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import gradio as gr
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_samples, silhouette_score
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
theme = gr.themes.Monochrome(
primary_hue="indigo",
secondary_hue="blue",
neutral_hue="slate",
)
def main(
n_clusters:int=2,
n_samples:int=500,
n_features:int=2,
n_centers:int=4,
cluster_std:int=1,
):
# Generating the sample data from make_blobs
# This particular setting has one distinct cluster and 3 clusters placed close
# together.
X, y = make_blobs(
n_samples=n_samples,
n_features=n_features,
centers=n_centers,
cluster_std=cluster_std,
center_box=(-10.0, 10.0),
shuffle=True,
random_state=1,
)
# For reproducibility
n_clusters = int(n_clusters)
fig1, ax1 = plt.subplots()
fig1.set_size_inches(9, 4)
fig2, ax2 = plt.subplots()
fig2.set_size_inches(9, 4)
# The 1st subplot is the silhouette plot
# The silhouette coefficient can range from -1, 1 but in this example all
# lie within [-0.1, 1]
ax1.set_xlim([-0.1, 1])
# The (n_clusters+1)*10 is for inserting blank space between silhouette
# plots of individual clusters, to demarcate them clearly.
ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])
# Initialize the clusterer with n_clusters value and a random generator
# seed of 10 for reproducibility.
clusterer = KMeans(n_clusters=n_clusters, n_init="auto", random_state=10)
cluster_labels = clusterer.fit_predict(X)
# The silhouette_score gives the average value for all the samples.
# This gives a perspective into the density and separation of the formed
# clusters
silhouette_avg = silhouette_score(X, cluster_labels)
print(
"For n_clusters =",
n_clusters,
"The average silhouette_score is :",
silhouette_avg,
)
# Compute the silhouette scores for each sample
sample_silhouette_values = silhouette_samples(X, cluster_labels)
y_lower = 10
for i in range(n_clusters):
# Aggregate the silhouette scores for samples belonging to
# cluster i, and sort them
ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]
ith_cluster_silhouette_values.sort()
size_cluster_i = ith_cluster_silhouette_values.shape[0]
y_upper = y_lower + size_cluster_i
color = cm.nipy_spectral(float(i) / n_clusters)
ax1.fill_betweenx(
np.arange(y_lower, y_upper),
0,
ith_cluster_silhouette_values,
facecolor=color,
edgecolor=color,
alpha=0.7,
)
# Label the silhouette plots with their cluster numbers at the middle
ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))
# Compute the new y_lower for next plot
y_lower = y_upper + 10 # 10 for the 0 samples
ax1.set_title("The silhouette plot for the various clusters.")
ax1.set_xlabel("The silhouette coefficient values")
ax1.set_ylabel("Cluster label")
# The vertical line for average silhouette score of all the values
ax1.axvline(x=silhouette_avg, color="red", linestyle="--")
ax1.set_yticks([]) # Clear the yaxis labels / ticks
ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])
# 2nd Plot showing the actual clusters formed
colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)
ax2.scatter(
X[:, 0], X[:, 1], marker=".", s=30, lw=0, alpha=0.7, c=colors, edgecolor="k"
)
# Labeling the clusters
centers = clusterer.cluster_centers_
# Draw white circles at cluster centers
ax2.scatter(
centers[:, 0],
centers[:, 1],
marker="o",
c="white",
alpha=1,
s=200,
edgecolor="k",
)
for i, c in enumerate(centers):
ax2.scatter(c[0], c[1], marker="$%d$" % i, alpha=1, s=50, edgecolor="k")
ax2.set_title("The visualization of the clustered data.")
ax2.set_xlabel("Feature space for the 1st feature")
ax2.set_ylabel("Feature space for the 2nd feature")
plt.suptitle(
"Silhouette analysis for KMeans clustering on sample data with n_clusters = %d"
% n_clusters,
fontsize=14,
fontweight="bold",
)
return fig1, fig2
title = '''# Selecting the number of clusters with silhouette analysis on KMeans clustering 📊'''
description = """
This app demonstrates a silhouette analysis for KMeans clustering on sample data.
The purpose of a clustering algorithm is to find groups of similar data points. The purpose of a silhouette analysis is to determine the optimal number of clusters for a given clustering algorithm. The silhouette analysis can be used on any clustering algorithm, but it is most commonly used with KMeans clustering.
"""
with gr.Blocks(theme=theme) as demo:
gr.Markdown(title)
gr.Markdown(description)
gr.Markdown('''### Dataset Generation Parameters''')
with gr.Row():
with gr.Column():
n_samples = gr.inputs.Slider(minimum=100, maximum=1000, default=500, step=50, label="n_samples")
n_features = gr.inputs.Slider(minimum=2, maximum=5, default=2, step=1, label="n_features")
n_clusters = gr.inputs.Slider(minimum=2, maximum=6, default=2, step=1, label="n_clusters")
n_centers = gr.inputs.Slider(minimum=2, maximum=5, default=4, step=1, label="n_centers")
cluster_std = gr.inputs.Slider(minimum=0.0, maximum=1.0, default=1, step=0.1, label="cluster_std")
run_button = gr.Button('Analyse Silhouette')
with gr.Row():
plot_silhouette = gr.Plot()
plot_clusters = gr.Plot()
outputs = [plot_silhouette, plot_clusters]
inputs = [n_clusters, n_samples,n_features,n_centers,cluster_std]
run_button.click(fn=main, inputs=inputs, outputs=outputs)
demo.launch() |