179 lines
7.3 KiB
Python
179 lines
7.3 KiB
Python
import numpy as np
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import pandas as pd
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from fastapi import APIRouter, HTTPException, Depends, Query
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from sqlalchemy.orm import Session
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from sklearn.svm import SVR
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from sklearn.preprocessing import MinMaxScaler, StandardScaler
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error
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import matplotlib.pyplot as plt
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from config.db import get_db
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from models.index import priceTomat
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from middleware.index import verify_token
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# Inisialisasi Router
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testingModel_router = APIRouter(
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prefix="/testing",
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tags=["Testing Model SVR"]
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)
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@testingModel_router.get("/predict", response_model=dict,dependencies=[Depends(verify_token)])
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def predict_price(
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db: Session = Depends(get_db),
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kernel: str = Query("rbf", enum=["linear", "poly", "sigmoid", "rbf"]),
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degree: int = Query(3, description="Degree untuk kernel Polynomial"),
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coef0: float = Query(0.0, description="Coef0 untuk kernel Polynomial dan Sigmoid"),
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C: float = Query(100.0, description="Parameter regulasi C"),
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gamma: float = Query(0.1, description="Parameter gamma untuk kernel RBF, Poly, dan Sigmoid"),
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epsilon: float = Query(0.01, description="Margin kesalahan untuk SVR")
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):
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# Ambil data dari database
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data = db.query(priceTomat).all()
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if not data:
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raise HTTPException(status_code=404, detail="Data harga tomat tidak ditemukan")
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# Konversi data ke DataFrame
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df = pd.DataFrame([{
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"Tanggal": item.tanggal,
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"Pasar_Bandung": item.pasar_bandung,
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"Pasar_Ngunut": item.pasar_ngunut,
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"Pasar_Ngemplak": item.pasar_ngemplak,
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"Harga_Kemarin": item.ratarata_kemarin,
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"Harga_Sekarang": item.ratarata_sekarang
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} for item in data])
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# Simpan data asli
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data_asli = df.to_dict(orient='records')
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# Cek apakah data cukup untuk diproses
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if df.shape[0] < 3:
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raise HTTPException(status_code=400, detail="Data tidak cukup untuk melakukan prediksi")
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# Preprocessing
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df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_Sekarang']] = df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_Sekarang']].apply(pd.to_numeric, errors='coerce')
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df.dropna(inplace=True)
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df['Tanggal'] = pd.to_datetime(df['Tanggal'])
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df['Harga_2Hari_Lalu'] = df['Harga_Kemarin'].shift(1)
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# df['Harga_2Hari_Lalu'] = df['Harga_Kemarin'].shift(1).bfill()
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df.dropna(inplace=True)
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# Simpan hasil preprocessing
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hasil_preprocessing = df.to_dict(orient='records')
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# Normalisasi Data
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# scaler = MinMaxScaler()
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# df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_2Hari_Lalu', 'Harga_Sekarang']] = scaler.fit_transform(df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_2Hari_Lalu', 'Harga_Sekarang']])
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scaler = StandardScaler()
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df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_2Hari_Lalu', 'Harga_Sekarang']] = scaler.fit_transform(df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_2Hari_Lalu', 'Harga_Sekarang']])
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# Simpan hasil normalisasi
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hasil_normalisasi = df.to_dict(orient='records')
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# Split Data
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X = df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_2Hari_Lalu']]
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y = df['Harga_Sekarang']
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)
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# Simpan hasil split data
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hasil_split_data = {
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"X_train": X_train.to_dict(orient='records'),
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"X_test": X_test.to_dict(orient='records'),
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"y_train": y_train.tolist(),
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"y_test": y_test.tolist()
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}
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# Inisialisasi Model SVR dengan parameter yang dipilih
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# svr_params = {
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# "kernel": kernel,
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# "C": C,
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# "gamma": gamma,
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# "epsilon": epsilon
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# }
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# # Jika kernel adalah Polynomial atau Sigmoid, tambahkan coef0
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# if kernel in ["poly", "sigmoid"]:
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# svr_params["coef0"] = coef0
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# # Jika kernel adalah Polynomial, tambahkan degree
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# if kernel == "poly":
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# svr_params["degree"] = degree
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# svr = SVR(**svr_params)
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if kernel == "linear":
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svr = SVR(kernel="linear", C=C, epsilon=epsilon)
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elif kernel == "rbf":
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svr = SVR(kernel="rbf", C=C, gamma=gamma, epsilon=epsilon)
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elif kernel == "sigmoid":
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svr = SVR(kernel="sigmoid", C=C, gamma=gamma, coef0=coef0, epsilon=epsilon)
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elif kernel == "poly":
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svr = SVR(kernel="poly", C=C, gamma=gamma, coef0=coef0, degree=degree, epsilon=epsilon)
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svr.fit(X_train, y_train)
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# Prediksi Harga
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y_pred = svr.predict(X_test)
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# Evaluasi Model
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mae = mean_absolute_error(y_test, y_pred)
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rmse = np.sqrt(mean_squared_error(y_test, y_pred))
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mape = mean_absolute_percentage_error(y_test, y_pred)
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# Kembalikan skala data
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df_prediksi = df.iloc[len(X_train):].copy()
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df_prediksi['Harga_Prediksi'] = y_pred
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df_prediksi[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_Sekarang', 'Harga_Prediksi']] = scaler.inverse_transform(df_prediksi[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_Sekarang', 'Harga_Prediksi']])
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# Simpan hasil prediksi
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hasil_prediksi = df_prediksi[['Tanggal', 'Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_Sekarang', 'Harga_Prediksi']].to_dict(orient='records')
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# Cek jumlah data bernilai 0 awal
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zero_data_log = {"Sebelum Preprocessing": (df == 0).sum().to_dict()}
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# Konversi ke numerik dan hitung 0 setelah konversi
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df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_Sekarang']] = df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_Sekarang']].apply(pd.to_numeric, errors='coerce')
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zero_data_log["Setelah Konversi Numerik"] = (df == 0).sum().to_dict()
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# Hapus NaN dan hitung 0 setelah drop NaN
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df.dropna(inplace=True)
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zero_data_log["Setelah Drop NaN Pertama"] = (df == 0).sum().to_dict()
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# Konversi tanggal
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df['Tanggal'] = pd.to_datetime(df['Tanggal'], errors='coerce')
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zero_data_log["Setelah Konversi Tanggal"] = (df == 0).sum().to_dict()
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# Tambah fitur Harga_2Hari_Lalu
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df['Harga_2Hari_Lalu'] = df['Harga_Kemarin'].shift(1)
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zero_data_log["Setelah Tambah Harga_2Hari_Lalu"] = (df == 0).sum().to_dict()
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# Drop NaN setelah penambahan fitur dan hitung 0
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df.dropna(inplace=True)
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zero_data_log["Setelah Drop NaN Kedua"] = (df == 0).sum().to_dict()
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# Kembalikan semua hasil dalam JSON
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return {
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"Kernel_Digunakan": kernel,
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"Degree": degree if kernel == "poly" else None,
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"Coef0": coef0 if kernel in ["poly", "sigmoid"] else None,
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"C": C,
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"Gamma": gamma,
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"Epsilon": epsilon,
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"Zero_Data_Log": zero_data_log,
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"Data_Asli": data_asli,
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"Hasil_Preprocessing": hasil_preprocessing,
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"Hasil_Normalisasi": hasil_normalisasi,
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"Hasil_Split_Data": hasil_split_data,
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"Hasil_Prediksi": hasil_prediksi,
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"Evaluasi_Model": {
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"MAE": mae,
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"RMSE": rmse,
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"MAPE": mape
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}
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}
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