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| import gradio as gr | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from sklearn import linear_model | |
| def plot(seed, num_points): | |
| # Error handling of non-numeric seeds | |
| if seed and not seed.isnumeric(): | |
| raise gr.Error("Invalid seed") | |
| # Setting the seed | |
| if seed: | |
| seed = int(seed) | |
| np.random.seed(seed) | |
| num_points = int(num_points) | |
| #Ensuring the number of points is even | |
| if num_points%2 != 0: | |
| num_points +=1 | |
| half_num_points = int(num_points/2) | |
| X = np.r_[np.random.randn(half_num_points, 2) + [1, 1], np.random.randn(half_num_points, 2)] | |
| y = [1] * half_num_points + [-1] * half_num_points | |
| sample_weight = 100 * np.abs(np.random.randn(num_points)) | |
| # and assign a bigger weight to the last 10 samples | |
| sample_weight[:half_num_points] *= 10 | |
| # plot the weighted data points | |
| xx, yy = np.meshgrid(np.linspace(-4, 5, 500), np.linspace(-4, 5, 500)) | |
| fig, ax = plt.subplots() | |
| ax.scatter( | |
| X[:, 0], | |
| X[:, 1], | |
| c=y, | |
| s=sample_weight, | |
| alpha=0.9, | |
| cmap=plt.cm.bone, | |
| edgecolor="black", | |
| ) | |
| # fit the unweighted model | |
| clf = linear_model.SGDClassifier(alpha=0.01, max_iter=100) | |
| clf.fit(X, y) | |
| Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) | |
| Z = Z.reshape(xx.shape) | |
| no_weights = ax.contour(xx, yy, Z, levels=[0], linestyles=["solid"]) | |
| # fit the weighted model | |
| clf = linear_model.SGDClassifier(alpha=0.01, max_iter=100) | |
| clf.fit(X, y, sample_weight=sample_weight) | |
| Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) | |
| Z = Z.reshape(xx.shape) | |
| samples_weights = ax.contour(xx, yy, Z, levels=[0], linestyles=["dashed"]) | |
| no_weights_handles, _ = no_weights.legend_elements() | |
| weights_handles, _ = samples_weights.legend_elements() | |
| ax.legend( | |
| [no_weights_handles[0], weights_handles[0]], | |
| ["no weights", "with weights"], | |
| loc="lower left", | |
| ) | |
| ax.set(xticks=(), yticks=()) | |
| return fig | |
| info = ''' # SGD: Weighted samples\n | |
| This is a demonstration of a modified version of [SGD](https://scikit-learn.org/stable/modules/sgd.html#id5) that takes into account the weights of the samples. Where the size of points is proportional to its weight.\n | |
| Created by [@Nahrawy](https://huggingface.co/Nahrawy) based on [scikit-learn docs](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_weighted_samples.html). | |
| ''' | |
| with gr.Blocks() as demo: | |
| gr.Markdown(info) | |
| with gr.Row(): | |
| with gr.Column(): | |
| seed = gr.Textbox(label="Seed", info="Leave empty to generate new random points each run ",value=None) | |
| num_points = gr.Slider(label="Number of Points", value="20", minimum=5, maximum=100, step=2) | |
| btn = gr.Button("Run") | |
| out = gr.Plot() | |
| btn.click(fn=plot, inputs=[seed,num_points] , outputs=out) | |
| demo.launch() |