### TimeseriesGenerator

```
keras.preprocessing.sequence.TimeseriesGenerator(data, targets, length, sampling_rate=1, stride=1, start_index=0, end_index=None, shuffle=False, reverse=False, batch_size=128)
```

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 instance.

**Examples**

```
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]]))
```

### pad_sequences

```
keras.preprocessing.sequence.pad_sequences(sequences, maxlen=None, dtype='int32', padding='pre', truncating='pre', value=0.0)
```

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.

### skipgrams

```
keras.preprocessing.sequence.skipgrams(sequence, vocabulary_size, window_size=4, negative_samples=1.0, 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

**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.

### make_sampling_table

```
keras.preprocessing.sequence.make_sampling_table(size, sampling_factor=1e-05)
```

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.