Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+
+from sklearn.metrics import r2_score
+from sklearn.linear_model import Lasso, ElasticNet
+
+
+theme = gr.themes.Monochrome(
+    primary_hue="indigo",
+    secondary_hue="blue",
+    neutral_hue="slate",
+)
+model_card = f"""
+## Description
+
+This demo estimates **Lasso** and **Elastic-Net** regression models on a manually generated sparse signal corrupted with an additive noise.
+You can play around with different ``regularization strength``, ``mixing ratio between L1 and L2``, ``number of samples``, ``number of features`` to see the effect
+
+## Dataset
+
+Simulation dataset
+"""
+
+
+
+def do_train(alpha, l1_ratio, n_samples, n_features):
+    np.random.seed(42)
+    X = np.random.randn(n_samples, n_features)
+
+    # Decreasing coef w. alternated signs for visualization
+    idx = np.arange(n_features)
+    coef = (-1) ** idx * np.exp(-idx / 10)
+    coef[10:] = 0  # sparsify coef
+    y = np.dot(X, coef)
+
+    # Add noise
+    y += 0.01 * np.random.normal(size=n_samples)
+
+    # Split data in train set and test set
+    n_samples = X.shape[0]
+    X_train, y_train = X[: n_samples // 2], y[: n_samples // 2]
+    X_test, y_test = X[n_samples // 2 :], y[n_samples // 2 :]
+
+    lasso = Lasso(alpha=alpha)
+    y_pred_lasso = lasso.fit(X_train, y_train).predict(X_test)
+    r2_score_lasso = r2_score(y_test, y_pred_lasso)
+
+
+    enet = ElasticNet(alpha=alpha, l1_ratio=l1_ratio)
+    y_pred_enet = enet.fit(X_train, y_train).predict(X_test)
+    r2_score_enet = r2_score(y_test, y_pred_enet)
+
+    fig, axes = plt.subplots()
+
+    m, s, _ = axes.stem(
+    np.where(enet.coef_)[0],
+    enet.coef_[enet.coef_ != 0],
+    markerfmt="x",
+    label="Elastic net coefficients",
+    )
+    plt.setp([m, s], color="#2ca02c")
+
+    m, s, _ = plt.stem(
+        np.where(lasso.coef_)[0],
+        lasso.coef_[lasso.coef_ != 0],
+        markerfmt="x",
+        label="Lasso coefficients",
+    )
+    plt.setp([m, s], color="#ff7f0e")
+
+    axes.stem(
+        np.where(coef)[0],
+        coef[coef != 0],
+        label="True coefficients",
+        markerfmt="bx",
+    )
+
+    axes.legend(loc="best")
+    axes.set_title("Elastic net and Lasso coefficients")
+    text = f"Lasso R^2: {r2_score_lasso:.3f}, Elastic Net R^2: {r2_score_enet:.3f}"
+    return fig, text
+
+
+
+with gr.Blocks(theme=theme) as demo:
+    gr.Markdown('''
+            <div>
+            <h1 style='text-align: center'>Lasso and Elastic Net for Sparse Signals</h1>
+            </div>
+        ''')
+    gr.Markdown(model_card)
+    gr.Markdown("Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>. Based on the example from <a href=\"https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_and_elasticnet.html#sphx-glr-auto-examples-linear-model-plot-lasso-and-elasticnet-py\">scikit-learn</a>")
+    alpha = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.1, label="Constant that multiplies the L1 term, controlling regularization strength. Using alpha = 0 with the Lasso object is not advised")
+    l1_ratio = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.7, label="The ElasticNet mixing parameter. For l1_ratio = 0 the penalty is an L2 penalty. For l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.")
+    n_samples = gr.Slider(minimum=50, maximum=500, step=50, value=50, label="Number of samples")
+    n_features = gr.Slider(minimum=50, maximum=200, step=50, value=50, label="Number of features")
+    with gr.Row():
+        with gr.Column():
+            plot = gr.Plot(label="Coefficients plot")
+        with gr.Column():
+            results = gr.Textbox(label="Results")
+
+    alpha.change(fn=do_train, inputs=[alpha, l1_ratio, n_samples, n_features], outputs=[plot, results])
+    l1_ratio.change(fn=do_train, inputs=[alpha, l1_ratio, n_samples, n_features], outputs=[plot, results])
+    n_samples.change(fn=do_train, inputs=[alpha, l1_ratio, n_samples, n_features], outputs=[plot, results])
+    n_features.change(fn=do_train, inputs=[alpha, l1_ratio, n_samples, n_features], outputs=[plot, results])
+
+demo.launch()