CODEWITHSUNDEEP
rr-caret
dleal
dleal

Reputation: 2314

user defined summaryFunction in caret, logloss

Using the caret package, I am having trouble getting the following user defined summary function to work. It is supposed to calculate the logloss, but I keep getting that logloss is not found. Below, a reproducible example:

data <- data.frame('target' = sample(c('Y','N'),100,replace = T), 'X1' = runif(100), 'X2' = runif(100))

log.loss2 <- function(data, lev = NULL, model = NULL) {
  logloss = -sum(data$obs*log(data$Y) + (1-data$obs)*log(1-data$Y))/length(data$obs)
  names(logloss) <- c('LL')
  logloss
}

fitControl <- trainControl(method="cv",number=1, classProbs = T, summaryFunction = log.loss2)

my.grid <- expand.grid(.decay = c(0.05), .size = c(2))

fit.nnet2 <- train(target ~., data = data,
                  method = "nnet", maxit = 500, metric = 'LL',
                  tuneGrid = my.grid, verbose = T)

Upvotes: 1

Views: 2030

Answers (2)

missuse
missuse

Reputation: 19716

The error was due to the fact you did not include trControl = fitControl in the call to train. However that would bring you to another error that is due to the fact data$obs and data$pred are factors - one needs to convert to numeric which gives 1 or 2, subtracting 1 gives desired 0 and 1

log.loss2 <- function(data, lev = NULL, model = NULL) {
  data$pred <- as.numeric(data$pred)-1
  data$obs <- as.numeric(data$obs)-1 
  logloss = -sum(data$obs*log(data$Y) + (1-data$obs)*log(1-data$Y))/length(data$obs)
  names(logloss) <- c('LL')
  logloss
}

fitControl <- trainControl(method="cv",number=1, classProbs = T, summaryFunction = log.loss2)

fit.nnet2 <- train(target ~., data = data,
                   method = "nnet", maxit = 500, metric = "LL" ,
                   tuneGrid = my.grid, verbose = T, trControl = fitControl,
                   maximize = FALSE)
#output
Neural Network 

100 samples
  2 predictor
  2 classes: 'N', 'Y' 

No pre-processing
Resampling: Cross-Validated (1 fold) 
Summary of sample sizes: 0 
Resampling results:

  LL       
  0.6931472

Tuning parameter 'size' was held constant at a value of 2
Tuning parameter 'decay' was held constant at a value of 0.05

Several things to note:

this loss function will work only with data containing N/Y as classes because probability is defined as data$Y, a better approach is to find the name of the class and use that. Additionally its good practice to truncate the probability values since log(0) is not a good idea:

LogLoss <- function (data, lev = NULL, model = NULL) 
  { 
    obs <- data[, "obs"]
    cls <- levels(obs) #find class names
    probs <- data[, cls[2]] #use second class name
    probs <- pmax(pmin(as.numeric(probs), 1 - 1e-15), 1e-15) #bound probability
    logPreds <- log(probs)        
    log1Preds <- log(1 - probs)
    real <- (as.numeric(data$obs) - 1)
    out <- c(mean(real * logPreds + (1 - real) * log1Preds)) * -1
    names(out) <- c("LogLoss")
    out
  }

Upvotes: 3

Sasa
Sasa

Reputation: 123

@missuse has answered the question, but I would like to add the option of weights in the logloss function:

# Cross-entropy error function
LogLoss <- function(pred, true, eps = 1e-15, weights = NULL) {
  # Bound the results
  pred = pmin(pmax(pred, eps), 1 - eps)

  if (is.null(weights)) {
    return(-(sum(
      true * log(pred) + (1 - true) * log(1 - pred)
    )) / length(true))
  } else{
    return(-weighted.mean(true * log(pred) + (1 - true) * log(1 - pred), weights))
  }
}

# Caret train weighted logloss summary function
caret_logloss <- function(data, lev = NULL, model = NULL) {
  cls <- levels(data$obs) #find class names
  loss <- LogLoss(
    pred = data[, cls[2]],
    true = as.numeric(data$obs) - 1,
    weights = data$weights
  )
  names(loss) <- c('MyLogLoss')
  loss
}

Upvotes: 2

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