None and num_itemsets is None:
         raise TypeError("If null values exist, num_itemsets must be provided.")
 
+    if num_itemsets is None:
+        if df_orig is not None:
+            num_itemsets = len(df_orig)
+        else:
+            num_itemsets = 1
     # check for valid input
     fpc.valid_input_check(df_orig, null_values)
 
     if not df.shape[0]:
         raise ValueError(
-            "The input DataFrame `df` containing " "the frequent itemsets is empty."
+            "The input DataFrame `df` containing the frequent itemsets is empty."
         )
 
     # check for mandatory columns
@@ -188,8 +193,22 @@ def certainty_metric_helper(sAC, sA, sC, disAC, disA, disC, dis_int, dis_int_):
 
     # metrics for association rules
     metric_dict = {
-        "antecedent support": lambda _, sA, ___, ____, _____, ______, _______, ________: sA,
-        "consequent support": lambda _, __, sC, ____, _____, ______, _______, ________: sC,
+        "antecedent support": lambda _,
+        sA,
+        ___,
+        ____,
+        _____,
+        ______,
+        _______,
+        ________: sA,
+        "consequent support": lambda _,
+        __,
+        sC,
+        ____,
+        _____,
+        ______,
+        _______,
+        ________: sC,
         "support": lambda sAC, _, __, ___, ____, _____, ______, _______: sAC,
         "confidence": lambda sAC, sA, _, disAC, disA, __, dis_int, ___: (
             sAC * (num_itemsets - disAC)
@@ -207,19 +226,47 @@ def certainty_metric_helper(sAC, sA, sC, disAC, disA, disC, dis_int, dis_int_):
             "support"
         ](sAC, sA, sC, disAC, disA, disC, dis_int, dis_int_)
         - sA * sC,
-        "conviction": lambda sAC, sA, sC, disAC, disA, disC, dis_int, dis_int_: conviction_helper(
+        "conviction": lambda sAC,
+        sA,
+        sC,
+        disAC,
+        disA,
+        disC,
+        dis_int,
+        dis_int_: conviction_helper(
             metric_dict["confidence"](
                 sAC, sA, sC, disAC, disA, disC, dis_int, dis_int_
             ),
             sC,
         ),
-        "zhangs_metric": lambda sAC, sA, sC, disAC, disA, disC, dis_int, dis_int_: zhangs_metric_helper(
+        "zhangs_metric": lambda sAC,
+        sA,
+        sC,
+        disAC,
+        disA,
+        disC,
+        dis_int,
+        dis_int_: zhangs_metric_helper(
             sAC, sA, sC, disAC, disA, disC, dis_int, dis_int_
         ),
-        "jaccard": lambda sAC, sA, sC, _, __, ____, _____, ______: jaccard_metric_helper(
+        "jaccard": lambda sAC,
+        sA,
+        sC,
+        _,
+        __,
+        ____,
+        _____,
+        ______: jaccard_metric_helper(
             sAC, sA, sC, disAC, disA, disC, dis_int, dis_int_
         ),
-        "certainty": lambda sAC, sA, sC, _, __, ____, _____, ______: certainty_metric_helper(
+        "certainty": lambda sAC,
+        sA,
+        sC,
+        _,
+        __,
+        ____,
+        _____,
+        ______: certainty_metric_helper(
             sAC, sA, sC, disAC, disA, disC, dis_int, dis_int_
         ),
         "kulczynski": lambda sAC, sA, sC, _, __, ____, _____, ______: kulczynski_helper(