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sequence.py
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sequence.py
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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.
# ==============================================================================
"""Utilities for preprocessing sequence data.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import random
import numpy as np
from six.moves import range # pylint: disable=redefined-builtin
from tensorflow.python.keras.utils.data_utils import Sequence
from tensorflow.python.util.tf_export import tf_export
@tf_export('keras.preprocessing.sequence.pad_sequences')
def pad_sequences(sequences,
maxlen=None,
dtype='int32',
padding='pre',
truncating='pre',
value=0.):
"""Pads sequences to the same length.
This function transforms a list of
`num_samples` sequences (lists of integers)
into a 2D Numpy array of shape `(num_samples, num_timesteps)`.
`num_timesteps` is either the `maxlen` argument if provided,
or the length of the longest sequence otherwise.
Sequences that are shorter than `num_timesteps`
are padded with `value` at the end.
Sequences longer than `num_timesteps` are truncated
so that they fit the desired length.
The position where padding or truncation happens is determined by
the arguments `padding` and `truncating`, respectively.
Pre-padding is the default.
Arguments:
sequences: List of lists, where each element is a sequence.
maxlen: Int, maximum length of all sequences.
dtype: Type of the output sequences.
padding: String, 'pre' or 'post':
pad either before or after each sequence.
truncating: String, 'pre' or 'post':
remove values from sequences larger than
`maxlen`, either at the beginning or at the end of the sequences.
value: Float, padding value.
Returns:
x: Numpy array with shape `(len(sequences), maxlen)`
Raises:
ValueError: In case of invalid values for `truncating` or `padding`,
or in case of invalid shape for a `sequences` entry.
"""
if not hasattr(sequences, '__len__'):
raise ValueError('`sequences` must be iterable.')
lengths = []
for x in sequences:
if not hasattr(x, '__len__'):
raise ValueError('`sequences` must be a list of iterables. '
'Found non-iterable: ' + str(x))
lengths.append(len(x))
num_samples = len(sequences)
if maxlen is None:
maxlen = np.max(lengths)
# take the sample shape from the first non empty sequence
# checking for consistency in the main loop below.
sample_shape = tuple()
for s in sequences:
if len(s) > 0: # pylint: disable=g-explicit-length-test
sample_shape = np.asarray(s).shape[1:]
break
x = (np.ones((num_samples, maxlen) + sample_shape) * value).astype(dtype)
for idx, s in enumerate(sequences):
if not len(s): # pylint: disable=g-explicit-length-test
continue # empty list/array was found
if truncating == 'pre':
trunc = s[-maxlen:] # pylint: disable=invalid-unary-operand-type
elif truncating == 'post':
trunc = s[:maxlen]
else:
raise ValueError('Truncating type "%s" not understood' % truncating)
# check `trunc` has expected shape
trunc = np.asarray(trunc, dtype=dtype)
if trunc.shape[1:] != sample_shape:
raise ValueError('Shape of sample %s of sequence at position %s '
'is different from expected shape %s' %
(trunc.shape[1:], idx, sample_shape))
if padding == 'post':
x[idx, :len(trunc)] = trunc
elif padding == 'pre':
x[idx, -len(trunc):] = trunc
else:
raise ValueError('Padding type "%s" not understood' % padding)
return x
@tf_export('keras.preprocessing.sequence.make_sampling_table')
def make_sampling_table(size, sampling_factor=1e-5):
"""Generates a word rank-based probabilistic sampling table.
Used for generating the `sampling_table` argument for `skipgrams`.
`sampling_table[i]` is the probability of sampling
the word i-th most common word in a dataset
(more common words should be sampled less frequently, for balance).
The sampling probabilities are generated according
to the sampling distribution used in word2vec:
`p(word) = min(1, sqrt(word_frequency / sampling_factor) / (word_frequency /
sampling_factor))`
We assume that the word frequencies follow Zipf's law (s=1) to derive
a numerical approximation of frequency(rank):
`frequency(rank) ~ 1/(rank * (log(rank) + gamma) + 1/2 - 1/(12*rank))`
where `gamma` is the Euler-Mascheroni constant.
Arguments:
size: Int, number of possible words to sample.
sampling_factor: The sampling factor in the word2vec formula.
Returns:
A 1D Numpy array of length `size` where the ith entry
is the probability that a word of rank i should be sampled.
"""
gamma = 0.577
rank = np.arange(size)
rank[0] = 1
inv_fq = rank * (np.log(rank) + gamma) + 0.5 - 1. / (12. * rank)
f = sampling_factor * inv_fq
return np.minimum(1., f / np.sqrt(f))
@tf_export('keras.preprocessing.sequence.skipgrams')
def skipgrams(sequence,
vocabulary_size,
window_size=4,
negative_samples=1.,
shuffle=True,
categorical=False,
sampling_table=None,
seed=None):
"""Generates skipgram word pairs.
This function transforms a sequence of word indexes (list of integers)
into tuples of words of the form:
- (word, word in the same window), with label 1 (positive samples).
- (word, random word from the vocabulary), with label 0 (negative samples).
Read more about Skipgram in this gnomic paper by Mikolov et al.:
[Efficient Estimation of Word Representations in
Vector Space](http://arxiv.org/pdf/1301.3781v3.pdf)
Arguments:
sequence: A word sequence (sentence), encoded as a list
of word indices (integers). If using a `sampling_table`,
word indices are expected to match the rank
of the words in a reference dataset (e.g. 10 would encode
the 10-th most frequently occurring token).
Note that index 0 is expected to be a non-word and will be skipped.
vocabulary_size: Int, maximum possible word index + 1
window_size: Int, size of sampling windows (technically half-window).
The window of a word `w_i` will be
`[i - window_size, i + window_size+1]`.
negative_samples: Float >= 0. 0 for no negative (i.e. random) samples.
1 for same number as positive samples.
shuffle: Whether to shuffle the word couples before returning them.
categorical: bool. if False, labels will be
integers (eg. `[0, 1, 1 .. ]`),
if `True`, labels will be categorical, e.g.
`[[1,0],[0,1],[0,1] .. ]`.
sampling_table: 1D array of size `vocabulary_size` where the entry i
encodes the probability to sample a word of rank i.
seed: Random seed.
Returns:
couples, labels: where `couples` are int pairs and
`labels` are either 0 or 1.
# Note
By convention, index 0 in the vocabulary is
a non-word and will be skipped.
"""
couples = []
labels = []
for i, wi in enumerate(sequence):
if not wi:
continue
if sampling_table is not None:
if sampling_table[wi] < random.random():
continue
window_start = max(0, i - window_size)
window_end = min(len(sequence), i + window_size + 1)
for j in range(window_start, window_end):
if j != i:
wj = sequence[j]
if not wj:
continue
couples.append([wi, wj])
if categorical:
labels.append([0, 1])
else:
labels.append(1)
if negative_samples > 0:
num_negative_samples = int(len(labels) * negative_samples)
words = [c[0] for c in couples]
random.shuffle(words)
couples += [[words[i % len(words)],
random.randint(1, vocabulary_size - 1)]
for i in range(num_negative_samples)]
if categorical:
labels += [[1, 0]] * num_negative_samples
else:
labels += [0] * num_negative_samples
if shuffle:
if seed is None:
seed = random.randint(0, 10e6)
random.seed(seed)
random.shuffle(couples)
random.seed(seed)
random.shuffle(labels)
return couples, labels
def _remove_long_seq(maxlen, seq, label):
"""Removes sequences that exceed the maximum length.
Arguments:
maxlen: Int, maximum length of the output sequences.
seq: List of lists, where each sublist is a sequence.
label: List where each element is an integer.
Returns:
new_seq, new_label: shortened lists for `seq` and `label`.
"""
new_seq, new_label = [], []
for x, y in zip(seq, label):
if len(x) < maxlen:
new_seq.append(x)
new_label.append(y)
return new_seq, new_label
@tf_export('keras.preprocessing.sequence.TimeseriesGenerator')
class TimeseriesGenerator(Sequence):
"""Utility class for generating batches of temporal data.
This class takes in a sequence of data-points gathered at
equal intervals, along with time series parameters such as
stride, length of history, etc., to produce batches for
training/validation.
Arguments:
data: Indexable generator (such as list or Numpy array)
containing consecutive data points (timesteps).
The data should be at 2D, and axis 0 is expected
to be the time dimension.
targets: Targets corresponding to timesteps in `data`.
It should have same length as `data`.
length: Length of the output sequences (in number of timesteps).
sampling_rate: Period between successive individual timesteps
within sequences. For rate `r`, timesteps
`data[i]`, `data[i-r]`, ... `data[i - length]`
are used for create a sample sequence.
stride: Period between successive output sequences.
For stride `s`, consecutive output samples would
be centered around `data[i]`, `data[i+s]`, `data[i+2*s]`, etc.
start_index, end_index: Data points earlier than `start_index`
or later than `end_index` will not be used in the output sequences.
This is useful to reserve part of the data for test or validation.
shuffle: Whether to shuffle output samples,
or instead draw them in chronological order.
reverse: Boolean: if `true`, timesteps in each output sample will be
in reverse chronological order.
batch_size: Number of timeseries samples in each batch
(except maybe the last one).
Returns:
A [Sequence](/utils/#sequence) instance.
Examples:
```python
from keras.preprocessing.sequence import TimeseriesGenerator
import numpy as np
data = np.array([[i] for i in range(50)])
targets = np.array([[i] for i in range(50)])
data_gen = TimeseriesGenerator(data, targets,
length=10, sampling_rate=2,
batch_size=2)
assert len(data_gen) == 20
batch_0 = data_gen[0]
x, y = batch_0
assert np.array_equal(x,
np.array([[[0], [2], [4], [6], [8]],
[[1], [3], [5], [7], [9]]]))
assert np.array_equal(y,
np.array([[10], [11]]))
```
"""
def __init__(self,
data,
targets,
length,
sampling_rate=1,
stride=1,
start_index=0,
end_index=None,
shuffle=False,
reverse=False,
batch_size=128):
self.data = data
self.targets = targets
self.length = length
self.sampling_rate = sampling_rate
self.stride = stride
self.start_index = start_index + length
if end_index is None:
end_index = len(data) - 1
self.end_index = end_index
self.shuffle = shuffle
self.reverse = reverse
self.batch_size = batch_size
if self.start_index > self.end_index:
raise ValueError('`start_index+length=%i > end_index=%i` '
'is disallowed, as no part of the sequence '
'would be left to be used as current step.' %
(self.start_index, self.end_index))
def __len__(self):
length = int(
np.ceil((self.end_index - self.start_index + 1) /
(self.batch_size * self.stride)))
return length if length >= 0 else 0
def _empty_batch(self, num_rows):
samples_shape = [num_rows, self.length // self.sampling_rate]
samples_shape.extend(self.data.shape[1:])
targets_shape = [num_rows]
targets_shape.extend(self.targets.shape[1:])
return np.empty(samples_shape), np.empty(targets_shape)
def __getitem__(self, index):
if self.shuffle:
rows = np.random.randint(
self.start_index, self.end_index + 1, size=self.batch_size)
else:
i = self.start_index + self.batch_size * self.stride * index
rows = np.arange(
i, min(i + self.batch_size * self.stride, self.end_index + 1),
self.stride)
samples, targets = self._empty_batch(len(rows))
for j in range(len(rows)):
indices = range(rows[j] - self.length, rows[j], self.sampling_rate)
samples[j] = self.data[indices]
targets[j] = self.targets[rows[j]]
if self.reverse:
return samples[:, ::-1, ...], targets
return samples, targets