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import numpy as np
from sklearn.cluster import AffinityPropagation
from sklearn import metrics
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('agg')
import gradio as gr
def generate_data(num_centers, num_samples):
all_centers = [[1, 1], [-1, -1], [1, -1], [-1, 1]]
centers = all_centers[:num_centers]
X, labels_true = make_blobs(n_samples=num_samples, centers=centers, cluster_std=0.5, random_state=0)
return X, labels_true
def create_plot(num_clusters, num_samples):
X, labels_true = generate_data(num_clusters, num_samples)
af = AffinityPropagation(preference=-50, random_state=0).fit(X)
cluster_centers_indices = af.cluster_centers_indices_
labels = af.labels_
n_clusters_ = len(cluster_centers_indices)
metrics_str = f"Estimated number of clusters: {n_clusters_}\n"
metrics_str += f"Homogeneity: {metrics.homogeneity_score(labels_true, labels):0.3f}\n"
metrics_str += f"Completeness: {metrics.completeness_score(labels_true, labels):0.3f}\n"
metrics_str += f"V-measure: {metrics.v_measure_score(labels_true, labels):0.3f}\n"
metrics_str += f"Adjusted Rand Index: {metrics.adjusted_rand_score(labels_true, labels):0.3f}\n"
metrics_str += f"Adjusted Mutual Information: {metrics.adjusted_mutual_info_score(labels_true, labels):0.3f}\n"
metrics_str += f"Silhouette Coefficient: {metrics.silhouette_score(X, labels, metric='sqeuclidean'):0.3f}\n"
fig = plt.figure(1)
plt.clf()
colors = plt.cycler("color", plt.cm.viridis(np.linspace(0, 1, n_clusters_)))
for k, col in zip(range(n_clusters_), colors):
class_members = labels == k
cluster_center = X[cluster_centers_indices[k]]
plt.scatter(
X[class_members, 0], X[class_members, 1], color=col["color"], marker="."
)
plt.scatter(
cluster_center[0], cluster_center[1], s=14, color=col["color"], marker="o"
)
for x in X[class_members]:
plt.plot(
[cluster_center[0], x[0]], [cluster_center[1], x[1]], color=col["color"]
)
plt.title("Estimated number of clusters: %d" % n_clusters_)
plt.xlabel("x")
plt.ylabel("y")
return fig, metrics_str
title = "Affinity propagation clustering algorithm"
description = """
This demo plots clusters of a synthetic 2D dataset that contains up to 4 clusters using the affinity propagation algorithm.\
The 2-dimensional dataset is generated around 2 to 4 predetermined cluster centers, by sampling a Gaussian distribution \
with 0.5 standard deviation around each center. The demo uses the affinity propagation clustering algorithm to assign the data into \
clusters. It also calculates a cluster center. \
The figure shows a scatter plot of the data points and their connection to the respective cluster center. The demo also \
presents several metrics based on the true and assigned labels.
"""
with gr.Blocks() as demo:
gr.Markdown(f"## {title}")
gr.Markdown(description)
num_clusters = gr.Slider(minimum=2, maximum=4, step=1, value=2, label="Number of clusters")
num_samples = gr.Slider(minimum=100, maximum=300, step=100, value=200, label="Number of samples")
with gr.Row():
plot = gr.Plot()
text_box = gr.Textbox(label="Results")
num_clusters.change(fn=create_plot, inputs=[num_clusters, num_samples], outputs=[plot, text_box])
num_samples.change(fn=create_plot, inputs=[num_clusters, num_samples], outputs=[plot, text_box])
demo.launch(enable_queue=True) |