adding SMM

quapy/method/aggregative.py

@@ -173,7 +173,7 @@ def _training_helper(learner,
             if isinstance(val_split, float):
                 if not (0 < val_split < 1):
                     raise ValueError(f'train/val split {val_split} out of range, must be in (0,1)')
-                train, unused = data.split_stratified(train_prop=1 - val_split,random_state=0)
+                train, unused = data.split_stratified(train_prop=1 - val_split)
             elif isinstance(val_split, LabelledCollection):
                 train = data
                 unused = val_split
@@ -712,6 +712,45 @@ class DyS(AggregativeProbabilisticQuantifier, BinaryQuantifier):
         return np.asarray([1 - class1_prev, class1_prev])
 
 
+class SMM(AggregativeProbabilisticQuantifier, BinaryQuantifier):
+    """
+    `SMM method <https://ieeexplore.ieee.org/document/9260028>`_ (SMM).
+    SMM is a simplification of matching distribution methods where the representation of the examples
+    is created using the mean instead of a histogram.
+
+    :param learner: a sklearn's Estimator that generates a binary classifier.
+    :param val_split: a float in range (0,1) indicating the proportion of data to be used as a stratified held-out
+        validation distribution, or a :class:`quapy.data.base.LabelledCollection` (the split itself).
+    """
+
+    def __init__(self, learner: BaseEstimator, val_split=0.4):
+        self.learner = learner
+        self.val_split = val_split
+      
+    def fit(self, data: LabelledCollection, fit_learner=True, val_split: Union[float, LabelledCollection] = None):
+        if val_split is None:
+            val_split = self.val_split
+
+        self._check_binary(data, self.__class__.__name__)
+        self.learner, validation = _training_helper(
+            self.learner, data, fit_learner, ensure_probabilistic=True, val_split=val_split)
+        Px = self.classify(validation.instances)[:, 1]  # takes only the P(y=+1|x)
+        self.Pxy1 = Px[validation.labels == self.learner.classes_[1]]
+        self.Pxy0 = Px[validation.labels == self.learner.classes_[0]]
+        self.Pxy1_mean = np.mean(self.Pxy1)
+        self.Pxy0_mean = np.mean(self.Pxy0)
+        return self
+
+    def aggregate(self, classif_posteriors):
+        Px = classif_posteriors[:, 1]  # takes only the P(y=+1|x)
+        Px_mean = np.mean(Px)
+     
+        class1_prev = (Px_mean - self.Pxy0_mean)/(self.Pxy1_mean - self.Pxy0_mean)
+        class1_prev = np.clip(class1_prev, 0, 1)
+
+        return np.asarray([1 - class1_prev, class1_prev])
+
+
 class ELM(AggregativeQuantifier, BinaryQuantifier):
     """
     Class of Explicit Loss Minimization (ELM) quantifiers.