From 881106aee61f6e431ae764d01f064bb4418427fa Mon Sep 17 00:00:00 2001
From: Charly Lamothe <charly.lamothe@univ-amu.fr>
Date: Fri, 28 Feb 2020 18:12:28 +0100
Subject: [PATCH] Add weights saving. TODO: density plots at least
---
code/bolsonaro/trainer.py | 8 +++++---
code/compute_results.py | 19 +++++++++++++------
2 files changed, 18 insertions(+), 9 deletions(-)
diff --git a/code/bolsonaro/trainer.py b/code/bolsonaro/trainer.py
index 5920fa2..389ab9d 100644
--- a/code/bolsonaro/trainer.py
+++ b/code/bolsonaro/trainer.py
@@ -128,10 +128,12 @@ class Trainer(object):
"""
model_weights = ''
- if type(model) == RandomForestRegressor:
- model_weights = model.coef_
- elif type(model) == OmpForestRegressor:
+ 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_weights=model_weights,
diff --git a/code/compute_results.py b/code/compute_results.py
index bad281c..4fce327 100644
--- a/code/compute_results.py
+++ b/code/compute_results.py
@@ -28,13 +28,12 @@ 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)
experiment_score_metrics = list()
- all_weights = list()
-
# For each seed results stored in models/{experiment_id}/seeds
seeds = os.listdir(experiment_seed_root_path)
seeds.sort(key=int)
@@ -46,6 +45,7 @@ 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)
@@ -62,6 +62,8 @@ 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:
@@ -69,7 +71,8 @@ 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}
@@ -228,8 +231,6 @@ if __name__ == "__main__":
ylabel=experiments_score_metric,
title='Loss values of {}\nusing best and default hyperparameters'.format(args.dataset_name)
)
-
- Plotter.plot_weight_density()
elif args.stage == 2:
if len(args.experiment_ids) != 4:
raise ValueError('In the case of stage 2, the number of specified experiment ids must be 4.')
@@ -353,6 +354,9 @@ 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])
@@ -375,7 +379,7 @@ 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(
@@ -386,6 +390,9 @@ if __name__ == "__main__":
xlabel='Number of trees extracted',
ylabel=experiments_score_metric,
title='Loss values of {}\nusing best params of previous stages'.format(args.dataset_name))
+
+ # experiment_weights
+ #Plotter.weight_density(experiment_weights, output_path + os.sep + 'weight_density.png')
else:
raise ValueError('This stage number is not supported yet, but it will be!')
--
GitLab