Add application files

app.py

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+from functools import partial
+
+import gradio as gr
+import matplotlib.pyplot as plt
+from matplotlib.ticker import NullFormatter
+import numpy as np
+from sklearn import datasets, manifold
+
+
+SEED = 0
+N_COMPONENTS = 2
+np.random.seed(SEED)
+
+
+def get_circles(n_samples):
+    X, color = datasets.make_circles(
+        n_samples=n_samples,
+        factor=0.5,
+        noise=0.05,
+        random_state=SEED
+    )
+    return X, color
+
+
+def get_s_curve(n_samples):
+    X, color = datasets.make_s_curve(n_samples=n_samples, random_state=SEED)
+    X[:, 1], X[:, 2] = X[:, 2], X[:, 1].copy()
+    return X, color
+
+
+def get_uniform_grid(n_samples):
+    x = np.linspace(0, 1, int(np.sqrt(n_samples)))
+    xx, yy = np.meshgrid(x, x)
+    X = np.hstack(
+        [
+            xx.ravel().reshape(-1, 1),
+            yy.ravel().reshape(-1, 1),
+        ]
+    )
+    color = xx.ravel()
+    return X, color
+
+
+DATA_MAPPING = {
+    'circles': get_circles,
+    's-curve': get_s_curve,
+    'uniform grid': get_uniform_grid,
+}
+
+
+
+def plot_data(dataset: str, perplexity: int, n_samples: int, tsne: bool):
+    if isinstance(perplexity, dict):
+        perplexity = perplexity['value']
+    else:
+        perplexity = int(perplexity)
+
+    X, color = DATA_MAPPING[dataset](n_samples)
+    if tsne:
+        tsne = manifold.TSNE(
+            n_components=N_COMPONENTS,
+            init="random",
+            random_state=0,
+            perplexity=perplexity,
+            n_iter=400,
+        )
+        Y = tsne.fit_transform(X)
+    else:
+        Y = X
+
+    fig, ax = plt.subplots(figsize=(7, 7))
+
+    ax.scatter(Y[:, 0], Y[:, 1], c=color)
+    ax.xaxis.set_major_formatter(NullFormatter())
+    ax.yaxis.set_major_formatter(NullFormatter())
+    ax.axis("tight")
+
+    return fig
+
+
+title = "t-SNE: The effect of various perplexity values on the shape"
+description = (
+    "An illustration of t-SNE on the two concentric circles and the"
+    "S-curve datasets for different perplexity values."
+)
+
+
+with gr.Blocks(title=title) as demo:
+    gr.HTML(f"<b>{title}</b>")
+    gr.Markdown(description)
+
+    input_data = gr.Radio(
+        list(DATA_MAPPING),
+        value="circles",
+        label="dataset"
+    )
+    n_samples = gr.Slider(
+        minimum=100,
+        maximum=1000,
+        value=150,
+        step=25,
+        label='Number of Samples'
+    )
+    perplexity = gr.Slider(
+        minimum=2,
+        maximum=100,
+        value=5,
+        step=1,
+        label='Perplexity'
+    )
+    with gr.Row():
+        with gr.Column():
+            plot = gr.Plot(label="Original data")
+            fn = partial(plot_data, tsne=False)
+            input_data.change(fn=fn, inputs=[input_data, perplexity, n_samples], outputs=plot)
+            perplexity.change(fn=fn, inputs=[input_data, perplexity, n_samples], outputs=plot)
+            n_samples.change(fn=fn, inputs=[input_data, perplexity, n_samples], outputs=plot)
+        with gr.Column():
+            plot = gr.Plot(label="t-SNE")
+            fn = partial(plot_data, tsne=True)
+            input_data.change(fn=fn, inputs=[input_data, perplexity, n_samples], outputs=plot)
+            perplexity.change(fn=fn, inputs=[input_data, perplexity, n_samples], outputs=plot)
+            n_samples.change(fn=fn, inputs=[input_data, perplexity, n_samples], outputs=plot)
+
+
+demo.launch()

requirements.txt

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+matplotlib
+numpy
+scikit-learn