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Bootstrapper

Module Interface

class torchmetrics.BootStrapper(base_metric, num_bootstraps=10, mean=True, std=True, quantile=None, raw=False, sampling_strategy='poisson', compute_on_step=None, **kwargs)[source]

Using Turn a Metric into a Bootstrapped

That can automate the process of getting confidence intervals for metric values. This wrapper class basically keeps multiple copies of the same base metric in memory and whenever update or forward is called, all input tensors are resampled (with replacement) along the first dimension.

Parameters
  • base_metric (Metric) – base metric class to wrap

  • num_bootstraps (int) – number of copies to make of the base metric for bootstrapping

  • mean (bool) – if True return the mean of the bootstraps

  • std (bool) – if True return the standard diviation of the bootstraps

  • quantile (Union[float, Tensor, None]) – if given, returns the quantile of the bootstraps. Can only be used with pytorch version 1.6 or higher

  • raw (bool) – if True, return all bootstrapped values

  • sampling_strategy (str) – Determines how to produce bootstrapped samplings. Either 'poisson' or multinomial. If 'possion' is chosen, the number of times each sample will be included in the bootstrap will be given by n\sim Poisson(\lambda=1), which approximates the true bootstrap distribution when the number of samples is large. If 'multinomial' is chosen, we will apply true bootstrapping at the batch level to approximate bootstrapping over the hole dataset.

  • compute_on_step (Optional[bool]) –

    Forward only calls update() and returns None if this is set to False.

    Deprecated since version v0.8: Argument has no use anymore and will be removed v0.9.

  • kwargs (Dict[str, Any]) – Additional keyword arguments, see Advanced metric settings for more info.

Example::
>>> from pprint import pprint
>>> from torchmetrics import Accuracy, BootStrapper
>>> _ = torch.manual_seed(123)
>>> base_metric = Accuracy()
>>> bootstrap = BootStrapper(base_metric, num_bootstraps=20)
>>> bootstrap.update(torch.randint(5, (20,)), torch.randint(5, (20,)))
>>> output = bootstrap.compute()
>>> pprint(output)
{'mean': tensor(0.2205), 'std': tensor(0.0859)}

Initializes internal Module state, shared by both nn.Module and ScriptModule.

compute()[source]

Computes the bootstrapped metric values.

Always returns a dict of tensors, which can contain the following keys: mean, std, quantile and raw depending on how the class was initialized.

Return type

Dict[str, Tensor]

update(*args, **kwargs)[source]

Updates the state of the base metric.

Any tensor passed in will be bootstrapped along dimension 0.

Return type

None

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