Delete pages 4_💃_Model_interpretation.py

pages/4_💃_Model_interpretation.py

@@ -1,114 +0,0 @@
-import streamlit as st
-import shap
-import matplotlib.pyplot as plt
-import numpy as np
-import joblib
-import pandas as pd
-from sklearn.metrics import roc_curve, roc_auc_score, precision_recall_curve, auc
-
-st.set_page_config(page_title="Model Analysis Dashboard", layout="wide")
-st.title("Model Analysis Dashboard")
-
-# --- Load model and test data ---
-@st.cache_data
-def load_model_and_data():
-    model = joblib.load("model_1mvp.pkl")  
-    df_test = pd.read_csv("test_data.csv") 
-    return model, df_test
-
-model, df_test = load_model_and_data()
-
-target = "y"
-X_test = df_test.drop(columns=[target])
-y_test = df_test[target]
-
-preprocessor = model.named_steps["preprocessor"]
-feature_names = preprocessor.get_feature_names_out()
-X_test_transformed = preprocessor.transform(X_test)
-
-# --- SHAP Explainer (precompute for efficiency) ---
-explainer = shap.LinearExplainer(model.named_steps["classifier"], X_test_transformed, feature_names=feature_names)
-shap_values = explainer.shap_values(X_test_transformed)
-expected_value = explainer.expected_value
-
-# --- Sidebar: Plot selection and controls ---
-with st.sidebar.form("plot_selector"):
-    st.markdown("## Select plots to display")
-    show_coeff = st.checkbox("Logistic Regression Coefficients", value=True)
-    show_shap_global = st.checkbox("SHAP Global (summary plot)", value=True)
-    show_shap_local = st.checkbox("SHAP Local (waterfall plot)", value=False)
-    show_roc = st.checkbox("ROC/PR Curves", value=True)
-    top_n = st.slider("Number of top features for LogReg coeffecients", 5, 30, 15)
-    local_idx = st.number_input("Local SHAP sample index", min_value=0, max_value=len(X_test)-1, value=0)
-    submitted = st.form_submit_button("Update plots")
-
-# --- Logistic Regression Coefficient Plot ---
-if show_coeff and submitted:
-    st.header("Logistic Regression Coefficients")
-    logreg_model = model.named_steps["classifier"]
-    coefficients = logreg_model.coef_[0]
-    importance = pd.DataFrame({
-        "feature": feature_names,
-        "coefficient": coefficients
-    }).sort_values(by="coefficient", key=abs, ascending=False)
-    fig, ax = plt.subplots(figsize=(8, 6))
-    importance.head(top_n).set_index("feature")["coefficient"].plot(kind="barh", ax=ax, color="#4C72B0")
-    ax.set_title("Logistic Regression Feature Importance (Coefficients)")
-    ax.set_xlabel("Coefficient Value")
-    ax.set_ylabel("Feature")
-    st.pyplot(fig)
-    st.dataframe(importance.head(top_n).style.format({"coefficient": "{:.3f}"}))
-
-# --- SHAP Analysis ---
-if (show_shap_global or show_shap_local) and submitted:
-    st.header("SHAP Analysis")
-    if show_shap_global:
-        st.subheader("Global Feature Importance (SHAP Summary Plot)")
-        fig, ax = plt.subplots(figsize=(10, 6))
-        shap.summary_plot(shap_values, X_test_transformed, feature_names=feature_names, show=False)
-        st.pyplot(fig)
-    if show_shap_local:
-        st.subheader("Local Explanation (SHAP Waterfall Plot)")
-        fig2, ax2 = plt.subplots(figsize=(10, 6))
-        shap.plots.waterfall(
-            shap.Explanation(
-                values=shap_values[local_idx],
-                base_values=expected_value,
-                data=X_test_transformed[local_idx],
-                feature_names=feature_names
-            ),
-            max_display=15,
-            show=False
-        )
-        st.pyplot(fig2)
-
-# --- ROC and PR Curves ---
-if show_roc and submitted:
-    st.header("Model Performance Metrics (ROC / PR Curves)")
-    y_pred_proba = model.predict_proba(X_test)[:, 1]
-    roc_auc = roc_auc_score(y_test, y_pred_proba)
-    fpr, tpr, _ = roc_curve(y_test, y_pred_proba)
-    precision, recall, _ = precision_recall_curve(y_test, y_pred_proba)
-    pr_auc = auc(recall, precision)
-
-    col1, col2 = st.columns(2)
-    with col1:
-        st.metric("ROC AUC", f"{roc_auc:.3f}")
-    with col2:
-        st.metric("PR AUC", f"{pr_auc:.3f}")
-
-    fig1, ax1 = plt.subplots(figsize=(5, 5))
-    ax1.plot(fpr, tpr, color="darkorange", lw=2, label=f"ROC curve (AUC = {roc_auc:.3f})")
-    ax1.plot([0, 1], [0, 1], color="navy", lw=2, linestyle="--", label="Random Guess")
-    ax1.set_xlabel("False Positive Rate")
-    ax1.set_ylabel("True Positive Rate")
-    ax1.set_title("ROC Curve")
-    ax1.legend()
-    st.pyplot(fig1)
-
-    fig2, ax2 = plt.subplots(figsize=(5, 5))
-    ax2.plot(recall, precision, color="#C44E52")
-    ax2.set_xlabel("Recall")
-    ax2.set_ylabel("Precision")
-    ax2.set_title("Precision-Recall Curve")
-    st.pyplot(fig2)