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| import gradio as gr | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from sklearn.datasets import make_blobs | |
| import time | |
| from sklearn.cluster import KMeans, MiniBatchKMeans | |
| from sklearn.metrics.pairwise import pairwise_distances_argmin | |
| model_card = f""" | |
| ## Description | |
| This demo compares the performance of the **MiniBatchKMeans** and **KMeans**. The MiniBatchKMeans is faster, but gives slightly different results. | |
| The points that are labelled differently between the two algorithms are also plotted. | |
| You can play around with different ``number of samples`` and ``number of mini batch size`` to see the effect | |
| ## Dataset | |
| Simulation dataset | |
| """ | |
| def do_train(n_samples, batch_size): | |
| np.random.seed(0) | |
| centers = np.random.rand(3, 2) | |
| n_clusters = len(centers) | |
| X, labels_true = make_blobs(n_samples=n_samples, centers=centers, cluster_std=0.7) | |
| k_means = KMeans(init="k-means++", n_clusters=n_clusters, n_init=10) | |
| t0 = time.time() | |
| k_means.fit(X) | |
| t_batch = time.time() - t0 | |
| mbk = MiniBatchKMeans( | |
| init="k-means++", | |
| n_clusters=n_clusters, | |
| batch_size=batch_size, | |
| n_init=10, | |
| max_no_improvement=10, | |
| verbose=0, | |
| ) | |
| t0 = time.time() | |
| mbk.fit(X) | |
| t_mini_batch = time.time() - t0 | |
| k_means_cluster_centers = k_means.cluster_centers_ | |
| order = pairwise_distances_argmin(k_means.cluster_centers_, mbk.cluster_centers_) | |
| mbk_means_cluster_centers = mbk.cluster_centers_[order] | |
| k_means_labels = pairwise_distances_argmin(X, k_means_cluster_centers) | |
| mbk_means_labels = pairwise_distances_argmin(X, mbk_means_cluster_centers) | |
| colors = ["#4EACC5", "#FF9C34", "#4E9A06"] | |
| # KMeans | |
| fig1, axes1 = plt.subplots() | |
| for k, col in zip(range(n_clusters), colors): | |
| my_members = k_means_labels == k | |
| cluster_center = k_means_cluster_centers[k] | |
| axes1.plot(X[my_members, 0], X[my_members, 1], "w", markerfacecolor=col, marker=".", markersize=15) | |
| axes1.plot( | |
| cluster_center[0], | |
| cluster_center[1], | |
| "o", | |
| markerfacecolor=col, | |
| markeredgecolor="k", | |
| markersize=12, | |
| ) | |
| axes1.set_title("KMeans") | |
| axes1.set_xticks(()) | |
| axes1.set_yticks(()) | |
| # MiniBatchKMeans | |
| fig2, axes2 = plt.subplots() | |
| for k, col in zip(range(n_clusters), colors): | |
| my_members = mbk_means_labels == k | |
| cluster_center = mbk_means_cluster_centers[k] | |
| axes2.plot(X[my_members, 0], X[my_members, 1], "w", markerfacecolor=col, marker=".", markersize=15) | |
| axes2.plot( | |
| cluster_center[0], | |
| cluster_center[1], | |
| "o", | |
| markerfacecolor=col, | |
| markeredgecolor="k", | |
| markersize=12, | |
| ) | |
| axes2.set_title("MiniBatchKMeans") | |
| axes2.set_xticks(()) | |
| axes2.set_yticks(()) | |
| # Initialize the different array to all False | |
| different = mbk_means_labels == 4 | |
| fig3, axes3 = plt.subplots() | |
| for k in range(n_clusters): | |
| different += (k_means_labels == k) != (mbk_means_labels == k) | |
| identic = np.logical_not(different) | |
| axes3.plot(X[identic, 0], X[identic, 1], "w", markerfacecolor="#bbbbbb", marker=".", markersize=15) | |
| axes3.plot(X[different, 0], X[different, 1], "w", markerfacecolor="m", marker=".", markersize=15) | |
| axes3.set_title("Difference") | |
| axes3.set_xticks(()) | |
| axes3.set_yticks(()) | |
| text = f"KMeans Train time: {t_batch:.2f}s Inertia: {k_means.inertia_:.4f}. MiniBatchKMeans Train time: {t_mini_batch:.2f}s Inertia: {mbk.inertia_:.4f}" | |
| plt.close() | |
| return fig1, fig2, fig3, text | |
| with gr.Blocks() as demo: | |
| gr.Markdown(''' | |
| <div> | |
| <h1 style='text-align: center'>Comparison of the K-Means and MiniBatchKMeans clustering algorithms</h1> | |
| </div> | |
| ''') | |
| gr.Markdown(model_card) | |
| gr.Markdown("Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>. Based on the example from <a href=\"https://scikit-learn.org/stable/auto_examples/cluster/plot_mini_batch_kmeans.html#sphx-glr-auto-examples-cluster-plot-mini-batch-kmeans-py\">scikit-learn</a>") | |
| n_samples = gr.Slider(minimum=500, maximum=5000, step=500, value=500, label="Number of samples") | |
| batch_size = gr.Slider(minimum=100, maximum=2000, step=100, value=100, label="Size of the mini batches") | |
| with gr.Row(): | |
| with gr.Column(): | |
| plot1 = gr.Plot(label="KMeans") | |
| with gr.Column(): | |
| plot2 = gr.Plot(label="MiniBatchKMeans") | |
| with gr.Column(): | |
| plot3 = gr.Plot(label="Difference") | |
| with gr.Row(): | |
| results = gr.Textbox(label="Results") | |
| n_samples.change(fn=do_train, inputs=[n_samples, batch_size], outputs=[plot1, plot2, plot3, results]) | |
| batch_size.change(fn=do_train, inputs=[n_samples, batch_size], outputs=[plot1, plot2, plot3, results]) | |
| demo.launch() |