Update app.py

app.py

@@ -58,92 +58,16 @@ def fn_assert_true():
 
 
 
-def visualize_input_data():
+def visualize_input_data(choice):
     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"
-
-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
+    if choice == "AIC criterion":
+      plt.plot(aic_criterion, color="tab:blue", marker="x", label="AIC criterion")
+    elif choice == "BIC criterion":
+      plt.plot(bic_criterion, color="tab:orange", marker="o", label="BIC criterion")
+    else:
+      plt.plot(aic_criterion, color="tab:blue", marker="*", label="AIC criterion")
+      plt.plot(bic_criterion, color="tab:orange", marker="o", label="BIC criterion")
 
-
-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(),
@@ -162,29 +86,24 @@ def visualize_input_data():
 
 title = " Lasso model selection via information criteria"
 
-with gr.Blocks(title=title) as demo:
+with gr.Blocks(title=title,theme=gr.themes.Default(font=[gr.themes.GoogleFont("Oxygen"), "Arial", "sans-serif"])) as demo:
   gr.Markdown(f"# {title}")
   gr.Markdown(
         """
-        Probabilistic model selection using Information Criterion. 
+        # Probabilistic model selection using Information Criterion. 
         This method in statistics is useful because they dont require a hold out set test set(cross validation set).
-        
         AIC and BIC are two ways of scoring a model based on its log-likelihood and complexity.
-        
-        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.
-        
-        Also one of the drawbacks of these kinds of Probabilistic model is that same general statistic cannot be used across models.
-        Instead, a careful metric must be devised for each of the models separately. 
-        The uncertainity of the model is not taken into account.
+        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.
+        Also one of the drawbacks of these kinds of Probabilistic model is that same general statistic cannot be used across models.Instead a careful metric must be deviced
+        for each of the models seperately.The uncertainity of the model is not taken into account.
         """
         
   )
     
 
     
-  gr.Markdown("See original example [here](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).")
+  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()
@@ -197,14 +116,12 @@ with gr.Blocks(title=title) as demo:
   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()
+      radio = gr.Radio(
+        ["AIC criterion", "BIC criterion", "Both"], label="What model selection criteria would you choose?"
+      )
+      # btn = gr.Button(value="Plot AIC BIC Criteria w Regularization")
+      # btn.click(visualize_input_data, outputs= gr.Plot(label='AIC BIC Criteria') )
+      radio.change(fn=visualize_input_data, inputs=radio, outputs=gr.Plot(label='AIC BIC Criteria'))