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code_wushen.Rmd
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---
title: "Power"
author: "WRQ"
date: "2018/10/22"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
- [Project Document](https://jiaxiangli.netlify.com/slides/phv/note.html)
- [trelliscope](https://jiaxiangli.netlify.com/slides/phv/trelliscope/index.html)
也可以访问
**p** https://jiaxiangli.netlify.com/slides/phv/trelliscope/p/index.html
**tsi** https://jiaxiangli.netlify.com/slides/phv/trelliscope/tsi/index.html
**tsi_real** https://jiaxiangli.netlify.com/slides/phv/trelliscope/tsi_real/index.html
[因子分析 Factor Analysis](https://jiaxiangli.netlify.com/2018/10/factor-analysis/)
- [DART booster — xgboost 0.80 documentation](https://xgboost.readthedocs.io/en/latest/tutorials/dart.html)
- [你羽翼下的风](https://mp.weixin.qq.com/s/Yix0xVp2SiqaAcuS6Q049g)
- [HyperparameterHunter](https://github.com/HunterMcGushion/hyperparameter_hunter)
- [20 interaction effect的解释](https://jiaxiangli.netlify.com/2018/05/intro-to-econometrics/#interactioneffect)
- [R包本地安装](https://jiaxiangli.netlify.com/2018/04/r-base/)
- [JiaxiangBU](https://github.com/JiaxiangBU/add2evaluation)
- [add to evaluation of model](https://jiaxiangbu.github.io/add2evaluation/)
```{r,message=FALSE}
library(mlr)
library(data.table)
library(tidyverse)
#library(lightgbm)
#library(catboost)
library(xgboost)
library(prophet)
library(rwavelet)
setwd('D:/power')
```
## load data
电站1，2，3，4的装机功率的值分别为10，10，40，50,预测功率小于0.03*实际功率时该样本不参与评分。
```{r}
train1 <- fread('train_1.csv',encoding = 'UTF-8',col.names = c('time','irradiance','Speed','Direction','Temp','Pressure','humidity','real_irradiance','Power'))
train2 <- fread('train_2.csv',encoding = 'UTF-8',col.names = c('time','irradiance','Speed','Direction','Temp','Pressure','humidity','real_irradiance','Power'))
train3 <- fread('train_3.csv',encoding = 'UTF-8',col.names = c('time','irradiance','Speed','Direction','Temp','Pressure','humidity','real_irradiance','Power'))
train4 <- fread('train_4.csv',encoding = 'UTF-8',col.names = c('time','irradiance','Speed','Direction','Temp','Pressure','humidity','real_irradiance','Power'))
test1 <- fread('test_1.csv',encoding = 'UTF-8',col.names = c('id','time','irradiance','Speed','Direction','Temp','Pressure','humidity'))
test2 <- fread('test_2.csv',encoding = 'UTF-8',col.names = c('id','time','irradiance','Speed','Direction','Temp','Pressure','humidity'))
test3 <- fread('test_3.csv',encoding = 'UTF-8',col.names = c('id','time','irradiance','Speed','Direction','Temp','Pressure','humidity'))
test4 <- fread('test_4.csv',encoding = 'UTF-8',col.names = c('id','time','irradiance','Speed','Direction','Temp','Pressure','humidity'))
```
```{r}
train1_ <- train1 %>%
separate(col = 'time',sep = ' ',into = c('Date','Time')) %>%
separate(col = 'Date',into = c('year','month','day'),sep = '-') %>%
separate(col = 'Time',into = c('H','M','S'), sep = ':') %>%
mutate(GRP = as.factor(1))
train2_ <- train2 %>%
separate(col = 'time',sep = ' ',into = c('Date','Time')) %>%
separate(col = 'Date',into = c('year','month','day'),sep = '-') %>%
separate(col = 'Time',into = c('H','M','S'), sep = ':') %>%
mutate(GRP = as.factor(2))
train3_ <- train3 %>%
separate(col = 'time',sep = ' ',into = c('Date','Time')) %>%
separate(col = 'Date',into = c('year','month','day'),sep = '-') %>%
separate(col = 'Time',into = c('H','M','S'), sep = ':') %>%
mutate(GRP = as.factor(3))
train4_ <- train4 %>%
separate(col = 'time',sep = ' ',into = c('Date','Time')) %>%
separate(col = 'Date',into = c('year','month','day'),sep = '-') %>%
separate(col = 'Time',into = c('H','M','S'), sep = ':') %>%
mutate(GRP = as.factor(4))
train <- rbindlist(l = list(train1_,train2_,train3_,train4_))
```
```{r}
train_mean <- train[,lapply(.SD,mean),by = .(GRP,year,month,day),.SDcols = c("irradiance","Speed","Direction","Temp","Pressure","humidity","real_irradiance","Power")]
fwrite(train_mean,file = 'train_mean.csv')
```
```{r}
test1_ <- test1 %>%
select(-id) %>%
separate(col = 'time',sep = ' ',into = c('Date','Time')) %>%
separate(col = 'Date',into = c('year','month','day'),sep = '-') %>%
separate(col = 'Time',into = c('H','M','S'), sep = ':') %>%
mutate(GRP = as.factor(1))
test2_ <- test2 %>%
select(-id) %>%
separate(col = 'time',sep = ' ',into = c('Date','Time')) %>%
separate(col = 'Date',into = c('year','month','day'),sep = '-') %>%
separate(col = 'Time',into = c('H','M','S'), sep = ':') %>%
mutate(GRP = as.factor(2))
test3_ <- test3 %>%
select(-id) %>%
separate(col = 'time',sep = ' ',into = c('Date','Time')) %>%
separate(col = 'Date',into = c('year','month','day'),sep = '-') %>%
separate(col = 'Time',into = c('H','M','S'), sep = ':') %>%
mutate(GRP = as.factor(3))
test4_ <- test4 %>%
select(-id) %>%
separate(col = 'time',sep = ' ',into = c('Date','Time')) %>%
separate(col = 'Date',into = c('year','month','day'),sep = '-') %>%
separate(col = 'Time',into = c('H','M','S'), sep = ':') %>%
mutate(GRP = as.factor(4))
test <- rbindlist(l = list(test1_,test2_,test3_,test4_))
```
```{r}
str(train)
```
```{r}
print(cat('train1相关系数',cor(train1_$real_irradiance,train1_$Power)))
print(cat('train2相关系数',cor(train2_$real_irradiance,train2_$Power)))
print(cat('train3相关系数',cor(train3_$real_irradiance,train3_$Power)))
print(cat('train4相关系数',cor(train4_$real_irradiance,train4_$Power)))
print(cat('train相关系数',cor(train$real_irradiance,train$Power)))
```
## mlr.xgboost
```{r}
train.xgb <- train[,.(month,day,H,irradiance,Speed,Direction,Temp,Pressure,humidity,real_irradiance,Power,GRP)]
train.xgb$GRP <- as.integer(train.xgb$GRP)
train.xgb <- createDummyFeatures(obj = as.data.frame(train.xgb),target = 'Power',cols = c('month','day','H'))
task <- makeRegrTask(id = 'power',data = train.xgb,target = 'Power')
lrn.xgb <- makeLearner('regr.xgboost',predict.type = 'response',par.vals = list(objective = 'reg:linear' ,eta = 0.1,nrounds = 1000,max_depth =6,
subsample = 0.9,colsample_bytree =0.9, early_stopping_rounds = 20))
rdesc = makeResampleDesc("CV", iters = 3, stratify.cols = "GRP")
r = resample(learner = lrn.xgb, task = task, resampling = rdesc, show.info = FALSE,measures = list(rmse))
model.xgb = train(lrn.xgb,task)
## model.xgb中包含real_irradiance实发辐照度 ,但是test文件中没有real_irradiance指标
```
Resample Result
Task: power
Learner: regr.xgboost
Aggr perf: rmse.test.rmse=1.2243323
Runtime: 2934.56
- `cut_interval` makes n groups with equal range,
- `cut_number` makes n groups with (approximately) equal numbers of observations;
- `cut_width` makes groups of width width.
```{r}
# 不包含日期特征
train.xgb <- train[,.(irradiance,Speed,Direction,Temp,Pressure,humidity,Power,GRP)]
train.xgb$GRP <- as.integer(train.xgb$GRP)
task <- makeRegrTask(id = 'power',data = train.xgb,target = 'Power')
lrn.xgb <- makeLearner('regr.xgboost',predict.type = 'response',par.vals = list(objective = 'reg:linear' ,
eta = 0.1,
nrounds = 2000,
max_depth = 6,
subsample = 0.9,
colsample_bytree =0.9,
early_stopping_rounds = 20))
rdesc = makeResampleDesc("CV", iters = 3, stratify.cols = "GRP")
r1 = resample(learner = lrn.xgb, task = task, resampling = rdesc, show.info = FALSE,measures = list(rmse))
model.xgb1 = train(lrn.xgb,task)
```
Resample Result
Task: power
Learner: regr.xgboost
Aggr perf: rmse.test.rmse=2.7772884
Runtime: 1419.54
```{r}
# 不含有实发辐照度,仅保留4,5,6,7,8,9,10月数据
# resampling
train.xgb2 <- train[month %in% c('04','05','06','07','08','09','10'),.(month,day,H,irradiance,Speed,Direction,Temp,Pressure,humidity,Power,GRP)]
train.xgb2$month <- as.factor(train.xgb2$month)
train.xgb2$day <- as.factor(train.xgb2$day)
train.xgb2$H <- as.factor(train.xgb2$H)
train.xgb2$GRP <- as.integer(train.xgb2$GRP)
train.xgb2 <- createDummyFeatures(obj = as.data.frame(train.xgb2),target = 'Power',cols = c('month','day','H'))
task2 <- makeRegrTask(id = 'power',data = train.xgb2,target = 'Power')
lrn.xgb <- makeLearner('regr.xgboost',predict.type = 'response',par.vals = list(objective = 'reg:linear' ,
eta = 0.1,
nrounds = 2000,
max_depth = 10,
subsample = 0.9,
colsample_bytree =0.9,
early_stopping_rounds = 20))
rdesc = makeResampleDesc("CV", iters = 3, stratify.cols = "GRP")
r2 = resample(learner = lrn.xgb, task = task2, resampling = rdesc, show.info = FALSE,measures = list(rmse))
model.xgb2 = train(lrn.xgb,task2)
```
nrounds = 1000,max_depth = 5:
Resample Result
Task: power
Learner: regr.xgboost
Aggr perf: rmse.test.rmse=2.1617714
Runtime: 1729.99
nrounds = 2000,max_depth = 10:
Resample Result
Task: power
Learner: regr.xgboost
Aggr perf: rmse.test.rmse=1.8885630
Runtime: 5790.29
```{r}
# 把booster由gbtree调整为gblinear,eval_metric = 'rmse'
# 看来gblinear的booster的效果不好
lrn.xgb2 <- makeLearner('regr.xgboost',predict.type = 'response',par.vals = list(booster = 'gblinear',
objective = 'reg:linear',
eta = 0.1,
nrounds = 1000,
max_depth = 8,
subsample = 0.9,
colsample_bytree =0.9,
early_stopping_rounds = 20,
eval_metric = 'rmse'))
#tree_method = 'gpu_hist',
#predictor = 'gpu_predictor'
rdesc = makeResampleDesc("CV", iters = 3, stratify.cols = "GRP")
r3 = resample(learner = lrn.xgb2, task = task2, resampling = rdesc, show.info = FALSE,measures = list(rmse))
model.xgb3 = train(lrn.xgb2,task2)
```
Resample Result
Task: power
Learner: regr.xgboost
Aggr perf: rmse.test.rmse=6.0056378
Runtime: 434.077
```{r}
# 把booster调整为dart,eval_metric = 'rmse'
lrn.xgb4 <- makeLearner('regr.xgboost',predict.type = 'response',par.vals = list(booster = 'dart',
objective = 'reg:linear',
eta = 0.1,
nrounds = 1000,
max_depth = 8,
subsample = 0.9,
colsample_bytree =0.9,
early_stopping_rounds = 20,
eval_metric = 'rmse'))
#tree_method = 'gpu_hist',
#predictor = 'gpu_predictor'
rdesc = makeResampleDesc("CV", iters = 3, stratify.cols = "GRP")
r4 = resample(learner = lrn.xgb4, task = task2, resampling = rdesc, show.info = FALSE,measures = list(rmse))
model.xgb4 = train(lrn.xgb4,task2)
```
Resample Result
Task: power
Learner: regr.xgboost
Aggr perf: rmse.test.rmse=1.9218663
Runtime: 5257.34
```{r}
# 把booster调整为dart,eval_metric = 'rmse'
lrn.xgb5 <- makeLearner('regr.xgboost',predict.type = 'response',par.vals = list(booster = 'dart',
objective = 'reg:linear',
eta = 0.1,
nrounds = 2000,
max_depth = 10,
subsample = 0.9,
colsample_bytree =0.9,
early_stopping_rounds = 20,
normalize_type = 'forest',
gamma = 2,
lambda =2,
eval_metric = 'rmse'))
#tree_method = 'gpu_hist',
#predictor = 'gpu_predictor'
rdesc = makeResampleDesc("CV", iters = 3, stratify.cols = "GRP")
r5 = resample(learner = lrn.xgb5, task = task2, resampling = rdesc, show.info = FALSE,measures = list(rmse))
model.xgb5 = train(lrn.xgb5,task2)
```
Resample Result
Task: power
Learner: regr.xgboost
Aggr perf: rmse.test.rmse=1.9172243
Runtime: 20977.2
增加normalize_type,gamma,lambda
Resample Result
Task: power
Learner: regr.xgboost
Aggr perf: rmse.test.rmse=1.8957285
Runtime: 15879
```{r}
# 把booster调整为dart,eval_metric = 'rmse'
lrn.xgb6 <- makeLearner('regr.xgboost',predict.type = 'response',par.vals = list(booster = 'gbtree',
objective = 'reg:linear',
eta = 0.1,
nrounds = 2000,
max_depth = 8,
colsample_bytree =0.9,
subsample = 0.9,
early_stopping_rounds = 20,
normalize_type = 'forest',
gamma = 2,
lambda =2,
eval_metric = 'rmse'))
#tree_method = 'gpu_hist',
#predictor = 'gpu_predictor'
rdesc = makeResampleDesc("CV", iters = 3, stratify.cols = "GRP")
r6 = resample(learner = lrn.xgb6, task = task2, resampling = rdesc, show.info = FALSE,measures = list(rmse))
lrn = makeTuneWrapper("classif.ksvm", resampling = inner, par.set = ps, control = ctrl, show.info = FALSE)
outer = makeResampleDesc("CV", iters = 3)
r6.outer = resample(lrn.xgb6, task2, resampling = outer, extract = getTuneResult, show.info = FALSE,measures = list(rmse))
model.xgb6 = train(lrn.xgb6,task2)
```
Resample Result
Task: power
Learner: regr.xgboost
Aggr perf: rmse.test.rmse=1.9181958
Runtime: 3085.82
```{r}
# 把booster调整为dart,eval_metric = 'rmse',objective = reg:tweedie
lrn.xgb7 <- makeLearner('regr.xgboost',predict.type = 'response',par.vals = list(booster = 'dart',
objective = 'reg:tweedie',
eta = 0.1,
nrounds = 2000,
max_depth = 10,
subsample = 0.9,
colsample_bytree =0.9,
early_stopping_rounds = 20,
normalize_type = 'forest',
gamma = 2,
lambda =2,
eval_metric = 'rmse'))
#tree_method = 'gpu_hist',
#predictor = 'gpu_predictor'
rdesc = makeResampleDesc("CV", iters = 3, stratify.cols = "GRP")
# Tuning in inner resampling loop
ps = makeParamSet(
makeIntegerParam("nrounds", lower = 100,upper = 1000,tunable = TRUE),
makeNumericParam("eta", lower = 0.01,upper = 1,tunable = TRUE),
makeIntegerParam('max_depth',lower = 6,upper = 8,tunable = TRUE),
makeNumericParam('subsample',lower = 0.5,upper = 0.9,tunable = TRUE),
makeNumericParam('colsample_bytree',lower = 0.5,upper = 0.9,tunable = TRUE),
makeDiscreteParam('normalize_type',values = c('forest','tree'),tunable = TRUE),
makeNumericParam('gamma',lower = 0,upper = 10,tunable = TRUE),
makeNumericParam('lambda',lower = 1,upper = 10,tunable = TRUE)
)
ctrl = makeTuneControlRandom(same.resampling.instance = TRUE,maxit = 100)
inner = makeResampleDesc("Subsample", iters = 2)
lrn = makeTuneWrapper("regr.xgboost", resampling = inner, par.set = ps, control = ctrl, show.info = FALSE)
# Outer resampling loop
outer = makeResampleDesc("CV", iters = 3)
r7.outer = resample(lrn, task2, resampling = outer, extract = getTuneResult, show.info = FALSE,measures = list(rmse))
model.xgb7 = train(lrn,task2)
```
```{r}
# 把booster由gbtree调整为gblinear,eval_metric = 'rmse',objective = 'reg:gamma',gamma分布要求y变量必须大于0
# Turn off parameter checking completely
# configureMlr(on.par.without.desc = "quiet")
lrn.xgb5 <- makeLearner('regr.xgboost',predict.type = 'response',par.vals = list(booster = 'gblinear',
objective = 'reg:gamma',
eta = 0.1,
nrounds = 1000,
max_depth = 8,
subsample = 0.9,
colsample_bytree =0.9,
early_stopping_rounds = 20,
eval_metric = 'rmse'))
#tree_method = 'gpu_hist',
#predictor = 'gpu_predictor'
```
```{r}
test.xgb2 <- test[,.(month,day,H,irradiance,Speed,Direction,Temp,Pressure,humidity,GRP)]
test.xgb2$month <- as.factor(test.xgb2$month)
test.xgb2$day <- as.factor(test.xgb2$day)
test.xgb2$H <- as.factor(test.xgb2$H)
test.xgb2$GRP <- as.integer(test.xgb2$GRP)
test.xgb2 <- createDummyFeatures(obj = as.data.frame(test.xgb2),cols = c('month','day','H'))
pred1 = predict(model.xgb1,newdata = test.xgb2[,c("irradiance","Speed","Direction","Temp","Pressure","humidity","GRP")])
fwrite(data.frame('id' = 1:46634,'predicition' = pred1$data),file = '/shzx_data/stock/shzx/tmp/result_xgb1.csv')
pred2 = predict(model.xgb2,newdata = test.xgb2)
fwrite(data.frame('id' = 1:46634,'predicition' = pred2$data),file = '/shzx_data/stock/shzx/tmp/result_xgb2.csv')
# 0.167904
pred3 = predict(model.xgb3,newdata = test.xgb2)
fwrite(data.frame('id' = 1:46634,'predicition' = pred3$data),file = '/shzx_data/stock/shzx/tmp/result_xgb3.csv')
# 0.30***9
pred4 = predict(model.xgb4,newdata = test.xgb2)
fwrite(data.frame('id' = 1:46634,'predicition' = pred4$data),file = '/shzx_data/stock/shzx/tmp/result_xgb4.csv')
# 0.167629
pred5 = predict(model.xgb5,newdata = test.xgb2)
fwrite(data.frame('id' = 1:46634,'predicition' = pred5$data),file = '/shzx_data/stock/shzx/tmp/result_xgb5.csv')
# 0.16***5
pred6 = predict(model.xgb6,newdata = test.xgb2)
fwrite(data.frame('id' = 1:46634,'predicition' = pred6$data),file = '/shzx_data/stock/shzx/tmp/result_xgb6.csv')
# 0.16***5
```
## catboost with mlr
```{r}
makeRLearner.regr.catboost = function() {
makeRLearnerRegr(
cl = "regr.catboost",
package = "catboost",
par.set = makeParamSet(
makeIntegerLearnerParam(id ="iterations", default=1000,lower=1),
makeIntegerLearnerParam(id ="border_count",default=128,lower = 1),
makeNumericLearnerParam(id = "learning_rate", default = 0.1, lower = 0),
makeIntegerLearnerParam(id = "depth", default = 6, lower = 1,upper = 16),
makeNumericLearnerParam(id = "rsm", default = 0.9, lower = 0, upper = 1),
makeDiscreteLearnerParam(id = 'loss_function', default = 'RMSE' , values = c('MAE','MAPE','Poisson','Quantile','RMSE','LogLinQuantile','SMAPE'),tunable = FALSE)
),
par.vals = list(task_type = 'GPU'), #
properties = c("numerics", "weights","factors","missings"),
name = "catboost",
short.name = "catboost",
note = "First try at catboost"
)
}
trainLearner.regr.catboost = function(.learner, .task, .subset, .weights = NULL, ...) {
f = getTaskDesc(.task)
data = getTaskData(.task, .subset,target.extra = TRUE)
train_pool <- catboost.load_pool(data = as.matrix(data$data),
label = data$target)
catboost::catboost.train(learn_pool = train_pool, test_pool = NULL)
}
predictLearner.regr.catboost= function(.learner, .model, .newdata, ...) {
catboost::catboost.predict(.model$learner.model, catboost.load_pool(as.matrix(.newdata)))
}
registerS3method("makeRLearner", "regr.catboost", makeRLearner.regr.catboost)
registerS3method("trainLearner", "regr.catboost", trainLearner.regr.catboost)
registerS3method("predictLearner", "regr.catboost", predictLearner.regr.catboost)
```
```{r,eval=FASLE}
split <- 0.7
setorder(tmp,Date,sec_code)
pool_data <- tmp[c_10_10_4 != -100,] # 剔除目标变量为-100的情况,剔除不需要的变量
unique_date <- pool_data[,unique(Date)]
uniqueN_date <- pool_data[,uniqueN(Date)]
train <- pool_data[Date %in% unique_date[1:round(uniqueN_date*split,digits = 0)],]
test <- pool_data[Date %in% unique_date[(round(uniqueN_date*split,digits = 0)+ 1):uniqueN_date],]
train <- train[,lapply(.SD, as.numeric)] #将变量都处理为数值型
test <- test[, lapply(.SD,as.numeric)] #将变量都处理为数值型
setDF(train)
setDF(test)
features <- setdiff(colnames(pool_data),c('c_10_10_4','sec_code','Date'))
train_alpha_pool <- catboost.load_pool(data = train[,features],
label = train[,'c_10_10_4'])
test_alpha_pool <- catboost.load_pool(data=test[,features],
label = test[,'c_10_10_4'])
fit_params <- list(iterations = 1000,
thread_count = 10,
loss_function = 'MultiClass', # MultiClass,MultiClassOneVsAll
# ignored_features = c(4,5),
# use_best_model = TRUE,
border_count = 32,
depth = 10, #树的最大深度
learning_rate = 0.1, #学习率
# class_weights = c(2,1,1,1,1,1),
# l2_leaf_reg = 1, # l2正则化
# class_weights = c(3,1,1,1,1,1), # 类别权重
# od_type = 'Iter',
# od_wait = 30,
# early_stopping_rounds = 10,
custom_loss = c('Accuracy', 'Recall','Precision'),
logging_level = 'Silent',
random_seed = 123,
task_type='CPU', #R不支持GPU计算,python支持
train_dir = file.path(train_dir_path,'cjy_alpha101_c_10_10_4'))
model_alpha <- catboost.train(learn_pool = train_alpha_pool, test_pool = test_alpha_pool, fit_params)
```
```{r}
rdesc = makeResampleDesc("CV", iters = 3, stratify.cols = "GRP")
# 采用嵌套采样评估模型
outer = makeResampleDesc("CV", iters = 3)
r = resample(lrn.catboost, task2, resampling = outer, extract = getTuneResult, show.info = FALSE,measures = list(rmse))
```
## lightgbm with mlr
```{r}
makeRLearner.regr.lightgbm = function() {
makeRLearnerRegr(
cl = "regr.lightgbm",
package = "lightgbm",
par.set = makeParamSet(
makeIntegerLearnerParam(id ="num_iterations", default=100,lower=1),
makeIntegerLearnerParam(id ="verbose",default=1),
makeDiscreteLearnerParam(id = "boosting", default = "gbdt", values = c("gbdt", "dart","goss")),
makeNumericLearnerParam(id = "learning_rate", default = 0.1, lower = 0),
makeIntegerLearnerParam(id = "max_depth", default = -1, lower = -1),
makeIntegerLearnerParam(id = "min_data_in_leaf", default = 20, lower = 0),
makeIntegerLearnerParam(id = "num_leaves", default=31, lower=1),
makeNumericLearnerParam(id = "feature_fraction", default = 1, lower = 0, upper = 1),
makeNumericLearnerParam(id = "bagging_fraction", default = 1, lower = 0, upper = 1),
makeNumericLearnerParam(id = "bagging_freq", default = 0, lower = 0),
makeNumericLearnerParam(id = "min_gain_to_split", default = 0, lower = 0),
makeLogicalLearnerParam(id="use_missing",default=TRUE,tunable = FALSE),
makeNumericLearnerParam(id = "min_sum_hessian", default=10),
makeDiscreteLearnerParam(id = "metric", default = "None",values = c('l1', 'l2','mape'),tunable = FALSE)
),
par.vals = list(objective="regression"),
properties = c("numerics", "weights","missings"),
name = "LightGBM",
short.name = "lightgbm",
note = "First try at this"
)
}
## 定义train方法
trainLearner.regr.lightgbm = function(.learner, .task, .subset, .weights = NULL, ...) {
f = getTaskDesc(.task)
data = getTaskData(.task, .subset,target.extra = TRUE)
lgb.data = lgb.Dataset(as.matrix(data$data), label = data$target)
lightgbm::lgb.train(data = lgb.data,
objective="regression",
verbosity = -1,
verbose = -1,
record = TRUE,...)
}
## 定义预测方法
predictLearner.regr.lightgbm= function(.learner, .model, .newdata, ...) {
predict(.model$learner.model, as.matrix(.newdata))
}
## 注册新方法
registerS3method("makeRLearner", "regr.lightgbm", makeRLearner.regr.lightgbm)
registerS3method("trainLearner", "regr.lightgbm", trainLearner.regr.lightgbm)
registerS3method("predictLearner", "regr.lightgbm", predictLearner.regr.lightgbm)
```
```{r}
lgb.Dataset_ringratio_list <- generate_lgb.Dataset(data_path = data_path_ringratio,invalid_features = invalid_features,target = 'c_10_10_4',split = 0.7)
dtrain <- lgb.Dataset_ringratio_list[[1]]
dtest <- lgb.Dataset_ringratio_list[[2]]
params <- list(objective = "multiclass", metric = "multi_error", num_class = 6)
# To train with valids, use lgb.train, which contains more advanced features
# valids allows us to monitor the evaluation result on all data in the list
params <- list(objective = "multiclass", metric = "multi_error", num_class = 6)
valids <- list(train = dtrain, test = dtest)
model <- lgb.train(params = params,
data = dtrain,
nrounds = 200,
valids = valids,
min_data = 1,
learning_rate = 1,
max_bin = 300, # default 255
early_stopping_rounds = 10)
```
```{r}
rdesc = makeResampleDesc("CV", iters = 3, stratify.cols = "GRP")
r = resample(learner = lrn.lightgbm, task = task2, resampling = rdesc, show.info = FALSE,measures = list(rmse))
```
## benchmark
```{r}
# lrn.xgb <- makeLearner('regr.xgboost',predict.type = 'response',par.vals = list(objective = 'reg:linear' ,eta = 0.1,nrounds = 1000,max_depth =6, subsample = 0.9, colsample_bytree =0.9, early_stopping_rounds = 20))
# lrn.lightgbm <- makeLearner('regr.lightgbm',predict.type = 'response',par.vals = list(objective = 'alpha' num_iterations = 1000, boosting = "goss", #gbdt, rf, dart, goss learning_rate = 0.1,metric = 'l2_root', # rmse
#bagging_fraction = 0.9, max_depth = 8))
# lrn.catboost <- makeLearner('regr.catboost',predict.type = 'response',par.vals = list(objective = 'reg:linear' ,eta = 0.1,nrounds = 1000,max_depth =6,subsample = 0.9,colsample_bytree =0.9,early_stopping_rounds = 20))
lrns <- list('regr.xgboost','regr.catboost')
task2 <- makeRegrTask(id = 'power',data = train.xgb2,target = 'Power')
rdesc = makeResampleDesc("CV", iters = 3, stratify.cols = "GRP")
bmr = benchmark(lrns, task2, rdesc,measures = list(rmse))
```
## catboost
```{r}
data_train <- train %>% as.data.table()
data_train <- data_train[month %in% c('04','05','06','07','08','09','10'),.(month,day,H,irradiance,Speed,Direction,Temp,Pressure,humidity,Power,GRP)]
data_train$month <- as.factor(data_train$month)
data_train$day <- as.factor(data_train$day)
data_train$H <- as.factor(data_train$H)
data_test <- test %>% as.data.table()
data_test <- data_test[,.(month,day,H,irradiance,Speed,Direction,Temp,Pressure,humidity,GRP)]
data_test$month <- as.factor(data_test$month)
data_test$day <- as.factor(data_test$day)
data_test$H <- as.factor(data_test$H)
```
```{r}
# 自定义数据读取，拆分函数
# data:数据路径；target：目标变量；split：训练集和测试集的拆分比例
generate_pool <- function(data,target,split){
setDF(data)
train_indices <- sample(1:nrow(data),split*nrow(data))
test_indices <- setdiff(1:nrow(data),train_indices)
features <- setdiff(colnames(data),target)
train_pool <- catboost.load_pool(data = data[train_indices,features],
label = data[train_indices,target],
cat_features = c(0,1,2,10))
test_pool <- catboost.load_pool(data=data[test_indices,features],
label = data[test_indices,target],
cat_features = c(0,1,2,10))
return(list(train_pool,test_pool))
}
```
```{r}
pool <- generate_pool(data = data_train,target = 'Power',split = 0.8)
train_pool <- pool[[1]]
test_pool <- pool[[2]]
```
```{r}
fit_params_rmse <- list(iterations = 1000,
thread_count = 10, #The number of threads to use during training.
loss_function = 'RMSE', # MAE,MAPE,Poisson,Quantile,RMSE,LogLinQuantile,SMAPE
# ignored_features = c(4,5),
# use_best_model = TRUE,
border_count = 128, # The number of splits for numerical features. Allowed values are integers from 1 to 255 inclusively.default 128
depth = 8, #树的最大深度
learning_rate = 0.1, #学习率
l2_leaf_reg = 1, # l2正则化
# class_weights = c(3,1,1,1,1,1), # 类别权重
od_type = 'Iter',
od_wait = 30,
rsm = 0.9,
# early_stopping_rounds = 10,
custom_loss = c('RMSE'),
random_seed = 123,
task_type='CPU', #R不支持GPU计算,python支持
train_dir = '/shzx_data/stock/shzx/tmp/train_dir')
model_catboost_fit_params_rmse <- catboost.train(learn_pool = train_pool, test_pool = test_pool, fit_params_rmse)
```
```{r}
predict_pool <- catboost.load_pool(data = data_test,
label = NULL,
cat_features = c(0,1,2,10))
predict_catboost1 <- catboost.predict(model = model_catboost_fit_params_rmse, pool = predict_pool, prediction_type = c('RawFormulaVal'))
fwrite(data.table('id' = 1:dim(predict_pool)[1],'predicition' = predict_catboost1),file = '/shzx_data/stock/shzx/tmp/result_catboost1.csv')
# 0.18**4
```
```{r,eval = FALSE}
跳转到jupyter.shzx.com 用shzx账户登陆
import pandas as pd
power = pd.read_csv('/shzx_data/stock/shzx/tmp/result_catboost1.csv')
power.to_csv('/home/shzx/power_predict_catboost.csv')
```
```{r}
fit_params_Quantile <- list(iterations = 10000,
thread_count = 10, #The number of threads to use during training.
loss_function = 'Quantile', # MAE,MAPE,Poisson,Quantile,RMSE,LogLinQuantile,SMAPE
# ignored_features = c(4,5),
# use_best_model = TRUE,
border_count = 128, # The number of splits for numerical features. Allowed values are integers from 1 to 255 inclusively.default 128
depth = 10, #树的最大深度
learning_rate = 0.1, #学习率
l2_leaf_reg = 1, # l2正则化
# class_weights = c(3,1,1,1,1,1), # 类别权重
od_type = 'Iter',
od_wait = 30,
rsm = 0.9,
# early_stopping_rounds = 10,
custom_loss = c('RMSE'),
random_seed = 123,
task_type='CPU', #R不支持GPU计算,python支持
train_dir = '/shzx_data/stock/shzx/tmp/train_dir/Quantile')
model_catboost_Quantile <- catboost.train(learn_pool = train_pool, test_pool = test_pool, fit_params_Quantile)
predict_catboost_Quantile <- catboost.predict(model = model_catboost_Quantile, pool = predict_pool, prediction_type = c('RawFormulaVal'))
fwrite(data.table('id' = 1:46634,'predicition' = predict_catboost_Quantile),file = '/shzx_data/stock/shzx/tmp/result_catboost_Quantile.csv')
# 0.16***9
fit_params_Poisson <- list(iterations = 10000,
thread_count = 10, #The number of threads to use during training.
loss_function = 'Poisson', # MAE,MAPE,Poisson,Quantile,RMSE,LogLinQuantile,SMAPE
# ignored_features = c(4,5),
# use_best_model = TRUE,
border_count = 128, # The number of splits for numerical features. Allowed values are integers from 1 to 255 inclusively.default 128
depth = 10, #树的最大深度
learning_rate = 0.1, #学习率
l2_leaf_reg = 1, # l2正则化
# class_weights = c(3,1,1,1,1,1), # 类别权重
od_type = 'Iter',
od_wait = 30,
rsm = 0.9,
# early_stopping_rounds = 10,
custom_loss = c('RMSE'),
random_seed = 123,
task_type='CPU', #R不支持GPU计算,python支持
train_dir = '/shzx_data/stock/shzx/tmp/train_dir/Poisson')
model_catboost_Poisson <- catboost.train(learn_pool = train_pool, test_pool = test_pool, fit_params_Poisson)
predict_catboost_Poisson <- catboost.predict(model = model_catboost_Poisson, pool = predict_pool, prediction_type = c('RawFormulaVal'))
fwrite(data.table('id' = 1:46634,'predicition' = predict_catboost_Poisson),file = '/shzx_data/stock/shzx/tmp/result_catboost_Poisson.csv')
# 0.36***1
fit_params_LogLinQuantile <- list(iterations = 10000,
thread_count = 10, #The number of threads to use during training.
loss_function = 'LogLinQuantile', # MAE,MAPE,Poisson,Quantile,RMSE,LogLinQuantile,SMAPE
# ignored_features = c(4,5),
# use_best_model = TRUE,
border_count = 128, # The number of splits for numerical features. Allowed values are integers from 1 to 255 inclusively.default 128
depth = 10, #树的最大深度
learning_rate = 0.1, #学习率
l2_leaf_reg = 1, # l2正则化
# class_weights = c(3,1,1,1,1,1), # 类别权重
od_type = 'Iter',
od_wait = 30,
rsm = 0.9,
# early_stopping_rounds = 10,
custom_loss = c('RMSE'),
random_seed = 123,
task_type='CPU', #R不支持GPU计算,python支持
train_dir = '/shzx_data/stock/shzx/tmp/train_dir/LogLinQuantile')
model_catboost_LogLinQuantile <- catboost.train(learn_pool = train_pool, test_pool = test_pool, fit_params_LogLinQuantile)
predict_catboost_LogLinQuantile <- catboost.predict(model = model_catboost_LogLinQuantile, pool = predict_pool, prediction_type = c('RawFormulaVal'))
fwrite(data.table('id' = 1:46634,'predicition' = predict_catboost_LogLinQuantile),file = '/shzx_data/stock/shzx/tmp/result_catboost_LogLinQuantile.csv')
# 0.34**5
####################################################### 无法运行 ################################################
fit_params_MAPE <- list(iterations = 10000,
thread_count = 10, #The number of threads to use during training.
loss_function = 'MAPE', # MAE,MAPE,Poisson,Quantile,RMSE,LogLinQuantile,SMAPE
# ignored_features = c(4,5),
# use_best_model = TRUE,
border_count = 128, # The number of splits for numerical features. Allowed values are integers from 1 to 255 inclusively.default 128
depth = 10, #树的最大深度
learning_rate = 0.1, #学习率
l2_leaf_reg = 50, # l2正则化
# class_weights = c(3,1,1,1,1,1), # 类别权重
od_type = 'Iter',
od_wait = 30,
rsm = 0.9,
# early_stopping_rounds = 10,
custom_loss = c('RMSE'),
random_seed = 123,
task_type='CPU', #R不支持GPU计算,python支持
train_dir = '/shzx_data/stock/shzx/tmp/train_dir/MAPE')
model_catboost_MAPE <- catboost.train(learn_pool = train_pool, test_pool = test_pool, fit_params_MAPE)
# Training has stopped (degenerate solution on iteration 0, probably too small l2-regularization, try to increase it)
predict_catboost_MAPE <- catboost.predict(model = model_catboost_MAPE, pool = predict_pool, prediction_type = c('RawFormulaVal'))
fwrite(data.table('id' = 1:46634,'predicition' = predict_catboost_MAPE),file = '/shzx_data/stock/shzx/tmp/result_catboost_MAPE.csv')
#####################################################################################################################
fit_params_MAE <- list(iterations = 10000,
thread_count = 10, #The number of threads to use during training.
loss_function = 'MAE', # MAE,MAPE,Poisson,Quantile,RMSE,LogLinQuantile,SMAPE
# ignored_features = c(4,5),
# use_best_model = TRUE,
border_count = 128, # The number of splits for numerical features. Allowed values are integers from 1 to 255 inclusively.default 128
depth = 8, #树的最大深度
learning_rate = 0.1, #学习率
l2_leaf_reg = 1, # l2正则化
# class_weights = c(3,1,1,1,1,1), # 类别权重
od_type = 'Iter',
od_wait = 30,
rsm = 0.9,
# early_stopping_rounds = 10,
custom_loss = c('RMSE'),
random_seed = 123,
task_type='CPU', #R不支持GPU计算,python支持
train_dir = '/shzx_data/stock/shzx/tmp/train_dir/MAE')
model_catboost_MAE <- catboost.train(learn_pool = train_pool, test_pool = test_pool, fit_params_MAE)
predict_catboost_MAE <- catboost.predict(model = model_catboost_MAE, pool = predict_pool, prediction_type = c('RawFormulaVal'))
fwrite(data.table('id' = 1:46634,'predicition' = predict_catboost_MAE),file = '/shzx_data/stock/shzx/tmp/result_catboost_MAE.csv')
# 0.16***9
```
## lightgbm
```{r}
data_train <- train %>% as.data.table() %>% .[month %in% c('04','05','06','07','08','09','10'),.(month,day,H,irradiance,Speed,Direction,Temp,Pressure,humidity,Power,GRP)]
# lgb.prepare:Attempts to prepare a clean dataset to prepare to put in a lgb.Dataset. Factors and characters are converted to numeric without integers. Please use lgb.prepare_rules if you want to apply this transformation to other datasets.
data_train <- lgb.prepare(data = data_train)
dtrain <- data_train[1:round(0.8*nrow(data_train)),]
dtest <- data_train[round(0.8*nrow(data_train)):nrow(data_train),]
dtrain <- lgb.Dataset(data = as.matrix(dtrain[,-'Power']),
label = dtrain[,'Power'])
# categorical_feature = c(1,2,3,10)
dtest <- lgb.Dataset(data = as.matrix(dtest[,-'Power']),
label = dtest[,'Power'])
data_valid <- test %>% as.data.table() %>% .[,.(month,day,H,irradiance,Speed,Direction,Temp,Pressure,humidity,GRP)]
data_valid <- lgb.prepare(data = data_valid)
# Creating the LightGBM dataset with categorical features
# The categorical features must be indexed like in R (1-indexed, not 0-indexed)
# We can now train a model
model <- lgb.train(params = list(objective = "tweedie",
metric = "l2",
min_data = 1,
learning_rate = 0.01,
min_hessian = 1,
max_depth = 10,
num_leaves = 60),
verbose = 0,
data = dtrain,
nrounds = 2000,
early_stopping_round = 30,
valids = list(train = dtrain, test = dtest))
```
```{r}
pred <- predict(model, as.matrix(data_valid))
```
# rwavelet 小波滤波
```{r}
train1 <- fread('train_1.csv',encoding = 'UTF-8',col.names = c('time','irradiance','Speed','Direction','Temp','Pressure','humidity','real_irradiance','Power'))
train2 <- fread('train_2.csv',encoding = 'UTF-8',col.names = c('time','irradiance','Speed','Direction','Temp','Pressure','humidity','real_irradiance','Power'))
train3 <- fread('train_3.csv',encoding = 'UTF-8',col.names = c('time','irradiance','Speed','Direction','Temp','Pressure','humidity','real_irradiance','Power'))
train4 <- fread('train_4.csv',encoding = 'UTF-8',col.names = c('time','irradiance','Speed','Direction','Temp','Pressure','humidity','real_irradiance','Power'))
test1 <- fread('test_1.csv',encoding = 'UTF-8',col.names = c('id','time','irradiance','Speed','Direction','Temp','Pressure','humidity'))
test2 <- fread('test_2.csv',encoding = 'UTF-8',col.names = c('id','time','irradiance','Speed','Direction','Temp','Pressure','humidity'))
test3 <- fread('test_3.csv',encoding = 'UTF-8',col.names = c('id','time','irradiance','Speed','Direction','Temp','Pressure','humidity'))
test4 <- fread('test_4.csv',encoding = 'UTF-8',col.names = c('id','time','irradiance','Speed','Direction','Temp','Pressure','humidity'))
train_original <- rbindlist(l = list(train1,train2,train3,train4))
test_original <- rbindlist(l = list(test1,test2,test3,test4))
```
```{r}
# MakeONFilter(Type, Par)
# Type:string, 'Haar', 'Beylkin', 'Coiflet', 'Daubechies' 'Symmlet', 'Vaidyanathan','Battle'.
# Par:integer, it is a parameter related to the support and vanishing moments of the wavelets, explained below for each wavelet.
Haar <- MakeONFilter('Haar', 10)
Beylkin <- MakeONFilter('Beylkin', 10)
Coiflet <- MakeONFilter('Coiflet', 1)
Daubechies <- MakeONFilter('Daubechies', 4)
Symmlet <- MakeONFilter('Symmlet', 4)
Vaidyanathan <- MakeONFilter('Vaidyanathan', 10)
Battle <- MakeONFilter('Battle', 1)
```
```{r}
# nrow(train1) 66859
# log2(66859) 16.02883
# 2^16 65536
n <- nrow(train1)
J <- 2^floor(log2(nrow(train1)))
wc <- train1[(n-J+1):n,Temp]
L <- 3
# Forward Wavelet Transform (periodized, orthogonal)前向小波变换
# IWT_PO Inverse Wavelet Transform (periodized, orthogonal).反向小波变换
fest <- IWT_PO(wc, L, qmf) #L << J
```
```{r}
# data:data.table
# col:character vector,column to transfer
# method: a MakeONFilter object
wave_transform <- function(data,col_transfer,col_preserve,method){
n <- nrow(data)
J <- 2^floor(log2(nrow(data)))
data_transform <- data[(n-J+1):n,]
L <- 3
data_output <- data_transform[,lapply(.SD,IWT_PO,L = 3,qmf = method),.SDcols = col_transfer]
return(cbind(data_transform[,col_preserve,with =FALSE],data_output))
}
```
```{r}
cols_train <- c("irradiance","Speed","Direction","Temp","Pressure","humidity","real_irradiance")
cols_preserve <- c('time','Power')
cols_test <- c("irradiance","Speed","Direction","Temp","Pressure","humidity")
train1_wavelet <- wave_transform(data = train1,col_transfer = cols_train,col_preserve = cols_preserve,method = Daubechies)
train2_wavelet <- wave_transform(data = train2,col_transfer = cols_train,col_preserve = cols_preserve,method = Daubechies)
train3_wavelet <- wave_transform(data = train3,col_transfer = cols_train,col_preserve = cols_preserve,method = Daubechies)
train4_wavelet <- wave_transform(data = train4,col_transfer = cols_train,col_preserve = cols_preserve,method = Daubechies)
fwrite(train1_wavelet,'D:/power/train1_wavelet.csv')
fwrite(train2_wavelet,'D:/power/train2_wavelet.csv')
fwrite(train3_wavelet,'D:/power/train3_wavelet.csv')
fwrite(train4_wavelet,'D:/power/train4_wavelet.csv')
```
# xgboost
### xgb1
利用除了year，S，real_irradiance 之外的特征建模;8:2,'reg:linear',
```{r}
train_numeric <- train[,lapply(.SD,FUN = as.numeric),.SDcols = c('year','month','day','H','M','S','irradiance','Speed','Direction','Temp','Pressure','humidity','real_irradiance','Power','GRP')]
train_prob <- 0.8
train_inds <- sample(x = train_numeric[,.N],size = train_numeric[,.N]*train_prob)
test_inds <- setdiff(1:train_numeric[,.N],train_inds)
dtrain <- xgb.DMatrix(data = as.matrix(train_numeric[train_inds,!c('year','S','real_irradiance','Power')]), label=as.matrix(train_numeric[train_inds,'Power']))
dtest <- xgb.DMatrix(data = as.matrix(train_numeric[test_inds,!c('year','S','real_irradiance','Power')]), label=as.matrix(train_numeric[test_inds,'Power']))
watchlist <- list(train=dtrain, test=dtest)
params = list(objective = "reg:linear",booster = 'gbtree', eta = 0.1, max_depth =8, subsample = 0.8, colsample_bytree = 0.8,eval_metric = 'mae',early_stopping_rounds = 10)
bst <- xgb.train(data=dtrain,params = params,nrounds = 5000, watchlist=watchlist)
test_numeric <- test[,lapply(.SD,FUN = as.numeric),.SDcols = c('year','month','day','H','M','S','irradiance','Speed','Direction','Temp','Pressure','humidity','GRP')]
pred <- predict(bst, as.matrix(test_numeric[,!c('year','S')]))
fwrite(data.table('id' = 1:46571,'predicition' = pred),file = 'D:/power/pred/pred1.csv')
# 0.19
```
### xgb2
只筛选power>=0.03*ci的数据
```{r}
train_above_3percent <- train[(GRP=='1' & Power>=0.03*10)|(GRP=='2' & Power>=0.03*10)|(GRP=='3' & Power>=0.03*40)|(GRP=='4' & Power>=0.03*50)]
train_numeric <- train_above_3percent[,lapply(.SD,FUN = as.numeric),.SDcols = c('year','month','day','H','M','S','irradiance','Speed','Direction','Temp','Pressure','humidity','real_irradiance','Power','GRP')]
train_prob <- 0.8
train_inds <- sample(x = train_numeric[,.N],size = train_numeric[,.N]*train_prob)
test_inds <- setdiff(1:train_numeric[,.N],train_inds)
dtrain <- xgb.DMatrix(data = as.matrix(train_numeric[train_inds,!c('year','S','real_irradiance','Power')]), label=as.matrix(train_numeric[train_inds,'Power']))
dtest <- xgb.DMatrix(data = as.matrix(train_numeric[test_inds,!c('year','S','real_irradiance','Power')]), label=as.matrix(train_numeric[test_inds,'Power']))
watchlist <- list(train=dtrain, test=dtest)
params = list(objective = "reg:linear",booster = 'gbtree', eta = 0.1, max_depth =8, subsample = 0.8, colsample_bytree = 0.8,eval_metric = 'mae',early_stopping_rounds = 10)
bst <- xgb.train(data=dtrain,params = params,nrounds = 5000, watchlist=watchlist)
test_numeric <- test[,lapply(.SD,FUN = as.numeric),.SDcols = c('year','month','day','H','M','S','irradiance','Speed','Direction','Temp','Pressure','humidity','GRP')]
pred <- predict(bst, as.matrix(test_numeric[,!c('year','S')]))
fwrite(data.table('id' = 1:46571,'predicition' = pred),file = 'D:/power/pred/pred2.csv')
# 0.19 预测结果和xgb1一样??
```
### xgb3
在xgb2的基础上把objective调整为'reg:tweedie'
```{r}
params = list(objective = "reg:tweedie",booster = 'gbtree', eta = 0.1, max_depth =8, subsample = 0.8, colsample_bytree = 0.8,eval_metric = 'mae',early_stopping_rounds = 10)
bst <- xgb.train(data=dtrain,params = params,nrounds = 5000, watchlist=watchlist)