Create app.py

app.py

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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
+
+theme = gr.themes.Monochrome(
+    primary_hue="indigo",
+    secondary_hue="blue",
+    neutral_hue="slate",
+)
+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(theme=theme) 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=50, maximum=500, step=50, value=50, 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()