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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()
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