Delete app.py

app.py

@@ -1,80 +0,0 @@
-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
-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.
-Multi-task lasso imposes that features that are selected at one time point are selected
-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://www.deamarialeon.com\">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()