vtreat package

John Mount, Nina Zumel

2018-06-19

‘vtreat’ is a data.frame processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner. A formal article on the method can be found here: arXiv:1611.09477 stat.AP.

A ‘vtreat’ clean data frame:

To achieve this a number of techniques are used. Principally:

For more details see: the ‘vtreat’ article and update.

The use pattern is:

  1. Use designTreatmentsC() or designTreatmentsN() to design a treatment plan
  2. Use the returned structure with prepare() to apply the plan to data frames.

The main feature of ‘vtreat’ is that all data preparation is “y-aware”: it uses the relations of effective variables to the dependent or outcome variable to encode the effective variables.

The structure returned from designTreatmentsN() or designTreatmentsC() includes a list of “treatments”: objects that encapsulate the transformation process from the original variables to the new “clean” variables.

In addition to the treatment objects designTreatmentsC() and designTreatmentsN() also return a data frame named scoreFrame which contains columns:

In all cases we have two undesirable upward biases on the scores:

‘vtreat’ uses a number of cross-training and jackknife style procedures to try to mitigate these effects. The suggested best practice (if you have enough data) is to split your data randomly into at least the following disjoint data sets:

Taking the extra step to perform the designTreatmentsC() or designTreatmentsN() on data disjoint from training makes the training data more exchangeable with test and avoids the issue that ‘vtreat’ may be hiding a large number of degrees of freedom in variables it derives from large categoricals.

Some trivial execution examples (not demonstrating any cal/train/test split) are given below. Variables that do not move during hold-out testing are considered “not to move.”


A Categorical Outcome Example

library(vtreat)
dTrainC <- data.frame(x=c('a','a','a','b','b',NA),
   z=c(1,2,3,4,NA,6),y=c(FALSE,FALSE,TRUE,FALSE,TRUE,TRUE))
head(dTrainC)
##      x  z     y
## 1    a  1 FALSE
## 2    a  2 FALSE
## 3    a  3  TRUE
## 4    b  4 FALSE
## 5    b NA  TRUE
## 6 <NA>  6  TRUE
dTestC <- data.frame(x=c('a','b','c',NA),z=c(10,20,30,NA))
head(dTestC)
##      x  z
## 1    a 10
## 2    b 20
## 3    c 30
## 4 <NA> NA
treatmentsC <- designTreatmentsC(dTrainC,colnames(dTrainC),'y',TRUE)
## [1] "vtreat 1.2.0 inspecting inputs Tue Jun 19 07:16:46 2018"
## [1] "designing treatments Tue Jun 19 07:16:46 2018"
## [1] " have initial level statistics Tue Jun 19 07:16:46 2018"
## [1] "design var x Tue Jun 19 07:16:46 2018"
## [1] "design var z Tue Jun 19 07:16:46 2018"
## [1] " scoring treatments Tue Jun 19 07:16:46 2018"
## [1] "have treatment plan Tue Jun 19 07:16:46 2018"
## [1] "rescoring complex variables Tue Jun 19 07:16:46 2018"
## [1] "done rescoring complex variables Tue Jun 19 07:16:46 2018"
print(treatmentsC)
##     varName varMoves        rsq        sig needsSplit extraModelDegrees
## 1    x_catP     TRUE 0.54085208 0.03392101       TRUE                 2
## 2    x_catB     TRUE 0.00000000 1.00000000       TRUE                 2
## 3   z_clean     TRUE 0.25792985 0.14299775      FALSE                 0
## 4   z_isBAD     TRUE 0.19087450 0.20766228      FALSE                 0
## 5  x_lev_NA     TRUE 0.19087450 0.20766228      FALSE                 0
## 6 x_lev_x_a     TRUE 0.08170417 0.40972582      FALSE                 0
## 7 x_lev_x_b     TRUE 0.00000000 1.00000000      FALSE                 0
##   origName  code
## 1        x  catP
## 2        x  catB
## 3        z clean
## 4        z isBAD
## 5        x   lev
## 6        x   lev
## 7        x   lev
print(treatmentsC$treatments[[1]])
## [1] "vtreat 'Categoric Indicators'('x'(integer,factor)->character->'x_lev_NA','x_lev_x_a','x_lev_x_b')"

Here we demonstrate the optional scaling feature of prepare(), which scales and centers all significant variables to mean 0, and slope 1 with respect to y: In other words, it rescales the variables to “y-units”. This is useful for downstream principal components analysis. Note: variables perfectly uncorrelated with y necessarily have slope 0 and can’t be “scaled” to slope 1, however for the same reason these variables will be insignificant and can be pruned by pruneSig.

scale=FALSE by default.

dTrainCTreated <- prepare(treatmentsC,dTrainC,pruneSig=c(),scale=TRUE)
head(dTrainCTreated)
##   x_catP      x_catB     z_clean z_isBAD x_lev_NA  x_lev_x_a x_lev_x_b
## 1   -0.2 -0.11976374 -0.38648649    -0.1     -0.1 -0.1666667         0
## 2   -0.2 -0.11976374 -0.21081081    -0.1     -0.1 -0.1666667         0
## 3   -0.2 -0.11976374 -0.03513514    -0.1     -0.1 -0.1666667         0
## 4    0.1 -0.07564865  0.14054054    -0.1     -0.1  0.1666667         0
## 5    0.1 -0.07564865  0.00000000     0.5     -0.1  0.1666667         0
## 6    0.4  0.51058851  0.49189189    -0.1      0.5  0.1666667         0
##       y
## 1 FALSE
## 2 FALSE
## 3  TRUE
## 4 FALSE
## 5  TRUE
## 6  TRUE
varsC <- setdiff(colnames(dTrainCTreated),'y')
# all input variables should be mean 0
sapply(dTrainCTreated[,varsC,drop=FALSE],mean)
##        x_catP        x_catB       z_clean       z_isBAD      x_lev_NA 
##  1.850372e-17  1.387779e-17  9.251859e-18 -6.938894e-18 -6.938894e-18 
##     x_lev_x_a     x_lev_x_b 
##  0.000000e+00  0.000000e+00
# all slopes should be 1 for variables with dTrainCTreated$scoreFrame$sig<1
sapply(varsC,function(c) { glm(paste('y',c,sep='~'),family=binomial,
   data=dTrainCTreated)$coefficients[[2]]})
##    x_catP    x_catB   z_clean   z_isBAD  x_lev_NA x_lev_x_a x_lev_x_b 
##  4.698112 15.815409  5.733441 31.619223 31.619223  4.158883        NA
dTestCTreated <- prepare(treatmentsC,dTestC,pruneSig=c(),scale=TRUE)
head(dTestCTreated)
##   x_catP      x_catB  z_clean z_isBAD x_lev_NA  x_lev_x_a x_lev_x_b
## 1   -0.2 -0.11976374 1.194595    -0.1     -0.1 -0.1666667         0
## 2    0.1 -0.07564865 2.951351    -0.1     -0.1  0.1666667         0
## 3    0.7 -0.07564865 4.708108    -0.1     -0.1  0.1666667         0
## 4    0.4  0.51058851 0.000000     0.5      0.5  0.1666667         0

A Numeric Outcome Example

# numeric example
dTrainN <- data.frame(x=c('a','a','a','a','b','b',NA),
   z=c(1,2,3,4,5,NA,7),y=c(0,0,0,1,0,1,1))
head(dTrainN)
##   x  z y
## 1 a  1 0
## 2 a  2 0
## 3 a  3 0
## 4 a  4 1
## 5 b  5 0
## 6 b NA 1
dTestN <- data.frame(x=c('a','b','c',NA),z=c(10,20,30,NA))
head(dTestN)
##      x  z
## 1    a 10
## 2    b 20
## 3    c 30
## 4 <NA> NA
treatmentsN = designTreatmentsN(dTrainN,colnames(dTrainN),'y')
## [1] "vtreat 1.2.0 inspecting inputs Tue Jun 19 07:16:46 2018"
## [1] "designing treatments Tue Jun 19 07:16:46 2018"
## [1] " have initial level statistics Tue Jun 19 07:16:46 2018"
## [1] "design var x Tue Jun 19 07:16:46 2018"
## [1] "design var z Tue Jun 19 07:16:46 2018"
## [1] " scoring treatments Tue Jun 19 07:16:46 2018"
## [1] "have treatment plan Tue Jun 19 07:16:46 2018"
## [1] "rescoring complex variables Tue Jun 19 07:16:46 2018"
## [1] "done rescoring complex variables Tue Jun 19 07:16:46 2018"
print(treatmentsN)
##     varName varMoves         rsq       sig needsSplit extraModelDegrees
## 1    x_catP     TRUE 0.156532606 0.3796474       TRUE                 2
## 2    x_catN     TRUE 0.045758227 0.6451033       TRUE                 2
## 3    x_catD     TRUE 0.173611111 0.3524132       TRUE                 2
## 4   z_clean     TRUE 0.336111111 0.1724763      FALSE                 0
## 5   z_isBAD     TRUE 0.222222222 0.2855909      FALSE                 0
## 6  x_lev_NA     TRUE 0.222222222 0.2855909      FALSE                 0
## 7 x_lev_x_a     TRUE 0.173611111 0.3524132      FALSE                 0
## 8 x_lev_x_b     TRUE 0.008333333 0.8456711      FALSE                 0
##   origName  code
## 1        x  catP
## 2        x  catN
## 3        x  catD
## 4        z clean
## 5        z isBAD
## 6        x   lev
## 7        x   lev
## 8        x   lev
dTrainNTreated <- prepare(treatmentsN,dTrainN,
                          pruneSig=c(),scale=TRUE)
head(dTrainNTreated)
##   x_catP      x_catN     x_catD     z_clean    z_isBAD   x_lev_NA
## 1   -0.2 -0.17857143 -0.1785714 -0.41904762 -0.0952381 -0.0952381
## 2   -0.2 -0.17857143 -0.1785714 -0.26190476 -0.0952381 -0.0952381
## 3   -0.2 -0.17857143 -0.1785714 -0.10476190 -0.0952381 -0.0952381
## 4   -0.2 -0.17857143 -0.1785714  0.05238095 -0.0952381 -0.0952381
## 5    0.2  0.07142857  0.2380952  0.20952381 -0.0952381 -0.0952381
## 6    0.2  0.07142857  0.2380952  0.00000000  0.5714286 -0.0952381
##    x_lev_x_a   x_lev_x_b y
## 1 -0.1785714 -0.02857143 0
## 2 -0.1785714 -0.02857143 0
## 3 -0.1785714 -0.02857143 0
## 4 -0.1785714 -0.02857143 1
## 5  0.2380952  0.07142857 0
## 6  0.2380952  0.07142857 1
varsN <- setdiff(colnames(dTrainNTreated),'y')
# all input variables should be mean 0
sapply(dTrainNTreated[,varsN,drop=FALSE],mean) 
##        x_catP        x_catN        x_catD       z_clean       z_isBAD 
## -5.551115e-17 -3.965082e-18 -9.515810e-17  4.757324e-17 -3.967986e-18 
##      x_lev_NA     x_lev_x_a     x_lev_x_b 
## -3.965082e-18  0.000000e+00 -2.974054e-18
# all slopes should be 1 for variables with treatmentsN$scoreFrame$sig<1
sapply(varsN,function(c) { lm(paste('y',c,sep='~'),
   data=dTrainNTreated)$coefficients[[2]]}) 
##    x_catP    x_catN    x_catD   z_clean   z_isBAD  x_lev_NA x_lev_x_a 
##         1         1         1         1         1         1         1 
## x_lev_x_b 
##         1
# prepared frame
dTestNTreated <- prepare(treatmentsN,dTestN,
                         pruneSig=c())
head(dTestNTreated)
##      x_catP      x_catN    x_catD   z_clean z_isBAD x_lev_NA x_lev_x_a
## 1 0.5714286 -0.17857143 0.5000000 10.000000       0        0         1
## 2 0.2857143  0.07142857 0.7071068 20.000000       0        0         0
## 3 0.0000000  0.00000000 0.7071068 30.000000       0        0         0
## 4 0.1428571  0.57142857 0.7071068  3.666667       1        1         0
##   x_lev_x_b
## 1         0
## 2         1
## 3         0
## 4         0
# scaled prepared frame
dTestNTreatedS <- prepare(treatmentsN,dTestN,
                         pruneSig=c(),scale=TRUE)
head(dTestNTreatedS)
##   x_catP        x_catN     x_catD   z_clean    z_isBAD   x_lev_NA
## 1   -0.2 -1.785714e-01 -0.1785714 0.9952381 -0.0952381 -0.0952381
## 2    0.2  7.142857e-02  0.2380952 2.5666667 -0.0952381 -0.0952381
## 3    0.6 -1.586033e-17  0.2380952 4.1380952 -0.0952381 -0.0952381
## 4    0.4  5.714286e-01  0.2380952 0.0000000  0.5714286  0.5714286
##    x_lev_x_a   x_lev_x_b
## 1 -0.1785714 -0.02857143
## 2  0.2380952  0.07142857
## 3  0.2380952 -0.02857143
## 4  0.2380952 -0.02857143

Related work: