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# Copyright The Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Sequence
from typing import Any, List, Optional, Union
from torch import Tensor
from torchmetrics.functional.clustering.dunn_index import dunn_index
from torchmetrics.metric import Metric
from torchmetrics.utilities.data import dim_zero_cat
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["DunnIndex.plot"]
class DunnIndex(Metric):
r"""Compute `Dunn Index`_.
.. math::
DI_m = \frac{\min_{1\leq i<j\leq m} \delta(C_i,C_j)}{\max_{1\leq k\leq m} \Delta_k}
Where :math:`C_i` is a cluster of tensors, :math:`C_j` is a cluster of tensors,
and :math:`\delta(C_i,C_j)` is the intercluster distance metric for :math:`m` clusters.
This clustering metric is an intrinsic measure, because it does not rely on ground truth labels for the evaluation.
Instead it examines how well the clusters are separated from each other. The score is higher when clusters are dense
and well separated, which relates to a standard concept of a cluster.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``data`` (:class:`~torch.Tensor`): float tensor with shape ``(N,d)`` with the embedded data. ``d`` is the
dimensionality of the embedding space.
- ``labels`` (:class:`~torch.Tensor`): single integer tensor with shape ``(N,)`` with cluster labels
As output of ``forward`` and ``compute`` the metric returns the following output:
- ``dunn_index`` (:class:`~torch.Tensor`): A tensor with the Dunn Index
Args:
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example::
>>> import torch
>>> from torchmetrics.clustering import DunnIndex
>>> data = torch.tensor([[0, 0], [0.5, 0], [1, 0], [0.5, 1]])
>>> labels = torch.tensor([0, 0, 0, 1])
>>> dunn_index = DunnIndex(p=2)
>>> dunn_index(data, labels)
tensor(2.)
"""
is_differentiable: bool = True
higher_is_better: bool = True
full_state_update: bool = False
plot_lower_bound: float = 0.0
data: List[Tensor]
labels: List[Tensor]
def __init__(self, p: float = 2, **kwargs: Any) -> None:
super().__init__(**kwargs)
self.p = p
self.add_state("data", default=[], dist_reduce_fx="cat")
self.add_state("labels", default=[], dist_reduce_fx="cat")
def update(self, data: Tensor, labels: Tensor) -> None:
"""Update state with predictions and targets."""
self.data.append(data)
self.labels.append(labels)
def compute(self) -> Tensor:
"""Compute mutual information over state."""
return dunn_index(dim_zero_cat(self.data), dim_zero_cat(self.labels), self.p)
def plot(self, val: Union[Tensor, Sequence[Tensor], None] = None, ax: Optional[_AX_TYPE] = None) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: 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: An matplotlib axis object. If provided will add plot to that axis
Returns:
Figure and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.clustering import DunnIndex
>>> data = torch.tensor([[0, 0], [0.5, 0], [1, 0], [0.5, 1]])
>>> labels = torch.tensor([0, 0, 0, 1])
>>> metric = DunnIndex(p=2)
>>> metric.update(data, labels)
>>> fig_, ax_ = metric.plot(metric.compute())
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.clustering import DunnIndex
>>> metric = DunnIndex(p=2)
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(torch.randn(50, 3), torch.randint(0, 2, (50,))))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
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