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from math import e |
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import gradio as gr |
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import numpy as np |
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import matplotlib.pyplot as plt |
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from matplotlib.collections import LineCollection |
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from sklearn.linear_model import LinearRegression |
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from sklearn.isotonic import IsotonicRegression |
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from sklearn.utils import check_random_state |
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def visualize_isotonic_regression(n, random_range_min, random_range_max, out_of_bounds): |
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if random_range_min >= random_range_max: |
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raise ValueError("Random Value Range (Min) must be less than Random Value Range (Max)") |
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x = np.arange(n) |
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rs = check_random_state(0) |
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y = rs.randint(random_range_min, random_range_max, size=(n,)) + 50.0 * np.log1p(np.arange(n)) |
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ir = IsotonicRegression(out_of_bounds=out_of_bounds if out_of_bounds else "clip") |
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y_ = ir.fit_transform(x, y) |
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lr = LinearRegression() |
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lr.fit(x[:, np.newaxis], y) |
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segments = [[[i, y[i]], [i, y_[i]]] for i in range(n)] |
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lc = LineCollection(segments, zorder=0) |
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lc.set_array(np.ones(len(y))) |
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lc.set_linewidths(np.full(n, 0.5)) |
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fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(12, 6)) |
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ax0.plot(x, y, "C0.", markersize=12) |
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ax0.plot(x, y_, "C1.-", markersize=12) |
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ax0.plot(x, lr.predict(x[:, np.newaxis]), "C2-") |
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ax0.add_collection(lc) |
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ax0.legend(("Training data", "Isotonic fit", "Linear fit"), loc="lower right") |
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ax0.set_title("Isotonic regression fit on noisy data (n=%d)" % n) |
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x_test = np.linspace(np.min(x), np.max(x), 1000) |
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ax1.plot(x_test, ir.predict(x_test), "C1-") |
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ax1.plot(ir.X_thresholds_, ir.y_thresholds_, "C1.", markersize=12) |
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ax1.set_title("Prediction function (%d thresholds)" % len(ir.X_thresholds_)) |
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return fig |
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parameters = [ |
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gr.inputs.Slider(10, 100, step=10, default=50, label="Number of data points (n)"), |
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gr.inputs.Slider(-50, 50, step=1, default=-50, label="Random Value Range (Min)"), |
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gr.inputs.Slider(-50, 50, step=1, default=50, label="Random Value Range (Max)"), |
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gr.inputs.Radio(["clip", "nan", "raise"], default="clip", label="Out of Bounds Strategy"), |
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] |
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description = "This app presents an illustration of the isotonic regression on generated data (non-linear monotonic trend with homoscedastic uniform noise). The isotonic regression algorithm finds a non-decreasing approximation of a function while minimizing the mean squared error on the training data. The benefit of such a non-parametric model is that it does not assume any shape for the target function besides monotonicity. For comparison a linear regression is also presented. See the original scikit-learn example here: https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_isotonic_regression.html" |
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examples = [ |
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[50, -30, 30, "clip"], |
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[30, -20, 40, "nan"], |
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[70, -10, 20, "raise"], |
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] |
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iface = gr.Interface(fn=visualize_isotonic_regression, inputs=parameters, outputs="plot", title="Isotonic Regression Visualization", description=description, examples=examples, live=True) |
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iface.launch() |
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