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prediction_base.py
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# -*- coding:utf-8 -*-
import pandas as pd
import torch.utils.data as Data
import torch
import numpy as np
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from statsmodels.tsa.ar_model import AutoReg
from statsmodels.tsa.arima_model import ARIMA
from math import sqrt
import xgboost as xgb
import time
from sklearn.metrics import f1_score,precision_score,recall_score,confusion_matrix,mean_squared_error,mean_absolute_error,mean_squared_log_error
def mape(y_true, y_pred):
return np.mean(np.abs((y_pred - y_true) / y_true))
def difference(dataset, interval=1):
diff = list()
for i in range(interval, len(dataset)):
value = dataset[i] - dataset[i - interval]
diff.append(value)
return np.array(diff)
def time_trans(x):
x = time.strptime(x,'%Y/%m/%d')
return time.strftime("%Y-%m-%d", x)
def prediction_base(args,Config,m=1):
if args.city==0:
if args.flag==0:
data = pd.read_csv(file1,index_col=0)
for c in ['mask','disinfectant','hand_sanitizer','vitamin','thermometer']:
data[c] = data[c].apply(lambda x:eval(x))
data = data[data['date']<'2020-04-01']
l =[[float(i[args.p_type][args.b_type]),float(i[5])] for i in data[['mask','hand_sanitizer','disinfectant','vitamin','thermometer','cases_pdf']].values.tolist()]
else:
data = pd.read_csv(file2)
data = data[data['time']<'2020-04-01']
l = [[float(i[args.p_type]),float(i[13])] for i in data[['milk', 'daily', 'vegtable', 'chocalate', 'disposable', 'small_bottal', 'drink', 'nut', 'cotton', 'present', 'china_wine', 'yogurt', 'apple', 'cases_pdf']].values.tolist()]
else:
data = pd.read_csv(file3,index_col=0).reset_index()
data = data[data['province']==args.province]
data['time'] = data['time'].apply(lambda x:time_trans(x))
data = data[data['time']<'2020-04-01']
l =[[float(i[0]),float(i[1])] for i in data[['mask','cases_pdf']].values.tolist()]
d = l
data_all = []
for index,i in enumerate(d):
if index>=args.src and index<len(d)-args.trg:
before = d[index-args.src+1:index+1]
future = d[index+1:index+1+args.trg]
data_all.append([[i[0] for i in before],[i[1] for i in before],[i[0] for i in future],[i[1] for i in future]])
train_data = data_all[:int(len(data_all)*Config.train_test_split)]
test_data = data_all[int(len(data_all)*Config.train_test_split):]
# AR, ARIMA
y_pred = []
y_test = []
if m in [1,2]:
for i in test_data:
train,test = i[0],i[2]
if m==1:
model = AutoReg(train,lags = 1)
model_fit = model.fit()
elif m==2:
model = ARIMA(train,order=[0,0,1])
model_fit = model.fit(disp=0)
predictions = model_fit.predict(start=len(train), end=len(train)+len(test)-1, dynamic=False)
y_pred += list(predictions)
y_test += test
y_pred = np.array(y_pred)
y_test = np.array(y_test)
print('mape:',mape(y_test,y_pred))
print('mae:',mean_absolute_error(y_test,y_pred))
print('rmse:',np.sqrt(mean_squared_error(y_test,y_pred)))
print('nrmse:',np.sqrt(mean_squared_error(y_test,y_pred))/(max(y_test)-min(y_test)))
if m in [3,4]:
# Xgboost
if m==3:
X_train = np.array([i[0]+i[1] for i in train_data])
X_test = np.array([i[0]+i[1] for i in test_data])
if m==4:
X_train = np.array([i[0] for i in train_data])
X_test = np.array([i[0] for i in test_data])
y_train = np.array([i[2][0] for i in train_data])
reg = xgb.XGBRegressor(n_estimators=1000)
reg.fit(X_train, y_train,verbose=False)
y_pred = []
for u in X_test:
u = u[np.newaxis, :]
temp = []
input = u
for d in range(args.trg):
pred = reg.predict(input)
input = np.array(list(u[0,1:args.src])+list(pred)+list(u[0,args.src:]))
input = input[np.newaxis,:]
temp.append(pred[0])
y_pred += temp
test = [i[2] for i in test_data]
y_test = []
for u in test:
y_test += u
y_test = np.array(y_test)
y_pred = np.array(y_pred)
print('mape:',mape(y_test,y_pred))
print('mae:',mean_absolute_error(y_test,y_pred))
print('rmse:',np.sqrt(mean_squared_error(y_test,y_pred)))
print('nrmse:',np.sqrt(mean_squared_error(y_test,y_pred))/(max(y_test)-min(y_test)))
return (mean_absolute_error(y_test,y_pred),np.sqrt(mean_squared_error(y_test,y_pred)),np.sqrt(mean_squared_error(y_test,y_pred))/(max(y_test)-min(y_test)))