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grid_search_final.py
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import time
import datetime
# 시간 표시 함수
def format_time(elapsed):
# 반올림
elapsed_rounded = int(round((elapsed)))
# hh:mm:ss으로 형태 변경
return str(datetime.timedelta(seconds=elapsed_rounded))
"""Training GCMC model on the MovieLens data set.
The script loads the full graph to the training device.
"""
import os, time
import argparse
import logging
import random
import string
import numpy as np
import pandas as pd
import torch as th
import torch.nn as nn
from data_rotten import RottenMovie
from utils import get_activation, get_optimizer, torch_total_param_num, torch_net_info, MetricLogger
import easydict
args = easydict.EasyDict({
"data_name": "rotten",
"use_one_hot_fea": False,
"gpu": 0,
"seed": 123,
"data_test_ratio": 0.1,
"data_valid_ratio": 0.1,
"model_activation": 'leaky',
"gcn_dropout": 0.5,
"gcn_agg_norm_symm": True,
"gcn_agg_units": 32,
"gcn_agg_accum": 'sum',
"gcn_out_units": 32, # 64, 128
"gen_r_num_basis_func": 2,
"train_max_epoch": 300,
"train_log_interval": 5,
"train_valid_interval": 5,
"train_optimizer": 'adam',
"train_grad_clip": 1.0,
"train_lr": 0.01,
"train_min_lr": 0.0008,
"train_lr_decay_factor": 0.5,
"train_decay_patience": 25,
"train_early_stopping_patience": 50,
"share_param": False,
"mix_cpu_gpu": False,
"minibatch_size": 40000,
"num_workers_per_gpu": 8,
"device": 'cpu',
"save_dir": './save/',
"save_id": 1,
"train_max_iter": 300
})
np.random.seed(args.seed)
th.manual_seed(args.seed)
if th.cuda.is_available():
th.cuda.manual_seed_all(args.seed)
from train import train
dataset = RottenMovie(
train_data='./data/trainset_filtered.csv',
test_data='./data/testset_filtered.csv',
movie_data = './data/movie_info.csv',
user_data = './data/user_info.csv',
emotion=False,
sentiment=False,
name='rotten',
device='cpu',
mix_cpu_gpu=False,
use_one_hot_fea=False,
symm=True,
valid_ratio=0.1,
)
dataset_es = RottenMovie(
train_data='./data/trainset_filtered.csv',
test_data='./data/testset_filtered.csv',
movie_data = './data/movie_info.csv',
user_data = './data/user_info.csv',
emotion=True,
sentiment=True,
name='rotten',
device='cpu',
mix_cpu_gpu=False,
use_one_hot_fea=False,
symm=True,
valid_ratio=0.1,
)
args.rating_vals = dataset.rating_values
args.gcn_dropout = 0.50
if __name__ == '__main__':
bests=100
bests_es=100
for dim in [64]:
args.gcn_out_units = dim
for agg in [64]:
args.gcn_agg_units = agg
for lr in [0.006*i for i in range(10)]:
args.train_lr = lr
args.save_dir = f'./test/test'
args.save_id = 'new_feature'
best = train(args, dataset)
print("****************************")
args.save_dir = f'./test/test_es'
args.save_id = 'new_feature_es'
best_es = train(args, dataset_es)
print(best,' VS ', best_es)
if bests>best:
bests = best
if bests_es>best_es:
bests_es=best_es
print(bests,' VS ', bests_es)