simplified predict and predict_proba using super(). Added duplicate class label check.

freqtrade/freqai/base_models/FreqaiMultiOutputClassifier.py

@@ -4,7 +4,9 @@ from sklearn.base import is_classifier
 from sklearn.multioutput import MultiOutputClassifier, _fit_estimator
 from sklearn.utils.fixes import delayed
 from sklearn.utils.multiclass import check_classification_targets
-from sklearn.utils.validation import check_is_fitted, has_fit_parameter
+from sklearn.utils.validation import has_fit_parameter
+
+from freqtrade.exceptions import OperationalException
 
 
 class FreqaiMultiOutputClassifier(MultiOutputClassifier):
@@ -65,6 +67,9 @@ class FreqaiMultiOutputClassifier(MultiOutputClassifier):
         self.classes_ = []
         for estimator in self.estimators_:
             self.classes_.extend(estimator.classes_)
+        if len(set(self.classes_)) != len(self.classes_):
+            raise OperationalException(f"Class labels must be unique across targets: "
+                                       f"{self.classes_}")
 
         if hasattr(self.estimators_[0], "n_features_in_"):
             self.n_features_in_ = self.estimators_[0].n_features_in_
@@ -74,56 +79,15 @@ class FreqaiMultiOutputClassifier(MultiOutputClassifier):
         return self
 
     def predict_proba(self, X):
-        """Return prediction probabilities for each class of each output.
+        """
-
+        Get predict_proba and stack arrays horizontally
-        This method will raise a ``ValueError`` if any of the
+        """
-        estimators do not have ``predict_proba``.
+        results = np.hstack(super().predict_proba(X))
-
+        return np.squeeze(results)
-        Parameters
-        ----------
-        X : array-like of shape (n_samples, n_features)
-            The input data.
-
-        Returns
-        -------
-        p : array of shape (n_samples, n_classes), or a list of n_outputs \
-                such arrays if n_outputs > 1.
-            The class probabilities of the input samples. The order of the
-            classes corresponds to that in the attribute :term:`classes_`.
-
-            .. versionchanged:: 0.19
-                This function now returns a list of arrays where the length of
-                the list is ``n_outputs``, and each array is (``n_samples``,
-                ``n_classes``) for that particular output.
-                """
-        check_is_fitted(self)
-        results = np.squeeze(np.hstack(
-            [estimator.predict_proba(X) for estimator in self.estimators_]
-        ))
-        return results
 
     def predict(self, X):
-        """Predict multi-output variable using model for each target variable.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            The input data.
-
-        Returns
-        -------
-        y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
-            Multi-output targets predicted across multiple predictors.
-            Note: Separate models are generated for each predictor.
         """
-        check_is_fitted(self)
+        Get predict and squeeze into 2D array
-        if not hasattr(self.estimators_[0], "predict"):
+        """
-            raise ValueError("The base estimator should implement a predict method")
+        results = super().predict(X)
-
+        return np.squeeze(results)
-        y = Parallel(n_jobs=self.n_jobs)(
-            delayed(e.predict)(X) for e in self.estimators_
-        )
-
-        results = np.squeeze(np.asarray(y).T)
-
-        return results