# Min / Max¶

## Module Interface¶

class torchmetrics.wrappers.MinMaxMetric(base_metric, **kwargs)[source]

Wrapper metric that tracks both the minimum and maximum of a scalar/tensor across an experiment.

The min/max value will be updated each time .compute is called.

Parameters:
Raises:

ValueError – If base_metric argument is not a subclasses instance of torchmetrics.Metric

Example::
>>> import torch
>>> from torchmetrics.wrappers import MinMaxMetric
>>> from torchmetrics.classification import BinaryAccuracy
>>> from pprint import pprint
>>> base_metric = BinaryAccuracy()
>>> minmax_metric = MinMaxMetric(base_metric)
>>> preds_1 = torch.Tensor([[0.1, 0.9], [0.2, 0.8]])
>>> preds_2 = torch.Tensor([[0.9, 0.1], [0.2, 0.8]])
>>> labels = torch.Tensor([[0, 1], [0, 1]]).long()
>>> pprint(minmax_metric(preds_1, labels))
{'max': tensor(1.), 'min': tensor(1.), 'raw': tensor(1.)}
>>> pprint(minmax_metric.compute())
{'max': tensor(1.), 'min': tensor(1.), 'raw': tensor(1.)}
>>> minmax_metric.update(preds_2, labels)
>>> pprint(minmax_metric.compute())
{'max': tensor(1.), 'min': tensor(0.7500), 'raw': tensor(0.7500)}

compute()[source]

Compute the underlying metric as well as max and min values for this metric.

Returns a dictionary that consists of the computed value (raw), as well as the minimum (min) and maximum (max) values.

Return type:
forward(*args, **kwargs)[source]

Use the original forward method of the base metric class.

Return type:

Any

plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

Parameters:
Return type:
Returns:

Figure and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.wrappers import MinMaxMetric
>>> from torchmetrics.classification import BinaryAccuracy
>>> metric = MinMaxMetric(BinaryAccuracy())
>>> metric.update(torch.randint(2, (20,)), torch.randint(2, (20,)))
>>> fig_, ax_ = metric.plot()

>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.wrappers import MinMaxMetric
>>> from torchmetrics.classification import BinaryAccuracy
>>> metric = MinMaxMetric(BinaryAccuracy())
>>> values = [ ]
>>> for _ in range(3):
...     values.append(metric(torch.randint(2, (20,)), torch.randint(2, (20,))))
>>> fig_, ax_ = metric.plot(values)

reset()[source]

Set max_val and min_val to the initialization bounds and resets the base metric.

Return type:

None

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

Update the underlying metric.

Return type:

None`