def get_word_index(path="reuters_word_index.pkl"): # pragma: no cover
path = get_file(
path,
origin="https://s3.amazonaws.com/text-datasets/reuters_word_index.pkl")
f = open(path, "rb")
if sys.version_info < (3, ):
data = pickle.load(f)
else:
data = pickle.load(f, encoding="latin1")
f.close()
return data
def get_word_index(path="reuters_word_index.pkl"): # pragma: no cover
path = get_file(
path, origin="https://s3.amazonaws.com/text-datasets/reuters_word_index.pkl"
)
f = open(path, "rb")
if sys.version_info < (3,):
data = pickle.load(f)
else:
data = pickle.load(f, encoding="latin1")
f.close()
return data
def load_mnist(path="mnist.pkl.gz"):
path = get_file(path, origin=URL)
if path.endswith(".gz"):
f = gzip.open(path, "rb")
else:
f = open(path, "rb")
data = pickle.load(f, encoding="bytes")
f.close()
return data # (X_train, y_train), (X_test, y_test)
def load_file(path: str, format: str = None):
format = _check_fmt(path, format)
if format == 'mpk':
with open(path, "rb") as f:
return msgpack.load(f)
elif format == "json":
with open(path, 'r') as f:
return json.load(f)
elif format == "pkl":
with open(path, 'rb') as f:
return pickle.load(f)
def load_reuters(
path="reuters.pkl",
nb_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 store the data (in `/.keras/dataset`)
nb_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 `nb_words`
or `skip_top` limit will be replaced with this character.
index_from: index actual words with this index and higher.
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 `nb_words` cut here.
Words that were not seen in the trining set but are in the test set
have simply been skipped.
"""
# https://raw.githubusercontent.com/fchollet/keras/master/keras/datasets/mnist.py
# Copyright Francois Chollet, Google, others (2015)
# Under MIT license
path = get_file(path, origin="https://s3.amazonaws.com/text-datasets/reuters.pkl")
f = open(path, "rb")
X, labels = pickle.load(f)
f.close()
numpy.random.seed(seed)
numpy.random.shuffle(X)
numpy.random.seed(seed)
numpy.random.shuffle(labels)
if start_char is not None:
X = [[start_char] + [w + index_from for w in x] for x in X]
elif index_from:
X = [[w + index_from for w in x] for x in X]
if maxlen:
new_X = []
new_labels = []
for x, y in zip(X, labels):
if len(x) < maxlen:
new_X.append(x)
new_labels.append(y)
X = new_X
labels = new_labels
if not nb_words:
nb_words = max([max(x) for x in X])
# 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:
X = [[oov_char if (w >= nb_words or w < skip_top) else w for w in x] for x in X]
else:
nX = []
for x in X:
nx = []
for w in x:
if w >= nb_words or w < skip_top:
nx.append(w)
nX.append(nx)
X = nX
X_train = X[: int(len(X) * (1 - test_split))]
y_train = labels[: int(len(X) * (1 - test_split))]
X_test = X[int(len(X) * (1 - test_split)) :]
y_test = labels[int(len(X) * (1 - test_split)) :]
return (X_train, y_train), (X_test, y_test)
def get_word_index(path="reuters_word_index.pkl"):
path = get_file(path, origin=URL)
f = open(path, "rb")
data = pickle.load(f, encoding="latin1")
f.close()
return data