sklearn-docs/Non_Linear_SVC_for_Binary_Classification

1import numpy as np2import matplotlib.pyplot as plt3from sklearn import svm4import gradio as gr5from PIL import Image6 7def calculate_score(clf):8    xx, yy = np.meshgrid(np.linspace(-3, 3, 500), np.linspace(-3, 3, 500))9    X_test = np.c_[xx.ravel(), yy.ravel()]10    Y_test = np.logical_xor(xx.ravel() > 0, yy.ravel() > 0)11    return clf.score(X_test, Y_test)12 13def getColorMap(kernel, gamma):14    # prepare the training dataset15    np.random.seed(0)16    X = np.random.randn(300, 2)17    Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)18 19    # fit the model20    clf = svm.NuSVC(kernel=kernel, gamma=gamma)21    clf.fit(X, Y)22    23    #create a grid for the plotting the decision function24    xx, yy = np.meshgrid(np.linspace(-3, 3, 500), np.linspace(-3, 3, 500))25    26    # plot the decision function for each datapoint on the grid27    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])28    Z = Z.reshape(xx.shape)29 30    plt.figure(figsize=(10, 4))31    plt.imshow(32        Z,33        interpolation="nearest",34        extent=(xx.min(), xx.max(), yy.min(), yy.max()),35        aspect="auto",36        origin="lower",37        cmap=plt.cm.PuOr_r,38    )39    contours = plt.contour(xx, yy, Z, levels=[0], linewidths=2, linestyles="dashed")40    plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired, edgecolors='k')41    plt.title(f"Decision function for Non-Linear SVC with the {kernel} kernel and '{gamma}' gamma ", fontsize='14')	#title42    plt.xlabel("X",fontsize='13')	#adds a label in the x axis43    plt.ylabel("Y",fontsize='13')	#adds a label in the y axis44    return plt, calculate_score(clf)45 46#XOR_TABLE markdown text47XOR_TABLE = """48<style type="text/css">49.tg  {border-collapse:collapse;border-spacing:10PX;width:50%;margin:auto}50.tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;51  overflow:hidden;padding:10px 5px;word-break:normal;}52.tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;53  font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}54.tg .tg-c3ow{border-color:inherit;text-align:center}55td, th {padding-left: 1rem}56</style>57<BR>58<H3>Table explaining the 'XOR' operator</H3>59<table class="tg">60<thead>61  <tr>62    <th class="tg-c3ow">A</th>63    <th class="tg-c3ow">B</th>64    <th class="tg-c3ow">A XOR B</th>65  </tr>66</thead>67<tbody>68  <tr>69    <td class="tg-c3ow">0</td>70    <td class="tg-c3ow">0</td>71    <td class="tg-c3ow">0</td>72  </tr>73  <tr>74    <td class="tg-c3ow">0</td>75    <td class="tg-c3ow">1</td>76    <td class="tg-c3ow">1</td>77  </tr>78  <tr>79    <td class="tg-c3ow">1</td>80    <td class="tg-c3ow">0</td>81    <td class="tg-c3ow">1</td>82  </tr>83  <tr>84    <td class="tg-c3ow">1</td>85    <td class="tg-c3ow">1</td>86    <td class="tg-c3ow">0</td>87  </tr>88</tbody>89</table>90"""91 92 93with gr.Blocks() as demo:94    gr.Markdown("## Learning the XOR function: An application of Binary Classification using Non-linear SVM")95    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).")96    gr.Markdown("### In this demo, we use a non-linear SVC (Support Vector Classifier) to learn the decision function of the XOR operator.")97                    98    gr.Markdown("### Furthermore, we observe that we get different decision function plots by varying the Kernel and Gamma hyperparameters of the non-linear SVC.")99 100    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.")101 102    inp1 = gr.Radio(['poly', 'rbf', 'sigmoid'], label="Kernel", info="Choose a kernel", value="poly")103    inp2 = gr.Radio(['scale', 'auto'], label="Gamma", info="Choose a gamma value", value="scale")104    105    with gr.Row().style(equal_height=True):106        with gr.Column(scale=2):        107            plot = gr.Plot(label=f"Decision function plot")108        with gr.Column(scale=1):109            num = gr.Textbox(label="Test Accuracy")110 111    inp1.change(getColorMap,  inputs=[inp1, inp2], outputs=[plot, num])112    inp2.change(getColorMap,  inputs=[inp1, inp2], outputs=[plot, num])113    demo.load(getColorMap, inputs=[inp1, inp2], outputs=[plot, num])114 115    gr.HTML(XOR_TABLE)116 117 118if __name__ == "__main__":119    demo.launch()