Write dmcas_fistat Json

Calculates fit statistics from user data and writes it to a JSON file for importing into the common model repository.

calculateFitStat(
  targetName,
  targetPredicted,
  validadedf = NULL,
  traindf = NULL,
  testdf = NULL,
  type = "binary",
  targetEventValue = 1,
  path = "./",
  label.ordering = c(0, 1),
  cutoff = 0.5,
  noFile = FALSE
)

Arguments

targetName

target variable column name (actuals)

targetPredicted

target variable column name. When type = "binary" it should be a probability.

validadedf

data.frame where the first column in the yActual (labels/value) and the second is yPrediction (target probability)

traindf

data.frame where the first column in the yActual (labels/value) and the second is yPrediction (target probability)

testdf

data.frame where the first column in the yActual (labels/value) and the second is yPrediction (target probability)

type

"binary" or "interval"

targetEventValue

if type = "binary" target class name for fit stat reference, if model is nominal, all other class will be counted as "not target"

path

default to current work dir

label.ordering

The default ordering (cf.details) of the classes can be changed by supplying a vector containing the negative and the positive class label. See ROCR::prediction()

cutoff

cutoff to be used for calculation of miss classification for binary

noFile

if you don't want to write to a file, only the output

Value

• list that reflects the 'dmcas_fitstat.json'

• 'dmcas_fitstat.json' file written to path

Examples

 
df <- data.frame(label = sample(c(1,0), 6000, replace = TRUE),
                 prob = runif(6000),
                 partition = rep_len(1:3, 6000))
               
calculateFitStat(targetName = "label",
                 targetPredicted = "prob",
                 df[df$partition == 1, ], 
                 df[df$partition == 2, ],
                 df[df$partition == 3, ],
                 noFile = TRUE)
                                    
                                    
df2 <- data.frame(actual = rnorm(6000, 1000, 100),
                  predicted = rnorm(6000, 1000, 100),
                  partition = rep_len(1:3, 6000))
 
calculateFitStat(targetName = "actual",
                 targetPredicted = "predicted",
                 df2[df2$partition == 1, ],
                 df2[df2$partition == 2, ],
                 df2[df2$partition == 3, ],
                 type = "interval",
                 noFile = TRUE)