Create app.py

app.py

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+import gradio as gr
+import matplotlib.pyplot as plt
+# from skops import hub_utils
+import time
+import pickle
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+from sklearn.linear_model import LassoLarsIC
+from sklearn.pipeline import make_pipeline
+from sklearn.datasets import load_diabetes
+
+
+
+def load_dataset():
+  X, y = load_diabetes(return_X_y=True, as_frame=True)
+  return X,y
+
+
+def aic_pipeline(X,y):
+  lasso_lars_ic = make_pipeline(StandardScaler(), LassoLarsIC(criterion="aic")).fit(X, y)
+  return lasso_lars_ic
+
+
+def zou_et_al_criterion_rescaling(criterion, n_samples, noise_variance):
+    """Rescale the information criterion to follow the definition of Zou et al."""
+    return criterion - n_samples * np.log(2 * np.pi * noise_variance) - n_samples
+
+
+def zou_et_all_aic(lasso_lars_ic):
+  aic_criterion = zou_et_al_criterion_rescaling(
+      lasso_lars_ic[-1].criterion_,
+      n_samples,
+      lasso_lars_ic[-1].noise_variance_,
+  )
+
+  index_alpha_path_aic = np.flatnonzero(
+      lasso_lars_ic[-1].alphas_ == lasso_lars_ic[-1].alpha_
+  )[0]
+
+  return index_alpha_path_aic, aic_criterion
+
+def zou_et_all_bic(lasso_lars_ic):
+  lasso_lars_ic.set_params(lassolarsic__criterion="bic").fit(X, y)
+  bic_criterion = zou_et_al_criterion_rescaling(
+    lasso_lars_ic[-1].criterion_,
+    n_samples,
+    lasso_lars_ic[-1].noise_variance_,
+  )
+
+  index_alpha_path_bic = np.flatnonzero(
+    lasso_lars_ic[-1].alphas_ == lasso_lars_ic[-1].alpha_
+  )[0]
+
+  return index_alpha_path_bic, bic_criterion
+
+def fn_assert_true():
+  assert index_alpha_path_bic == index_alpha_path_aic
+
+
+
+def visualize_input_data():
+    fig = plt.figure(1, facecolor="w", figsize=(5, 5))
+    plt.plot(aic_criterion, color="tab:blue", marker="o", label="AIC criterion")
+    plt.plot(bic_criterion, color="tab:orange", marker="o", label="BIC criterion")
+    plt.vlines(
+        index_alpha_path_bic,
+        aic_criterion.min(),
+        aic_criterion.max(),
+        color="black",
+        linestyle="--",
+        label="Selected alpha",
+    )
+    plt.legend()
+    plt.ylabel("Information criterion")
+    plt.xlabel("Lasso model sequence")
+    _ = plt.title("Lasso model selection via AIC and BIC")
+
+
+    return fig
+
+title = " Lasso model selection via information criteria"
+
+with gr.Blocks(title=title) as demo:
+  gr.Markdown(f"# {title}")
+  gr.Markdown(
+        """
+        A LassoLarsIC estimator is fit on a diabetes dataset and the AIC and the BIC criteria are used to select the best model.
+        It is important to note that the optimization to find alpha with LassoLarsIC relies on the AIC or BIC criteria that are computed in-sample,
+        thus on the training set directly. This approach differs from the cross-validation procedure
+        """
+        
+  )
+    
+
+    
+  gr.Markdown(" **https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lars_ic.html#sphx-glr-auto-examples-linear-model-plot-lasso-lars-ic-py**")
+    
+  ##process
+  X,y = load_dataset()
+  lasso_lars_ic = aic_pipeline(X,y)
+  n_samples = X.shape[0]
+  index_alpha_path_aic, aic_criterion = zou_et_all_aic(lasso_lars_ic)
+  
+  index_alpha_path_bic, bic_criterion = zou_et_all_bic(lasso_lars_ic)
+
+  fn_assert_true()
+
+  with gr.Tab("AIC BIC Criteria"):
+      btn = gr.Button(value="Plot AIC BIC Criteria w Regularization")
+      btn.click(visualize_input_data, outputs= gr.Plot(label='AIC BIC Criteria') )
+
+    
+
+    
+
+demo.launch()