from typing import Any, Callable, Union
import torch
from torch import Tensor
from ignite.metrics.clustering._base import _ClusteringMetricBase
__all__ = ["DaviesBouldinScore"]
def _davies_bouldin_score(features: Tensor, labels: Tensor) -> float:
from sklearn.metrics import davies_bouldin_score
np_features = features.cpu().numpy()
np_labels = labels.cpu().numpy()
score = davies_bouldin_score(np_features, np_labels)
return score
[docs]class DaviesBouldinScore(_ClusteringMetricBase):
r"""Calculates the
`Davies-Bouldin score <https://en.wikipedia.org/wiki/Davies%E2%80%93Bouldin_index>`_.
The Davies-Bouldin score evaluates the quality of clustering results.
More details can be found
`here <https://scikit-learn.org/1.5/modules/clustering.html#davies-bouldin-index>`_.
The Davies-Bouldin score is non-negative,
where values closer to zero indicate that the clustering result is good (i.e., clusters are well-separated).
The computation of this metric is implemented with
`sklearn.metrics.davies_bouldin_score
<https://scikit-learn.org/1.5/modules/generated/sklearn.metrics.davies_bouldin_score.html>`_.
- ``update`` must receive output of the form ``(features, labels)``
or ``{'features': features, 'labels': labels}``.
- `features` and `labels` must be of same shape `(B, D)` and `(B,)`.
Parameters are inherited from ``EpochMetric.__init__``.
Args:
output_transform: a callable that is used to transform the
:class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
form expected by the metric. This can be useful if, for example, you have a multi-output model and
you want to compute the metric with respect to one of the outputs.
By default, metrics require the output as ``(features, labels)``
or ``{'features': features, 'labels': labels}``.
check_compute_fn: if True, ``compute_fn`` is run on the first batch of data to ensure there are no
issues. If issues exist, user is warned that there might be an issue with the ``compute_fn``.
Default, True.
device: specifies which device updates are accumulated on. Setting the
metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
non-blocking. By default, CPU.
skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be
true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)``
Alternatively, ``output_transform`` can be used to handle this.
Examples:
To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine.
The output of the engine's ``process_function`` needs to be in format of
``(features, labels)`` or ``{'features': features, 'labels': labels, ...}``.
.. include:: defaults.rst
:start-after: :orphan:
.. testcode::
metric = DaviesBouldinScore()
metric.attach(default_evaluator, "davies_bouldin_score")
X = torch.tensor([
[-1.04, -0.71, -1.42, -0.28, -0.43],
[0.47, 0.96, -0.43, 1.57, -2.24],
[-0.62, -0.29, 0.10, -0.72, -1.69],
[0.96, -0.77, 0.60, -0.89, 0.49],
[-1.33, -1.53, 0.25, -1.60, -2.0],
[-0.63, -0.55, -1.03, -0.89, -0.77],
[-0.26, -1.67, -0.24, -1.33, -0.40],
[-0.20, -1.34, -0.52, -1.55, -1.50],
[2.68, 1.13, 2.51, 0.80, 0.92],
[0.33, 2.88, 1.35, -0.56, 1.71]
])
Y = torch.tensor([0, 0, 0, 0, 1, 1, 1, 1, 2, 2])
state = default_evaluator.run([{"features": X, "labels": Y}])
print(state.metrics["davies_bouldin_score"])
.. testoutput::
1.3838673743829881
.. versionadded:: 0.5.2
"""
def __init__(
self,
output_transform: Callable[..., Any] = lambda x: x,
check_compute_fn: bool = True,
device: Union[str, torch.device] = torch.device("cpu"),
skip_unrolling: bool = False,
) -> None:
try:
from sklearn.metrics import davies_bouldin_score # noqa: F401
except ImportError:
raise ModuleNotFoundError("This module requires scikit-learn to be installed.")
super().__init__(_davies_bouldin_score, output_transform, check_compute_fn, device, skip_unrolling)