| import numpy as np |
| import matplotlib.pyplot as plt |
| from sklearn import svm |
| import gradio as gr |
| from PIL import Image |
|
|
| def calculate_score(clf): |
| xx, yy = np.meshgrid(np.linspace(-3, 3, 500), np.linspace(-3, 3, 500)) |
| X_test = np.c_[xx.ravel(), yy.ravel()] |
| Y_test = np.logical_xor(xx.ravel() > 0, yy.ravel() > 0) |
| return clf.score(X_test, Y_test) |
|
|
| def getColorMap(kernel, gamma): |
| |
| np.random.seed(0) |
| X = np.random.randn(300, 2) |
| Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0) |
|
|
| |
| clf = svm.NuSVC(kernel=kernel, gamma=gamma) |
| clf.fit(X, Y) |
| |
| |
| xx, yy = np.meshgrid(np.linspace(-3, 3, 500), np.linspace(-3, 3, 500)) |
| |
| |
| Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) |
| Z = Z.reshape(xx.shape) |
|
|
| plt.figure(figsize=(10, 4)) |
| plt.imshow( |
| Z, |
| interpolation="nearest", |
| extent=(xx.min(), xx.max(), yy.min(), yy.max()), |
| aspect="auto", |
| origin="lower", |
| cmap=plt.cm.PuOr_r, |
| ) |
| contours = plt.contour(xx, yy, Z, levels=[0], linewidths=2, linestyles="dashed") |
| plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired, edgecolors='k') |
| plt.title(f"Decision function for Non-Linear SVC with the {kernel} kernel and '{gamma}' gamma ", fontsize='14') |
| plt.xlabel("X",fontsize='13') |
| plt.ylabel("Y",fontsize='13') |
| return plt, calculate_score(clf) |
|
|
|
|
| with gr.Blocks() as demo: |
| gr.Markdown("## Learning the XOR function: An application of Binary Classification using Non-linear SVM") |
| gr.Markdown("### This demo is based on this [scikit-learn example](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_nonlinear.html#sphx-glr-auto-examples-svm-plot-svm-nonlinear-py).") |
| gr.Markdown("### In this demo, we use a non-linear SVC (Support Vector Classifier) to learn the decision function of the XOR operator.") |
| |
| gr.Markdown("### Furthermore, we observe that we get different decision function plots by varying the Kernel and Gamma hyperparameters of the non-linear SVC.") |
|
|
| gr.Markdown("### Feel free to experiment with kernel and gamma values below to see how the quality of the decision function changes with the hyperparameters.") |
|
|
| inp1 = gr.Radio(['poly', 'rbf', 'sigmoid'], label="Kernel", info="Choose a kernel", value="poly") |
| inp2 = gr.Radio(['scale', 'auto'], label="Gamma", info="Choose a gamma value", value="scale") |
| |
| with gr.Row(): |
| plot = gr.Plot(label=f"Decision function plot for Non-Linear SVC with the '{inp1}' kernel and '{inp2}' gamma ") |
| num = gr.Textbox(label="Test Accuracy") |
|
|
| inp1.change(getColorMap, inputs=[inp1, inp2], outputs=[plot, num]) |
| inp2.change(getColorMap, inputs=[inp1, inp2], outputs=[plot, num]) |
| demo.load(getColorMap, inputs=[inp1, inp2], outputs=[plot, num]) |
|
|
| gr.HTML("<hr>") |
|
|
| xor_image = Image.open("xor.png") |
| gr.Image(xor_image, label="Table explaining the 'XOR' operator") |
|
|
|
|
| if __name__ == "__main__": |
| demo.launch() |
