import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import MultiTaskLasso, Lasso import gradio as gr import time 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, progress=gr.Progress()): progress(0, desc="Starting...") time.sleep(1) for i in progress.tqdm(range(100)): time.sleep(0.1) 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.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), ncol=3, fancybox=True, shadow=True) 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 us to jointly fit multiple regression problems by enforcing the selected features to be the same across tasks. This example simulates sequential measurement. Each task is a time instant, and the relevant features, while being the same, vary in amplitude over time. Multi-task lasso imposes that features that are selected at one time point are selected for all time points. This makes feature selection more stable than by regular Lasso. ## 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(theme=gr.themes.Glass(primary_hue=gr.themes.colors.gray, secondary_hue=gr.themes.colors.sky, text_size=gr.themes.sizes.text_lg), css=".gradio-container {background-color: #9ea9a9 }") as demo: gr.Markdown('''

Joint feature selection with multi-task Lasso

''') gr.Markdown(model_card) gr.Markdown("Original example Author: Alexandre Gramfort ") gr.Markdown( "Iterative conversion by: Dea María Léon" ) gr.Markdown("### Please select values and click submit:") with gr.Row().style(equal_height=True): n_samples = gr.Slider(50,500,value=100,step=50,label='Number of samples') n_features = gr.Slider(5,50,value=30,step=5,label='Features') n_tasks = gr.Slider(5,50,value=40,step=5,label='Tasks') n_relevant_features = gr.Slider(1,10,value=5,step=1,label='Relevant features') alpha = gr.Slider(0,10,value=1.0,step=0.5,label='Alpha Range') btn = gr.Button(value = 'Submit') btn.click(make_plot,inputs=[n_samples,n_features, n_tasks, n_relevant_features, alpha],outputs=[gr.Plot()]) demo.queue().launch()