app.py

import gradio as gr
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

import matplotlib.cm as cm
from sklearn.utils import shuffle
from sklearn.utils import check_random_state
from sklearn.linear_model import BayesianRidge

theme = gr.themes.Monochrome(
    primary_hue="indigo",
    secondary_hue="blue",
    neutral_hue="slate",
)

description = f"""
## Description

This demo computes a Bayesian Ridge Regression of Sinusoids.

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)
"""

def func(x):
    return np.sin(2 * np.pi * x)

def curve_fit(size, alpha, lam):
    rng = np.random.RandomState(1234)
    x_train = rng.uniform(0.0, 1.0, size)
    y_train = func(x_train) + rng.normal(scale=0.1, size=size)
    x_test = np.linspace(0.0, 1.0, 100)

    n_order = 3
    X_train = np.vander(x_train, n_order + 1, increasing=True)
    X_test = np.vander(x_test, n_order + 1, increasing=True)
    reg = BayesianRidge(tol=1e-6, fit_intercept=False, compute_score=True)

    fig, axes = plt.subplots(1, 2, figsize=(8, 4))
    for i, ax in enumerate(axes):
    # Bayesian ridge regression with different initial value pairs
        if i == 0:
            init = [1 / np.var(y_train), 1.0]  # Default values
        elif i == 1:
            init = [alpha, lam]
            reg.set_params(alpha_init=init[0], lambda_init=init[1])
        reg.fit(X_train, y_train)
        ymean, ystd = reg.predict(X_test, return_std=True)
    
        ax.plot(x_test, func(x_test), color="blue", label="sin($2\\pi x$)")
        ax.scatter(x_train, y_train, s=50, alpha=0.5, label="observation")
        ax.plot(x_test, ymean, color="red", label="predict mean")
        ax.fill_between(
            x_test, ymean - ystd, ymean + ystd, color="pink", alpha=0.5, label="predict std"
        )
        ax.set_ylim(-1.3, 1.3)
        ax.legend()
        title = "$\\alpha$_init$={:.2f},\\ \\lambda$_init$={}$".format(init[0], init[1])
        if i == 0:
            title += " (Default)"
        ax.set_title(title, fontsize=12)
        text = "$\\alpha={:.1f}$\n$\\lambda={:.3f}$\n$L={:.1f}$".format(
            reg.alpha_, reg.lambda_, reg.scores_[-1]
        )
        ax.text(0.05, -1.0, text, fontsize=12)
    return fig


with gr.Blocks(theme=theme) as demo:
    gr.Markdown('''
            <h1 style='text-align: center'>Curve Fitting with Bayesian Ridge Regression 📈</h1>
        ''')
    gr.Markdown(description)
    
    with gr.Row():
        size = gr.Slider(minimum=10, maximum=100, step=5, value=25, label="Number of Data Points")
        alpha = gr.Slider(minimum=1e-2, maximum=2, step=0.1, value=1, label="Initial Alpha")
        lam = gr.Slider(minimum=1e-5, maximum=1, step=1e-4, value=1e-3, label="Initial Lambda")
    with gr.Row():
        run_button = gr.Button('Fit the Curve')
    with gr.Row():
        plot_result = gr.Plot()
    run_button.click(fn=curve_fit, inputs=[size, alpha, lam], outputs=[plot_result])

demo.launch()