diff --git a/code/bolsonaro/trainer.py b/code/bolsonaro/trainer.py
index fc289afd1f8301197f5b1dd8be3bb134deca4a91..7070126e2a9a8f449757bdab9381b4bffab99b2d 100644
--- a/code/bolsonaro/trainer.py
+++ b/code/bolsonaro/trainer.py
@@ -1,192 +1,190 @@
-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.models.similarity_forest_regressor import SimilarityForestRegressor
-from bolsonaro.error_handling.logger_factory import LoggerFactory
-from bolsonaro.data.task import Task
-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
-
-
-class Trainer(object):
- """
- Class capable of fitting any model object to some prepared data then evaluate and save results through the `train` method.
- """
-
- def __init__(self, dataset, regression_score_metric=mean_squared_error, classification_score_metric=accuracy_score,
- base_regression_score_metric=mean_squared_error, base_classification_score_metric=accuracy_score):
- """
-
- :param dataset: Object with X_train, y_train, X_dev, y_dev, X_test and Y_test attributes
- """
- self._dataset = dataset
- self._logger = LoggerFactory.create(LOG_PATH, __name__)
- self._regression_score_metric = regression_score_metric
- self._classification_score_metric = classification_score_metric
- self._base_regression_score_metric = base_regression_score_metric
- self._base_classification_score_metric = base_classification_score_metric
- self._score_metric_name = regression_score_metric.__name__ if dataset.task == Task.REGRESSION \
- else classification_score_metric.__name__
- self._base_score_metric_name = base_regression_score_metric.__name__ if dataset.task == Task.REGRESSION \
- else base_classification_score_metric.__name__
-
- @property
- def score_metric_name(self):
- return self._score_metric_name
-
- @property
- def base_score_metric_name(self):
- return self._base_score_metric_name
-
- def init(self, model, subsets_used='train,dev'):
- if type(model) in [RandomForestRegressor, RandomForestClassifier]:
- if subsets_used == 'train,dev':
- self._X_forest = self._dataset.X_train
- self._y_forest = self._dataset.y_train
- else:
- self._X_forest = np.concatenate([self._dataset.X_train, self._dataset.X_dev])
- self._y_forest = np.concatenate([self._dataset.y_train, self._dataset.y_dev])
- self._logger.debug('Fitting the forest on train subset')
- elif model.models_parameters.subsets_used == 'train,dev':
- self._X_forest = self._dataset.X_train
- self._y_forest = self._dataset.y_train
- self._X_omp = self._dataset.X_dev
- self._y_omp = self._dataset.y_dev
- self._logger.debug('Fitting the forest on train subset and OMP on dev subset.')
- elif model.models_parameters.subsets_used == 'train+dev,train+dev':
- self._X_forest = np.concatenate([self._dataset.X_train, self._dataset.X_dev])
- self._X_omp = self._X_forest
- self._y_forest = np.concatenate([self._dataset.y_train, self._dataset.y_dev])
- self._y_omp = self._y_forest
- self._logger.debug('Fitting both the forest and OMP on train+dev subsets.')
- elif model.models_parameters.subsets_used == 'train,train+dev':
- self._X_forest = self._dataset.X_train
- self._y_forest = self._dataset.y_train
- self._X_omp = np.concatenate([self._dataset.X_train, self._dataset.X_dev])
- self._y_omp = np.concatenate([self._dataset.y_train, self._dataset.y_dev])
- else:
- raise ValueError("Unknown specified subsets_used parameter '{}'".format(model.models_parameters.subsets_used))
-
- def train(self, model):
- """
- :param model: An instance of either RandomForestRegressor, RandomForestClassifier, OmpForestRegressor,
- OmpForestBinaryClassifier, OmpForestMulticlassClassifier.
- :return:
- """
-
- self._logger.debug('Training model using train set...')
- self._begin_time = time.time()
- if type(model) in [RandomForestRegressor, RandomForestClassifier]:
- model.fit(
- X=self._X_forest,
- y=self._y_forest
- )
- else:
- model.fit(
- self._X_forest,
- self._y_forest,
- self._X_omp,
- self._y_omp
- )
- self._end_time = time.time()
-
- def __score_func(self, model, X, y_true, weights=True):
- if type(model) in [OmpForestRegressor, RandomForestRegressor, SimilarityForestRegressor]:
- if weights:
- y_pred = model.predict(X)
- else:
- y_pred = model.predict_no_weights(X)
- result = self._regression_score_metric(y_true, y_pred)
- elif type(model) in [OmpForestBinaryClassifier, OmpForestMulticlassClassifier, RandomForestClassifier]:
- if weights:
- y_pred = model.predict(X)
- else:
- y_pred = model.predict_no_weights(X)
- if type(model) is OmpForestBinaryClassifier:
- y_pred = np.sign(y_pred)
- y_pred = np.where(y_pred==0, 1, y_pred)
- result = self._classification_score_metric(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 = self._base_regression_score_metric(y_true, y_pred)
- elif type(model) in [OmpForestBinaryClassifier, OmpForestMulticlassClassifier]:
- y_pred = model.predict_base_estimator(X)
- result = self._base_classification_score_metric(y_true, y_pred)
- elif type(model) == RandomForestClassifier:
- y_pred = model.predict(X)
- result = self._base_classification_score_metric(y_true, y_pred)
- elif type(model) in [RandomForestRegressor, SimilarityForestRegressor]:
- y_pred = model.predict(X)
- result = self._base_regression_score_metric(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
- """
-
- model_weights = ''
- if type(model) in [OmpForestRegressor, OmpForestBinaryClassifier]:
- model_weights = model._omp.coef_
- elif type(model) == OmpForestMulticlassClassifier:
- model_weights = model._dct_class_omp
- elif type(model) == OmpForestBinaryClassifier:
- model_weights = model._omp
-
- results = ModelRawResults(
- model_weights=model_weights,
- training_time=self._end_time - self._begin_time,
- datetime=datetime.datetime.now(),
- 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=self._score_metric_name,
- base_score_metric=self._base_score_metric_name
- )
- results.save(models_dir)
- 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_base))
- self._logger.info("Performance on train: {}".format(results.train_score))
-
- self._logger.info("Base performance on dev: {}".format(results.dev_score_base))
- self._logger.info("Performance on dev: {}".format(results.dev_score))
-
- if type(model) not in [RandomForestRegressor, RandomForestClassifier]:
- results = ModelRawResults(
- model_weights='',
- training_time=self._end_time - self._begin_time,
- datetime=datetime.datetime.now(),
- train_score=self.__score_func(model, self._dataset.X_train, self._dataset.y_train, False),
- dev_score=self.__score_func(model, self._dataset.X_dev, self._dataset.y_dev, False),
- test_score=self.__score_func(model, self._dataset.X_test, self._dataset.y_test, False),
- 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=self._score_metric_name,
- base_score_metric=self._base_score_metric_name
- )
- results.save(models_dir+'_no_weights')
- self._logger.info("Base performance on test without weights: {}".format(results.test_score_base))
- self._logger.info("Performance on test: {}".format(results.test_score))
-
- self._logger.info("Base performance on train without weights: {}".format(results.train_score_base))
- self._logger.info("Performance on train: {}".format(results.train_score))
-
- self._logger.info("Base performance on dev without weights: {}".format(results.dev_score_base))
- self._logger.info("Performance on dev: {}".format(results.dev_score))
-
-
+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.models.similarity_forest_regressor import SimilarityForestRegressor
+from bolsonaro.error_handling.logger_factory import LoggerFactory
+from bolsonaro.data.task import Task
+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
+
+
+class Trainer(object):
+ """
+ Class capable of fitting any model object to some prepared data then evaluate and save results through the `train` method.
+ """
+
+ def __init__(self, dataset, regression_score_metric=mean_squared_error, classification_score_metric=accuracy_score,
+ base_regression_score_metric=mean_squared_error, base_classification_score_metric=accuracy_score):
+ """
+
+ :param dataset: Object with X_train, y_train, X_dev, y_dev, X_test and Y_test attributes
+ """
+ self._dataset = dataset
+ self._logger = LoggerFactory.create(LOG_PATH, __name__)
+ self._regression_score_metric = regression_score_metric
+ self._classification_score_metric = classification_score_metric
+ self._base_regression_score_metric = base_regression_score_metric
+ self._base_classification_score_metric = base_classification_score_metric
+ self._score_metric_name = regression_score_metric.__name__ if dataset.task == Task.REGRESSION \
+ else classification_score_metric.__name__
+ self._base_score_metric_name = base_regression_score_metric.__name__ if dataset.task == Task.REGRESSION \
+ else base_classification_score_metric.__name__
+
+ @property
+ def score_metric_name(self):
+ return self._score_metric_name
+
+ @property
+ def base_score_metric_name(self):
+ return self._base_score_metric_name
+
+ def init(self, model, subsets_used='train,dev'):
+ if type(model) in [RandomForestRegressor, RandomForestClassifier]:
+ if subsets_used == 'train,dev':
+ self._X_forest = self._dataset.X_train
+ self._y_forest = self._dataset.y_train
+ else:
+ self._X_forest = np.concatenate([self._dataset.X_train, self._dataset.X_dev])
+ self._y_forest = np.concatenate([self._dataset.y_train, self._dataset.y_dev])
+ self._logger.debug('Fitting the forest on train subset')
+ elif model.models_parameters.subsets_used == 'train,dev':
+ self._X_forest = self._dataset.X_train
+ self._y_forest = self._dataset.y_train
+ self._X_omp = self._dataset.X_dev
+ self._y_omp = self._dataset.y_dev
+ self._logger.debug('Fitting the forest on train subset and OMP on dev subset.')
+ elif model.models_parameters.subsets_used == 'train+dev,train+dev':
+ self._X_forest = np.concatenate([self._dataset.X_train, self._dataset.X_dev])
+ self._X_omp = self._X_forest
+ self._y_forest = np.concatenate([self._dataset.y_train, self._dataset.y_dev])
+ self._y_omp = self._y_forest
+ self._logger.debug('Fitting both the forest and OMP on train+dev subsets.')
+ elif model.models_parameters.subsets_used == 'train,train+dev':
+ self._X_forest = self._dataset.X_train
+ self._y_forest = self._dataset.y_train
+ self._X_omp = np.concatenate([self._dataset.X_train, self._dataset.X_dev])
+ self._y_omp = np.concatenate([self._dataset.y_train, self._dataset.y_dev])
+ else:
+ raise ValueError("Unknown specified subsets_used parameter '{}'".format(model.models_parameters.subsets_used))
+
+ def train(self, model):
+ """
+ :param model: An instance of either RandomForestRegressor, RandomForestClassifier, OmpForestRegressor,
+ OmpForestBinaryClassifier, OmpForestMulticlassClassifier.
+ :return:
+ """
+
+ self._logger.debug('Training model using train set...')
+ self._begin_time = time.time()
+ if type(model) in [RandomForestRegressor, RandomForestClassifier]:
+ model.fit(
+ X=self._X_forest,
+ y=self._y_forest
+ )
+ else:
+ model.fit(
+ self._X_forest,
+ self._y_forest,
+ self._X_omp,
+ self._y_omp
+ )
+ self._end_time = time.time()
+
+ def __score_func(self, model, X, y_true, weights=True):
+ if type(model) in [OmpForestRegressor, RandomForestRegressor, SimilarityForestRegressor]:
+ if weights:
+ y_pred = model.predict(X)
+ else:
+ y_pred = model.predict_no_weights(X)
+ result = self._regression_score_metric(y_true, y_pred)
+ elif type(model) in [OmpForestBinaryClassifier, OmpForestMulticlassClassifier, RandomForestClassifier]:
+ if weights:
+ y_pred = model.predict(X)
+ else:
+ y_pred = model.predict_no_weights(X)
+ if type(model) is OmpForestBinaryClassifier:
+ y_pred = np.sign(y_pred)
+ y_pred = np.where(y_pred==0, 1, y_pred)
+ result = self._classification_score_metric(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 = self._base_regression_score_metric(y_true, y_pred)
+ elif type(model) in [OmpForestBinaryClassifier, OmpForestMulticlassClassifier]:
+ y_pred = model.predict_base_estimator(X)
+ result = self._base_classification_score_metric(y_true, y_pred)
+ elif type(model) == RandomForestClassifier:
+ y_pred = model.predict(X)
+ result = self._base_classification_score_metric(y_true, y_pred)
+ elif type(model) in [RandomForestRegressor, SimilarityForestRegressor]:
+ y_pred = model.predict(X)
+ result = self._base_regression_score_metric(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
+ """
+
+ model_weights = ''
+ if type(model) in [OmpForestRegressor, OmpForestBinaryClassifier]:
+ model_weights = model._omp.coef_
+ elif type(model) == OmpForestMulticlassClassifier:
+ model_weights = model._dct_class_omp
+ elif type(model) == OmpForestBinaryClassifier:
+ model_weights = model._omp
+
+ results = ModelRawResults(
+ model_weights=model_weights,
+ training_time=self._end_time - self._begin_time,
+ datetime=datetime.datetime.now(),
+ 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=self._score_metric_name,
+ base_score_metric=self._base_score_metric_name
+ )
+ results.save(models_dir)
+ 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_base))
+ self._logger.info("Performance on train: {}".format(results.train_score))
+
+ self._logger.info("Base performance on dev: {}".format(results.dev_score_base))
+ self._logger.info("Performance on dev: {}".format(results.dev_score))
+
+ if type(model) not in [RandomForestRegressor, RandomForestClassifier]:
+ results = ModelRawResults(
+ model_weights='',
+ training_time=self._end_time - self._begin_time,
+ datetime=datetime.datetime.now(),
+ train_score=self.__score_func(model, self._dataset.X_train, self._dataset.y_train, False),
+ dev_score=self.__score_func(model, self._dataset.X_dev, self._dataset.y_dev, False),
+ test_score=self.__score_func(model, self._dataset.X_test, self._dataset.y_test, False),
+ 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=self._score_metric_name,
+ base_score_metric=self._base_score_metric_name
+ )
+ results.save(models_dir+'_no_weights')
+ self._logger.info("Base performance on test without weights: {}".format(results.test_score_base))
+ self._logger.info("Performance on test: {}".format(results.test_score))
+
+ self._logger.info("Base performance on train without weights: {}".format(results.train_score_base))
+ self._logger.info("Performance on train: {}".format(results.train_score))
+
+ self._logger.info("Base performance on dev without weights: {}".format(results.dev_score_base))
+ self._logger.info("Performance on dev: {}".format(results.dev_score))