Permalink
122 lines (101 sloc) 4.33 KB
"""IMDB sentiment classification dataset.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from ..utils.data_utils import get_file
from ..preprocessing.sequence import _remove_long_seq
import numpy as np
import json
import warnings
def load_data(path='imdb.npz', num_words=None, skip_top=0,
maxlen=None, seed=113,
start_char=1, oov_char=2, index_from=3, **kwargs):
"""Loads the IMDB dataset.
# Arguments
path: where to cache the data (relative to `~/.keras/dataset`).
num_words: max number of words to include. Words are ranked
by how often they occur (in the training set) and only
the most frequent words are kept
skip_top: skip the top N most frequently occurring words
(which may not be informative).
maxlen: sequences longer than this will be filtered out.
seed: random seed for sample shuffling.
start_char: The start of a sequence will be marked with this character.
Set to 1 because 0 is usually the padding character.
oov_char: words that were cut out because of the `num_words`
or `skip_top` limit will be replaced with this character.
index_from: index actual words with this index and higher.
# Returns
Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
# Raises
ValueError: in case `maxlen` is so low
that no input sequence could be kept.
Note that the 'out of vocabulary' character is only used for
words that were present in the training set but are not included
because they're not making the `num_words` cut here.
Words that were not seen in the training set but are in the test set
have simply been skipped.
"""
# Legacy support
if 'nb_words' in kwargs:
warnings.warn('The `nb_words` argument in `load_data` '
'has been renamed `num_words`.')
num_words = kwargs.pop('nb_words')
if kwargs:
raise TypeError('Unrecognized keyword arguments: ' + str(kwargs))
path = get_file(path,
origin='https://s3.amazonaws.com/text-datasets/imdb.npz',
file_hash='599dadb1135973df5b59232a0e9a887c')
with np.load(path) as f:
x_train, labels_train = f['x_train'], f['y_train']
x_test, labels_test = f['x_test'], f['y_test']
np.random.seed(seed)
indices = np.arange(len(x_train))
np.random.shuffle(indices)
x_train = x_train[indices]
labels_train = labels_train[indices]
indices = np.arange(len(x_test))
np.random.shuffle(indices)
x_test = x_test[indices]
labels_test = labels_test[indices]
xs = np.concatenate([x_train, x_test])
labels = np.concatenate([labels_train, labels_test])
if start_char is not None:
xs = [[start_char] + [w + index_from for w in x] for x in xs]
elif index_from:
xs = [[w + index_from for w in x] for x in xs]
if maxlen:
xs, labels = _remove_long_seq(maxlen, xs, labels)
if not xs:
raise ValueError('After filtering for sequences shorter than maxlen=' +
str(maxlen) + ', no sequence was kept. '
'Increase maxlen.')
if not num_words:
num_words = max([max(x) for x in xs])
# by convention, use 2 as OOV word
# reserve 'index_from' (=3 by default) characters:
# 0 (padding), 1 (start), 2 (OOV)
if oov_char is not None:
xs = [[w if (skip_top <= w < num_words) else oov_char for w in x]
for x in xs]
else:
xs = [[w for w in x if skip_top <= w < num_words]
for x in xs]
idx = len(x_train)
x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])
x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])
return (x_train, y_train), (x_test, y_test)
def get_word_index(path='imdb_word_index.json'):
"""Retrieves the dictionary mapping words to word indices.
# Arguments
path: where to cache the data (relative to `~/.keras/dataset`).
# Returns
The word index dictionary.
"""
path = get_file(
path,
origin='https://s3.amazonaws.com/text-datasets/imdb_word_index.json',
file_hash='bfafd718b763782e994055a2d397834f')
with open(path) as f:
return json.load(f)