README.md

In atamakita/xgboostOLR: Custom Objective/Evaluate Functions for xgboost

xgboostOLR

xgboostOLR package provides custom objective / evaluation function for XGBoost. Objective function is negative logloss of ordered logit model. Evaluation functions are negative logloss and accuracy of labels.

Installation

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Example

# necessary
library(xgboostOLR)
library(xgboost)

# for vis
library(tidyverse)

Visualising dataset.

data("scatter", package = "xgboostOLR")

scatter %>% 
  dplyr::mutate(cls = as.character(cls)) %>% 
  ggplot(aes(x = V1, y = V2, col = cls)) + 
  geom_point(size = 0.5) + 
  facet_wrap(~ type) + 
  labs(title = "Distribution of labels on train/test dataset")

Preparing R6 class for loss function for xgboost.

• Initialize by xgboostOLR::createOLRlossClass().
  • Set criterion between each labels by criterion args.
  • Set margin of hit ratio by notchdiff args if you use eval_hitratio as eval_metric.

# metric class
cls <- xgboostOLR::createOLRlossClass(criterion = c(2, 4, 6, 8),
                                      notchdiff = 1L)

Preparing train/test dataset.

# train 
idx_col <- colnames(scatter) %in% c("V1", "V2")
idx_row <- scatter$type == "train"
dtrain <- xgb.DMatrix(data = as.matrix(scatter[idx_row, idx_col]),
                      label = scatter[["cls"]][idx_row])
# test
idx_row <- scatter$type == "test"
dtest <- xgb.DMatrix(data = as.matrix(scatter[idx_row, idx_col]),
                      label = scatter[["cls"]][idx_row])

Construct model.

# train model
param <- list(max_depth = 5, eta = 0.01, nthread = 2, verbosity = 1L,
              objective=cls$obj_ordered_logit,
              eval_metric=cls$eval_hitratio)
bst <- xgb.train(params = param, 
                 data = dtrain, 
                 nrounds = 100L, 
                 watchlist = list(eval = dtest, train = dtrain), 
                 verbose = 0L)

Comparing predicted label.

• pred_class_criterion generates predicted labels comparing criterion and margins.

# vis pred labels
pred_score <- c(predict(bst, dtrain), predict(bst, dtest))
pred_label <- cls$pred_class_criterion(pred_score)
scatter %>% 
  dplyr::mutate(pred_score = pred_score, 
                pred_label = as.character(pred_label)) %>% 
  ggplot(aes(x = V1, y = V2, col = pred_label)) + 
  geom_point(size = 0.5) + 
  facet_wrap(~ type) + 
  labs(title = "Distribution of predicted labels on train/test dataset")

atamakita/xgboostOLR documentation built on Aug. 8, 2020, 5:39 p.m.