omnixai.explanations.image package

`pixel_importance`	Pixel importance explanations for vision tasks.
`mask`	Image mask explanations for vision tasks.
`contrast`	Contrastive explanations for vision tasks.
`counterfactual`	Counterfactual explanations for vision tasks.

omnixai.explanations.image.pixel_importance module

Pixel importance explanations for vision tasks.

class omnixai.explanations.image.pixel_importance.PixelImportance(mode, explanations=None, use_heatmap=False)

Bases: ExplanationBase

The class for pixel importance explanations. The pixel importance scores of the input instances are stored in a list. Each item in the list is a dict with the following format: {“image”: the input image, “scores”: the pixel importance scores}. If the task is classification, the dict has an additional entry {“target_label”: the predicted label of the input instance}.

Parameters

mode – The task type, e.g., classification or regression.
explanations – The explanation results for initializing PixelImportance, which is optional.
use_heatmap – True if plot heatmaps (for Grad-CAM methods) or False if plot raw values (for integrated gradients and SHAP).

add(image, target_label, importance_scores, **kwargs)

Adds the generated explanation of one image.

Parameters

image – The input image.
target_label – The label to be explained, which is ignored for regression.
importance_scores – The list of the corresponding pixel importance scores.

get_explanations(index=None)

Gets the generated explanations.

Parameters: index – The index of an explanation result stored in PixelImportance. When index is None, the function returns a list of all the explanations.
Returns: The explanation for one specific image (a dict) or the explanations for all the instances (a list of dicts). Each dict has the following format: {“image”: the input image, “scores”: the pixel importance scores}. If the task is classification, the dict has an additional entry {“target_label”: the predicted label of the input instance}.

plot(index=None, class_names=None, max_num_figures=20, **kwargs)

Returns matplotlib figures plotting pixel importance scores.

Parameters

index – The index of an explanation result stored in PixelImportance, e.g., it will plot the first explanation result when index = 0. When index is None, it returns a figure plotting the pixel importance scores for the first 10 instances.
class_names – A list of the class names indexed by the labels, e.g., class_name = ['dog', 'cat'] means that label 0 corresponds to ‘dog’ and label 1 corresponds to ‘cat’.
max_num_figures – The maximum number of figures to plot.

Returns

A list of matplotlib figures plotting pixel importance scores.

plotly_plot(index=0, class_names=None, max_num_figures=20, **kwargs)

Returns a plotly dash figure plotting the pixel importance scores for one specific instance.

Parameters

index – The index of the instance which cannot be None, e.g., it will plot the first explanation result when index = 0.
class_names – A list of the class names indexed by the labels, e.g., class_name = ['dog', 'cat'] means that label 0 corresponds to ‘dog’ and label 1 corresponds to ‘cat’.
max_num_figures – The maximum number of figures to plot.

Returns

A plotly dash figure plotting the pixel importance scores.

ipython_plot(index=0, class_names=None, max_num_figures=20, **kwargs)

Plots the pixel importance scores in IPython.

Parameters

index – The index of the instance which cannot be None, e.g., it will plot the first explanation result when index = 0.
class_names – A list of the class names indexed by the labels, e.g., class_name = ['dog', 'cat'] means that label 0 corresponds to ‘dog’ and label 1 corresponds to ‘cat’.
max_num_figures – The maximum number of figures to plot.

classmethod from_dict(d)

omnixai.explanations.image.mask module

Image mask explanations for vision tasks.

class omnixai.explanations.image.mask.MaskExplanation

Bases: ExplanationBase

The class for image mask explanations used by LIME. It uses a list to store image mask explanations of the input instances. Each item in the list is a dict with the following format: {“images”: the input images, “labels”: the predicted labels, “masks”: the masks returned by the explainer, e.g., LIME, “boundary”: the boundaries extracted from “masks”}.

add(labels, images, masks)

Adds the generated explanation of one image.

Parameters

labels – The predicted labels.
images – The input images.
masks – The mask explanation results.

get_explanations(index=None)

Gets the generated explanations.

Parameters: index – The index or name of the instance. When index is None, this method return all the explanations.
Returns: The explanation for one specific instance (a dict) or the explanations for all the instances (a list of dicts). Each dict has the following format: {“images”: the input images, “labels”: the predicted labels, “masks”: the masks returned by the explainer, e.g., LIME, “boundary”: the boundaries extracted from “masks”}.

plot(index=None, class_names=None, **kwargs)

Returns a matplotlib figure showing the explanations.

Parameters

index – The index of the instance, e.g., it will plot the first explanation result when index = 0. When index is None, it returns a figure showing the mask explanations for the first 10 instances.
class_names – A list of the class names indexed by the labels, e.g., class_name = ['dog', 'cat'] means that label 0 corresponds to ‘dog’ and label 1 corresponds to ‘cat’.

Returns

A matplotlib figure plotting mask explanations.

plotly_plot(index=0, class_names=None, **kwargs)

Returns a plotly dash figure showing the explanations for one specific instance.

Parameters

index – The index of the instance which cannot be None, e.g., it will plot the first explanation result when index = 0.
class_names – A list of the class names indexed by the labels, e.g., class_name = ['dog', 'cat'] means that label 0 corresponds to ‘dog’ and label 1 corresponds to ‘cat’.

Returns

A plotly dash figure plotting mask explanations.

ipython_plot(index=0, class_names=None, **kwargs)

Plots mask explanations in IPython.

Parameters

index – The index of the instance which cannot be None, e.g., it will plot the first explanation result when index = 0.
class_names – A list of the class names indexed by the labels, e.g., class_name = ['dog', 'cat'] means that label 0 corresponds to ‘dog’ and label 1 corresponds to ‘cat’.

classmethod from_dict(d)

omnixai.explanations.image.contrast module

Contrastive explanations for vision tasks.

class omnixai.explanations.image.contrast.ContrastiveExplanation

Bases: ExplanationBase

The class for image contrastive explanations. It uses a list to store the contrastive explanations of the input instances. Each item in the list is a dict with the following format: {“image”: the input image, “label”: the predicted label of the input, “pn”: the pertinent negative, “pn_label”: the predicted label of the pertinent negative, “pp”: the pertinent positive, “pp_label”: the predicted label of the pertinent positive}.

add(image, label, pn, pn_label, pp, pp_label)

Adds the generated explanation of one image.

Parameters

image – The input image.
label – The label of the input.
pn – The pertinent negative (PN).
pn_label – The label of the pertinent negative.
pp – The pertinent positive (PP).
pp_label – The label of the pertinent positive.

get_explanations(index=None)

Gets the generated explanations.

Parameters: index – The index of an explanation result stored in ContrastiveExplanation. When index is None, the function returns a list of all the explanations.
Returns: The explanation for one specific image (a dict) or the explanations for all the instances (a list of dicts). Each dict has the following format: {“image”: the input image, “label”: the predicted label of the input, “pn”: the pertinent negative, “pn_label”: the predicted label of the pertinent negative, “pp”: the pertinent positive, “pp_label”: the predicted label of the pertinent positive}.

plot(index=None, class_names=None, **kwargs)

Returns a matplotlib figure showing the contrastive explanations.

Parameters

index – The index of an explanation result stored in ContrastiveExplanation, e.g., it will plot the first explanation result when index = 0. When index is None, it returns a figure showing the PNs and PPs for the first 5 instances.
class_names – A list of the class names indexed by the labels, e.g., class_name = ['dog', 'cat'] means that label 0 corresponds to ‘dog’ and label 1 corresponds to ‘cat’.

Returns

A matplotlib figure plotting PNs and PPs.

plotly_plot(index=0, class_names=None, **kwargs)

Returns a plotly dash figure showing the contrastive explanations for one specific instance.

Parameters

index – The index of an explanation result stored in ContrastiveExplanation which cannot be None, e.g., it will plot the first explanation result when index = 0.
class_names – A list of the class names indexed by the labels, e.g., class_name = ['dog', 'cat'] means that label 0 corresponds to ‘dog’ and label 1 corresponds to ‘cat’.

Returns

A plotly dash figure plotting PNs and PPs.

ipython_plot(index=0, class_names=None, **kwargs)

Plots PNs and PPs in IPython.

Parameters

index – The index of an explanation result stored in ContrastiveExplanation which cannot be None, e.g., it will plot the first explanation result when index = 0.
class_names – A list of the class names indexed by the labels, e.g., class_name = ['dog', 'cat'] means that label 0 corresponds to ‘dog’ and label 1 corresponds to ‘cat’.

classmethod from_dict(d)

omnixai.explanations.image.counterfactual module

Counterfactual explanations for vision tasks.

class omnixai.explanations.image.counterfactual.CFExplanation

Bases: ExplanationBase

The class for image counterfactual explanations. The counterfactual examples of the input instances are stored in a list. Each item in the list is a dict with the following format: {“image”: the input image, “label”: the predicted label of the input image, “cf”: the counterfactual example, “cf_label”: the predicted label of the counterfactual example}.

add(image, label, cf, cf_label)

Adds the generated explanation of one image.

Parameters

image – The input image.
label – The label of the input.
cf – The counterfactual image.
cf_label – The label of the counterfactual image.

get_explanations(index=None)

Gets the generated explanations.

Parameters: index – The index of an explanation result stored in CounterfactualExplanation. When index is None, the function returns a list of all the explanations.
Returns: The explanation for one specific image (a dict) or the explanations for all the instances (a list of dicts). Each dict has the following format: {“image”: the input image, “label”: the predicted label of the input image, “cf”: the counterfactual example, “cf_label”: the predicted label of the counterfactual example}.

plot(index=None, class_names=None, **kwargs)

Returns a matplotlib figure showing the counterfactual explanations.

Parameters

index – The index of an explanation result stored in CounterfactualExplanation, e.g., it will plot the first explanation result when index = 0. When index is None, it returns a figure showing the PNs and PPs for the first 5 instances.
class_names – A list of the class names indexed by the labels, e.g., class_name = ['dog', 'cat'] means that label 0 corresponds to ‘dog’ and label 1 corresponds to ‘cat’.

Returns

A matplotlib figure plotting counterfactuals.

plotly_plot(index=0, class_names=None, **kwargs)

Returns a plotly dash figure showing the counterfactual explanations for one specific instance.

Parameters

index – The index of an explanation result stored in CounterfactualExplanation which cannot be None, e.g., it will plot the first explanation result when index = 0.
class_names – A list of the class names indexed by the labels, e.g., class_name = ['dog', 'cat'] means that label 0 corresponds to ‘dog’ and label 1 corresponds to ‘cat’.

Returns

A plotly dash figure plotting counterfactual examples.

ipython_plot(index=0, class_names=None, **kwargs)

Plots counterfactual examples in IPython.

Parameters

index – The index of an explanation result stored in CounterfactualExplanation which cannot be None, e.g., it will plot the first explanation result when index = 0.
class_names – A list of the class names indexed by the labels, e.g., class_name = ['dog', 'cat'] means that label 0 corresponds to ‘dog’ and label 1 corresponds to ‘cat’.

classmethod from_dict(d)