-
Notifications
You must be signed in to change notification settings - Fork 387
/
calinski_harabasz_score.py
62 lines (52 loc) · 2.4 KB
/
calinski_harabasz_score.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
# 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 (
_validate_intrinsic_cluster_data,
_validate_intrinsic_labels_to_samples,
)
def calinski_harabasz_score(data: Tensor, labels: Tensor) -> Tensor:
"""Compute the Calinski Harabasz Score (also known as variance ratio criterion) for clustering algorithms.
Args:
data: float tensor with shape ``(N,d)`` with the embedded data.
labels: single integer tensor with shape ``(N,)`` with cluster labels
Returns:
Scalar tensor with the Calinski Harabasz Score
Example:
>>> import torch
>>> from torchmetrics.functional.clustering import calinski_harabasz_score
>>> _ = torch.manual_seed(42)
>>> data = torch.randn(10, 3)
>>> labels = torch.randint(0, 2, (10,))
>>> calinski_harabasz_score(data, labels)
tensor(3.4998)
"""
_validate_intrinsic_cluster_data(data, labels)
# convert to zero indexed labels
unique_labels, labels = torch.unique(labels, return_inverse=True)
num_labels = len(unique_labels)
num_samples = data.shape[0]
_validate_intrinsic_labels_to_samples(num_labels, num_samples)
mean = data.mean(dim=0)
between_cluster_dispersion = torch.tensor(0.0, device=data.device)
within_cluster_dispersion = torch.tensor(0.0, device=data.device)
for k in range(num_labels):
cluster_k = data[labels == k, :]
mean_k = cluster_k.mean(dim=0)
between_cluster_dispersion += ((mean_k - mean) ** 2).sum() * cluster_k.shape[0]
within_cluster_dispersion += ((cluster_k - mean_k) ** 2).sum()
if within_cluster_dispersion == 0:
return torch.tensor(1.0, device=data.device, dtype=torch.float32)
return between_cluster_dispersion * (num_samples - num_labels) / (within_cluster_dispersion * (num_labels - 1.0))