Minimum

Module Interface

class torchmetrics.MinMetric(nan_strategy='warn', **kwargs)

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 arbitary 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[str, float]) – options: - 'error': if any nan values are encounted will give a RuntimeError - 'warn': if any nan values are encounted will give a warning and continue - 'ignore': all nan values are silently removed - a float: if a float is provided will impude 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 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.)

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

plot(val=None, ax=None)

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()

(Source code, png, hires.png, pdf)

>>> # 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)

(Source code, png, hires.png, pdf)