R Client

promote.library <- function(name, src="CRAN", version=NULL, user=NULL, install=TRUE)

name: The name of the package to be added.

src: Optionally indicate the source from which the package will be installed on Promote, such as github or CRAN.

version: Optionally indicate the version of the package to be added.

user: Optionally indicate the Github username associated with the package

install: Optionally indicate if the package should be installed on the Promote server. Set this to False if the package is already part of the base image.

promote.library("MASS")
promote.library(c("rjson", "stringr"))
promote.library("cats", src="github", user="hilaryparker")
promote.library("hilaryparker/cats")
promote.library("my_proprietary_package", install=FALSE)

promote.metadata("name",value)

name: The name of the metadata.

value: A string or object that displays in Promote. Must be 50 characters or less.

promote.metadata("aic",my.glm$aic)

promote.unload("name")

name: The name of the package to be excluded during deployment.

promote.unload("ggplot2")

input: The API reads in a data frame input.

output: The API returns a data frame output.

model.predict <- function(data) {
  #LMModel, a linear regression model trained previously, is executed
  pred <- predict(LMModel, newdata=data.frame(data))
  result <- data.frame(pred)
  print(pred)
}

install.packages("promote")
library(promote)

promote.config  <- c(
  username="kermit",
  apikey="123456789101112131415",
  env="https://promote.yourcompany.com"
)

promote.deploy(model_name)

model_name: The name of the model.

promote.config <- c(
 username = "your username",
 apikey = "your apikey",
 env = "https://promote.yourcompany.com"
)

#simple example model
iris$Sepal.Width_sq <- iris$Sepal.Width^2
fit <- glm(I(Species)=="virginica" ~ ., data=iris)

model.require <- function() {
    require("randomForest")
}

model.predict <- function(df) {
 data.frame("prediction"=predict(fit, df, type="response"))
}

promote.deploy("irisModel")

promote.predict(model_name = , data = , model_owner = , raw_input = , silent = )

model_name: The name of the prediction model.

data: The JSON data to be sent to the model.

model_owner: Optionally indicate the name of the model owner.

raw_input: Optionally indicate if data returned by the model is coerced into a data.frame. Be default, the data is returned as a data.frame.

silent: Optionally indicate if the request url is printed to the console. By default, the url is returned.

data <- rjson::fromJSON('{"name":"Sam"}')
promote.predict("HelloWorld",data)

#Output:
#       greeting
#1 Hello Sam!

#################### model_owner and silent example #############################

# send request to another users model, with silent = FALSE
promote.predict("HelloWorld",data, 'brandon',silent = FALSE)

# Output:

# https://promote.yourcompany.com/brandon/models/HelloWorld/?username=colin&apikey=123455667781238912389
#greeting
#1 Bye Sam!

############################## raw_input example ################################

jsonTest <- '{
    "name": "colin",
    "favorite_nums": [1, 2, 3, 4, 5],
    "favorite_words": ["cat", "dog", "mouse",null]
}'

model.require <- function(){
  library("RJSONIO")
}

# If we want to manually preprocess the JSON data
model.predict <- function(data){
  data <- list(json=data)
  # Below, parsing our JSON object manually with RJSONIO
  d <-RJSONIO::fromJSON(data$json,nullValue = 'my_na_value')
}

promote.deploy('non_tabular_JSON')

# this will fail:
promote.predict('non_tabular_JSON',jsonTest)

# this will succeed because we are sending non-tabular data:
promote.predict('non_tabular_JSON',jsonTest,raw_input = TRUE)