Data frame is assumed to have only atomic columns except for dates (which are converted to numeric). Note: each column is processed independently of all others.
designTreatmentsZ(
dframe,
varlist,
...,
minFraction = 0,
weights = c(),
rareCount = 0,
collarProb = 0,
codeRestriction = NULL,
customCoders = NULL,
verbose = TRUE,
parallelCluster = NULL,
use_parallel = TRUE,
missingness_imputation = NULL,
imputation_map = NULL
)treatment plan (for use with prepare)
Data frame to learn treatments from (training data), must have at least 1 row.
Names of columns to treat (effective variables).
no additional arguments, declared to forced named binding of later arguments
optional minimum frequency a categorical level must have to be converted to an indicator column.
optional training weights for each row
optional integer, allow levels with this count or below to be pooled into a shared rare-level. Defaults to 0 or off.
what fraction of the data (pseudo-probability) to collar data at if doCollar is set during prepare.treatmentplan.
what types of variables to produce (character array of level codes, NULL means no restriction).
map from code names to custom categorical variable encoding functions (please see https://github.com/WinVector/vtreat/blob/main/extras/CustomLevelCoders.md).
if TRUE print progress.
(optional) a cluster object created by package parallel or package snow.
logical, if TRUE use parallel methods (if parallel cluster is set).
function of signature f(values: numeric, weights: numeric), simple missing value imputer.
map from column names to functions of signature f(values: numeric, weights: numeric), simple missing value imputers.
The main fields are mostly vectors with names (all with the same names in the same order):
- vars : (character array without names) names of variables (in same order as names on the other diagnostic vectors) - varMoves : logical TRUE if the variable varied during hold out scoring, only variables that move will be in the treated frame
See the vtreat vignette for a bit more detail and a worked example.
Columns that do not vary are not passed through.
prepare.treatmentplan, designTreatmentsC, designTreatmentsN
dTrainZ <- data.frame(x=c('a','a','a','a','b','b',NA,'e','e'),
z=c(1,2,3,4,5,6,7,NA,9))
dTestZ <- data.frame(x=c('a','x','c',NA),
z=c(10,20,30,NA))
treatmentsZ = designTreatmentsZ(dTrainZ, colnames(dTrainZ),
rareCount=0)
dTrainZTreated <- prepare(treatmentsZ, dTrainZ)
dTestZTreated <- prepare(treatmentsZ, dTestZ)
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