386 lines
14 KiB
Python
386 lines
14 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Utilities for preprocessing sequence data.
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Deprecated: `tf.keras.preprocessing.sequence` APIs are not recommended for new
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code. Prefer `tf.keras.utils.timeseries_dataset_from_array` and
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the `tf.data` APIs which provide a much more flexible mechanisms for dealing
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with sequences. See the [tf.data guide](https://www.tensorflow.org/guide/data)
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for more details.
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"""
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import json
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import random
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import numpy as np
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from keras.utils import data_utils
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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def _remove_long_seq(maxlen, seq, label):
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"""Removes sequences that exceed the maximum length.
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Args:
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maxlen: Int, maximum length of the output sequences.
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seq: List of lists, where each sublist is a sequence.
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label: List where each element is an integer.
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Returns:
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new_seq, new_label: shortened lists for `seq` and `label`.
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"""
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new_seq, new_label = [], []
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for x, y in zip(seq, label):
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if len(x) < maxlen:
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new_seq.append(x)
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new_label.append(y)
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return new_seq, new_label
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@keras_export("keras.preprocessing.sequence.TimeseriesGenerator")
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class TimeseriesGenerator(data_utils.Sequence):
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"""Utility class for generating batches of temporal data.
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Deprecated: `tf.keras.preprocessing.sequence.TimeseriesGenerator` does not
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operate on tensors and is not recommended for new code. Prefer using a
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`tf.data.Dataset` which provides a more efficient and flexible mechanism for
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batching, shuffling, and windowing input. See the
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[tf.data guide](https://www.tensorflow.org/guide/data) for more details.
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This class takes in a sequence of data-points gathered at
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equal intervals, along with time series parameters such as
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stride, length of history, etc., to produce batches for
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training/validation.
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Arguments:
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data: Indexable generator (such as list or Numpy array)
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containing consecutive data points (timesteps).
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The data should be at 2D, and axis 0 is expected
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to be the time dimension.
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targets: Targets corresponding to timesteps in `data`.
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It should have same length as `data`.
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length: Length of the output sequences (in number of timesteps).
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sampling_rate: Period between successive individual timesteps
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within sequences. For rate `r`, timesteps
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`data[i]`, `data[i-r]`, ... `data[i - length]`
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are used for create a sample sequence.
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stride: Period between successive output sequences.
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For stride `s`, consecutive output samples would
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be centered around `data[i]`, `data[i+s]`, `data[i+2*s]`, etc.
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start_index: Data points earlier than `start_index` will not be used
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in the output sequences. This is useful to reserve part of the
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data for test or validation.
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end_index: Data points later than `end_index` will not be used
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in the output sequences. This is useful to reserve part of the
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data for test or validation.
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shuffle: Whether to shuffle output samples,
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or instead draw them in chronological order.
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reverse: Boolean: if `true`, timesteps in each output sample will be
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in reverse chronological order.
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batch_size: Number of timeseries samples in each batch
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(except maybe the last one).
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Returns:
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A [Sequence](
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https://www.tensorflow.org/api_docs/python/tf/keras/utils/Sequence)
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instance.
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Examples:
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```python
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from keras.preprocessing.sequence import TimeseriesGenerator
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import numpy as np
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data = np.array([[i] for i in range(50)])
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targets = np.array([[i] for i in range(50)])
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data_gen = TimeseriesGenerator(data, targets,
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length=10, sampling_rate=2,
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batch_size=2)
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assert len(data_gen) == 20
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batch_0 = data_gen[0]
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x, y = batch_0
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assert np.array_equal(x,
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np.array([[[0], [2], [4], [6], [8]],
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[[1], [3], [5], [7], [9]]]))
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assert np.array_equal(y,
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np.array([[10], [11]]))
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```
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"""
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def __init__(
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self,
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data,
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targets,
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length,
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sampling_rate=1,
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stride=1,
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start_index=0,
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end_index=None,
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shuffle=False,
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reverse=False,
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batch_size=128,
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):
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if len(data) != len(targets):
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raise ValueError(
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"Data and targets have to be"
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+ f" of same length. Data length is {len(data)}"
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+ f" while target length is {len(targets)}"
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)
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self.data = data
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self.targets = targets
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self.length = length
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self.sampling_rate = sampling_rate
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self.stride = stride
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self.start_index = start_index + length
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if end_index is None:
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end_index = len(data) - 1
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self.end_index = end_index
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self.shuffle = shuffle
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self.reverse = reverse
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self.batch_size = batch_size
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if self.start_index > self.end_index:
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raise ValueError(
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"`start_index+length=%i > end_index=%i` "
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"is disallowed, as no part of the sequence "
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"would be left to be used as current step."
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% (self.start_index, self.end_index)
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)
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def __len__(self):
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return (
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self.end_index - self.start_index + self.batch_size * self.stride
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) // (self.batch_size * self.stride)
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def __getitem__(self, index):
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if self.shuffle:
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rows = np.random.randint(
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self.start_index, self.end_index + 1, size=self.batch_size
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)
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else:
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i = self.start_index + self.batch_size * self.stride * index
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rows = np.arange(
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i,
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min(i + self.batch_size * self.stride, self.end_index + 1),
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self.stride,
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)
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samples = np.array(
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[
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self.data[row - self.length : row : self.sampling_rate]
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for row in rows
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]
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)
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targets = np.array([self.targets[row] for row in rows])
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if self.reverse:
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return samples[:, ::-1, ...], targets
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return samples, targets
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def get_config(self):
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"""Returns the TimeseriesGenerator configuration as Python dictionary.
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Returns:
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A Python dictionary with the TimeseriesGenerator configuration.
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"""
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data = self.data
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if type(self.data).__module__ == np.__name__:
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data = self.data.tolist()
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try:
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json_data = json.dumps(data)
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except TypeError as e:
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raise TypeError("Data not JSON Serializable:", data) from e
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targets = self.targets
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if type(self.targets).__module__ == np.__name__:
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targets = self.targets.tolist()
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try:
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json_targets = json.dumps(targets)
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except TypeError as e:
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raise TypeError("Targets not JSON Serializable:", targets) from e
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return {
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"data": json_data,
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"targets": json_targets,
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"length": self.length,
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"sampling_rate": self.sampling_rate,
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"stride": self.stride,
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"start_index": self.start_index,
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"end_index": self.end_index,
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"shuffle": self.shuffle,
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"reverse": self.reverse,
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"batch_size": self.batch_size,
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}
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def to_json(self, **kwargs):
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"""Returns a JSON string containing the generator's configuration.
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Args:
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**kwargs: Additional keyword arguments to be passed
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to `json.dumps()`.
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Returns:
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A JSON string containing the tokenizer configuration.
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"""
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config = self.get_config()
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timeseries_generator_config = {
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"class_name": self.__class__.__name__,
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"config": config,
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}
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return json.dumps(timeseries_generator_config, **kwargs)
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@keras_export("keras.preprocessing.sequence.make_sampling_table")
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def make_sampling_table(size, sampling_factor=1e-5):
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"""Generates a word rank-based probabilistic sampling table.
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Used for generating the `sampling_table` argument for `skipgrams`.
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`sampling_table[i]` is the probability of sampling
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the word i-th most common word in a dataset
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(more common words should be sampled less frequently, for balance).
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The sampling probabilities are generated according
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to the sampling distribution used in word2vec:
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```
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p(word) = (min(1, sqrt(word_frequency / sampling_factor) /
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(word_frequency / sampling_factor)))
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```
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We assume that the word frequencies follow Zipf's law (s=1) to derive
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a numerical approximation of frequency(rank):
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`frequency(rank) ~ 1/(rank * (log(rank) + gamma) + 1/2 - 1/(12*rank))`
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where `gamma` is the Euler-Mascheroni constant.
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Args:
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size: Int, number of possible words to sample.
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sampling_factor: The sampling factor in the word2vec formula.
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Returns:
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A 1D Numpy array of length `size` where the ith entry
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is the probability that a word of rank i should be sampled.
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"""
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gamma = 0.577
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rank = np.arange(size)
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rank[0] = 1
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inv_fq = rank * (np.log(rank) + gamma) + 0.5 - 1.0 / (12.0 * rank)
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f = sampling_factor * inv_fq
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return np.minimum(1.0, f / np.sqrt(f))
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@keras_export("keras.preprocessing.sequence.skipgrams")
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def skipgrams(
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sequence,
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vocabulary_size,
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window_size=4,
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negative_samples=1.0,
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shuffle=True,
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categorical=False,
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sampling_table=None,
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seed=None,
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):
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"""Generates skipgram word pairs.
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This function transforms a sequence of word indexes (list of integers)
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into tuples of words of the form:
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- (word, word in the same window), with label 1 (positive samples).
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- (word, random word from the vocabulary), with label 0 (negative samples).
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Read more about Skipgram in this gnomic paper by Mikolov et al.:
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[Efficient Estimation of Word Representations in
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Vector Space](http://arxiv.org/pdf/1301.3781v3.pdf)
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Args:
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sequence: A word sequence (sentence), encoded as a list
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of word indices (integers). If using a `sampling_table`,
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word indices are expected to match the rank
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of the words in a reference dataset (e.g. 10 would encode
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the 10-th most frequently occurring token).
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Note that index 0 is expected to be a non-word and will be skipped.
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vocabulary_size: Int, maximum possible word index + 1
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window_size: Int, size of sampling windows (technically half-window).
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The window of a word `w_i` will be
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`[i - window_size, i + window_size+1]`.
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negative_samples: Float >= 0. 0 for no negative (i.e. random) samples.
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1 for same number as positive samples.
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shuffle: Whether to shuffle the word couples before returning them.
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categorical: bool. if False, labels will be
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integers (eg. `[0, 1, 1 .. ]`),
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if `True`, labels will be categorical, e.g.
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`[[1,0],[0,1],[0,1] .. ]`.
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sampling_table: 1D array of size `vocabulary_size` where the entry i
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encodes the probability to sample a word of rank i.
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seed: Random seed.
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Returns:
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couples, labels: where `couples` are int pairs and
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`labels` are either 0 or 1.
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Note:
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By convention, index 0 in the vocabulary is
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a non-word and will be skipped.
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"""
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couples = []
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labels = []
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for i, wi in enumerate(sequence):
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if not wi:
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continue
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if sampling_table is not None:
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if sampling_table[wi] < random.random():
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continue
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window_start = max(0, i - window_size)
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window_end = min(len(sequence), i + window_size + 1)
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for j in range(window_start, window_end):
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if j != i:
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wj = sequence[j]
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if not wj:
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continue
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couples.append([wi, wj])
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if categorical:
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labels.append([0, 1])
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else:
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labels.append(1)
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if negative_samples > 0:
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num_negative_samples = int(len(labels) * negative_samples)
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words = [c[0] for c in couples]
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random.shuffle(words)
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couples += [
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[words[i % len(words)], random.randint(1, vocabulary_size - 1)]
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for i in range(num_negative_samples)
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]
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if categorical:
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labels += [[1, 0]] * num_negative_samples
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else:
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labels += [0] * num_negative_samples
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|
|
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
|