extras/PerfTests.md
In WinVector/vtreat: A Statistically Sound 'data.frame' Processor/Conditioner
perf tests
Win-Vector LLC
2/15/2018
library("vtreat")
packageVersion("vtreat")
## [1] '1.0.2'
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 <- 500000
Get a base timing of a moderately large task.
d <- mkEx(n_rows = n_rows,
n_cat_columns = 2,
n_num_columns = 2,
n_irrel_columns = 10,
n_cat_levels_a = 5,
n_cat_levels_b = 0)
yName <- "yC"
yTarget <- "Y"
varNames <- colnames(d)[grep("^var", colnames(d))]
system.time(
tplan <-
vtreat::mkCrossFrameCExperiment(
d,
varNames,
yName,
yTarget,
parallelCluster = parallelCluster))
## user system elapsed
## 12.238 1.637 64.085
knitr::kable(tplan$treatments$scoreFrame)
| varName | varMoves | rsq| sig| needsSplit | extraModelDegrees| origName | code |
|:------------------------------|:---------|--------:|----------:|:-----------|------------------:|:------------|:------|
| var_cat_1_lev_x.lev_a_1 | TRUE | 1.0e-06| 0.4112751| FALSE | 0| var_cat_1 | lev |
| var_cat_1_lev_x.lev_a_2 | TRUE | 1.0e-07| 0.8048765| FALSE | 0| var_cat_1 | lev |
| var_cat_1_lev_x.lev_a_3 | TRUE | 1.0e-07| 0.8061192| FALSE | 0| var_cat_1 | lev |
| var_cat_1_lev_x.lev_a_4 | TRUE | 2.5e-06| 0.1859667| FALSE | 0| var_cat_1 | lev |
| var_cat_1_lev_x.lev_a_5 | TRUE | 4.0e-07| 0.6160177| FALSE | 0| var_cat_1 | lev |
| var_cat_1_catP | TRUE | 2.0e-07| 0.7315422| TRUE | 4| var_cat_1 | catP |
| var_cat_1_catB | TRUE | 5.0e-07| 0.5493606| TRUE | 4| var_cat_1 | catB |
| var_cat_2_lev_x.lev_a_1 | TRUE | 0.0e+00| 0.8724265| FALSE | 0| var_cat_2 | lev |
| var_cat_2_lev_x.lev_a_2 | TRUE | 0.0e+00| 0.9556600| FALSE | 0| var_cat_2 | lev |
| var_cat_2_lev_x.lev_a_3 | TRUE | 1.0e-07| 0.7956594| FALSE | 0| var_cat_2 | lev |
| var_cat_2_lev_x.lev_a_4 | TRUE | 3.4e-06| 0.1251896| FALSE | 0| var_cat_2 | lev |
| var_cat_2_lev_x.lev_a_5 | TRUE | 5.2e-06| 0.0582204| FALSE | 0| var_cat_2 | lev |
| var_cat_2_catP | TRUE | 6.0e-07| 0.5091260| TRUE | 4| var_cat_2 | catP |
| var_cat_2_catB | TRUE | 0.0e+00| 0.8726200| TRUE | 4| var_cat_2 | catB |
| var_num_1_clean | TRUE | 8.0e-07| 0.4661966| FALSE | 0| var_num_1 | clean |
| var_num_2_clean | TRUE | 1.0e-07| 0.8320938| FALSE | 0| var_num_2 | clean |
Measure the effect of irrelevant columns.
d <- mkEx(n_rows = n_rows,
n_cat_columns = 2,
n_num_columns = 2,
n_irrel_columns = 300,
n_cat_levels_a = 5,
n_cat_levels_b = 0)
yName <- "yC"
yTarget <- "Y"
varNames <- colnames(d)[grep("^var", colnames(d))]
system.time(
tplan <-
vtreat::mkCrossFrameCExperiment(
d,
varNames,
yName,
yTarget,
parallelCluster = parallelCluster))
## user system elapsed
## 13.430 3.010 68.907
knitr::kable(tplan$treatments$scoreFrame)
| varName | varMoves | rsq| sig| needsSplit | extraModelDegrees| origName | code |
|:------------------------------|:---------|--------:|----------:|:-----------|------------------:|:------------|:------|
| var_cat_1_lev_x.lev_a_1 | TRUE | 1.0e-07| 0.8013785| FALSE | 0| var_cat_1 | lev |
| var_cat_1_lev_x.lev_a_2 | TRUE | 2.2e-06| 0.2190508| FALSE | 0| var_cat_1 | lev |
| var_cat_1_lev_x.lev_a_3 | TRUE | 1.0e-07| 0.8260429| FALSE | 0| var_cat_1 | lev |
| var_cat_1_lev_x.lev_a_4 | TRUE | 0.0e+00| 0.9764722| FALSE | 0| var_cat_1 | lev |
| var_cat_1_lev_x.lev_a_5 | TRUE | 2.2e-06| 0.2189264| FALSE | 0| var_cat_1 | lev |
| var_cat_1_catP | TRUE | 6.1e-06| 0.0399115| TRUE | 4| var_cat_1 | catP |
| var_cat_1_catB | TRUE | 0.0e+00| 0.9390311| TRUE | 4| var_cat_1 | catB |
| var_cat_2_lev_x.lev_a_1 | TRUE | 3.3e-06| 0.1320445| FALSE | 0| var_cat_2 | lev |
| var_cat_2_lev_x.lev_a_2 | TRUE | 2.0e-07| 0.7357801| FALSE | 0| var_cat_2 | lev |
| var_cat_2_lev_x.lev_a_3 | TRUE | 2.0e-07| 0.7053845| FALSE | 0| var_cat_2 | lev |
| var_cat_2_lev_x.lev_a_4 | TRUE | 0.0e+00| 0.8904021| FALSE | 0| var_cat_2 | lev |
| var_cat_2_lev_x.lev_a_5 | TRUE | 6.0e-07| 0.5150741| FALSE | 0| var_cat_2 | lev |
| var_cat_2_catP | TRUE | 0.0e+00| 0.9718014| TRUE | 4| var_cat_2 | catP |
| var_cat_2_catB | TRUE | 9.0e-07| 0.4359623| TRUE | 4| var_cat_2 | catB |
| var_num_1_clean | TRUE | 1.0e-07| 0.8472534| FALSE | 0| var_num_1 | clean |
| var_num_2_clean | TRUE | 5.0e-07| 0.5397267| FALSE | 0| var_num_2 | clean |
Measure the effect of more levels (both common and uncommon).
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))]
system.time(
tplan <-
vtreat::mkCrossFrameCExperiment(
d,
varNames,
yName,
yTarget,
parallelCluster = parallelCluster))
## user system elapsed
## 32.666 2.402 139.161
knitr::kable(tplan$treatments$scoreFrame)
| varName | varMoves | rsq| sig| needsSplit | extraModelDegrees| origName | code |
|:-------------------------------|:---------|--------:|----------:|:-----------|------------------:|:------------|:------|
| var_cat_1_lev_x.lev_a_1 | TRUE | 0.0e+00| 0.9162907| FALSE | 0| var_cat_1 | lev |
| var_cat_1_lev_x.lev_a_10 | TRUE | 4.0e-07| 0.5798830| FALSE | 0| var_cat_1 | lev |
| var_cat_1_lev_x.lev_a_2 | TRUE | 2.0e-07| 0.7325213| FALSE | 0| var_cat_1 | lev |
| var_cat_1_lev_x.lev_a_3 | TRUE | 3.0e-07| 0.6535017| FALSE | 0| var_cat_1 | lev |
| var_cat_1_lev_x.lev_a_4 | TRUE | 1.0e-07| 0.8433908| FALSE | 0| var_cat_1 | lev |
| var_cat_1_lev_x.lev_a_5 | TRUE | 3.0e-07| 0.6463082| FALSE | 0| var_cat_1 | lev |
| var_cat_1_lev_x.lev_a_6 | TRUE | 0.0e+00| 0.8529945| FALSE | 0| var_cat_1 | lev |
| var_cat_1_lev_x.lev_a_7 | TRUE | 3.0e-07| 0.6554978| FALSE | 0| var_cat_1 | lev |
| var_cat_1_lev_x.lev_a_8 | TRUE | 7.0e-07| 0.4943275| FALSE | 0| var_cat_1 | lev |
| var_cat_1_lev_x.lev_a_9 | TRUE | 5.0e-07| 0.5647106| FALSE | 0| var_cat_1 | lev |
| var_cat_1_catP | TRUE | 0.0e+00| 0.9976207| TRUE | 49676| var_cat_1 | catP |
| var_cat_1_catB | TRUE | 5.0e-07| 0.5501446| TRUE | 49676| var_cat_1 | catB |
| var_cat_2_lev_x.lev_a_1 | TRUE | 4.0e-07| 0.5970799| FALSE | 0| var_cat_2 | lev |
| var_cat_2_lev_x.lev_a_10 | TRUE | 6.3e-06| 0.0370985| FALSE | 0| var_cat_2 | lev |
| var_cat_2_lev_x.lev_a_2 | TRUE | 1.0e-07| 0.7530640| FALSE | 0| var_cat_2 | lev |
| var_cat_2_lev_x.lev_a_3 | TRUE | 0.0e+00| 0.9139013| FALSE | 0| var_cat_2 | lev |
| var_cat_2_lev_x.lev_a_4 | TRUE | 8.0e-07| 0.4447940| FALSE | 0| var_cat_2 | lev |
| var_cat_2_lev_x.lev_a_5 | TRUE | 3.9e-06| 0.1004540| FALSE | 0| var_cat_2 | lev |
| var_cat_2_lev_x.lev_a_6 | TRUE | 0.0e+00| 0.9355194| FALSE | 0| var_cat_2 | lev |
| var_cat_2_lev_x.lev_a_7 | TRUE | 1.2e-06| 0.3550048| FALSE | 0| var_cat_2 | lev |
| var_cat_2_lev_x.lev_a_8 | TRUE | 6.0e-07| 0.5193514| FALSE | 0| var_cat_2 | lev |
| var_cat_2_lev_x.lev_a_9 | TRUE | 2.7e-06| 0.1738158| FALSE | 0| var_cat_2 | lev |
| var_cat_2_catP | TRUE | 1.4e-06| 0.3193110| TRUE | 49660| var_cat_2 | catP |
| var_cat_2_catB | TRUE | 0.0e+00| 0.8788629| TRUE | 49660| var_cat_2 | catB |
| var_num_1_clean | TRUE | 2.3e-06| 0.2086929| FALSE | 0| var_num_1 | clean |
| var_num_2_clean | TRUE | 1.0e-07| 0.8351640| FALSE | 0| var_num_2 | clean |
See if it is the indicators.
Measure the effect of more levels (both common and uncommon).
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))]
system.time(
tplan <-
vtreat::mkCrossFrameCExperiment(
d,
varNames,
yName,
yTarget,
minFraction = 2.0,
parallelCluster = parallelCluster))
## user system elapsed
## 33.751 2.390 80.806
knitr::kable(tplan$treatments$scoreFrame)
| varName | varMoves | rsq| sig| needsSplit | extraModelDegrees| origName | code |
|:-------------------|:---------|--------:|----------:|:-----------|------------------:|:------------|:------|
| var_cat_1_catP | TRUE | 7.8e-06| 0.0201505| TRUE | 49698| var_cat_1 | catP |
| var_cat_1_catB | TRUE | 7.0e-07| 0.4897245| TRUE | 49698| var_cat_1 | catB |
| var_cat_2_catP | TRUE | 1.1e-06| 0.3746776| TRUE | 49673| var_cat_2 | catP |
| var_cat_2_catB | TRUE | 7.5e-06| 0.0226816| TRUE | 49673| var_cat_2 | catB |
| var_num_1_clean | TRUE | 9.0e-07| 0.4406701| FALSE | 0| var_num_1 | clean |
| var_num_2_clean | TRUE | 1.0e-07| 0.8112024| FALSE | 0| var_num_2 | clean |
if(!is.null(parallelCluster)) {
parallel::stopCluster(parallelCluster)
parallelCluster <- NULL
}
WinVector/vtreat documentation built on Aug. 29, 2023, 4:49 a.m.
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perf tests
Win-Vector LLC 2/15/2018
library("vtreat")
packageVersion("vtreat")
## [1] '1.0.2'
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 <- 500000
Get a base timing of a moderately large task.
d <- mkEx(n_rows = n_rows,
n_cat_columns = 2,
n_num_columns = 2,
n_irrel_columns = 10,
n_cat_levels_a = 5,
n_cat_levels_b = 0)
yName <- "yC"
yTarget <- "Y"
varNames <- colnames(d)[grep("^var", colnames(d))]
system.time(
tplan <-
vtreat::mkCrossFrameCExperiment(
d,
varNames,
yName,
yTarget,
parallelCluster = parallelCluster))
## user system elapsed
## 12.238 1.637 64.085
knitr::kable(tplan$treatments$scoreFrame)
| varName | varMoves | rsq| sig| needsSplit | extraModelDegrees| origName | code | |:------------------------------|:---------|--------:|----------:|:-----------|------------------:|:------------|:------| | var_cat_1_lev_x.lev_a_1 | TRUE | 1.0e-06| 0.4112751| FALSE | 0| var_cat_1 | lev | | var_cat_1_lev_x.lev_a_2 | TRUE | 1.0e-07| 0.8048765| FALSE | 0| var_cat_1 | lev | | var_cat_1_lev_x.lev_a_3 | TRUE | 1.0e-07| 0.8061192| FALSE | 0| var_cat_1 | lev | | var_cat_1_lev_x.lev_a_4 | TRUE | 2.5e-06| 0.1859667| FALSE | 0| var_cat_1 | lev | | var_cat_1_lev_x.lev_a_5 | TRUE | 4.0e-07| 0.6160177| FALSE | 0| var_cat_1 | lev | | var_cat_1_catP | TRUE | 2.0e-07| 0.7315422| TRUE | 4| var_cat_1 | catP | | var_cat_1_catB | TRUE | 5.0e-07| 0.5493606| TRUE | 4| var_cat_1 | catB | | var_cat_2_lev_x.lev_a_1 | TRUE | 0.0e+00| 0.8724265| FALSE | 0| var_cat_2 | lev | | var_cat_2_lev_x.lev_a_2 | TRUE | 0.0e+00| 0.9556600| FALSE | 0| var_cat_2 | lev | | var_cat_2_lev_x.lev_a_3 | TRUE | 1.0e-07| 0.7956594| FALSE | 0| var_cat_2 | lev | | var_cat_2_lev_x.lev_a_4 | TRUE | 3.4e-06| 0.1251896| FALSE | 0| var_cat_2 | lev | | var_cat_2_lev_x.lev_a_5 | TRUE | 5.2e-06| 0.0582204| FALSE | 0| var_cat_2 | lev | | var_cat_2_catP | TRUE | 6.0e-07| 0.5091260| TRUE | 4| var_cat_2 | catP | | var_cat_2_catB | TRUE | 0.0e+00| 0.8726200| TRUE | 4| var_cat_2 | catB | | var_num_1_clean | TRUE | 8.0e-07| 0.4661966| FALSE | 0| var_num_1 | clean | | var_num_2_clean | TRUE | 1.0e-07| 0.8320938| FALSE | 0| var_num_2 | clean |
Measure the effect of irrelevant columns.
d <- mkEx(n_rows = n_rows,
n_cat_columns = 2,
n_num_columns = 2,
n_irrel_columns = 300,
n_cat_levels_a = 5,
n_cat_levels_b = 0)
yName <- "yC"
yTarget <- "Y"
varNames <- colnames(d)[grep("^var", colnames(d))]
system.time(
tplan <-
vtreat::mkCrossFrameCExperiment(
d,
varNames,
yName,
yTarget,
parallelCluster = parallelCluster))
## user system elapsed
## 13.430 3.010 68.907
knitr::kable(tplan$treatments$scoreFrame)
| varName | varMoves | rsq| sig| needsSplit | extraModelDegrees| origName | code | |:------------------------------|:---------|--------:|----------:|:-----------|------------------:|:------------|:------| | var_cat_1_lev_x.lev_a_1 | TRUE | 1.0e-07| 0.8013785| FALSE | 0| var_cat_1 | lev | | var_cat_1_lev_x.lev_a_2 | TRUE | 2.2e-06| 0.2190508| FALSE | 0| var_cat_1 | lev | | var_cat_1_lev_x.lev_a_3 | TRUE | 1.0e-07| 0.8260429| FALSE | 0| var_cat_1 | lev | | var_cat_1_lev_x.lev_a_4 | TRUE | 0.0e+00| 0.9764722| FALSE | 0| var_cat_1 | lev | | var_cat_1_lev_x.lev_a_5 | TRUE | 2.2e-06| 0.2189264| FALSE | 0| var_cat_1 | lev | | var_cat_1_catP | TRUE | 6.1e-06| 0.0399115| TRUE | 4| var_cat_1 | catP | | var_cat_1_catB | TRUE | 0.0e+00| 0.9390311| TRUE | 4| var_cat_1 | catB | | var_cat_2_lev_x.lev_a_1 | TRUE | 3.3e-06| 0.1320445| FALSE | 0| var_cat_2 | lev | | var_cat_2_lev_x.lev_a_2 | TRUE | 2.0e-07| 0.7357801| FALSE | 0| var_cat_2 | lev | | var_cat_2_lev_x.lev_a_3 | TRUE | 2.0e-07| 0.7053845| FALSE | 0| var_cat_2 | lev | | var_cat_2_lev_x.lev_a_4 | TRUE | 0.0e+00| 0.8904021| FALSE | 0| var_cat_2 | lev | | var_cat_2_lev_x.lev_a_5 | TRUE | 6.0e-07| 0.5150741| FALSE | 0| var_cat_2 | lev | | var_cat_2_catP | TRUE | 0.0e+00| 0.9718014| TRUE | 4| var_cat_2 | catP | | var_cat_2_catB | TRUE | 9.0e-07| 0.4359623| TRUE | 4| var_cat_2 | catB | | var_num_1_clean | TRUE | 1.0e-07| 0.8472534| FALSE | 0| var_num_1 | clean | | var_num_2_clean | TRUE | 5.0e-07| 0.5397267| FALSE | 0| var_num_2 | clean |
Measure the effect of more levels (both common and uncommon).
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))]
system.time(
tplan <-
vtreat::mkCrossFrameCExperiment(
d,
varNames,
yName,
yTarget,
parallelCluster = parallelCluster))
## user system elapsed
## 32.666 2.402 139.161
knitr::kable(tplan$treatments$scoreFrame)
| varName | varMoves | rsq| sig| needsSplit | extraModelDegrees| origName | code | |:-------------------------------|:---------|--------:|----------:|:-----------|------------------:|:------------|:------| | var_cat_1_lev_x.lev_a_1 | TRUE | 0.0e+00| 0.9162907| FALSE | 0| var_cat_1 | lev | | var_cat_1_lev_x.lev_a_10 | TRUE | 4.0e-07| 0.5798830| FALSE | 0| var_cat_1 | lev | | var_cat_1_lev_x.lev_a_2 | TRUE | 2.0e-07| 0.7325213| FALSE | 0| var_cat_1 | lev | | var_cat_1_lev_x.lev_a_3 | TRUE | 3.0e-07| 0.6535017| FALSE | 0| var_cat_1 | lev | | var_cat_1_lev_x.lev_a_4 | TRUE | 1.0e-07| 0.8433908| FALSE | 0| var_cat_1 | lev | | var_cat_1_lev_x.lev_a_5 | TRUE | 3.0e-07| 0.6463082| FALSE | 0| var_cat_1 | lev | | var_cat_1_lev_x.lev_a_6 | TRUE | 0.0e+00| 0.8529945| FALSE | 0| var_cat_1 | lev | | var_cat_1_lev_x.lev_a_7 | TRUE | 3.0e-07| 0.6554978| FALSE | 0| var_cat_1 | lev | | var_cat_1_lev_x.lev_a_8 | TRUE | 7.0e-07| 0.4943275| FALSE | 0| var_cat_1 | lev | | var_cat_1_lev_x.lev_a_9 | TRUE | 5.0e-07| 0.5647106| FALSE | 0| var_cat_1 | lev | | var_cat_1_catP | TRUE | 0.0e+00| 0.9976207| TRUE | 49676| var_cat_1 | catP | | var_cat_1_catB | TRUE | 5.0e-07| 0.5501446| TRUE | 49676| var_cat_1 | catB | | var_cat_2_lev_x.lev_a_1 | TRUE | 4.0e-07| 0.5970799| FALSE | 0| var_cat_2 | lev | | var_cat_2_lev_x.lev_a_10 | TRUE | 6.3e-06| 0.0370985| FALSE | 0| var_cat_2 | lev | | var_cat_2_lev_x.lev_a_2 | TRUE | 1.0e-07| 0.7530640| FALSE | 0| var_cat_2 | lev | | var_cat_2_lev_x.lev_a_3 | TRUE | 0.0e+00| 0.9139013| FALSE | 0| var_cat_2 | lev | | var_cat_2_lev_x.lev_a_4 | TRUE | 8.0e-07| 0.4447940| FALSE | 0| var_cat_2 | lev | | var_cat_2_lev_x.lev_a_5 | TRUE | 3.9e-06| 0.1004540| FALSE | 0| var_cat_2 | lev | | var_cat_2_lev_x.lev_a_6 | TRUE | 0.0e+00| 0.9355194| FALSE | 0| var_cat_2 | lev | | var_cat_2_lev_x.lev_a_7 | TRUE | 1.2e-06| 0.3550048| FALSE | 0| var_cat_2 | lev | | var_cat_2_lev_x.lev_a_8 | TRUE | 6.0e-07| 0.5193514| FALSE | 0| var_cat_2 | lev | | var_cat_2_lev_x.lev_a_9 | TRUE | 2.7e-06| 0.1738158| FALSE | 0| var_cat_2 | lev | | var_cat_2_catP | TRUE | 1.4e-06| 0.3193110| TRUE | 49660| var_cat_2 | catP | | var_cat_2_catB | TRUE | 0.0e+00| 0.8788629| TRUE | 49660| var_cat_2 | catB | | var_num_1_clean | TRUE | 2.3e-06| 0.2086929| FALSE | 0| var_num_1 | clean | | var_num_2_clean | TRUE | 1.0e-07| 0.8351640| FALSE | 0| var_num_2 | clean |
See if it is the indicators.
Measure the effect of more levels (both common and uncommon).
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))]
system.time(
tplan <-
vtreat::mkCrossFrameCExperiment(
d,
varNames,
yName,
yTarget,
minFraction = 2.0,
parallelCluster = parallelCluster))
## user system elapsed
## 33.751 2.390 80.806
knitr::kable(tplan$treatments$scoreFrame)
| varName | varMoves | rsq| sig| needsSplit | extraModelDegrees| origName | code | |:-------------------|:---------|--------:|----------:|:-----------|------------------:|:------------|:------| | var_cat_1_catP | TRUE | 7.8e-06| 0.0201505| TRUE | 49698| var_cat_1 | catP | | var_cat_1_catB | TRUE | 7.0e-07| 0.4897245| TRUE | 49698| var_cat_1 | catB | | var_cat_2_catP | TRUE | 1.1e-06| 0.3746776| TRUE | 49673| var_cat_2 | catP | | var_cat_2_catB | TRUE | 7.5e-06| 0.0226816| TRUE | 49673| var_cat_2 | catB | | var_num_1_clean | TRUE | 9.0e-07| 0.4406701| FALSE | 0| var_num_1 | clean | | var_num_2_clean | TRUE | 1.0e-07| 0.8112024| FALSE | 0| var_num_2 | clean |
if(!is.null(parallelCluster)) {
parallel::stopCluster(parallelCluster)
parallelCluster <- NULL
}
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