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import os
import glob
import io
from .. import data
class IMDB(data.Dataset):
urls = ['http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz']
name = 'imdb'
dirname = 'aclImdb'
@staticmethod
def sort_key(ex):
return len(ex.text)
def __init__(self, path, text_field, label_field, **kwargs):
"""Create an IMDB dataset instance given a path and fields.
Arguments:
path: Path to the dataset's highest level directory
text_field: The field that will be used for text data.
label_field: The field that will be used for label data.
Remaining keyword arguments: Passed to the constructor of
data.Dataset.
"""
fields = [('text', text_field), ('label', label_field)]
examples = []
for label in ['pos', 'neg']:
for fname in glob.iglob(os.path.join(path, label, '*.txt')):
with io.open(fname, 'r', encoding="utf-8") as f:
text = f.readline()
examples.append(data.Example.fromlist([text, label], fields))
super(IMDB, self).__init__(examples, fields, **kwargs)
@classmethod
def splits(cls, text_field, label_field, root='.data',
train='train', test='test', **kwargs):
"""Create dataset objects for splits of the IMDB dataset.
Arguments:
text_field: The field that will be used for the sentence.
label_field: The field that will be used for label data.
root: Root dataset storage directory. Default is '.data'.
train: The directory that contains the training examples
test: The directory that contains the test examples
Remaining keyword arguments: Passed to the splits method of
Dataset.
"""
return super(IMDB, cls).splits(
root=root, text_field=text_field, label_field=label_field,
train=train, validation=None, test=test, **kwargs)
@classmethod
def iters(cls, batch_size=32, device=0, root='.data', vectors=None, **kwargs):
"""Create iterator objects for splits of the IMDB dataset.
Arguments:
batch_size: Batch_size
device: Device to create batches on. Use - 1 for CPU and None for
the currently active GPU device.
root: The root directory that contains the imdb dataset subdirectory
vectors: one of the available pretrained vectors or a list with each
element one of the available pretrained vectors (see Vocab.load_vectors)
Remaining keyword arguments: Passed to the splits method.
"""
TEXT = data.Field()
LABEL = data.Field(sequential=False)
train, test = cls.splits(TEXT, LABEL, root=root, **kwargs)
TEXT.build_vocab(train, vectors=vectors)
LABEL.build_vocab(train)
return data.BucketIterator.splits(
(train, test), batch_size=batch_size, device=device)