Resolve "integration-sota"

Closes #15 (closed)

code/bolsonaro/models/similarity_forest_regressor.py

 import time
 
-from sklearn.ensemble import RandomForestRegressor
+from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
 from sklearn.metrics import mean_squared_error
 from sklearn.base import BaseEstimator
 from abc import abstractmethod, ABCMeta
@@ -19,10 +19,15 @@ class SimilarityForest(BaseEstimator, metaclass=ABCMeta):
 
     def __init__(self, models_parameters):
         self._models_parameters = models_parameters
-        self._estimator = RandomForestRegressor(**self._models_parameters.hyperparameters,
-                                                random_state=self._models_parameters.seed, n_jobs=-1)
         self._extracted_forest_size = self._models_parameters.extracted_forest_size
         self._selected_trees = list()
+        self._base_estimator = self.init_estimator(models_parameters)
+
+    @staticmethod
+    @abstractmethod
+    def init_estimator(model_parameters):
+        pass
+
 
     @property
     def models_parameters(self):
@@ -32,32 +37,33 @@ class SimilarityForest(BaseEstimator, metaclass=ABCMeta):
     def selected_trees(self):
         return self._selected_trees
 
+    def _base_estimator_predictions(self, X):
+        base_predictions = np.array([tree.predict(X) for tree in self._base_estimator.estimators_]).T
+        return base_predictions
+
+    def _selected_tree_predictions(self, X):
+        base_predictions = np.array([tree.predict(X) for tree in self.selected_trees]).T
+        return base_predictions
+
     def predict(self, X):
-        predictions = np.empty((len(self._selected_trees), X.shape[0]))
-        for idx_tree, tree in enumerate(self._selected_trees):
-            predictions[idx_tree, :] = tree.predict(X)
+        predictions = self._selected_tree_predictions(X).T
         final_predictions = self._aggregate(predictions)
         return final_predictions
 
     def predict_base_estimator(self, X):
-        return self._estimator.predict(X)
+        return self._base_estimator.predict(X)
 
     def fit(self, X_train, y_train, X_val, y_val):
-        self._estimator.fit(X_train, y_train)
+        self._base_estimator.fit(X_train, y_train)
 
         param = self._models_parameters.extraction_strategy
 
         # get score of base forest on val
-        tree_list = list(self._estimator.estimators_)        # get score of base forest on val
+        tree_list = list(self._base_estimator.estimators_)        # get score of base forest on val
         trees_to_remove = list()
 
         # get score of each single tree of forest on val
-        val_predictions = np.empty((len(tree_list), X_val.shape[0]))
-        with tqdm(tree_list) as tree_pred_bar:
-            tree_pred_bar.set_description('[Initial tree predictions]')
-            for idx_tree, tree in enumerate(tree_pred_bar):
-                val_predictions[idx_tree, :] = tree.predict(X_val)
-            tree_pred_bar.update(1)
+        val_predictions = self._base_estimator_predictions(X_val).T
 
         # boolean mask of trees to take into account for next evaluation of trees importance
         mask_trees_to_consider = np.ones(val_predictions.shape[0], dtype=bool)
@@ -132,6 +138,12 @@ class SimilarityForest(BaseEstimator, metaclass=ABCMeta):
 
 
 class SimilarityForestRegressor(SimilarityForest, metaclass=ABCMeta):
+
+    @staticmethod
+    def init_estimator(model_parameters):
+        return RandomForestRegressor(**model_parameters.hyperparameters,
+                              random_state=model_parameters.seed, n_jobs=2)
+
     def _aggregate(self, predictions):
         return aggregation_regression(predictions)
 
@@ -143,6 +155,12 @@ class SimilarityForestRegressor(SimilarityForest, metaclass=ABCMeta):
 
 
 class SimilarityForestClassifier(SimilarityForest, metaclass=ABCMeta):
+
+    @staticmethod
+    def init_estimator(model_parameters):
+        return RandomForestClassifier(**model_parameters.hyperparameters,
+                                    random_state=model_parameters.seed, n_jobs=2)
+
     def _aggregate(self, predictions):
         return aggregation_classification(predictions)
 
@@ -152,3 +170,12 @@ class SimilarityForestClassifier(SimilarityForest, metaclass=ABCMeta):
     def _activation(self, predictions):
         return np.sign(predictions)
 
+    def _selected_tree_predictions(self, X):
+        predictions_0_1 = super()._selected_tree_predictions(X)
+        predictions = (predictions_0_1 - 0.5) * 2
+        return predictions
+
+    def _base_estimator_predictions(self, X):
+        predictions_0_1 = super()._base_estimator_predictions(X)
+        predictions = (predictions_0_1 - 0.5) * 2
+        return predictions