Minimum¶

Module Interface¶

class torchmetrics.aggregation.MinMetric(nan_strategy='warn', **kwargs)[source]¶

Aggregate a stream of value into their minimum value.

As input to forward and update the metric accepts the following input

value (float or Tensor): a single float or an tensor of float values with arbitrary shape (...,).

As output of forward and compute the metric returns the following output

agg (Tensor): scalar float tensor with aggregated minimum value over all inputs received

Parameters:

nan_strategy¶ (Union[Literal['error', 'warn', 'ignore', 'disable'], float]) – options: - 'error': if any nan values are encountered will give a RuntimeError - 'warn': if any nan values are encountered will give a warning and continue - 'ignore': all nan values are silently removed - 'disable': disable all nan checks - a float: if a float is provided will impute any nan values with this value
kwargs¶ (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Raises:

ValueError – If nan_strategy is not one of error, warn, ignore, disable or a float

Example

>>> from torch import tensor
>>> from torchmetrics.aggregation import MinMetric
>>> metric = MinMetric()
>>> metric.update(1)
>>> metric.update(tensor([2, 3]))
>>> metric.compute()
tensor(1.)

plot(val=None, ax=None)[source]¶

Plot a single or multiple values from the metric.

Parameters:

val¶ (Union[Tensor, Sequence[Tensor], None]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.
ax¶ (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis

Return type:

tuple[Figure, Union[Axes, ndarray]]

Returns:

Figure and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> # Example plotting a single value
>>> from torchmetrics.aggregation import MinMetric
>>> metric = MinMetric()
>>> metric.update([1, 2, 3])
>>> fig_, ax_ = metric.plot()

>>> # Example plotting multiple values
>>> from torchmetrics.aggregation import MinMetric
>>> metric = MinMetric()
>>> values = [ ]
>>> for i in range(10):
...     values.append(metric(i))
>>> fig_, ax_ = metric.plot(values)