alibi.explainers.integrated_gradients module
- class alibi.explainers.integrated_gradients.IntegratedGradients(model, layer=None, target_fn=None, method='gausslegendre', n_steps=50, internal_batch_size=100)[source]
Bases:
Explainer- __init__(model, layer=None, target_fn=None, method='gausslegendre', n_steps=50, internal_batch_size=100)[source]
An implementation of the integrated gradients method for tensorflow models.
For details of the method see the original paper: https://arxiv.org/abs/1703.01365 .
- Parameters:
model (
Model) – tensorflow model.layer (
Union[Callable[[Model],Layer],Layer,None]) – A layer or a function having as parameter the model and returning a layer with respect to which the gradients are calculated. If not provided, the gradients are calculated with respect to the input. To guarantee saving and loading of the explainer, the layer has to be specified as a callable which returns a layer given the model. E.g.lambda model: model.layers[0].embeddings.target_fn (
Optional[Callable]) – A scalar function that is applied to the predictions of the model. This can be used to specify which scalar output the attributions should be calculated for. This can be particularly useful if the desired output is not known before calling the model (e.g. explaining the argmax output for a probabilistic classifier, in this case we could passtarget_fn=partial(np.argmax, axis=1)).method (
str) – Method for the integral approximation. Methods available:"riemann_left","riemann_right","riemann_middle","riemann_trapezoid","gausslegendre".n_steps (
int) – Number of step in the path integral approximation from the baseline to the input instance.internal_batch_size (
int) – Batch size for the internal batching.
- explain(X, forward_kwargs=None, baselines=None, target=None, attribute_to_layer_inputs=False)[source]
Calculates the attributions for each input feature or element of layer and returns an Explanation object.
- Parameters:
X (
Union[ndarray,List[ndarray]]) – Instance for which integrated gradients attribution are computed.forward_kwargs (
Optional[dict]) – Input keyword args. If it’s notNone, it must be a dict with numpy arrays as values. The first dimension of the arrays must correspond to the number of examples. It will be repeated for each of n_steps along the integrated path. The attributions are not computed with respect to these arguments.baselines (
Union[int,float,ndarray,List[int],List[float],List[ndarray],None]) – Baselines (starting point of the path integral) for each instance. If the passed value is an np.ndarray must have the same shape as X. If not provided, all features values for the baselines are set to 0.target (
Union[int,list,ndarray,None]) – Defines which element of the model output is considered to compute the gradients. Target can be a numpy array, a list or a numeric value. Numeric values are only valid if the model’s output is a rank-n tensor with n <= 2 (regression and classification models). If a numeric value is passed, the gradients are calculated for the same element of the output for all data points. For regression models whose output is a scalar, target should not be provided. For classification models target can be either the true classes or the classes predicted by the model. It must be provided for classification models and regression models whose output is a vector. If the model’s output is a rank-n tensor with n > 2, the target must be a rank-2 numpy array or a list of lists (a matrix) with dimensions nb_samples X (n-1) .attribute_to_layer_inputs (
bool) – In case of layers gradients, controls whether the gradients are computed for the layer’s inputs or outputs. IfTrue, gradients are computed for the layer’s inputs, ifFalsefor the layer’s outputs.
- Return type:
- Returns:
explanation – Explanation object including meta and data attributes with integrated gradients attributions for each feature. See usage at IG examples for details.