【发布时间】：2021-05-20 10:48:16
【问题描述】：
我有一个数据表，它总是有不同数量的列和列名，还有一个名为 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/训练一个线性模型 (= LM)，我想在其中预测 DE 列，并计算变量相对于 days 变量的重要性。见以下代码sn -p：
## 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$terms , "term.labels")
varimp <- caret::varImp(xgbModel)
importance <- data[, .(date, imp = t(varimp))]
}
## Train Data PREDICTION with iteratively xgbModel: ##
dt.train <- dt.train[, c('prediction') := calcPred(.SD), by = seq_len(nrow(dt.train)) %/% days]
## Iteratively variable importance:##
dt.importance <- data.table::copy(dt.train[, c("prediction") := NULL])
dt.importance <- dt.importance[, varImportance(.SD), by = seq_len(nrow(dt.train)) %/% days]
这里发生了什么：我的模型总是训练 50 天，然后准确地在这段时间内预测这些训练完成的 50 天。这一直持续到我的桌子的结束日期。此外，varImportance() 函数给出了预测变量（所有列，不包括 date 和 DE）在训练间隔（此处为每 50 天）中的变量重要性。
最初我认为我也可以将函数 calcPred() 和 varImportance() 用于广义加法模型 (= GAM) 和多元自适应回归样条 (= MARS) 或梯度提升 (= GB)，但不幸的是这个版本仅适用于 LM。
我现在想简要描述一下其他三个模型的模型拟合，但我也需要你的帮助，以便最终计算 GAM、MARS 和 GB 模型以及 LM。
GAM：
## Create data-vector with dates of dt.train: ##
v.trainDate <- dt.train$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)
GB：
## 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？希望有人能帮助我。 很抱歉这个问题拖了这么久。
【问题讨论】：
标签： r time-series iteration prediction forecasting