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| # Code source: Gaël Varoquaux | |
| # License: BSD 3 clause | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from sklearn import svm | |
| import gradio as gr | |
| plt.switch_backend("agg") | |
| kernels = ["linear", "poly", "rbf"] | |
| font1 = {'family':'DejaVu Sans','size':20} | |
| cmaps = {'Set1': plt.cm.Set1, 'Set2': plt.cm.Set2, 'Set3': plt.cm.Set3, | |
| 'tab10': plt.cm.tab10, 'tab20': plt.cm.tab20} | |
| # fit the model | |
| def clf_kernel(kernel, cmap, dpi = 300, use_random = False): | |
| #example data | |
| if use_random == False: | |
| X = np.c_[ | |
| (0.4, -0.7), | |
| (-1.5, -1), | |
| (-1.4, -0.9), | |
| (-1.3, -1.2), | |
| (-1.5, 0.2), | |
| (-1.2, -0.4), | |
| (-0.5, 1.2), | |
| (-1.5, 2.1), | |
| (1, 1), | |
| # -- | |
| (1.3, 0.8), | |
| (1.5, 0.5), | |
| (0.2, -2), | |
| (0.5, -2.4), | |
| (0.2, -2.3), | |
| (0, -2.7), | |
| (1.3, 2.8), | |
| ].T | |
| else: | |
| #emulate some random data | |
| x = np.random.uniform(-2, 2, size=(16, 1)) | |
| y = np.random.uniform(-2, 2, size=(16, 1)) | |
| X = np.hstack((x, y)) | |
| Y = [0] * 8 + [1] * 8 | |
| clf = svm.SVC(kernel=kernel, gamma=2) | |
| clf.fit(X, Y) | |
| # plot the line, the points, and the nearest vectors to the plane | |
| fig= plt.figure(figsize=(10, 6), facecolor = 'none', | |
| frameon = False, dpi = dpi) | |
| ax = fig.add_subplot(111) | |
| ax.scatter( | |
| clf.support_vectors_[:, 0], | |
| clf.support_vectors_[:, 1], | |
| s=80, | |
| facecolors="none", | |
| zorder=10, | |
| edgecolors="k", | |
| ) | |
| ax.scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap=cmap, edgecolors="k") | |
| ax.axis("tight") | |
| x_min = -3 | |
| x_max = 3 | |
| y_min = -3 | |
| y_max = 3 | |
| XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j] | |
| Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()]) | |
| # Put the result into a color plot | |
| Z = Z.reshape(XX.shape) | |
| ax.pcolormesh(XX, YY, Z > 0, cmap=cmap) | |
| ax.contour( | |
| XX, | |
| YY, | |
| Z, | |
| colors=["k", "k", "k"], | |
| linestyles=["--", "-", "--"], | |
| levels=[-0.5, 0, 0.5], | |
| ) | |
| ax.set_xlim(x_min, x_max) | |
| ax.set_ylim(y_min, y_max) | |
| ax.set_xticks(()) | |
| ax.set_yticks(()) | |
| ax.set_title('Type of kernel: ' + kernel, | |
| color = "white", fontdict = font1, pad=20, | |
| bbox=dict(boxstyle="round,pad=0.3", | |
| color = "#6366F1")) | |
| plt.close() | |
| return fig | |
| intro = """<h1 style="text-align: center;">🤗 Introducing SVM-Kernels 🤗</h1> | |
| """ | |
| desc = """<h3 style="text-align: center;">Three different types of SVM-Kernels are displayed below. | |
| The polynomial and RBF are especially useful when the data-points are not linearly separable. </h3> | |
| """ | |
| notice = """<div style = "text-align: left;"> <em>Notice: Run the model on example data or press | |
| <strong>Randomize data</strong> button to check out the model on emulated data-points.</em></div>""" | |
| made ="""<div style="text-align: center;"> | |
| <p>Made with ❤</p>""" | |
| link = """<div style="text-align: center;"> | |
| <a href="https://scikit-learn.org/stable/auto_examples/svm/plot_svm_kernels.html#sphx-glr-auto-examples-svm-plot-svm-kernels-py" target="_blank" rel="noopener noreferrer"> | |
| Demo is based on this script from scikit-learn documentation</a>""" | |
| with gr.Blocks(theme=gr.themes.Soft(primary_hue="indigo", | |
| secondary_hue="violet", | |
| neutral_hue="slate", | |
| font = gr.themes.GoogleFont("Inter")), | |
| title="SVM-Kernels") as demo: | |
| gr.HTML(intro) | |
| gr.HTML(desc) | |
| with gr.Column(): | |
| kernel = gr.Radio(kernels, label="Select kernel:", | |
| show_label = True, value = 'linear') | |
| plot = gr.Plot(label="Plot") | |
| with gr.Accordion(label = "More options", open = True): | |
| cmap = gr.Radio(['Set1', 'Set2', 'Set3', 'tab10', 'tab20'], label="Choose color map: ", value = 'Set2') | |
| dpi = gr.Slider(50, 150, value = 75, step = 1, label = "Set the resolution: ") | |
| gr.HTML(notice) | |
| random = gr.Button("Randomize data").style(full_width = False) | |
| cmap.change(fn=clf_kernel, inputs=[kernel,cmap,dpi], outputs=plot) | |
| dpi.change(fn=clf_kernel, inputs=[kernel,cmap,dpi], outputs=plot) | |
| kernel.change(fn=clf_kernel, inputs=[kernel,cmap,dpi], outputs=plot) | |
| random.click(fn=clf_kernel, inputs=[kernel,cmap,dpi,random], outputs=plot) | |
| demo.load(fn=clf_kernel, inputs=[kernel,cmap,dpi], outputs=plot) | |
| gr.HTML(made) | |
| gr.HTML(link) | |
| demo.launch() |