PFA with the R Package aurelius

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I first heard about the Portable Format for Analytics (PFA) through KDnuggets in 2016. There was a post that in vague way talked about the virtues and benefits of PFA, but not to completely supplant PMML, which I was already familiar with. PMML was pretty cool, but I couldn’t find good support and felt that the XML-based approach was clumsy and outdated so when I heard about PFA, I was pretty excited. A new JSON-based standard for encoding any type of analytics, not just certain supported models like PMML. I mainly work in the R programming language, so I was disappointed to find resources lacking that would convert models made in R into the PFA standard. The great folks at the Data Mining Group (DMG) had started on an R package called aurelius that contained the bare bones functions to construct the syntax. After reading through the Project pages on GitHub, I saw everything was there, but it wasn’t as convenient as I would like so I started writing “model producers”. Model producers are functions that convert model objects in R into their PFA equivalent to be exported and used in other systems. In this process I learned a great deal. I thought I knew how models worked, but actually reading through the mechanics of scoring has made me appreciate them so much more. Below is an outline of working with the aurelius package, but note that this work has yet to be integrated into the project and exists on forked branch in my GitHub (installation command shown below).

# you only need to run this once because it 
# installs the package

In this example we’ll load the randomForest package to demonstrate how to convert randomForest models into PFA. Currently, the package converts models of many types: lm, glm, glmnet, gbm, randomForest, naivebayes, lda, kcca, and kmeans.

options(stringsAsFactors = FALSE)

PFA Structure

Before we get started it’s helpful to understand the basic components of a valid PFA document. Every PFA document has an input spec, output spec, and an action. The reason for the specifications on the input and output is so that the machine executing your PFA can safely and reliably excute. PFA relies on the Avro serialization format for its type system, whichsupports your typical primitive data types like boolean, integer, and string, but also, arrays, maps, and more. You can define these types in R by using any of the functions like avro_*.

#> [1] "double"

avro_enum(list("one", "two"))
#> $type
#> [1] "enum"
#> $symbols
#> $symbols[[1]]
#> [1] "one"
#> $symbols[[2]]
#> [1] "two"
#> $name
#> [1] "Enum_1"

After specifying inputs and outputs, you must specify an action. With aurelius an action can be specified from an R expression. A simple example is 2+2. This can be expanded to manipulate the input and/or derivatives of it. Here is a simple analytics spec that takes a double and adds 10 to it.

pfa_document(input = avro_double, 
             output = avro_double, 
             action = expression(input + 10))
#> $input
#> [1] "double"
#> $output
#> [1] "double"
#> $action
#> $action[[1]]
#> $action[[1]]$`+`
#> $action[[1]]$`+`[[1]]
#> [1] "input"
#> $action[[1]]$`+`[[2]]
#> [1] 10

You’ll notice that the resulting output is a list. PFA is based on JSON, so it’s natural for the document, when it’s in R, to be a list that can always later be converted to JSON for export.

Converting a Tree Model

Let’s step through a more realistic example that predicts the species in the iris dataset, based on the other variables using a random forest model. First, format the columns to meet Avro guidelines: [A-Za-z_][A-Za-z0-9_]*. Having periods in the variable names (e.g. Sepal.Length) is not encouraged.

# make sure column names dont have periods in them
iris2 <- iris
colnames(iris2) <- gsub('\\.', '_', colnames(iris2))

Creating the PFA is very easy. Simply fit your model, then call pfa().

# fit a random forest model
rf_model <- randomForest(Species ~ ., data=iris2) 
# convert that model to PFA
# pred_type='prob' means that the output is the 
# probability of all classes. If you want 
# the majority vote, then specify pred_type='response'
# check all options by running ?pfa.randomForest
rf_model_as_pfa <- pfa(rf_model, pred_type='prob')

If you have the rPython package installed and Titus (PFA for Python), then you can create an engine in R and see how the PFA behaves. In this case it predicts the same as the predict() function in R. This way we can test that the behavior of the PFA is what we expect.

# you can check the predictions from this model
# by first converting to a pfa_engine
pfa_model <- pfa_engine(rf_model_as_pfa) 
# confirm that the pfa engine produces the 
# same prediction as the "predict" method in R
test_dat <- iris2[73,1:4]
round(pfa_model$action(as.list(test_dat)), 6)
#>     setosa versicolor  virginica 
#>       0.00       0.83       0.17
round(unclass(predict(object=rf_model, newdata=test_dat, type='prob')), 6) 
#>    setosa versicolor virginica
#> 73      0       0.83      0.17

Once you’re happy with your model, just export to a flat file using write_pfa(). This format can be read and used by any PFA interpreter. With R you can read the file back in using read_pfa().

# you can export your model for use in other systems
write_pfa(rf_model_as_pfa, file = '~/my_rf_model.pfa')

my_model <- read_pfa(file("~/my_rf_model.pfa"))

# see that all PFA components are there
#> [1] "input"  "output" "action" "cells"

Final Thoughts

PFA is a measurable step in open, portable, and reprodcible analytics. I hope to see the standard more widely adopted and tools developed to help others implement it. One final thing: Check out the unit tests. I’ve recently added quite a bit of unit test coverage and these tests are an excellent source of examples because they cover most all cases of utilizing the package functions.

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