[英]How to iteratively train forecast models (GAM, MARS, …) based on selected days and calculate the variable importance in the time period
我有一个数据表,它总是有不同数量的列和列名以及一个名为days
的数字变量(这个变量也不同;现在/这里:50):
library(data.table)
library(caret)
days -> 50
## Create random data table: ##
dt.train <- data.table(date = seq(as.Date('2020-01-01'), by = '1 day', length.out = 366),
"DE" = rnorm(366, 35, 1), "Wind" = rnorm(366, 5000, 2), "Solar" = rnorm(366, 3, 2),
"Nuclear" = rnorm(366, 100, 5), "ResLoad" = rnorm(366, 200, 3), check.names = FALSE)
我正在建模/训练线性 Model (= LM),我想预测 DE 列并计算变量相对于days
变量的重要性。 请参阅以下代码片段:
## MODEL FITTING: ##
## Linear Model: ##
## Function that calculates the iteratively prediction: ##
calcPred <- function(data){
## Model fitting: ##
xgbModel <- stats::lm(DE ~ .-1-date, data = data)
## Model training: ##
stats::predict.lm(xgbModel, data)
}
## Function that calculates the iteratively variable importance: ##
varImportance <- function(data){
## Model fitting: ##
xgbModel <- stats::lm(DE ~ .-1-date, data = data)
terms <- attr(xgbModel$terms , "term.labels")
varimp <- caret::varImp(xgbModel)
importance <- data[, .(date, imp = t(varimp))]
}
## Train Data PREDICTION with iteratively xgbModel: ##
dt.train <- dt.train[, c('prediction') := calcPred(.SD), by = seq_len(nrow(dt.train)) %/% days]
## Iteratively variable importance:##
dt.importance <- data.table::copy(dt.train[, c("prediction") := NULL])
dt.importance <- dt.importance[, varImportance(.SD), by = seq_len(nrow(dt.train)) %/% days]
这里发生了什么:我的 model 总是训练 50 天,然后准确地在这段时间内预测这些训练完成 50 天。 这一直持续到我的桌子的结束日期。 此外, varImportance()
function 给出了训练间隔(此处为每 50 天)中预测变量(所有列,不包括date
和DE
)的变量重要性。
最初我认为我也可以将函数calcPred()
和varImportance()
用于广义加法 Model (= GAM) 和多元自适应回归样条 (= MARS) 或梯度提升 (= GB),但不幸的是,此版本仅适用于LM。
我现在想简要介绍一下 model 适合其他三个模型,但我也需要你的帮助,以便最终计算 GAM、MARS 和 GB model 以及 LM。
游戏:
## Create data-vector with dates of dt.train: ##
v.trainDate <- dt.train$date
## Delete column "date" of train data for model fitting: ##
dt.train <- dt.train[, c("date") := NULL]
## Preparation for GAM: ##
trainDataNames <- names(dt.train)
responseVar <- trainDataNames[1]
trainDataNames <- trainDataNames[trainDataNames != responseVar]
## Create right-hand side of GAM model in string/character format: ##
formulaRight <- paste('s(', trainDataNames, ')', sep = '', collapse = ' + ')
## Create the whole formula for GAM model in string/character format: ##
formulaGAM <- paste(responseVar, '~', formulaRight, collapse = ' ')
## Coerce to a formula object: ##
formulaGAM <- as.formula(formulaGAM)
## MODEL FITTING: ##
## Generalized Additive Model: ##
xgbModel <- mgcv::gam(formulaGAM, data = dt.train)
## Train and Test Data PREDICTION with xgbModel: ##
dt.train$prediction <- mgcv::predict.gam(xgbModel, dt.train)
## Add date columns to dt.train and dt.test: ##
dt.train <- data.table(date = v.trainDate, dt.train)
火星:
## Create vectors with all DE values of train data set: ##
v.trainY <- dt.train$DE
## Save dates of train data in an extra vector: ##
v.trainDate <- dt.train$date
## Create train matrices for GB model fitting: ##
m.trainData <- as.matrix(dt.train[, c("date", "DE") := list(NULL, NULL)])
## Model fitting with grid-search: ##: ##
hyper_grid <- expand.grid(degree = 1:3,
nprune = seq(2, 100, length.out = 10) %>% floor()
)
## MODEL FITTING: ##
## Multivariate Adaptive Regression Spline: ##
xgbModel <- caret::train(x = m.trainData,
y = v.trainY,
method = "earth",
metric = "RMSE",
trControl = trainControl(method = "cv", number = 10),
tuneGrid = hyper_grid
)
## Train Data PREDICTION with xgbModel: ##
dt.train$prediction <- stats::predict(xgbModel, dt.train)
国标:
## Create vectors with all DE values of train data set: ##
v.trainY <- dt.train$DE
## Save dates of train data in an extra vector: ##
v.trainDate <- dt.train$date
## Create train matrices for GB model fitting: ##
m.trainData <- as.matrix(dt.train[, c("date", "DE") := list(NULL, NULL)])
## Gradient Boosting with hyper parameter tuning: ##
xgb_trcontrol <- caret::trainControl(method = "cv",
number = 3,
allowParallel = TRUE,
verboseIter = TRUE,
returnData = FALSE
)
xgbgrid <- base::expand.grid(nrounds = c(15000), # 15000
max_depth = c(2),
eta = c(0.01),
gamma = c(1),
colsample_bytree = c(1),
min_child_weight = c(2),
subsample = c(0.6)
)
## MODEL FITTING: ##
## Gradient Boosting: ##
xgbModel <- caret::train(x = m.trainData,
y = v.trainY,
trControl = xgb_trcontrol,
tuneGrid = xgbgrid,
method = "xgbTree"
)
## Train data PREDICTION with xgbModel: ##
dt.train$prediction <- stats::predict(xgbModel, m.trainData)
## Add DE and date columns to dt.train: ##
dt.train <- data.table(DE = v.trainY, dt.train)
dt.train <- data.table(date = v.trainDate, dt.train)
我如何计算其他三个模型与 LM 相同的值? 我希望有一个人可以帮助我。 很抱歉这个问题拖了这么久。
您可以将 model 定义为 function 作为参数传递给calcPred
和varImportance
。
例如使用LM
model <- function(data) {stats::lm(DE ~ .-1-date, data = data)}
使用GAM
model <- function(data) {mgcv::gam(formulaGAM, data = data)}
与MARS
:
model <- function(data) {
hyper_grid <- expand.grid(degree = 1:3,
nprune = seq(2, 100, length.out = 10) %>% floor())
caret::train(x = subset(data, select = -DE),
y = data$DE,
method = "earth",
metric = "RMSE",
trControl = trainControl(method = "cv", number = 10),
tuneGrid = hyper_grid)
}
我更新了代码以考虑到这个新参数:
library(data.table)
library(caret)
library(magrittr)
days <- 50
## Create random data table: ##
dt.train <- data.table(date = seq(as.Date('2020-01-01'), by = '1 day', length.out = 366),
"DE" = rnorm(366, 35, 1), "Wind" = rnorm(366, 5000, 2), "Solar" = rnorm(366, 3, 2),
"Nuclear" = rnorm(366, 100, 5), "ResLoad" = rnorm(366, 200, 3), check.names = FALSE)
dt.importance <- data.table::copy(dt.train)
## Define model & prediction functions ##
model <- function(data) {stats::lm(DE ~ .-1-date, data = data)}
predict <- function(data,model) {stats::predict(model, data)}
calcPred <- function(data,model){
if (nrow(data)==days) {
stats::predict(model,data) } else {
NULL }
}
## Function that calculates the iteratively variable importance: ##
varImportance <- function(data,model){
cat(nrow(data),'\n')
if (nrow(data)==days) {
terms <- attr(model$terms , "term.labels")
varimp <- caret::varImp(model)
importance <- data[, .(date, imp = t(varimp))]} else
{ NULL }
}
## Train Data PREDICTION with iteratively xgbModel: ##
dt.train <- dt.train[, c('prediction') := calcPred(.SD,model(.SD)), by = (seq_len(nrow(dt.train))-1) %/% days]
## Iteratively variable importance:##
dt.importance <- dt.importance[, varImportance(.SD,model(.SD)), by = (seq_len(nrow(dt.train))-1) %/% days]
要使用其他型号,只需在上述代码中使用您希望的 model function。 这适用于您提供的数据集上的LM
或GAM
。
不幸的是, varImp
似乎不适用于MARS
的数据集,尽管这似乎可行。
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