From 95f543a14563816200eb438e4492a893f8dec63e Mon Sep 17 00:00:00 2001
From: Charly Lamothe <charly.lamothe@univ-amu.fr>
Date: Wed, 25 Mar 2020 15:20:18 +0100
Subject: [PATCH] Fix omp_wo_weights when coupled with normalize_D. Fix missing
selected trees saving for omp and random. Update compute results (not done
yet). Fix omp_distillation.
---
code/bolsonaro/models/omp_forest.py | 7 +------
code/bolsonaro/models/omp_forest_classifier.py | 7 ++-----
code/bolsonaro/trainer.py | 15 +++++----------
code/compute_results.py | 13 ++++++++-----
4 files changed, 16 insertions(+), 26 deletions(-)
diff --git a/code/bolsonaro/models/omp_forest.py b/code/bolsonaro/models/omp_forest.py
index e4830f0..5918eea 100644
--- a/code/bolsonaro/models/omp_forest.py
+++ b/code/bolsonaro/models/omp_forest.py
@@ -41,7 +41,7 @@ class OmpForest(BaseEstimator, metaclass=ABCMeta):
# print(set([type(y) for y in y_forest]))
self._base_forest_estimator.fit(X_forest, y_forest)
self._extract_subforest(X_omp,
- self.predict_base_estimator(X_forest) if use_distillation else y_omp) # type: OrthogonalMatchingPursuit
+ self.predict_base_estimator(X_omp) if use_distillation else y_omp) # type: OrthogonalMatchingPursuit
return self
def _extract_subforest(self, X, y):
@@ -153,11 +153,6 @@ class SingleOmpForest(OmpForest):
"""
forest_predictions = np.array([tree.predict(X) for tree in self._base_forest_estimator.estimators_])
- 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_
select_trees = np.mean(forest_predictions[weights != 0], axis=0)
return select_trees
diff --git a/code/bolsonaro/models/omp_forest_classifier.py b/code/bolsonaro/models/omp_forest_classifier.py
index 4255eeb..002dd93 100644
--- a/code/bolsonaro/models/omp_forest_classifier.py
+++ b/code/bolsonaro/models/omp_forest_classifier.py
@@ -19,11 +19,11 @@ class OmpForestBinaryClassifier(SingleOmpForest):
def _check_classes(self, y):
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):
+ def fit(self, X_forest, y_forest, X_omp, y_omp, use_distillation=False):
self._check_classes(y_forest)
self._check_classes(y_omp)
- return super().fit(X_forest, y_forest, X_omp, y_omp)
+ return super().fit(X_forest, y_forest, X_omp, y_omp, use_distillation=use_distillation)
def _base_estimator_predictions(self, X):
predictions_0_1 = super()._base_estimator_predictions(X)
@@ -42,9 +42,6 @@ class OmpForestBinaryClassifier(SingleOmpForest):
forest_predictions = self._base_estimator_predictions(X)
- if self._models_parameters.normalize_D:
- forest_predictions /= self._forest_norms
-
weights = self._omp.coef_
omp_trees_predictions = forest_predictions[:, weights != 0]
diff --git a/code/bolsonaro/trainer.py b/code/bolsonaro/trainer.py
index 0986200..7761c53 100644
--- a/code/bolsonaro/trainer.py
+++ b/code/bolsonaro/trainer.py
@@ -155,16 +155,7 @@ class Trainer(object):
return result
def _evaluate_predictions(self, model, X, aggregation_function):
- if type(model) in [OmpForestRegressor, OmpForestBinaryClassifier, OmpForestMulticlassClassifier]:
- estimators = model.forest
- estimators = np.asarray(estimators)[model._omp.coef_ != 0]
- elif type(model) in [SimilarityForestRegressor, KMeansForestRegressor, EnsembleSelectionForestRegressor,
- SimilarityForestClassifier, KMeansForestClassifier, EnsembleSelectionForestClassifier]:
- estimators = model.selected_trees
- elif type(model) in [RandomForestRegressor, RandomForestClassifier]:
- estimators = model.estimators_
-
- predictions = np.array([tree.predict(X) for tree in estimators])
+ predictions = np.array([tree.predict(X) for tree in self._selected_trees])
predictions = normalize(predictions)
@@ -187,6 +178,10 @@ class Trainer(object):
if type(model) in [SimilarityForestRegressor, KMeansForestRegressor, EnsembleSelectionForestRegressor,
SimilarityForestClassifier, KMeansForestClassifier, EnsembleSelectionForestClassifier]:
self._selected_trees = model.selected_trees
+ elif type(model) in [OmpForestRegressor, OmpForestMulticlassClassifier, OmpForestBinaryClassifier]:
+ self._selected_trees = np.asarray(model.forest)[model._omp.coef_ != 0]
+ elif type(model) in [RandomForestRegressor, RandomForestClassifier]:
+ self._selected_trees = model.estimators_
if len(self._selected_trees) > 0:
with open(os.path.join(models_dir, 'selected_trees.pickle'), 'wb') as output_file:
diff --git a/code/compute_results.py b/code/compute_results.py
index ab90b85..28b08ac 100644
--- a/code/compute_results.py
+++ b/code/compute_results.py
@@ -473,9 +473,10 @@ if __name__ == "__main__":
30 + 1,
endpoint=True)[1:]).astype(np.int)).tolist()"""
- extracted_forest_sizes = [4, 7, 11, 14, 18, 22, 25, 29, 32, 36, 40, 43, 47, 50, 54, 58, 61, 65, 68, 72, 76, 79, 83, 86, 90, 94, 97, 101, 104, 108]
+ #extracted_forest_sizes = [4, 7, 11, 14, 18, 22, 25, 29, 32, 36, 40, 43, 47, 50, 54, 58, 61, 65, 68, 72, 76, 79, 83, 86, 90, 94, 97, 101, 104, 108]
- extracted_forest_sizes = [str(forest_size) for forest_size in extracted_forest_sizes]
+ #extracted_forest_sizes = [str(forest_size) for forest_size in extracted_forest_sizes]
+ extracted_forest_sizes= list()
# base_with_params
logger.info('Loading base_with_params experiment scores...')
@@ -508,8 +509,10 @@ if __name__ == "__main__":
for i in range(3, len(args.experiment_ids)):
if 'kmeans' in args.experiment_ids[i]:
label = 'kmeans'
- elif 'similarity' in args.experiment_ids[i]:
- label = 'similarity'
+ elif 'similarity_similarities' in args.experiment_ids[i]:
+ label = 'similarity_similarities'
+ elif 'similarity_predictions' in args.experiment_ids[i]:
+ label = 'similarity_predictions'
elif 'ensemble' in args.experiment_ids[i]:
label = 'ensemble'
else:
@@ -528,7 +531,7 @@ if __name__ == "__main__":
pathlib.Path(output_path).mkdir(parents=True, exist_ok=True)
Plotter.plot_stage2_losses(
- file_path=output_path + os.sep + f"losses_{'-'.join(all_labels)}_test.png",
+ file_path=output_path + os.sep + f"losses_{'-'.join(all_labels)}_test_train,dev.png",
all_experiment_scores=all_scores,
all_labels=all_labels,
x_value=with_params_extracted_forest_sizes,
--
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