Shortcuts

# 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

© Copyright Copyright (c) 2020-2023, Lightning-AI et al... Revision 99d6d9d6`.

Built with Sphinx using a theme provided by Read the Docs.
Versions
latest
stable
v1.1.0
v1.0.3
v1.0.2
v1.0.1
v1.0.0
v0.11.4
v0.11.3
v0.11.2
v0.11.1
v0.11.0
v0.10.3
v0.10.2
v0.10.1
v0.10.0
v0.9.3
v0.9.2
v0.9.1
v0.9.0
v0.8.2
v0.8.1
v0.8.0
v0.7.3
v0.7.2
v0.7.1
v0.7.0
v0.6.2
v0.6.1
v0.6.0
v0.5.1
v0.5.0
v0.4.1
v0.4.0
v0.3.2
v0.3.1
v0.3.0
v0.2.0
v0.1.0