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normalized_mutual_info_score.py
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normalized_mutual_info_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.
from typing import Literal
import torch
from torch import Tensor
from torchmetrics.functional.clustering.mutual_info_score import mutual_info_score
from torchmetrics.functional.clustering.utils import (
_validate_average_method_arg,
calculate_entropy,
calculate_generalized_mean,
check_cluster_labels,
)
def normalized_mutual_info_score(
preds: Tensor, target: Tensor, average_method: Literal["min", "geometric", "arithmetic", "max"] = "arithmetic"
) -> Tensor:
"""Compute normalized mutual information between two clusterings.
Args:
preds: predicted cluster labels
target: ground truth cluster labels
average_method: normalizer computation method
Returns:
Scalar tensor with normalized mutual info score between 0.0 and 1.0
Example:
>>> from torchmetrics.functional.clustering import normalized_mutual_info_score
>>> target = torch.tensor([0, 3, 2, 2, 1])
>>> preds = torch.tensor([1, 3, 2, 0, 1])
>>> normalized_mutual_info_score(preds, target, "arithmetic")
tensor(0.7919)
"""
check_cluster_labels(preds, target)
_validate_average_method_arg(average_method)
mutual_info = mutual_info_score(preds, target)
if torch.allclose(mutual_info, torch.tensor(0.0), atol=torch.finfo().eps):
return mutual_info
normalizer = calculate_generalized_mean(
torch.stack([calculate_entropy(preds), calculate_entropy(target)]), average_method
)
return mutual_info / normalizer