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app.py

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+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()

requirements.txt

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+matplotlib==3.5.3
+numpy==1.24.2
+scikit_learn==1.2.2