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adjusted_rand_score.py
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adjusted_rand_score.py
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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
from torchmetrics.functional.clustering.utils import (
calculate_contingency_matrix,
calculate_pair_cluster_confusion_matrix,
check_cluster_labels,
)
def _adjusted_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)
def _adjusted_rand_score_compute(contingency: Tensor) -> Tensor:
"""Compute the rand score based on the contingency matrix.
Args:
contingency: contingency matrix
Returns:
rand_score: rand score
"""
(tn, fp), (fn, tp) = calculate_pair_cluster_confusion_matrix(contingency=contingency)
if fn == 0 and fp == 0:
return torch.ones_like(tn, dtype=torch.float32)
return 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn))
def adjusted_rand_score(preds: Tensor, target: Tensor) -> Tensor:
"""Compute the Adjusted Rand score between two clusterings.
Args:
preds: predicted cluster labels
target: ground truth cluster labels
Returns:
Scalar tensor with adjusted rand score
Example:
>>> from torchmetrics.functional.clustering import adjusted_rand_score
>>> import torch
>>> adjusted_rand_score(torch.tensor([0, 0, 1, 1]), torch.tensor([0, 0, 1, 1]))
tensor(1.)
>>> adjusted_rand_score(torch.tensor([0, 0, 1, 2]), torch.tensor([0, 0, 1, 1]))
tensor(0.5714)
"""
contingency = _adjusted_rand_score_update(preds, target)
return _adjusted_rand_score_compute(contingency)