Inverter

class gtda.images.Inverter(max_value=None, n_jobs=None)[source]

Invert all 2D/3D images in a collection.

Applies an inversion function to the value of all pixels of all images in the input collection. If the images are binary, the inversion function is defined as the logical NOT function. Otherwise, it is the function \(f(x) = M - x\), where x is a pixel value and M is max_value_.

Parameters

  • max_value (bool, int, float or None, optional, default: None) – Maximum possible pixel value in the images. It should be a boolean if input images are binary and an int or a float if they are greyscale. If None, it is calculated from the collection of images passed in fit.

  • n_jobs (int or None, optional, default: None) – The number of jobs to use for the computation. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.

n_dimensions_

Dimension of the images. Set in fit.

Type

2 or 3

max_value_

Effective maximum value of the images’ pixels. Set in fit.

Type

int, float or bool

References

1

A. Garin and G. Tauzin, “A topological reading lesson: Classification of MNIST using TDA”; 19th International IEEE Conference on Machine Learning and Applications (ICMLA 2020), 2019; arXiv:1910.08345.

__init__(max_value=None, n_jobs=None)[source]

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

fit(X, y=None)[source]

Calculate n_dimensions_ and max_value_ from the collection of images. Then, return the estimator.

This method is here to implement the usual scikit-learn API and hence work in pipelines.

Parameters

  • X (ndarray of shape (n_samples, n_pixels_x, n_pixels_y [, n_pixels_z])) – Input data. Each entry along axis 0 is interpreted as a 2D or 3D image.

  • y (None) – There is no need of a target in a transformer, yet the pipeline API requires this parameter.

Returns

self

Return type

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 (ndarray of shape (n_samples, n_pixels_x, n_pixels_y [, n_pixels_z])) – Input data. Each entry along axis 0 is interpreted as a 2D or 3D image.

  • y (None) – There is no need of a target in a transformer, yet the pipeline API requires this parameter.

Returns

Xt – Transformed collection of images. Each entry along axis 0 is a 2D or 3D binary image.

Return type

ndarray of shape (n_samples, n_pixels_x, n_pixels_y [, n_pixels_z])

fit_transform_plot(X, y=None, sample=0, **plot_params)

Fit to data, then apply transform_plot.

Parameters

  • X (ndarray of shape (n_samples, ..)) – Input data.

  • y (ndarray of shape (n_samples,) or None) – Target values for supervised problems.

  • sample (int) – Sample to be plotted.

  • **plot_params – Optional plotting parameters.

Returns

Xt – Transformed one-sample slice from the input.

Return type

ndarray of shape (1, ..)

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 – Parameter names mapped to their values.

Return type

mapping of string to any

static plot(Xt, sample=0, colorscale='greys', origin='upper', plotly_params=None)[source]

Plot a sample from a collection of 2D binary images.

Parameters

  • Xt (ndarray of shape (n_samples, n_pixels_x, n_pixels_y)) – Collection of 2D binary images, such as returned by transform.

  • sample (int, optional, default: 0) – Index of the sample in Xt to be plotted.

  • colorscale (str, optional, default: 'greys') – Color scale to be used in the heat map. Can be anything allowed by plotly.graph_objects.Heatmap.

  • origin ('upper' | 'lower', optional, default: 'upper') – Position of the [0, 0] pixel of data, in the upper left or lower left corner. The convention 'upper' is typically used for matrices and images.

  • plotly_params (dict or None, optional, default: None) – Custom parameters to configure the plotly figure. Allowed keys are "trace" and "layout", and the corresponding values should be dictionaries containing keyword arguments as would be fed to the update_traces and update_layout methods of plotly.graph_objects.Figure.

Returns

fig – Plotly figure.

Return type

plotly.graph_objects.Figure object

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). 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.

Return type

object

transform(X, y=None)[source]

For each binary image in the collection X, calculate its negation. Return the collection of negated binary images.

Parameters

  • X (ndarray of shape (n_samples, n_pixels_x, n_pixels_y [, n_pixels_z])) – Input data. Each entry along axis 0 is interpreted as a 2D or 3D binary image.

  • y (None) – There is no need of a target in a transformer, yet the pipeline API requires this parameter.

Returns

Xt – Transformed collection of images. Each entry along axis 0 is a 2D or 3D binary image.

Return type

ndarray of shape (n_samples, n_pixels_x, n_pixels_y [, n_pixels_z])

transform_plot(X, sample=0, **plot_params)

Take a one-sample slice from the input collection and transform it. Before returning the transformed object, plot the transformed sample.

Parameters

  • X (ndarray of shape (n_samples, ..)) – Input data.

  • sample (int) – Sample to be plotted.

  • **plot_params – Optional plotting parameters.

Returns

Xt – Transformed one-sample slice from the input.

Return type

ndarray of shape (1, ..)