# alibi\_detect.cd.lsdd\_online ## 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. ## `LSDDDriftOnline` *Inherits from:* `DriftConfigMixin` ### Constructor ```python LSDDDriftOnline(self, x_ref: Union[numpy.ndarray, list], ert: float, window_size: int, backend: str = 'tensorflow', preprocess_fn: Optional[Callable] = None, x_ref_preprocessed: bool = False, sigma: Optional[numpy.ndarray] = None, n_bootstraps: int = 1000, n_kernel_centers: Optional[int] = None, lambda_rd_max: float = 0.2, device: Union[typing_extensions.Literal['cuda', 'gpu', 'cpu'], ForwardRef('torch.device'), NoneType] = None, verbose: bool = True, 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. | | `ert` | `float` | | The expected run-time (ERT) in the absence of drift. For the multivariate detectors, the ERT is defined as the expected run-time from t=0. | | `window_size` | `int` | | The size of the sliding test-window used to compute the test-statistic. Smaller windows focus on responding quickly to severe drift, larger windows focus on ability to detect slight drift. | | `backend` | `str` | `'tensorflow'` | Backend used for the LSDD implementation and configuration. | | `preprocess_fn` | `Optional[Callable]` | `None` | Function to preprocess the data before computing the data drift metrics. | | `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. | | `sigma` | `Optional[numpy.ndarray]` | `None` | Optionally set the bandwidth of the Gaussian kernel used in estimating the LSDD. Can also pass multiple bandwidth values as an array. The kernel evaluation is then averaged over those bandwidths. If `sigma` is not specified, the 'median heuristic' is adopted whereby `sigma` is set as the median pairwise distance between reference samples. | | `n_bootstraps` | `int` | `1000` | The number of bootstrap simulations used to configure the thresholds. The larger this is the more accurately the desired ERT will be targeted. Should ideally be at least an order of magnitude larger than the ert. | | `n_kernel_centers` | `Optional[int]` | `None` | The number of reference samples to use as centers in the Gaussian kernel model used to estimate LSDD. Defaults to 2\*window\_size. | | `lambda_rd_max` | `float` | `0.2` | The maximum relative difference between two estimates of LSDD that the regularization parameter lambda is allowed to cause. Defaults to 0.2 as in the paper. | | `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`. Only relevant for 'pytorch' backend. | | `verbose` | `bool` | `True` | Whether or not to print progress during configuration. | | `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. | ### Properties | Property | Type | Description | | ------------ | ---- | ----------- | | `t` | \`\` | | | `test_stats` | \`\` | | | `thresholds` | \`\` | | ### Methods #### `get_config` ```python get_config() -> dict ``` **Returns** * Type: `dict` #### `load_state` ```python load_state(filepath: Union[str, os.PathLike]) ``` Load the detector's state from disk, in order to restart from a checkpoint previously generated with :meth:`~save_state`. | Name | Type | Default | Description | | ---------- | ------------------------- | ------- | --------------------------------- | | `filepath` | `Union[str, os.PathLike]` | | The directory to load state from. | #### `predict` ```python predict(x_t: Union[numpy.ndarray, typing.Any], return_test_stat: bool = True) -> Dict[Dict[str, str], Dict[str, Union[int, float]]] ``` Predict whether the most recent window of data has drifted from the reference data. | Name | Type | Default | Description | | ------------------ | ---------------------------------- | ------- | ---------------------------------------------------------- | | `x_t` | `Union[numpy.ndarray, typing.Any]` | | A single instance to be added to the test-window. | | `return_test_stat` | `bool` | `True` | Whether to return the test statistic (LSDD) and threshold. | **Returns** * Type: `Dict[Dict[str, str], Dict[str, Union[int, float]]]` #### `reset_state` ```python reset_state() ``` Resets the detector to its initial state (`t=0`). This does not include reconfiguring thresholds. #### `save_state` ```python save_state(filepath: Union[str, os.PathLike]) ``` Save a detector's state to disk in order to generate a checkpoint. | Name | Type | Default | Description | | ---------- | ------------------------- | ------- | ------------------------------- | | `filepath` | `Union[str, os.PathLike]` | | The directory to save state to. | #### `score` ```python score(x_t: Union[numpy.ndarray, typing.Any]) -> float ``` Compute the test-statistic (LSDD) between the reference window and test window. | Name | Type | Default | Description | | ----- | ---------------------------------- | ------- | ------------------------------------------------- | | `x_t` | `Union[numpy.ndarray, typing.Any]` | | A single instance to be added to the test-window. | **Returns** * Type: `float` --- # 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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