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app.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.linear_model import MultiTaskLasso, Lasso
+import gradio as gr
+
+rng = np.random.RandomState(42)
+
+# Generate some 2D coefficients with sine waves with random frequency and phase
+def make_plot(n_samples, n_features, n_tasks, n_relevant_features, alpha):
+
+    coef = np.zeros((n_tasks, n_features))
+    times = np.linspace(0, 2 * np.pi, n_tasks)
+    for k in range(n_relevant_features):
+        coef[:, k] = np.sin((1.0 + rng.randn(1)) * times + 3 * rng.randn(1))
+
+    X = rng.randn(n_samples, n_features)
+    Y = np.dot(X, coef.T) + rng.randn(n_samples, n_tasks)
+
+    coef_lasso_ = np.array([Lasso(alpha=0.5).fit(X, y).coef_ for y in Y.T])
+    coef_multi_task_lasso_ = MultiTaskLasso(alpha=alpha).fit(X, Y).coef_
+
+    fig = plt.figure(figsize=(8, 5))
+
+    feature_to_plot = 0
+    fig = plt.figure()
+    lw = 2
+    plt.plot(coef[:, feature_to_plot], color="seagreen", linewidth=lw, label="Ground truth")
+    plt.plot(
+        coef_lasso_[:, feature_to_plot], color="cornflowerblue", linewidth=lw, label="Lasso"
+    )
+    plt.plot(
+        coef_multi_task_lasso_[:, feature_to_plot],
+        color="gold",
+        linewidth=lw,
+        label="MultiTaskLasso",
+    )
+    plt.legend(loc="upper center")
+    plt.axis("tight")
+    plt.ylim([-1.1, 1.1])
+    fig.suptitle("Lasso, MultiTaskLasso and Ground truth time series")
+    return fig
+
+
+model_card=f"""
+## Description
+The multi-task lasso allows to fit multiple regression problems jointly enforcing the selected
+features to be the same across tasks. This example simulates sequential measurements, each task
+is a time instant, and the relevant features vary in amplitude over time while being the same.
+The multi-task lasso imposes that features that are selected at one time point are select
+for all time point. This makes feature selection by the Lasso more stable.
+## Model
+currentmodule: sklearn.linear_model
+class:`Lasso` and class: `MultiTaskLasso` are used in this example.
+Plots represent Lasso, MultiTaskLasso and Ground truth time series
+"""
+
+with gr.Blocks() as demo:
+    gr.Markdown('''
+            <div>
+            <h1 style='text-align: center'> Joint feature selection with multi-task Lasso </h1>
+            </div>
+        ''')
+    gr.Markdown(model_card)
+    gr.Markdown("Original example Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>")
+    gr.Markdown(
+        "Iterative conversion by: <a href=\"https://github.com/DeaMariaLeon\">Dea María Léon</a>"
+    )
+    n_samples = gr.Slider(50,500,value=100,step=50,label='Select number of samples')
+    n_features = gr.Slider(5,50,value=30,step=5,label='Select number of features')
+    n_tasks = gr.Slider(5,50,value=40,step=5,label='Select number of tasks')
+    n_relevant_features = gr.Slider(1,10,value=5,step=1,label='Select number of relevant_features')
+    with gr.Column():
+        with gr.Tab('Select Alpha Range'):
+            alpha = gr.Slider(0,10,value=1.0,step=0.5,label='alpha')
+
+    btn = gr.Button(value = 'Submit')
+
+    btn.click(make_plot,inputs=[n_samples,n_features, n_tasks, n_relevant_features, alpha],outputs=[gr.Plot()])
+
+demo.launch()

requirements.txt

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+matplotlib==3.6.3
+scikit-learn==1.2.1