R Client

Use the promote.library method to list the libraries needed to deploy your model.

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

promote.library("MASS")

promote.library(c("rjson", "stringr"))

promote.library("cats", src="github", version="0.1", user="hilaryparker")

promote.library("my_proprietary_package", install=FALSE)

Use the promote.metadata method to assign related metadata to the model so the Promote platform can view it.

promote.metadata("name", value)

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

Use the promote.unload method to remove from the model's dependency list any libraries that are not required for deployment.

promote.unload("name")

promote.unload("ggplot2")

Use the model.predict method to process requests for predictions. When you use this method, the Promote API endpoint receives the request, executes the prediction code, and then issues a response.

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

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

promote.config <- c(
    username = "USERNAME",
    apikey = "APIKEY"
    env = "YOURURL"
)

promote.config <- c(
    username="kermit",
    apikey="1234556789101112131415",
    env="https://promote.yourcompany.com"
)

Use the promote.deploy method to deploy your model and its related dependencies to the URL of your Promote instance.

promote.deploy(model_name)

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

# A 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 = MODELNAME, data = DATA, model_owner = OWNER, raw_input = INPUT, silent = TRUE)

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)

# Below (for example) is incoming JSON, parsed BEFORE it gets sent to model.predict().
# API Requests --> model.predict(jsonlite::fromJSON(df))

model.predict <- function(df) {
  lg <- df
  m <- lapply(lg[[1]]$users, function(x) c(paste(x$user['name'],sample(1:2,1)), x$user['user_id'], x['ts']))
  m <- do.call(rbind, m)
  # Below, we split the dataframe so it is returned as JSON arrays.
  m <- unname(split(m, 1:nrow(m)))
  m
}

# jsonlite::toJSON() --> API Response