# alibi\_detect.cd.learned\_kernel ## Constants ### `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. ### `has_keops` ```python has_keops: 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. ## `LearnedKernelDrift` *Inherits from:* `DriftConfigMixin` ### Constructor ```python LearnedKernelDrift(self, x_ref: Union[numpy.ndarray, list], kernel: Callable, backend: str = 'tensorflow', p_val: float = 0.05, x_ref_preprocessed: bool = False, preprocess_at_init: bool = True, update_x_ref: Optional[Dict[str, int]] = None, preprocess_fn: Optional[Callable] = None, n_permutations: int = 100, batch_size_permutations: int = 1000000, var_reg: float = 1e-05, reg_loss_fn: Callable = at 0x28fe7ea60>, train_size: Optional[float] = 0.75, retrain_from_scratch: bool = True, optimizer: Optional[Callable] = None, learning_rate: float = 0.001, batch_size: int = 32, batch_size_predict: int = 32, preprocess_batch_fn: Optional[Callable] = None, epochs: int = 3, num_workers: int = 0, verbose: int = 0, train_kwargs: Optional[dict] = None, device: Union[typing_extensions.Literal['cuda', 'gpu', 'cpu'], ForwardRef('torch.device'), NoneType] = None, dataset: Optional[Callable] = None, dataloader: Optional[Callable] = None, input_shape: Optional[tuple] = None, data_type: Optional[str] = None) -> None ``` | Name | Type | Default | Description | | ------------------------- | ------------------------------------------------------------------ | ------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | `x_ref` | `Union[numpy.ndarray, list]` | | Data used as reference distribution. | | `kernel` | `Callable` | | Trainable PyTorch or TensorFlow module that returns a similarity between two instances. | | `backend` | `str` | `'tensorflow'` | Backend used by the kernel and training loop. | | `p_val` | `float` | `0.05` | p-value used for the significance of the test. | | `x_ref_preprocessed` | `bool` | `False` | Whether the given reference data `x_ref` has been preprocessed yet. If `x_ref_preprocessed=True`, only the test data `x` will be preprocessed at prediction time. If `x_ref_preprocessed=False`, the reference data will also be preprocessed. | | `preprocess_at_init` | `bool` | `True` | Whether to preprocess the reference data when the detector is instantiated. Otherwise, the reference data will be preprocessed at prediction time. Only applies if `x_ref_preprocessed=False`. | | `update_x_ref` | `Optional[Dict[str, int]]` | `None` | Reference data can optionally be updated to the last n instances seen by the detector or via reservoir sampling with size n. For the former, the parameter equals {'last': n} while for reservoir sampling {'reservoir\_sampling': n} is passed. | | `preprocess_fn` | `Optional[Callable]` | `None` | Function to preprocess the data before applying the kernel. | | `n_permutations` | `int` | `100` | The number of permutations to use in the permutation test once the MMD has been computed. | | `batch_size_permutations` | `int` | `1000000` | KeOps computes the n\_permutations of the MMD^2 statistics in chunks of batch\_size\_permutations. Only relevant for 'keops' backend. | | `var_reg` | `float` | `1e-05` | Constant added to the estimated variance of the MMD for stability. | | `reg_loss_fn` | `Callable` | ` at 0x28fe7ea60>` | The regularisation term reg\_loss\_fn(kernel) is added to the loss function being optimized. | | `train_size` | `Optional[float]` | `0.75` | Optional fraction (float between 0 and 1) of the dataset used to train the kernel. The drift is detected on `1 - train_size`. | | `retrain_from_scratch` | `bool` | `True` | Whether the kernel should be retrained from scratch for each set of test data or whether it should instead continue training from where it left off on the previous set. | | `optimizer` | `Optional[Callable]` | `None` | Optimizer used during training of the kernel. | | `learning_rate` | `float` | `0.001` | Learning rate used by optimizer. | | `batch_size` | `int` | `32` | Batch size used during training of the kernel. | | `batch_size_predict` | `int` | `32` | Batch size used for the trained drift detector predictions. | | `preprocess_batch_fn` | `Optional[Callable]` | `None` | Optional batch preprocessing function. For example to convert a list of objects to a batch which can be processed by the kernel. | | `epochs` | `int` | `3` | Number of training epochs for the kernel. Corresponds to the smaller of the reference and test sets. | | `num_workers` | `int` | `0` | Number of workers for the dataloader. The default (`num_workers=0`) means multi-process data loading is disabled. Setting `num_workers>0` may be unreliable on Windows. | | `verbose` | `int` | `0` | Verbosity level during the training of the kernel. 0 is silent, 1 a progress bar. | | `train_kwargs` | `Optional[dict]` | `None` | Optional additional kwargs when training the kernel. | | `device` | `Union[Literal[cuda, gpu, cpu], ForwardRef('torch.device'), None]` | `None` | Device type used. The default tries to use the GPU and falls back on CPU if needed. Can be specified by passing either `'cuda'`, `'gpu'`, `'cpu'` or an instance of `torch.device`. Relevant for 'pytorch' and 'keops' backends. | | `dataset` | `Optional[Callable]` | `None` | Dataset object used during training. | | `dataloader` | `Optional[Callable]` | `None` | Dataloader object used during training. Relevant for 'pytorch' and 'keops' backends. | | `input_shape` | `Optional[tuple]` | `None` | Shape of input data. | | `data_type` | `Optional[str]` | `None` | Optionally specify the data type (tabular, image or time-series). Added to metadata. | ### Methods #### `predict` ```python predict(x: Union[numpy.ndarray, list], return_p_val: bool = True, return_distance: bool = True, return_kernel: bool = True) -> Dict[Dict[str, str], Dict[str, Union[int, float, Callable]]] ``` Predict whether a batch of data has drifted from the reference data. | Name | Type | Default | Description | | ----------------- | ---------------------------- | ------- | ------------------------------------------------------------------------------------------ | | `x` | `Union[numpy.ndarray, list]` | | Batch of instances. | | `return_p_val` | `bool` | `True` | Whether to return the p-value of the permutation test. | | `return_distance` | `bool` | `True` | Whether to return the MMD metric between the new batch and reference data. | | `return_kernel` | `bool` | `True` | Whether to return the updated kernel trained to discriminate reference and test instances. | **Returns** * Type: `Dict[Dict[str, str], Dict[str, Union[int, float, Callable]]]` --- # Agent Instructions: 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. 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