在插入符号中哪些模型可以对X使用稀疏矩阵？

Question

我希望能够像x一样在caret::train中使用稀疏矩阵，看起来他们中的许多人都希望有一个数据框架。我已经能够在XGboost和caret中使用稀疏矩阵，但是nnet和ELM似乎都需要一个数据框架。我在代码中注意到，插入符号试图将x转换为nnet和ELM模型的数据框架。

是否有支持稀疏矩阵的模型列表？

Answer 1

您可以使用这段代码来查找哪些模型在fit函数中使用as.matrix。

请注意，as.matrix将稀疏矩阵转换为完整的矩阵。您可能会遇到内存问题。我还没有测试个别的底层模型是否接受稀疏矩阵。

library(caret)  # run on version 6.0-71

model_list <- getModelInfo()
df <- data.frame(models = names(model_list), 
                 fit = rep("", length(model_list)), 
                 stringsAsFactors = FALSE)

for (i in 1:length(model_list)) {
  df$fit[i] <- as.expression(functionBody(model_list[[i]]$fit))
}

# find xgboost matrix   
df$models[grep("xgb.DMatrix", df$fit)]
[1] "xgbLinear" "xgbTree"  

# find all models where fit contains as.matrix(x)
df$models[grep("as.matrix\\(x\\)", df$fit)]

[1] "bdk"               "binda"             "blasso"            "blassoAveraged"    "bridge"            "brnn"             
[7] "dnn"               "dwdLinear"         "dwdPoly"           "dwdRadial"         "enet"              "enpls.fs"         
[13] "enpls"             "foba"              "gaussprLinear"     "gaussprPoly"       "gaussprRadial"     "glmnet"           
[19] "knn"               "lars"              "lars2"             "lasso"             "logicBag"          "LogitBoost"       
[25] "lssvmLinear"       "lssvmPoly"         "lssvmRadial"       "mlpSGD"            "nnls"              "ordinalNet"       
[31] "ORFlog"            "ORFpls"            "ORFridge"          "ORFsvm"            "ownn"              "PenalizedLDA"     
[37] "ppr"               "qrnn"              "randomGLM"         "relaxo"            "ridge"             "rocc"             
[43] "rqlasso"           "rqnc"              "rvmLinear"         "rvmPoly"           "rvmRadial"         "sda"              
[49] "sddaLDA"           "sddaQDA"           "sdwd"              "snn"               "spikeslab"         "svmLinear"        
[55] "svmLinear2"        "svmLinear3"        "svmLinearWeights"  "svmLinearWeights2" "svmPoly"           "svmRadial"        
[61] "svmRadialCost"     "svmRadialSigma"    "svmRadialWeights"  "xgbLinear"         "xgbTree"           "xyf"