pyts.image.MarkovTransitionField

class pyts.image.MarkovTransitionField(image_size=1.0, n_bins=8, strategy='quantile', overlapping=False, flatten=False)[source]

Markov Transition Field.

Parameters:
image_size : int or float (default = 1.)

Shape of the output images. If float, it represents a percentage of the size of each time series and must be between 0 and 1. Output images are square, thus providing the size of one dimension is enough.

n_bins : int (default = 5)

Number of bins (also known as the size of the alphabet)

strategy : ‘uniform’, ‘quantile’ or ‘normal’ (default = ‘quantile’)

Strategy used to define the widths of the bins:

  • ‘uniform’: All bins in each sample have identical widths
  • ‘quantile’: All bins in each sample have the same number of points
  • ‘normal’: Bin edges are quantiles from a standard normal distribution
overlapping : bool (default = False)

If False, reducing the image with the blurring kernel will be applied on non-overlapping rectangles. If True, it will be applied on possibly overlapping squares.

flatten : bool (default = False)

If True, images are flattened to be one-dimensional.

References

[1]Z. Wang and T. Oates, “Encoding Time Series as Images for Visual Inspection and Classification Using Tiled Convolutional Neural Networks.” AAAI Workshop (2015).

Examples

>>> from pyts.datasets import load_gunpoint
>>> from pyts.image import MarkovTransitionField
>>> X, _, _, _ = load_gunpoint(return_X_y=True)
>>> transformer = MarkovTransitionField()
>>> X_new = transformer.transform(X)
>>> X_new.shape
(50, 150, 150)

Methods

__init__([image_size, n_bins, strategy, …]) Initialize self.
fit([X, y]) Pass.
fit_transform(X[, y]) Fit to data, then transform it.
get_params([deep]) Get parameters for this estimator.
set_params(**params) Set the parameters of this estimator.
transform(X) Transform each time series into a MTF image.
__init__(image_size=1.0, n_bins=8, strategy='quantile', overlapping=False, flatten=False)[source]

Initialize self. See help(type(self)) for accurate signature.

fit(X=None, y=None)[source]

Pass.

Parameters:
X

Ignored

y

Ignored
Returns:
self : object
fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:
X : array-like, shape = (n_samples, n_timestamps)

Univariate time series.

y : None or array-like, shape = (n_samples,) (default = None)

Target values (None for unsupervised transformations).

**fit_params : dict

Additional fit parameters.
Returns:
X_new : array

Transformed array.
get_params(deep=True)

Get parameters for this estimator.

Parameters:
deep : bool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:
params : dict

Parameter names mapped to their values.
set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**params : dict

Estimator parameters.
Returns:
self : estimator instance

Estimator instance.
transform(X)[source]

Transform each time series into a MTF image.

Parameters:
X : array-like, shape = (n_samples, n_timestamps)

Input data
Returns:
X_new : array-like, shape = (n_samples, image_size, image_size)

Transformed data. If flatten=True, the shape is (n_samples, image_size * image_size).

Examples using pyts.image.MarkovTransitionField

Data set of Markov transition fields

Data set of Markov transition fields

Data set of Markov transition fields
Single Markov transition field

Single Markov transition field

Single Markov transition field