fix KDE code with scores generation

Author: luizfernandolj

docs/source/modules/using_aggregative.rst

@@ -41,14 +41,15 @@ Examples
    # Create a classifier
    clf = LogisticRegression()
 
-   ac = AC()
+   ac = AC(clf)
 
    # used when you just have the sample to predict
    ac.fit(X_train, y_train)
    prevalence = ac.predict(X_test)
 
    # used when you already have the sample predictions (usually from cross-validation for training predictions)
-   prevalence = ac.aggregate(posteriors, train_posteriors, y_train)
+   ac2 = AC()
+   prevalence = ac2.aggregate(posteriors, train_posteriors, y_train)
 
 
 

mlquantify/neighbors/_base.py

@@ -86,7 +86,7 @@ def __init__(self, learner=None, bandwidth: float = 0.1, kernel: str = "gaussian
         self.best_distance = None
 
     @_fit_context(prefer_skip_nested_validation=True)
-    def fit(self, X, y, learner_fitted=False):
+    def fit(self, X, y, learner_fitted=False, cv=5, stratified=True, shuffle=False):
         X, y = validate_data(self, X, y, ensure_2d=True, ensure_min_samples=2)
         validate_y(self, y)
 
@@ -100,8 +100,8 @@ def fit(self, X, y, learner_fitted=False):
         else:
             train_predictions, y_train = apply_cross_validation(
                 self.learner, X, y,
-                function=learner_function, cv=5,
-                stratified=True, shuffle=True
+                function=learner_function, cv=cv,
+                stratified=stratified, shuffle=shuffle
             )
 
         self.train_predictions = train_predictions