Add plot for without_weights

code/bolsonaro/models/model_raw_results.py

@@ -68,6 +68,8 @@ class ModelRawResults(object):
         return self._base_score_metric
 
     def save(self, models_dir):
+        if not os.path.exists(models_dir):
+            os.mkdir(models_dir)
         save_obj_to_pickle(models_dir + os.sep + 'model_raw_results.pickle',
             self.__dict__)
 

code/bolsonaro/models/omp_forest.py

@@ -24,7 +24,6 @@ class OmpForest(BaseEstimator, metaclass=ABCMeta):
         return self._base_forest_estimator.score(X, y)
 
     def _base_estimator_predictions(self, X):
-        # We need to use predict_proba to get the probabilities of each class
         return np.array([tree.predict(X) for tree in self._base_forest_estimator.estimators_]).T
 
     @property
@@ -123,3 +122,24 @@ class SingleOmpForest(OmpForest):
             forest_predictions /= self._forest_norms
 
         return self._make_omp_weighted_prediction(forest_predictions, self._omp, self._models_parameters.normalize_weights)
+
+    def predict_no_weights(self, X):
+        """
+        Apply the SingleOmpForest to X without using the weights.
+
+        Make all the base tree predictions
+
+        :param X: a Forest
+        :return: a np.array of the predictions of the entire forest
+        """
+        forest_predictions = self._base_estimator_predictions(X).T
+
+        if self._models_parameters.normalize_D:
+            forest_predictions /= self._forest_norms
+
+        weights = self._omp.coef_
+        omp_trees_indices = np.nonzero(weights)
+
+        select_trees = np.mean(forest_predictions[omp_trees_indices], axis=0)
+
+        return select_trees

code/bolsonaro/models/omp_forest_classifier.py

@@ -106,6 +106,36 @@ class OmpForestMulticlassClassifier(OmpForest):
         max_preds = np.argmax(preds, axis=1)
         return np.array(label_names)[max_preds]
 
+    def predict_no_weights(self, X):
+        """
+        Apply the SingleOmpForest to X without using the weights.
+
+        Make all the base tree predictions
+
+        :param X: a Forest
+        :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_]).T
+
+        if self._models_parameters.normalize_D:
+            forest_predictions /= self._forest_norms
+
+        label_names = []
+        preds = []
+        num_class = 0
+        for class_label, omp_class in self._dct_class_omp.items():
+            weights = omp_class.coef_
+            omp_trees_indices = np.nonzero(weights)
+            label_names.append(class_label)
+            atoms_binary = (forest_predictions[num_class].T - 0.5) * 2 # centré réduit de 0/1 à -1/1
+            preds.append(np.sum(atoms_binary[omp_trees_indices], axis=0))
+            num_class += 1
+
+        preds = np.array(preds).T
+        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)
 

code/bolsonaro/trainer.py

@@ -95,12 +95,18 @@ class Trainer(object):
             )
         self._end_time = time.time()
 
-    def __score_func(self, model, X, y_true):
+    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 = y_pred.round()
             result = self._classification_score_metric(y_true, y_pred)
@@ -148,3 +154,29 @@ class Trainer(object):
 
         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_object='',
+                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))
+
+

code/bolsonaro/visualization/plotter.py

@@ -109,22 +109,23 @@ class Plotter(object):
 
         fig, ax = plt.subplots()
 
-        n = len(all_experiment_scores)
+        nb_experiments = len(all_experiment_scores)
 
         """
         Get as many different colors from the specified cmap (here nipy_spectral)
         as there are curve to plot.
         """
-        colors = Plotter.get_colors_from_cmap(n)
+        colors = Plotter.get_colors_from_cmap(nb_experiments)
 
         # For each curve to plot
-        for i in range(n):
+        for i in range(nb_experiments):
             # Retreive the scores in a list for each seed
             experiment_scores = list(all_experiment_scores[i].values())
             # Compute the mean and the std for the CI
             mean_experiment_scores = np.average(experiment_scores, axis=0)
             std_experiment_scores = np.std(experiment_scores, axis=0)
             # Plot the score curve with the CI
+            print(len(mean_experiment_scores))
             Plotter.plot_mean_and_CI(
                 ax=ax,
                 mean=mean_experiment_scores,

code/compute_results.py

@@ -17,7 +17,7 @@ def retreive_extracted_forest_sizes_number(models_dir, experiment_id):
     extracted_forest_sizes_root_path = experiment_seed_path + os.sep + 'extracted_forest_sizes'
     return len(os.listdir(extracted_forest_sizes_root_path))
 
-def extract_scores_across_seeds_and_extracted_forest_sizes(models_dir, results_dir, experiment_id):
+def extract_scores_across_seeds_and_extracted_forest_sizes(models_dir, results_dir, experiment_id, weights=True):
     experiment_id_path = models_dir + os.sep + str(experiment_id) # models/{experiment_id}
     experiment_seed_root_path = experiment_id_path + os.sep + 'seeds' # models/{experiment_id}/seeds
 
@@ -47,11 +47,15 @@ def extract_scores_across_seeds_and_extracted_forest_sizes(models_dir, results_d
 
         # List the forest sizes in models/{experiment_id}/seeds/{seed}/extracted_forest_sizes
         extracted_forest_sizes = os.listdir(extracted_forest_sizes_root_path)
+        extracted_forest_sizes = [nb_tree for nb_tree in extracted_forest_sizes if not 'no_weights' in nb_tree ]
         extracted_forest_sizes.sort(key=int)
         all_extracted_forest_sizes.append(list(map(int, extracted_forest_sizes)))
         for extracted_forest_size in extracted_forest_sizes:
             # models/{experiment_id}/seeds/{seed}/extracted_forest_sizes/{extracted_forest_size}
+            if weights:
                 extracted_forest_size_path = extracted_forest_sizes_root_path + os.sep + extracted_forest_size
+            else:
+                extracted_forest_size_path = extracted_forest_sizes_root_path + os.sep + extracted_forest_size + '_no_weights'
             # Load models/{experiment_id}/seeds/{seed}/extracted_forest_sizes/{extracted_forest_size}/model_raw_results.pickle file
             model_raw_results = ModelRawResults.load(extracted_forest_size_path)
             # Save the scores
@@ -350,6 +354,11 @@ if __name__ == "__main__":
         omp_with_params_train_scores, omp_with_params_dev_scores, omp_with_params_test_scores, _, \
             omp_with_params_experiment_score_metric = extract_scores_across_seeds_and_extracted_forest_sizes(
                 args.models_dir, args.results_dir, args.experiment_ids[2])
+        #omp_with_params_without_weights
+        logger.info('Loading omp_with_params experiment scores...')
+        omp_with_params_without_weights_train_scores, omp_with_params_without_weights_dev_scores, omp_with_params_without_weights_test_scores, _, \
+            omp_with_params_experiment_score_metric = extract_scores_across_seeds_and_extracted_forest_sizes(
+                args.models_dir, args.results_dir, args.experiment_ids[2], weights=False)
 
         """# base_with_params
         logger.info('Loading base_with_params experiment scores 2...')
@@ -374,8 +383,9 @@ if __name__ == "__main__":
 
         Plotter.plot_stage2_losses(
             file_path=output_path + os.sep + 'losses.png',
-            all_experiment_scores=[base_with_params_test_scores, random_with_params_test_scores, omp_with_params_test_scores],
-            all_labels=['base', 'random', 'omp'],
+            all_experiment_scores=[base_with_params_test_scores, random_with_params_test_scores, omp_with_params_test_scores,
+                                   omp_with_params_without_weights_test_scores],
+            all_labels=['base', 'random', 'omp', 'omp_without_weights'],
             x_value=with_params_extracted_forest_sizes,
             xlabel='Number of trees extracted',
             ylabel=experiments_score_metric,

experiments/iris/stage1/none_with_params.json

@@ -13,9 +13,11 @@
     "test_size": 0.2,
     "random_seed_number": 1,
     "seeds": [
-        58,
-        43535,
-        234234
+        1,
+        2,
+        3,
+        4,
+        5
     ],
     "subsets_used": "train,dev",
     "normalize_weights": false,

experiments/iris/stage1/omp_with_params.json

@@ -13,9 +13,11 @@
     "test_size": 0.2,
     "random_seed_number": 1,
     "seeds": [
-        58,
-        43535,
-        234234
+        1,
+        2,
+        3,
+        4,
+        5
     ],
     "subsets_used": "train,dev",
     "normalize_weights": false,

scripts/run_compute_results.sh

-for dataset in diamonds california_housing boston iris diabetes digits linnerud wine breast_cancer olivetti_faces 20newsgroups_vectorized lfw_pairs
+seeds='1 2 3'
+for dataset in boston iris
 do
-    python code/compute_results.py --stage=1 --experiment_ids 1 2 3 4 5 6 --dataset_name=$dataset --models_dir=models/$dataset/stage1
-    python code/compute_results.py --stage=2 --experiment_ids 1 2 3 4 --dataset_name=$dataset --models_dir=models/$dataset/stage2
-    python code/compute_results.py --stage=3 --experiment_ids 1 2 3 --dataset_name=$dataset --models_dir=models/$dataset/stage3
+    python code/train.py --dataset_name=$dataset --seeds $seeds --extraction_strategy=none --save_experiment_configuration 4 none_with_params --extracted_forest_size_stop=0.40 --extracted_forest_size_samples=30 --experiment_id=1 --models_dir=models/$dataset/stage4 --subsets_used train+dev,train+dev
+    python code/train.py --dataset_name=$dataset --seeds $seeds --extraction_strategy=random --save_experiment_configuration 4 random_with_params --extracted_forest_size_stop=0.40 --extracted_forest_size_samples=30 --experiment_id=2 --models_dir=models/$dataset/stage4 --subsets_used train+dev,train+dev
+    python code/train.py --dataset_name=$dataset --seeds $seeds --extraction_strategy=omp --save_experiment_configuration 4 omp_with_params --extracted_forest_size_stop=0.40 --extracted_forest_size_samples=30 --experiment_id=3 --models_dir=models/$dataset/stage4 --subsets_used train+dev,train+dev
     python code/compute_results.py --stage=4 --experiment_ids 1 2 3 --dataset_name=$dataset --models_dir=models/$dataset/stage4
 done