diff --git a/code/bolsonaro/visualization/plotter.py b/code/bolsonaro/visualization/plotter.py
index 5a5f72ad9fade836dcfed3c2ef6f452653dcf3d1..7d3154e1b15f153c85ef0be360fd990f4395fde5 100644
--- a/code/bolsonaro/visualization/plotter.py
+++ b/code/bolsonaro/visualization/plotter.py
@@ -51,6 +51,7 @@ class Plotter(object):
@staticmethod
def plot_mean_and_CI(ax, mean, lb, ub, x_value, color_mean=None, facecolor=None, label=None):
+ #print(x_value, mean, lb, ub)
# plot the shaded range of the confidence intervals
ax.fill_between(x_value, ub, lb, facecolor=facecolor, alpha=.5)
# plot the mean on top
@@ -105,7 +106,7 @@ class Plotter(object):
@staticmethod
def plot_stage2_losses(file_path, all_experiment_scores, x_value,
- xlabel, ylabel, all_labels, title):
+ xlabel, ylabel, all_labels, title, filter_num=-1):
fig, ax = plt.subplots()
@@ -124,13 +125,14 @@ class Plotter(object):
# 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
Plotter.plot_mean_and_CI(
ax=ax,
mean=mean_experiment_scores,
lb=mean_experiment_scores + std_experiment_scores,
ub=mean_experiment_scores - std_experiment_scores,
- x_value=x_value,
+ x_value=x_value[:filter_num] if len(mean_experiment_scores) == filter_num else x_value,
color_mean=colors[i],
facecolor=colors[i],
label=all_labels[i]
diff --git a/code/compute_results.py b/code/compute_results.py
index 28b08ac26599fd716d130d06ef80b54e2d1338e7..0d3850931c83f1a1ef349e519501b4d3955cb684 100644
--- a/code/compute_results.py
+++ b/code/compute_results.py
@@ -150,6 +150,35 @@ def extract_weights_across_seeds(models_dir, results_dir, experiment_id):
return experiment_weights
+def extract_correlations_across_seeds(models_dir, results_dir, experiment_id):
+ 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
+ experiment_correlations = dict()
+
+ # For each seed results stored in models/{experiment_id}/seeds
+ seeds = os.listdir(experiment_seed_root_path)
+ seeds.sort(key=int)
+ for seed in seeds:
+ experiment_seed_path = experiment_seed_root_path + os.sep + seed # models/{experiment_id}/seeds/{seed}
+ extracted_forest_sizes_root_path = experiment_seed_path + os.sep + 'extracted_forest_sizes' # models/{experiment_id}/seeds/{seed}/forest_size
+
+ # {{seed}:[]}
+ experiment_correlations[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)
+ for extracted_forest_size in extracted_forest_sizes:
+ # models/{experiment_id}/seeds/{seed}/extracted_forest_sizes/{extracted_forest_size}
+ extracted_forest_size_path = extracted_forest_sizes_root_path + os.sep + extracted_forest_size
+ # 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 weights
+ experiment_correlations[seed].append(model_raw_results.correlation)
+
+ return experiment_correlations
+
def extract_coherences_across_seeds(models_dir, results_dir, experiment_id):
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
@@ -179,7 +208,6 @@ def extract_coherences_across_seeds(models_dir, results_dir, experiment_id):
return experiment_coherences
-
if __name__ == "__main__":
# get environment variables in .env
load_dotenv(find_dotenv('.env'))
@@ -203,6 +231,7 @@ if __name__ == "__main__":
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.')
parser.add_argument('--wo_loss_plots', action='store_true', default=DEFAULT_WO_LOSS_PLOTS, help='Do not compute the loss plots.')
parser.add_argument('--plot_preds_coherence', action='store_true', default=DEFAULT_PLOT_PREDS_COHERENCE, help='Plot the coherence of the prediction trees.')
+ parser.add_argument('--plot_preds_correlation', action='store_true', default=DEFAULT_PLOT_PREDS_COHERENCE, help='Plot the correlation of the prediction trees.')
args = parser.parse_args()
if args.stage not in list(range(1, 6)):
@@ -501,8 +530,20 @@ if __name__ == "__main__":
omp_with_params_experiment_score_metric = extract_scores_across_seeds_and_extracted_forest_sizes(
args.models_dir, args.results_dir, int(args.experiment_ids[2]), weights=False, extracted_forest_sizes=extracted_forest_sizes)
+ """print(omp_with_params_dev_scores)
+ import sys
+ sys.exit(0)"""
+
+ #all_labels = ['base', 'random', 'omp', 'omp_wo_weights']
all_labels = ['base', 'random', 'omp']
- all_scores = [base_with_params_test_scores, random_with_params_test_scores, omp_with_params_test_scores]
+ omp_with_params_test_scores_new = dict()
+ filter_num = -1
+ """filter_num = 9
+ for key, value in omp_with_params_test_scores.items():
+ omp_with_params_test_scores_new[key] = value[:filter_num]"""
+ #all_scores = [base_with_params_test_scores, random_with_params_test_scores, omp_with_params_test_scores,
+ # omp_with_params_without_weights_test_scores]
+ all_scores = [base_with_params_dev_scores, random_with_params_dev_scores, omp_with_params_dev_scores]
#all_scores = [base_with_params_train_scores, random_with_params_train_scores, omp_with_params_train_scores,
# omp_with_params_without_weights_train_scores]
@@ -515,29 +556,32 @@ if __name__ == "__main__":
label = 'similarity_predictions'
elif 'ensemble' in args.experiment_ids[i]:
label = 'ensemble'
+ elif 'omp_distillation' in args.experiment_ids[i]:
+ label = 'omp_distillation'
else:
logger.error('Invalid value encountered')
continue
logger.info(f'Loading {label} experiment scores...')
current_experiment_id = int(args.experiment_ids[i].split('=')[1])
- current_train_scores, _, current_test_scores, _, _ = extract_scores_across_seeds_and_extracted_forest_sizes(
+ current_train_scores, current_dev_scores, current_test_scores, _, _ = extract_scores_across_seeds_and_extracted_forest_sizes(
args.models_dir, args.results_dir, current_experiment_id)
all_labels.append(label)
- all_scores.append(current_test_scores)
+ #all_scores.append(current_test_scores)
#all_scores.append(current_train_scores)
+ all_scores.append(current_dev_scores)
- output_path = os.path.join(args.results_dir, args.dataset_name, 'stage5')
+ output_path = os.path.join(args.results_dir, args.dataset_name, 'stage5_new')
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_train,dev.png",
+ file_path=output_path + os.sep + f"losses_{'-'.join(all_labels)}_dev_clean.png",
all_experiment_scores=all_scores,
all_labels=all_labels,
x_value=with_params_extracted_forest_sizes,
xlabel='Number of trees extracted',
ylabel=base_with_params_experiment_score_metric,
- title='Loss values of {}\nusing best params of previous stages'.format(args.dataset_name))
+ title='Loss values of {}\nusing best params of previous stages'.format(args.dataset_name), filter_num=filter_num)
"""if args.plot_weight_density:
root_output_path = os.path.join(args.results_dir, args.dataset_name, f'stage{args.stage}')
@@ -581,14 +625,14 @@ if __name__ == "__main__":
experiment_weights = extract_weights_across_seeds(args.models_dir, args.results_dir, experiment_id)
Plotter.weight_density(experiment_weights, os.path.join(root_output_path, f'weight_density_{experiment_label}.png'))
if args.plot_preds_coherence:
- root_output_path = os.path.join(args.results_dir, args.dataset_name, f'stage5')
- all_labels = ['random', 'omp', 'omp_normalize_D']
- random_with_params_train_scores, random_with_params_dev_scores, random_with_params_test_scores, \
- with_params_extracted_forest_sizes, random_with_params_experiment_score_metric = \
+ root_output_path = os.path.join(args.results_dir, args.dataset_name, f'stage5_new')
+ pathlib.Path(root_output_path).mkdir(parents=True, exist_ok=True)
+ all_labels = ['random', 'omp', 'kmeans', 'similarity_similarities', 'similarity_predictions', 'ensemble']
+ _, _, _, with_params_extracted_forest_sizes, _ = \
extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir, 2)
- coherence_values = [extract_coherences_across_seeds(args.models_dir, args.results_dir, i) for i in [2, 3, 4]]
+ coherence_values = [extract_coherences_across_seeds(args.models_dir, args.results_dir, i) for i in [2, 3, 5, 6, 7, 8]]
Plotter.plot_stage2_losses(
- file_path=root_output_path + os.sep + f"coherences_{'-'.join(all_labels)}_30_all.png",
+ file_path=root_output_path + os.sep + f"coherences_{'-'.join(all_labels)}.png",
all_experiment_scores=coherence_values,
all_labels=all_labels,
x_value=with_params_extracted_forest_sizes,
@@ -596,5 +640,21 @@ if __name__ == "__main__":
ylabel='Coherence',
title='Coherence values of {}'.format(args.dataset_name))
logger.info(f'Computing preds coherence plot...')
+ if args.plot_preds_correlation:
+ root_output_path = os.path.join(args.results_dir, args.dataset_name, f'stage5_new')
+ pathlib.Path(root_output_path).mkdir(parents=True, exist_ok=True)
+ all_labels = ['random', 'omp', 'kmeans', 'similarity_similarities', 'similarity_predictions', 'ensemble']
+ _, _, _, with_params_extracted_forest_sizes, _ = \
+ extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir, 2)
+ correlation_values = [extract_correlations_across_seeds(args.models_dir, args.results_dir, i) for i in [2, 3, 5, 6, 7, 8]]
+ Plotter.plot_stage2_losses(
+ file_path=root_output_path + os.sep + f"correlations_{'-'.join(all_labels)}.png",
+ all_experiment_scores=correlation_values,
+ all_labels=all_labels,
+ x_value=with_params_extracted_forest_sizes,
+ xlabel='Number of trees extracted',
+ ylabel='correlation',
+ title='correlation values of {}'.format(args.dataset_name))
+ logger.info(f'Computing preds correlation plot...')
logger.info('Done.')
diff --git a/code/prepare_models.py b/code/prepare_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..04a2ec3f4446b48268bddaa2e452ef31af9d7eb3
--- /dev/null
+++ b/code/prepare_models.py
@@ -0,0 +1,31 @@
+import pathlib
+import glob2
+import os
+import shutil
+from tqdm import tqdm
+
+
+if __name__ == "__main__":
+ models_source_path = 'models'
+ models_destination_path = 'bolsonaro_models_25-03-20'
+ datasets = ['boston', 'diabetes', 'linnerud', 'breast_cancer', 'california_housing', 'diamonds',
+ 'steel-plates', 'kr-vs-kp', 'kin8nm', 'spambase', 'gamma', 'lfw_pairs']
+
+ pathlib.Path(models_destination_path).mkdir(parents=True, exist_ok=True)
+
+ with tqdm(datasets) as dataset_bar:
+ for dataset in dataset_bar:
+ dataset_bar.set_description(dataset)
+ found_paths = glob2.glob(os.path.join(models_source_path, dataset, 'stage5_new',
+ '**', 'model_raw_results.pickle'), recursive=True)
+ pathlib.Path(os.path.join(models_destination_path, dataset)).mkdir(parents=True, exist_ok=True)
+ with tqdm(found_paths) as found_paths_bar:
+ for path in found_paths_bar:
+ found_paths_bar.set_description(path)
+ new_path = path.replace(f'models/{dataset}/stage5_new/', '')
+ (new_path, filename) = os.path.split(new_path)
+ new_path = os.path.join(models_destination_path, dataset, new_path)
+ pathlib.Path(new_path).mkdir(parents=True, exist_ok=True)
+ shutil.copyfile(src=path, dst=os.path.join(new_path, filename))
+ found_paths_bar.update(1)
+ dataset_bar.update(1)