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DaviesBouldinScore#

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

Calculates the Davies-Bouldin score.

The Davies-Bouldin score evaluates the quality of clustering results.

More details can be found here.

The Davies-Bouldin score is non-negative, where values closer to zero indicate that the clustering result is good (i.e., clusters are well-separated).

When the number of unique labels is less than 2, or equals the number of samples (i.e., the index is undefined), float('nan') is returned.

The computation of this metric is implemented with sklearn.metrics.davies_bouldin_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 (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-output 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.fairness import *
from ignite.metrics.rec_sys 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 = DaviesBouldinScore()
metric.attach(default_evaluator, "davies_bouldin_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["davies_bouldin_score"])

1.3838673743829881

New in version 0.5.2.

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