CTR-DeepFM/DeepFM_with_csv_input.py at main · anilanlar/CTR-DeepFM · GitHub

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import sys
sys.path.append('../')
import os
import logging
from fuxictr.utils import load_config, set_logger, print_to_json
from fuxictr.features import FeatureMap
from fuxictr.pytorch.torch_utils import seed_everything
from fuxictr.pytorch.dataloaders import H5DataLoader
from fuxictr.preprocess import FeatureProcessor, build_dataset
from model_zoo import DeepFM


if __name__ == '__main__':
    # Load params from config files
    # config_dir = './config/example3_config'
    config_dir = os.path.join(os.path.dirname(__file__), 'config')
    experiment_id = 'DeepFM_test_csv' # corresponds to h5 input `data/tiny_h5`
    params = load_config(config_dir, experiment_id)

    # set up logger and random seed
    set_logger(params)
    logging.info("Params: " + print_to_json(params))
    seed_everything(seed=params['seed'])

    # Set feature_encoder that defines how to preprocess data
    feature_encoder = FeatureProcessor(feature_cols=params["feature_cols"],
                                       label_col=params["label_col"],
                                       dataset_id=params["dataset_id"],
                                       data_root=params["data_root"])

    # Build dataset from csv to h5, and remap data paths to h5 files
    params["train_data"], params["valid_data"], params["test_data"] = \
        build_dataset(feature_encoder,
                      train_data=params["train_data"],
                      valid_data=params["valid_data"],
                      test_data=params["test_data"])

    # Get feature_map that defines feature specs
    data_dir = os.path.join(params['data_root'], params['dataset_id'])
    feature_map = FeatureMap(params['dataset_id'], data_dir)
    feature_map.load(os.path.join(data_dir, "feature_map.json"), params)
    logging.info("Feature specs: " + print_to_json(feature_map.features))

    # Get train and validation data generators from h5
    train_gen, valid_gen = H5DataLoader(feature_map,
                                        stage='train',
                                        train_data=params['train_data'],
                                        valid_data=params['valid_data'],
                                        batch_size=params['batch_size'],
                                        shuffle=params['shuffle']).make_iterator()

    # Model initialization and fitting
    model = DeepFM(feature_map, **params)
    model.fit(train_gen, validation_data=valid_gen, epochs=params['epochs'])

    logging.info('***** Validation evaluation *****')
    model.evaluate(valid_gen)

    logging.info('***** Test evaluation *****')
    test_gen = H5DataLoader(feature_map,
                            stage='test',
                            test_data=params['test_data'],
                            batch_size=params['batch_size'],
                            shuffle=False).make_iterator()
    model.evaluate(test_gen)