sklearn-docs/SGD-max-margin-seperation-hyperplane

1import numpy as np2import matplotlib.pyplot as plt3from sklearn.linear_model import SGDClassifier4from sklearn.datasets import make_blobs5import gradio as gr 6 7def plot_max_margin_hyperplane():8    # we create 50 separable points9    X, Y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=0.60)10    # fit the model11    clf = SGDClassifier(loss="hinge", alpha=0.01, max_iter=200)12    clf.fit(X, Y)13    # plot the line, the points, and the nearest vectors to the plane14    xx = np.linspace(-1, 5, 10)15    yy = np.linspace(-1, 5, 10)16 17    X1, X2 = np.meshgrid(xx, yy)18    Z = np.empty(X1.shape)19    for (i, j), val in np.ndenumerate(X1):20        x1 = val21        x2 = X2[i, j]22        p = clf.decision_function([[x1, x2]])23        Z[i, j] = p[0]24    levels = [-1.0, 0.0, 1.0]25    linestyles = ["dashed", "solid", "dashed"]26    colors = "k"27    fig = plt.figure()28    plt.contour(X1, X2, Z, levels, colors=colors, linestyles=linestyles)29    plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired, edgecolor="black", s=20)30 31    plt.axis("tight")32    #plt.show()33    return fig 34 35heading = '🤗🧡🤍💙 SGD: Maximum Margin Separating Hyperplane'36 37with gr.Blocks(title = heading, theme= 'snehilsanyal/scikit-learn') as demo:38    gr.Markdown("# {}".format(heading))39    gr.Markdown(40        """41        ### This demo visualizes the maximum margin hyperplane that seperates\42        a two-class separable dataset using a linear SVM classifier trained using SGD.43        """44    )45    gr.Markdown('**[Demo is based on this script from scikit-learn documentation](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_separating_hyperplane.html#sphx-glr-auto-examples-linear-model-plot-sgd-separating-hyperplane-py)**')46 47    button = gr.Button(value = 'Visualize Maximum Margin Hyperplane')48    button.click(plot_max_margin_hyperplane, outputs = gr.Plot())49 50demo.launch()