R/vtreat.R
designTreatmentsN.RdFunction to design variable treatments for binary prediction of a
numeric outcome. 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.
Note: re-encoding high cardinality on training data
categorical variables can introduce undesirable nested model bias, for such data consider
using mkCrossFrameNExperiment.
designTreatmentsN( dframe, varlist, outcomename, ..., weights = c(), minFraction = 0.02, smFactor = 0, rareCount = 0, rareSig = NULL, collarProb = 0, codeRestriction = NULL, customCoders = NULL, splitFunction = NULL, ncross = 3, forceSplit = FALSE, verbose = TRUE, parallelCluster = NULL, use_parallel = TRUE, missingness_imputation = NULL, imputation_map = NULL )
| dframe | Data frame to learn treatments from (training data), must have at least 1 row. |
|---|---|
| varlist | Names of columns to treat (effective variables). |
| outcomename | Name of column holding outcome variable. dframe[[outcomename]] must be only finite non-missing values and there must be a cut such that dframe[[outcomename]] is both above the cut at least twice and below the cut at least twice. |
| ... | no additional arguments, declared to forced named binding of later arguments |
| weights | optional training weights for each row |
| minFraction | optional minimum frequency a categorical level must have to be converted to an indicator column. |
| smFactor | optional smoothing factor for impact coding models. |
| rareCount | optional integer, allow levels with this count or below to be pooled into a shared rare-level. Defaults to 0 or off. |
| rareSig | optional numeric, suppress levels from pooling at this significance value greater. Defaults to NULL or off. |
| collarProb | what fraction of the data (pseudo-probability) to collar data at if doCollar is set during |
| codeRestriction | what types of variables to produce (character array of level codes, NULL means no restriction). |
| customCoders | map from code names to custom categorical variable encoding functions (please see https://github.com/WinVector/vtreat/blob/main/extras/CustomLevelCoders.md). |
| splitFunction | (optional) see vtreat::buildEvalSets . |
| ncross | optional scalar >=2 number of cross validation splits use in rescoring complex variables. |
| forceSplit | logical, if TRUE force cross-validated significance calculations on all variables. |
| verbose | if TRUE print progress. |
| parallelCluster | (optional) a cluster object created by package parallel or package snow. |
| use_parallel | logical, if TRUE use parallel methods (when parallel cluster is set). |
| missingness_imputation | function of signature f(values: numeric, weights: numeric), simple missing value imputer. |
| imputation_map | map from column names to functions of signature f(values: numeric, weights: numeric), simple missing value imputers. |
treatment plan (for use with prepare)
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 - sig : an estimate significance of effect
See the vtreat vignette for a bit more detail and a worked example.
Columns that do not vary are not passed through.
dTrainN <- data.frame(x=c('a','a','a','a','b','b','b'), z=c(1,2,3,4,5,6,7),y=c(0,0,0,1,0,1,1)) dTestN <- data.frame(x=c('a','b','c',NA), z=c(10,20,30,NA)) treatmentsN = designTreatmentsN(dTrainN,colnames(dTrainN),'y')#> [1] "vtreat 1.6.3 inspecting inputs Fri Jun 11 07:01:19 2021" #> [1] "designing treatments Fri Jun 11 07:01:19 2021" #> [1] " have initial level statistics Fri Jun 11 07:01:19 2021" #> [1] " scoring treatments Fri Jun 11 07:01:19 2021" #> [1] "have treatment plan Fri Jun 11 07:01:19 2021"