train.py

from bolsonaro.data.dataset_parameters import DatasetParameters
from bolsonaro.data.dataset_loader import DatasetLoader
from bolsonaro.models.model_factory import ModelFactory
from bolsonaro.models.model_parameters import ModelParameters
from bolsonaro.trainer import Trainer
from bolsonaro.utils import resolve_experiment_id
from bolsonaro import LOG_PATH
from bolsonaro.error_handling.logger_factory import LoggerFactory

from dotenv import find_dotenv, load_dotenv
import argparse
import json
import pathlib
import random
import os
from concurrent import futures
import threading
import json


def process_job(seed, parameters, experiment_id, hyperparameters):
    """
    Experiment function.

    Will be used as base function for worker in multithreaded application.

    :param seed:
    :param parameters:
    :param experiment_id:
    :return:
    """
    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 = 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=parameters['dataset_name'],
        test_size=parameters['test_size'],
        dev_size=parameters['dev_size'],
        random_state=seed,
        dataset_normalizer=parameters['dataset_normalizer']
    )
    dataset_parameters.save(models_dir, experiment_id_str)
    dataset = DatasetLoader.load(dataset_parameters)

    trainer = Trainer(dataset)

    for extracted_forest_size in parameters['extracted_forest_size']:
        # question if training is too long, one may also split experiments for different forest sizes into different workers
        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(
            extracted_forest_size=extracted_forest_size,
            normalize_D=parameters['normalize_D'],
            subsets_used=parameters['subsets_used'],
            normalize_weights=parameters['normalize_weights'],
            seed=seed,
            hyperparameters=hyperparameters
        )
        model_parameters.save(sub_models_dir, experiment_id)

        model = ModelFactory.build(dataset.task, model_parameters)

        trainer.init(model)
        trainer.train(model)
        trainer.compute_results(model, sub_models_dir)
    logger.info('Training done')

if __name__ == "__main__":
    load_dotenv(find_dotenv('.env'))

    DEFAULT_EXPERIMENT_CONFIGURATION_PATH = 'experiments'
    DEFAULT_DATASET_NAME = 'boston'
    DEFAULT_NORMALIZE_D = False
    DEFAULT_DATASET_NORMALIZER = None
    DEFAULT_FOREST_SIZE = 100
    DEFAULT_EXTRACTED_FOREST_SIZE = 10
    # the models will be stored in a directory structure like: models/{experiment_id}/seeds/{seed_nb}/extracted_forest_size/{nb_extracted_trees}
    DEFAULT_MODELS_DIR = os.environ["project_dir"] + os.sep + 'models'
    DEFAULT_DEV_SIZE = 0.2
    DEFAULT_TEST_SIZE = 0.2
    DEFAULT_RANDOM_SEED_NUMBER = 1
    DEFAULT_SUBSETS_USED = 'train,dev'
    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).')
    parser.add_argument('--forest_size', nargs='?', type=int, default=DEFAULT_FOREST_SIZE, help='The number of trees of the random forest.')
    parser.add_argument('--extracted_forest_size', nargs='+', type=int, default=DEFAULT_EXTRACTED_FOREST_SIZE, help='The number of trees selected by OMP.')
    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=DEFAULT_DEV_SIZE, help='Dev subset ratio.')
    parser.add_argument('--test_size', nargs='?', type=float, default=DEFAULT_TEST_SIZE, help='Test subset ratio.')
    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.')
    args = parser.parse_args()

    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__))

    # The number of tree to extract from forest (K)
    parameters['extracted_forest_size'] = parameters['extracted_forest_size'] \
        if type(parameters['extracted_forest_size']) == list \
        else [parameters['extracted_forest_size']]

    hyperparameters_path = os.path.join('experiments', args.dataset_name, 'stage1', 'params.json')
    if os.path.exists(hyperparameters_path):
        logger.info("Hyperparameters found for this dataset at '{}'".format(hyperparameters_path))
        with open(hyperparameters_path, 'r+') as file_hyperparameter:
            hyperparameters = json.load(file_hyperparameter)['best_parameters']
    else:
        hyperparameters = {}

    if parameters['forest_size'] is not None:
        hyperparameters['n_estimators'] = parameters['forest_size']

    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 are either provided as parameters or generated at random
    seeds = parameters['seeds'] if parameters['seeds'] is not None \
        else [random.randint(begin_random_seed_range, end_random_seed_range) \
        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 coming 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,
            parameters, experiment_id, hyperparameters) for seed in seeds))