> 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_base.md). # alibi.explainers.cfrl\_base ## 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. ### `DEFAULT_DATA_CFRL` ```python DEFAULT_DATA_CFRL: dict = {'orig': None, 'cf': None, 'target': None, 'condition': None} ``` ### `DEFAULT_META_CFRL` ```python DEFAULT_META_CFRL: dict = {'name': None, 'type': ['blackbox'], 'explanations': ['local'], 'params': {},... ``` ### `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. ### `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. ### `DEFAULT_BASE_PARAMS` ```python DEFAULT_BASE_PARAMS: dict = {'act_noise': 0.1, 'act_low': -1.0, 'act_high': 1.0, 'replay_buffer_size': 10... ``` ## `Callback` *Inherits from:* `ABC` Training callback class. ## `CounterfactualRL` *Inherits from:* `Explainer`, `FitMixin`, `ABC`, `Base` Counterfactual Reinforcement Learning. ### Constructor ```python CounterfactualRL(self, predictor: Callable[[numpy.ndarray], numpy.ndarray], encoder: 'Union[tensorflow.keras.Model, torch.nn.Module]', decoder: 'Union[tensorflow.keras.Model, torch.nn.Module]', coeff_sparsity: float, coeff_consistency: float, 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 encoder network. | | `decoder` | `Union[tensorflow.keras.Model, torch.nn.Module]` | | Pretrained decoder network. | | `coeff_sparsity` | `float` | | Sparsity loss coefficient. | | `coeff_consistency` | `float` | | Consistency loss coefficient. | | `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[numpy.ndarray] = None, batch_size: int = 100) -> alibi.api.interfaces.Explanation ``` | Name | Type | Default | Description | | ------------ | ------------------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------ | | `X` | `numpy.ndarray` | | Instances to be explained. | | `Y_t` | `numpy.ndarray` | | Counterfactual targets. | | `C` | `Optional[numpy.ndarray]` | `None` | Conditional vectors. If `None`, it means that no conditioning was used during training (i.e. the `conditional_func` returns `None`). | | `batch_size` | `int` | `100` | Batch size to be used when generating counterfactuals. | **Returns** * Type: `alibi.api.interfaces.Explanation` #### `fit` ```python fit(X: numpy.ndarray) -> alibi.api.interfaces.Explainer ``` | Name | Type | Default | Description | | ---- | --------------- | ------- | -------------------- | | `X` | `numpy.ndarray` | | Training data array. | **Returns** * Type: `alibi.api.interfaces.Explainer` #### `load` ```python load(path: Union[str, os.PathLike], predictor: typing.Any) -> alibi.api.interfaces.Explainer ``` | Name | Type | Default | Description | | ----------- | ------------------------- | ------- | ----------- | | `path` | `Union[str, os.PathLike]` | | | | `predictor` | `typing.Any` | | | **Returns** * Type: `alibi.api.interfaces.Explainer` #### `reset_predictor` ```python reset_predictor(predictor: typing.Any) -> None ``` | Name | Type | Default | Description | | ----------- | ------------ | ------- | ----------- | | `predictor` | `typing.Any` | | | **Returns** * Type: `None` #### `save` ```python save(path: Union[str, os.PathLike]) -> None ``` | Name | Type | Default | Description | | ------ | ------------------------- | ------- | ----------- | | `path` | `Union[str, os.PathLike]` | | | **Returns** * Type: `None` ## `NormalActionNoise` Normal noise generator. ### Constructor ```python NormalActionNoise(self, mu: float, sigma: float) -> None ``` | Name | Type | Default | Description | | ------- | ------- | ------- | -------------------------------- | | `mu` | `float` | | Mean of the normal noise. | | `sigma` | `float` | | Standard deviation of the noise. | ## `Postprocessing` *Inherits from:* `ABC` ## `ReplayBuffer` Circular experience replay buffer for `CounterfactualRL` (DDPG). When the buffer is filled, then the oldest experience is replaced by the new one (FIFO). The experience batch size is kept constant and inferred when the first batch of data is stored. Allowing flexible batch size can generate `tensorflow` warning due to the `tf.function` retracing, which can lead to a drop in performance. ### Constructor ```python ReplayBuffer(self, size: int = 1000) -> None ``` | Name | Type | Default | Description | | ------ | ----- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `size` | `int` | `1000` | Dimension of the buffer in batch size. This that the total memory allocated is proportional with the `size x batch_size`, where `batch_size` is inferred from the first array to be stored. | ### Methods #### `append` ```python append(X: numpy.ndarray, Y_m: numpy.ndarray, Y_t: numpy.ndarray, Z: numpy.ndarray, Z_cf_tilde: numpy.ndarray, C: Optional[numpy.ndarray], R_tilde: numpy.ndarray, kwargs) -> None ``` | Name | Type | Default | Description | | ------------ | ------------------------- | ------- | ----------------------------------- | | `X` | `numpy.ndarray` | | Input array. | | `Y_m` | `numpy.ndarray` | | Model's prediction class of `X`. | | `Y_t` | `numpy.ndarray` | | Counterfactual target class. | | `Z` | `numpy.ndarray` | | Input's embedding. | | `Z_cf_tilde` | `numpy.ndarray` | | Noised counterfactual embedding. | | `C` | `Optional[numpy.ndarray]` | | Conditional array. | | `R_tilde` | `numpy.ndarray` | | Noised counterfactual reward array. | | `**kwargs` | | | Other arguments. Not used. | **Returns** * Type: `None` #### `sample` ```python sample() -> Dict[str, Optional[numpy.ndarray]] ``` Sample a batch of experience form the replay buffer. **Returns** * Type: `Dict[str, Optional[numpy.ndarray]]` --- # 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/cfrl_base.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.