Inverter¶

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

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

Parameters: 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.

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__(n_jobs=None)[source]¶: Initialize self. See help(type(self)) for accurate signature.

fit(X, y=None)[source]¶

Do nothing and return the estimator unchanged.

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 binary 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 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])

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')[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.

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 (dict) – Optional plotting parameters.

Returns

Xt – Transformed one-sample slice from the input.

Return type

ndarray of shape (1, ..)