xgboard.eval.error: Xgboard Metric Evaluation Error (Binary Accuracy)

In Laurae2/Laurae: Advanced High Performance Data Science Toolbox for R

Description

This function is a custom metric for the logging of the (binary) Accuracy.

Usage

xgboard.eval.error(preds, dtrain, dump)

Arguments

preds	Type: numeric. The predictions.
dtrain	Type: xgb.DMatrix. The training data.
dump	Type: environment. An environment created by xgboard.init.

Value

The maximum accuracy for binary data.

Examples

## Not run: 
# First, we must load libraries: xgboost, data.table, and R.utils
library(xgboost)
library(data.table)
library(R.utils)

# Second, we load some data
data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')

# Third, we create the xgb.DMatrices and the watchlist
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
watchlist <- list(train = dtrain, eval = dtest)

# Fourth, we prepare environment for Accuracy/Threshold logging on Train/Test
# Stored in D:/debug/log.txt
my_envir <- xgboard.init(what = c("Accuracy", "Threshold"),
                         watchnames = c("Train", "Test"),
                         maximizer = c(TRUE, TRUE),
                         log = "D:/debug/log.txt")

# Fifth we spawn the xgboard to open in browser
xgboard.run(my_envir)

# Fifth, the model is set for training using these parameters
# Take note of eval_metric needing xgboard.xgb(f = your metric, dumper = envir)
param <- list(max_depth = 2,
              eta = 0.05,
              silent = 1,
              nthread = 2, 
              objective = "binary:logistic",
              eval_metric = xgboard.xgb(f = xgboard.eval.error, dumper = my_envir))

# Sixth, we train a model with full logging
# We can notice it will update in real time
# The number of warning messages = number of file locks which xgboost waits
# because the log file is LOCKED when read by Xgboard (to avoid crashes)
set.seed(0)
bst <- xgb.train(param,
                 dtrain,
                 nrounds = 500,
                 watchlist)

# If you intend to run again xgboost, you have to do the following:
# - Reset the dump environment using xgboard.init
# - Reset the eval_metric from the parameters (because it will use the previous envir!)
# - If you are using an interactive console, use xgboard.time before setting the seed!

## End(Not run)

Laurae2/Laurae documentation built on May 8, 2019, 7:59 p.m.