# vtreat Rare Levels

#### John Mount

#### 2020-10-17

For some categorical variables rarity can reflect structural features. For instance with United States Zip codes rare zip codes often represent low population density regions.

When this is the case it can make sense to pool the rare levels into a new re-coded level called ``rare.’’ If this new level is statistically significant it can be a usable modeling feature. This sort of pooling is only potentially useful if below a given training count behave similarly.

This capability was more of an experimental demonstration of possible extensions of `vtreat` to have more inference capabilities about rare level than a commonly useful feature. Most of this power has since been captured in the more useful `catP` feature (also demonstrated here). Even more power is found in using an interaction of `catN` or `catB` with `catP`.

An example of the rare level feature using `vtreat` is given below.

First we set up some data by defining a set of population centers (`populationFrame`) and code to observe individuals (with replacement) uniformly from the combined population with a rare condition (`inClass`) that has elevated occurrence in observations coming from the small population centers (`rareCodes`).

    library('vtreat')

    set.seed(2325)
    populationFrame <- data.frame(
       popsize = round(rlnorm(100,meanlog=log(4000),sdlog=1)),
       stringsAsFactors = FALSE)
    populationFrame$code <- paste0('z',formatC(sample.int(100000,
                                                  size=nrow(populationFrame),
                                                  replace=FALSE),width=5,flag='0'))
    rareCodes <- populationFrame$code[populationFrame$popsize<1000]

    # Draw individuals from code-regions proportional to size of code region
    # (or uniformly over all individuals labeled by code region).
    # Also add the outcome which has altered conditional probability for rareCodes.
    drawIndividualsAndReturnCodes <- function(n) {
      ords <- sort(sample.int(sum(populationFrame$popsize),size=n,replace=TRUE))
      cs <- cumsum(populationFrame$popsize)
      indexes <- findInterval(ords,cs)+1
      indexes <- indexes[sample.int(n,size=n,replace=FALSE)]
      samp <- data.frame(code=populationFrame$code[indexes],
                         stringsAsFactors = FALSE)
      samp$inClass <- runif(n) < ifelse(samp$code %in% rareCodes,0.3,0.01)
      samp
    }

We then draw a sample we want to make some observations on.

    testSet <- drawIndividualsAndReturnCodes(2000)
    table(generatedAsRare=testSet$code %in% rareCodes,inClass=testSet$inClass)

    ##                inClass
    ## generatedAsRare FALSE TRUE
    ##           FALSE  1957   19
    ##           TRUE     17    7

Notice that in the sample we can observe the elevated rate of `inClass==TRUE` conditioned on coming from a `code` that is one of the `rareCodes`.

We could try to learn this relation using `vtreat`. To do this we set up another sample (`designSet`) to work on, so we are not inferring from `testSet` (where we will evaluate results).

    designSet <- drawIndividualsAndReturnCodes(2000)
    treatments <- vtreat::designTreatmentsC(designSet,'code','inClass',TRUE,
                                            rareCount=5,rareSig=NULL,
                                            verbose=FALSE)
    treatments$scoreFrame[,c('varName','sig'),drop=FALSE]

    ##              varName         sig
    ## 1          code_catP 0.035934754
    ## 2          code_catB 0.025765020
    ## 3      code_lev_rare 0.006440297
    ## 4  code_lev_x_z01318 0.944465050
    ## 5  code_lev_x_z05023 0.255244077
    ## 6  code_lev_x_z05141 0.932425672
    ## 7  code_lev_x_z13059 0.766518335
    ## 8  code_lev_x_z22752 0.168315399
    ## 9  code_lev_x_z27896 0.249886934
    ## 10 code_lev_x_z37337 0.999706030
    ## 11 code_lev_x_z45874 0.261182774
    ## 12 code_lev_x_z46558 0.213118876
    ## 13 code_lev_x_z54516 0.859663802
    ## 14 code_lev_x_z59854 0.031292097
    ## 15 code_lev_x_z60281 0.222826006
    ## 16 code_lev_x_z71826 0.249467981
    ## 17 code_lev_x_z79197 0.255244077
    ## 18 code_lev_x_z86061 0.944465050
    ## 19 code_lev_x_z86248 0.178878966

We see in `treatments$scoreFrame` we have a level called `code_lev_rare`, which is where a number of rare levels are re-coding. We can also confirm levels that occur `rareCount` or fewer times are eligible to code to to `code_lev_rare`.

    designSetTreated <-  vtreat::prepare(treatments,designSet,pruneSig=0.5)

    ## Warning in prepare.treatmentplan(treatments, designSet, pruneSig = 0.5):
    ## possibly called prepare() on same data frame as designTreatments*()/
    ## mkCrossFrame*Experiment(), this can lead to over-fit. To avoid this, please use
    ## mkCrossFrame*Experiment$crossFrame.

    designSetTreated$code <- designSet$code
    summary(as.numeric(table(designSetTreated$code[designSetTreated$code_lev_rare==1])))

    ##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
    ##   1.000   2.000   2.000   2.655   4.000   5.000

    summary(as.numeric(table(designSetTreated$code[designSetTreated$code_lev_rare!=1])))

    ##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
    ##    6.00   10.25   17.00   27.47   38.75  143.00

We can now apply this treatment to `testSet` to see how this inferred rare level performs. Notice also the `code_catP` which directly encodes prevalence or frequency of the level during training also gives usable estimate of size (likely a more useful one then the rare-level code itself).

As we can see below the `code_lev_rare` correlates with the condition, and usefully re-codes novel levels (levels in `testSet` that were not seen in `designSet`) to rare.

    testSetTreated <- vtreat::prepare(treatments,testSet,pruneSig=0.5)
    testSetTreated$code <- testSet$code
    testSetTreated$newCode <- !(testSetTreated$code %in% unique(designSet$code))
    testSetTreated$generatedAsRareCode <- testSetTreated$code %in% rareCodes

    # Show code_lev_rare==1 corresponds to a subset of rows with elevated inClass==TRUE rate.
    table(code_lev_rare=testSetTreated$code_lev_rare,
          inClass=testSetTreated$inClass)

    ##              inClass
    ## code_lev_rare FALSE TRUE
    ##             0  1894   18
    ##             1    80    8

    # Show newCodes get coded with code_level_rare==1.

    table(newCode=testSetTreated$newCode,code_lev_rare=testSetTreated$code_lev_rare)

    ##        code_lev_rare
    ## newCode    0    1
    ##   FALSE 1912   88

    # Show newCodes tend to come from defined rareCodes.
    table(newCode=testSetTreated$newCode,
          generatedAsRare=testSetTreated$generatedAsRareCode)

    ##        generatedAsRare
    ## newCode FALSE TRUE
    ##   FALSE  1976   24

    # Show code_catP's behavior on rare and novel levels.
    summary(testSetTreated$code_catP)

    ##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
    ## 0.00050 0.00950 0.01950 0.02541 0.03450 0.07150

    summary(testSetTreated$code_catP[testSetTreated$code_lev_rare==1])

    ##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.
    ## 0.000500 0.001000 0.001500 0.001398 0.002000 0.002500

    summary(testSetTreated$code_catP[testSetTreated$newCode])

    ##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
    ##

    summary(testSetTreated$code_catP[testSetTreated$generatedAsRareCode])

    ##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.
    ## 0.0005000 0.0005000 0.0010000 0.0009792 0.0015000 0.0020000