68 lines
1.7 KiB
Python
68 lines
1.7 KiB
Python
import pickle
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import pandas as pd
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import numpy as np
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from sklearn.metrics import mean_absolute_error
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# ======================
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# LOAD MODEL
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# ======================
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with open("model_pegas.pkl", "rb") as f:
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model_pegas = pickle.load(f)
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print("Model berhasil dimuat")
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# ======================
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# LOAD DATA
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# ======================
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df_gabungan = pd.read_excel("Data_Gabungan.xlsx")
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df_eka = pd.read_excel("Data_Penjualan_Eka.xlsx")
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# ======================
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# PREPROCESSING
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# ======================
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df_gabungan['ds'] = pd.to_datetime(df_gabungan['tanggal_penjualan'])
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df_eka['ds'] = pd.to_datetime(df_eka['tanggal_penjualan'])
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df_gab_w = df_gabungan.resample('W', on='ds')[['jumlah']].sum().reset_index().rename(columns={'jumlah':'y'})
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df_eka_w = df_eka.resample('W', on='ds')[['jumlah']].sum().reset_index().rename(columns={'jumlah':'y'})
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# ======================
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# REGRESSOR
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# ======================
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df_gab_w['is_pengetatan'] = (df_gab_w['ds'] >= '2025-10-01').astype(int)
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# ======================
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# HITUNG RASIO
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overlap = pd.merge(df_gab_w, df_eka_w, on='ds', suffixes=('_gab','_eka'))
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ratio = overlap['y_eka'].sum() / overlap['y_gab'].sum()
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print("Rasio:", ratio)
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# PREDIKSI
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forecast = model_pegas.predict(df_gab_w)
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eval_df = pd.merge(df_eka_w[['ds','y']], forecast[['ds','yhat']], on='ds')
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y_true = eval_df['y'].values
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y_pred_scaled = eval_df['yhat'].values * ratio
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# EVALUASI
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mae = mean_absolute_error(y_true, y_pred_scaled)
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mape = np.mean(
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np.abs((y_true[y_true>0] - y_pred_scaled[y_true>0]) / y_true[y_true>0])
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) * 100
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print("----------------------------")
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print("HASIL EVALUASI MODEL PEGAS")
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print("MAE :", round(mae,2), "tabung")
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print("MAPE:", round(mape,2), "%")
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print("----------------------------") |