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import numpy as np |
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import matplotlib.pyplot as plt |
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import gradio as gr |
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from PIL import Image |
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from sklearn.gaussian_process import GaussianProcessClassifier |
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from sklearn.gaussian_process.kernels import RBF, DotProduct |
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def classify_xor_dataset(kernel_name): |
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xx, yy = np.meshgrid(np.linspace(-3, 3, 50), np.linspace(-3, 3, 50)) |
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rng = np.random.RandomState(0) |
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X = rng.randn(200, 2) |
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Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0) |
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fig, ax = plt.subplots(figsize=(10, 5)) |
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kernels = [1.0 * RBF(length_scale=1.15), 1.0 * DotProduct(sigma_0=1.0) ** 2] |
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kernel_idx = 0 if kernel_name == "RBF" else 1 |
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kernel = kernels[kernel_idx] |
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clf = GaussianProcessClassifier(kernel=kernel, warm_start=True).fit(X, Y) |
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Z = clf.predict_proba(np.vstack((xx.ravel(), yy.ravel())).T)[:, 1] |
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Z = Z.reshape(xx.shape) |
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ax.imshow( |
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Z, |
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interpolation="nearest", |
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extent=(xx.min(), xx.max(), yy.min(), yy.max()), |
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aspect="auto", |
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origin="lower", |
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cmap=plt.cm.PuOr_r, |
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) |
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ax.contour(xx, yy, Z, levels=[0.5], linewidths=2, colors=["k"]) |
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ax.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired, edgecolors=(0, 0, 0)) |
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ax.set_xticks(()) |
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ax.set_yticks(()) |
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ax.axis([-3, 3, -3, 3]) |
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ax.set_title( |
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"%s\n Log-Marginal-Likelihood:%.3f" |
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% (clf.kernel_, clf.log_marginal_likelihood(clf.kernel_.theta)), |
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fontsize=12, |
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) |
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fig.canvas.draw() |
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pil_image = Image.frombytes("RGB", fig.canvas.get_width_height(), fig.canvas.tostring_rgb()) |
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plt.close(fig) |
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return pil_image |
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title = "Gaussian Process Classification on the XOR Dataset" |
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description = "This example illustrates GPC on XOR data. Compared are a stationary, isotropic kernel (RBF) and a non-stationary kernel (DotProduct). On this particular dataset, the DotProduct kernel obtains considerably better results because the class-boundaries are linear and coincide with the coordinate axes. In general, stationary kernels often obtain better results. See the original scikit-learn example at https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_xor.html" |
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kernel_options = ["RBF", "DotProduct"] |
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iface = gr.Interface( |
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classify_xor_dataset, |
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gr.inputs.Radio(choices=kernel_options, label="Kernel"), |
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gr.outputs.Image(label="Decision Boundary", type="pil"), |
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title=title, |
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description=description, |
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theme="default", |
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layout="vertical", |
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analytics_enabled=False |
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) |
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iface.launch() |
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