Spaces:
Sleeping
Sleeping
| # 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 | |
| from matplotlib.colors import ListedColormap | |
| plt.switch_backend("agg") | |
| font1 = {'family':'DejaVu Sans','size':20} | |
| def create_data(random, size_num, x_min, x_max, y_min, y_max): | |
| #emulate some random data | |
| if random: | |
| size_num = int(size_num) | |
| x = np.random.uniform(x_min, x_max, size=(size_num, 1)) | |
| y = np.random.uniform(y_min, y_max, size=(size_num, 1)) | |
| X = np.hstack((x, y)) | |
| Y = [0] * int(size_num/2) + [1] * int(size_num/2) | |
| else: | |
| 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 | |
| Y = [0] * 8 + [1] * 8 | |
| return X, Y | |
| # fit the model | |
| def clf_kernel(color1, color2, dpi, size_num = None, x_min = None, | |
| x_max = None, y_min = None, | |
| y_max = None, random = False): | |
| if size_num is not None or x_min is not None or x_max is not None or y_min is not None or y_max is not None: | |
| random = True | |
| X, Y = create_data(random, size_num, x_min, x_max, y_min, y_max) | |
| kernels = ["linear", "poly", "rbf"] | |
| # plot the line, the points, and the nearest vectors to the plane | |
| fig, axs = plt.subplots(1,3, figsize = (16,8), facecolor='none', dpi = res[dpi]) | |
| cmap = ListedColormap([color1, color2], N=2, name = 'braincell') | |
| for i, kernel in enumerate(kernels): | |
| clf = svm.SVC(kernel=kernel, gamma=2) | |
| clf.fit(X, Y) | |
| axs[i].scatter( | |
| clf.support_vectors_[:, 0], | |
| clf.support_vectors_[:, 1], | |
| s=80, | |
| facecolors="none", | |
| zorder=10, | |
| edgecolors="k", | |
| ) | |
| axs[i].scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap=cmap, edgecolors="k") | |
| axs[i].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) | |
| axs[i].pcolormesh(XX, YY, Z > 0, cmap=cmap) | |
| axs[i].contour( | |
| XX, | |
| YY, | |
| Z, | |
| colors=["k", "k", "k"], | |
| linestyles=["--", "-", "--"], | |
| levels=[-0.5, 0, 0.5], | |
| ) | |
| axs[i].set_xlim(x_min, x_max) | |
| axs[i].set_ylim(y_min, y_max) | |
| axs[i].set_xticks(()) | |
| axs[i].set_yticks(()) | |
| axs[i].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, np.round(X, decimals=2) | |
| 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 = """<br><div style = "text-align: left;"> <em>Notice: Run the model on example data or use <strong>Randomize data</strong> | |
| button below to check out the model on randomized data-points. Any changes to visual parameters will reset the data!</em></div>""" | |
| notice2 = """<br><div style = "text-align: left;"> <em>Notice: The data points are categorized into two distinct classes, and they are evenly distributed on the plots to visually represent these classes.</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>""" | |
| res = {'Small': 50, 'Medium': 75, 'Large': 100} | |
| 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.Tab("Plotted results"): | |
| plot = gr.Plot(label="Kernel comparison:") | |
| with gr.Tab("Data coordinates"): | |
| gr.HTML(notice2) | |
| X = gr.Numpy(headers = ['x','y'], interactive=False) | |
| with gr.Column(): | |
| with gr.Accordion(label = 'Randomize data'): | |
| gr.HTML(notice) | |
| samples = gr.Slider(4, 16, value = 8, step = 2, label = "Number of samples:") | |
| x_min = gr.Slider(-3, 0, value=-2, step=0.1, label="X Min:") | |
| x_max = gr.Slider(0, 3, value=2, step=0.1, label="X Max:") | |
| y_min = gr.Slider(-3, 0, value=-2, step=0.1, label="Y Min:") | |
| y_max = gr.Slider(0, 3, value=2, step=0.1, label="Y Max:") | |
| random = gr.Button("Randomize data") | |
| with gr.Accordion(label = "Visual parameters"): | |
| with gr.Row(): | |
| color1 = gr.ColorPicker(label = 'Pick color one:', value = '#9abfd8') | |
| color2 = gr.ColorPicker(label = 'Pick color two:', value = '#371c4b') | |
| #dpi = gr.Slider(50, 100, value = 75, step = 1, label = "Set the resolution: ") | |
| dpi = gr.Radio(list(res.keys()), value = 'Medium', label = "Select the plot size:") | |
| params2 = [color1, color2, dpi] | |
| random.click(fn=clf_kernel, inputs=[color1, color2, dpi,samples, x_min, x_max, y_min, y_max], outputs=[plot,X]) | |
| for i in params2: | |
| i.change(fn=clf_kernel, inputs=[color1, color2,dpi], outputs=[plot, X]) | |
| demo.load(fn=clf_kernel, inputs=[color1, color2, dpi], outputs=[plot,X]) | |
| gr.HTML(made) | |
| gr.HTML(link) | |
| demo.launch() |