> For the complete documentation index, see [llms.txt](https://docs.seldon.ai/alibi-explain/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.seldon.ai/alibi-explain/api-reference/explainers/integrated_gradients.md). # alibi.explainers.integrated\_gradients ## Constants ### `DEFAULT_DATA_INTGRAD` ```python DEFAULT_DATA_INTGRAD: dict = {'attributions': None, 'X': None, 'forward_kwargs': None, 'baselines': None, ... ``` ### `DEFAULT_META_INTGRAD` ```python DEFAULT_META_INTGRAD: dict = {'name': None, 'type': ['whitebox'], 'explanations': ['local'], 'params': {},... ``` ### `logger` ```python logger: logging.Logger = ``` 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 ```python 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[[.[]], 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` ```python 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` ```python 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. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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