# 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.""" # pylint: disable=invalid-name from keras_preprocessing import sequence from keras.utils import data_utils from tensorflow.python.util.tf_export import keras_export make_sampling_table = sequence.make_sampling_table skipgrams = sequence.skipgrams # TODO(fchollet): consider making `_remove_long_seq` public. _remove_long_seq = sequence._remove_long_seq # pylint: disable=protected-access @keras_export('keras.preprocessing.sequence.TimeseriesGenerator') class TimeseriesGenerator(sequence.TimeseriesGenerator, data_utils.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: Data points earlier 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: Data points 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](https://www.tensorflow.org/api_docs/python/tf/keras/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]])) ``` """ pass @keras_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 length `num_samples`) of 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 in the list. Sequences that are shorter than `num_timesteps` are padded with `value` until they are `num_timesteps` long. 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 or removing values from the beginning of the sequence is the default. >>> sequence = [[1], [2, 3], [4, 5, 6]] >>> tf.keras.preprocessing.sequence.pad_sequences(sequence) array([[0, 0, 1], [0, 2, 3], [4, 5, 6]], dtype=int32) >>> tf.keras.preprocessing.sequence.pad_sequences(sequence, value=-1) array([[-1, -1, 1], [-1, 2, 3], [ 4, 5, 6]], dtype=int32) >>> tf.keras.preprocessing.sequence.pad_sequences(sequence, padding='post') array([[1, 0, 0], [2, 3, 0], [4, 5, 6]], dtype=int32) >>> tf.keras.preprocessing.sequence.pad_sequences(sequence, maxlen=2) array([[0, 1], [2, 3], [5, 6]], dtype=int32) Args: sequences: List of sequences (each sequence is a list of integers). maxlen: Optional Int, maximum length of all sequences. If not provided, sequences will be padded to the length of the longest individual sequence. dtype: (Optional, defaults to int32). Type of the output sequences. To pad sequences with variable length strings, you can use `object`. padding: String, 'pre' or 'post' (optional, defaults to 'pre'): pad either before or after each sequence. truncating: String, 'pre' or 'post' (optional, defaults to 'pre'): remove values from sequences larger than `maxlen`, either at the beginning or at the end of the sequences. value: Float or String, padding value. (Optional, defaults to 0.) Returns: 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. """ return sequence.pad_sequences( sequences, maxlen=maxlen, dtype=dtype, padding=padding, truncating=truncating, value=value) keras_export( 'keras.preprocessing.sequence.make_sampling_table', allow_multiple_exports=True)(make_sampling_table) keras_export('keras.preprocessing.sequence.skipgrams', allow_multiple_exports=True)(skipgrams)