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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Reuters newswire topic classification dataset.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import numpy as np
from six.moves import zip # pylint: disable=redefined-builtin
from tensorflow.contrib.keras.python.keras.utils.data_utils import get_file
def load_data(path='reuters.npz',
num_words=None,
skip_top=0,
maxlen=None,
test_split=0.2,
seed=113,
start_char=1,
oov_char=2,
index_from=3):
"""Loads the Reuters newswire classification 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 occuring words
(which may not be informative).
maxlen: truncate sequences after this length.
test_split: Fraction of the dataset to be used as test data.
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)`.
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.
"""
path = get_file(
path, origin='https://s3.amazonaws.com/text-datasets/reuters.npz')
npzfile = np.load(path)
xs = npzfile['x']
labels = npzfile['y']
npzfile.close()
np.random.seed(seed)
np.random.shuffle(xs)
np.random.seed(seed)
np.random.shuffle(labels)
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:
new_xs = []
new_labels = []
for x, y in zip(xs, labels):
if len(x) < maxlen:
new_xs.append(x)
new_labels.append(y)
xs = new_xs
labels = new_labels
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 = [[oov_char if (w >= num_words or w < skip_top) else w for w in x]
for x in xs]
else:
new_xs = []
for x in xs:
nx = []
for w in x:
if w >= num_words or w < skip_top:
nx.append(w)
new_xs.append(nx)
xs = new_xs
x_train = np.array(xs[:int(len(xs) * (1 - test_split))])
y_train = np.array(labels[:int(len(xs) * (1 - test_split))])
x_test = np.array(xs[int(len(xs) * (1 - test_split)):])
y_test = np.array(labels[int(len(xs) * (1 - test_split)):])
return (x_train, y_train), (x_test, y_test)
def get_word_index(path='reuters_word_index.json'):
"""Retrieves the dictionary mapping word indices back to words.
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/reuters_word_index.json')
f = open(path)
data = json.load(f)
f.close()
return data
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