Hardcoded metric functions for comparison

code/bolsonaro/models/model_raw_results.py

@@ -8,8 +8,8 @@ class ModelRawResults(object):
 
     def __init__(self, model_object, training_time,
         datetime, train_score, dev_score, test_score,
-        score_metric, train_score_regressor, dev_score_regressor,
-        test_score_regressor):
+        score_metric, train_score_base, dev_score_base,
+        test_score_base):
 
         self._model_object = model_object
         self._training_time = training_time
@@ -18,9 +18,9 @@ class ModelRawResults(object):
         self._dev_score = dev_score
         self._test_score = test_score
         self._score_metric = score_metric
-        self._train_score_regressor = train_score_regressor
-        self._dev_score_regressor = dev_score_regressor
-        self._test_score_regressor = test_score_regressor
+        self._train_score_base = train_score_base
+        self._dev_score_base = dev_score_base
+        self._test_score_base = test_score_base
     
     @property
     def model_object(self):
@@ -51,16 +51,16 @@ class ModelRawResults(object):
         return self._score_metric
 
     @property
-    def train_score_regressor(self):
-        return self._train_score_regressor
+    def train_score_base(self):
+        return self._train_score_base
 
     @property
-    def dev_score_regressor(self):
-        return self._dev_score_regressor
+    def dev_score_base(self):
+        return self._dev_score_base
 
     @property
-    def test_score_regressor(self):
-        return self._test_score_regressor
+    def test_score_base(self):
+        return self._test_score_base
 
     def save(self, models_dir):
         save_obj_to_pickle(models_dir + os.sep + 'model_raw_results.pickle',

code/bolsonaro/models/omp_forest.py

@@ -17,6 +17,9 @@ class OmpForest(BaseEstimator, metaclass=ABCMeta):
     def models_parameters(self):
         return self._models_parameters
 
+    def predict_base_estimator(self, X):
+        return self._base_forest_estimator.predict(X)
+
     def score_base_estimator(self, X, y):
         return self._base_forest_estimator.score(X, y)
 

code/bolsonaro/trainer.py

 from bolsonaro.models.model_raw_results import ModelRawResults
+from bolsonaro.models.omp_forest_regressor import OmpForestRegressor
+from bolsonaro.models.omp_forest_classifier import OmpForestBinaryClassifier, OmpForestMulticlassClassifier
 from bolsonaro.error_handling.logger_factory import LoggerFactory
 from . import LOG_PATH
 
 from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
+from sklearn.metrics import mean_squared_error, accuracy_score
 import time
 import datetime
 import numpy as np
@@ -68,32 +71,60 @@ class Trainer(object):
             )
         self._end_time = time.time()
 
+    def __score_func(self, model, X, y_true):
+        if type(model) == OmpForestRegressor:
+            y_pred = model.predict(X)
+            result = mean_squared_error(y_true, y_pred)
+
+        elif type(model) in [OmpForestBinaryClassifier, OmpForestMulticlassClassifier]:
+            y_pred = model.predict(X)
+            result = accuracy_score(y_true, y_pred)
+
+        else:
+            y_pred = model.predict(X)
+            result = model.score(y_true, y_pred)
+
+        return result
+
+    def __score_func_base(self, model, X, y_true):
+        if type(model) == OmpForestRegressor:
+            y_pred = model.predict_base_estimator(X)
+            result = mean_squared_error(y_true, y_pred)
+
+        elif type(model) in [OmpForestBinaryClassifier, OmpForestMulticlassClassifier]:
+            y_pred = model.predict_base_estimator(X)
+            result = accuracy_score(y_true, y_pred)
+
+        else:
+            y_pred = model.predict_base_estimator(X)
+            result = model.score(y_true, y_pred)
+
+        return result
+
     def compute_results(self, model, models_dir):
         """
         :param model: Object with
         :param models_dir: Where the results will be saved
         """
-        score_func = model.score if type(model) in [RandomForestRegressor, RandomForestClassifier] \
-            else model.score_base_estimator
         results = ModelRawResults(
             model_object=model,
             training_time=self._end_time - self._begin_time,
             datetime=datetime.datetime.now(),
-            train_score=model.score(self._dataset.X_train, self._dataset.y_train),
-            dev_score=model.score(self._dataset.X_dev, self._dataset.y_dev),
-            test_score=model.score(self._dataset.X_test, self._dataset.y_test),
+            train_score=self.__score_func(model, self._dataset.X_train, self._dataset.y_train),
+            dev_score=self.__score_func(model, self._dataset.X_dev, self._dataset.y_dev),
+            test_score=self.__score_func(model, self._dataset.X_test, self._dataset.y_test),
+            train_score_base=self.__score_func_base(model, self._dataset.X_train, self._dataset.y_train),
+            dev_score_base=self.__score_func_base(model, self._dataset.X_dev, self._dataset.y_dev),
+            test_score_base=self.__score_func_base(model, self._dataset.X_test, self._dataset.y_test),
             score_metric='mse' if type(model) in [RandomForestRegressor, RandomForestClassifier] \
                 else model.DEFAULT_SCORE_METRIC,  # TODO: resolve the used metric in a proper way
-            train_score_regressor=score_func(self._dataset.X_train, self._dataset.y_train),
-            dev_score_regressor=score_func(self._dataset.X_dev, self._dataset.y_dev),
-            test_score_regressor=score_func(self._dataset.X_test, self._dataset.y_test)
         )
         results.save(models_dir)
-        self._logger.info("Base performance on test: {}".format(results.test_score_regressor))
+        self._logger.info("Base performance on test: {}".format(results.test_score_base))
         self._logger.info("Performance on test: {}".format(results.test_score))
 
-        self._logger.info("Base performance on train: {}".format(results.train_score_regressor))
+        self._logger.info("Base performance on train: {}".format(results.train_score_base))
         self._logger.info("Performance on train: {}".format(results.train_score))
 
-        self._logger.info("Base performance on dev: {}".format(results.dev_score_regressor))
+        self._logger.info("Base performance on dev: {}".format(results.dev_score_base))
         self._logger.info("Performance on dev: {}".format(results.dev_score))