Merge branch 'master' into 15-integration-sota

code/bolsonaro/models/model_raw_results.py

@@ -6,12 +6,12 @@ import datetime
 
 class ModelRawResults(object):
 
-    def __init__(self, model_object, training_time,
+    def __init__(self, model_weights, training_time,
         datetime, train_score, dev_score, test_score,
         train_score_base, dev_score_base,
         test_score_base, score_metric, base_score_metric):
 
-        self._model_object = model_object
+        self._model_weights = model_weights
         self._training_time = training_time
         self._datetime = datetime
         self._train_score = train_score
@@ -24,8 +24,8 @@ class ModelRawResults(object):
         self._base_score_metric = base_score_metric
     
     @property
-    def model_object(self):
-        return self.model_object
+    def model_weights(self):
+        return self.model_weights
 
     @property
     def training_time(self):
@@ -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

@@ -8,6 +8,7 @@ from sklearn.base import BaseEstimator
 
 
 class OmpForest(BaseEstimator, metaclass=ABCMeta):
+
     def __init__(self, models_parameters, base_forest_estimator):
         self._base_forest_estimator = base_forest_estimator
         self._models_parameters = models_parameters
@@ -24,7 +25,6 @@ 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
@@ -96,6 +96,7 @@ class OmpForest(BaseEstimator, metaclass=ABCMeta):
         pass
 
 class SingleOmpForest(OmpForest):
+
     def __init__(self, models_parameters, base_forest_estimator):
         # fit_intercept shouldn't be set to False as the data isn't necessarily centered here
         # normalization is handled outsite OMP
@@ -123,3 +124,24 @@ 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

@@ -2,7 +2,6 @@ from bolsonaro.models.model_raw_results import ModelRawResults
 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.error_handling.logger_factory import LoggerFactory
 from bolsonaro.data.task import Task
 from . import LOG_PATH
@@ -96,21 +95,25 @@ class Trainer(object):
             )
         self._end_time = time.time()
 
-    def __score_func(self, model, X, y_true):
-        if type(model) in [OmpForestRegressor, RandomForestRegressor]:
+    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)
-        elif type(model) in [SimilarityForestRegressor, KMeansForestRegressor]:
-            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) == OmpForestRegressor:
             y_pred = model.predict_base_estimator(X)
             result = self._base_regression_score_metric(y_true, y_pred)
         elif type(model) in [OmpForestBinaryClassifier, OmpForestMulticlassClassifier]:
@@ -119,7 +122,7 @@ class Trainer(object):
         elif type(model) == RandomForestClassifier:
             y_pred = model.predict(X)
             result = self._base_classification_score_metric(y_true, y_pred)
-        elif type(model) is RandomForestRegressor:
+        elif type(model) in [RandomForestRegressor, SimilarityForestRegressor]:
             y_pred = model.predict(X)
             result = self._base_regression_score_metric(y_true, y_pred)
         return result
@@ -129,8 +132,17 @@ class Trainer(object):
         :param model: Object with
         :param models_dir: Where the results will be saved
         """
+
+        model_weights = ''
+        if type(model) in [OmpForestRegressor, OmpForestBinaryClassifier]:
+            model_weights = model._omp.coef_
+        elif type(model) == OmpForestMulticlassClassifier:
+            model_weights = model._dct_class_omp
+        elif type(model) == OmpForestBinaryClassifier:
+            model_weights = model._omp
+
         results = ModelRawResults(
-            model_object='',
+            model_weights=model_weights,
             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),
@@ -151,3 +163,29 @@ 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_weights='',
+                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

@@ -109,16 +109,16 @@ 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

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
 
@@ -28,6 +28,7 @@ def extract_scores_across_seeds_and_extracted_forest_sizes(models_dir, results_d
     experiment_train_scores = dict()
     experiment_dev_scores = dict()
     experiment_test_scores = dict()
+    experiment_weights = dict()
     all_extracted_forest_sizes = list()
 
     # Used to check if all losses were computed using the same metric (it should be the case)
@@ -44,14 +45,19 @@ def extract_scores_across_seeds_and_extracted_forest_sizes(models_dir, results_d
         experiment_train_scores[seed] = list()
         experiment_dev_scores[seed] = list()
         experiment_test_scores[seed] = list()
+        experiment_weights[seed] = list()
 
         # 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
@@ -60,6 +66,8 @@ def extract_scores_across_seeds_and_extracted_forest_sizes(models_dir, results_d
             experiment_test_scores[seed].append(model_raw_results.test_score)
             # Save the metric
             experiment_score_metrics.append(model_raw_results.score_metric)
+            # Save the weights
+            #experiment_weights[seed].append(model_raw_results.model_weights)
 
     # Sanity checks
     if len(set(experiment_score_metrics)) > 1:
@@ -67,7 +75,8 @@ def extract_scores_across_seeds_and_extracted_forest_sizes(models_dir, results_d
     if len(set([sum(extracted_forest_sizes) for extracted_forest_sizes in all_extracted_forest_sizes])) != 1:
         raise ValueError("The extracted forest sizes aren't the sames across seeds.")
 
-    return experiment_train_scores, experiment_dev_scores, experiment_test_scores, all_extracted_forest_sizes[0], experiment_score_metrics[0]
+    return experiment_train_scores, experiment_dev_scores, experiment_test_scores, \
+        all_extracted_forest_sizes[0], experiment_score_metrics[0]#, experiment_weights
 
 def extract_scores_across_seeds_and_forest_size(models_dir, results_dir, experiment_id, extracted_forest_sizes_number):
     experiment_id_path = models_dir + os.sep + str(experiment_id) # models/{experiment_id}
@@ -120,6 +129,7 @@ if __name__ == "__main__":
 
     DEFAULT_RESULTS_DIR = os.environ["project_dir"] + os.sep + 'results'
     DEFAULT_MODELS_DIR = os.environ["project_dir"] + os.sep + 'models'
+    DEFAULT_PLOT_WEIGHT_DENSITY = False
 
     parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
     parser.add_argument('--stage', nargs='?', type=int, required=True, help='Specify the stage number among [1, 5].')
@@ -130,6 +140,7 @@ if __name__ == "__main__":
     parser.add_argument('--dataset_name', nargs='?', type=str, required=True, help='Specify the dataset name. TODO: read it from models dir directly.')
     parser.add_argument('--results_dir', nargs='?', type=str, default=DEFAULT_RESULTS_DIR, help='The output directory of the results.')
     parser.add_argument('--models_dir', nargs='?', type=str, default=DEFAULT_MODELS_DIR, help='The output directory of the trained models.')
+    parser.add_argument('--plot_weight_density', action='store_true', default=DEFAULT_PLOT_WEIGHT_DENSITY, help='Plot the weight density. Only working for regressor models for now.')
     args = parser.parse_args()
 
     if args.stage not in list(range(1, 6)):
@@ -347,9 +358,17 @@ if __name__ == "__main__":
             extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir, args.experiment_ids[1])
         # omp_with_params
         logger.info('Loading omp_with_params experiment scores...')
+        """omp_with_params_train_scores, omp_with_params_dev_scores, omp_with_params_test_scores, _, \
+            omp_with_params_experiment_score_metric, experiment_weights = extract_scores_across_seeds_and_extracted_forest_sizes(
+                args.models_dir, args.results_dir, args.experiment_ids[2])"""
         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...')
@@ -369,13 +388,14 @@ if __name__ == "__main__":
             raise ValueError('Score metrics of all experiments must be the same.')
         experiments_score_metric = base_with_params_experiment_score_metric
 
-        output_path = os.path.join(args.results_dir, args.dataset_name, 'stage4')
+        output_path = os.path.join(args.results_dir, args.dataset_name, 'stage4_fix')
         pathlib.Path(output_path).mkdir(parents=True, exist_ok=True)
 
         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,

code/ensemble_selection.py

+# Implemenation of the paper 'Ensemble selection from libraries of models' by Rich Caruana et al.
+# A set of trees is trained, then those performing the best on the dev set are added to the forest.
+
+
+
+from sklearn.datasets import fetch_california_housing
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.externals import joblib
+import numpy as np
+from sklearn.metrics import r2_score
+import matplotlib.pyplot as plt
+
+(data, target) = fetch_california_housing(return_X_y=True)
+X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=10000, random_state=2019)
+X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=3000, random_state=2019)
+
+criterion_arr = ["mse"]#, "friedman_mse", "mae"]
+splitter_arr = ["best"]#, "random"]
+depth_arr = [i for i in range(5, 20, 1)]
+min_samples_split_arr = [i for i in range(2, 20, 1)]
+min_samples_leaf_arr = [i for i in range(2, 20, 1)]
+max_features_arr = ["sqrt"]#["auto", "sqrt", "log2"]
+
+library = list()
+
+for criterion in criterion_arr:
+    for splitter in splitter_arr:
+        for depth in depth_arr:
+            for min_samples_split in min_samples_split_arr:
+                for min_samples_leaf in min_samples_leaf_arr:
+                    for max_features in max_features_arr:
+                        t = DecisionTreeRegressor(criterion=criterion, splitter=splitter, max_depth=depth, min_samples_split=min_samples_split,
+                        min_samples_leaf=min_samples_leaf, max_features=max_features, random_state=2017)
+                        t.fit(X_train, y_train)
+                        #filename= "t_{}_{}_{}_{}_{}_{}.sav".format(criterion, splitter, depth, min_sample_split, min_sample_leaf, max_features)
+                        library.append(t)
+                        
+                                                
+print("classifiers", len(library))
+
+scores_list = list()
+
+for classif in library:
+    r2 = classif.score(X_val, y_val)
+    scores_list.append(r2)
+    
+print("scores", len(scores_list))
+#print(scores_list)
+
+##########################
+
+np_scores_list = np.array(scores_list)
+#sort_ind = np.argsort(np_scores_list)[::-1]
+#sorted_scores = [scores_list[i] for i in sort_ind]
+#sorted_class = [class_list[i] for i in sort_ind]
+
+#print(sorted_class)
+#print(sorted_scores)
+
+#res = list()
+#for s in [10, 20, 30]:
+#    best_class = sorted_class[:s]
+#    temp_res = list()
+#    for r in best_class:
+#        r2 = r.score(X_test, y_test)
+#        temp_res.append(r2)
+#    res.append(np.mean(temp_res))
+    
+#print("scores on test set", res)
+    
+
+###########################
+
+
+    
+
+
+
+
+#for k in range(num_sel_tree-1):
+#        cand_index = 0
+#        best_mean = 0
+#        #del scores_sel[-1]
+#        for j in range(len(scores_list)):
+#            scores_sel.append(scores_list[j])
+#            temp_scores_sel = np.array(scores_sel)
+#            temp_mean = np.mean(temp_scores_sel)
+#            if (temp_mean > best_mean):
+#                best_mean = temp_mean
+#                cand_index = j
+#            del scores_sel[-1]
+#        ens_sel.append(class_list[cand_index])
+#        scores_sel.append(scores_list[cand_index])
+#        del scores_list[cand_index]
+#        del class_list[cand_index]
+#print("selected models",ens_sel)
+#print("selected_scores", scores_sel)
+
+trees_in_forest = list()
+perf_prun_forest = list()
+
+for num_sel_tree in [2, 4, 6, 8, 10, 15, 20, 30, 40, 50]:
+    class_list = list(library)
+    print("class list", len(class_list))
+    m = np.argmax(np_scores_list)
+    ens_sel = [class_list[m]]
+    #scores_sel = [scores_list[m]]
+    #del scores_list[m]
+    temp_pred = class_list[m].predict(X_val)
+    del class_list[m]
+    #print("prima di entrare nel for", len(class_list))  
+    for k in range(num_sel_tree-1):
+        cand_index = 0
+        r2_best = -10000
+        #print("ad ogni loop", len(class_list))
+        for j in range(len(class_list)):
+            temp_pred = np.vstack((temp_pred, class_list[j].predict(X_val)))
+            temp_mean = np.mean(temp_pred, axis=0)
+            #print("temp pred and temp  mean shapes", temp_pred.shape, temp_mean.shape)
+            r2_temp = r2_score(y_val, temp_mean)
+            if (r2_temp > r2_best):
+                r2_best = r2_temp
+                cand_index = j
+            temp_pred = np.delete(temp_pred, -1, 0)
+            #print(temp_pred.shape)
+        ens_sel.append(class_list[cand_index])
+        #scores_sel.append(scores_list[cand_index])
+        temp_pred = np.vstack((temp_pred, class_list[cand_index].predict(X_val)))
+        #del scores_list[cand_index]
+        del class_list[cand_index]
+        
+    #print("ens_sel", len(ens_sel))
+    test_list = list()
+    for mod in ens_sel:
+        test_pred = mod.predict(X_test)
+        test_list.append(test_pred)
+        #print("scores sep", mod.score(X_test, y_test))
+        
+    test_list = np.array(test_list)
+    #print("test list shape", test_list.shape)
+    test_mean = np.mean(test_list, axis=0)
+    #print("test list shape", test_mean.shape)
+    r2_test = r2_score(test_mean, y_test)
+    #print(r2_test)
+    #print(ens_sel[0].score(X_test, y_test), ens_sel[1].score(X_test, y_test))
+    print(num_sel_tree, r2_test)
+    trees_in_forest.append(num_sel_tree)
+    perf_prun_forest.append(r2_test)
+
+
+print(trees_in_forest)
+print(perf_prun_forest)
+ax = plt.gca()
+ax.plot(trees_in_forest, perf_prun_forest, label='ensemble selection')
+ax.legend()
+#plt.title('fashion mnist')
+plt.xlabel('num trees')
+plt.ylabel('r2 score')
+plt.savefig("ensemble_selection.pdf") 
+plt.show()

code/forest_similarity.py

+from sklearn.datasets import fetch_california_housing
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.externals import joblib
+import numpy as np
+from sklearn.metrics import r2_score
+from sklearn.ensemble import RandomForestRegressor
+import matplotlib.pyplot as plt
+
+(data, target) = fetch_california_housing(return_X_y=True)
+X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=10000, random_state=2019)
+X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=3000, random_state=2019)
+
+num_trees = 100
+prun_for_size=[2, 4, 6, 8, 10, 12, 15, 20]
+
+randfor = RandomForestRegressor(num_trees, max_depth=7, random_state=2019)
+randfor.fit(X_train, y_train)
+randfor_pred = randfor.score(X_val, y_val)
+
+trees_forest = randfor.estimators_
+
+trees_in_forest = list()
+perf_prun_forest = list()
+
+for k in range(len(prun_for_size)):
+    ens_sel = list()
+    trees_list = list(randfor.estimators_)
+    #print("dovrebbe essere la taglia iniziale", len(trees_list))
+    for j in range(num_trees - prun_for_size[k]):
+        best_simil = 100000
+        cand_ind = 0
+        for i in range(len(trees_list)):
+            lonely_tree = trees_list[i]
+            del trees_list[i]
+            val_list = list()
+            #print("quando poto", len(trees_list))
+            for tree in trees_list:
+                val_pred = tree.predict(X_val)
+                val_list.append(val_pred)
+            val_list = np.array(val_list)
+            val_mean = np.mean(val_list, axis=0)
+            r2_val = r2_score(val_mean, y_val)
+            temp_simil = abs(randfor_pred-r2_val)
+            if (temp_simil < best_simil):
+                cand_ind = i
+                best_simil = temp_simil
+            trees_list.insert(i, lonely_tree)
+            #print("quando innesto", len(trees_list))
+        ens_sel.append(trees_list[cand_ind])
+        del trees_list[cand_ind]
+
+    prun_for = list(set(trees_forest) - set(ens_sel))
+    print("prun_for", len(prun_for))
+    print("trees forest", len(trees_forest))
+    print("ens_sel", len(ens_sel))
+
+    test_list = list()
+    for mod in prun_for:
+        test_pred = mod.predict(X_test)
+        test_list.append(test_pred)
+        #print("scores sep", mod.score(X_test, y_test))
+            
+    test_list = np.array(test_list)
+    #print("test list shape", test_list.shape)
+    test_mean = np.mean(test_list, axis=0)
+    #print("test list shape", test_mean.shape)
+    r2_test = r2_score(test_mean, y_test)
+    #print(r2_test)
+    #print(ens_sel[0].score(X_test, y_test), ens_sel[1].score(X_test, y_test))
+    print(len(prun_for), r2_test)
+    trees_in_forest.append(len(prun_for))
+    perf_prun_forest.append(r2_test)
+
+
+print(trees_in_forest)
+print(r2_test)
+ax = plt.gca()
+ax.plot(trees_in_forest, perf_prun_forest, label='pruned forest')
+ax.legend()
+#plt.title('fashion mnist')
+plt.xlabel('num trees')
+plt.ylabel('r2 score')
+plt.savefig("pruned_forest.pdf") 
+plt.show()

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 diabetes digits linnerud wine breast_cancer olivetti_faces 20newsgroups_vectorized lfw_pairs california_housing diamonds
 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/compute_results.py --stage=4 --experiment_ids 1 2 3 --dataset_name=$dataset --models_dir=models/$dataset/stage4
 done