| 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) |