| import gradio as gr |
| import numpy as np |
| import pandas as pd |
| from sklearn.cluster import KMeans, AgglomerativeClustering, DBSCAN, Birch, MeanShift |
| from sklearn.mixture import GaussianMixture |
| from sklearn.decomposition import PCA |
| from scipy.cluster.hierarchy import linkage, dendrogram |
| import matplotlib.pyplot as plt |
| import skfuzzy as fuzz |
| import hdbscan |
|
|
| def apply_clustering(algorithm, n_clusters, dataset): |
| |
| data = pd.read_csv(dataset.name) |
| data_matrix = data.values |
|
|
| |
| pca = PCA(n_components=2) |
| reduced_data = pca.fit_transform(data_matrix) |
|
|
| |
| if algorithm == "KMeans": |
| model = KMeans(n_clusters=n_clusters, random_state=42) |
| labels = model.fit_predict(data_matrix) |
| elif algorithm == "Fuzzy C-Means (FCM)": |
| |
| cntr, u, _, _, _, _, _ = fuzz.cmeans(data_matrix.T, n_clusters, 2, error=0.005, maxiter=1000) |
| labels = np.argmax(u, axis=0) |
| elif algorithm == "Agglomerative Hierarchical Clustering (AHC)": |
| model = AgglomerativeClustering(n_clusters=n_clusters) |
| labels = model.fit_predict(data_matrix) |
|
|
| |
| Z = linkage(data_matrix, 'ward') |
| plt.figure(figsize=(10, 7)) |
| dendrogram(Z) |
| plt.title("Dendrogram for Agglomerative Clustering") |
| plt.xlabel("Sample index") |
| plt.ylabel("Distance") |
| plt.tight_layout() |
| plt.savefig("dendrogram.png") |
| plt.close() |
| return f"Agglomerative clustering applied successfully.", "dendrogram.png" |
| |
| elif algorithm == "BIRCH": |
| model = Birch(n_clusters=n_clusters) |
| labels = model.fit_predict(data_matrix) |
| elif algorithm == "DBSCAN": |
| model = DBSCAN(eps=0.5, min_samples=5) |
| labels = model.fit_predict(data_matrix) |
| elif algorithm == "HDBSCAN": |
| model = hdbscan.HDBSCAN(min_samples=5) |
| labels = model.fit_predict(data_matrix) |
| elif algorithm == "Mean-Shift": |
| model = MeanShift() |
| labels = model.fit_predict(data_matrix) |
| elif algorithm == "Gaussian Mixture Models (GMM)": |
| model = GaussianMixture(n_components=n_clusters) |
| model.fit(data_matrix) |
| labels = model.predict(data_matrix) |
| else: |
| return "Algorithm not supported yet.", None |
|
|
| |
| plt.figure(figsize=(8, 6)) |
| plt.scatter(reduced_data[:, 0], reduced_data[:, 1], c=labels, cmap='viridis', s=50) |
| plt.colorbar(label='Cluster Label') |
| plt.title(f"Clusters Visualization ({algorithm})") |
| plt.xlabel("PCA Component 1") |
| plt.ylabel("PCA Component 2") |
| plt.tight_layout() |
| plt.savefig("clusters_plot.png") |
| plt.close() |
|
|
| return f"{algorithm} clustering applied successfully.", "clusters_plot.png" |
|
|
| |
| def main_interface(): |
| dataset = gr.File(label="Upload Dataset (CSV format)") |
| algorithm = gr.Dropdown( |
| choices=[ |
| "KMeans", |
| "Fuzzy C-Means (FCM)", |
| "Agglomerative Hierarchical Clustering (AHC)", |
| "BIRCH", |
| "DBSCAN", |
| "HDBSCAN", |
| "Mean-Shift", |
| "Gaussian Mixture Models (GMM)" |
| ], |
| label="Select Algorithm" |
| ) |
| n_clusters = gr.Slider(minimum=2, maximum=10, value=3, step=1, label="Number of Clusters (for applicable algorithms)") |
|
|
| output_text = gr.Textbox(label="Result") |
| output_image = gr.Image(label="Cluster Visualization") |
|
|
| gr.Interface( |
| fn=apply_clustering, |
| inputs=[algorithm, n_clusters, dataset], |
| outputs=[output_text, output_image] |
| ).launch() |
|
|
| |
| if __name__ == "__main__": |
| main_interface() |
|
|
| |
| |
