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utils.py
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utils.py
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import numpy as np
import torch
import os
# creates wordPairs for the Training & Validation Set
def createWordPairs(indexes, tokens, window_size):
idx_pairs = []
# for each sentence
for sentence in tokens.tokenzied_corpus:
# if it can be indexed else skip to next iteration
try:
indices = [indexes.word2idx[word] for word in sentence]
except:
continue
# for each word, threated as center word
for center_word_pos in range(len(indices)):
# for each window position
for w in range(-window_size, window_size + 1):
context_word_pos = center_word_pos + w
# make soure not jump out sentence
if context_word_pos < 0 or context_word_pos >= len(indices) or center_word_pos == context_word_pos:
continue
context_word_idx = indices[context_word_pos]
idx_pairs.append((indices[center_word_pos], context_word_idx))
return np.array(idx_pairs)
class AverageMeter():
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
self.train_loss = 0
self.valid_loss = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
class EarlyStopping:
"""Early stops the training if validation loss doesn't improve after a given patience."""
def __init__(self, patience, verbose, save_name):
"""
Args:
patience (int): How long to wait after last time validation loss improved.
Default: 7
verbose (bool): If True, prints a message for each validation loss improvement.
Default: False
"""
self.patience = patience
self.verbose = verbose
self.counter = 0
self.best_score = None
self.early_stop = False
self.val_loss_min = np.Inf
self.save_name = save_name
def __call__(self, val_loss, model):
score = -val_loss
if self.best_score is None:
self.best_score = score
self.save_checkpoint(val_loss, model)
elif score < self.best_score:
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.save_checkpoint(val_loss, model)
self.counter = 0
def save_checkpoint(self, val_loss, model):
'''Saves model when validation loss decrease.'''
if self.verbose:
print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...')
torch.save(model.input_embeddings(), os.path.join("SOFTMAX_trained", self.save_name))
self.val_loss_min = val_loss