> 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/cfrl_tabular.md). # alibi.explainers.cfrl\_tabular ## Constants ### `TYPE_CHECKING` ```python 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` ```python 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` ```python 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 ```python 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[[.[]], 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` ```python 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` ```python 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 ```python 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. 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