utils.py

from tqdm import tqdm
import numpy as np
import torch
from sklearn.utils import shuffle
import random
import datetime as dt
import os
from glob import glob
from torch_geometric.data import Data, DataLoader

class EarlyStopping:
    """주어진 patience 이후로 validation loss가 개선되지 않으면 학습을 조기 중지"""
    def __init__(self, patience=7, verbose=False, delta=0):
        """
        Args:
            patience (int): validation loss가 개선된 후 기다리는 기간
                            Default: 7
            verbose (bool): True일 경우 각 validation loss의 개선 사항 메세지 출력
                            Default: False
            delta (float): 개선되었다고 인정되는 monitered quantity의 최소 변화
                            Default: 0
        """
        self.patience = patience
        self.verbose = verbose
        self.counter = 0
        self.best_score = None
        self.early_stop = False
        self.val_loss_min = np.Inf
        self.delta = delta

    def __call__(self, val_loss):

        score = -val_loss

        if self.best_score is None:
            self.best_score = score
            self.check_loss(val_loss)
        elif score < self.best_score + self.delta:
            self.counter += 1
            print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
            if self.counter >= self.patience:
                self.early_stop = True
        else:
            self.best_score = score
            self.check_loss(val_loss)
            self.counter = 0

    def check_loss(self, val_loss):
        '''validation loss가 감소하면 감소를 출력한다.'''
        if self.verbose:
            print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). ')
        self.val_loss_min = val_loss