GraphCTR/code/train.py at main · CRIPAC-DIG/GraphCTR · GitHub

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import math
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.metrics import make_scorer
from sklearn.model_selection import StratifiedKFold
from time import time
from .autoint import AutoInt
from .fignn import FiGNN
from .graphfm import GraphFM
from .InterHAt import InterHAt
import argparse
import os


def str2list(v):
    v=v.split(',')
    v=[int(_.strip('[]')) for _ in v]

    return v


def str2list2(v):
    v=v.split(',')
    v=[float(_.strip('[]')) for _ in v]

    return v


def str2bool(v):
    if v.lower() in ['yes', 'true', 't', 'y', '1']:
        return True
    elif v.lower() in ['no', 'false', 'f', 'n', '0']:
        return False
    else:
        raise argparse.ArgumentTypeError('Unsupported value encountered.')

def parse_args():
    parser = argparse.ArgumentParser()

    parser.add_argument('--is_save', action='store_true')
    parser.add_argument('--greater_is_better', action='store_true', help='early stop criterion')
    parser.add_argument('--has_residual', action='store_true', help='add residual')

    parser.add_argument('--model_type', type=str, default='FiGNN')

    parser.add_argument('--k', type=int, default=15, help='keep the top k nodes')
    parser.add_argument('--blocks', type=int, default=2, help='#blocks')
    parser.add_argument('--block_shape', type=str2list, default=[16,16], help='output shape of each block')
    parser.add_argument('--ks', type=str2list, default=[39, 10, 5], help='the size of sampled neighborhood')
    parser.add_argument('--heads', type=int, default=2, help='#heads')
    parser.add_argument('--embedding_size', type=int, default=16)
    parser.add_argument('--dropout_keep_prob', type=str2list2, default=[1, 1, 0.5])
    parser.add_argument('--epoch', type=int, default=2)
    parser.add_argument('--batch_size', type=int, default=1024)
    parser.add_argument('--learning_rate', type=float, default=0.001)
    parser.add_argument('--optimizer_type', type=str, default='adam')
    parser.add_argument('--l2_reg', type=float, default=0.0)
    parser.add_argument('--random_seed', type=int, default=2018)
    parser.add_argument('--save_path', type=str, default='./model/')
    parser.add_argument('--field_size', type=int, default=23, help='#fields')
    parser.add_argument('--loss_type', type=str, default='logloss')
    parser.add_argument('--verbose', type=int, default=1)
    parser.add_argument('--run_times', type=int, default=3,help='run multiple times to eliminate error')
    parser.add_argument('--deep_layers', type=str2list, default=None, help='config for dnn in joint train')
    parser.add_argument('--batch_norm', type=int, default=0)
    parser.add_argument('--batch_norm_decay', type=float, default=0.995)
    parser.add_argument('--data', type=str, help='data name')
    parser.add_argument('--data_path', type=str, help='root path for all the data')
    return parser.parse_args()


def _run_(args, file_name, run_cnt):
    path_prefix = os.path.join(args.data_path, args.data)
    feature_size = np.load(path_prefix + '/feature_size.npy')[0]

    # test: file1, valid: file2, train: file3-10
    # test: file1, valid: file2, train: file3-10
    if args.model_type == 'AutoInt':
        model = AutoInt(args=args, feature_size=feature_size, run_cnt=run_cnt)
    elif args.model_type == 'FiGNN':
        model = FiGNN(args=args, feature_size=feature_size, run_cnt=run_cnt)
    elif args.model_type == 'GraphFM':
        model = GraphFM(args=args, feature_size=feature_size, run_cnt=run_cnt)
    elif args.model_type == 'InterHAt':
        model = InterHAt(args=args, feature_size=feature_size, run_cnt=run_cnt)


    Xi_valid = np.load(path_prefix + '/part2/' + file_name[0])
    Xv_valid = np.load(path_prefix + '/part2/' + file_name[1])
    y_valid = np.load(path_prefix + '/part2/' + file_name[2])

    is_continue = True
    for k in range(model.epoch):
        if not is_continue:
            print('early stopping at epoch %d' % (k+1))
            break
        file_count = 0
        time_epoch = 0
        for j in range(3, 11):
            if not is_continue:
                print('early stopping at epoch %d file %d' % (k+1, j))
                break
            file_count += 1
            Xi_train = np.load(path_prefix + '/part' + str(j) + '/' + file_name[0])
            Xv_train = np.load(path_prefix + '/part' + str(j) + '/' + file_name[1])
            y_train = np.load(path_prefix + '/part' + str(j) + '/' + file_name[2])

            print("epoch %d, file %d" %(k+1, j))
            t1 = time()
            is_continue = model.fit_once(Xi_train, Xv_train, y_train, k+1, file_count,
                      Xi_valid, Xv_valid, y_valid, early_stopping=True)
            time_epoch += time() - t1

        print("epoch %d, time %d" % (k+1, time_epoch))


    print('start testing!...')
    Xi_test = np.load(path_prefix + '/part1/' + file_name[0])
    Xv_test = np.load(path_prefix + '/part1/' + file_name[1])
    y_test = np.load(path_prefix + '/part1/' + file_name[2])

    if args.is_save == True: model.restore()

    test_result, test_loss = model.evaluate(Xi_test, Xv_test, y_test)
    print("test-result = %.4lf, test-logloss = %.4lf" % (test_result, test_loss))

    visualization = model.visualize(Xi_test, Xv_test, y_test)

    return test_result, test_loss

if __name__ == "__main__":
    args = parse_args()
    print(args.__dict__)
    print('**************')
    if args.data in ['Avazu', 'Wiki']:
        # Avazu does not have numerical features so we didn't scale the data.
        file_name = ['train_i.npy', 'train_x.npy', 'train_y.npy']
    elif args.data in ['Criteo', 'KDD2012']:
        file_name = ['train_i.npy', 'train_x2.npy', 'train_y.npy']
    test_auc = []
    test_log = []

    print('run time : %d' % args.run_times)
    for i in range(1, args.run_times + 1):
        test_result, test_loss = _run_(args, file_name, i)
        test_auc.append(test_result)
        test_log.append(test_loss)
    print('test_auc', test_auc)
    print('test_log_loss', test_log)
    print('avg_auc', sum(test_auc)/len(test_auc))
    print('avg_log_loss', sum(test_log)/len(test_log))