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| import gradio as gr | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from sklearn.cluster import MeanShift, estimate_bandwidth | |
| from sklearn.datasets import make_blobs | |
| def get_clusters_plot(n_blobs, quantile, cluster_std): | |
| X, _, centers = make_blobs( | |
| n_samples=10000, cluster_std=cluster_std, centers=n_blobs, return_centers=True | |
| ) | |
| bandwidth = estimate_bandwidth(X, quantile=quantile, n_samples=500) | |
| ms = MeanShift(bandwidth=bandwidth, bin_seeding=True) | |
| ms.fit(X) | |
| labels = ms.labels_ | |
| cluster_centers = ms.cluster_centers_ | |
| labels_unique = np.unique(labels) | |
| n_clusters_ = len(labels_unique) | |
| fig = plt.figure() | |
| for k in range(n_clusters_): | |
| my_members = labels == k | |
| cluster_center = cluster_centers[k] | |
| plt.scatter(X[my_members, 0], X[my_members, 1]) | |
| plt.plot( | |
| cluster_center[0], | |
| cluster_center[1], | |
| "x", | |
| markeredgecolor="k", | |
| markersize=14, | |
| ) | |
| plt.xlabel("Feature 1") | |
| plt.ylabel("Feature 2") | |
| plt.title(f"Estimated number of clusters: {n_clusters_}") | |
| if len(centers) != n_clusters_: | |
| message = ( | |
| '<p style="text-align: center;">' | |
| + f"The number of estimated clusters ({n_clusters_})" | |
| + f" differs from the true number of clusters ({n_blobs})." | |
| + " Try changing the `Quantile` parameter.</p>" | |
| ) | |
| else: | |
| message = ( | |
| '<p style="text-align: center;">' | |
| + f"The number of estimated clusters ({n_clusters_})" | |
| + f" matches the true number of clusters ({n_blobs})!</p>" | |
| ) | |
| return fig, message | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| """ | |
| # Mean Shift Clustering | |
| This space shows how to use the [Mean Shift Clustering](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.MeanShift.html) algorithm to cluster 2D data points. You can change the parameters using the sliders and see how the model performs. | |
| This space is based on [sklearn's original demo](https://scikit-learn.org/stable/auto_examples/cluster/plot_mean_shift.html#sphx-glr-auto-examples-cluster-plot-mean-shift-py). | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| n_blobs = gr.Slider( | |
| minimum=2, | |
| maximum=10, | |
| label="Number of clusters in the data", | |
| step=1, | |
| value=3, | |
| ) | |
| quantile = gr.Slider( | |
| minimum=0, | |
| maximum=1, | |
| step=0.05, | |
| value=0.2, | |
| label="Quantile", | |
| info="Used to determine clustering's bandwidth.", | |
| ) | |
| cluster_std = gr.Slider( | |
| minimum=0.1, | |
| maximum=1, | |
| label="Clusters' standard deviation", | |
| step=0.1, | |
| value=0.6, | |
| ) | |
| with gr.Column(scale=4): | |
| clusters_plots = gr.Plot(label="Clusters' Plot") | |
| message = gr.HTML() | |
| n_blobs.change( | |
| get_clusters_plot, | |
| [n_blobs, quantile, cluster_std], | |
| [clusters_plots, message], | |
| queue=False, | |
| ) | |
| quantile.change( | |
| get_clusters_plot, | |
| [n_blobs, quantile, cluster_std], | |
| [clusters_plots, message], | |
| queue=False, | |
| ) | |
| cluster_std.change( | |
| get_clusters_plot, | |
| [n_blobs, quantile, cluster_std], | |
| [clusters_plots, message], | |
| queue=False, | |
| ) | |
| demo.load( | |
| get_clusters_plot, | |
| [n_blobs, quantile, cluster_std], | |
| [clusters_plots, message], | |
| queue=False, | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |