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
+from sklearn.metrics import mean_squared_error, mean_absolute_error
 
 # =========================
 # Constants (GR)
@@ -77,7 +77,7 @@ st.markdown("""
 section[data-testid="stFileUploader"] div[data-testid="stMarkdownContainer"]{display:none !important;}
 section[data-testid="stFileUploader"] [data-testid="stFileUploaderDropzone"] > div:first-child{display:none !important;}
 section[data-testid="stFileUploader"] [data-testid="stFileUploaderInstructions"]{display:none !important;}
-section[data-testid="stFileUploader"] p, section[data-testid="stFileUploader"] small{display:none !important;}
+section[data-testid="stFileUploader"] p, section[data-testid="stFileUploader"] small{display:none !重要;}
 </style>
 """, unsafe_allow_html=True)
 
@@ -167,13 +167,6 @@ def pearson_r(y_true, y_pred) -> float:
         return float("nan")
     return float(np.corrcoef(a, p)[0, 1])
 
-def mape(y_true, y_pred, eps: float = 1e-8) -> float:
-    """Mean Absolute Percentage Error in percent; ignores near-zero actuals."""
-    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))
-    return float(np.nanmean(np.abs(a - p) / denom) * 100.0)
-
 @st.cache_resource(show_spinner=False)
 def load_model(model_path: str):
     return joblib.load(model_path)
@@ -735,31 +728,31 @@ if st.session_state.app_step == "dev":
         st.session_state.results["m_train"]={
             "R": pearson_r(tr["GR_Actual"], tr["GR_Pred"]),
             "RMSE": rmse(tr["GR_Actual"], tr["GR_Pred"]),
-            "MAPE": mape(tr["GR_Actual"], tr["GR_Pred"])
+            "MAE": mean_absolute_error(tr["GR_Actual"], tr["GR_Pred"])
         }
         st.session_state.results["m_test"]={
             "R": pearson_r(te["GR_Actual"], te["GR_Pred"]),
             "RMSE": rmse(te["GR_Actual"], te["GR_Pred"]),
-            "MAPE": mape(te["GR_Actual"], te["GR_Pred"])
+            "MAE": mean_absolute_error(te["GR_Actual"], te["GR_Pred"])
         }
 
         tr_min = tr[FEATURES].min().to_dict(); tr_max = tr[FEATURES].max().to_dict()
         st.session_state.train_ranges = {f:(float(tr_min[f]), float(tr_max[f])) for f in FEATURES}
         st.markdown('<div class="st-message-box st-success">Case has been built and results are displayed below.</div>', unsafe_allow_html=True)
 
-    # -------- Metrics + Plots --------
+    # -------- Metrics + Plots (3 decimals here) --------
     def _dev_block(df, m):
         c1, c2, c3 = st.columns(3)
-        c1.metric("R",          f"{m['R']:.3f}")
-        c2.metric("RMSE",       f"{m['RMSE']:.3f}")
-        c3.metric("MAPE (%)",   f"{m['MAPE']:.2f}")
+        c1.metric("R",    f"{m['R']:.3f}")
+        c2.metric("RMSE", f"{m['RMSE']:.3f}")
+        c3.metric("MAE",  f"{m['MAE']:.3f}")
 
         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>MAPE:</strong> Mean Absolute Percentage Error
+                <strong>MAE:</strong> Mean Absolute Error
             </div>
             """,
             unsafe_allow_html=True,
@@ -813,7 +806,7 @@ if st.session_state.app_step == "validate":
             st.markdown('<div class="st-message-box st-error">Missing required feature columns.</div>', unsafe_allow_html=True)
             st.stop()
 
-        pred_raw = model.predict(df[FEATURES])
+        pred_raw = model.predict(df[FEATURES])  # <-- fixed here
         df["GR_Pred"] = inverse_target(np.asarray(pred_raw, dtype=float), TARGET_TRANSFORM)
         try:
             df["GR_Actual"] = to_actual_series(df, TARGET, ACTUAL_COL, TARGET_TRANSFORM)
@@ -837,7 +830,7 @@ if st.session_state.app_step == "validate":
         st.session_state.results["m_val"]={
             "R": pearson_r(df["GR_Actual"], df["GR_Pred"]),
             "RMSE": rmse(df["GR_Actual"], df["GR_Pred"]),
-            "MAPE": mape(df["GR_Actual"], df["GR_Pred"])
+            "MAE": mean_absolute_error(df["GR_Actual"], df["GR_Pred"])
         }
         st.session_state.results["sv_val"]={"n":len(df),"pred_min":float(df["GR_Pred"].min()),"pred_max":float(df["GR_Pred"].max()),"oor":oor_pct}
         st.session_state.results["oor_tbl"]=tbl
@@ -845,14 +838,14 @@ 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}")
-        c2.metric("RMSE",      f"{m['RMSE']:.2f}")
-        c3.metric("MAPE (%)",  f"{m['MAPE']:.2f}")
+        c1.metric("R", f"{m['R']:.2f}")
+        c2.metric("RMSE", f"{m['RMSE']:.2f}")
+        c3.metric("MAE", f"{m['MAE']:.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>MAPE:</strong> Mean Absolute Percentage Error
+                <strong>MAE:</strong> Mean Absolute Error
             </div>
         """, unsafe_allow_html=True)
 
@@ -898,9 +891,7 @@ if st.session_state.app_step == "predict":
             if not ensure_cols(df, FEATURES):
                 st.markdown('<div class="st-message-box st-error">Missing required feature columns.</div>', unsafe_allow_html=True)
                 st.stop()
-            pred_raw = model.predict(df[FEATURES"])
-            # Fix typo if occurred: ensure correct line in your code as:
-            # pred_raw = model.predict(df[FEATURES])
+            pred_raw = model.predict(df[FEATURES])
             df["GR_Pred"] = inverse_target(np.asarray(pred_raw, dtype=float), TARGET_TRANSFORM)
             st.session_state.results["PredictOnly"]=df
             ranges = st.session_state.train_ranges