64 lines
2.2 KiB
Python
64 lines
2.2 KiB
Python
from fastapi import APIRouter, HTTPException
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from schema.prediction import (
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AutoPredictionRequest,
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AutoPredictionResponse,
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ManualPredictionRequest,
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ManualPredictionResponse,
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)
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from utils.statistic.auto_arima import auto_arima_forecast
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from utils.statistic.manual_arima import manual_arima_forecast
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from utils.data_preparation import read_csv_string_to_df, df_group_by_interval
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router = APIRouter()
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@router.post("/predict/private/auto", response_model=AutoPredictionResponse)
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def predict_auto(request: AutoPredictionRequest):
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try:
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df = read_csv_string_to_df(request.csv_string)
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series = df['amount']
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result = auto_arima_forecast(series, forecast_periods=request.future_steps)
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return AutoPredictionResponse(
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rmse=result["rmse"],
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mape=result["mape"],
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arima_order=tuple(result["arima_order"]),
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prediction=result["prediction"],
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lower=result["lower"],
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upper=result["upper"],
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success=True
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)
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except ValueError as ve:
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raise HTTPException(status_code=422, detail=str(ve))
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Terjadi kesalahan saat memproses data: {e}")
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@router.post("/predict/private/manual", response_model=ManualPredictionResponse)
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def predict_manual(request: ManualPredictionRequest):
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try:
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df = read_csv_string_to_df(request.csv_string)
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series = df['amount']
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# Validasi arimaModel
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if len(request.arima_model) != 3:
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raise HTTPException(status_code=400, detail="Parameter arimaModel harus terdiri dari 3 elemen (p, d, q).")
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p, d, q = request.arima_model
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result = manual_arima_forecast(series, p=p, d=d, q=q, forecast_periods=request.future_steps)
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return ManualPredictionResponse(
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arima_order=tuple(result["arima_order"]),
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prediction=result["prediction"],
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lower=result["lower"],
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upper=result["upper"],
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success=True,
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)
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except ValueError as ve:
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raise HTTPException(status_code=422, detail=str(ve))
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Terjadi kesalahan saat memproses data: {e}")
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