In WinVector/vtreat: A Statistically Sound 'data.frame' Processor/Conditioner

vtreat is a an R 'data.frame' processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner. vtreat prepares variables so that data has fewer exceptional cases, making it easier to safely use models in production. Common problems vtreat defends against: 'Inf', 'NA', too many categorical levels, rare categorical levels, and new categorical levels (levels seen during application, but not during training). Reference: "vtreat: a data.frame Processor for Predictive Modeling", Zumel, Mount, 2016, DOI:10.5281/zenodo.1173314.

vtreat::mkCrossFrameCExperiment() and vtreat::mkCrossFrameCExperiment() are the most statistically efficient methods vtreat supplies. So we often advise them, especially for data with few rows. However they have non-trivial computational cost. For data with very many rows we suggest the more computationally efficient vtreat::designTreamentsC() and vtreat::designTreatmensN().

Here is an example (based on vtreat issue 12, perf tests, and perf tests2).

Example data (in this case no variables are truly related to the outcome to be predicted).

library("vtreat")
packageVersion("vtreat")

useParallel <- TRUE

mkEx <- function(n_rows, 
                 n_cat_columns, n_num_columns, n_irrel_columns,
                 n_cat_levels_a, n_cat_levels_b) {
  n_cols <- n_cat_columns + n_num_columns + n_irrel_columns + 2
  d <- as.data.frame(matrix(data = rnorm(n_rows * n_cols), 
                            nrow = n_rows, ncol = n_cols))
  cat_names <- NULL
  num_names <- NULL
  irrel_names <- NULL
  if(n_cat_columns>0) {
    cat_names <- paste0('var_cat_', seq_len(n_cat_columns))
  }
  if(n_num_columns>0) {
    num_names <- paste0('var_num_', seq_len(n_num_columns))
  }
  if(n_irrel_columns>0) {
    irrel_names <- paste0('irrel_', seq_len(n_irrel_columns))
  }
  y_names <- c("yC", "yN")
  colnames(d) <- c(cat_names, num_names, irrel_names, y_names)
  d$yC <- ifelse(d$yC>=0, "Y", "N")
  levels_a <- paste0("lev_a_", seq_len(n_cat_levels_a))
  levels_b <- NULL
  if(n_cat_levels_b>0) {
    levels_b <- paste0("lev_b_", seq_len(n_cat_levels_b))
  }
  for(ci in cat_names) {
    a_set <- rep(TRUE, n_rows)
    if(n_cat_levels_b>0) {
      a_set <- runif(n_rows)>=0.5
    }
    na <- sum(a_set)
    nb <- n_rows - na
    if(na>0) {
      d[[ci]][a_set] <- sample(levels_a, na, replace = TRUE)
    }
    if(nb>0) {
       d[[ci]][!a_set] <- sample(levels_b, nb, replace = TRUE)
    }
  }
  d
}

parallelCluster <- NULL
if(useParallel) {
  ncores <- parallel::detectCores()
  parallelCluster <- parallel::makeCluster(ncores)
}

n_rows <- 2000000

Convert the large cardinality categorical variables into new single column sub-models ready to be used in later modeling.

d <- mkEx(n_rows = n_rows,
          n_cat_columns = 2,
          n_num_columns = 2,
          n_irrel_columns = 10,
          n_cat_levels_a = 10,
          n_cat_levels_b = 50000)
yName <- "yC"
yTarget <- "Y"
varNames <- colnames(d)[grep("^var", colnames(d))]
codeTargets <- c("catB", "catP", "clean", "isBAD")
system.time(
  {
    splitGroup <- sample.int(2, nrow(d), replace = TRUE)
    tplan <- vtreat::designTreatmentsC(d[splitGroup==1, , drop = FALSE],
                                       varNames,
                                       yName,
                                       yTarget,
                                       codeRestriction = codeTargets,
                                       parallelCluster = parallelCluster)
    scoreFrame <- tplan$scoreFrame
    print(scoreFrame[, c("varName", "rsq", "sig", "extraModelDegrees", "origName")])
    # newVars <- scoreFrame$varName[scoreFrame$sig < 1/nrow(scoreFrame)]
    newVars <- scoreFrame$varName
    trainFrame <- vtreat::prepare(tplan, 
                                  d[splitGroup==2, , drop= FALSE],
                                  varRestriction = newVars,
                                  parallelCluster = parallelCluster)
})

if(!is.null(parallelCluster)) {
  parallel::stopCluster(parallelCluster)
  parallelCluster <- NULL
}

WinVector/vtreat documentation built on Jan. 12, 2025, 6:04 a.m.