model.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score

from sklearn.preprocessing import StandardScaler

from src.algorithms import DAGMM,LSTMAD

scaler = StandardScaler()

data_file_path = "./DuEx_DuEn.csv"

data_sheet = pd.read_csv(data_file_path,parse_dates=True)

X = data_sheet[['si_average_speed','si_density','si_flow']]
y = np.asarray(data_sheet['cong'])
y = np.reshape(y,[y.shape[0],1])

# X = scaler.fit_transform(X)

X = pd.DataFrame(X)
y = pd.DataFrame(y)

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.33,random_state=42)

mod = DAGMM(sequence_length=480)
mod.fit(X_train)
error = mod.predict(X_test)
print(roc_auc_score(y_test,error))