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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 sklearn.model_selection import train_test_split |
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import matplotlib.cm as cm |
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from sklearn.utils import shuffle |
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from sklearn.utils import check_random_state |
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from sklearn.linear_model import BayesianRidge |
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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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description = f""" |
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## Description |
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This demo computes a Bayesian Ridge Regression of Sinusoids. |
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The demo is based on the [scikit-learn docs](https://scikit-learn.org/stable/auto_examples/linear_model/plot_bayesian_ridge_curvefit.html#sphx-glr-auto-examples-linear-model-plot-bayesian-ridge-curvefit-py) |
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""" |
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def func(x): |
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return np.sin(2 * np.pi * x) |
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size = 25 |
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rng = np.random.RandomState(1234) |
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x_train = rng.uniform(0.0, 1.0, size) |
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y_train = func(x_train) + rng.normal(scale=0.1, size=size) |
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x_test = np.linspace(0.0, 1.0, 100) |
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n_order = 3 |
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X_train = np.vander(x_train, n_order + 1, increasing=True) |
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X_test = np.vander(x_test, n_order + 1, increasing=True) |
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reg = BayesianRidge(tol=1e-6, fit_intercept=False, compute_score=True) |
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def curve_fit(): |
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fig, axes = plt.subplots(1, 2, figsize=(8, 4)) |
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for i, ax in enumerate(axes): |
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if i == 0: |
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init = [1 / np.var(y_train), 1.0] |
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elif i == 1: |
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init = [1.0, 1e-3] |
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reg.set_params(alpha_init=init[0], lambda_init=init[1]) |
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reg.fit(X_train, y_train) |
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ymean, ystd = reg.predict(X_test, return_std=True) |
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ax.plot(x_test, func(x_test), color="blue", label="sin($2\\pi x$)") |
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ax.scatter(x_train, y_train, s=50, alpha=0.5, label="observation") |
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ax.plot(x_test, ymean, color="red", label="predict mean") |
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ax.fill_between( |
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x_test, ymean - ystd, ymean + ystd, color="pink", alpha=0.5, label="predict std" |
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) |
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ax.set_ylim(-1.3, 1.3) |
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ax.legend() |
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title = "$\\alpha$_init$={:.2f},\\ \\lambda$_init$={}$".format(init[0], init[1]) |
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if i == 0: |
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title += " (Default)" |
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ax.set_title(title, fontsize=12) |
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text = "$\\alpha={:.1f}$\n$\\lambda={:.3f}$\n$L={:.1f}$".format( |
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reg.alpha_, reg.lambda_, reg.scores_[-1] |
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) |
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ax.text(0.05, -1.0, text, fontsize=12) |
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return fig |
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with gr.Blocks(theme=theme) as demo: |
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gr.Markdown(''' |
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<h1 style='text-align: center'>Curve Fitting with Bayesian Ridge Regression 📈</h1> |
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''') |
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gr.Markdown(description) |
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with gr.Row(): |
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run_button = gr.Button('Fit the Curve') |
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with gr.Row(): |
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plot_result = gr.Plot() |
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run_button.click(fn=curve_fit, inputs=[], outputs=[plot_result]) |
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demo.launch() |
