Agenda for the workshop I conducted at ML@Enterprise conference that took place on 14th of December 2017 in Warsaw. Machine Learning is not only about algorithms. Machine learning is about value and this can be achieved only after proper deployment of Machine Learning solutions. I will present best practices regarding managing R based ML projects. I will use our open-source tool R Suite (http://rsuite.io/). During the workshop I will talk about: – project structure – development cycle – deployment – test

17. Rscript R/master.R
--port=7137

22. ●
○
○

31. > rsuite install
Detecting repositories ...
Will use repositories:
CRAN.CRAN = https://mran.microsoft.com/snapshot/2017-10-15
CRAN.CRANextra = http://www.stats.ox.ac.uk/pub/RWin
Other = http://wlog-rsuite.s3.amazonaws.com
Installing RSuite(v0.17x) package ...
installing the source package 'RSuite'
All done.

33. > rsuite proj start -n spmf

34. Commands:
update
Checks if newest version of RSuite CLI is installed. If not
installer for newest version is downloaded and installation
is initiated.
install
Install RSuite with all the dependencies.
proj
Use it to manage project, its dependencies, and build
project packages.
repo
Use to manage repositories. e.g. upload packages.
pkgzip
Use to create PKGZIP packages to fillup remove repository.
version
Show RSuite CLI version.
help
Show this message and exit.
Call 'rsuite [command] help' to get information on acceptable [args].

49. logs/.gitignore

57. PARAMETERS
●
●
●
○
○
○
○
●

58. ●
●
●
LogLevel: INFO
N_days: 365
solver_max_iterations: 10
solver_opt_horizon: 8

60. ●
●
○ main
○ if __name__ == "__main__":

66. predmodel

68. ● ==
● >=
● <=
●

69. master.R

71. spmf/libs

72. packages_import.R

73. master.R

77. import_training.R (I)
● import/<session_id>/
● work/<session_id>/
library(predmodel)
import_path <- file.path(script_path, "../import")
work_path <- file.path(script_path, "../work")
# required
session_id <- args$get(name = "session_id", default = "201711122000", required = FALSE)
loginfo("--> Session id:%s", session_id)
session_work <- file.path(work_path, session_id)
if(!dir.exists(session_work)) {
dir.create(session_work)
}
import_training_data(file.path(import_path, session_id),
session_work)

78. import_training.R (II)

80. devtools

81. import_training_data
#' @export
import_training_data <- function(import_path, work_path) {
pkg_loginfo("Importing from %s into %s",
import_path,
work_path)
n <- 10000
dt <- data.table(feature1 = rnorm(n), feature2 = rnorm(n))
m <- round(n*0.3)
dt[, resp := c(rep(1, m), rep(0, n - m))]
fwrite(x = dt,
file = file.path(work_path, "training.csv"),
sep = ";")
}

84. estimate_model.R (I)
●
●
library(predmodel)
work_path <- file.path(script_path, "../work")
# required
session_id <- args$get(name = "session_id", required =
FALSE, default = "201710111655")
loginfo("--> Session id:%s", session_id)
session_work <- file.path(work_path, session_id)
h2o.init(max_mem_size = "4g",
nthreads = 2)
logdebug("---> H2O started")
train_file <- file.path(session_work, "training.csv")
stopifnot(file.exists(train_file))
train_file %>%
transform_training() %>%
estimate_model(session_id) %>%
save_model(session_work)

85. transform_training
#' @export
transform_training <- function(train_file) {
dt <- h2o.importFile(path = train_file,
destination_frame = "train_dt",
parse = TRUE,
header = TRUE,
sep = ";")
dt$resp <- as.factor(dt$resp)
dt <- h2o.assign(data=dt, key = "train_dt")
return(dt)
}

86. estimate_model
#'@export
estimate_model <- function(dt, session_id) {
model <- h2o.gbm(x = colnames(dt),
y = "resp",
training_frame = dt,
model_id = sprintf("gbm_%s", session_id),
ntrees = 10,
learn_rate = 0.1)
}

87. save_model
#' @export
save_model <- function(model, session_work) {
h2o.saveModel(model,
path = session_work,
force =TRUE)
}

90. import_test.R (I)
● import/<session_id>/
● work/<session_id>/
library(predmodel)
import_path <- file.path(script_path, "../import")
work_path <- file.path(script_path, "../work")
# required
session_id <- args$get(name = "session_id", default = "201711122000", required = FALSE)
loginfo("--> Session id:%s", session_id)
session_work <- file.path(work_path, session_id)
if(!dir.exists(session_work)) {
dir.create(session_work)
}
import_test_data(file.path(import_path, session_id),
session_work)

91. import_test_data
#' @export
import_test_data <- function(import_path, work_path) {
pkg_loginfo("Importing from %s into %s",
import_path,
work_path)
n <- 1000
dt <- data.table(feature1 = rnorm(n), feature2 = rnorm(n))
fwrite(x = dt,
file = file.path(work_path, "test.csv"),
sep = ";")
}

94. score_model.R (I)
● work/<score_session_id>
● work/<train_session_id>
● export/<score_session_id>

95. score_model.R (II)
library(h2o)
library(magrittr)
library(predmodel)
work_path <- file.path(script_path, "../work")
export_path <- file.path(script_path, "../export")
# required
train_session_id <- args$get(name = "train_session_id",
required = FALSE, default = "201710111655")
score_session_id <- args$get(name = "score_session_id",
required = FALSE, default = "201710111655")
loginfo("--> train session id:%s", train_session_id)
loginfo("--> score session id:%s", score_session_id)
score_session_export <- export_path
train_session_work <- file.path(work_path, train_session_id)
score_session_work <- file.path(work_path, score_session_id)
h2o.init(max_mem_size = "4g",
nthreads = 2)
logdebug("---> H2O started")
test_file <- file.path(score_session_work, "test.csv")
model_file <- file.path(train_session_work,
sprintf("gbm_%s", train_session_id))
stopifnot(file.exists(test_file))
stopifnot(file.exists(model_file))
test_dt <- test_file %>%
transform_test()
score_model(test_dt = test_dt,
model_path = model_file) %>%
export_score(export_path = export_path,
score_session_id = score_session_id)

96. transform_test
#' @export
transform_test <- function(test_file) {
h2o.importFile(path = test_file,
destination_frame = "test_dt",
parse = TRUE,
header = TRUE,
sep = ";")
}

97. score_model
#' @export
score_model <- function(test_dt, model_path) {
model <- h2o.loadModel(model_path)
pred_dt <- h2o.predict(model, test_dt)
pred_dt
}

98. export_score
#' @export
export_score <- function(score_dt, score_session_id, export_path) {
score_dt <- as.data.table(score_dt)
score_dt[, score_session_id := score_session_id]
fwrite(x = score_dt,
file = file.path(export_path, "score.csv"),
sep = ";",
append = TRUE)
}

105. Production
spmf_0.1_001.zip
Production/spmf import export
work

106. Production/spmf/R
a. Rscript import_training.R
b. Rscript estimate_model.R
c. Rscript import_test.R
d. Rscript score_model.R
Production/spmf/export

108. print

110. loginfo("Phase 1 passed")
logdebug("Iter %d done", i)
logtrace("Iter %d done", i)
logwarning("Are you sure?")
logerror("I failed :(")
Packages
pkg_loginfo("Phase 1 passed")
pkg_logdebug("Iter %d done", i)
pkg_logtrace("Iter %d done", i)
pkg_logwarning("Are you sure?")
pkg_logerror("I failed :(")

111. 2017-11-13 13:47:03 INFO::--> Session id:201711122000
2017-11-13 13:47:03 INFO:predmodel:Importing from
C:/Workplace/Sandbox/Production/spmf/R/../import/201711122000 into
C:/Workplace/Sandbox/Production/spmf/R/../work/201711122000
2017-11-13 13:47:14 INFO::--> Session id:201711122000
2017-11-13 13:47:51 INFO::--> Session id:201711131000
2017-11-13 13:47:51 INFO:predmodel:Importing from
C:/Workplace/Sandbox/Production/spmf/R/../import/201711131000 into
C:/Workplace/Sandbox/Production/spmf/R/../work/201711131000
2017-11-13 13:47:57 INFO::--> train session id:201711122000
2017-11-13 13:47:57 INFO::--> score session id:201711131000

112. LogLevel: INFO
LogLevel: DEBUG
LogLevel: TRACE

113. import_training.R

116. tests/test_spmf.R
library(predmodel)
library(testthat)
context("Testing context")
test_that(desc = "Test",
code = {
expect_true(5 > 3)
expect_true(pi < 3)
})