A silhouette plot visualizes the quality of clustering results by showing the silhouette coefficient for each sample, grouped by cluster assignment. Each horizontal bar represents a sample's silhouette score (-1 to 1), where positive values indicate good cluster membership and negative values suggest potential misclassification. This visualization helps evaluate cluster cohesion (how similar samples are to their own cluster) and separation (how distinct they are from neighboring clusters).
""" anyplot.ai
silhouette-basic: Silhouette Plot
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 89/100 | Updated: 2026-05-10
"""
import os
import numpy as np
import pandas as pd
from plotnine import (
aes,
element_blank,
element_line,
element_rect,
element_text,
geom_segment,
geom_text,
geom_vline,
ggplot,
labs,
scale_color_manual,
theme,
theme_minimal,
xlim,
)
from sklearn.cluster import KMeans
from sklearn.datasets import load_iris
from sklearn.metrics import silhouette_samples, silhouette_score
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
# Okabe-Ito palette
IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
# Data - Clustering iris dataset into 3 groups
np.random.seed(42)
iris = load_iris()
X = iris.data
n_clusters = 3
# Perform K-means clustering
kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
cluster_labels = kmeans.fit_predict(X)
# Calculate silhouette scores
silhouette_vals = silhouette_samples(X, cluster_labels)
avg_silhouette = silhouette_score(X, cluster_labels)
# Build dataframe for plotting - sort samples within each cluster by silhouette score
data_rows = []
y_position = 0
cluster_centers = []
cluster_avg_scores = []
for cluster_idx in range(n_clusters):
# Get samples in this cluster
mask = cluster_labels == cluster_idx
cluster_silhouettes = silhouette_vals[mask]
cluster_silhouettes_sorted = np.sort(cluster_silhouettes)
# Calculate cluster average
cluster_avg = cluster_silhouettes.mean()
cluster_avg_scores.append(cluster_avg)
# Track the center position for annotation
cluster_start = y_position
# Add each sample as a row
for sil_val in cluster_silhouettes_sorted:
data_rows.append({"y": y_position, "silhouette": sil_val, "cluster": f"Cluster {cluster_idx}"})
y_position += 1
cluster_end = y_position - 1
cluster_centers.append((cluster_start + cluster_end) / 2)
# Add small gap between clusters
y_position += 8
df = pd.DataFrame(data_rows)
df["x_start"] = 0 # Starting x position for horizontal bars
# Create annotation dataframe for cluster labels
annotation_df = pd.DataFrame(
{
"y": cluster_centers,
"x": [-0.08] * n_clusters,
"label": [f"Cluster {i}\n(avg: {cluster_avg_scores[i]:.2f})" for i in range(n_clusters)],
}
)
# Create average line label dataframe
avg_label_df = pd.DataFrame(
{"x": [avg_silhouette + 0.02], "y": [max(df["y"]) * 0.95], "label": [f"Avg: {avg_silhouette:.2f}"]}
)
# Create the silhouette plot using horizontal segments
plot = (
ggplot()
+ geom_segment(aes(x="x_start", xend="silhouette", y="y", yend="y", color="cluster"), data=df, size=1.5)
+ geom_vline(xintercept=avg_silhouette, color=INK_SOFT, linetype="dashed", size=1.2, alpha=0.6)
+ geom_text(aes(x="x", y="y", label="label"), data=annotation_df, size=12, ha="right", color=INK_SOFT)
+ geom_text(aes(x="x", y="y", label="label"), data=avg_label_df, size=11, ha="left", color=INK_SOFT)
+ scale_color_manual(values=IMPRINT)
+ labs(
x="Silhouette Coefficient",
y="Sample Index (sorted within cluster)",
title="silhouette-basic · plotnine · anyplot.ai",
)
+ xlim(-0.25, 1.0)
+ theme_minimal()
+ theme(
figure_size=(16, 9),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
panel_grid_major_y=element_blank(),
panel_grid_minor_y=element_blank(),
panel_grid_major_x=element_line(color=INK, size=0.3, alpha=0.10),
panel_grid_minor_x=element_line(color=INK, size=0.2, alpha=0.05),
panel_border=element_rect(color=INK_SOFT, fill=None, size=0.5),
plot_title=element_text(size=24, color=INK),
axis_title=element_text(size=20, color=INK),
axis_text_x=element_text(size=16, color=INK_SOFT),
axis_text_y=element_blank(),
axis_ticks_major_y=element_blank(),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_text=element_text(size=16, color=INK_SOFT),
legend_title=element_text(size=18, color=INK),
legend_position="right",
)
)
# Save as PNG
plot.save(f"plot-{THEME}.png", dpi=300, verbose=False)
Part of Silhouette Plot on anyplot.ai.