# -*- coding: utf-8 -*-
"""XGBoostWB_Forecasting_Using_Hybrid_DL_Framework_Pm2.5_(1,6)
"""
import sys
sys.version
#Import Libraries
import itertools
import pandas as pd
import numpy as np
import os
#import matplotlib
#import matplotlib.pyplot as plt
import random
# %matplotlib inline
import shutil
from random import shuffle
from sklearn.multioutput import MultiOutputRegressor
import xgboost as xgb
#TF Version
tf.__version__
num_periods_output = 6 #to predict
num_periods_input=1 #input
ALL_Test_Data=[]
ALL_Test_Prediction=[]
"""## preprocessing"""
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import Normalizer
def preprocessing(df_,num_features):
cols=df_.columns
#print('shape before',df_.shape)
#print('date',df_['Date'].dtype)
df_['Date'] = pd.to_datetime(df_['Date'], format='%Y-%m-%d')
#print('date',df_['Date'].dtype)
df_['DayofWeek']=df_['Date'].dt.dayofweek
df_['Week']=df_['Date'].dt.week
df_['dayofyear']=df_['Date'].dt.dayofyear
Train=df_[(df_.year==2010) | (df_.year==2011)| (df_.year==2012)| (df_.year==2013)]
#Train=df_[(df_.year==2010) | (df_.year==2011)| (df_.year==2012)]
Test=df_[(df_.year==2014)]
Train=Train.fillna(Train.mean())
Test=Test.fillna(Test.mean())
################################################encoding########################
Train=Train[['pm2.5','No','year', 'month', 'day', 'hour', 'DEWP', 'TEMP', 'PRES', 'cbwd', 'Iws', 'Is', 'Ir']]
cbwd=Train.pop('cbwd')
Train.loc[:,'cbwd_cv']=(cbwd=='cv')*1.0
Train.loc[:,'cbwd_NE']=(cbwd=='NE')*1.0
Train.loc[:,'cbwd_NW']=(cbwd=='NW')*1.0
Train.loc[:,'cbwd_SE']=(cbwd=='SE')*1.0
Train=Train.values
Train = Train.astype('float32')
#################################################################################
Test=Test[['pm2.5','No','year', 'month', 'day', 'hour', 'DEWP', 'TEMP', 'PRES', 'cbwd', 'Iws', 'Is', 'Ir']]
cbwd=Test.pop('cbwd')
Test.loc[:,'cbwd_cv']=(cbwd=='cv')*1.0
Test.loc[:,'cbwd_NE']=(cbwd=='NE')*1.0
Test.loc[:,'cbwd_NW']=(cbwd=='NW')*1.0
Test.loc[:,'cbwd_SE']=(cbwd=='SE')*1.0
Test=Test.values
Test = Test.astype('float32')
#################################################################################
Number_Of_Features=num_features
#split=num_periods_output+num_periods_input
############################# Normalization on train #############
PM_Train=Train[:,0]
Train=np.delete(Train,[0],1)
#normalizing data
print('Len of training ',Train)
Train = Train.astype('float32')
normalizer = MinMaxScaler().fit(Train)
Train=normalizer.transform(Train)
PM_Train=np.reshape(PM_Train,(len(PM_Train),1))
Train=np.append(PM_Train,Train, axis=1)
############################################ TRAIN windows ##################################
end=len(Train)
start=0
next=0
x_batches=[]
y_batches=[]
count=0
#print('len',len(Train))
limit=num_periods_output+num_periods_input
while start+(limit)<=end:
next=start+num_periods_input
x_batches.append(Train[start:next,:])
y_batches.append(Train[next:next+num_periods_output,0])
start=start+1
x_batches=np.asarray(x_batches)
#print('x-------------',len(x_batches))
x_batches = x_batches.reshape(-1, num_periods_input, Number_Of_Features)
y_batches=np.asarray(y_batches)
#print('y=======',len(y_batches))
y_batches = y_batches.reshape(-1, num_periods_output, 1)
#print('len x_batches ',len(x_batches))
###########################################TEST Normalization##################################
PM_Test=Test[:,0]
Test=np.delete(Test,[0],1)
Test = Test.astype('float32')
Test=normalizer.transform(Test)
PM_Test=np.reshape(PM_Test,(len(PM_Test),1))
Test=np.append(PM_Test,Test, axis=1)
#------------------
############################################ TEST windows ##################################
end_test=len(Test)
start_test=0
next_test=0
x_testbatches=[]
y_testbatches=[]
#print('len',len(Train))
while start_test+(limit)<=end_test:
next_test=start_test+num_periods_input
x_testbatches.append(Test[start_test:next_test,:])
y_testbatches.append(Test[next_test:next_test+num_periods_output,0])
start_test=start_test+num_periods_output
x_testbatches=np.asarray(x_testbatches)
#print('x----------',len(x_testbatches))
x_testbatches = x_testbatches.reshape(-1, num_periods_input, Number_Of_Features)
y_testbatches=np.asarray(y_testbatches)
#print('y====',len(y_testbatches))
y_testbatches = y_testbatches.reshape(-1, num_periods_output, 1)
print('len Test',len(Test))
print('len xTestbatches',len(x_testbatches))
return x_batches, y_batches, x_testbatches, y_testbatches
data_All=pd.DataFrame()
x_batches_Full=[]
y_batches_Full=[]
X_Test_Full=[]
Y_Test_Full=[]
range_list = [1]
data=pd.read_csv('/GBRT-for-TSF/Data/Multivariate/PM2_5.csv')
header=list(data.columns.values)
#print(header)
data=pd.DataFrame(data,columns=header)
x_batches_Full, y_batches_Full,X_Test_Full,Y_Test_Full=preprocessing(data,16)
#---------------------shuffle minibatches X and Y together-------------------------------------
#print(len(x_batches_Full),' length of all file : ',len(y_batches_Full))
combined = list(zip(x_batches_Full, y_batches_Full))
random.shuffle(combined)
shuffled_batch_features, shuffled_batch_y = zip(*combined)
#xgboost part
#print(len(x_batches_Full))
All_Training_Instances=[]
#=============== change each window into Instance =================================
for i in range(0,len(shuffled_batch_features)):
hold=[]
temp=[]
for j in range(0,len(shuffled_batch_features[i])):
#print(len(hold))
if j==(len(shuffled_batch_features[i])-1):
hold=np.concatenate((hold, shuffled_batch_features[i][j][:]), axis=None)
else:
hold=np.concatenate((hold, shuffled_batch_features[i][j][0]), axis=None)
#print(len(hold))
All_Training_Instances.append(hold)
#print(len(All_Training_Instances[0]))
#=============== change each window into Instance =================================
All_Testing_Instances=[]
#print(len(X_Test_Full))
for i in range(0,len(X_Test_Full)):
hold=[]
temp=[]
for j in range(0,len(X_Test_Full[i])):
#print(len(hold))
if j==(len(X_Test_Full[i])-1):
hold=np.concatenate((hold, X_Test_Full[i][j][:]), axis=None)
else:
hold=np.concatenate((hold, X_Test_Full[i][j][0]), axis=None)
All_Testing_Instances.append(hold)
#prediction=multioutput.predict(All_Testing_Instances)
#print(len(All_Testing_Instances[0]))
#===========================calling MultiOutput XGoost=========================
All_Testing_Instances=np.reshape(All_Testing_Instances, (len(All_Testing_Instances),len(All_Testing_Instances[0])))
Y_Test_Full=np.reshape(Y_Test_Full, (len(Y_Test_Full),num_periods_output))
#========== reshape train ==============================
All_Training_Instances=np.reshape(All_Training_Instances, (len(All_Training_Instances),len(All_Training_Instances[0])))
shuffled_batch_y=np.reshape(shuffled_batch_y, (len(shuffled_batch_y),num_periods_output))
#print(All_Training_Instances.shape)
model=xgb.XGBRegressor(learning_rate =0.02,
n_estimators=420,
max_depth=3,
min_child_weight=1,
gamma=0.0,
subsample=0.95,
colsample_bytree=0.95,
scale_pos_weight=0.9,
seed=42,silent=False)
multioutput=MultiOutputRegressor(model).fit(All_Training_Instances,shuffled_batch_y)
print('Fitting Done!')
prediction=multioutput.predict(All_Testing_Instances)
print('prediction ',prediction.shape)
print('test ',Y_Test_Full.shape)
MSE=np.mean(( prediction- Y_Test_Full)**2)
print('RMSE: ',MSE**0.5)
MAE=np.mean(np.abs( prediction- Y_Test_Full))
print('MAE: ',MAE)
View Code
一、数据预处理
cols=df_.columns #取出列数
df_['Date'] = pd.to_datetime(df_['Date'], format='%Y-%m-%d') #将数据类型转换成datetime格式
df_['DayofWeek']=df_['Date'].dt.dayofweek #DAYOFWEEK函数返回日期的工作日索引值，即星期日为1，星期一为2，星期六为7。
Train=Train.fillna(Train.mean()) #用平均值填充缺失值
Train=df_[(df_.year==2010) | (df_.year==2011)| (df_.year==2012)| (df_.year==2013)]
Test=df_[(df_.year==2014)] #2014年作为测试数据，其他作为训练数据
cbwd=Train.pop('cbwd') #返回pop删除的值并赋值给对象，原列表改变。pop()默认为最后一个元素，即pop(-1)，pop(index)指定索引
Train.loc[:,'cbwd_cv']=(cbwd=='cv')*1.0 #loc定位，[x:y,a:b] 前者为行范围，后者为列范围 后面没懂？？
Test=Test.values #values取出除了行列名称的所有值（此处用处？）
Test = Test.astype('float32') #astype 更改数据类型
PM_Train=Train[:,0] #取出所有行和第0列的数值
Train=np.delete(Train,[0],1) #numpy.delete(arr, obj, axis) 处理Train矩阵，在[0]处处理，axis=1代表按列删除。delete用法
normalizer = MinMaxScaler().fit(Train) #归一化处理 MinMaxScaler为其中一种归一化类型
标签：Do,Forecasting,_.,Models,cbwd,df,Train,year,Test From： https://www.cnblogs.com/mowanying/p/16753604.html