add permutation

pages/ICE_and_PDP.py

@@ -50,13 +50,12 @@ def main():
     if global_model is None:
         train_model()
     # Define feature names
-    feature_names = ["Age", "Workclass", "Education-Num", "Marital Status", "Occupation",
-                     "Relationship", "Race", "Sex", "Capital Gain", "Capital Loss", "Hours per week", "Country"]
-    print(X_test.columns)  # Check the actual feature names
-
-    selected_feature = st.sidebar.selectbox("Select a feature for PDP/ICE analysis:", feature_names)
     
-    kind = st.sidebar.selectbox("Select plot type:", ["average", "both", "individual"])
+
+    selected_feature = st.sidebar.selectbox("Select a feature for PDP/ICE analysis:", ("Age", "Workclass", "Education-Num", "Marital Status", "Occupation",
+                     "Relationship", "Race", "Sex", "Capital Gain", "Capital Loss", "Hours per week", "Country"))
+    st.write(f"selected feature is {selected_feature}")
+    kind = st.sidebar.selectbox("Select plot type:", ("average", "both", "individual"))
     
     st.title("ICE (Individual Conditional Expectation) and PDP (Partial Dependence Plot)")
     st.write(prompt_params.ICE_INTRODUCTION)

pages/PermutationFeatureImportance.py

@@ -0,0 +1,75 @@
+# Import Libraries
+import matplotlib.pyplot as plt
+import streamlit as st
+import src.prompt_config as prompt_params
+# Models
+import xgboost
+from sklearn.model_selection import train_test_split
+from alepython import ale_plot
+# XAI (Explainability)
+import shap
+from sklearn.inspection import permutation_importance
+# Global Variables to Store Model & Data
+global_model = None
+X_train, X_test, y_train, y_test = None, None, None, None
+
+
+def train_model():
+    """ Train the XGBoost model only once and store it globally. """
+    global global_model, X_train, X_test, y_train, y_test
+    
+    if global_model is None:
+        # Load Data from SHAP library
+        X, y = shap.datasets.adult()
+        
+        # Split data
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
+        
+        # Train XGBoost model
+        global_model = xgboost.XGBClassifier()
+        global_model.fit(X_train, y_train)
+        
+        print("XGBoost Model training completed!")
+
+def explain_example():
+    """ Explain a given sample without retraining the model. """
+    global global_model, X_train, X_test, y_train, y_test
+    
+    if global_model is None:
+        train_model()
+    
+    fig, ax = plt.subplots(figsize=(10, 5))
+    st.write("1D Main Effect ALE Plot")
+    perm_imp = permutation_importance(global_model, X_test, y_test,
+                                      n_repeats=30,
+                                      random_state=0)
+    sorted_idx = perm_imp.importances_mean.argsort()
+    ax.barh(X_test.columns[sorted_idx], perm_imp.importances[sorted_idx].mean(axis=1).T)
+    ax.set_title("Permutation Importances")
+    fig.tight_layout()
+    st.pyplot(fig)
+
+    fig, ax = plt.subplots(figsize=(10, 5))
+    st.write("2D Second-Order ALE Plot")
+    ax.boxplot(perm_imp.importances[sorted_idx].T,
+               vert=False, labels=X_test.columns[sorted_idx])
+    ax.set_title("Permutation Importances")
+    fig.tight_layout()
+    st.pyplot(fig)
+
+def main():
+    global global_model
+    
+    # Ensure the model is trained only once
+    if global_model is None:
+        train_model()
+    
+    st.title("ALE (Accumulated Local Effects)")
+    st.write(prompt_params.ALE_INTRODUCTION)
+    # Explain the selected sample
+    if st.button("Explain Sample"):
+        explain_example()
+
+
+if __name__ == '__main__':
+    main()

requirements.txt

@@ -3,4 +3,5 @@ lime
 xgboost
 shap
 anchor-exp
-scikit-learn
+scikit-learn
+git+https://github.com/MaximeJumelle/ALEPython.git@dev#egg=alepython