POC of possible wrong way to compute best hyperparams. Are there the best only...

POC of possible wrong way to compute best hyperparams. Are there the best only before the application of OMP extraction?

code/bolsonaro/data/dataset_loader.py

@@ -19,7 +19,8 @@ class DatasetLoader(object):
     DEFAULT_NORMALIZE_D = False
     DEFAULT_DATASET_NORMALIZER = 'standard'
     DEFAULT_FOREST_SIZE = 100
-    DEFAULT_EXTRACTED_FOREST_SIZE_SAMPLES = 4
+    DEFAULT_EXTRACTED_FOREST_SIZE_SAMPLES = 10
+    DEFAULT_EXTRACTED_FOREST_SIZE_STOP = 0.3
     DEFAULT_DEV_SIZE = 0.2
     DEFAULT_TEST_SIZE = 0.2
     DEFAULT_RANDOM_SEED_NUMBER = 1

code/compute_results.py

@@ -24,13 +24,79 @@ if __name__ == "__main__":
     parser.add_argument('--models_dir', nargs='?', type=str, default=DEFAULT_MODELS_DIR, help='The output directory of the trained models.')
     args = parser.parse_args()
 
-    if int(args.stage_number) not in list(range(1, 5)):
+    if args.stage_number not in list(range(1, 5)):
         raise ValueError('stage_number must be a supported stage id (i.e. [1, 4]).')
 
     # Create recursively the results dir tree
     pathlib.Path(args.results_dir).mkdir(parents=True, exist_ok=True)
 
+    if args.stage_number == 1:
+        for experiment_id in args.experiment_ids:
+            experiment_id_path = args.models_dir + os.sep + str(experiment_id) # models/{experiment_id}
+            # Create recursively the tree results/{experiment_id}
+            pathlib.Path(args.results_dir + os.sep + str(experiment_id)).mkdir(parents=True, exist_ok=True)
+            experiment_seed_root_path = experiment_id_path + os.sep + 'seeds' # models/{experiment_id}/seeds
 
+            """
+            Dictionaries to temporarly store the scalar results with the following structure:
+            {seed_1: [score_1, ..., score_m], ... seed_n: [score_1, ..., score_k]}
+            TODO: to complete to retreive more results
+            """
+            experiment_train_scores = dict()
+            experiment_dev_scores = dict()
+            experiment_test_scores = dict()
+
+            # Used to check if all losses were computed using the same metric (it should be the case)
+            experiment_score_metrics = list()
+
+            # For each seed results stored in models/{experiment_id}/seeds
+            for seed in os.listdir(experiment_seed_root_path):
+                experiment_seed_path = experiment_seed_root_path + os.sep + seed # models/{experiment_id}/seeds/{seed}
+                dataset_parameters = DatasetParameters.load(experiment_seed_path, experiment_id) # Load the dataset parameters of this experiment, with this specific seed
+                dataset = DatasetLoader.load(dataset_parameters) # Load the dataset using the previously loaded dataset parameters
+                extracted_forest_size_root_path = experiment_seed_path + os.sep + 'extracted_forest_size' # models/{experiment_id}/seeds/{seed}/extracted_forest_size
+
+                # {{seed}:[]}
+                experiment_train_scores[seed] = list()
+                experiment_dev_scores[seed] = list()
+                experiment_test_scores[seed] = list()
+
+                # List the forest sizes in models/{experiment_id}/seeds/{seed}/extracted_forest_size
+                extracted_forest_sizes = os.listdir(extracted_forest_size_root_path)
+                extracted_forest_sizes.sort(key=int)
+                for extracted_forest_size in extracted_forest_sizes:
+                    # models/{experiment_id}/seeds/{seed}/extracted_forest_size/{extracted_forest_size}
+                    extracted_forest_size_path = extracted_forest_size_root_path + os.sep + extracted_forest_size
+                    # Load models/{experiment_id}/seeds/{seed}/extracted_forest_size/{extracted_forest_size}/model_raw_results.pickle file
+                    model_raw_results = ModelRawResults.load(extracted_forest_size_path)
+                    # Save temporarly some raw results (TODO: to complete to retreive more results)
+                    # Save the scores
+                    experiment_train_scores[seed].append(model_raw_results.train_score)
+                    experiment_dev_scores[seed].append(model_raw_results.dev_score)
+                    experiment_test_scores[seed].append(model_raw_results.test_score)
+                    # Save the metric
+                    experiment_score_metrics.append(model_raw_results.score_metric)
+
+            if len(set(experiment_score_metrics)) > 1:
+                raise ValueError("The metrics used to compute the dev score aren't the same everytime")
+
+            """
+            Example of plot that just plots the losses computed
+            on the train, dev and test subsets using a trained
+            model, with the CI, and depending on the extracted
+            forest size.
+            """
+            Plotter.plot_losses(
+                file_path=args.results_dir + os.sep + str(experiment_id) + os.sep + 'losses.png',
+                all_experiment_scores=[experiment_train_scores, experiment_dev_scores, experiment_test_scores],
+                x_value=extracted_forest_sizes,
+                xlabel='Number of trees extracted',
+                ylabel=experiment_score_metrics[0],
+                all_labels=['train', 'dev', 'test'],
+                title='Loss values of the trained model'
+            )
+    else:
+        raise ValueError('This stage number is not supported yet, but it will be!')
 
     """
     TODO:

code/train.py

@@ -97,6 +97,7 @@ if __name__ == "__main__":
     parser.add_argument('--dataset_normalizer', nargs='?', type=str, default=DatasetLoader.DEFAULT_DATASET_NORMALIZER, help='Specify which dataset normalizer use (either standard, minmax, robust or normalizer).')
     parser.add_argument('--forest_size', nargs='?', type=int, default=None, help='The number of trees of the random forest.')
     parser.add_argument('--extracted_forest_size_samples', nargs='?', type=int, default=DatasetLoader.DEFAULT_EXTRACTED_FOREST_SIZE_SAMPLES, help='The number of extracted forest sizes (proportional to the forest size) selected by OMP.')
+    parser.add_argument('--extracted_forest_size_stop', nargs='?', type=float, default=DatasetLoader.DEFAULT_EXTRACTED_FOREST_SIZE_STOP, help='Specify the upper bound of the extracted forest sizes linspace.')
     parser.add_argument('--models_dir', nargs='?', type=str, default=DEFAULT_MODELS_DIR, help='The output directory of the trained models.')
     parser.add_argument('--dev_size', nargs='?', type=float, default=DatasetLoader.DEFAULT_DEV_SIZE, help='Dev subset ratio.')
     parser.add_argument('--test_size', nargs='?', type=float, default=DatasetLoader.DEFAULT_TEST_SIZE, help='Test subset ratio.')
@@ -142,9 +143,10 @@ if __name__ == "__main__":
         hyperparameters['n_estimators'] = parameters['forest_size']
 
     # The number of tree to extract from forest (K)
-    parameters['extracted_forest_size'] = [int(hyperparameters['n_estimators'] * coeff) \
-         for coeff in np.linspace(0, 1, parameters['extracted_forest_size_samples'] + 1,
-         endpoint=False)[1:]]
+    parameters['extracted_forest_size'] = (hyperparameters['n_estimators'] *
+        np.linspace(0, args.extracted_forest_size_stop,
+        parameters['extracted_forest_size_samples'] + 1,
+        endpoint=False)[1:]).astype(np.int).tolist()
 
     if parameters['seeds'] != None and parameters['random_seed_number'] > 1:
         logger.warning('seeds and random_seed_number parameters are both specified. Seeds will be used.')    

experiments/iris/stage1/with_best_params_16.json

-{
-    "experiment_configuration": null,
-    "experiment_configuration_path": "experiments",
-    "dataset_name": "iris",
-    "normalize_D": false,
-    "dataset_normalizer": "standard",
-    "forest_size": null,
-    "extracted_forest_size_samples": 4,
-    "models_dir": ".\\models",
-    "dev_size": 0.2,
-    "test_size": 0.2,
-    "random_seed_number": 1,
-    "seeds": [
-        1,
-        2,
-        3,
-        4,
-        5
-    ],
-    "subsets_used": "train,dev",
-    "normalize_weights": false,
-    "verbose": false,
-    "skip_best_hyperparams": false,
-    "save_experiment_configuration": [
-        "1",
-        "with_best_params"
-    ],
-    "job_number": -1,
-    "extracted_forest_size": [
-        200,
-        400,
-        600,
-        800
-    ],
-    "experiment_id": 16
-}
\ No newline at end of file

experiments/iris/stage1/wo_best_params_17.json

-{
-    "experiment_configuration": null,
-    "experiment_configuration_path": "experiments",
-    "dataset_name": "iris",
-    "normalize_D": false,
-    "dataset_normalizer": "standard",
-    "forest_size": null,
-    "extracted_forest_size_samples": 4,
-    "models_dir": ".\\models",
-    "dev_size": 0.2,
-    "test_size": 0.2,
-    "random_seed_number": 1,
-    "seeds": [
-        1,
-        2,
-        3,
-        4,
-        5
-    ],
-    "subsets_used": "train,dev",
-    "normalize_weights": false,
-    "verbose": false,
-    "skip_best_hyperparams": true,
-    "save_experiment_configuration": [
-        "1",
-        "wo_best_params"
-    ],
-    "job_number": -1,
-    "extracted_forest_size": [
-        20,
-        40,
-        60,
-        80
-    ],
-    "experiment_id": 17
-}
\ No newline at end of file