plot_silhouette with examples#

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

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

Import scikit-plots#

16 from sklearn.cluster import KMeans
17 from sklearn.datasets import (
18     load_iris as data_3_classes,
19 )
20 from sklearn.model_selection import train_test_split
21
22 import numpy as np
23
24 np.random.seed(0)  # reproducibility
25 # importing pylab or pyplot
26 import matplotlib.pyplot as plt
27
28 # Import scikit-plot
29 import scikitplot as sp

Loading the dataset#

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

Model Training#

43 # Create an instance of the LogisticRegression
44 model = KMeans(n_clusters=4, random_state=0)
45
46 cluster_labels = model.fit_predict(X_train)

Plot!#

52 # Plot!
53 ax = sp.metrics.plot_silhouette(
54     X_train,
55     cluster_labels,
56     save_fig=True,
57     save_fig_filename="",
58     # overwrite=True,
59     add_timestamp=True,
60     # verbose=True,
61 )
Silhouette Analysis

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

Total running time of the script: (0 minutes 0.303 seconds)

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