acfx.AcfxLinear
- class acfx.AcfxLinear(blackbox: LinearClassifierMixin)
Bases:
ACFXAcfxLinear: A Counterfactual Explanation Model (using linear additive model as blackbox)
- counterfactual(query_instance: ndarray, desired_class: int, num_counterfactuals: int = 1, proximity_weight: float = 1, sparsity_weight: float = 1, plausibility_weight: float = 0, diversity_weight: float = 1, init_points: int = 10, n_iter: int = 1000, sampling_from_model: bool = True) ndarray
Generates counterfactuals
Parameters
- query_instance:
The instance to generate counterfactuals for.
- desired_class:
The target class for the counterfactuals.
- num_counterfactuals:
The number of counterfactual instances to generate.
- proximity_weight:
Weight for proximity loss component
- sparsity_weight:
Weight for sparsity loss component
- plausibility_weight:
Weight for plausibility loss component
- diversity_weight:
Weight for diversity loss component
- init_points:
Number of initial points for Bayesian Optimization.
- n_iter:
Number of iterations for Bayesian Optimization.
- sampling_from_model:
true if you want to generate samples from model after sampling from data
Returns
- np.ndarray:
The generated counterfactuals that minimize the loss function.
- fit(X: DataFrame, pbounds: Dict[str, Tuple[float, float]], causal_order: Sequence[int] | None = None, adjacency_matrix: ndarray | None = None, y=None, masked_features: List[str] | None = None, categorical_indicator: List[bool] | None = None, features_order: List[str] | None = None, bayesian_causality: bool = False, bayesian_model: DiscreteBayesianNetwork | None = None, num_bins: int | None = None) Self
Fits explainer to the sampled data and blackbox model provided in the constructor
Returns
- self
Fitted estimator.
Parameters
- X{sparse matrix} of shape (n_samples, n_features)
Dataset used for counterfactuals generation
- pbounds:
The bounds for each feature to search over (dict with feature names as keys and tuple (min, max) as values).
- causal_order:
The order of variables in the causal graph.
- adjacency_matrix:
The adjacency matrix representing the causal structure.
- yarray-like of shape (n_samples,).
Dataset target values used for blackbox model fitting only. You can provide fitted blackbox to constructor or fit it in this method by providing this parameter
- masked_features:
List of interchangeable features
- categorical_indicator:
True at the index where the variable should be treated as categorical
- features_order:
order of features in query instance
- bayesian_causality:
calculate discrete bayesian network for causal loss. If bayesian_causality is True, then adjacency_matrix and causal_order are not used. Instead, a discrete bayesian network will be used to calculate causal loss component
- bayesian_model:
optionally, provide pre-fitted discrete bayesian model, if bayesian_causality is True. If it is not provided and bayesian_causality is True, then it will be initialized internally and fitted with the num_bins param
- num_bins:
Number of bins to use for discretizing continuous features (bayesian causality only)