CalinskiHarabaszScore#

class ignite.metrics.clustering.CalinskiHarabaszScore(output_transform=<function CalinskiHarabaszScore.<lambda>>, check_compute_fn=True, device=device(type='cpu'), skip_unrolling=False)[source]#

Calculates the Calinski-Harabasz score.

The Calinski-Harabasz score evaluates the quality of clustering results.

More details can be found here.

A higher Calinski-Harabasz score indicates that the clustering result is good (i.e., clusters are well-separated).

The computation of this metric is implemented with sklearn.metrics.calinski_harabasz_score.

update must receive output of the form (features, labels) or {'features': features, 'labels': labels}.
features and labels must be of same shape (B, D) and (B,).

Parameters are inherited from EpochMetric.__init__.

Parameters

output_transform (Callable[[...], Any]) – a callable that is used to transform the Engine’s process_function’s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as (features, labels) or {'features': features, 'labels': labels}.
check_compute_fn (bool) – if True, compute_fn is run on the first batch of data to ensure there are no issues. If issues exist, user is warned that there might be an issue with the compute_fn. Default, True.
device (Union[str, device]) – specifies which device updates are accumulated on. Setting the metric’s device to be the same as your update arguments ensures the update method is non-blocking. By default, CPU.
skip_unrolling (bool) – specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if y_pred contains multi-ouput as (y_pred_a, y_pred_b) Alternatively, output_transform can be used to handle this.

Examples

To use with Engine and process_function, simply attach the metric instance to the engine. The output of the engine’s process_function needs to be in format of (features, labels) or {'features': features, 'labels': labels, ...}.

from collections import OrderedDict

import torch
from torch import nn, optim

from ignite.engine import *
from ignite.handlers import *
from ignite.metrics import *
from ignite.metrics.clustering import *
from ignite.metrics.regression import *
from ignite.utils import *

# create default evaluator for doctests

def eval_step(engine, batch):
    return batch

default_evaluator = Engine(eval_step)

# create default optimizer for doctests

param_tensor = torch.zeros([1], requires_grad=True)
default_optimizer = torch.optim.SGD([param_tensor], lr=0.1)

# create default trainer for doctests
# as handlers could be attached to the trainer,
# each test must define his own trainer using `.. testsetup:`

def get_default_trainer():

    def train_step(engine, batch):
        return batch

    return Engine(train_step)

# create default model for doctests

default_model = nn.Sequential(OrderedDict([
    ('base', nn.Linear(4, 2)),
    ('fc', nn.Linear(2, 1))
]))

manual_seed(666)

metric = CalinskiHarabaszScore()
metric.attach(default_evaluator, "calinski_harabasz_score")
X = torch.tensor([
        [-1.04, -0.71, -1.42, -0.28, -0.43],
        [0.47, 0.96, -0.43, 1.57, -2.24],
        [-0.62, -0.29, 0.10, -0.72, -1.69],
        [0.96, -0.77, 0.60, -0.89, 0.49],
        [-1.33, -1.53, 0.25, -1.60, -2.0],
        [-0.63, -0.55, -1.03, -0.89, -0.77],
        [-0.26, -1.67, -0.24, -1.33, -0.40],
        [-0.20, -1.34, -0.52, -1.55, -1.50],
        [2.68, 1.13, 2.51, 0.80, 0.92],
        [0.33, 2.88, 1.35, -0.56, 1.71]
])
Y = torch.tensor([0, 0, 0, 0, 1, 1, 1, 1, 2, 2])
state = default_evaluator.run([{"features": X, "labels": Y}])
print(state.metrics["calinski_harabasz_score"])

5.733935121807529

New in version 0.5.2.

Methods