| 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) |
|
|
| |
| 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=(16, 20)) |
| |
| 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', 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 |
| """ |
|
|
| theme = gr.themes.Glass(primary_hue=gr.themes.colors.gray, |
| secondary_hue=gr.themes.colors.sky, |
| text_size=gr.themes.sizes.text_lg).set(slider_color="#b2dcf3") |
|
|
| with gr.Blocks(theme=theme, |
| css=".gradio-container {background-color: #9ea9a9}") 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>" |
| ) |
| 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() |
