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utils.py
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utils.py
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# ---------------------------------
# --------人生苦短,我用python--------
# ---------------------------------
# ---------------------------------
# --------人生苦短,我用python--------
# ---------------------------------
import datetime
import itertools
import os
import re
import torch
import numpy as np
import pandas as pd
import requests
PAD_ID = 0
class DateData:
def __init__(self):
data_path = "./model_comparison_data50.txt"
self.date_train = [] # 记录中国日期数据 例如 31-04-26
self.date_target = [] # 记录外国日期数据 例如 26/Apr/2031
self.vocab = set()
# -------------------------------------产生训练数据 和 目标数据-------------------------------
with open(data_path, 'r', encoding='iso-8859-1') as f:
lines = f.read().split('\n')
for line in lines:
input_text, target_text = line.split('/-*/')
if(len(input_text) != 50 or len(target_text) != 1):
continue
target_text = '\t' + target_text + '\n'
self.date_train.append(input_text)
self.date_target.append(target_text)
# -------------------------------------产生训练数据 和 目标数据-------------------------------
# ********************************************将数据向量化****************************************************************
for char in input_text:
if char not in self.vocab:
self.vocab.add(char)
for char in target_text:
if char not in self.vocab:
self.vocab.add(char)
self.v2i = {v: i for i, v in enumerate(sorted(list(self.vocab)), start=1)} # 字典 V代表数据 I代表对于索引 用于向量化
# 注意sorted 为内嵌参数 ,不改变原来列表 而 sort 的使用 a.sort()
self.v2i["<PAD>"] = PAD_ID # 给字典增加新的键值对,用于覆盖填充。
self.vocab.add("<PAD>")
self.i2v = {i: v for v, i in self.v2i.items()} # 将字典v2i的键和值反转 用于将预测数据的 向量转化数据
self.x, self.y = [], []
for d_in, d_get in zip(self.date_train, self.date_target): # 其实就是将这些序列中对应位置的元素重新组合,生成一个个新的元组。
self.x.append([self.v2i[v] for v in d_in])
self.y.append([self.v2i[v] for v in d_get])
self.x = np.array(self.x) # 列表中的列表转化为数组
self.y = np.array(self.y)
# ********************************************将数据向量化****************************************************************
self.start_token = self.v2i["\t"]
self.end_token = self.v2i["\n"]
def sample(self, n=64): # 在数据集种抽样
bi = np.random.randint(0, len(self.x), size=n)
bx, by = self.x[bi], self.y[bi]
dec_input = [i[:2] for i in by]
dec_output = [i[1:3] for i in by]
dec_input = np.array(dec_input)
dec_output = np.array(dec_output)
return torch.LongTensor(bx), torch.LongTensor(dec_input), torch.LongTensor(dec_output)
def idx2str(self, idx):
x = []
for i in idx:
x.append(self.i2v[i])
if i == self.end_token:
break
return "".join(x) # join()连接任意数量的字符串。
@property
def num_word(self):
return len(self.vocab)
def pad_zero(seqs, max_len): # 将矩阵(X,y)转化为(X,max_len)
padded = np.full((len(seqs), max_len), fill_value=PAD_ID, dtype=np.long) # len(seqs) 是将seqs看成 列表元素为列表 的列表
for i, seq in enumerate(seqs):
padded[i, :len(seq)] = seq
return padded
def maybe_download_mrpc(save_dir="./MRPC/", proxy=None):
train_url = 'https://mofanpy.com/static/files/MRPC/msr_paraphrase_train.txt'
test_url = 'https://mofanpy.com/static/files/MRPC/msr_paraphrase_test.txt'
os.makedirs(save_dir, exist_ok=True)
proxies = {"http": proxy, "https": proxy}
for url in [train_url, test_url]:
raw_path = os.path.join(save_dir, url.split("/")[-1])
if not os.path.isfile(raw_path):
print("downloading from %s" % url)
r = requests.get(url, proxies=proxies)
with open(raw_path, "w", encoding="utf-8") as f:
f.write(r.text.replace('"', "<QUOTE>"))
print("completed")
def _text_standardize(text):
text = re.sub(r'—', '-', text)
text = re.sub(r'–', '-', text)
text = re.sub(r'―', '-', text)
text = re.sub(r" \d+(,\d+)?(\.\d+)? ", " <NUM> ", text)
text = re.sub(r" \d+-+?\d*", " <NUM>-", text)
return text.strip()
def _process_mrpc(dir="./MRPC", rows=None):
data = {"train": None, "test": None}
files = os.listdir(dir)
for f in files:
df = pd.read_csv(os.path.join(dir, f), sep='\t', nrows=rows)
k = "train" if "train" in f else "test"
data[k] = {"is_same": df.iloc[:, 0].values, "s1": df["#1 String"].values, "s2": df["#2 String"].values}
vocab = set()
for n in ["train", "test"]:
for m in ["s1", "s2"]:
for i in range(len(data[n][m])):
data[n][m][i] = _text_standardize(data[n][m][i].lower())
cs = data[n][m][i].split(" ")
vocab.update(set(cs))
v2i = {v: i for i, v in enumerate(sorted(vocab), start=1)}
v2i["<PAD>"] = PAD_ID
v2i["<MASK>"] = len(v2i)
v2i["<SEP>"] = len(v2i)
v2i["<GO>"] = len(v2i)
i2v = {i: v for v, i in v2i.items()}
for n in ["train", "test"]:
for m in ["s1", "s2"]:
data[n][m + "id"] = [[v2i[v] for v in c.split(" ")] for c in data[n][m]]
return data, v2i, i2v
class MRPCData:
num_seg = 3
pad_id = PAD_ID
def __init__(self, data_dir="./MRPC/", rows=None, proxy=None):
maybe_download_mrpc(save_dir=data_dir, proxy=proxy)
data, self.v2i, self.i2v = _process_mrpc(data_dir, rows)
self.max_len = max(
[len(s1) + len(s2) + 3 for s1, s2 in zip(
data["train"]["s1id"] + data["test"]["s1id"], data["train"]["s2id"] + data["test"]["s2id"])])
self.xlen = np.array([
[
len(data["train"]["s1id"][i]), len(data["train"]["s2id"][i])
] for i in range(len(data["train"]["s1id"]))], dtype=int)
x = [
[self.v2i["<GO>"]] + data["train"]["s1id"][i] + [self.v2i["<SEP>"]] + data["train"]["s2id"][i] + [
self.v2i["<SEP>"]]
for i in range(len(self.xlen))
]
self.x = pad_zero(x, max_len=self.max_len)
self.nsp_y = data["train"]["is_same"][:, None]
self.seg = np.full(self.x.shape, self.num_seg - 1, np.int32)
for i in range(len(x)):
si = self.xlen[i][0] + 2
self.seg[i, :si] = 0
si_ = si + self.xlen[i][1] + 1
self.seg[i, si:si_] = 1
self.word_ids = np.array(list(set(self.i2v.keys()).difference(
[self.v2i[v] for v in ["<PAD>", "<MASK>", "<SEP>"]])))
def sample(self, n):
bi = np.random.randint(0, self.x.shape[0], size=n)
bx, bs, bl, by = self.x[bi], self.seg[bi], self.xlen[bi], self.nsp_y[bi]
return bx, bs, bl, by
@property
def num_word(self):
return len(self.v2i)
@property
def mask_id(self):
return self.v2i["<MASK>"]
class MRPCSingle:
pad_id = PAD_ID
def __init__(self, data_dir="./MRPC/", rows=None, proxy=None):
maybe_download_mrpc(save_dir=data_dir, proxy=proxy)
data, self.v2i, self.i2v = _process_mrpc(data_dir, rows)
self.max_len = max([len(s) + 2 for s in data["train"]["s1id"] + data["train"]["s2id"]])
x = [
[self.v2i["<GO>"]] + data["train"]["s1id"][i] + [self.v2i["<SEP>"]]
for i in range(len(data["train"]["s1id"]))
]
x += [
[self.v2i["<GO>"]] + data["train"]["s2id"][i] + [self.v2i["<SEP>"]]
for i in range(len(data["train"]["s2id"]))
]
self.x = pad_zero(x, max_len=self.max_len)
self.word_ids = np.array(list(set(self.i2v.keys()).difference([self.v2i["<PAD>"]])))
def sample(self, n):
bi = np.random.randint(0, self.x.shape[0], size=n)
bx = self.x[bi]
return bx
@property
def num_word(self):
return len(self.v2i)
class Dataset:
def __init__(self, x, y, v2i, i2v):
self.x, self.y = x, y
self.v2i, self.i2v = v2i, i2v
self.vocab = v2i.keys()
def sample(self, n):
b_idx = np.random.randint(0, len(self.x), n)
bx, by = self.x[b_idx], self.y[b_idx]
return bx, by
@property
def num_word(self):
return len(self.v2i)
def process_w2v_data(corpus, skip_window=2, method="skip_gram"):
all_words = [sentence.split(" ") for sentence in corpus]
all_words = np.array(list(itertools.chain(*all_words)))
# vocab sort by decreasing frequency for the negative sampling below (nce_loss).
vocab, v_count = np.unique(all_words, return_counts=True)
vocab = vocab[np.argsort(v_count)[::-1]]
print("all vocabularies sorted from more frequent to less frequent:\n", vocab)
v2i = {v: i for i, v in enumerate(vocab)}
i2v = {i: v for v, i in v2i.items()}
# pair data
pairs = []
js = [i for i in range(-skip_window, skip_window + 1) if i != 0]
for c in corpus:
words = c.split(" ")
w_idx = [v2i[w] for w in words]
if method == "skip_gram":
for i in range(len(w_idx)):
for j in js:
if i + j < 0 or i + j >= len(w_idx):
continue
pairs.append((w_idx[i], w_idx[i + j])) # (center, context) or (feature, target)
elif method.lower() == "cbow":
for i in range(skip_window, len(w_idx) - skip_window):
context = []
for j in js:
context.append(w_idx[i + j])
pairs.append(context + [w_idx[i]]) # (contexts, center) or (feature, target)
else:
raise ValueError
pairs = np.array(pairs)
print("5 example pairs:\n", pairs[:5])
if method.lower() == "skip_gram":
x, y = pairs[:, 0], pairs[:, 1]
elif method.lower() == "cbow":
x, y = pairs[:, :-1], pairs[:, -1]
else:
raise ValueError
return Dataset(x, y, v2i, i2v) # 数据向量化
def set_soft_gpu(soft_gpu):
import tensorflow as tf
if soft_gpu:
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
# Currently, memory growth needs to be the same across GPUs
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs") # 设置Gpu