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import gradio as gr
import matplotlib.pyplot as plt
from matplotlib import ticker
from sklearn import manifold, datasets
from mpl_toolkits.mplot3d import Axes3D
def compare_manifold_learning(methods, n_samples, n_neighbors, n_components, perplexity):
S_points, S_color = datasets.make_s_curve(n_samples, random_state=0)
transformed_data = []
if len(methods) == 1:
method = methods[0]
manifold_method = {
"Locally Linear Embeddings Standard": manifold.LocallyLinearEmbedding(method="standard", n_neighbors=n_neighbors, n_components=n_components, eigen_solver="auto", random_state=0),
"Locally Linear Embeddings LTSA": manifold.LocallyLinearEmbedding(method="ltsa", n_neighbors=n_neighbors, n_components=n_components, eigen_solver="auto", random_state=0),
"Locally Linear Embeddings Hessian": manifold.LocallyLinearEmbedding(method="hessian", n_neighbors=n_neighbors, n_components=n_components, eigen_solver="auto", random_state=0),
"Locally Linear Embeddings Modified": manifold.LocallyLinearEmbedding(method="modified", n_neighbors=n_neighbors, n_components=n_components, eigen_solver="auto", random_state=0),
"Isomap": manifold.Isomap(n_neighbors=n_neighbors, n_components=n_components, p=1),
"MultiDimensional Scaling": manifold.MDS(n_components=n_components, max_iter=50, n_init=4, random_state=0, normalized_stress=False),
"Spectral Embedding": manifold.SpectralEmbedding(n_components=n_components, n_neighbors=n_neighbors),
"T-distributed Stochastic Neighbor Embedding": manifold.TSNE(n_components=n_components, perplexity=perplexity, init="random", n_iter=250, random_state=0)
}[method]
S_transformed = manifold_method.fit_transform(S_points)
transformed_data.append(S_transformed)
else:
for method in methods:
manifold_method = {
"Locally Linear Embeddings Standard": manifold.LocallyLinearEmbedding(method="standard", n_neighbors=n_neighbors, n_components=n_components, eigen_solver="auto", random_state=0),
"Locally Linear Embeddings LTSA": manifold.LocallyLinearEmbedding(method="ltsa", n_neighbors=n_neighbors, n_components=n_components, eigen_solver="auto", random_state=0),
"Locally Linear Embeddings Hessian": manifold.LocallyLinearEmbedding(method="hessian", n_neighbors=n_neighbors, n_components=n_components, eigen_solver="auto", random_state=0),
"Locally Linear Embeddings Modified": manifold.LocallyLinearEmbedding(method="modified", n_neighbors=n_neighbors, n_components=n_components, eigen_solver="auto", random_state=0),
"Isomap": manifold.Isomap(n_neighbors=n_neighbors, n_components=n_components, p=1),
"MultiDimensional Scaling": manifold.MDS(n_components=n_components, max_iter=50, n_init=4, random_state=0, normalized_stress=False),
"Spectral Embedding": manifold.SpectralEmbedding(n_components=n_components, n_neighbors=n_neighbors),
"T-distributed Stochastic Neighbor Embedding": manifold.TSNE(n_components=n_components, perplexity=perplexity, init="random", n_iter=250, random_state=0)
}[method]
S_transformed = manifold_method.fit_transform(S_points)
transformed_data.append(S_transformed)
fig, axs = plt.subplots(1, len(transformed_data), figsize=(6 * len(transformed_data), 6))
fig.suptitle("Manifold Learning Comparison", fontsize=16)
if len(methods) == 1:
ax = axs
method = methods[0]
data = transformed_data[0]
ax.scatter(data[:, 0], data[:, 1], c=S_color, cmap=plt.cm.Spectral)
ax.set_title(f"Method: {method}")
ax.axis("tight")
ax.axis("off")
ax.xaxis.set_major_locator(ticker.NullLocator())
ax.yaxis.set_major_locator(ticker.NullLocator())
else:
for ax, method, data in zip(axs, methods, transformed_data):
ax.scatter(data[:, 0], data[:, 1], c=S_color, cmap=plt.cm.Spectral)
ax.set_title(f"Method: {method}")
ax.axis("tight")
ax.axis("off")
ax.xaxis.set_major_locator(ticker.NullLocator())
ax.yaxis.set_major_locator(ticker.NullLocator())
plt.tight_layout()
plt.savefig("plot.png")
plt.close()
return "plot.png"
method_options = [
"Locally Linear Embeddings Standard",
"Locally Linear Embeddings LTSA",
"Locally Linear Embeddings Hessian",
"Locally Linear Embeddings Modified",
"Isomap",
"MultiDimensional Scaling",
"Spectral Embedding",
"T-distributed Stochastic Neighbor Embedding"
]
inputs = [
gr.components.CheckboxGroup(method_options, label="Manifold Learning Methods"),
gr.inputs.Slider(default=1500, label="Number of Samples", maximum=5000),
gr.inputs.Slider(default=12, label="Number of Neighbors"),
gr.inputs.Slider(default=2, label="Number of Components"),
gr.inputs.Slider(default=30, label="Perplexity (for t-SNE)")
]
gr.Interface(
fn=compare_manifold_learning,
inputs=inputs,
outputs="image",
examples=[
[method_options, 1500, 12, 2, 30]
],
title="Manifold Learning Comparison",
description="This code demonstrates a comparison of manifold learning methods using the S-curve dataset. Manifold learning techniques aim to uncover the underlying structure and relationships within high-dimensional data by projecting it onto a lower-dimensional space. This comparison allows you to explore the effects of different methods on the dataset. See the original scikit-learn example here: https://scikit-learn.org/stable/auto_examples/manifold/plot_compare_methods.html"
).launch()
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