Update app.py

app.py

@@ -14,7 +14,7 @@ import matplotlib.pyplot as plt
 from matplotlib.ticker import FuncFormatter
 
 import plotly.graph_objects as go
-from sklearn.metrics import mean_squared_error, mean_absolute_error
+from sklearn.metrics import mean_squared_error
 
 # =========================
 # Constants (Ym variant)
@@ -220,6 +220,12 @@ def _excel_safe_name(name: str) -> str:
     safe = ''.join('_' if ch in bad else ch for ch in str(name))
     return safe[:31]
 
+def mape(y_true, y_pred, eps: float = 1e-8) -> float:
+    a = np.asarray(y_true, dtype=float)
+    p = np.asarray(y_pred, dtype=float)
+    denom = np.where(np.abs(a) < eps, np.nan, np.abs(a))   # ignore near-zero actuals
+    return float(np.nanmean(np.abs(a - p) / denom) * 100.0)  # percent
+
 def _round_numeric(df: pd.DataFrame, ndigits: int = 3) -> pd.DataFrame:
     out = df.copy()
     for c in out.columns:
@@ -772,12 +778,12 @@ if st.session_state.app_step == "dev":
         st.session_state.results["m_train"]={
             "R":   pearson_r(tr[TARGET], tr[PRED_COL]),
             "RMSE": rmse(tr[TARGET], tr[PRED_COL]),
-            "MAE":  mean_absolute_error(tr[TARGET], tr[PRED_COL])
+            "MAPE":  mape(tr[TARGET], tr[PRED_COL])
         }
         st.session_state.results["m_test"]={
             "R":   pearson_r(te[TARGET], te[PRED_COL]),
             "RMSE": rmse(te[TARGET], te[PRED_COL]),
-            "MAE":  mean_absolute_error(te[TARGET], te[PRED_COL])
+            "MAPE":  mape(te[TARGET], te[PRED_COL])
         }
 
         tr_min = tr[FEATURES].min().to_dict(); tr_max = tr[FEATURES].max().to_dict()
@@ -786,15 +792,15 @@ if st.session_state.app_step == "dev":
 
     def _dev_block(df, m):
         c1,c2,c3 = st.columns(3)
-        c1.metric("R", f"{m['R']:.2f}")
+        c1.metric("R", f"{m['R']:.3f}")
         c2.metric("RMSE", f"{m['RMSE']:.2f}")
-        c3.metric("MAE", f"{m['MAE']:.2f}")
+        c3.metric("MAPE (%)", f"{m['MAPE']:.2f}")
 
         st.markdown("""
             <div style='text-align: left; font-size: 0.8em; color: #6b7280; margin-top: -16px; margin-bottom: 8px;'>
                 <strong>R:</strong> Pearson Correlation Coefficient<br>
                 <strong>RMSE:</strong> Root Mean Square Error<br>
-                <strong>MAE:</strong> Mean Absolute Error
+                <strong>MAPE:</strong> Mean Absolute Percentage Error
             </div>
         """, unsafe_allow_html=True)
 
@@ -860,7 +866,7 @@ if st.session_state.app_step == "validate":
         st.session_state.results["m_val"]={
             "R": pearson_r(df[TARGET], df[PRED_COL]),
             "RMSE": rmse(df[TARGET], df[PRED_COL]),
-            "MAE": mean_absolute_error(df[TARGET], df[PRED_COL])
+            "MAPE": mape(df[TARGET], df[PRED_COL])
         }
         st.session_state.results["sv_val"]={"n":len(df), "pred_min":float(df[PRED_COL].min()), "pred_max":float(df[PRED_COL].max()), "oor":oor_pct}
         st.session_state.results["oor_tbl"]=tbl
@@ -868,15 +874,15 @@ if st.session_state.app_step == "validate":
     if "Validate" in st.session_state.results:
         m = st.session_state.results["m_val"]
         c1,c2,c3 = st.columns(3)
-        c1.metric("R", f"{m['R']:.2f}")
+        c1.metric("R", f"{m['R']:.3f}")
         c2.metric("RMSE", f"{m['RMSE']:.2f}")
-        c3.metric("MAE", f"{m['MAE']:.2f}")
+        c3.metric("MAPE (%)", f"{m['MAPE']:.2f}")
 
         st.markdown("""
             <div style='text-align: left; font-size: 0.8em; color: #6b7280; margin-top: -16px; margin-bottom: 8px;'>
                 <strong>R:</strong> Pearson Correlation Coefficient<br>
                 <strong>RMSE:</strong> Root Mean Square Error<br>
-                <strong>MAE:</strong> Mean Absolute Error
+                <strong>MAPE:</strong> Mean Absolute Percentage Error
             </div>
         """, unsafe_allow_html=True)