R/source_detailed_metrics_out.R

In wing328/namsor-r-client: R Package Client for NamSor API v2

# NamSor API v2
#
# NamSor API v2 : enpoints to process personal names (gender, cultural origin or ethnicity) in all alphabets or languages. Use GET methods for small tests, but prefer POST methods for higher throughput (batch processing of up to 1000 names at a time). Need something you can't find here? We have many more features coming soon. Let us know, we'll do our best to add it! 
#
# OpenAPI spec version: 2.0.2-beta
# Contact: contact@namsor.com
# Generated by: https://openapi-generator.tech


#' SourceDetailedMetricsOut Class
#'
#' @field classifierName 
#' @field source 
#' @field aiEstimateTotal 
#' @field aiEstimatePrecision 
#' @field aiEstimateRecall 
#' @field metricTimeStamp 
#' @field aiStartTime 
#' @field aiLearnTotal 
#' @field snapshotDate 
#' @field expectedClassMetrics 
#'
#' @importFrom R6 R6Class
#' @importFrom jsonlite fromJSON toJSON
#' @export
SourceDetailedMetricsOut <- R6::R6Class(
  'SourceDetailedMetricsOut',
  public = list(
    `classifierName` = NULL,
    `source` = NULL,
    `aiEstimateTotal` = NULL,
    `aiEstimatePrecision` = NULL,
    `aiEstimateRecall` = NULL,
    `metricTimeStamp` = NULL,
    `aiStartTime` = NULL,
    `aiLearnTotal` = NULL,
    `snapshotDate` = NULL,
    `expectedClassMetrics` = NULL,
    initialize = function(`classifierName`, `source`, `aiEstimateTotal`, `aiEstimatePrecision`, `aiEstimateRecall`, `metricTimeStamp`, `aiStartTime`, `aiLearnTotal`, `snapshotDate`, `expectedClassMetrics`){
      if (!missing(`classifierName`)) {
        stopifnot(is.character(`classifierName`), length(`classifierName`) == 1)
        self$`classifierName` <- `classifierName`
      }
      if (!missing(`source`)) {
        stopifnot(R6::is.R6(`source`))
        self$`source` <- `source`
      }
      if (!missing(`aiEstimateTotal`)) {
        stopifnot(is.numeric(`aiEstimateTotal`), length(`aiEstimateTotal`) == 1)
        self$`aiEstimateTotal` <- `aiEstimateTotal`
      }
      if (!missing(`aiEstimatePrecision`)) {
        stopifnot(is.numeric(`aiEstimatePrecision`), length(`aiEstimatePrecision`) == 1)
        self$`aiEstimatePrecision` <- `aiEstimatePrecision`
      }
      if (!missing(`aiEstimateRecall`)) {
        stopifnot(is.numeric(`aiEstimateRecall`), length(`aiEstimateRecall`) == 1)
        self$`aiEstimateRecall` <- `aiEstimateRecall`
      }
      if (!missing(`metricTimeStamp`)) {
        stopifnot(is.numeric(`metricTimeStamp`), length(`metricTimeStamp`) == 1)
        self$`metricTimeStamp` <- `metricTimeStamp`
      }
      if (!missing(`aiStartTime`)) {
        stopifnot(is.numeric(`aiStartTime`), length(`aiStartTime`) == 1)
        self$`aiStartTime` <- `aiStartTime`
      }
      if (!missing(`aiLearnTotal`)) {
        stopifnot(is.numeric(`aiLearnTotal`), length(`aiLearnTotal`) == 1)
        self$`aiLearnTotal` <- `aiLearnTotal`
      }
      if (!missing(`snapshotDate`)) {
        stopifnot(is.numeric(`snapshotDate`), length(`snapshotDate`) == 1)
        self$`snapshotDate` <- `snapshotDate`
      }
      if (!missing(`expectedClassMetrics`)) {
        stopifnot(is.vector(`expectedClassMetrics`), length(`expectedClassMetrics`) != 0)
        sapply(`expectedClassMetrics`, function(x) stopifnot(R6::is.R6(x)))
        self$`expectedClassMetrics` <- `expectedClassMetrics`
      }
    },
    toJSON = function() {
      SourceDetailedMetricsOutObject <- list()
      if (!is.null(self$`classifierName`)) {
        SourceDetailedMetricsOutObject[['classifierName']] <-
          self$`classifierName`
      }
      if (!is.null(self$`source`)) {
        SourceDetailedMetricsOutObject[['source']] <-
          self$`source`$toJSON()
      }
      if (!is.null(self$`aiEstimateTotal`)) {
        SourceDetailedMetricsOutObject[['aiEstimateTotal']] <-
          self$`aiEstimateTotal`
      }
      if (!is.null(self$`aiEstimatePrecision`)) {
        SourceDetailedMetricsOutObject[['aiEstimatePrecision']] <-
          self$`aiEstimatePrecision`
      }
      if (!is.null(self$`aiEstimateRecall`)) {
        SourceDetailedMetricsOutObject[['aiEstimateRecall']] <-
          self$`aiEstimateRecall`
      }
      if (!is.null(self$`metricTimeStamp`)) {
        SourceDetailedMetricsOutObject[['metricTimeStamp']] <-
          self$`metricTimeStamp`
      }
      if (!is.null(self$`aiStartTime`)) {
        SourceDetailedMetricsOutObject[['aiStartTime']] <-
          self$`aiStartTime`
      }
      if (!is.null(self$`aiLearnTotal`)) {
        SourceDetailedMetricsOutObject[['aiLearnTotal']] <-
          self$`aiLearnTotal`
      }
      if (!is.null(self$`snapshotDate`)) {
        SourceDetailedMetricsOutObject[['snapshotDate']] <-
          self$`snapshotDate`
      }
      if (!is.null(self$`expectedClassMetrics`)) {
        SourceDetailedMetricsOutObject[['expectedClassMetrics']] <-
          sapply(self$`expectedClassMetrics`, function(x) x$toJSON())
      }

      SourceDetailedMetricsOutObject
    },
    fromJSON = function(SourceDetailedMetricsOutJson) {
      SourceDetailedMetricsOutObject <- jsonlite::fromJSON(SourceDetailedMetricsOutJson)
      if (!is.null(SourceDetailedMetricsOutObject$`classifierName`)) {
        self$`classifierName` <- SourceDetailedMetricsOutObject$`classifierName`
      }
      if (!is.null(SourceDetailedMetricsOutObject$`source`)) {
        sourceObject <- APIKeyOut$new()
        sourceObject$fromJSON(jsonlite::toJSON(SourceDetailedMetricsOutObject$source, auto_unbox = TRUE))
        self$`source` <- sourceObject
      }
      if (!is.null(SourceDetailedMetricsOutObject$`aiEstimateTotal`)) {
        self$`aiEstimateTotal` <- SourceDetailedMetricsOutObject$`aiEstimateTotal`
      }
      if (!is.null(SourceDetailedMetricsOutObject$`aiEstimatePrecision`)) {
        self$`aiEstimatePrecision` <- SourceDetailedMetricsOutObject$`aiEstimatePrecision`
      }
      if (!is.null(SourceDetailedMetricsOutObject$`aiEstimateRecall`)) {
        self$`aiEstimateRecall` <- SourceDetailedMetricsOutObject$`aiEstimateRecall`
      }
      if (!is.null(SourceDetailedMetricsOutObject$`metricTimeStamp`)) {
        self$`metricTimeStamp` <- SourceDetailedMetricsOutObject$`metricTimeStamp`
      }
      if (!is.null(SourceDetailedMetricsOutObject$`aiStartTime`)) {
        self$`aiStartTime` <- SourceDetailedMetricsOutObject$`aiStartTime`
      }
      if (!is.null(SourceDetailedMetricsOutObject$`aiLearnTotal`)) {
        self$`aiLearnTotal` <- SourceDetailedMetricsOutObject$`aiLearnTotal`
      }
      if (!is.null(SourceDetailedMetricsOutObject$`snapshotDate`)) {
        self$`snapshotDate` <- SourceDetailedMetricsOutObject$`snapshotDate`
      }
      if (!is.null(SourceDetailedMetricsOutObject$`expectedClassMetrics`)) {
        self$`expectedClassMetrics` <- sapply(SourceDetailedMetricsOutObject$`expectedClassMetrics`, function(x) {
          expectedClassMetricsObject <- ExpectedClassMetricsOut$new()
          expectedClassMetricsObject$fromJSON(jsonlite::toJSON(x, auto_unbox = TRUE))
          expectedClassMetricsObject
        })
      }
    },
    toJSONString = function() {
      sprintf(
        '{
           "classifierName":
             "%s",
           "source":
             %s,
           "aiEstimateTotal":
             %d,
           "aiEstimatePrecision":
             %d,
           "aiEstimateRecall":
             %d,
           "metricTimeStamp":
             %d,
           "aiStartTime":
             %d,
           "aiLearnTotal":
             %d,
           "snapshotDate":
             %d,
           "expectedClassMetrics":
             [%s]
        }',
        self$`classifierName`,
        jsonlite::toJSON(self$`source`$toJSON(), auto_unbox=TRUE),
        self$`aiEstimateTotal`,
        self$`aiEstimatePrecision`,
        self$`aiEstimateRecall`,
        self$`metricTimeStamp`,
        self$`aiStartTime`,
        self$`aiLearnTotal`,
        self$`snapshotDate`,
        paste(unlist(lapply(self$`expectedClassMetrics`, function(x) jsonlite::toJSON(x$toJSON(), auto_unbox=TRUE))), collapse=",")
      )
    },
    fromJSONString = function(SourceDetailedMetricsOutJson) {
      SourceDetailedMetricsOutObject <- jsonlite::fromJSON(SourceDetailedMetricsOutJson)
      self$`classifierName` <- SourceDetailedMetricsOutObject$`classifierName`
      self$`source` <- APIKeyOut$new()$fromJSON(jsonlite::toJSON(SourceDetailedMetricsOutObject$source, auto_unbox = TRUE))
      self$`aiEstimateTotal` <- SourceDetailedMetricsOutObject$`aiEstimateTotal`
      self$`aiEstimatePrecision` <- SourceDetailedMetricsOutObject$`aiEstimatePrecision`
      self$`aiEstimateRecall` <- SourceDetailedMetricsOutObject$`aiEstimateRecall`
      self$`metricTimeStamp` <- SourceDetailedMetricsOutObject$`metricTimeStamp`
      self$`aiStartTime` <- SourceDetailedMetricsOutObject$`aiStartTime`
      self$`aiLearnTotal` <- SourceDetailedMetricsOutObject$`aiLearnTotal`
      self$`snapshotDate` <- SourceDetailedMetricsOutObject$`snapshotDate`
      data.frame <- SourceDetailedMetricsOutObject$`expectedClassMetrics`
      self$`expectedClassMetrics` <- vector("list", length = nrow(data.frame))
      for (row in 1:nrow(data.frame)) {
          expectedClassMetrics.node <- ExpectedClassMetricsOut$new()
          expectedClassMetrics.node$fromJSON(jsonlite::toJSON(data.frame[row,,drop = TRUE], auto_unbox = TRUE))
          self$`expectedClassMetrics`[[row]] <- expectedClassMetrics.node
      }
      self
    }
  )
)