> 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/counterfactual.md). # alibi.explainers.counterfactual ## Constants ### `DEFAULT_DATA_CF` ```python DEFAULT_DATA_CF: dict = {'cf': None, 'all': [], 'orig_class': None, 'orig_proba': None, 'success': None} ``` ### `DEFAULT_META_CF` ```python DEFAULT_META_CF: dict = {'name': None, 'type': ['blackbox', 'tensorflow', 'keras'], 'explanations': [... ``` ### `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. ## `Counterfactual` *Inherits from:* `Explainer`, `ABC`, `Base` ### Constructor ```python Counterfactual(self, predict_fn: Union[Callable[[numpy.ndarray], numpy.ndarray], keras.src.models.model.Model], shape: Tuple[int, ...], distance_fn: str = 'l1', target_proba: float = 1.0, target_class: Union[str, int] = 'other', max_iter: int = 1000, early_stop: int = 50, lam_init: float = 0.1, max_lam_steps: int = 10, tol: float = 0.05, learning_rate_init=0.1, feature_range: Union[Tuple, str] = (-10000000000.0, 10000000000.0), eps: Union[float, numpy.ndarray] = 0.01, init: str = 'identity', decay: bool = True, write_dir: Optional[str] = None, debug: bool = False, sess: Optional[tensorflow.python.client.session.Session] = None) -> None ``` | Name | Type | Default | Description | | -------------------- | -------------------------------------------------------------------------------------------- | --------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `predict_fn` | `Union[Callable[[.[]], numpy.ndarray], keras.src.models.model.Model]` | | `tensorflow` model or any other model's prediction function returning class probabilities. | | `shape` | `Tuple[int, .Ellipsis]` | | Shape of input data starting with batch size. | | `distance_fn` | `str` | `'l1'` | Distance function to use in the loss term. | | `target_proba` | `float` | `1.0` | Target probability for the counterfactual to reach. | | `target_class` | `Union[str, int]` | `'other'` | Target class for the counterfactual to reach, one of `'other'`, `'same'` or an integer denoting desired class membership for the counterfactual instance. | | `max_iter` | `int` | `1000` | Maximum number of iterations to run the gradient descent for (inner loop). | | `early_stop` | `int` | `50` | Number of steps after which to terminate gradient descent if all or none of found instances are solutions. | | `lam_init` | `float` | `0.1` | Initial regularization constant for the prediction part of the Wachter loss. | | `max_lam_steps` | `int` | `10` | Maximum number of times to adjust the regularization constant (outer loop) before terminating the search. | | `tol` | `float` | `0.05` | Tolerance for the counterfactual target probability. | | `learning_rate_init` | | `0.1` | Initial learning rate for each outer loop of `lambda`. | | `feature_range` | `Union[Tuple, str]` | `(-10000000000.0, 10000000000.0)` | Tuple with `min` and `max` ranges to allow for perturbed instances. `Min` and `max` ranges can be `float` or `numpy` arrays with dimension (1 x nb of features) for feature-wise ranges. | | `eps` | `Union[float, numpy.ndarray]` | `0.01` | Gradient step sizes used in calculating numerical gradients, defaults to a single value for all features, but can be passed an array for feature-wise step sizes. | | `init` | `str` | `'identity'` | Initialization method for the search of counterfactuals, currently must be `'identity'`. | | `decay` | `bool` | `True` | Flag to decay learning rate to zero for each outer loop over lambda. | | `write_dir` | `Optional[str]` | `None` | Directory to write `tensorboard` files to. | | `debug` | `bool` | `False` | Flag to write `tensorboard` summaries for debugging. | | `sess` | `Optional[tensorflow.python.client.session.Session]` | `None` | Optional `tensorflow` session that will be used if passed instead of creating or inferring one internally. | ### Methods #### `explain` ```python explain(X: numpy.ndarray) -> alibi.api.interfaces.Explanation ``` | Name | Type | Default | Description | | ---- | --------------- | ------- | ------------------------- | | `X` | `numpy.ndarray` | | Instance to be explained. | **Returns** * Type: `alibi.api.interfaces.Explanation` #### `fit` ```python fit(X: numpy.ndarray, y: Optional[numpy.ndarray]) -> alibi.explainers.counterfactual.Counterfactual ``` | Name | Type | Default | Description | | ---- | ------------------------- | ------- | ----------------------------------- | | `X` | `numpy.ndarray` | | Not used. Included for consistency. | | `y` | `Optional[numpy.ndarray]` | | Not used. Included for consistency. | **Returns** * Type: `alibi.explainers.counterfactual.Counterfactual` #### `reset_predictor` ```python reset_predictor(predictor: Union[Callable, keras.src.models.model.Model]) -> None ``` | Name | Type | Default | Description | | ----------- | ----------------------------------------------- | ------- | ----------------------------- | | `predictor` | `Union[Callable, keras.src.models.model.Model]` | | New predictor function/model. | **Returns** * Type: `None` ## Functions ### `CounterFactual` ```python CounterFactual(args, kwargs) ``` The class name `CounterFactual` is deprecated, please use `Counterfactual`. --- # 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. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.seldon.ai/alibi-explain/api-reference/explainers/counterfactual.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.