omnixai.explainers.vision.agnostic package

`lime`	The LIME explainer for image classification.
`shap`	The SHAP explainer for vision tasks.
`pdp`	The partial dependence plots for vision tasks.
`l2x`	The L2X explainer for image data.

omnixai.explainers.vision.agnostic.lime module

The LIME explainer for image classification.

class omnixai.explainers.vision.agnostic.lime.LimeImage(predict_function, mode='classification', **kwargs)

Bases: ExplainerBase

The LIME explainer for image classification. If using this explainer, please cite the original work: https://github.com/marcotcr/lime. This explainer only supports image classification.

Parameters

predict_function (Callable) – The prediction function corresponding to the machine learning model to explain. For classification, the outputs of the predict_function are the class probabilities.
mode (str) – The task type can be classification only.

explanation_type = 'local'

alias = ['lime']

explain(X, **kwargs)

Generates the explanations for the input instances.

Parameters

X – A batch of input instances.
kwargs – Additional parameters, e.g., top_labels – the number of the top labels to explain. Please refer to the doc of LimeImageExplainer.explain_instance.

Return type

MaskExplanation

Returns

The explanations for all the input instances.

omnixai.explainers.vision.agnostic.shap module

The SHAP explainer for vision tasks.

class omnixai.explainers.vision.agnostic.shap.ShapImage(model, preprocess_function, mode='classification', background_data=None, **kwargs)

Bases: ExplainerBase

The SHAP explainer for vision tasks. If using this explainer, please cite the original work: https://github.com/slundberg/shap.

Parameters

model – The model to explain, whose type can be tf.keras.Model or torch.nn.Module.
preprocess_function – The preprocessing function that converts the raw input features into the inputs of model.
mode (str) – The task type, e.g., classification or regression.
background_data (Image) – The background images to compare with.

explanation_type = 'local'

alias = ['shap']

explain(X, y=None, **kwargs)

Generates the pixel-importance explanations for the input instances.

Parameters

X (Image) – A batch of input instances.
y – A batch of labels to explain. For regression, y is ignored. For classification, the top predicted label of each input instance will be explained when y = None.
kwargs – Additional parameters, e.g., nsamples – the maximum number of images sampled for the background.

Return type

PixelImportance

Returns

The explanations for all the input instances, e.g., pixel importance scores.

omnixai.explainers.vision.agnostic.pdp module

The partial dependence plots for vision tasks.

class omnixai.explainers.vision.agnostic.pdp.PartialDependenceImage(predict_function, mode='classification', **kwargs)

Bases: ExplainerBase

The partial dependence plots for vision tasks. The input image is segmented by a particular segmentation method, e.g., “quickshift”. For each segment, its importance score is measured by the average change of the predicted value when the segment is replaced by new segments constructed in the grid search.

Parameters

predict_function – The prediction function corresponding to the model to explain. When the model is for classification, the outputs of the predict_function are the class probabilities. When the model is for regression, the outputs of the predict_function are the estimated values.
mode – The task type, e.g., classification or regression.

explanation_type = 'local'

alias = ['pdp', 'partial_dependence']

explain(X, y=None, **kwargs)

Generates PDP explanations.

Parameters

X (Image) – A batch of input instances.
y – A batch of labels to explain. For regression, y is ignored. For classification, the top predicted label of each input instance will be explained when y = None.
kwargs – Additional parameters in the PDP explainer, e.g., grid_resolution – the resolution in the grid search, and n_segments – the number of image segments used by image segmentation methods.

Return type

PixelImportance

Returns

The generated explanations, e.g., the importance scores for image segments.

omnixai.explainers.vision.agnostic.l2x module

The L2X explainer for image data.

class omnixai.explainers.vision.agnostic.l2x.DefaultSelectionModel(explainer, **kwargs)

Bases: _DefaultModelBase

The default selection model in L2X, which is designed for MNIST.

Parameters

explainer – A L2XImage explainer.
kwargs – Additional parameters.

forward(inputs)

Parameters: inputs – The model inputs.

postprocess(inputs)

Upsamples to the original image size.

Parameters: inputs – The outputs of forward.

training: bool

class omnixai.explainers.vision.agnostic.l2x.DefaultPredictionModel(explainer, **kwargs)

Bases: _DefaultModelBase

The default prediction model in L2X, which is designed for MNIST.

Parameters

explainer – A L2XImage explainer.
kwargs – Additional parameters.

forward(inputs, weights)

Parameters

inputs – The model inputs.
weights – The weights generated via Gumbel-Softmax sampling.

training: bool

class omnixai.explainers.vision.agnostic.l2x.L2XImage(training_data, predict_function, mode='classification', tau=0.5, k=10, selection_model=None, prediction_model=None, loss_function=None, optimizer=None, learning_rate=0.001, batch_size=None, num_epochs=20, **kwargs)

Bases: ExplainerBase

The LIME explainer for vision tasks. If using this explainer, please cite the original work: Learning to Explain: An Information-Theoretic Perspective on Model Interpretation, Jianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan, https://arxiv.org/abs/1802.07814.

Parameters

training_data (Image) – The data used to train the explainer. training_data should be the training dataset for training the machine learning model.
predict_function (Callable) – The prediction function corresponding to the model to explain. When the model is for classification, the outputs of the predict_function are the class probabilities. When the model is for regression, the outputs of the predict_function are the estimated values.
mode (str) – The task type, e.g., classification or regression.
tau (float) – Parameter tau in Gumbel-Softmax.
k (int) – The maximum number of the selected features in L2X.
selection_model – A pytorch model class for estimating P(S|X) in L2X. If selection_model = None, a default model DefaultSelectionModel will be used.
prediction_model – A pytorch model class for estimating Q(X_S) in L2X. If prediction_model = None, a default model DefaultPredictionModel will be used.
loss_function (Optional[Callable]) – The loss function for the task, e.g., nn.CrossEntropyLoss() for classification.
optimizer – The optimizer class for training the explainer, e.g., torch.optim.Adam.
learning_rate (float) – The learning rate for training the explainer.
batch_size (Optional[int]) – The batch size for training the explainer. If batch_size is None, batch_size will be picked from [32, 64, 128, 256] based on the sample size.
num_epochs (int) – The number of epochs for training the explainer.
kwargs – Additional parameters, e.g., parameters for selection_model and prediction_model.

explanation_type = 'local'

alias = ['l2x', 'L2X']

explain(X, **kwargs)

Generates the explanations for the input instances. For classification, it explains the top predicted label for each input instance.

Parameters: X (Image) – A batch of input instances.
Return type: PixelImportance
Returns: The explanations for all the input instances.

save(directory, filename=None, **kwargs)

Saves the initialized explainer.

Parameters

directory (str) – The folder for the dumped explainer.
filename (Optional[str]) – The filename (the explainer class name if it is None).