diff --git a/code/train.py b/code/train.py
index 2d3264d6755c64be3c7bdb1b09785fa02266a2a1..dca0b2156267e9108082bf7051f02981badfbe07 100644
--- a/code/train.py
+++ b/code/train.py
@@ -14,24 +14,25 @@ import random
import os
from concurrent import futures
import threading
+import json
-def process_job(seed, args, experiment_id):
+def process_job(seed, parameters, experiment_id):
logger = LoggerFactory.create(LOG_PATH, 'training_seed{}_ti{}'.format(
seed, threading.get_ident()))
logger.info('seed={}'.format(seed))
seed_str = str(seed)
experiment_id_str = str(experiment_id)
- models_dir = args.models_dir + os.sep + experiment_id_str + os.sep + 'seeds' + \
+ models_dir = parameters['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,
+ name=parameters['dataset_name'],
+ test_size=parameters['test_size'],
+ dev_size=parameters['dev_size'],
random_state=seed,
- dataset_normalizer=args.dataset_normalizer
+ dataset_normalizer=parameters['dataset_normalizer']
)
dataset_parameters.save(models_dir, experiment_id_str)
@@ -39,17 +40,17 @@ def process_job(seed, args, experiment_id):
trainer = Trainer(dataset)
- for extracted_forest_size in args.extracted_forest_size:
+ for extracted_forest_size in parameters['extracted_forest_size']:
logger.info('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,
+ forest_size=parameters['forest_size'],
extracted_forest_size=extracted_forest_size,
- normalize_D=args.normalize_D,
- subsets_used=args.subsets_used,
- normalize_weights=args.normalize_weights,
+ normalize_D=parameters['normalize_D'],
+ subsets_used=parameters['subsets_used'],
+ normalize_weights=parameters['normalize_weights'],
seed=seed
)
model_parameters.save(sub_models_dir, experiment_id)
@@ -63,6 +64,7 @@ if __name__ == "__main__":
# get environment variables in .env
load_dotenv(find_dotenv('.env.example'))
+ DEFAULT_EXPERIMENT_CONFIGURATION_PATH = 'experiments'
DEFAULT_DATASET_NAME = 'boston'
DEFAULT_NORMALIZE_D = False
DEFAULT_DATASET_NORMALIZER = None
@@ -74,12 +76,14 @@ if __name__ == "__main__":
DEFAULT_TEST_SIZE = 0.2
DEFAULT_RANDOM_SEED_NUMBER = 1
DEFAULT_SUBSETS_USED = 'train,dev'
- DEFAULT_normalize_weights = False
+ DEFAULT_NORMALIZE_WEIGHTS = False
begin_random_seed_range = 1
end_random_seed_range = 2000
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+ parser.add_argument('--experiment_configuration', nargs='?', type=str, default=None, help='Specify an experiment configuration file name. Overload all other parameters.')
+ parser.add_argument('--experiment_configuration_path', nargs='?', type=str, default=DEFAULT_EXPERIMENT_CONFIGURATION_PATH, help='Specify the experiment configuration directory path.')
parser.add_argument('--dataset_name', nargs='?', type=str, default=DEFAULT_DATASET_NAME, help='Specify the dataset. Regression: boston, diabetes, linnerud, california_housing. Classification: iris, digits, wine, breast_cancer, olivetti_faces, 20newsgroups, 20newsgroups_vectorized, lfw_people, lfw_pairs, covtype, rcv1, kddcup99.')
parser.add_argument('--normalize_D', action='store_true', default=DEFAULT_NORMALIZE_D, help='Specify if we want to normalize the prediction of the forest by doing the L2 division of the pred vectors.')
parser.add_argument('--dataset_normalizer', nargs='?', type=str, default=DEFAULT_DATASET_NORMALIZER, help='Specify which dataset normalizer use (either standard, minmax, robust or normalizer).')
@@ -91,28 +95,50 @@ if __name__ == "__main__":
parser.add_argument('--random_seed_number', nargs='?', type=int, default=DEFAULT_RANDOM_SEED_NUMBER, help='Number of random seeds used.')
parser.add_argument('--seeds', nargs='+', type=int, default=None, help='Specific a list of seeds instead of generate them randomly')
parser.add_argument('--subsets_used', nargs='+', type=str, default=DEFAULT_SUBSETS_USED, help='train,dev: forest on train, OMP on dev. train+dev,train+dev: both forest and OMP on train+dev. train,train+dev: forest on train+dev and OMP on dev.')
- parser.add_argument('--normalize_weights', action='store_true', default=DEFAULT_normalize_weights, help='Divide the predictions by the weights sum.')
+ parser.add_argument('--normalize_weights', action='store_true', default=DEFAULT_NORMALIZE_WEIGHTS, help='Divide the predictions by the weights sum.')
args = parser.parse_args()
- pathlib.Path(args.models_dir).mkdir(parents=True, exist_ok=True)
+ if args.experiment_configuration:
+ with open(args.experiment_configuration_path + os.sep + \
+ args.experiment_configuration + '.json', 'r') as input_file:
+ parameters = json.load(input_file)
+ else:
+ parameters = args.__dict__
+
+ pathlib.Path(parameters['models_dir']).mkdir(parents=True, exist_ok=True)
logger = LoggerFactory.create(LOG_PATH, os.path.basename(__file__))
- args.extracted_forest_size = args.extracted_forest_size \
- if type(args.extracted_forest_size) == list \
- else [args.extracted_forest_size]
+ parameters['extracted_forest_size'] = parameters['extracted_forest_size'] \
+ if type(parameters['extracted_forest_size']) == list \
+ else [parameters['extracted_forest_size']]
- if args.seeds != None and args.random_seed_number > 1:
+ 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.')
- seeds = args.seeds if args.seeds is not None \
+ seeds = parameters['seeds'] if parameters['seeds'] is not None \
else [random.randint(begin_random_seed_range, end_random_seed_range) \
- for i in range(args.random_seed_number)]
-
- experiment_id = resolve_experiment_id(args.models_dir)
+ for i in range(parameters['random_seed_number'])]
+ # Resolve the next experiment id number (last id + 1)
+ experiment_id = resolve_experiment_id(parameters['models_dir'])
logger.info('Experiment id: {}'.format(experiment_id))
+ """
+ If the experiment configuration isn't comming from
+ an already existing file, save it to a json file to
+ keep trace of it.
+ """
+ if args.experiment_configuration is None:
+ with open(args.experiment_configuration_path + os.sep + 'unnamed_{}.json'.format(
+ experiment_id), 'w') as output_file:
+ json.dump(
+ parameters,
+ output_file,
+ indent=4
+ )
+
+ # Train as much job as there are seeds
with futures.ProcessPoolExecutor(len(seeds)) as executor:
list(f.result() for f in futures.as_completed(executor.submit(process_job, seed,
- args, experiment_id) for seed in seeds))
+ parameters, experiment_id) for seed in seeds))