Clean up some imports

code/bolsonaro/models/model_factory.py

@@ -27,6 +27,6 @@ class ModelFactory(object):
         model_parameters = ModelParameters.load(directory_path, experiment_id)
         model = ModelFactory.build(task, model_parameters)
         # todo faire ce qu'il faut ici pour rétablir correctement le modèle
-        # model.set_forest(model_raw_results.forest)
-        # model.set_weights(model_raw_results.weights)
+        model.set_forest(model_raw_results.model_object.forest)
+        model.set_weights(model_raw_results.model_object.weights)
         return model

code/bolsonaro/models/omp_forest.py

-from abc import abstractmethod, ABCMeta
+from bolsonaro import LOG_PATH
+from bolsonaro.error_handling.logger_factory import LoggerFactory
 
+from abc import abstractmethod, ABCMeta
 import numpy as np
 from sklearn.linear_model import OrthogonalMatchingPursuit
-
-from bolsonaro import LOG_PATH
-from bolsonaro.error_handling.logger_factory import LoggerFactory
 from sklearn.base import BaseEstimator
 
 
@@ -21,7 +20,6 @@ class OmpForest(BaseEstimator, metaclass=ABCMeta):
     def score_base_estimator(self, X, y):
         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
 
@@ -120,4 +118,4 @@ class SingleOmpForest(OmpForest):
         if self._models_parameters.normalize_D:
             forest_predictions /= self._forest_norms
 
-        return self._make_omp_weighted_prediction(forest_predictions, self._omp, self._models_parameters.normalize_weights)
+        return self._make_omp_weighted_prediction(forest_predictions, self._omp, self._models_parameters.normalize_weights)
\ No newline at end of file

code/bolsonaro/models/omp_forest_classifier.py

-from collections import namedtuple
-from copy import deepcopy
+from bolsonaro.models.omp_forest import OmpForest, SingleOmpForest
+from bolsonaro.utils import binarize_class_data
 
-from sklearn.base import BaseEstimator
+import numpy as np
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.linear_model import OrthogonalMatchingPursuit
 
-from bolsonaro import LOG_PATH
-from bolsonaro.error_handling.logger_factory import LoggerFactory
-from bolsonaro.models.omp_forest import OmpForest, SingleOmpForest
-import numpy as np
-
-from bolsonaro.utils import binarize_class_data
-
 
 class OmpForestBinaryClassifier(SingleOmpForest):
 
     DEFAULT_SCORE_METRIC = 'indicator'
 
     def __init__(self, models_parameters):
-        estimator = RandomForestClassifier(n_estimators=models_parameters.forest_size,
+        estimator = RandomForestClassifier(**models_parameters.hyperparameters,
                                            random_state=models_parameters.seed, n_jobs=-1)
         super().__init__(models_parameters, estimator)
 
     def _check_classes(self, y):
-        assert len(set(y).difference({-1, 1})) == 0, "Classes for binary classifier should be {-1, +1}"
+        assert len(set(y).difference({-1, 1})) == 0, "Classes for binary classifier must be {-1, +1}"
 
     def fit(self, X_forest, y_forest, X_omp, y_omp):
         self._check_classes(y_forest)
@@ -31,7 +24,6 @@ class OmpForestBinaryClassifier(SingleOmpForest):
 
         return super().fit(X_forest, y_forest, X_omp, y_omp)
 
-
     def score(self, X, y, metric=DEFAULT_SCORE_METRIC):
         """
         Evaluate OMPForestClassifer on (`X`, `y`) using `metric`
@@ -56,7 +48,7 @@ class OmpForestMulticlassClassifier(OmpForest):
     DEFAULT_SCORE_METRIC = 'indicator'
 
     def __init__(self, models_parameters):
-        estimator = RandomForestClassifier(n_estimators=models_parameters.forest_size,
+        estimator = RandomForestClassifier(**models_parameters.hyperparameters,
                                            random_state=models_parameters.seed, n_jobs=-1)
         super().__init__(models_parameters, estimator)
         # question: peut-être initialiser les omps dans le __init__? comme pour le SingleOmpForest
@@ -95,7 +87,6 @@ class OmpForestMulticlassClassifier(OmpForest):
         max_preds = np.argmax(preds, axis=1)
         return np.array(label_names)[max_preds]
 
-
     def score(self, X, y, metric=DEFAULT_SCORE_METRIC):
         predictions = self.predict(X)
 
@@ -107,8 +98,6 @@ class OmpForestMulticlassClassifier(OmpForest):
         return evaluation
 
 
-
-
 if __name__ == "__main__":
     forest = RandomForestClassifier(n_estimators=10)
     X = np.random.rand(10, 5)

code/bolsonaro/models/omp_forest_regressor.py

-
+from bolsonaro.models.omp_forest import SingleOmpForest
 
 from sklearn.ensemble import RandomForestRegressor
 import numpy as np
 
-from bolsonaro.models.omp_forest import SingleOmpForest
-
 
 class OmpForestRegressor(SingleOmpForest):