alibi.explainers.integrated_gradients

Constants

DEFAULT_DATA_INTGRAD

DEFAULT_DATA_INTGRAD: dict = {'attributions': None, 'X': None, 'forward_kwargs': None, 'baselines': None, ...

DEFAULT_META_INTGRAD

DEFAULT_META_INTGRAD: dict = {'name': None, 'type': ['whitebox'], 'explanations': ['local'], 'params': {},...

logger

logger: logging.Logger = <Logger alibi.explainers.integrated_gradients (WARNING)>

Instances of the Logger class represent a single logging channel. A "logging channel" indicates an area of an application. Exactly how an "area" is defined is up to the application developer. Since an application can have any number of areas, logging channels are identified by a unique string. Application areas can be nested (e.g. an area of "input processing" might include sub-areas "read CSV files", "read XLS files" and "read Gnumeric files"). To cater for this natural nesting, channel names are organized into a namespace hierarchy where levels are separated by periods, much like the Java or Python package namespace. So in the instance given above, channel names might be "input" for the upper level, and "input.csv", "input.xls" and "input.gnu" for the sub-levels. There is no arbitrary limit to the depth of nesting.

IntegratedGradients

Inherits from: Explainer, ABC, Base

Constructor

IntegratedGradients(self, model: keras.src.models.model.Model, layer: Union[Callable[[keras.src.models.model.Model], keras.src.layers.layer.Layer], keras.src.layers.layer.Layer, NoneType] = None, target_fn: Optional[Callable] = None, method: str = 'gausslegendre', n_steps: int = 50, internal_batch_size: int = 100) -> None

Name	Type	Default	Description
model	keras.src.models.model.Model		tensorflow model.
layer	Union[Callable[[.[<class 'keras.src.models.model.Model'>]], keras.src.layers.layer.Layer], keras.src.layers.layer.Layer, None]	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]	None	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 pass target_fn=partial(np.argmax, axis=1)).
method	str	'gausslegendre'	Method for the integral approximation. Methods available: "riemann_left", "riemann_right", "riemann_middle", "riemann_trapezoid", "gausslegendre".
n_steps	int	50	Number of step in the path integral approximation from the baseline to the input instance.
internal_batch_size	int	100	Batch size for the internal batching.

Methods

explain

explain(X: Union[numpy.ndarray, List[numpy.ndarray]], forward_kwargs: Optional[dict] = None, baselines: Union[int, float, numpy.ndarray, List[int], List[float], List[numpy.ndarray], None] = None, target: Union[int, list, numpy.ndarray, None] = None, attribute_to_layer_inputs: bool = False) -> alibi.api.interfaces.Explanation

Name	Type	Default	Description
X	Union[numpy.ndarray, List[numpy.ndarray]]		Instance for which integrated gradients attribution are computed.
forward_kwargs	Optional[dict]	None	Input keyword args. If it's not None, 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, numpy.ndarray, List[int], List[float], List[numpy.ndarray], None]	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, numpy.ndarray, None]	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	False	In case of layers gradients, controls whether the gradients are computed for the layer's inputs or outputs. If True, gradients are computed for the layer's inputs, if False for the layer's outputs.

Returns

• Type: alibi.api.interfaces.Explanation

reset_predictor

reset_predictor(predictor: keras.src.models.model.Model) -> None

Name	Type	Default	Description
predictor	keras.src.models.model.Model		New prediction model.

Returns

• Type: None

LayerState

Inherits from: str, Enum

An enumeration.