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feat: Implement upsert functionality for prediction data, updating existing records or inserting new ones, and remove the previous deletion of all predictions.

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main
MrWaradana 3 weeks ago
parent d2861e1a35
commit 0e954d2dc0
2 changed files with 59 additions and 57 deletions
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116
src/modules/equipment/Prediksi.py
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@ -239,57 +239,39 @@ class Prediksi:
# print(f"HTTP error occurred: {e}")
# return {}
# Menyiapkan data untuk batch insert
# print(f"Data to be inserted: {data}")
# Menyiapkan data untuk batch insert atau update
records_to_insert = []
check_existence_query = """
SELECT id FROM lcc_equipment_tr_data
WHERE assetnum = %s AND tahun = %s AND is_actual = 0
"""
update_query = """
UPDATE lcc_equipment_tr_data
SET
rc_cm_material_cost = %s,
rc_cm_labor_cost = %s,
rc_pm_material_cost = %s,
rc_pm_labor_cost = %s,
rc_oh_material_cost = %s,
rc_oh_labor_cost = %s,
rc_predictive_material_cost = %s,
rc_predictive_labor_cost = %s,
updated_by = 'Sys',
updated_at = NOW()
WHERE id = %s
"""
for _, row in data.iterrows():
max_seq = max_seq + 1
# (token already stored before defining fetch_api_data)
# maintain previous cm_interval between iterations using attribute on fetch_api_data
# if not hasattr(fetch_api_data, "prev_cm"):
# fetch_api_data.prev_cm = None
# Update values from API (current year)
# api_data = await fetch_api_data(equipment_id, row["year"])
# if api_data and "data" in api_data and isinstance(api_data["data"], list) and len(api_data["data"]) > 0:
# try:
# cur_cm = float(api_data["data"][0].get("num_fail", row.get("cm_interval", 1)))
# except Exception:
# cur_cm = float(row.get("cm_interval", 1)) if not pd.isna(row.get("cm_interval", None)) else 1.0
# else:
# try:
# val = float(row.get("cm_interval", 1))
# cur_cm = val if val >= 1 else 1.0
# except Exception:
# cur_cm = 1.0
# Determine previous cm_interval: prefer stored prev_cm, otherwise try API for previous year, else fallback to cur_cm
# if fetch_api_data.prev_cm is not None:
# prev_cm = float(fetch_api_data.prev_cm)
# else:
# try:
# api_prev = await fetch_api_data(equipment_id, int(row["year"]) - 1)
# if api_prev and "data" in api_prev and isinstance(api_prev["data"], list) and len(api_prev["data"]) > 0:
# prev_cm = float(api_prev["data"][0].get("num_fail", cur_cm))
# else:
# # attempt to use any available previous value from the row if present, otherwise fallback to current
# prev_cm = float(row.get("cm_interval", cur_cm)) if not pd.isna(row.get("cm_interval", None)) else cur_cm
# except Exception:
# prev_cm = cur_cm
# compute difference: current year interval minus previous year interval
# try:
# cm_interval_diff = float(cur_cm) - float(prev_cm)
# except Exception:
# cm_interval_diff = 0.0
# append record using the difference for raw_cm_interval
records_to_insert.append(
(
str(uuid4()), # id
int(max_seq), # seq
int(row["year"]),
equipment_id,
# Check if data exists
cursor.execute(check_existence_query, (equipment_id, int(row["year"])))
existing_record = cursor.fetchone()
if existing_record:
# Update existing record
record_id = existing_record[0]
cursor.execute(update_query, (
float(row.get("rc_cm_material_cost", 0)) if not pd.isna(row.get("rc_cm_material_cost", 0)) else 0.0,
float(row.get("rc_cm_labor_cost", 0)) if not pd.isna(row.get("rc_cm_labor_cost", 0)) else 0.0,
float(row.get("rc_pm_material_cost", 0)) if not pd.isna(row.get("rc_pm_material_cost", 0)) else 0.0,
@ -298,16 +280,36 @@ class Prediksi:
float(row.get("rc_oh_labor_cost", 0)) if not pd.isna(row.get("rc_oh_labor_cost", 0)) else 0.0,
float(row.get("rc_predictive_material_cost", 0)) if not pd.isna(row.get("rc_predictive_material_cost", 0)) else 0.0,
float(row.get("rc_predictive_labor_cost", 0)) if not pd.isna(row.get("rc_predictive_labor_cost", 0)) else 0.0,
record_id
))
else:
max_seq = max_seq + 1
# Prepare for insert
records_to_insert.append(
(
str(uuid4()), # id
int(max_seq), # seq
int(row["year"]),
equipment_id,
float(row.get("rc_cm_material_cost", 0)) if not pd.isna(row.get("rc_cm_material_cost", 0)) else 0.0,
float(row.get("rc_cm_labor_cost", 0)) if not pd.isna(row.get("rc_cm_labor_cost", 0)) else 0.0,
float(row.get("rc_pm_material_cost", 0)) if not pd.isna(row.get("rc_pm_material_cost", 0)) else 0.0,
float(row.get("rc_pm_labor_cost", 0)) if not pd.isna(row.get("rc_pm_labor_cost", 0)) else 0.0,
float(row.get("rc_oh_material_cost", 0)) if not pd.isna(row.get("rc_oh_material_cost", 0)) else 0.0,
float(row.get("rc_oh_labor_cost", 0)) if not pd.isna(row.get("rc_oh_labor_cost", 0)) else 0.0,
float(row.get("rc_predictive_material_cost", 0)) if not pd.isna(row.get("rc_predictive_material_cost", 0)) else 0.0,
float(row.get("rc_predictive_labor_cost", 0)) if not pd.isna(row.get("rc_predictive_labor_cost", 0)) else 0.0,
)
)
)
# store current cm for next iteration
# fetch_api_data.prev_cm = cur_cm
# Eksekusi batch insert
cursor.executemany(insert_query, records_to_insert)
# Eksekusi batch insert jika ada data baru
if records_to_insert:
cursor.executemany(insert_query, records_to_insert)
connection.commit()
# print("Data proyeksi berhasil dimasukkan ke database.")
# Recalculate total costs and update asset criticality
self.__update_data_lcc(equipment_id)
except Exception as e:
print(f"Error saat menyimpan data ke database: {e}")
@ -930,7 +932,7 @@ class Prediksi:
predictions_df = pd.DataFrame(predictions)
# print(predictions_df)
# Hapus data prediksi yang ada sebelumnya
self.__delete_predictions_from_db(assetnum)
# self.__delete_predictions_from_db(assetnum)
# Insert hasil prediksi ke database
try:

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