example3_DeepFM_with_npz_input.py

# =========================================================================
# Copyright (C) 2024. The FuxiCTR Library. All rights reserved.
# 
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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# =========================================================================

import sys
sys.path.append('../')
import os
import logging
from fuxictr import datasets
from datetime import datetime
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 RankDataLoader
from model_zoo import DeepFM


if __name__ == '__main__':
    # Load params from config files
    config_dir = './config/example3_config'
    experiment_id = 'DeepFM_test_npz' # corresponds to input `data/tiny_npz`
    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'])

    # Load feature_map from json
    data_dir = os.path.join(params['data_root'], params['dataset_id'])
    feature_map_json = os.path.join(data_dir, "feature_map.json")
    feature_map = FeatureMap(params['dataset_id'], data_dir)
    feature_map.load(feature_map_json, params)
    logging.info("Feature specs: " + print_to_json(feature_map.features))
    
    # Get train and validation data generators
    train_gen, valid_gen = RankDataLoader(feature_map,
                                          stage='train',
                                          train_data=params['train_data'],
                                          valid_data=params['valid_data'],
                                          batch_size=params['batch_size'],
                                          data_format=params["data_format"],
                                          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 = RankDataLoader(feature_map, 
                              stage='test',
                              test_data=params['test_data'],
                              batch_size=params['batch_size'],
                              data_format=params["data_format"],
                              shuffle=False).make_iterator()
    model.evaluate(test_gen)