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utils.py
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utils.py
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import numpy as np
import datetime
import os
import requests
import pandas as pd
import re
import itertools
PAD_ID = 0
class DateData:
def __init__(self, n):
np.random.seed(1)
self.date_cn = []
self.date_en = []
for timestamp in np.random.randint(143835585, 2043835585, n):
date = datetime.datetime.fromtimestamp(timestamp)
self.date_cn.append(date.strftime("%y-%m-%d"))
self.date_en.append(date.strftime("%d/%b/%Y"))
self.vocab = set(
[str(i) for i in range(0, 10)] + ["-", "/", "<GO>", "<EOS>"] + [
i.split("/")[1] for i in self.date_en])
self.v2i = {v: i for i, v in enumerate(sorted(list(self.vocab)), start=1)}
self.v2i["<PAD>"] = PAD_ID
self.vocab.add("<PAD>")
self.i2v = {i: v for v, i in self.v2i.items()}
self.x, self.y = [], []
for cn, en in zip(self.date_cn, self.date_en):
self.x.append([self.v2i[v] for v in cn])
self.y.append(
[self.v2i["<GO>"], ] + [self.v2i[v] for v in en[:3]] + [
self.v2i[en[3:6]], ] + [self.v2i[v] for v in en[6:]] + [
self.v2i["<EOS>"], ])
self.x, self.y = np.array(self.x), np.array(self.y)
self.start_token = self.v2i["<GO>"]
self.end_token = self.v2i["<EOS>"]
def sample(self, n=64):
bi = np.random.randint(0, len(self.x), size=n)
bx, by = self.x[bi], self.y[bi]
decoder_len = np.full((len(bx),), by.shape[1] - 1, dtype=np.int32)
return bx, by, decoder_len
def idx2str(self, idx):
x = []
for i in idx:
x.append(self.i2v[i])
if i == self.end_token:
break
return "".join(x)
@property
def num_word(self):
return len(self.vocab)
def pad_zero(seqs, max_len):
padded = np.full((len(seqs), max_len), fill_value=PAD_ID, dtype=np.long)
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)
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")