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: plotly 6.7.0 | Python 3.13.13
Quality: 77/100 | Updated: 2026-05-10
"""
import numpy as np
import plotly.graph_objects as go
from sklearn.cluster import KMeans
from sklearn.datasets import load_iris
from sklearn.metrics import silhouette_samples, silhouette_score
# Data - load iris dataset and perform clustering
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)
# Compute silhouette scores
silhouette_vals = silhouette_samples(X, cluster_labels)
silhouette_avg = silhouette_score(X, cluster_labels)
# Colors for clusters (Python blue, Python yellow, and a colorblind-safe third color)
colors = ["#306998", "#FFD43B", "#E84A5F"]
# Create figure
fig = go.Figure()
y_lower = 10
cluster_info = []
for i in range(n_clusters):
# Get silhouette values for this cluster
cluster_silhouette_vals = silhouette_vals[cluster_labels == i]
cluster_silhouette_vals.sort()
cluster_size = cluster_silhouette_vals.shape[0]
y_upper = y_lower + cluster_size
cluster_avg = np.mean(cluster_silhouette_vals)
# Create y positions for bars
y_positions = np.arange(y_lower, y_upper)
# Add horizontal bars for each sample
fig.add_trace(
go.Bar(
x=cluster_silhouette_vals,
y=y_positions,
orientation="h",
marker=dict(color=colors[i], line=dict(width=0)),
name=f"Cluster {i} (avg: {cluster_avg:.2f})",
hovertemplate=f"Cluster {i}<br>Silhouette: %{{x:.3f}}<extra></extra>",
)
)
# Store cluster info for annotation
cluster_info.append({"y_center": y_lower + 0.5 * cluster_size, "avg": cluster_avg, "cluster": i})
y_lower = y_upper + 10 # Gap between clusters
# Add vertical line for average silhouette score
fig.add_vline(
x=silhouette_avg,
line=dict(color="red", width=3, dash="dash"),
annotation_text=f"Average: {silhouette_avg:.3f}",
annotation_position="top",
annotation_font=dict(size=20, color="red"),
)
# Add vertical line at 0
fig.add_vline(x=0, line=dict(color="gray", width=2))
# Update layout
fig.update_layout(
title=dict(text="silhouette-basic \u00b7 plotly \u00b7 pyplots.ai", font=dict(size=32), x=0.5, xanchor="center"),
xaxis=dict(
title=dict(text="Silhouette Coefficient", font=dict(size=24)),
tickfont=dict(size=18),
range=[-0.2, 1.0],
showgrid=True,
gridcolor="rgba(0,0,0,0.1)",
zeroline=True,
zerolinecolor="gray",
zerolinewidth=2,
),
yaxis=dict(
title=dict(text="Samples (grouped by cluster)", font=dict(size=24)),
tickfont=dict(size=18),
showticklabels=False,
),
legend=dict(font=dict(size=20), x=0.98, xanchor="right", y=0.98, yanchor="top"),
template="plotly_white",
margin=dict(l=100, r=100, t=120, b=100),
bargap=0,
showlegend=True,
)
# Add cluster annotations on y-axis
for info in cluster_info:
fig.add_annotation(
x=-0.18,
y=info["y_center"],
text=f"Cluster {info['cluster']}",
showarrow=False,
font=dict(size=18),
xanchor="center",
)
# Save as PNG and HTML
fig.write_image("plot.png", width=1600, height=900, scale=3)
fig.write_html("plot.html", include_plotlyjs="cdn")
Part of Silhouette Plot on anyplot.ai.