vignettes/test/workerHotspots.R

In Azure/AzureDSVM: Create and Manage Azure Data Science Virtual Machine(s) for Convenient Execution of Scalable and Elastic Analytics.

# ---------------------------------------------------------------------------
# THIS IS A HEADER ADDED BY COMPUTE INTERFACE
# ---------------------------------------------------------------------------
CI_MACHINES <- c( "mytbmm" )
CI_DNS <- c( "", "" )
CI_VMUSER <- c( "zhle" )
CI_MASTER <- c( "" )
CI_SLAVES <- c( "" )
CI_DATA <- ""
CI_CONTEXT <- "localParallel"

library(RevoScaleR)
library(doParallel)
# library(readr)
# --------- Set compute context
rxSetComputeContext(RxLocalParallel())
# --------- Load data.
# ciData <- ifelse(CI_DATA != '', read_csv(CI_DATA), data.frame(0))
# ---------------------------------------------------------------------------
# END OF THE HEADER ADDED BY COMPUTE INTERFACE
# ---------------------------------------------------------------------------
# source the script to load functions used for the analysis.

source("workerHotspotsSetup.R")
source("workerHotspotsFuncs.R")
source("workerHotspotsTrain.R")
source("workerHotspotsTest.R")
source("workerHotspotsProcess.R")

# initial parameter definition.

number_of_clust <- 2:10 
train_ratio     <- 0.7

lib  <- "~/lib" # install packages on a personal lib. Note this merely works for Linux machine.
pkgs <- c("dplyr", "stringr", "stringi", "magrittr", "readr", "rattle", "ggplot2", "DMwR")

data_url <- "https://zhledata.blob.core.windows.net/mldata/creditcard.xdf"

download.file(data_url,
              destfile="./data.xdf",
              mode="wb")

# install and load packages.

installPkgs(list_of_pkgs=pkgs, lib=lib)

sapply(pkgs, require, character.only=TRUE)

# Hotspots analysis.

eval <- hotSpotsProcess(data=RxXdfData("./data.xdf"),
                        number.of.clust=number_of_clust,
                        train.ratio=train_ratio)

# save results.

save(eval, file="./results.RData")