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Principal Component Analysis (PCA)

PCA is used for dimensionality reduction to obtain lower-dimensional representation of the data while preserving as much of the variation as possible.

We will create a function that takes a multi-dimensional JSON array as input and returns principal coordinate axes as JSON.

You will learn how to pre-load libraries and process the request data using a statistical method on the fly.

You'll need the prerequisites listed here.

Create a new function using a template

Create a new function called r-pca.

faas-cli new --lang rstats-base-plumber r-pca

Customize the function

Change the ./r-pca/handler.R file. Note: loading libraries is good practice, it makes trouble shooting installation related issues much easier (i.e. when shared objects are not found doe to not building the package against specific libraries). Startup messages can also be useful.

library(vegan)
#* PCA
#* @post /
function(req) {
  x <- jsonlite::fromJSON(paste(req$postBody))
  vegan::rda(x)$CA$u
}

Edit the ./r-pca/DESCRIPTION file.

Package: OpenFaaStR
Version: 0.0.1
Imports:
  vegan
Remotes:
SystemRequirements:
  libgfortran5
VersionedPackages:

Edit the r-pca.yml file as required, see configuration options.

Build, push, deploy the function

faas-cli up is a shorthand for automating faas-cli build, faas-cli push, and faas-cli deploy.

faas-cli up -f r-pca.yml

Testing

Test the Docker image locally after docker run -p 5000:8080 $OPENFAAS_PREFIX/r-pca:

curl http://localhost:5000/ -H \
  "Content-Type: application/json" -d \
  '[[-1,3,16],[10,-10,9],[-5,10,-14],[14,3,-12]]'

Test the deployed instance:

curl $OPENFAAS_URL/function/r-pca -H \
  "Content-Type: application/json" -d \
  '[[-1,3,16],[10,-10,9],[-5,10,-14],[14,3,-12]]'

The output should be:

[[0.5099,0.5251,-0.4629],[0.479,-0.4319,0.5779], 
[-0.598,0.4699,0.4143],[-0.391,-0.563,-0.5293]]