data-raw/otu_small.R

In SchlossLab/mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines

## code to prepare `otu_small` dataset
otu_large <- read.delim(system.file("data-raw", "otu_large_bin.csv",
  package = "mikropml"
), sep = ",")
otu_small <- otu_large[1:200, 1:61]
usethis::use_data(otu_small, overwrite = TRUE)

## code to prepare models with the `otu_small` otu_small
set.seed(2019)
outcome_colname <- "dx"

inTraining <-
  caret::createDataPartition(otu_small[, outcome_colname], p = .80, list = FALSE)
train_data_sm <- otu_small[inTraining, ]
usethis::use_data(train_data_sm, overwrite = TRUE)
test_data_sm <- otu_small[-inTraining, ]
usethis::use_data(test_data_sm, overwrite = TRUE)

default_hyperparams <- structure(
  list(
    param = c(
      "lambda", "lambda", "lambda", "lambda", "lambda",
      "lambda", "lambda", "lambda", "lambda", "lambda", "lambda", "lambda", "lambda",
      "alpha", "sigma", "sigma", "sigma", "sigma", "sigma",
      "sigma", "sigma", "sigma", "C", "C", "C", "C", "C", "C", "C",
      "C", "C", "maxdepth", "maxdepth", "maxdepth", "maxdepth", "maxdepth",
      "maxdepth", "nrounds", "gamma", "eta", "eta", "eta", "eta", "max_depth",
      "colsample_bytree", "min_child_weight", "subsample", "subsample",
      "subsample", "subsample", "mtry", "mtry"
    ),
    value = c(
      "1e-6",
      "1e-5", "1e-4", "1e-3", "0.0025", "0.005", "0.01", "0.05", "0.1",
      "0.25", "0.5", "1", "10", "1", "0.00000001",
      "0.0000001", "0.000001", "0.00001", "0.0001", "0.001", "0.01",
      "0.1", "0.0000001", "0.000001", "0.00001", "0.0001", "0.001",
      "0.01", "0.1", "1", "10", "1", "2", "3", "4", "5", "6", "500",
      "0", "0.001", "0.01", "0.1", "1", "8", "0.8", "1", "0.4", "0.5",
      "0.6", "0.7", "500", "1000"
    ),
    method = c(
      "glmnet", "glmnet",
      "glmnet", "glmnet", "glmnet", "glmnet", "glmnet",
      "glmnet", "glmnet", "glmnet", "glmnet", "glmnet",
      "glmnet", "glmnet", "svmRadial", "svmRadial",
      "svmRadial", "svmRadial", "svmRadial", "svmRadial", "svmRadial",
      "svmRadial", "svmRadial", "svmRadial", "svmRadial", "svmRadial",
      "svmRadial", "svmRadial", "svmRadial", "svmRadial", "svmRadial",
      "rpart2", "rpart2", "rpart2", "rpart2", "rpart2", "rpart2", "xgbTree",
      "xgbTree", "xgbTree", "xgbTree", "xgbTree", "xgbTree", "xgbTree",
      "xgbTree", "xgbTree", "xgbTree", "xgbTree", "xgbTree", "xgbTree", "rf", "rf"
    )
  ),
  class = c("spec_tbl_df", "tbl_df", "tbl", "data.frame"),
  row.names = c(NA, -52L),
  spec = structure(
    list(
      cols = list(
        param = structure(list(), class = c("collector_character", "collector")),
        val = structure(list(), class = c("collector_character", "collector")),
        method = structure(list(), class = c("collector_character", "collector"))
      ),
      default = structure(list(), class = c("collector_guess", "collector")), skip = 1
    ),
    class = "col_spec"
  )
)

set.seed(2019)

otu_large_bin_svmRadial <- mikropml::run_ml(
  otu_small,
  "svmRadial",
  outcome_colname = "dx",
  find_feature_importance = FALSE,
  kfold = 5,
  cv_times = 2,
  seed = 2019
)