Lets train a fancy XGBoost on the iris dataset, which is a massive overkill but it's a good example.
data(iris)
library(jug)
library(caret)
library(dplyr)
library(xgboost)
library(jsonlite)
df <- iris
We will center and scale the iris data first and save the method that allows us to do this. This is because the centering and scaling is dependent on the data used to train the model.
preprocessing <- df %>%
select(-Species) %>%
preProcess(method = c('center', 'scale'))
dfCenterScaled <- predict(preprocessing, df)
head(dfCenterScaled)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 -0.8976739 1.01560199 -1.335752 -1.311052 setosa
## 2 -1.1392005 -0.13153881 -1.335752 -1.311052 setosa
## 3 -1.3807271 0.32731751 -1.392399 -1.311052 setosa
## 4 -1.5014904 0.09788935 -1.279104 -1.311052 setosa
## 5 -1.0184372 1.24503015 -1.335752 -1.311052 setosa
## 6 -0.5353840 1.93331463 -1.165809 -1.048667 setosa
Looks good, let's save it for later.
saveRDS(preprocessing, file = 'preprocessing.RDS')
Let's train an XGBoost model with 5 fold cross validation 10 times
First let's set the fitting control parameters for the cross validation
fitControl <- trainControl(
method = "repeatedcv",
number = 10,
repeats = 2)
And now let's train...
set.seed(825)
model <- train(Species ~ .,
data = dfCenterScaled,
method = "xgbTree",
trControl = fitControl,
verbose = FALSE)
model
Cool, let's save that model for later in the plumbeR app.
saveRDS(model, file = 'model.RDS')
The following has been saved as app.R
We will be using Jug here in order to create the API as I like the syntax. I hear it has been discontinued though which is sad. You could also use plumber for this but I was drawn to Jug because of syntax and CORS support.
The aim is to read the serialised model and preprocessing steps, then use them to make a prediction.
#' Jug Application for exposing model as an API
#'
#' @author Alistair Rogers
#'
#' ======= IMPORTING LIBRARIES ===========================================
#'
suppressMessages(library(jsonlite, quietly = T))
suppressMessages(library(dplyr, quietly = T))
suppressMessages(library(jug, quietly = T))
#' ======= LOADING SERIALISED MODEL AND PREPROCESSING ====================
#'
model <- readRDS('model.RDS')
preprocessing <- readRDS('preprocessing.RDS')
#' ============= PREDICTION METHOD =======================================
#' @param json: JSON string of Sepal.Length, Sepal.Width, Petal.Length,
#' Petal.Width
#' @concepts Read in JSON string of predictors (described above) and apply
#' preprocessing and predict Species.
#' @return result: JSONised dataframe of predictor columns with their species prediction
prediction <- function(json) {
if (validate(json)){
df <- fromJSON(json) %>%
as.data.frame()
result <- df %>%
predict(preprocessing, newdata = .) %>% # Apply Preprocessing - Centering and Scaling
predict(model, newdata = .) %>% # Apply XGBoost Model
data.frame(Species = .) %>% # Turn predictions into Dataframe
cbind(df, .) %>% # Concatenate
toJSON(pretty = T)
} else {
result <- list(error = 400, message = 'Not Valid JSON')
}
return(result)
}
#' ========== TEST METHOD ==============================================
test_func <- function() {
message <- 'Why Hello there chap'
message %>% toJSON()
}
#' ========== MAIN METHOD - API ENDPOINT ===============================
main <- function() {
jug() %>%
post("/prediction", decorate(prediction)) %>% # Prediction Method
get("/", decorate(test_func)) %>% # Test Method
simple_error_handler_json() %>%
serve_it(host = '0.0.0.0', port = 8080) # Docker container will not work unless it's on this host.
}
main()
We will test the API predictions on a sample of data from the iris dataset.
To get this going, run the following
cd <PROJECT NAME>
Rscript app.R
You should get the response
Serving the jug at http://0.0.0.0:8080
So let's test out the testing method in Bash
curl http://0.0.0.0:8080/
## % Total % Received % Xferd Average Speed Time Time Time Current
## Dload Upload Total Spent Left Speed
##
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
100 24 100 24 0 0 845 0 --:--:-- --:--:-- --:--:-- 857
## ["Why Hello there chap"]
Now the prediction method. Let's use a sample of 10 rows of the iris dataset (without Species)
set.seed(100)
json_test <- df[sample(1:nrow(iris), 10), ] %>%
select(-Species) %>%
toJSON()
json_test
## [{"Sepal.Length":5.1,"Sepal.Width":3.8,"Petal.Length":1.6,"Petal.Width":0.2},{"Sepal.Length":4.4,"Sepal.Width":3,"Petal.Length":1.3,"Petal.Width":0.2},{"Sepal.Length":5.5,"Sepal.Width":2.4,"Petal.Length":3.7,"Petal.Width":1},{"Sepal.Length":4.4,"Sepal.Width":2.9,"Petal.Length":1.4,"Petal.Width":0.2},{"Sepal.Length":6.2,"Sepal.Width":2.2,"Petal.Length":4.5,"Petal.Width":1.5},{"Sepal.Length":5.9,"Sepal.Width":3.2,"Petal.Length":4.8,"Petal.Width":1.8},{"Sepal.Length":6.5,"Sepal.Width":3,"Petal.Length":5.5,"Petal.Width":1.8},{"Sepal.Length":6.9,"Sepal.Width":3.1,"Petal.Length":4.9,"Petal.Width":1.5},{"Sepal.Length":6.7,"Sepal.Width":3,"Petal.Length":5,"Petal.Width":1.7},{"Sepal.Length":4.8,"Sepal.Width":3.4,"Petal.Length":1.9,"Petal.Width":0.2}]
Now let's POST this to the API in bash
curl -X POST -d 'json=[{"Sepal.Length":5.1,"Sepal.Width":3.8,"Petal.Length":1.6,"Petal.Width":0.2},{"Sepal.Length":4.4,"Sepal.Width":3,"Petal.Length":1.3,"Petal.Width":0.2},{"Sepal.Length":5.5,"Sepal.Width":2.4,"Petal.Length":3.7,"Petal.Width":1},{"Sepal.Length":4.4,"Sepal.Width":2.9,"Petal.Length":1.4,"Petal.Width":0.2},{"Sepal.Length":6.2,"Sepal.Width":2.2,"Petal.Length":4.5,"Petal.Width":1.5},{"Sepal.Length":5.9,"Sepal.Width":3.2,"Petal.Length":4.8,"Petal.Width":1.8},{"Sepal.Length":6.5,"Sepal.Width":3,"Petal.Length":5.5,"Petal.Width":1.8},{"Sepal.Length":6.9,"Sepal.Width":3.1,"Petal.Length":4.9,"Petal.Width":1.5},{"Sepal.Length":6.7,"Sepal.Width":3,"Petal.Length":5,"Petal.Width":1.7},{"Sepal.Length":4.8,"Sepal.Width":3.4,"Petal.Length":1.9,"Petal.Width":0.2}]' http://localhost:8080/prediction
## % Total % Received % Xferd Average Speed Time Time Time Current
## Dload Upload Total Spent Left Speed
##
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
100 756 0 0 100 756 0 3640 --:--:-- --:--:-- --:--:-- 3634
100 2080 100 1324 100 756 3645 2081 --:--:-- --:--:-- --:--:-- 3637
## [
## {
## "Sepal.Length": 5.1,
## "Sepal.Width": 3.8,
## "Petal.Length": 1.6,
## "Petal.Width": 0.2,
## "Species": "setosa"
## },
## {
## "Sepal.Length": 4.4,
## "Sepal.Width": 3,
## "Petal.Length": 1.3,
## "Petal.Width": 0.2,
## "Species": "setosa"
## },
## {
## "Sepal.Length": 5.5,
## "Sepal.Width": 2.4,
## "Petal.Length": 3.7,
## "Petal.Width": 1,
## "Species": "versicolor"
## },
## {
## "Sepal.Length": 4.4,
## "Sepal.Width": 2.9,
## "Petal.Length": 1.4,
## "Petal.Width": 0.2,
## "Species": "setosa"
## },
## {
## "Sepal.Length": 6.2,
## "Sepal.Width": 2.2,
## "Petal.Length": 4.5,
## "Petal.Width": 1.5,
## "Species": "versicolor"
## },
## {
## "Sepal.Length": 5.9,
## "Sepal.Width": 3.2,
## "Petal.Length": 4.8,
## "Petal.Width": 1.8,
## "Species": "versicolor"
## },
## {
## "Sepal.Length": 6.5,
## "Sepal.Width": 3,
## "Petal.Length": 5.5,
## "Petal.Width": 1.8,
## "Species": "virginica"
## },
## {
## "Sepal.Length": 6.9,
## "Sepal.Width": 3.1,
## "Petal.Length": 4.9,
## "Petal.Width": 1.5,
## "Species": "versicolor"
## },
## {
## "Sepal.Length": 6.7,
## "Sepal.Width": 3,
## "Petal.Length": 5,
## "Petal.Width": 1.7,
## "Species": "virginica"
## },
## {
## "Sepal.Length": 4.8,
## "Sepal.Width": 3.4,
## "Petal.Length": 1.9,
## "Petal.Width": 0.2,
## "Species": "setosa"
## }
## ]
woohoo, that all works... now for building the Docker image
We will need a requirements file, a Dockerfile specifying the commands to build the container and the Jug app (as well as the serialised model and preprocessing steps).
First let's build a requirements file specifying what we need
requirements.R
install.packages(c('jug',
'jsonlite',
'dplyr',
'caret',
'xgboost'),
repos="http://cran.us.r-project.org")
Now let's make the Dockerfile specifying the instructions. Pay attention to the comments.
Dockerfile
FROM rocker/r-base # Base R image, Rocker contains many images for R.
MAINTAINER Alistair Rogers
WORKDIR /app/
# Required in order to get Jug to work in Debian Linux. Docker containers are Debian Linux and jug requires the use of libcurl. This must be installed before Jug is.
RUN apt-get update && apt-get install libcurl4-openssl-dev
COPY app.R requirements.R /app/
COPY model.RDS preprocessing.RDS /app/
RUN Rscript /app/requirements.R
EXPOSE 8080
ENTRYPOINT Rscript ./app.R
Now run the following (in Bash) in order to build your Docker image. I will not show the output here because there are 5 R packages to install and the output is EXTREMELY verbose!
Trust me that it works.
docker build . -t xgboost_iris
So after this has been built, you can test if it works with the following command Make sure to specify a port outside of the port that the app has running inside of the container. e.g. If it's exposed on port 8080 in the jug app, use the port range 808X:8080 where X does not equal 0, for example.
docker run -p 8083:8080 -it xgboost_iris
You should receive something that looks like this (this message is from inside of the container):
Serving the jug at http://0.0.0.0:8080
So let's test it. (remember, port 8083!)
curl -X POST -d 'json=[{"Sepal.Length":5.1,"Sepal.Width":3.8,"Petal.Length":1.6,"Petal.Width":0.2},{"Sepal.Length":4.4,"Sepal.Width":3,"Petal.Length":1.3,"Petal.Width":0.2},{"Sepal.Length":5.5,"Sepal.Width":2.4,"Petal.Length":3.7,"Petal.Width":1},{"Sepal.Length":4.4,"Sepal.Width":2.9,"Petal.Length":1.4,"Petal.Width":0.2},{"Sepal.Length":6.2,"Sepal.Width":2.2,"Petal.Length":4.5,"Petal.Width":1.5},{"Sepal.Length":5.9,"Sepal.Width":3.2,"Petal.Length":4.8,"Petal.Width":1.8},{"Sepal.Length":6.5,"Sepal.Width":3,"Petal.Length":5.5,"Petal.Width":1.8},{"Sepal.Length":6.9,"Sepal.Width":3.1,"Petal.Length":4.9,"Petal.Width":1.5},{"Sepal.Length":6.7,"Sepal.Width":3,"Petal.Length":5,"Petal.Width":1.7},{"Sepal.Length":4.8,"Sepal.Width":3.4,"Petal.Length":1.9,"Petal.Width":0.2}]' http://localhost:8083/prediction
## % Total % Received % Xferd Average Speed Time Time Time Current
## Dload Upload Total Spent Left Speed
##
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
100 2080 100 1324 100 756 3624 2069 --:--:-- --:--:-- --:--:-- 3627
## [
## {
## "Sepal.Length": 5.1,
## "Sepal.Width": 3.8,
## "Petal.Length": 1.6,
## "Petal.Width": 0.2,
## "Species": "setosa"
## },
## {
## "Sepal.Length": 4.4,
## "Sepal.Width": 3,
## "Petal.Length": 1.3,
## "Petal.Width": 0.2,
## "Species": "setosa"
## },
## {
## "Sepal.Length": 5.5,
## "Sepal.Width": 2.4,
## "Petal.Length": 3.7,
## "Petal.Width": 1,
## "Species": "versicolor"
## },
## {
## "Sepal.Length": 4.4,
## "Sepal.Width": 2.9,
## "Petal.Length": 1.4,
## "Petal.Width": 0.2,
## "Species": "setosa"
## },
## {
## "Sepal.Length": 6.2,
## "Sepal.Width": 2.2,
## "Petal.Length": 4.5,
## "Petal.Width": 1.5,
## "Species": "versicolor"
## },
## {
## "Sepal.Length": 5.9,
## "Sepal.Width": 3.2,
## "Petal.Length": 4.8,
## "Petal.Width": 1.8,
## "Species": "versicolor"
## },
## {
## "Sepal.Length": 6.5,
## "Sepal.Width": 3,
## "Petal.Length": 5.5,
## "Petal.Width": 1.8,
## "Species": "virginica"
## },
## {
## "Sepal.Length": 6.9,
## "Sepal.Width": 3.1,
## "Petal.Length": 4.9,
## "Petal.Width": 1.5,
## "Species": "versicolor"
## },
## {
## "Sepal.Length": 6.7,
## "Sepal.Width": 3,
## "Petal.Length": 5,
## "Petal.Width": 1.7,
## "Species": "virginica"
## },
## {
## "Sepal.Length": 4.8,
## "Sepal.Width": 3.4,
## "Petal.Length": 1.9,
## "Petal.Width": 0.2,
## "Species": "setosa"
## }
## ]
AMAZING!
Now we can save the docker image for use later on (maybe in a cloud service)
docker save xgboost_iris > xgboost_iris.tar
WOOHOO
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent