sil-score 0.1.5

Exact and approximate silhouette scoring with micro, macro, and weighted cluster averages.

sil_score

sil-score is a small Python package for exact and fast approximate silhouette scoring.

It extends the usual silhouette workflow with:

• per-sample silhouette scores
• micro-averaged silhouette score
• macro-averaged silhouette score
• cluster-weighted macro silhouette score
• exact vs approximate comparison report

The exact mode uses scikit-learn's silhouette_samples.
The approximate mode uses Euclidean distances to cluster centroids, making it faster but not identical to the classical silhouette definition.

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Installation

Install from PyPI:

pip install sil-score

Quick example

import numpy as np
from sil_score import (
    sil_samples,
    micro_sil_score,
    macro_sil_score,
    weighted_macro_sil_score,
    sil_approximation_report,
)

X = np.array([
    [0.0],
    [2.0],
    [10.0],
    [12.0],
])

labels = np.array([0, 0, 1, 1])

samples = sil_samples(X, labels)
micro = micro_sil_score(X, labels)
macro = macro_sil_score(X, labels)

print(samples)
print(micro)
print(macro)

Output:

[0.81818182 0.77777778 0.77777778 0.81818182]
0.797979797979798
0.797979797979798

Functions

sil_samples

sil_samples(X, labels, approximation=False, centers=None)

Computes the silhouette score for each sample.

By default, it computes the exact silhouette values using scikit-learn.

scores = sil_samples(X, labels)

For a faster centroid-based approximation:

scores = sil_samples(X, labels, approximation=True)

You can also pass precomputed cluster centers:

scores = sil_samples(
    X,
    labels,
    approximation=True,
    centers=centers,
)

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micro_sil_score

micro_sil_score(X, labels, approximation=False, centers=None)

Computes the mean of all sample-level silhouette scores. This is the usual average silhouette score. Larger clusters naturally have more influence because they contain more samples.

# Standard usage
score = micro_sil_score(X, labels)

# Approximate version
score = micro_sil_score(X, labels, approximation=True)

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macro_sil_score

macro_sil_score(X, labels, approximation=False, centers=None)

Computes the mean silhouette score inside each cluster, then averages the cluster means equally. This gives every cluster the same importance, regardless of its size.

# Standard usage
score = macro_sil_score(X, labels)

# Approximate version
score = macro_sil_score(X, labels, approximation=True)

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weighted_macro_sil_score

weighted_macro_sil_score(X, labels, cluster_weights, approximation=False, centers=None)

Computes a cluster-weighted macro silhouette score. First, it computes the mean silhouette score for each cluster, then combines those cluster means using custom cluster weights.

Using a dictionary:

weights = {
    0: 0.2,
    1: 0.3,
    2: 0.5,
}

score = weighted_macro_sil_score(X, labels, cluster_weights=weights)

Using an array:

weights = [0.2, 0.3, 0.5]

score = weighted_macro_sil_score(X, labels, cluster_weights=weights)

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sil_approximation_report

sil_approximation_report(X, labels, centers=None, return_samples=False)

Compares exact silhouette scores with centroid-based approximate scores. It returns(Pearson) correlation and error metrics:

report = sil_approximation_report(X, labels)
print(report)

Example output:

{
    "correlation": 0.96,
    "mean_absolute_error": 0.03,
    "mean_squared_error": 0.002,
    "root_mean_squared_error": 0.045,
    "max_absolute_error": 0.12,
    "mean_error": 0.01,
    "mean_exact_score": 0.52,
    "mean_approximate_score": 0.53,
    "n_samples": 300,
}

Use return_samples=True to also include the exact scores, approximate scores, and per-sample errors.

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Exact vs Approximate mode

• Exact mode: sil_samples(X, labels, approximation=False). Uses the classical silhouette definition based on distances between samples.
• Approximate mode: sil_samples(X, labels, approximation=True). Uses distances from each sample to cluster centroids. This can be significantly faster for larger datasets.

Requirements

sil-score depends on:

• NumPy
• scikit-learn

License

This project is licensed under the MIT License.