Dea22 commited on
Commit
0609eee
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1 Parent(s): 22e50a4
Files changed (1) hide show
  1. app.py +10 -9
app.py CHANGED
@@ -25,7 +25,7 @@ def make_plot(n_samples, n_features, n_tasks, n_relevant_features, alpha, progre
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  coef_lasso_ = np.array([Lasso(alpha=0.5).fit(X, y).coef_ for y in Y.T])
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  coef_multi_task_lasso_ = MultiTaskLasso(alpha=alpha).fit(X, Y).coef_
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- fig = plt.figure(figsize=(8, 5))
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  feature_to_plot = 0
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  fig = plt.figure()
@@ -51,20 +51,21 @@ def make_plot(n_samples, n_features, n_tasks, n_relevant_features, alpha, progre
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  model_card = f"""
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  ## Description
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- 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.
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- 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
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- are selected for all time points. This makes feature selection more stable than by regular Lasso.
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  ## Model
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  currentmodule: sklearn.linear_model
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  class:`Lasso` and class: `MultiTaskLasso` are used in this example.
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  Plots represent Lasso, MultiTaskLasso and Ground truth time series
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  """
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- with gr.Blocks(theme=gr.themes.Glass(primary_hue=gr.themes.colors.gray,
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- secondary_hue=gr.themes.colors.stone,
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- text_size=gr.themes.sizes.text_lg) as demo:#,
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- #css=".gradio-container {background-color: #9ea9a9 }")
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-
 
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  gr.Markdown('''
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  <div>
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  <h1 style='text-align: center'> Joint feature selection with multi-task Lasso </h1>
 
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  coef_lasso_ = np.array([Lasso(alpha=0.5).fit(X, y).coef_ for y in Y.T])
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  coef_multi_task_lasso_ = MultiTaskLasso(alpha=alpha).fit(X, Y).coef_
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+ fig = plt.figure(figsize=(16, 20))
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  feature_to_plot = 0
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  fig = plt.figure()
 
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  model_card = f"""
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  ## Description
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+ 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
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+ 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
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+ for all time points. This makes feature selection more stable than by regular Lasso.
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  ## Model
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  currentmodule: sklearn.linear_model
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  class:`Lasso` and class: `MultiTaskLasso` are used in this example.
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  Plots represent Lasso, MultiTaskLasso and Ground truth time series
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  """
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+ theme = gr.themes.Glass(primary_hue=gr.themes.colors.gray,
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+ secondary_hue=gr.themes.colors.sky,
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+ text_size=gr.themes.sizes.text_lg).set(slider_color="#b2dcf3")
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+
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+ with gr.Blocks(theme=theme) as demo:
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+
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  gr.Markdown('''
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  <div>
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  <h1 style='text-align: center'> Joint feature selection with multi-task Lasso </h1>