alibi.explainers.cfrl_tabular

Constants

TYPE_CHECKING

TYPE_CHECKING: bool = False

bool(x) -> bool

Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.

has_pytorch

has_pytorch: bool = True

bool(x) -> bool

Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.

has_tensorflow

has_tensorflow: bool = True

bool(x) -> bool

Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.

ConcatTabularPostprocessing

Inherits from: Postprocessing, ABC

Tabular feature columns concatenation post-processing.

CounterfactualRLTabular

Inherits from: CounterfactualRL, Explainer, FitMixin, ABC, Base

Counterfactual Reinforcement Learning Tabular.

Constructor

CounterfactualRLTabular(self, predictor: Callable[[numpy.ndarray], numpy.ndarray], encoder: 'Union[tensorflow.keras.Model, torch.nn.Module]', decoder: 'Union[tensorflow.keras.Model, torch.nn.Module]', encoder_preprocessor: Callable, decoder_inv_preprocessor: Callable, coeff_sparsity: float, coeff_consistency: float, feature_names: List[str], category_map: Dict[int, List[str]], immutable_features: Optional[List[str]] = None, ranges: Optional[Dict[str, Tuple[int, int]]] = None, weight_num: float = 1.0, weight_cat: float = 1.0, latent_dim: Optional[int] = None, backend: str = 'tensorflow', seed: int = 0, **kwargs)

Name	Type	Default	Description
predictor	Callable[[.[<class 'numpy.ndarray'>]], numpy.ndarray]		A callable that takes a numpy array of N data points as inputs and returns N outputs. For classification task, the second dimension of the output should match the number of classes. Thus, the output can be either a soft label distribution or a hard label distribution (i.e. one-hot encoding) without affecting the performance since argmax is applied to the predictor's output.
encoder	Union[tensorflow.keras.Model, torch.nn.Module]		Pretrained heterogeneous encoder network.
decoder	Union[tensorflow.keras.Model, torch.nn.Module]		Pretrained heterogeneous decoder network. The output of the decoder must be a list of tensors.
encoder_preprocessor	Callable		Auto-encoder data pre-processor. Depending on the input format, the pre-processor can normalize numerical attributes, transform label encoding to one-hot encoding etc.
decoder_inv_preprocessor	Callable		Auto-encoder data inverse pre-processor. This is the inverse function of the pre-processor. It can denormalize numerical attributes, transform one-hot encoding to label encoding, feature type casting etc.
coeff_sparsity	float		Sparsity loss coefficient.
coeff_consistency	float		Consistency loss coefficient.
feature_names	List[str]		List of feature names. This should be provided by the dataset.
category_map	Dict[int, List[str]]		Dictionary of category mapping. The keys are column indexes and the values are lists containing the possible values for a feature. This should be provided by the dataset.
immutable_features	Optional[List[str]]	None	List of immutable features.
ranges	Optional[Dict[str, Tuple[int, int]]]	None	Numerical feature ranges. Note that exist numerical features such as 'Age', which are allowed to increase only. We denote those by 'inc_feat'. Similarly, there exist features allowed to decrease only. We denote them by 'dec_feat'. Finally, there are some free feature, which we denote by 'free_feat'. With the previous notation, we can define range = {'inc_feat': [0, 1], 'dec_feat': [-1, 0], 'free_feat': [-1, 1]}. 'free_feat' can be omitted, as any unspecified feature is considered free. Having the ranges of a feature {'feat': [a_low, a_high}, when sampling is performed the numerical value will be clipped between [a_low * (max_val - min_val), a_high * [max_val - min_val]], where a_low and a_high are the minimum and maximum values the feature 'feat'. This implies that a_low and a_high are not restricted to {-1, 0} and {0, 1}, but can be any float number in-between [-1, 0] and [0, 1].
weight_num	float	1.0	Numerical loss weight.
weight_cat	float	1.0	Categorical loss weight.
latent_dim	Optional[int]	None	Auto-encoder latent dimension. Can be omitted if the actor network is user specified.
backend	str	'tensorflow'	Deep learning backend: 'tensorflow'
seed	int	0	Seed for reproducibility. The results are not reproducible for 'tensorflow' backend.
**kwargs			Used to replace any default parameter from :py:data:alibi.explainers.cfrl_base.DEFAULT_BASE_PARAMS.

Methods

explain

explain(X: numpy.ndarray, Y_t: numpy.ndarray, C: Optional[List[Dict[str, List[Union[float, str]]]]] = None, batch_size: int = 100, diversity: bool = False, num_samples: int = 1, patience: int = 1000, tolerance: float = 0.001) -> alibi.api.interfaces.Explanation

Name	Type	Default	Description
X	numpy.ndarray		Input instances to generate counterfactuals for.
Y_t	numpy.ndarray		Target labels.
C	Optional[List[Dict[str, List[Union[float, str]]]]]	None	List of conditional dictionaries. If None, it means that no conditioning was used during training (i.e. the conditional_func returns None). If conditioning was used during training but no conditioning is desired for the current input, an empty list is expected.
batch_size	int	100	Batch size to use when generating counterfactuals.
diversity	bool	False	Whether to generate diverse counterfactual set for the given instance. Only supported for a single input instance.
num_samples	int	1	Number of diversity samples to be generated. Considered only if diversity=True.
patience	int	1000	Maximum number of iterations to perform diversity search stops. If -1, the search stops only if the desired number of samples has been found.
tolerance	float	0.001	Tolerance to distinguish two counterfactual instances.

Returns

• Type: alibi.api.interfaces.Explanation

fit

fit(X: numpy.ndarray) -> alibi.api.interfaces.Explainer

Name	Type	Default	Description
X	numpy.ndarray

Returns

• Type: alibi.api.interfaces.Explainer

SampleTabularPostprocessing

Inherits from: Postprocessing, ABC

Tabular sampling post-processing. Given the output of the heterogeneous auto-encoder the post-processing functions samples the output according to the conditional vector. Note that the original input instance is required to perform the conditional sampling.

Constructor

SampleTabularPostprocessing(self, category_map: Dict[int, List[str]], stats: Dict[int, Dict[str, float]])

Name	Type	Default	Description
category_map	Dict[int, List[str]]		Dictionary of category mapping. The keys are column indexes and the values are lists containing the possible feature values.
stats	Dict[int, Dict[str, float]]		Dictionary of statistic of the training data. Contains the minimum and maximum value of each numerical feature in the training set. Each key is an index of the column and each value is another dictionary containing 'min' and 'max' keys.