tensorflow/python/keras/preprocessing/timeseries.py - platform/external/tensorflow - Git at Google

 # Copyright 2020 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.
 # ==============================================================================
 """Keras timeseries dataset utilities."""
 # pylint: disable=g-classes-have-attributes

 import numpy as np

 from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.util.tf_export import keras_export


 @keras_export('keras.utils.timeseries_dataset_from_array',
               'keras.preprocessing.timeseries_dataset_from_array',
               v1=[])
 def timeseries_dataset_from_array(
     data,
     targets,
     sequence_length,
     sequence_stride=1,
     sampling_rate=1,
     batch_size=128,
     shuffle=False,
     seed=None,
     start_index=None,
     end_index=None):
   """Creates a dataset of sliding windows over a timeseries provided as array.

   This function takes in a sequence of data-points gathered at
   equal intervals, along with time series parameters such as
   length of the sequences/windows, spacing between two sequence/windows, etc.,
   to produce batches of timeseries inputs and targets.

   Args:
     data: Numpy array or eager tensor
       containing consecutive data points (timesteps).
       Axis 0 is expected to be the time dimension.
     targets: Targets corresponding to timesteps in `data`.
       `targets[i]` should be the target
       corresponding to the window that starts at index `i`
       (see example 2 below).
       Pass None if you don't have target data (in this case the dataset will
       only yield the input data).
     sequence_length: Length of the output sequences (in number of timesteps).
     sequence_stride: Period between successive output sequences.
       For stride `s`, output samples would
       start at index `data[i]`, `data[i + s]`, `data[i + 2 * s]`, etc.
     sampling_rate: Period between successive individual timesteps
       within sequences. For rate `r`, timesteps
       `data[i], data[i + r], ... data[i + sequence_length]`
       are used for create a sample sequence.
     batch_size: Number of timeseries samples in each batch
       (except maybe the last one).
     shuffle: Whether to shuffle output samples,
       or instead draw them in chronological order.
     seed: Optional int; random seed for shuffling.
     start_index: Optional int; data points earlier (exclusive)
       than `start_index` will not be used
       in the output sequences. This is useful to reserve part of the
       data for test or validation.
     end_index: Optional int; data points later (exclusive) than `end_index`
       will not be used in the output sequences.
       This is useful to reserve part of the data for test or validation.

   Returns:
     A tf.data.Dataset instance. If `targets` was passed, the dataset yields
     tuple `(batch_of_sequences, batch_of_targets)`. If not, the dataset yields
     only `batch_of_sequences`.

   Example 1:

   Consider indices `[0, 1, ... 99]`.
   With `sequence_length=10,  sampling_rate=2, sequence_stride=3`,
   `shuffle=False`, the dataset will yield batches of sequences
   composed of the following indices:

   ```
   First sequence:  [0  2  4  6  8 10 12 14 16 18]
   Second sequence: [3  5  7  9 11 13 15 17 19 21]
   Third sequence:  [6  8 10 12 14 16 18 20 22 24]
   ...
   Last sequence:   [78 80 82 84 86 88 90 92 94 96]
   ```

   In this case the last 3 data points are discarded since no full sequence
   can be generated to include them (the next sequence would have started
   at index 81, and thus its last step would have gone over 99).

   Example 2: Temporal regression.

   Consider an array `data` of scalar values, of shape `(steps,)`.
   To generate a dataset that uses the past 10
   timesteps to predict the next timestep, you would use:

   ```python
   input_data = data[:-10]
   targets = data[10:]
   dataset = tf.keras.preprocessing.timeseries_dataset_from_array(
       input_data, targets, sequence_length=10)
   for batch in dataset:
     inputs, targets = batch
     assert np.array_equal(inputs[0], data[:10])  # First sequence: steps [0-9]
     assert np.array_equal(targets[0], data[10])  # Corresponding target: step 10
     break
   ```

   Example 3: Temporal regression for many-to-many architectures.

   Consider two arrays of scalar values `X` and `Y`,
   both of shape `(100,)`. The resulting dataset should consist samples with
   20 timestamps each. The samples should not overlap.
   To generate a dataset that uses the current timestamp
   to predict the corresponding target timestep, you would use:

   ```python
   X = np.arange(100)
   Y = X*2

   sample_length = 20
   input_dataset = tf.keras.preprocessing.timeseries_dataset_from_array(
     X, None, sequence_length=sample_length, sequence_stride=sample_length)
   target_dataset = tf.keras.preprocessing.timeseries_dataset_from_array(
     Y, None, sequence_length=sample_length, sequence_stride=sample_length)

   for batch in zip(input_dataset, target_dataset):
     inputs, targets = batch
     assert np.array_equal(inputs[0], X[:sample_length])

     # second sample equals output timestamps 20-40
     assert np.array_equal(targets[1], Y[sample_length:2*sample_length])
     break
   ```
   """
   if start_index and (start_index < 0 or start_index >= len(data)):
     raise ValueError('start_index must be higher than 0 and lower than the '
                      'length of the data. Got: start_index=%s '
                      'for data of length %s.' % (start_index, len(data)))
   if end_index:
     if start_index and end_index <= start_index:
       raise ValueError('end_index must be higher than start_index. Got: '
                        'start_index=%s, end_index=%s.' %
                        (start_index, end_index))
     if end_index >= len(data):
       raise ValueError('end_index must be lower than the length of the data. '
                        'Got: end_index=%s' % (end_index,))
     if end_index <= 0:
       raise ValueError('end_index must be higher than 0. '
                        'Got: end_index=%s' % (end_index,))

   # Validate strides
   if sampling_rate <= 0 or sampling_rate >= len(data):
     raise ValueError(
         'sampling_rate must be higher than 0 and lower than '
         'the length of the data. Got: '
         'sampling_rate=%s for data of length %s.' % (sampling_rate, len(data)))
   if sequence_stride <= 0 or sequence_stride >= len(data):
     raise ValueError(
         'sequence_stride must be higher than 0 and lower than '
         'the length of the data. Got: sequence_stride=%s '
         'for data of length %s.' % (sequence_stride, len(data)))

   if start_index is None:
     start_index = 0
   if end_index is None:
     end_index = len(data)

   # Determine the lowest dtype to store start positions (to lower memory usage).
   num_seqs = end_index - start_index - (sequence_length * sampling_rate) + 1
   if targets is not None:
     num_seqs = min(num_seqs, len(targets))
   if num_seqs < 2147483647:
     index_dtype = 'int32'
   else:
     index_dtype = 'int64'

   # Generate start positions
   start_positions = np.arange(0, num_seqs, sequence_stride, dtype=index_dtype)
   if shuffle:
     if seed is None:
       seed = np.random.randint(1e6)
     rng = np.random.RandomState(seed)
     rng.shuffle(start_positions)

   sequence_length = math_ops.cast(sequence_length, dtype=index_dtype)
   sampling_rate = math_ops.cast(sampling_rate, dtype=index_dtype)

   positions_ds = dataset_ops.Dataset.from_tensors(start_positions).repeat()

   # For each initial window position, generates indices of the window elements
   indices = dataset_ops.Dataset.zip(
       (dataset_ops.Dataset.range(len(start_positions)), positions_ds)).map(
           lambda i, positions: math_ops.range(  # pylint: disable=g-long-lambda
               positions[i],
               positions[i] + sequence_length * sampling_rate,
               sampling_rate),
           num_parallel_calls=dataset_ops.AUTOTUNE)

   dataset = sequences_from_indices(data, indices, start_index, end_index)
   if targets is not None:
     indices = dataset_ops.Dataset.zip(
         (dataset_ops.Dataset.range(len(start_positions)), positions_ds)).map(
             lambda i, positions: positions[i],
             num_parallel_calls=dataset_ops.AUTOTUNE)
     target_ds = sequences_from_indices(
         targets, indices, start_index, end_index)
     dataset = dataset_ops.Dataset.zip((dataset, target_ds))
   if shuffle:
     # Shuffle locally at each iteration
     dataset = dataset.shuffle(buffer_size=batch_size * 8, seed=seed)
   dataset = dataset.batch(batch_size)
   return dataset


 def sequences_from_indices(array, indices_ds, start_index, end_index):
   dataset = dataset_ops.Dataset.from_tensors(array[start_index : end_index])
   dataset = dataset_ops.Dataset.zip((dataset.repeat(), indices_ds)).map(
       lambda steps, inds: array_ops.gather(steps, inds),  # pylint: disable=unnecessary-lambda
       num_parallel_calls=dataset_ops.AUTOTUNE)
   return dataset
	# Copyright 2020 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.
	# ==============================================================================
	"""Keras timeseries dataset utilities."""
	# pylint: disable=g-classes-have-attributes

	import numpy as np

	from tensorflow.python.data.ops import dataset_ops
	from tensorflow.python.ops import array_ops
	from tensorflow.python.ops import math_ops
	from tensorflow.python.util.tf_export import keras_export


	@keras_export('keras.utils.timeseries_dataset_from_array',
	'keras.preprocessing.timeseries_dataset_from_array',
	v1=[])
	def timeseries_dataset_from_array(
	data,
	targets,
	sequence_length,
	sequence_stride=1,
	sampling_rate=1,
	batch_size=128,
	shuffle=False,
	seed=None,
	start_index=None,
	end_index=None):
	"""Creates a dataset of sliding windows over a timeseries provided as array.

	This function takes in a sequence of data-points gathered at
	equal intervals, along with time series parameters such as
	length of the sequences/windows, spacing between two sequence/windows, etc.,
	to produce batches of timeseries inputs and targets.

	Args:
	data: Numpy array or eager tensor
	containing consecutive data points (timesteps).
	Axis 0 is expected to be the time dimension.
	targets: Targets corresponding to timesteps in `data`.
	`targets[i]` should be the target
	corresponding to the window that starts at index `i`
	(see example 2 below).
	Pass None if you don't have target data (in this case the dataset will
	only yield the input data).
	sequence_length: Length of the output sequences (in number of timesteps).
	sequence_stride: Period between successive output sequences.
	For stride `s`, output samples would
	start at index `data[i]`, `data[i + s]`, `data[i + 2 * s]`, etc.
	sampling_rate: Period between successive individual timesteps
	within sequences. For rate `r`, timesteps
	`data[i], data[i + r], ... data[i + sequence_length]`
	are used for create a sample sequence.
	batch_size: Number of timeseries samples in each batch
	(except maybe the last one).
	shuffle: Whether to shuffle output samples,
	or instead draw them in chronological order.
	seed: Optional int; random seed for shuffling.
	start_index: Optional int; data points earlier (exclusive)
	than `start_index` will not be used
	in the output sequences. This is useful to reserve part of the
	data for test or validation.
	end_index: Optional int; data points later (exclusive) than `end_index`
	will not be used in the output sequences.
	This is useful to reserve part of the data for test or validation.

	Returns:
	A tf.data.Dataset instance. If `targets` was passed, the dataset yields
	tuple `(batch_of_sequences, batch_of_targets)`. If not, the dataset yields
	only `batch_of_sequences`.

	Example 1:

	Consider indices `[0, 1, ... 99]`.
	With `sequence_length=10, sampling_rate=2, sequence_stride=3`,
	`shuffle=False`, the dataset will yield batches of sequences
	composed of the following indices:

	```
	First sequence: [0 2 4 6 8 10 12 14 16 18]
	Second sequence: [3 5 7 9 11 13 15 17 19 21]
	Third sequence: [6 8 10 12 14 16 18 20 22 24]
	...
	Last sequence: [78 80 82 84 86 88 90 92 94 96]
	```

	In this case the last 3 data points are discarded since no full sequence
	can be generated to include them (the next sequence would have started
	at index 81, and thus its last step would have gone over 99).

	Example 2: Temporal regression.

	Consider an array `data` of scalar values, of shape `(steps,)`.
	To generate a dataset that uses the past 10
	timesteps to predict the next timestep, you would use:

	```python
	input_data = data[:-10]
	targets = data[10:]
	dataset = tf.keras.preprocessing.timeseries_dataset_from_array(
	input_data, targets, sequence_length=10)
	for batch in dataset:
	inputs, targets = batch
	assert np.array_equal(inputs[0], data[:10]) # First sequence: steps [0-9]
	assert np.array_equal(targets[0], data[10]) # Corresponding target: step 10
	break
	```

	Example 3: Temporal regression for many-to-many architectures.

	Consider two arrays of scalar values `X` and `Y`,
	both of shape `(100,)`. The resulting dataset should consist samples with
	20 timestamps each. The samples should not overlap.
	To generate a dataset that uses the current timestamp
	to predict the corresponding target timestep, you would use:

	```python
	X = np.arange(100)
	Y = X*2

	sample_length = 20
	input_dataset = tf.keras.preprocessing.timeseries_dataset_from_array(
	X, None, sequence_length=sample_length, sequence_stride=sample_length)
	target_dataset = tf.keras.preprocessing.timeseries_dataset_from_array(
	Y, None, sequence_length=sample_length, sequence_stride=sample_length)

	for batch in zip(input_dataset, target_dataset):
	inputs, targets = batch
	assert np.array_equal(inputs[0], X[:sample_length])

	# second sample equals output timestamps 20-40
	assert np.array_equal(targets[1], Y[sample_length:2*sample_length])
	break
	```
	"""
	if start_index and (start_index < 0 or start_index >= len(data)):
	raise ValueError('start_index must be higher than 0 and lower than the '
	'length of the data. Got: start_index=%s '
	'for data of length %s.' % (start_index, len(data)))
	if end_index:
	if start_index and end_index <= start_index:
	raise ValueError('end_index must be higher than start_index. Got: '
	'start_index=%s, end_index=%s.' %
	(start_index, end_index))
	if end_index >= len(data):
	raise ValueError('end_index must be lower than the length of the data. '
	'Got: end_index=%s' % (end_index,))
	if end_index <= 0:
	raise ValueError('end_index must be higher than 0. '
	'Got: end_index=%s' % (end_index,))

	# Validate strides
	if sampling_rate <= 0 or sampling_rate >= len(data):
	raise ValueError(
	'sampling_rate must be higher than 0 and lower than '
	'the length of the data. Got: '
	'sampling_rate=%s for data of length %s.' % (sampling_rate, len(data)))
	if sequence_stride <= 0 or sequence_stride >= len(data):
	raise ValueError(
	'sequence_stride must be higher than 0 and lower than '
	'the length of the data. Got: sequence_stride=%s '
	'for data of length %s.' % (sequence_stride, len(data)))

	if start_index is None:
	start_index = 0
	if end_index is None:
	end_index = len(data)

	# Determine the lowest dtype to store start positions (to lower memory usage).
	num_seqs = end_index - start_index - (sequence_length * sampling_rate) + 1
	if targets is not None:
	num_seqs = min(num_seqs, len(targets))
	if num_seqs < 2147483647:
	index_dtype = 'int32'
	else:
	index_dtype = 'int64'

	# Generate start positions
	start_positions = np.arange(0, num_seqs, sequence_stride, dtype=index_dtype)
	if shuffle:
	if seed is None:
	seed = np.random.randint(1e6)
	rng = np.random.RandomState(seed)
	rng.shuffle(start_positions)

	sequence_length = math_ops.cast(sequence_length, dtype=index_dtype)
	sampling_rate = math_ops.cast(sampling_rate, dtype=index_dtype)

	positions_ds = dataset_ops.Dataset.from_tensors(start_positions).repeat()

	# For each initial window position, generates indices of the window elements
	indices = dataset_ops.Dataset.zip(
	(dataset_ops.Dataset.range(len(start_positions)), positions_ds)).map(
	lambda i, positions: math_ops.range( # pylint: disable=g-long-lambda
	positions[i],
	positions[i] + sequence_length * sampling_rate,
	sampling_rate),
	num_parallel_calls=dataset_ops.AUTOTUNE)

	dataset = sequences_from_indices(data, indices, start_index, end_index)
	if targets is not None:
	indices = dataset_ops.Dataset.zip(
	(dataset_ops.Dataset.range(len(start_positions)), positions_ds)).map(
	lambda i, positions: positions[i],
	num_parallel_calls=dataset_ops.AUTOTUNE)
	target_ds = sequences_from_indices(
	targets, indices, start_index, end_index)
	dataset = dataset_ops.Dataset.zip((dataset, target_ds))
	if shuffle:
	# Shuffle locally at each iteration
	dataset = dataset.shuffle(buffer_size=batch_size * 8, seed=seed)
	dataset = dataset.batch(batch_size)
	return dataset


	def sequences_from_indices(array, indices_ds, start_index, end_index):
	dataset = dataset_ops.Dataset.from_tensors(array[start_index : end_index])
	dataset = dataset_ops.Dataset.zip((dataset.repeat(), indices_ds)).map(
	lambda steps, inds: array_ops.gather(steps, inds), # pylint: disable=unnecessary-lambda
	num_parallel_calls=dataset_ops.AUTOTUNE)
	return dataset