exampleAPI: Example of using the API inference

In bogdanoancea/inference: R package for computing the number of individuals in the target population conditioned on the number of individuals detected by the MNO and auxiliary information.

Description

This is just an example on how to use the REST API of this package to generate the distribution of the population count.

Usage

exampleAPI()

Details

This is a script that shows how to use the REST API of this package to compute the distribution of the initial target population count, the distribution of the population count at successive time instants and the origin-destination matrix.

References

https://github.com/MobilePhoneESSnetBigData

Examples

########################################
# First, in a separate R console run the following instructions that start the http API
# on your local computer
# One can replace 127.0.0.1 with the API address of another Web server
library(plumber)
pathPL <-'plumber'
initPop_api <- plumber::plumb(system.file(pathPL, 'plumb.R', package = 'inference'))
initPop_api$run(host = '127.0.0.1', port = 8000)
########################################

library(httr)
library(jsonlite)
library(data.table)

# set the folder where the necessary input files are stored and the prefix of the input file names.
path      <- 'extdata'
prefix <- 'postLocDevice'
postLocPath <- system.file(path, package = 'inference')

# compute the deduplication factors
dpFileName <- system.file(path, 'duplicity.csv', package = 'inference')
rgFileName <- system.file(path, 'regions.csv', package = 'inference')

# prepare the body of the http request
body <- list(
  .dupFileName = dpFileName,
  .regsFileName = rgFileName,
  .postLocPrefix = prefix,
  .postLocPath = postLocPath
)

# set API path
pathDedup <- 'computeDeduplicationFactors'

# send POST Request to API
url <- "http://127.0.0.1:8000"
raw.result <- POST(url = url, path = pathDedup, body = body, encode = 'json')

# check status code
raw.result$status_code

# transform back the results from json format
omega_r <- as.data.table(fromJSON(rawToChar(raw.result$content)))

# Compute the parameters of the distribution
# First reads the number of individuals detected by network
nFileName <- system.file(path, 'nnet.csv', package = 'inference')
nnet <- readNnetInitial(nFileName)

pRFileName <- system.file(path, 'pop_reg.csv', package = 'inference')
pRateFileName <- system.file(path, 'pnt_rate.csv', package = 'inference')
grFileName <- system.file(path, 'grid.csv', package = 'inference')

# prepare the body of the http request
body <- list(
   .omega = omega_r,
  .popRegFileName = pRFileName,
  .pntRateFileName  = pRateFileName,
  .regsFileName = rgFileName,
  .gridFileName = grFileName,
  .rel_bias = 0,
  .cv = 1e-5
)

# set API path
pathDistr <- 'computeDistrParams'

 # send POST Request to API
raw.result <- POST(url = url, path = pathDistr, body = body, encode = 'json')

# check status code
raw.result$status_code

# transform back the results from json format
params <- as.data.table(fromJSON(rawToChar(raw.result$content)))

# Compute the population count distribution at t0 using the Beta Negative Binomial distribution

# prepare the body of the http request
body <- list(
  .nnet = nnet,
  .params = params,
  .popDistr = 'BetaNegBin',
  .rndVal = TRUE,
  .ciprob = 0.95,
  .method = 'ETI'
)

# set API path
pathInit <- 'computeInitialPopulation'

 # send POST Request to API
raw.result <- POST(url = url, path = pathInit, body = body, encode = 'json')

# check status code
raw.result$status_code

# transform back the results from json format
n_bnb <- fromJSON(rawToChar(raw.result$content))

# display results
n_bnb$stats
head(n_bnb$rnd_values)

# Compute the population count distribution at time instants t > t0 using the
# Beta Negative Binomial distribution
# first set the name of the file with the population moving from one region
# to another (output of the aggregation package)
nnetODFile <- system.file(path, 'nnetOD.zip', package = 'inference')

# prepare the body of the http request
body <- list(
  .nt0 = as.data.table(n_bnb$rnd_values),
  .nnetODFileName = nnetODFile,
  .zip = TRUE,
  .rndVal = TRUE,
  .ciprob = 0.95,
  .method = 'ETI'
)

# set API path
pathT <- 'computePopulationT'

 # send POST Request to API
raw.result <- POST(url = url, path = pathT, body = body, encode = 'json')

# check status code
raw.result$status_code

# transform back the results from json format
nt_bnb <- fromJSON(rawToChar(raw.result$content))

# display results
# first, select a random time instant
times <- names(nt_bnb)
t <- sample(1:length(times), size = 1)
t
nt_bnb[[t]]$stats
head(nt_bnb[[t]]$rnd_values)


# Compute the Origin-Destination matrices for all pairs of time instants
# time_from-time_to using the Beta Negative Binomial distribution

# prepare the body of the http request
body <- list(
  .nt0 = as.data.table(n_bnb$rnd_values),
  .nnetODFileName = nnetODFile,
  .zip = TRUE,
  .rndVal = TRUE,
  .ciprob = 0.95,
  .method = 'ETI'
)

# set API path
pathOD <- 'computePopulationOD'

 # send POST Request to API
raw.result <- POST(url = url, path = pathOD, body = body, encode = 'json')

# check status code
raw.result$status_code

# transform back the results from json format
OD_bnb <- fromJSON(rawToChar(raw.result$content))

# display results
time_pairs <- names(OD_bnb)
# first, select a random time instants pair
i <- sample(1:length(time_pairs), size = 1)
time_pairs[i]
OD_bnb[[i]]$stats
head(OD_bnb[[i]]$rnd_values)

bogdanoancea/inference documentation built on Nov. 28, 2020, 9:20 p.m.