These are two implementations of the silhouette score. They are compatible with the scikit learn implementation but offers different drawbacks in term of complexity and memory usage. The slow version needs no memory but is painfully slow and should, I think, not be used. The second one is based on a block strategy: distance between samples and c…

unsupervised_alt.py

""" Unsupervised evaluation metrics. """

# License: BSD Style.

from itertools import combinations
import numpy as np

from sklearn.utils import check_random_state
from sklearn.metrics.pairwise import distance_metrics
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.externals.joblib import Parallel, delayed


def silhouette_score_slow(X, labels, metric='euclidean', sample_size=None,
                          random_state=None, **kwds):
    """Compute the mean Silhouette Coefficient of all samples.

    This method is computationally expensive compared to the reference one.

    The Silhouette Coefficient is calculated using the mean intra-cluster
    distance (a) and the mean nearest-cluster distance (b) for each sample.
    The Silhouette Coefficient for a sample is ``(b - a) / max(a, b)``.
    To clarrify, b is the distance between a sample and the nearest cluster
    that b is not a part of.

    This function returns the mean Silhoeutte Coefficient over all samples.
    To obtain the values for each sample, use silhouette_samples

    The best value is 1 and the worst value is -1. Values near 0 indicate
    overlapping clusters. Negative values generally indicate that a sample has
    been assigned to the wrong cluster, as a different cluster is more similar.

    Parameters
    ----------
    X : array [n_samples_a, n_features]
        Feature array.

    labels : array, shape = [n_samples]
        label values for each sample

    metric : string, or callable
        The metric to use when calculating distance between instances in a
        feature array. If metric is a string, it must be one of the options
        allowed by metrics.pairwise.pairwise_distances. If X is the distance
        array itself, use "precomputed" as the metric.

    sample_size : int or None
        The size of the sample to use when computing the Silhouette
        Coefficient. If sample_size is None, no sampling is used.

    random_state : integer or numpy.RandomState, optional
        The generator used to initialize the centers. If an integer is
        given, it fixes the seed. Defaults to the global numpy random
        number generator.

    `**kwds` : optional keyword parameters
        Any further parameters are passed directly to the distance function.
        If using a scipy.spatial.distance metric, the parameters are still
        metric dependent. See the scipy docs for usage examples.

    Returns
    -------
    silhouette : float
        Mean Silhouette Coefficient for all samples.

    References
    ----------

    Peter J. Rousseeuw (1987). "Silhouettes: a Graphical Aid to the
        Interpretation and Validation of Cluster Analysis". Computational
        and Applied Mathematics 20: 53-65. doi:10.1016/0377-0427(87)90125-7.

    http://en.wikipedia.org/wiki/Silhouette_(clustering)

    """
    if sample_size is not None:
        random_state = check_random_state(random_state)
        indices = random_state.permutation(X.shape[0])[:sample_size]
        if metric == "precomputed":
            raise ValueError('Distance matrix cannot be precomputed')
        else:
            X, labels = X[indices], labels[indices]
    return np.mean(silhouette_samples_slow(X, labels, metric=metric, **kwds))


def silhouette_samples_slow(X, labels, metric='euclidean', **kwds):
    """Compute the Silhouette Coefficient for each sample.

    The Silhoeutte Coefficient is a measure of how well samples are clustered
    with samples that are similar to themselves. Clustering models with a high
    Silhouette Coefficient are said to be dense, where samples in the same
    cluster are similar to each other, and well separated, where samples in
    different clusters are not very similar to each other.

    The Silhouette Coefficient is calculated using the mean intra-cluster
    distance (a) and the mean nearest-cluster distance (b) for each sample.
    The Silhouette Coefficient for a sample is ``(b - a) / max(a, b)``.

    This function returns the Silhoeutte Coefficient for each sample.

    The best value is 1 and the worst value is -1. Values near 0 indicate
    overlapping clusters.

    Parameters
    ----------
    X : array [n_samples_a, n_features]
        Feature array.

    labels : array, shape = [n_samples]
             label values for each sample

    metric : string, or callable
        The metric to use when calculating distance between instances in a
        feature array. If metric is a string, it must be one of the options
        allowed by metrics.pairwise.pairwise_distances. If X is the distance
        array itself, use "precomputed" as the metric.

    `**kwds` : optional keyword parameters
        Any further parameters are passed directly to the distance function.
        If using a scipy.spatial.distance metric, the parameters are still
        metric dependent. See the scipy docs for usage examples.

    Returns
    -------
    silhouette : array, shape = [n_samples]
        Silhouette Coefficient for each samples.

    References
    ----------

    Peter J. Rousseeuw (1987). "Silhouettes: a Graphical Aid to the
        Interpretation and Validation of Cluster Analysis". Computational
        and Applied Mathematics 20: 53-65. doi:10.1016/0377-0427(87)90125-7.

    http://en.wikipedia.org/wiki/Silhouette_(clustering)

    """
    metric = distance_metrics()[metric]
    n = labels.shape[0]
    A = np.array([_intra_cluster_distance_slow(X, labels, metric, i)
                  for i in range(n)])
    B = np.array([_nearest_cluster_distance_slow(X, labels, metric, i)
                  for i in range(n)])
    sil_samples = (B - A) / np.maximum(A, B)
    # nan values are for clusters of size 1, and should be 0
    return np.nan_to_num(sil_samples)


def _intra_cluster_distance_slow(X, labels, metric, i):
    """Calculate the mean intra-cluster distance for sample i.

    Parameters
    ----------
    X : array [n_samples_a, n_features]
        Feature array.

    labels : array, shape = [n_samples]
        label values for each sample

    metric: function
        Pairwise metric function

    i : int
        Sample index being calculated. It is excluded from calculation and
        used to determine the current label

    Returns
    -------
    a : float
        Mean intra-cluster distance for sample i
    """
    indices = np.where(labels == labels[i])[0]
    if len(indices) == 0:
        return 0.
    a = np.mean([metric(X[i], X[j]) for j in indices if not i == j])
    return a


def _nearest_cluster_distance_slow(X, labels, metric, i):
    """Calculate the mean nearest-cluster distance for sample i.

    Parameters
    ----------
    X : array [n_samples_a, n_features]
        Feature array.

    labels : array, shape = [n_samples]
        label values for each sample

    metric: function
        Pairwise metric function

    i : int
        Sample index being calculated. It is used to determine the current
        label.

    Returns
    -------
    b : float
        Mean nearest-cluster distance for sample i
    """
    label = labels[i]
    b = np.min(
            [np.mean(
                [metric(X[i], X[j]) for j in np.where(labels == cur_label)[0]]
            ) for cur_label in set(labels) if not cur_label == label])
    return b


def silhouette_score_block(X, labels, metric='euclidean', sample_size=None,
                           random_state=None, n_jobs=1, **kwds):
    """Compute the mean Silhouette Coefficient of all samples.

    The Silhouette Coefficient is calculated using the mean intra-cluster
    distance (a) and the mean nearest-cluster distance (b) for each sample.
    The Silhouette Coefficient for a sample is ``(b - a) / max(a, b)``.
    To clarrify, b is the distance between a sample and the nearest cluster
    that b is not a part of.

    This function returns the mean Silhoeutte Coefficient over all samples.
    To obtain the values for each sample, use silhouette_samples

    The best value is 1 and the worst value is -1. Values near 0 indicate
    overlapping clusters. Negative values generally indicate that a sample has
    been assigned to the wrong cluster, as a different cluster is more similar.

    Parameters
    ----------
    X : array [n_samples_a, n_features]
        Feature array.

    labels : array, shape = [n_samples]
             label values for each sample

    metric : string, or callable
        The metric to use when calculating distance between instances in a
        feature array. If metric is a string, it must be one of the options
        allowed by metrics.pairwise.pairwise_distances. If X is the distance
        array itself, use "precomputed" as the metric.

    sample_size : int or None
        The size of the sample to use when computing the Silhouette
        Coefficient. If sample_size is None, no sampling is used.

    random_state : integer or numpy.RandomState, optional
        The generator used to initialize the centers. If an integer is
        given, it fixes the seed. Defaults to the global numpy random
        number generator.

    `**kwds` : optional keyword parameters
        Any further parameters are passed directly to the distance function.
        If using a scipy.spatial.distance metric, the parameters are still
        metric dependent. See the scipy docs for usage examples.

    Returns
    -------
    silhouette : float
        Mean Silhouette Coefficient for all samples.

    References
    ----------

    Peter J. Rousseeuw (1987). "Silhouettes: a Graphical Aid to the
        Interpretation and Validation of Cluster Analysis". Computational
        and Applied Mathematics 20: 53-65. doi:10.1016/0377-0427(87)90125-7.

    http://en.wikipedia.org/wiki/Silhouette_(clustering)

    """
    if sample_size is not None:
        random_state = check_random_state(random_state)
        indices = random_state.permutation(X.shape[0])[:sample_size]
        if metric == "precomputed":
            raise ValueError('Distance matrix cannot be precomputed')
        else:
            X, labels = X[indices], labels[indices]
    return np.mean(silhouette_samples_block(
        X, labels, metric=metric, n_jobs=n_jobs, **kwds))


def silhouette_samples_block(X, labels, metric='euclidean', n_jobs=1, **kwds):
    """Compute the Silhouette Coefficient for each sample.

    The Silhoeutte Coefficient is a measure of how well samples are clustered
    with samples that are similar to themselves. Clustering models with a high
    Silhouette Coefficient are said to be dense, where samples in the same
    cluster are similar to each other, and well separated, where samples in
    different clusters are not very similar to each other.

    The Silhouette Coefficient is calculated using the mean intra-cluster
    distance (a) and the mean nearest-cluster distance (b) for each sample.
    The Silhouette Coefficient for a sample is ``(b - a) / max(a, b)``.

    This function returns the Silhoeutte Coefficient for each sample.

    The best value is 1 and the worst value is -1. Values near 0 indicate
    overlapping clusters.

    Parameters
    ----------
    X : array [n_samples_a, n_features]
        Feature array.

    labels : array, shape = [n_samples]
             label values for each sample

    metric : string, or callable
        The metric to use when calculating distance between instances in a
        feature array. If metric is a string, it must be one of the options
        allowed by metrics.pairwise.pairwise_distances. If X is the distance
        array itself, use "precomputed" as the metric.

    `**kwds` : optional keyword parameters
        Any further parameters are passed directly to the distance function.
        If using a scipy.spatial.distance metric, the parameters are still
        metric dependent. See the scipy docs for usage examples.

    Returns
    -------
    silhouette : array, shape = [n_samples]
        Silhouette Coefficient for each samples.

    References
    ----------

    Peter J. Rousseeuw (1987). "Silhouettes: a Graphical Aid to the
        Interpretation and Validation of Cluster Analysis". Computational
        and Applied Mathematics 20: 53-65. doi:10.1016/0377-0427(87)90125-7.

    http://en.wikipedia.org/wiki/Silhouette_(clustering)

    """
    A = _intra_cluster_distances_block(X, labels, metric, n_jobs=n_jobs,
                                       **kwds)
    B = _nearest_cluster_distance_block(X, labels, metric, n_jobs=n_jobs,
                                        **kwds)
    sil_samples = (B - A) / np.maximum(A, B)
    # nan values are for clusters of size 1, and should be 0
    return np.nan_to_num(sil_samples)


def _intra_cluster_distances_block_(subX, metric, **kwds):
    distances = pairwise_distances(subX, metric=metric, **kwds)
    return distances.sum(axis=1) / (distances.shape[0] - 1)


def _intra_cluster_distances_block(X, labels, metric, n_jobs=1, **kwds):
    """Calculate the mean intra-cluster distance for sample i.

    Parameters
    ----------
    X : array [n_samples_a, n_features]
        Feature array.

    labels : array, shape = [n_samples]
        label values for each sample

    metric : string, or callable
        The metric to use when calculating distance between instances in a
        feature array. If metric is a string, it must be one of the options
        allowed by metrics.pairwise.pairwise_distances. If X is the distance
        array itself, use "precomputed" as the metric.

    `**kwds` : optional keyword parameters
        Any further parameters are passed directly to the distance function.
        If using a scipy.spatial.distance metric, the parameters are still
        metric dependent. See the scipy docs for usage examples.

    Returns
    -------
    a : array [n_samples_a]
        Mean intra-cluster distance
    """
    intra_dist = np.zeros(labels.size, dtype=float)
    values = Parallel(n_jobs=n_jobs)(
            delayed(_intra_cluster_distances_block_)
                (X[np.where(labels == label)[0]], metric, **kwds)
                for label in np.unique(labels))
    for label, values_ in zip(np.unique(labels), values):
        intra_dist[np.where(labels == label)[0]] = values_
    return intra_dist


def _nearest_cluster_distance_block_(subX_a, subX_b, metric, **kwds):
    dist = pairwise_distances(subX_a, subX_b, metric=metric, **kwds)
    dist_a = dist.mean(axis=1)
    dist_b = dist.mean(axis=0)
    return dist_a, dist_b


def _nearest_cluster_distance_block(X, labels, metric, n_jobs=1, **kwds):
    """Calculate the mean nearest-cluster distance for sample i.

    Parameters
    ----------
    X : array [n_samples_a, n_features]
        Feature array.

    labels : array, shape = [n_samples]
        label values for each sample

    metric : string, or callable
        The metric to use when calculating distance between instances in a
        feature array. If metric is a string, it must be one of the options
        allowed by metrics.pairwise.pairwise_distances. If X is the distance
        array itself, use "precomputed" as the metric.

    `**kwds` : optional keyword parameters
        Any further parameters are passed directly to the distance function.
        If using a scipy.spatial.distance metric, the parameters are still
        metric dependent. See the scipy docs for usage examples.
    X : array [n_samples_a, n_features]
        Feature array.

    Returns
    -------
    b : float
        Mean nearest-cluster distance for sample i
    """
    inter_dist = np.empty(labels.size, dtype=float)
    inter_dist.fill(np.inf)
    # Compute cluster distance between pairs of clusters
    unique_labels = np.unique(labels)

    values = Parallel(n_jobs=n_jobs)(
            delayed(_nearest_cluster_distance_block_)(
                X[np.where(labels == label_a)[0]],
                X[np.where(labels == label_b)[0]],
                metric, **kwds)
                for label_a, label_b in combinations(unique_labels, 2))

    for (label_a, label_b), (values_a, values_b) in \
            zip(combinations(unique_labels, 2), values):

            indices_a = np.where(labels == label_a)[0]
            inter_dist[indices_a] = np.minimum(values_a, inter_dist[indices_a])
            del indices_a
            indices_b = np.where(labels == label_b)[0]
            inter_dist[indices_b] = np.minimum(values_b, inter_dist[indices_b])
            del indices_b
    return inter_dist

if __name__ == '__main__':
    import time

    from sklearn.metrics.cluster.unsupervised import silhouette_score

    np.random.seed(0)
    X = np.random.random((10000, 100))
    y = np.repeat(np.arange(100), 100)
    t0 = time.time()
    s = silhouette_score(X, y)
    t = time.time() - t0
    print 'Scikit silhouette (%fs): %f' % (t, s)
    t0 = time.time()
    s = silhouette_score_block(X, y)
    t = time.time() - t0
    print 'Block silhouette (%fs): %f' % (t, s)
    t0 = time.time()
    s = silhouette_score_block(X, y, n_jobs=2)
    t = time.time() - t0
    print 'Block silhouette parallel (%fs): %f' % (t, s)

Do you mean? ->
from sklearn.metrics import *
silhouette_score(data, labels)

can I use silhouette_score to KMedoid not Kmeans?
if it's possible, could you please make an example python code of it? thank you

Hi
Im getting the following error, can you help me out please?

Traceback (most recent call last):
File "cluster_clim.py", line 229, in
random_state=10, n_jobs=1)
File "/home/peter_wassall_jamieson/demand_clusters/silhouette_calc.py", line 20, in silhouette_score_block
X, labels, metric=metric, n_jobs=n_jobs, **kwds))
File "/home/peter_wassall_jamieson/demand_clusters/silhouette_calc.py", line 25, in silhouette_samples_block
**kwds)
File "/home/peter_wassall_jamieson/demand_clusters/silhouette_calc.py", line 43, in _intra_cluster_distances_block
for label in np.unique(labels))
File "/home/peter_wassall_jamieson/anaconda3/envs/insights_production/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 779,
in call
while self.dispatch_one_batch(iterator):
File "/home/peter_wassall_jamieson/anaconda3/envs/insights_production/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 620,
in dispatch_one_batch
tasks = BatchedCalls(itertools.islice(iterator, batch_size))
File "/home/peter_wassall_jamieson/anaconda3/envs/insights_production/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 127,
in init
self.items = list(iterator_slice)
File "/home/peter_wassall_jamieson/demand_clusters/silhouette_calc.py", line 43, in
for label in np.unique(labels))
TypeError: only integer scalar arrays can be converted to a scalar index

Thanks

This is really great, thank you!

Error: only integer scalar arrays can be converted to a scalar index

Pass parameters in numpy.array() when calling silhouette_score_block.
e.g., silhouette_score_block( np.array(X), np.array(y))

Error: only integer scalar arrays can be converted to a scalar index

Pass parameters in numpy.array() when calling silhouette_score_block.
e.g., silhouette_score_block( np.array(X), np.array(y))

This code is a bit old, to say the least. Feel free to use scikit learn, its implementation should be able to scale now, this code is not needed anymore.

For those interested as @AlexandreAbraham stated scikit-learn is faster now:

sklearn 1.0.2: 1.54 s ± 67.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
this impl.: 2.28 s ± 131 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Using more jobs helps a bit but still does not beat scikit-learn. Bummer ... I am still looking for something faster when dealing with 1.5B samples.

Hey @paulgavrikov, I did not look hard nor test but there are implementations of silhouette in numba lying around. Could it help?
https://gist.github.com/mangecoeur/f7c419506bb009d69c20565e2b6f7887

@AlexandreAbraham thanks for the link! There's something wrong with the numba part there. I'll try to fix it. Cheers!

@paulgavrikov maybe try using Simplified Silhouette (it is way much faster) and, in general, provides quite good results. You can find a descripition here: https://dl.acm.org/doi/abs/10.1145/2484838.2484844.