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import gradio as gr |
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import numpy as np |
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from matplotlib import pyplot as plt |
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from sklearn import linear_model, datasets |
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theme = gr.themes.Monochrome( |
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primary_hue="indigo", |
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secondary_hue="blue", |
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neutral_hue="slate", |
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) |
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model_card = f""" |
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## Description |
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**Random sample consensus (RANSAC)** is a method to estimate a mathematical model from a set of observed data that may have some wrong information. |
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The number of times it tries affects how likely it is to get a good answer. **RANSAC** is commonly used in photogrammetry to solve problems with linear or non-linear regression. |
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It works by separating the input data into two groups: inliers (which may have some noise) and outliers (which are wrong data). It estimates the model only using the inliers. |
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In this demo, a simulation regression dataset with noise is created, and then compare the results of fitting data in **Linear model** and **RANSAC**. |
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You can play around with different ``number of samples`` and ``number of outliers`` to see the effect |
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## Dataset |
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Simulation dataset |
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""" |
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def do_train(n_samples, n_outliers): |
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X, y, coef = datasets.make_regression( |
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n_samples=n_samples, |
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n_features=1, |
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n_informative=1, |
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noise=10, |
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coef=True, |
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random_state=0, |
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) |
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np.random.seed(0) |
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X[:n_outliers] = 3 + 0.5 * np.random.normal(size=(n_outliers, 1)) |
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y[:n_outliers] = -3 + 10 * np.random.normal(size=n_outliers) |
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lr = linear_model.LinearRegression() |
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lr.fit(X, y) |
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ransac = linear_model.RANSACRegressor() |
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ransac.fit(X, y) |
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inlier_mask = ransac.inlier_mask_ |
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outlier_mask = np.logical_not(inlier_mask) |
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line_X = np.arange(X.min(), X.max())[:, np.newaxis] |
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line_y = lr.predict(line_X) |
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line_y_ransac = ransac.predict(line_X) |
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text = f"True coefficients: {coef:.4f}.\nLinear regression coefficients: {lr.coef_[0]:.4f}.\nRANSAC coefficients: {ransac.estimator_.coef_[0]:.4f}." |
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fig, axes = plt.subplots() |
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axes.scatter( |
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X[inlier_mask], y[inlier_mask], color="yellowgreen", marker=".", label="Inliers" |
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) |
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axes.scatter( |
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X[outlier_mask], y[outlier_mask], color="gold", marker=".", label="Outliers" |
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) |
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axes.plot(line_X, line_y, color="navy", linewidth=2, label="Linear regressor") |
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axes.plot( |
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line_X, |
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line_y_ransac, |
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color="cornflowerblue", |
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linewidth=2, |
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label="RANSAC regressor", |
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) |
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axes.legend(loc="lower right") |
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axes.set_xlabel("Input") |
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axes.set_ylabel("Response") |
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return fig, text |
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with gr.Blocks(theme=theme) as demo: |
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gr.Markdown(''' |
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<div> |
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<h1 style='text-align: center'>Robust linear model estimation using RANSAC</h1> |
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</div> |
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''') |
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gr.Markdown(model_card) |
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gr.Markdown("Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>. Based on the example from <a href=\"https://scikit-learn.org/stable/auto_examples/linear_model/plot_ransac.html#sphx-glr-auto-examples-linear-model-plot-ransac-py\">scikit-learn</a>") |
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n_samples = gr.Slider(minimum=500, maximum=5000, step=500, value=500, label="Number of samples") |
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n_outliers = gr.Slider(minimum=25, maximum=250, step=25, value=25, label="Number of outliers") |
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with gr.Row(): |
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with gr.Column(): |
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plot = gr.Plot(label="Compare Linear regressor and RANSAC") |
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with gr.Column(): |
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results = gr.Textbox(label="Results") |
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n_samples.change(fn=do_train, inputs=[n_samples, n_outliers], outputs=[plot, results]) |
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n_outliers.change(fn=do_train, inputs=[n_samples, n_outliers], outputs=[plot, results]) |
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demo.launch() |
