dataeval.detectors.drift.preprocess_drift¶

dataeval.detectors.drift.preprocess_drift(x, model, device=None, preprocess_batch_fn=None, batch_size=int(10000000000.0), dtype=np.float32)¶

Prediction function used for preprocessing step of drift detector.

Parameters:¶

x : NDArray¶

Batch of instances.

model : nn.Module¶

Model used for preprocessing.

device : torch.device | None, default None¶

Device type used. The default None tries to use the GPU and falls back on CPU. Can be specified by passing either torch.device(‘cuda’) or torch.device(‘cpu’).

preprocess_batch_fn : Callable | None, default None¶

Optional batch preprocessing function. For example to convert a list of objects to a batch which can be processed by the PyTorch model.

batch_size : int, default 1e10¶

Batch size used during prediction.

dtype : np.dtype | torch.dtype, default np.float32¶

Model output type, either a NumPy or torch dtype, e.g. np.float32 or torch.float32.

Returns:¶

Numpy array, torch tensor or tuples of those with model outputs.

Return type:¶

NDArray | torch.Tensor | tuple