XGBoost custom loss function cannot reproduce binary:logistic objective

Question

I am workin in RStudio and am looking to develop a custom objective function for XGBoost. In order to make sure I have understood how the process works, I have tried to write an objective function which reproduces the "binary:logistic" objective. However, my custom objective function yields significantly different results (often a lot worse).

Based on the examples on the XGBoost github repo my custom objective function looks like this:

# custom objective function
logloss <- function(preds, dtrain){

  # Get weights and labels
  labels<- getinfo(dtrain, "label")

  # Apply logistic transform to predictions
  preds <- 1/(1 + exp(-preds))

  # Find gradient and hessian
  grad <- (preds - labels)
  hess <- preds * (1-preds)

  return(list("grad" = grad, "hess" = hess))
}

Based on this medium blog post this seems to match what is implemented in XGBoost binary objective.

Using some simple test data, my final training-rmse for the built-in objective is ~0.468 and using my custom objective it is ~0.72.

The code below can be used to generate test data and reproduce the problem.

Can somebody explain why my code does not reproduce the behavious of objective "binary:logistic"? I am using the XGBoost R-Package v0.90.0.2.

library(data.table)
library(xgboost)

# Generate test data
generate_test_data <- function(n_rows = 1e5, feature_count = 5, train_fraction = 0.5){

  # Make targets
  test_data <- data.table(
    target = sign(runif(n = n_rows, min=-1, max=1))
  )

  # Add feature columns.These are normally distributed and shifted by the target
  # in order to create a noisy signal
  for(feature in 1:feature_count){

    # Randomly create features of the noise
    mu <- runif(1, min=-1, max=1)
    sdev <- runif(1, min=5, max=10)

    # Create noisy signal
    test_data[, paste0("feature_", feature) := rnorm(
      n=n_rows, mean = mu, sd = sdev)*target + target]
  }

  # Split data into test/train
  test_data[, index_fraction := .I/.N]
  split_data <- list(
    "train" = test_data[index_fraction < (train_fraction)],
    "test" = test_data[index_fraction >= (train_fraction)]
  )

  # Make vector of feature names
  feature_names <- paste0("feature_", 1:feature_count)

  # Make test/train matrix and labels
  split_data[["test_trix"]] <- as.matrix(split_data$test[, feature_names, with=FALSE])
  split_data[["train_trix"]] <- as.matrix(split_data$train[, feature_names, with=FALSE])
  split_data[["test_labels"]] <- as.logical(split_data$test$target + 1)    
  split_data[["train_labels"]] <- as.logical(split_data$train$target + 1)

  return(split_data)
}

# Build the tree
build_model <- function(split_data, objective){

  # Make evaluation matrix
  train_dtrix <-
    xgb.DMatrix(
      data = split_data$train_trix, label = split_data$train_labels)

  # Train the model
  model <- xgb.train(
    data = train_dtrix,
    watchlist = list(
      train = train_dtrix),
    nrounds = 5,
    objective =  objective,
    eval_metric = "rmse"
  )

  return(model)
}

split_data <- generate_test_data()
cat("\nUsing built-in binary:logistic objective.\n")
test_1 <- build_model(split_data, "binary:logistic")
cat("\n\nUsing custom objective")
test_2 <- build_model(split_data, logloss)

XGBoost custom loss function cannot reproduce binary:logistic objective

0 Answers0