diff --git a/code/bolsonaro/models/ensemble_selection_forest_regressor.py b/code/bolsonaro/models/ensemble_selection_forest_regressor.py
index 1e63c8cf3c34424e277d5a79d6f2349aee2aa5a5..b82e131d296392963e31d85f5c1444fc5cb7fd09 100644
--- a/code/bolsonaro/models/ensemble_selection_forest_regressor.py
+++ b/code/bolsonaro/models/ensemble_selection_forest_regressor.py
@@ -53,14 +53,14 @@ class EnsembleSelectionForestRegressor(BaseEstimator, metaclass=ABCMeta):
def score(self, X, y):
predictions = self._predict_base_estimator(X)
- mean_predictions = np.mean(predictions, axis=0)
- return self._score_metric(mean_predictions, y)
+ return self._score_metric(predictions, y)
def predict_base_estimator(self, X):
predictions = list()
for tree in self._ensemble_selected:
predictions.append(tree.predict(X))
- return np.array(predictions)
+ mean_predictions = np.mean(np.array(predictions), axis=0)
+ return mean_predictions
@staticmethod
def generate_library(X_train, y_train, random_state=None):
diff --git a/code/bolsonaro/models/model_factory.py b/code/bolsonaro/models/model_factory.py
index eb3e8b50d7411a2beee8e79bf7da46f6561558a2..335816b1dd33d28175f4865da2fddbbf73b8027d 100644
--- a/code/bolsonaro/models/model_factory.py
+++ b/code/bolsonaro/models/model_factory.py
@@ -22,10 +22,10 @@ class ModelFactory(object):
if model_parameters.extraction_strategy == 'omp':
return OmpForestBinaryClassifier(model_parameters)
elif model_parameters.extraction_strategy == 'random':
- return RandomForestClassifier(n_estimators=model_parameters.extracted_forest_size,
+ return RandomForestClassifier(**model_parameters.hyperparameters,
random_state=model_parameters.seed)
elif model_parameters.extraction_strategy == 'none':
- return RandomForestClassifier(n_estimators=model_parameters.hyperparameters['n_estimators'],
+ return RandomForestClassifier(**model_parameters.hyperparameters,
random_state=model_parameters.seed)
else:
raise ValueError('Invalid extraction strategy')
@@ -33,7 +33,7 @@ class ModelFactory(object):
if model_parameters.extraction_strategy == 'omp':
return OmpForestRegressor(model_parameters)
elif model_parameters.extraction_strategy == 'random':
- return RandomForestRegressor(n_estimators=model_parameters.extracted_forest_size,
+ return RandomForestRegressor(**model_parameters.hyperparameters,
random_state=model_parameters.seed)
elif model_parameters.extraction_strategy == 'similarity':
return SimilarityForestRegressor(model_parameters)
@@ -42,7 +42,7 @@ class ModelFactory(object):
elif model_parameters.extraction_strategy == 'ensemble':
return EnsembleSelectionForestRegressor(model_parameters, library=library)
elif model_parameters.extraction_strategy == 'none':
- return RandomForestRegressor(n_estimators=model_parameters.hyperparameters['n_estimators'],
+ return RandomForestRegressor(**model_parameters.hyperparameters,
random_state=model_parameters.seed)
else:
raise ValueError('Invalid extraction strategy')
@@ -50,10 +50,10 @@ class ModelFactory(object):
if model_parameters.extraction_strategy == 'omp':
return OmpForestMulticlassClassifier(model_parameters)
elif model_parameters.extraction_strategy == 'random':
- return RandomForestClassifier(n_estimators=model_parameters.extracted_forest_size,
+ return RandomForestClassifier(**model_parameters.hyperparameters,
random_state=model_parameters.seed)
elif model_parameters.extraction_strategy == 'none':
- return RandomForestClassifier(n_estimators=model_parameters.hyperparameters['n_estimators'],
+ return RandomForestClassifier(**model_parameters.hyperparameters,
random_state=model_parameters.seed)
else:
raise ValueError('Invalid extraction strategy')
diff --git a/code/bolsonaro/models/omp_forest.py b/code/bolsonaro/models/omp_forest.py
index 5b947d327693020b51c7da778d4855274454de93..e4a5667b0d759fe1344fe14bb89dcf601c14f610 100644
--- a/code/bolsonaro/models/omp_forest.py
+++ b/code/bolsonaro/models/omp_forest.py
@@ -145,5 +145,4 @@ class SingleOmpForest(OmpForest):
omp_trees_indices = np.nonzero(weights)[0]
select_trees = np.mean(forest_predictions[omp_trees_indices], axis=0)
- print(len(omp_trees_indices))
return select_trees
diff --git a/code/bolsonaro/trainer.py b/code/bolsonaro/trainer.py
index 6ec39094fccdac553a09adbddebda27d621cd4e8..ebcfe80440b97510c02e948ffc1e72ca752ca626 100644
--- a/code/bolsonaro/trainer.py
+++ b/code/bolsonaro/trainer.py
@@ -3,6 +3,7 @@ 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.models.kmeans_forest_regressor import KMeansForestRegressor
+from bolsonaro.models.ensemble_selection_forest_regressor import EnsembleSelectionForestRegressor
from bolsonaro.error_handling.logger_factory import LoggerFactory
from bolsonaro.data.task import Task
from . import LOG_PATH
@@ -73,7 +74,7 @@ class Trainer(object):
else:
raise ValueError("Unknown specified subsets_used parameter '{}'".format(model.models_parameters.subsets_used))
- def train(self, model):
+ def train(self, model, extracted_forest_size=None):
"""
:param model: An instance of either RandomForestRegressor, RandomForestClassifier, OmpForestRegressor,
OmpForestBinaryClassifier, OmpForestMulticlassClassifier.
@@ -83,10 +84,13 @@ class Trainer(object):
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
- )
+ if extracted_forest_size is not None:
+ model.estimators_ = np.random.choice(model.estimators_, extracted_forest_size)
+ else:
+ model.fit(
+ X=self._X_forest,
+ y=self._y_forest
+ )
else:
model.fit(
self._X_forest,
@@ -112,12 +116,12 @@ class Trainer(object):
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)
- elif type(model) in [SimilarityForestRegressor, KMeansForestRegressor]:
+ elif type(model) in [SimilarityForestRegressor, KMeansForestRegressor, EnsembleSelectionForestRegressor]:
result = model.score(X, y_true)
return result
def __score_func_base(self, model, X, y_true):
- if type(model) in [OmpForestRegressor, SimilarityForestRegressor, KMeansForestRegressor]:
+ if type(model) in [OmpForestRegressor, SimilarityForestRegressor, KMeansForestRegressor, EnsembleSelectionForestRegressor]:
y_pred = model.predict_base_estimator(X)
result = self._base_regression_score_metric(y_true, y_pred)
elif type(model) in [OmpForestBinaryClassifier, OmpForestMulticlassClassifier]:
diff --git a/code/train.py b/code/train.py
index b7ea2a77ab08cac3e544c12e2d0c3036ae83f379..72c91d8b004bdf0205b5c89856cae4e388c6a225 100644
--- a/code/train.py
+++ b/code/train.py
@@ -59,10 +59,26 @@ def seed_job(seed_job_pb, seed, parameters, experiment_id, hyperparameters, verb
else:
library = None
+ if parameters['extraction_strategy'] == 'random':
+ pretrained_model_parameters = ModelParameters(
+ extracted_forest_size=parameters['forest_size'],
+ normalize_D=parameters['normalize_D'],
+ subsets_used=parameters['subsets_used'],
+ normalize_weights=parameters['normalize_weights'],
+ seed=seed,
+ hyperparameters=hyperparameters,
+ extraction_strategy=parameters['extraction_strategy']
+ )
+ pretrained_estimator = ModelFactory.build(dataset.task, pretrained_model_parameters, library=library)
+ else:
+ pretrained_estimator = None
+ pretrained_model_parameters = None
+
if parameters['extraction_strategy'] != 'none':
with tqdm_joblib(tqdm(total=len(parameters['extracted_forest_size']), disable=not verbose)) as extracted_forest_size_job_pb:
Parallel(n_jobs=-1)(delayed(extracted_forest_size_job)(extracted_forest_size_job_pb, parameters['extracted_forest_size'][i],
- models_dir, seed, parameters, dataset, hyperparameters, experiment_id, trainer, library)
+ models_dir, seed, parameters, dataset, hyperparameters, experiment_id, trainer, library,
+ pretrained_estimator=pretrained_estimator, pretrained_model_parameters=pretrained_model_parameters)
for i in range(len(parameters['extracted_forest_size'])))
else:
forest_size = hyperparameters['n_estimators']
@@ -103,7 +119,8 @@ def seed_job(seed_job_pb, seed, parameters, experiment_id, hyperparameters, verb
seed_job_pb.update(1)
def extracted_forest_size_job(extracted_forest_size_job_pb, extracted_forest_size, models_dir,
- seed, parameters, dataset, hyperparameters, experiment_id, trainer, library):
+ seed, parameters, dataset, hyperparameters, experiment_id, trainer, library,
+ pretrained_estimator=None, pretrained_model_parameters=None):
logger = LoggerFactory.create(LOG_PATH, 'training_seed{}_extracted_forest_size{}_ti{}'.format(
seed, extracted_forest_size, threading.get_ident()))
@@ -127,21 +144,24 @@ def extracted_forest_size_job(extracted_forest_size_job_pb, extracted_forest_siz
pathlib.Path(sub_models_dir).mkdir(parents=True, exist_ok=True)
- model_parameters = ModelParameters(
- extracted_forest_size=extracted_forest_size,
- normalize_D=parameters['normalize_D'],
- subsets_used=parameters['subsets_used'],
- normalize_weights=parameters['normalize_weights'],
- seed=seed,
- hyperparameters=hyperparameters,
- extraction_strategy=parameters['extraction_strategy']
- )
- model_parameters.save(sub_models_dir, experiment_id)
-
- model = ModelFactory.build(dataset.task, model_parameters, library=library)
+ if not pretrained_estimator:
+ model_parameters = ModelParameters(
+ extracted_forest_size=extracted_forest_size,
+ normalize_D=parameters['normalize_D'],
+ subsets_used=parameters['subsets_used'],
+ normalize_weights=parameters['normalize_weights'],
+ seed=seed,
+ hyperparameters=hyperparameters,
+ extraction_strategy=parameters['extraction_strategy']
+ )
+ model_parameters.save(sub_models_dir, experiment_id)
+ model = ModelFactory.build(dataset.task, model_parameters, library=library)
+ else:
+ model = pretrained_estimator
+ pretrained_model_parameters.save(sub_models_dir, experiment_id)
trainer.init(model, subsets_used=parameters['subsets_used'])
- trainer.train(model)
+ trainer.train(model, extracted_forest_size=extracted_forest_size)
trainer.compute_results(model, sub_models_dir)
"""