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R interface for MLeap

Travis build status Coverage status CRAN status

mleap is a sparklyr extension that provides an interface to MLeap, which allows us to take Spark pipelines to production.

Getting started

mleap can be installed from CRAN via

install.packages("mleap")

or, for the latest development version from GitHub, using

devtools::install_github("rstudio/mleap")

Once mleap has been installed, we can install the external dependencies using

library(mleap)
install_maven()
# Alternatively, if you already have Maven installed, you can 
#  set options(maven.home = "path/to/maven")
install_mleap()

We can now export Spark ML pipelines from sparklyr.

library(sparklyr)
sc <- spark_connect(master = "local", version = "2.4.0")
mtcars_tbl <- sdf_copy_to(sc, mtcars, overwrite = TRUE)

# Create a pipeline and fit it
pipeline <- ml_pipeline(sc) %>%
  ft_binarizer("hp", "big_hp", threshold = 100) %>%
  ft_vector_assembler(c("big_hp", "wt", "qsec"), "features") %>%
  ml_gbt_regressor(label_col = "mpg")
pipeline_model <- ml_fit(pipeline, mtcars_tbl)

# Export model
model_path <- file.path(tempdir(), "mtcars_model.zip")
ml_write_bundle(pipeline_model, sample_input = mtcars_tbl, path = model_path)

# Disconnect from Spark
spark_disconnect(sc)
## NULL

At this point, we can share mtcars_model.zip with our deployment/implementation engineers, and they would be able to embed the model in another application. See the MLeap docs for details.

We also provide R functions for testing that the saved models behave as expected. Here we load the previously saved model:

model <- mleap_load_bundle(model_path)
model
## MLeap Transformer
## <7e2f61ed-154b-4c9e-9926-85fa326d69ef> 
##   Name: pipeline_c1754f374a53 
##   Format: json 
##   MLeap Version: 0.14.0

We can retrieve the schema associated with the model:

mleap_model_schema(model)
## # A tibble: 6 x 5
##   name       type   nullable dimension io    
##   <chr>      <chr>  <lgl>    <chr>     <chr> 
## 1 qsec       double TRUE     <NA>      input 
## 2 hp         double FALSE    <NA>      input 
## 3 wt         double TRUE     <NA>      input 
## 4 big_hp     double FALSE    <NA>      output
## 5 features   double TRUE     (3)       output
## 6 prediction double FALSE    <NA>      output

Then, we create a new data frame to be scored, and make predictions using our model:

newdata <- tibble::tribble(
  ~qsec, ~hp, ~wt,
  16.2,  101, 2.68,
  18.1,  99,  3.08
)

# Transform the data frame
transformed_df <- mleap_transform(model, newdata)
dplyr::glimpse(transformed_df)
## Observations: 2
## Variables: 6
## $ qsec       <dbl> 16.2, 18.1
## $ hp         <dbl> 101, 99
## $ wt         <dbl> 2.68, 3.08
## $ big_hp     <dbl> 1, 0
## $ features   <list> [[[1, 2.68, 16.2], [3]], [[0, 3.08, 18.1], [3]]]
## $ prediction <dbl> 21.00084, 20.56445