In WinVector/vtreat: A Statistically Sound 'data.frame' Processor/Conditioner

Using vtreat with unsupervised problems and non-Y-aware data treatment

Nina Zumel and John Mount updated February 2020

This article documents vtreat's "fit_prepare" variation for unsupervised problems. This API was inspired by the pyvtreat API, which was in turn based on the .fit(), .transform(), .fit_transform() workflow of scikit-learn in Python.

The same example in the original R vtreat notation can be found here.

The same example in the Python version of vtreat can be found here.

Preliminaries

Load modules/packages.

library(vtreat)
packageVersion('vtreat')
suppressPackageStartupMessages(library(ggplot2))
library(WVPlots)
library(rqdatatable)

Generate example data.

• y is a noisy sinusoidal plus linear function of the variable x
• Input xc is a categorical variable that represents a discretization of y, along with some NaNs
• Input x2 is a pure noise variable with no relationship to the output
• Input x3 is a constant variable

set.seed(2020)

make_data <- function(nrows) {
    d <- data.frame(x = 5*rnorm(nrows))
    d['y'] = sin(d[['x']]) + 0.01*d[['x']] + 0.1*rnorm(n = nrows)
    d[4:10, 'x'] = NA                  # introduce NAs
    d['xc'] = paste0('level_', 5*round(d$y/5, 1))
    d['x2'] = rnorm(n = nrows)
    d['x3'] = 1
    d[d['xc']=='level_-1', 'xc'] = NA  # introduce a NA level
    return(d)
}

d = make_data(500)

d %.>%
  head(.) %.>%
  knitr::kable(.)

Some quick data exploration

Check how many levels xc has, and their distribution (including NaN)

unique(d['xc'])

table(d$xc, useNA = 'always')

Build a transform appropriate for unsupervised (or non-y-aware) problems.

The vtreat package is primarily intended for data treatment prior to supervised learning, as detailed in the Classification and Regression examples. In these situations, vtreat specifically uses the relationship between the inputs and the outcomes in the training data to create certain types of synthetic variables. We call these more complex synthetic variables y-aware variables.

However, you may also want to use vtreat for basic data treatment for unsupervised problems, when there is no outcome variable. Or, you may not want to create any y-aware variables when preparing the data for supervised modeling. For these applications, vtreat is a convenient alternative to model.matrix() that keeps information about the levels of factor variables observed in the data, and can manage novel levels that appear in future data.

In any case, we still want training data where all the input variables are numeric and have no missing values or NaNs.

First create the data treatment transform object, in this case a treatment for an unsupervised problem.

transform_design <- vtreat::UnsupervisedTreatment(
    var_list = setdiff(colnames(d), c('y')),   # columns to transform
    cols_to_copy = 'y'                         # copy y to the treated data
)

# learn transform from data
treatment_plan <- fit(transform_design, d)

# prepare the data using the treatment plan
d_prepared <- prepare(treatment_plan, d)

# for unsupervised problems fit_transform(transform_design, d)
# will produce the same treatment plan and treated data set
# as the above, in one step

# unpack[treatment_plan = treatments,
#        d_prepared = cross_frame] <- fit_prepare(transform_design, d)


# list the derived variables
get_feature_names(treatment_plan)

The treated training set should be clean: completely numeric, with no missing values.

The Score Frame

Now examine the score frame, which gives information about each new variable, including its type and which original variable it is derived from. Some of the columns of the score frame (rsq, sig) are not relevant to the unsupervised case; those columns are used by the Regression and Classification transforms.

score_frame <- get_score_frame(treatment_plan)
knitr::kable(score_frame)

Notice that the variable xc has been converted to multiple variables:

• an indicator variable for each possible level, including NA or missing (xc_lev*)
• a variable that returns how prevalent this particular value of xc is in the training data (xc_catP)

The numeric variable x has been converted to two variables:

• a clean version of x that has no NaNs or missing values
• a variable indicating when x was NaN or NA in the original data (xd_isBAD)

Any or all of these new variables are available for downstream modeling.

Also note that the variable x3 does not appear in the score frame (or in the treated data), as it had no range (didn't vary), so the unsupervised treatment dropped it.

Let's look at the top of d_prepared, which includes all the new variables, plus y (and excluding x3).

d_prepared %.>%
  head(.) %.>%
  knitr::kable(.)

Using the Prepared Data to Model

Of course, what we really want to do with the prepared training data is to model.

K-means clustering

Let's start with an unsupervised analysis: clustering.

# don't use y to cluster
not_variables <- c('y')
model_vars <- setdiff(colnames(d_prepared), not_variables)

clusters = kmeans(d_prepared[, model_vars, drop = FALSE], centers = 5)

d_prepared['clusterID'] <- clusters$cluster
head(d_prepared$clusterID)

ggplot(data = d_prepared, aes(x=x, y=y, color=as.character(clusterID))) +
  geom_point() +
  ggtitle('y as a function of x, points colored by (unsupervised) clusterID') +
  theme(legend.position="none") +
  scale_colour_brewer(palette = "Dark2")

Supervised modeling with non-y-aware variables

Since in this case we have an outcome variable, y, we can try fitting a linear regression model to d_prepared.

f <- wrapr::mk_formula('y', model_vars)

model = lm(f, data = d_prepared)

# now predict
d_prepared['prediction'] = predict(
  model,
  newdata = d_prepared)

# look at the fit (on the training data)
WVPlots::ScatterHist(
  d_prepared, 
  xvar = 'prediction',
  yvar = 'y',
  smoothmethod = 'identity',
  estimate_sig = TRUE,
  title = 'Relationship between prediction and y')

Now apply the model to new data.

# create the new data
dtest <- make_data(450)

# prepare the new data with vtreat
dtest_prepared = prepare(treatment_plan, dtest)
dtest_prepared$y = dtest$y

# apply the model to the prepared data
dtest_prepared['prediction'] = predict(
  model,
  newdata = dtest_prepared)

# compare the predictions to the outcome (on the test data)
WVPlots::ScatterHist(
  dtest_prepared, 
  xvar = 'prediction',
  yvar = 'y',
  smoothmethod = 'identity',
  estimate_sig = TRUE,
  title = 'Relationship between prediction and y')

# get r-squared
sigr::wrapFTest(dtest_prepared, 
                predictionColumnName = 'prediction',
                yColumnName = 'y',
                nParameters = length(model_vars) + 1)

Parameters for UnsupervisedTreatment

We've tried to set the defaults for all parameters so that vtreat is usable out of the box for most applications.

unsupervised_parameters()

Some parameters of note include:

codeRestriction: The types of synthetic variables that vtreat will (potentially) produce. By default, all possible applicable types will be produced. See Types of prepared variables below.

minFraction (default: 0): For categorical variables, indicator variables (type levs) are only produced for levels that are present at least minFraction of the time. A consequence of this is that 1/minFraction is the maximum number of indicators that will be produced for a given categorical variable. By default, all possible indicator variables are produced.

missingness_imputation: The function or value that vtreat uses to impute or "fill in" missing numerical values. The default is mean. To change the imputation function or use different functions/values for different columns, see the Imputation example for examples.

customCoders: For passing in user-defined transforms for custom data preparation. Won't be needed in most situations, but see here for an example of applying a GAM transform to input variables.

Example: Restrict the number of indicator variables

# calculate the prevalence of each level of xc by hand, including NA
table(d$xc, useNA = "ifany")/nrow(d)

# create a parameter list, overriding the default for minFraction
newparams = unsupervised_parameters(
  list(minFraction = 0.2)     # only make indicators for levels that appear more than 20% of the time
)

transform_common = UnsupervisedTreatment(
    var_list = setdiff(colnames(d), c('y')), # columns to transform
    params = newparams                       # set the parameters                        
)

# learn transform from data
treatment_plan <- fit(transform_common, d)

# prepare the data using the treatment plan
d_prepared <- prepare(treatment_plan, d)

# examine the score frame
knitr::kable(get_score_frame(treatment_plan))  

In this case, the unsupervised treatment only created levels for the two most common levels, level_1 and NA, which are both present more than 20% of the time.

In unsupervised situations, this may only be desirable when there are an unworkably large number of possible levels (for example, when using ZIP code as a variable). It is more useful in conjunction with the y-aware variables produced by NumericOutcomeTreatment, BinomialOutcomeTreatment, or MultinomialOutcomeTreatment.

Types of prepared variables

clean: Produced from numerical variables: a clean numerical variable with no NaNs or missing values

lev: Produced from categorical variables, one for each level: for each level of the variable, indicates if that level was "on"

catP: Produced from categorical variables: indicates how often each level of the variable was "on" (its prevalence)

isBAD: Produced for numerical variables: an indicator variable that marks when the original variable was missing or NaN

Conclusion

In all cases (classification, regression, unsupervised, and multinomial classification) the intent is that vtreat transforms are essentially one liners.

The preparation commands are organized as follows:

• Regression: R regression example, fit/prepare interface, R regression example, design/prepare/experiment interface, Python regression example.
• Classification: R classification example, fit/prepare interface, R classification example, design/prepare/experiment interface, Python classification example.
• Unsupervised tasks: R unsupervised example, fit/prepare interface, R unsupervised example, design/prepare/experiment interface, Python unsupervised example.
• Multinomial classification: R multinomial classification example, fit/prepare interface, R multinomial classification example, design/prepare/experiment interface, Python multinomial classification example.

These current revisions of the examples are designed to be small, yet complete. So as a set they have some overlap, but the user can rely mostly on a single example for a single task type.

WinVector/vtreat documentation built on Aug. 29, 2023, 4:49 a.m.