- Add progress bar during training;

- Update requirements packages.

code/train.py

@@ -12,7 +12,7 @@ import argparse
 import pathlib
 import random
 import os
-import errno
+from tqdm import tqdm
 
 
 if __name__ == "__main__":
@@ -67,51 +67,42 @@ if __name__ == "__main__":
     experiment_id = resolve_experiment_id(args.models_dir)
     experiment_id_str = str(experiment_id)
 
-    for seed in seeds:
-        logger.debug('Seed={}'.format(seed))
-        seed_str = str(seed)
-        models_dir = args.models_dir + os.sep + experiment_id_str + os.sep + 'seeds' + \
-            os.sep + seed_str
-        try:
-            os.makedirs(models_dir)
-        except OSError as e:
-            if e.errno != errno.EEXIST:
-                raise
-
-        dataset_parameters = DatasetParameters(
-            name=args.dataset_name,
-            test_size=args.test_size,
-            dev_size=args.dev_size,
-            random_state=seed,
-            normalize=normalize,
-            train_on_subset=args.train_on_subset
-        )
-        dataset_parameters.save(models_dir, experiment_id_str)
-
-        dataset = DatasetLoader.load(dataset_parameters)
-
-        trainer = Trainer(dataset)
-
-        for extracted_forest_size in args.extracted_forest_size:
-            logger.debug('extracted_forest_size={}'.format(extracted_forest_size))
-            sub_models_dir = models_dir + os.sep + 'extracted_forest_size' + os.sep + str(extracted_forest_size)
-            try:
-                os.makedirs(sub_models_dir)
-            except OSError as e:
-                if e.errno != errno.EEXIST:
-                    raise
-
-            model_parameters = ModelParameters(
-                forest_size=args.forest_size,
-                extracted_forest_size=extracted_forest_size,
-                seed=seed,
-                normalize=normalize
+    with tqdm(seeds) as seed_bar:
+        for seed in seed_bar:
+            seed_bar.set_description('seed={}'.format(seed))
+            seed_str = str(seed)
+            models_dir = args.models_dir + os.sep + experiment_id_str + os.sep + 'seeds' + \
+                os.sep + seed_str
+            pathlib.Path(models_dir).mkdir(parents=True, exist_ok=True)
+
+            dataset_parameters = DatasetParameters(
+                name=args.dataset_name,
+                test_size=args.test_size,
+                dev_size=args.dev_size,
+                random_state=seed,
+                normalize=normalize,
+                train_on_subset=args.train_on_subset
             )
-            model_parameters.save(sub_models_dir, experiment_id)
+            dataset_parameters.save(models_dir, experiment_id_str)
 
-            model = ModelFactory.build(dataset.task, model_parameters)
+            dataset = DatasetLoader.load(dataset_parameters)
 
-            trainer.train(model, sub_models_dir)
+            trainer = Trainer(dataset)
 
-            logger.info('Error on test set: {}'.format(model.score(dataset.X_test, dataset.y_test)))
-            logger.info('Accuracy on test set: {}'.format(model.score_regressor(dataset.X_test, dataset.y_test)))
+            with tqdm(args.extracted_forest_size) as extracted_forest_size_bar:
+                for extracted_forest_size in extracted_forest_size_bar:
+                    extracted_forest_size_bar.set_description('extracted_forest_size={}'.format(extracted_forest_size))
+                    sub_models_dir = models_dir + os.sep + 'extracted_forest_size' + os.sep + str(extracted_forest_size)
+                    pathlib.Path(sub_models_dir).mkdir(parents=True, exist_ok=True)
+
+                    model_parameters = ModelParameters(
+                        forest_size=args.forest_size,
+                        extracted_forest_size=extracted_forest_size,
+                        seed=seed,
+                        normalize=normalize
+                    )
+                    model_parameters.save(sub_models_dir, experiment_id)
+
+                    model = ModelFactory.build(dataset.task, model_parameters)
+
+                    trainer.train(model, sub_models_dir)

requirements.txt

@@ -8,4 +8,6 @@ coverage
 awscli
 flake8
 python-dotenv>=0.5.1
-scikit-learn
\ No newline at end of file
+scikit-learn
+python-dotenv
+tqdm
\ No newline at end of file