fix:memperbaiki predictTomat, testingModel dan landingpage
This commit is contained in:
parent
6408166ced
commit
89ce77b631
Binary file not shown.
Binary file not shown.
|
|
@ -6,7 +6,7 @@ from sqlalchemy import select, insert, text, join, delete
|
|||
from sklearn.svm import SVR
|
||||
from sklearn.preprocessing import MinMaxScaler, StandardScaler
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.metrics import mean_absolute_error, mean_squared_error
|
||||
from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error
|
||||
from config.db import get_db, conn
|
||||
from models.index import priceTomat, settingPredict, resultPredict
|
||||
from datetime import datetime, timedelta
|
||||
|
|
@ -21,13 +21,28 @@ predict_router = APIRouter(
|
|||
@predict_router.get("/date")
|
||||
async def read_data(db: Session = Depends(get_db)):
|
||||
try:
|
||||
query = text("SELECT tanggal FROM price_tomat ORDER BY tanggal DESC LIMIT 1;")
|
||||
result = db.execute(query).fetchone()
|
||||
|
||||
if result:
|
||||
return {"tanggal": result[0]}
|
||||
else:
|
||||
return {"message": "No data found"}
|
||||
query = text("""
|
||||
SELECT
|
||||
(SELECT pt.tanggal
|
||||
FROM predict.price_tomat AS pt
|
||||
JOIN predict.result_predict AS rp ON pt.id = rp.id
|
||||
ORDER BY rp.id ASC
|
||||
LIMIT 1 OFFSET 30) AS tanggal_old,
|
||||
|
||||
(SELECT tanggal
|
||||
FROM predict.price_tomat
|
||||
ORDER BY tanggal DESC
|
||||
LIMIT 1) AS tanggal_new;
|
||||
""")
|
||||
result = db.execute(query).fetchone()
|
||||
|
||||
if result:
|
||||
return {
|
||||
"tanggal_old": result[0],
|
||||
"tanggal_new": result[1]
|
||||
}
|
||||
else:
|
||||
return {"message": "No data found"}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
|
@ -76,6 +91,15 @@ def predict_price(db: Session = Depends(get_db)):
|
|||
df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'RataRata_Kemarin', 'RataRata_2Hari_Lalu', 'RataRata_Sekarang']]
|
||||
)
|
||||
|
||||
X = df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'RataRata_Kemarin', 'RataRata_2Hari_Lalu']].values
|
||||
y = df['RataRata_Sekarang'].values
|
||||
ids = df['id'].values
|
||||
tanggal = df['Tanggal'].values
|
||||
|
||||
X_train, X_test, y_train, y_test, id_train, id_test, tanggal_train, tanggal_test = train_test_split(
|
||||
X, y, ids, tanggal, test_size=0.2, shuffle=False
|
||||
)
|
||||
|
||||
# Ambil parameter model dari database
|
||||
kernel = settings.nama_kernel
|
||||
C = float(settings.nilai_c) if settings.nilai_c is not None else 1.0
|
||||
|
|
@ -110,41 +134,58 @@ def predict_price(db: Session = Depends(get_db)):
|
|||
X = df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'RataRata_Kemarin', 'RataRata_2Hari_Lalu']].values
|
||||
y = df['RataRata_Sekarang'].values
|
||||
|
||||
# Latih model dengan semua data yang tersedia
|
||||
svr.fit(X, y)
|
||||
# # Latih model dengan semua data yang tersedia
|
||||
# svr.fit(X, y)
|
||||
|
||||
# Prediksi harga untuk semua tanggal di masa depan
|
||||
for i in range(len(df)):
|
||||
fitur_input = X[i]
|
||||
prediksi = svr.predict([fitur_input])[0]
|
||||
hasil_prediksi.append(prediksi)
|
||||
# # Prediksi harga untuk semua tanggal di masa depan
|
||||
# for i in range(len(df)):
|
||||
# fitur_input = X[i]
|
||||
# prediksi = svr.predict([fitur_input])[0]
|
||||
# hasil_prediksi.append(prediksi)
|
||||
|
||||
# Latih model dengan data latih
|
||||
svr.fit(X_train, y_train)
|
||||
|
||||
# Prediksi untuk data uji
|
||||
y_pred = svr.predict(X_test)
|
||||
|
||||
# Evaluasi Model
|
||||
mae = mean_absolute_error(y, hasil_prediksi)
|
||||
rmse = np.sqrt(mean_squared_error(y, hasil_prediksi))
|
||||
mape = np.mean(np.abs((y - hasil_prediksi) / y)) * 100
|
||||
|
||||
# Simpan hasil ke database
|
||||
for i in range(len(hasil_prediksi)):
|
||||
id_tomat = df.iloc[i]['id'] # Ambil ID dari tabel price_tomat
|
||||
prediksi_value = float(scaler.inverse_transform([[0, 0, 0, 0, 0, hasil_prediksi[i]]])[0][5])
|
||||
mae = mean_absolute_error(y_test, y_pred)
|
||||
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
|
||||
mape = mean_absolute_percentage_error(y_test, y_pred)
|
||||
|
||||
jumlah_data_dikirim = 0
|
||||
|
||||
# Gabungkan hasil prediksi ke data uji
|
||||
for i in range(len(y_pred)):
|
||||
id_tomat = id_test[i]
|
||||
tanggal_pred = tanggal_test[i]
|
||||
hasil = y_pred[i]
|
||||
hasil_asli = y_test[i]
|
||||
|
||||
# Invers hasil prediksi
|
||||
dummy_row = np.zeros((1, 6)) # [0, 0, 0, 0, 0, hasil_prediksi]
|
||||
dummy_row[0][5] = hasil
|
||||
prediksi_asli = float(scaler.inverse_transform(dummy_row)[0][5])
|
||||
|
||||
# Perbarui atau masukkan data baru
|
||||
existing = db.execute(select(resultPredict).where(resultPredict.c.id == id_tomat)).fetchone()
|
||||
if existing:
|
||||
db.execute(
|
||||
resultPredict.update()
|
||||
.where(resultPredict.c.id == id_tomat)
|
||||
.values(hasil_prediksi=prediksi_value)
|
||||
.values(hasil_prediksi=prediksi_asli)
|
||||
)
|
||||
else:
|
||||
db.execute(insert(resultPredict).values(id=id_tomat, hasil_prediksi=prediksi_value))
|
||||
db.execute(insert(resultPredict).values(id=id_tomat, hasil_prediksi=prediksi_asli))
|
||||
|
||||
jumlah_data_dikirim += 1
|
||||
|
||||
db.commit()
|
||||
|
||||
return {
|
||||
"Kernel": kernel,
|
||||
"Evaluasi": { "MAE": mae, "RMSE": rmse, "MAPE": mape },
|
||||
"Jumlah_data_dikirim": jumlah_data_dikirim,
|
||||
"Pesan": "Prediksi seluruh data berhasil disimpan ke database"
|
||||
}
|
||||
|
||||
|
|
@ -256,7 +297,7 @@ def get_price_history(
|
|||
# 10. Evaluasi Model
|
||||
mae = mean_absolute_error(y_test, y_pred)
|
||||
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
|
||||
mape = np.mean(np.abs((y_test - y_pred) / y_test)) * 100
|
||||
mape = mape = mean_absolute_percentage_error(y_test, y_pred)
|
||||
|
||||
|
||||
# 11. Prediksi 30 Hari ke Depan
|
||||
|
|
@ -311,7 +352,7 @@ def get_price_history(
|
|||
|
||||
else:
|
||||
# Ambil data historis jika tanggal bukan yang terbaru
|
||||
start_date = tanggal_input - timedelta(days=30)
|
||||
start_date = tanggal_input - timedelta(days=29)
|
||||
end_date = tanggal_input
|
||||
|
||||
query = (
|
||||
|
|
|
|||
|
|
@ -123,36 +123,60 @@ def predict_price(
|
|||
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
|
||||
mape = mean_absolute_percentage_error(y_test, y_pred)
|
||||
|
||||
# Kembalikan skala data
|
||||
# Kembalikan skala data
|
||||
df_prediksi = df.iloc[len(X_train):].copy()
|
||||
df_prediksi['Tanggal'] = df_prediksi['Tanggal'].dt.date
|
||||
df_prediksi['Harga_Prediksi'] = y_pred
|
||||
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']])
|
||||
|
||||
# Simpan hasil prediksi
|
||||
hasil_prediksi = df_prediksi[['Tanggal', 'Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_Sekarang', 'Harga_Prediksi']].to_dict(orient='records')
|
||||
# Daftar kolom yang telah dinormalisasi
|
||||
cols_scaled = ['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_Sekarang']
|
||||
|
||||
# Cek jumlah data bernilai 0 awal
|
||||
zero_data_log = {"Sebelum Preprocessing": (df == 0).sum().to_dict()}
|
||||
# Invers scaling satu per satu kolom berdasarkan mean dan std dari StandardScaler
|
||||
feature_names = scaler.feature_names_in_.tolist()
|
||||
|
||||
# Konversi ke numerik dan hitung 0 setelah konversi
|
||||
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')
|
||||
zero_data_log["Setelah Konversi Numerik"] = (df == 0).sum().to_dict()
|
||||
# Invers Harga_Prediksi berdasarkan skala 'Harga_Sekarang'
|
||||
index_harga_sekarang = feature_names.index('Harga_Sekarang')
|
||||
mean_sekarang = scaler.mean_[index_harga_sekarang]
|
||||
std_sekarang = np.sqrt(scaler.var_[index_harga_sekarang])
|
||||
df_prediksi['Harga_Prediksi'] = df_prediksi['Harga_Prediksi'] * std_sekarang + mean_sekarang
|
||||
|
||||
# Hapus NaN dan hitung 0 setelah drop NaN
|
||||
df.dropna(inplace=True)
|
||||
zero_data_log["Setelah Drop NaN Pertama"] = (df == 0).sum().to_dict()
|
||||
df_prediksi['Harga_Prediksi'] = df_prediksi['Harga_Prediksi'].round(0)
|
||||
|
||||
# Konversi tanggal
|
||||
df['Tanggal'] = pd.to_datetime(df['Tanggal'], errors='coerce')
|
||||
zero_data_log["Setelah Konversi Tanggal"] = (df == 0).sum().to_dict()
|
||||
# Invers kolom lain yang tersedia di df_prediksi
|
||||
for kolom in cols_scaled:
|
||||
if kolom in df_prediksi.columns:
|
||||
index = feature_names.index(kolom)
|
||||
mean = scaler.mean_[index]
|
||||
std = np.sqrt(scaler.var_[index])
|
||||
df_prediksi[kolom] = df_prediksi[kolom] * std + mean
|
||||
|
||||
# Tambah fitur Harga_2Hari_Lalu
|
||||
df['Harga_2Hari_Lalu'] = df['Harga_Kemarin'].shift(1)
|
||||
zero_data_log["Setelah Tambah Harga_2Hari_Lalu"] = (df == 0).sum().to_dict()
|
||||
# Export atau tampilkan hasil
|
||||
hasil_prediksi = df_prediksi[['Tanggal', 'Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak',
|
||||
'Harga_Kemarin', 'Harga_Sekarang', 'Harga_Prediksi']].to_dict(orient='records')
|
||||
|
||||
# Drop NaN setelah penambahan fitur dan hitung 0
|
||||
df.dropna(inplace=True)
|
||||
zero_data_log["Setelah Drop NaN Kedua"] = (df == 0).sum().to_dict()
|
||||
|
||||
# # Cek jumlah data bernilai 0 awal
|
||||
# zero_data_log = {"Sebelum Preprocessing": (df == 0).sum().to_dict()}
|
||||
|
||||
# # Konversi ke numerik dan hitung 0 setelah konversi
|
||||
# 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')
|
||||
# zero_data_log["Setelah Konversi Numerik"] = (df == 0).sum().to_dict()
|
||||
|
||||
# # Hapus NaN dan hitung 0 setelah drop NaN
|
||||
# df.dropna(inplace=True)
|
||||
# zero_data_log["Setelah Drop NaN Pertama"] = (df == 0).sum().to_dict()
|
||||
|
||||
# # Konversi tanggal
|
||||
# df['Tanggal'] = pd.to_datetime(df['Tanggal'], errors='coerce')
|
||||
# zero_data_log["Setelah Konversi Tanggal"] = (df == 0).sum().to_dict()
|
||||
|
||||
# # Tambah fitur Harga_2Hari_Lalu
|
||||
# df['Harga_2Hari_Lalu'] = df['Harga_Kemarin'].shift(1)
|
||||
# zero_data_log["Setelah Tambah Harga_2Hari_Lalu"] = (df == 0).sum().to_dict()
|
||||
|
||||
# # Drop NaN setelah penambahan fitur dan hitung 0
|
||||
# df.dropna(inplace=True)
|
||||
# zero_data_log["Setelah Drop NaN Kedua"] = (df == 0).sum().to_dict()
|
||||
|
||||
|
||||
|
||||
|
|
@ -164,7 +188,7 @@ def predict_price(
|
|||
"C": C,
|
||||
"Gamma": gamma,
|
||||
"Epsilon": epsilon,
|
||||
"Zero_Data_Log": zero_data_log,
|
||||
# "Zero_Data_Log": zero_data_log,
|
||||
"Data_Asli": data_asli,
|
||||
"Hasil_Preprocessing": hasil_preprocessing,
|
||||
"Hasil_Normalisasi": hasil_normalisasi,
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
import React, { useState, useEffect } from "react";
|
||||
import { format, parseISO, isAfter, startOfDay } from "date-fns";
|
||||
import { format, parseISO, isBefore, isAfter, startOfDay } from "date-fns";
|
||||
import { CalendarIcon } from "lucide-react";
|
||||
import { cn } from "@/lib/utils";
|
||||
import { Button } from "@/components/ui/button";
|
||||
|
|
@ -43,6 +43,8 @@ import { useToast } from '@/hooks/use-toast';
|
|||
const ViewGrafik = ({ date, setDate, dataYAxis, setDataYAxis, priceType, setPriceType, chartData, setChartData, setTabelDataAktual, setTabelDataPredict }) => {
|
||||
const { toast } = useToast();
|
||||
const [dateTerbaru, setDateTerbaru] = useState(null);
|
||||
const [dateTerlama, setDateTerlama] = useState(null);
|
||||
const oldestDate = dateTerlama ? parseISO(dateTerlama) : null;
|
||||
const latestDate = dateTerbaru ? parseISO(dateTerbaru) : null;
|
||||
|
||||
const handleDateChange = (selectedDate) => setDate(selectedDate);
|
||||
|
|
@ -52,8 +54,9 @@ const ViewGrafik = ({ date, setDate, dataYAxis, setDataYAxis, priceType, setPric
|
|||
try {
|
||||
const response = await axios.get(`${API_URL}/predict/date`);
|
||||
|
||||
console.log(response.data.tanggal)
|
||||
setDateTerbaru(response.data.tanggal)
|
||||
console.log(response.data.tanggal_old)
|
||||
setDateTerbaru(response.data.tanggal_new)
|
||||
setDateTerlama(response.data.tanggal_old)
|
||||
} catch (error) {
|
||||
console.error("Error fetching data", error);
|
||||
}
|
||||
|
|
@ -119,7 +122,12 @@ const ViewGrafik = ({ date, setDate, dataYAxis, setDataYAxis, priceType, setPric
|
|||
onSelect={handleDateChange}
|
||||
initialFocus
|
||||
defaultMonth={latestDate}
|
||||
disabled={(day) => latestDate && isAfter(startOfDay(day), latestDate)}
|
||||
disabled={(day) =>
|
||||
// Nonaktifkan tanggal SETELAH tanggal terbaru
|
||||
(latestDate && isAfter(startOfDay(day), latestDate)) ||
|
||||
// Nonaktifkan tanggal SEBELUM tanggal terlama
|
||||
(oldestDate && isBefore(startOfDay(day), oldestDate))
|
||||
}
|
||||
/>
|
||||
</PopoverContent>
|
||||
</Popover>
|
||||
|
|
|
|||
Loading…
Reference in New Issue