From 0dd0ce1fe28bee4432111e61b7daf6546c9e419c Mon Sep 17 00:00:00 2001
From: MrWaradana <muhammadridhowaradana@gmail.com>
Date: Mon, 22 Dec 2025 16:23:30 +0700
Subject: [PATCH] refactor: Exclude `seq=0` from historical data, simplify CM
 prediction logic, and remove hardcoded assetnum from run script.

---
 src/modules/equipment/Prediksi.py             |  19 +++++++-----------
 .../__pycache__/Prediksi.cpython-311.pyc      | Bin 41028 -> 40721 bytes
 .../equipment/__pycache__/run.cpython-311.pyc | Bin 3029 -> 2686 bytes
 src/modules/equipment/run.py                  |  12 +++++------
 4 files changed, 13 insertions(+), 18 deletions(-)

diff --git a/src/modules/equipment/Prediksi.py b/src/modules/equipment/Prediksi.py
index 14161ec..bd69dae 100644
--- a/src/modules/equipment/Prediksi.py
+++ b/src/modules/equipment/Prediksi.py
@@ -95,6 +95,7 @@ class Prediksi:
             FROM lcc_equipment_tr_data
             WHERE assetnum = %s
             and is_actual=1
+            and seq != 0
             ;
             """
             cursor.execute(query, (equipment_id,))
@@ -711,7 +712,6 @@ class Prediksi:
             if df is None:
                 print("Data tidak tersedia untuk prediksi.")
                 return
-
             # Mendapatkan tahun proyeksi dari DB
             par_tahun_target = self.__get_param(assetnum)
 
@@ -769,19 +769,14 @@ class Prediksi:
                 try:
                     # Case untuk kolom yang terkait dengan corrective maintenance (cm)
                     if "cm" in col_lower:
-                        # Tentukan jumlah baris recent yang dianggap actual jika kolom is_actual ada
-                        if "is_actual" in df.columns:
-                            recent_df = df[df["is_actual"] == 1]
-                            recent_n = recent_df.shape[0]
-                            avg_recent = recent_df[column].mean()
-                            print(f"avg_recent: {avg_recent}")
-                        else:
-                            recent_df = df
-                            recent_n = df.shape[0]
+
+                        recent_df = df
+                        recent_n = df.shape[0]
 
                         recent_n = max(1, recent_n)
                         recent_vals = (
-                            recent_df.sort_values("year", ascending=True)
+                            recent_df
+                            .sort_values("year", ascending=True)
                             .head(recent_n)[column]
                             .dropna()
                         )
@@ -798,7 +793,7 @@ class Prediksi:
                             avg = 0.0 if pd.isna(avg) else float(avg)
 
                         preds = np.repeat(float(avg), n_future)
-                        print(preds)
+                        # print(preds)
                     else:
                         # Untuk kolom non-cm, gunakan nilai dari last actual year bila ada,
                         # jika tidak ada gunakan last available non-NA value, jika tidak ada pakai 0.0
diff --git a/src/modules/equipment/__pycache__/Prediksi.cpython-311.pyc b/src/modules/equipment/__pycache__/Prediksi.cpython-311.pyc
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diff --git a/src/modules/equipment/run.py b/src/modules/equipment/run.py
index 400a047..ebadb24 100644
--- a/src/modules/equipment/run.py
+++ b/src/modules/equipment/run.py
@@ -10,14 +10,14 @@ from src.modules.equipment.Eac import Eac, main as eac_run
 async def main():
     start_time = time.time()
 
-    try:
-        await query_data()
-    except Exception as e:
-        print(f"Error in query_data: {str(e)}")
-        return
+    # try:
+    #     await query_data()
+    # except Exception as e:
+    #     print(f"Error in query_data: {str(e)}")
+    #     return
 
     try:
-        prediction_result = await predict_run(assetnum="A22277")
+        prediction_result = await predict_run()
         if prediction_result is False:
             print("Prediction step failed or was skipped. Skipping EAC run.")
             return