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mydatasets.py
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mydatasets.py
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import re
import os
import random
import tarfile
import urllib
from torchtext import data
class TarDataset(data.Dataset):
"""Defines a Dataset loaded from a downloadable tar archive.
Attributes:
url: URL where the tar archive can be downloaded.
filename: Filename of the downloaded tar archive.
dirname: Name of the top-level directory within the zip archive that
contains the data files.
"""
@classmethod
def download_or_unzip(cls, root):
path = os.path.join(root, cls.dirname)
if not os.path.isdir(path):
tpath = os.path.join(root, cls.filename)
if not os.path.isfile(tpath):
print('downloading')
urllib.request.urlretrieve(cls.url, tpath)
with tarfile.open(tpath, 'r') as tfile:
print('extracting')
tfile.extractall(root)
return os.path.join(path, '')
class MR(TarDataset):
url = 'https://www.cs.cornell.edu/people/pabo/movie-review-data/rt-polaritydata.tar.gz'
filename = 'rt-polaritydata.tar.gz'
dirname = 'rt-polaritydata'
@staticmethod
def sort_key(ex):
return len(ex.text)
def __init__(self, text_field, label_field, path=None, examples=None, **kwargs):
"""Create an MR dataset instance given a path and fields.
Arguments:
text_field: The field that will be used for text data.
label_field: The field that will be used for label data.
path: Path to the data file.
examples: The examples contain all the data.
Remaining keyword arguments: Passed to the constructor of
data.Dataset.
"""
def clean_str(string):
"""
Tokenization/string cleaning for all datasets except for SST.
Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py
"""
string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
string = re.sub(r"\'s", " \'s", string)
string = re.sub(r"\'ve", " \'ve", string)
string = re.sub(r"n\'t", " n\'t", string)
string = re.sub(r"\'re", " \'re", string)
string = re.sub(r"\'d", " \'d", string)
string = re.sub(r"\'ll", " \'ll", string)
string = re.sub(r",", " , ", string)
string = re.sub(r"!", " ! ", string)
string = re.sub(r"\(", " \( ", string)
string = re.sub(r"\)", " \) ", string)
string = re.sub(r"\?", " \? ", string)
string = re.sub(r"\s{2,}", " ", string)
return string.strip()
text_field.tokenize = lambda x: clean_str(x).split()
fields = [('text', text_field), ('label', label_field)]
if examples is None:
path = self.dirname if path is None else path
examples = []
with open(os.path.join(path, 'rt-polarity.neg'), errors='ignore') as f:
examples += [
data.Example.fromlist([line, 'negative'], fields) for line in f]
with open(os.path.join(path, 'rt-polarity.pos'), errors='ignore') as f:
examples += [
data.Example.fromlist([line, 'positive'], fields) for line in f]
super(MR, self).__init__(examples, fields, **kwargs)
@classmethod
def splits(cls, text_field, label_field, dev_ratio=.1, shuffle=True, root='.', **kwargs):
"""Create dataset objects for splits of the MR dataset.
Arguments:
text_field: The field that will be used for the sentence.
label_field: The field that will be used for label data.
dev_ratio: The ratio that will be used to get split validation dataset.
shuffle: Whether to shuffle the data before split.
root: The root directory that the dataset's zip archive will be
expanded into; therefore the directory in whose trees
subdirectory the data files will be stored.
train: The filename of the train data. Default: 'train.txt'.
Remaining keyword arguments: Passed to the splits method of
Dataset.
"""
path = cls.download_or_unzip(root)
examples = cls(text_field, label_field, path=path, **kwargs).examples
if shuffle: random.shuffle(examples)
dev_index = -1 * int(dev_ratio*len(examples))
return (cls(text_field, label_field, examples=examples[:dev_index]),
cls(text_field, label_field, examples=examples[dev_index:]))