diff --git a/code/bolsonaro/models/kmeans_forest_regressor.py b/code/bolsonaro/models/kmeans_forest_regressor.py
index 4f15372186898ba66792d39ac5c2f8b810e682f9..ba1d9b4e56d98fbb2607d12917eb9d75b013044d 100644
--- a/code/bolsonaro/models/kmeans_forest_regressor.py
+++ b/code/bolsonaro/models/kmeans_forest_regressor.py
@@ -3,7 +3,7 @@ import time
from bolsonaro.models.utils import score_metric_mse, score_metric_indicator, aggregation_classification, aggregation_regression
from bolsonaro.utils import tqdm_joblib
-from sklearn.ensemble import RandomForestRegressor
+from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.metrics import mean_squared_error
from sklearn.base import BaseEstimator
from sklearn.cluster import KMeans
@@ -20,10 +20,22 @@ class KmeansForest(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=2)
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
+
+ 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
@property
def models_parameters(self):
@@ -34,13 +46,10 @@ class KmeansForest(BaseEstimator, metaclass=ABCMeta):
return self._selected_trees
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)
- predictions_val = np.empty((len(self._estimator.estimators_), X_val.shape[0]))
- predictions = np.empty((len(self._estimator.estimators_), X_train.shape[0]))
- for i_tree, tree in enumerate(self._estimator.estimators_):
- predictions_val[i_tree, :] = tree.predict(X_val)
- predictions[i_tree, :] = tree.predict(X_train)
+ predictions_val = self._base_estimator_predictions(X_val).T
+ predictions = self._base_estimator_predictions(X_train).T
kmeans = KMeans(n_clusters=self._extracted_forest_size, random_state=self._models_parameters.seed).fit(predictions)
labels = np.array(kmeans.labels_)
@@ -51,10 +60,9 @@ class KmeansForest(BaseEstimator, metaclass=ABCMeta):
index_trees_cluster = np.where(labels == cluster_idx)[0]
predictions_val_cluster = predictions_val[index_trees_cluster] # get predictions of trees in cluster
best_tree_index = self._get_best_tree_index(predictions_val_cluster, y_val)
- lst_pruned_forest.append(self._estimator.estimators_[index_trees_cluster[best_tree_index]])
+ lst_pruned_forest.append(self._base_estimator.estimators_[index_trees_cluster[best_tree_index]])
self._selected_trees = lst_pruned_forest
- # self._estimator.estimators_ = lst_pruned_forest
def score(self, X, y):
final_predictions = self.predict(X)
@@ -62,14 +70,12 @@ class KmeansForest(BaseEstimator, metaclass=ABCMeta):
return score
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 _get_best_tree_index(self, y_preds, y_true):
score = self._score_metric(y_preds, y_true)
@@ -114,6 +120,12 @@ class KmeansForest(BaseEstimator, metaclass=ABCMeta):
class KMeansForestRegressor(KmeansForest, 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)
@@ -127,12 +139,27 @@ class KMeansForestRegressor(KmeansForest, metaclass=ABCMeta):
class KMeansForestClassifier(KmeansForest, 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)
def _score_metric(self, y_preds, y_true):
return score_metric_indicator(y_preds, y_true)
+ 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
+
@staticmethod
def _best(array):
return np.argmax(array)
diff --git a/code/bolsonaro/models/omp_forest.py b/code/bolsonaro/models/omp_forest.py
index d539f45314a244c410453bb84f726502c6ffe082..90a6fc30edac540b23cf907a3fbd440ec60a86cc 100644
--- a/code/bolsonaro/models/omp_forest.py
+++ b/code/bolsonaro/models/omp_forest.py
@@ -28,7 +28,8 @@ class OmpForest(BaseEstimator, metaclass=ABCMeta):
return self._base_forest_estimator.score(X, y)
def _base_estimator_predictions(self, X):
- return np.array([tree.predict(X) for tree in self._base_forest_estimator.estimators_]).T
+ base_predictions = np.array([tree.predict(X) for tree in self._base_forest_estimator.estimators_]).T
+ return base_predictions
@property
def forest(self):
diff --git a/code/bolsonaro/models/omp_forest_classifier.py b/code/bolsonaro/models/omp_forest_classifier.py
index 7a22337b2fcf48b5181e4971836470e17d0f4f62..4255eeb129aa04f797c1fd690809f5163b66c5c2 100644
--- a/code/bolsonaro/models/omp_forest_classifier.py
+++ b/code/bolsonaro/models/omp_forest_classifier.py
@@ -25,6 +25,11 @@ class OmpForestBinaryClassifier(SingleOmpForest):
return super().fit(X_forest, y_forest, X_omp, y_omp)
+ def _base_estimator_predictions(self, X):
+ predictions_0_1 = super()._base_estimator_predictions(X)
+ predictions = (predictions_0_1 - 0.5) * 2
+ return predictions
+
def predict_no_weights(self, X):
"""
Apply the SingleOmpForest to X without using the weights.
@@ -35,21 +40,18 @@ class OmpForestBinaryClassifier(SingleOmpForest):
: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_])
+ forest_predictions = self._base_estimator_predictions(X)
if self._models_parameters.normalize_D:
- forest_predictions = forest_predictions.T
forest_predictions /= self._forest_norms
- forest_predictions = forest_predictions.T
weights = self._omp.coef_
- omp_trees_predictions = forest_predictions[weights != 0].T[1]
+ omp_trees_predictions = forest_predictions[:, weights != 0]
# 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
diff --git a/code/bolsonaro/trainer.py b/code/bolsonaro/trainer.py
index 1adb387ca5cf639b8bea16b72d27b46ac190fb14..8d82b3d6872655e6406944f8bb4e7f1be058f20f 100644
--- a/code/bolsonaro/trainer.py
+++ b/code/bolsonaro/trainer.py
@@ -130,7 +130,7 @@ class Trainer(object):
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, KMeansForestClassifier, SimilarityForestRegressor]:
+ elif type(model) in [OmpForestBinaryClassifier, OmpForestMulticlassClassifier, KMeansForestClassifier, SimilarityForestClassifier]:
y_pred = model.predict_base_estimator(X)
result = self._base_classification_score_metric(y_true, y_pred)
elif type(model) == RandomForestClassifier: