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.. customcarditem:: | ||
:header: Rand Score | ||
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/clustering.svg | ||
:tags: Clustering | ||
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.. include:: ../links.rst | ||
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########## | ||
Rand Score | ||
########## | ||
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Module Interface | ||
________________ | ||
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.. autoclass:: torchmetrics.clustering.RandScore | ||
:exclude-members: update, compute | ||
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Functional Interface | ||
____________________ | ||
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.. autofunction:: torchmetrics.functional.clustering.rand_score |
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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 typing import Any, List, Optional, Sequence, Union | ||
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from torch import Tensor | ||
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from torchmetrics.functional.clustering.rand_score import rand_score | ||
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 | ||
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if not _MATPLOTLIB_AVAILABLE: | ||
__doctest_skip__ = ["RandScore.plot"] | ||
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class RandScore(Metric): | ||
r"""Compute `Rand Score`_ (alternatively known as Rand Index). | ||
.. math:: | ||
RS(U, V) = \text{number of agreeing pairs} / \text{number of pairs} | ||
The number of agreeing pairs is every :math:`(i, j)` pair of samples where :math:`i \in U` and :math:`j \in V` | ||
(the predicted and true clusterings, respectively) that are in the same cluster for both clusterings. | ||
The metric is symmetric, therefore swapping :math:`U` and :math:`V` yields the same rand score. | ||
As input to ``forward`` and ``update`` the metric accepts the following input: | ||
- ``preds`` (:class:`~torch.Tensor`): single integer tensor with shape ``(N,)`` with predicted cluster labels | ||
- ``target`` (:class:`~torch.Tensor`): single integer tensor with shape ``(N,)`` with ground truth cluster labels | ||
As output of ``forward`` and ``compute`` the metric returns the following output: | ||
- ``rand_score`` (:class:`~torch.Tensor`): A tensor with the Rand Score | ||
Args: | ||
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. | ||
Example: | ||
>>> import torch | ||
>>> from torchmetrics.clustering import RandScore | ||
>>> preds = torch.tensor([2, 1, 0, 1, 0]) | ||
>>> target = torch.tensor([0, 2, 1, 1, 0]) | ||
>>> metric = RandScore() | ||
>>> metric(preds, target) | ||
tensor(0.6000) | ||
""" | ||
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is_differentiable = True | ||
higher_is_better = None | ||
full_state_update: bool = True | ||
plot_lower_bound: float = 0.0 | ||
preds: List[Tensor] | ||
target: List[Tensor] | ||
contingency: Tensor | ||
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def __init__(self, **kwargs: Any) -> None: | ||
super().__init__(**kwargs) | ||
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self.add_state("preds", default=[], dist_reduce_fx="cat") | ||
self.add_state("target", default=[], dist_reduce_fx="cat") | ||
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def update(self, preds: Tensor, target: Tensor) -> None: | ||
"""Update state with predictions and targets.""" | ||
self.preds.append(preds) | ||
self.target.append(target) | ||
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def compute(self) -> Tensor: | ||
"""Compute rand score over state.""" | ||
return rand_score(dim_zero_cat(self.preds), dim_zero_cat(self.target)) | ||
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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 RandScore | ||
>>> metric = RandScore() | ||
>>> metric.update(torch.randint(0, 4, (10,)), torch.randint(0, 4, (10,))) | ||
>>> fig_, ax_ = metric.plot(metric.compute()) | ||
.. plot:: | ||
:scale: 75 | ||
>>> # Example plotting multiple values | ||
>>> import torch | ||
>>> from torchmetrics.clustering import RandScore | ||
>>> metric = RandScore() | ||
>>> for _ in range(10): | ||
... metric.update(torch.randint(0, 4, (10,)), torch.randint(0, 4, (10,))) | ||
>>> fig_, ax_ = metric.plot(metric.compute()) | ||
""" | ||
return self._plot(val, ax) |
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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. | ||
import torch | ||
from torch import Tensor | ||
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from torchmetrics.functional.clustering.utils import ( | ||
calcualte_pair_cluster_confusion_matrix, | ||
calculate_contingency_matrix, | ||
check_cluster_labels, | ||
) | ||
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def _rand_score_update(preds: Tensor, target: Tensor) -> Tensor: | ||
"""Update and return variables required to compute the rand score. | ||
Args: | ||
preds: predicted cluster labels | ||
target: ground truth cluster labels | ||
Returns: | ||
contingency: contingency matrix | ||
""" | ||
check_cluster_labels(preds, target) | ||
return calculate_contingency_matrix(preds, target) | ||
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def _rand_score_compute(contingency: Tensor) -> Tensor: | ||
"""Compute the rand score based on the contingency matrix. | ||
Args: | ||
contingency: contingency matrix | ||
Returns: | ||
rand_score: rand score | ||
""" | ||
pair_matrix = calcualte_pair_cluster_confusion_matrix(contingency=contingency) | ||
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numerator = pair_matrix.diagonal().sum() | ||
denominator = pair_matrix.sum() | ||
if numerator == denominator or denominator == 0: | ||
# Special limit cases: no clustering since the data is not split; | ||
# or trivial clustering where each document is assigned a unique | ||
# cluster. These are perfect matches hence return 1.0. | ||
return torch.ones_like(numerator, dtype=torch.float32) | ||
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return numerator / denominator | ||
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def rand_score(preds: Tensor, target: Tensor) -> Tensor: | ||
"""Compute the Rand score between two clusterings. | ||
Args: | ||
preds: predicted cluster labels | ||
target: ground truth cluster labels | ||
Returns: | ||
scalar tensor with the rand score | ||
Example: | ||
>>> from torchmetrics.functional.clustering import rand_score | ||
>>> import torch | ||
>>> rand_score(torch.tensor([0, 0, 1, 1]), torch.tensor([1, 1, 0, 0])) | ||
tensor(1.) | ||
>>> rand_score(torch.tensor([0, 0, 1, 2]), torch.tensor([0, 0, 1, 1])) | ||
tensor(0.8333) | ||
""" | ||
contingency = _rand_score_update(preds, target) | ||
return _rand_score_compute(contingency) |
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