plot_silhouette with examples#

An example showing the plot_silhouette function used by a scikit-learn clusterer.

# Authors: The scikit-plots developers
# SPDX-License-Identifier: BSD-3-Clause

import numpy as np
from sklearn.cluster import KMeans
from sklearn.datasets import (
    load_iris as data_3_classes,
)
from sklearn.model_selection import train_test_split

np.random.seed(0)  # reproducibility
# importing pylab or pyplot
import matplotlib.pyplot as plt

# Import scikit-plot
import scikitplot as sp

# Load the data
X, y = data_3_classes(return_X_y=True, as_frame=False)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.5, random_state=0)

# Create an instance of the LogisticRegression
model = KMeans(n_clusters=4, random_state=0)

cluster_labels = model.fit_predict(X_train)

# Plot!
ax = sp.metrics.plot_silhouette(X_train, cluster_labels)

# Adjust layout to make sure everything fits
plt.tight_layout()

# Save the plot with a filename based on the current script's name
# sp.api._utils.save_plot()

# Display the plot
plt.show(block=True)

/opt/conda/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning:

The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning

Tags: model-type: clustering model-type: k-means model-workflow: model evaluation plot-type: bar plot-type: silhouette plot level: beginner purpose: showcase