fixed cross_entropy carte_multitable

src/carte_estimator.py

@@ -1265,9 +1265,8 @@ def _get_predict_prob(self, X):
             idx_ = np.where(np.array(self.source_list_) == source_name)[0]
             model_list = [self.model_list_[idx] for idx in idx_]
             out += [self._generate_output(X, model_list=model_list, weights=None)]
-        out = np.array(out).squeeze().transpose()
-
-        out = np.average(out, weights=self.weights_, axis=1)
+        out = np.array(out).squeeze()
+        out = np.average(out, weights=self.weights_, axis=0)
 
         # Transform according to loss
         if self.loss == "binary_crossentropy":