| import gradio as gr | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from sklearn.linear_model import BayesianRidge | |
| SEED = 1234 | |
| ORDER = 3 | |
| MAX_SAMPLES = 100 | |
| def sin_wave(x: np.array): | |
| """Sinusoidal wave function""" | |
| return np.sin(2 * np.pi * x) | |
| def generate_train_data(n_samples: int): | |
| """Generates sinuosidal data with noise""" | |
| rng = np.random.RandomState(SEED) | |
| x_train = rng.uniform(0.0, 1.0, n_samples) | |
| y_train = sin_wave(x_train) + rng.normal(scale=0.1, size=n_samples) | |
| X_train = np.vander(x_train, ORDER + 1, increasing=True) | |
| return x_train, X_train, y_train | |
| def get_app_fn(): | |
| """Returns the demo function with pre-generated data and model""" | |
| x_test = np.linspace(0.0, 1.0, 100) | |
| X_test = np.vander(x_test, ORDER + 1, increasing=True) | |
| y_test = sin_wave(x_test) | |
| reg = BayesianRidge(tol=1e-6, fit_intercept=False, compute_score=True) | |
| x_train_full, X_train_full, y_train_full = generate_train_data(MAX_SAMPLES) | |
| def app_fn(n_samples: int, alpha_init: float, lambda_init: float): | |
| """Train a Bayesian Ridge regression model and plot the predicted points""" | |
| rng = np.random.RandomState(SEED) | |
| subset_idx = rng.randint(0, MAX_SAMPLES, n_samples) | |
| x_train, X_train, y_train = ( | |
| x_train_full[subset_idx], | |
| X_train_full[subset_idx], | |
| y_train_full[subset_idx], | |
| ) | |
| reg.set_params(alpha_init=alpha_init, lambda_init=lambda_init) | |
| reg.fit(X_train, y_train) | |
| ymean, ystd = reg.predict(X_test, return_std=True) | |
| fig, ax = plt.subplots() | |
| ax.plot(x_test, y_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() | |
| 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 | |
| return app_fn | |
| title = "Bayesian Ridge Regression" | |
| with gr.Blocks(title=title) as demo: | |
| gr.Markdown(f"## {title}") | |
| n_samples_input = gr.Slider(minimum=5, maximum=100, value=25, step=1, label="#observations") | |
| alpha_input = gr.Slider(minimum=0.001, maximum=5, value=1.9, step=0.01, label="alpha_init") | |
| lambda_input = gr.Slider(minimum=0.001, maximum=5, value=1., step=0.01, label="lambda_init") | |
| outputs = gr.Plot(label="Output") | |
| inputs = [n_samples_input, alpha_input, lambda_input] | |
| app_fn = get_app_fn() | |
| n_samples_input.change(fn=app_fn, inputs=inputs, outputs=outputs) | |
| alpha_input.change(fn=app_fn, inputs=inputs, outputs=outputs) | |
| lambda_input.change(fn=app_fn, inputs=inputs, outputs=outputs) | |
| demo.launch() | |