Resolve "Adding new datasets"

Closes #17 (closed)

code/bolsonaro/models/omp_forest_classifier.py

@@ -24,6 +24,34 @@ class OmpForestBinaryClassifier(SingleOmpForest):
 
         return super().fit(X_forest, y_forest, X_omp, y_omp)
 
+    def predict_no_weights(self, X):
+        """
+        Apply the SingleOmpForest to X without using the weights.
+
+        Make all the base tree predictions
+
+        :param X: a Forest
+        :return: a np.array of the predictions of the entire forest
+        """
+
+        forest_predictions = np.array([tree.predict_proba(X) for tree in self._base_forest_estimator.estimators_])
+
+        if self._models_parameters.normalize_D:
+            forest_predictions /= self._forest_norms
+
+        weights = self._omp.coef_
+        omp_trees_indices = np.nonzero(weights)
+
+        omp_trees_predictions = forest_predictions[omp_trees_indices].T[1]
+
+        # Here forest_pred is the probability of being class 1.
+
+        result_omp = np.mean(omp_trees_predictions, axis=1)
+
+        result_omp = (result_omp - 0.5) * 2
+
+        return result_omp
+
     def score(self, X, y, metric=DEFAULT_SCORE_METRIC):
         """
         Evaluate OMPForestClassifer on (`X`, `y`) using `metric`
@@ -129,7 +157,7 @@ class OmpForestMulticlassClassifier(OmpForest):
             omp_trees_indices = np.nonzero(weights)
             label_names.append(class_label)
             atoms_binary = (forest_predictions[num_class].T - 0.5) * 2 # centré réduit de 0/1 à -1/1
-            preds.append(np.sum(atoms_binary[omp_trees_indices], axis=0))
+            preds.append(np.sum(atoms_binary[omp_trees_indices], axis=0)/len(omp_trees_indices))
             num_class += 1
 
         preds = np.array(preds).T