In nanxstats/stackgbm: Stacked Gradient Boosting Machines

knitr::opts_chunk$set(
  comment = "#>",
  collapse = TRUE
)

run <- if (rlang::is_installed(c("catboost", "lightgbm", "xgboost"))) TRUE else FALSE
knitr::opts_chunk$set(eval = run)

Introduction

Model stacking [@wolpert1992stacked] is a method for ensemble learning that combines the strength of multiple base learners to drive up predictive performance. It is a particularly popular and effective strategy used in machine learning competitions.

stackgbm implements a two-layer stacking model: the first layer generates "features" produced by gradient boosting trees. The boosted tree models are built by xgboost [@chen2016xgboost], lightgbm [@ke2017lightgbm], and catboost [@prokhorenkova2018catboost]. The second layer is a logistic regression that uses these features as inputs.

library("stackgbm")

Generate data

Let's generate some data for demonstrate purposes. The simulated data has a $1000 \times 50$ predictor matrix with a binary outcome vector. 800 samples will be in the training set and the rest 200 will be in the (independent) test set. 25 out of the 50 features will be informative and follows $N(0, 10)$.

sim_data <- msaenet::msaenet.sim.binomial(
  n = 5000,
  p = 100,
  rho = 0.8,
  coef = c(
    rnorm(20, mean = 0, sd = 5),
    rnorm(20, mean = 0, sd = 2),
    rnorm(20, mean = 0, sd = 1)
  ),
  snr = 0.5,
  p.train = 0.8,
  seed = 42
)

x_train <- sim_data$x.tr
x_test <- sim_data$x.te
y_train <- as.vector(sim_data$y.tr)
y_test <- as.vector(sim_data$y.te)

Parameter tuning

cv_xgboost(), cv_lightgbm() and cv_catboost() provide wrappers for tuning the most essential hyperparameters for each type of boosted tree models with k-fold cross-validation. The "optimal" parameters will be used to fit the stacking model later.

params_xgboost <- cv_xgboost(x_train, y_train)
params_lightgbm <- cv_lightgbm(x_train, y_train)
params_catboost <- cv_catboost(x_train, y_train)

saveRDS(params_xgboost, file = "vignettes/params_xgboost.rds")
saveRDS(params_lightgbm, file = "vignettes/params_lightgbm.rds")
saveRDS(params_catboost, file = "vignettes/params_catboost.rds")

temp_dir <- "catboost_info"
temp_file <- "lightgbm.model"
if (dir.exists(temp_dir)) unlink(temp_dir, recursive = TRUE)
if (file.exists(temp_file)) unlink(temp_file)

params_xgboost <- readRDS("params_xgboost.rds")
params_lightgbm <- readRDS("params_lightgbm.rds")
params_catboost <- readRDS("params_catboost.rds")

Train the stackgbm model

model_stackgbm <- stackgbm(
  sim_data$x.tr,
  sim_data$y.tr,
  params = list(
    params_xgboost,
    params_lightgbm,
    params_catboost
  )
)

Inference

roc_stackgbm_train <- pROC::roc(
  y_train,
  predict(model_stackgbm, x_train)$prob,
  quiet = TRUE
)
roc_stackgbm_test <- pROC::roc(
  y_test,
  predict(model_stackgbm, x_test)$prob,
  quiet = TRUE
)
roc_stackgbm_train$auc
roc_stackgbm_test$auc

Performance evaluation

Let's compare the predictive performance between the stacking model and the three types of tree boosting models (base learners) fitted individually. Note that the models and performance metrics should be (bitwise) reproducible on the same operating system but they might vary on different platforms.

model_xgboost <- xgboost_train(
  params = list(
    objective = "binary:logistic",
    eval_metric = "auc",
    max_depth = params_xgboost$max_depth,
    eta = params_xgboost$eta
  ),
  data = xgboost_dmatrix(x_train, label = y_train),
  nrounds = params_xgboost$nrounds
)

model_lightgbm <- lightgbm_train(
  data = x_train,
  label = y_train,
  params = list(
    objective = "binary",
    learning_rate = params_lightgbm$learning_rate,
    num_iterations = params_lightgbm$num_iterations,
    max_depth = params_lightgbm$max_depth,
    num_leaves = 2^params_lightgbm$max_depth - 1
  ),
  verbose = -1
)

model_catboost <- catboost_train(
  catboost_load_pool(data = x_train, label = y_train),
  NULL,
  params = list(
    loss_function = "Logloss",
    iterations = params_catboost$iterations,
    depth = params_catboost$depth,
    logging_level = "Silent"
  )
)

xgboost

roc_xgboost_train <- pROC::roc(
  y_train,
  predict(model_xgboost, x_train),
  quiet = TRUE
)
roc_xgboost_test <- pROC::roc(
  y_test,
  predict(model_xgboost, x_test),
  quiet = TRUE
)
roc_xgboost_train$auc
roc_xgboost_test$auc

lightgbm

roc_lightgbm_train <- pROC::roc(
  y_train,
  predict(model_lightgbm, x_train),
  quiet = TRUE
)
roc_lightgbm_test <- pROC::roc(
  y_test,
  predict(model_lightgbm, x_test),
  quiet = TRUE
)
roc_lightgbm_train$auc
roc_lightgbm_test$auc

catboost

roc_catboost_train <- pROC::roc(
  y_train,
  catboost_predict(
    model_catboost,
    catboost_load_pool(data = x_train, label = NULL)
  ),
  quiet = TRUE
)
roc_catboost_test <- pROC::roc(
  y_test,
  catboost_predict(
    model_catboost,
    catboost_load_pool(data = x_test, label = NULL)
  ),
  quiet = TRUE
)
roc_catboost_train$auc
roc_catboost_test$auc

Tabular summary

We can summarize the AUC values in a table.

df <- as.data.frame(matrix(NA, ncol = 4, nrow = 2))
names(df) <- c("stackgbm", "xgboost", "lightgbm", "catboost")
rownames(df) <- c("Training", "Testing")

df$stackgbm <- c(roc_stackgbm_train$auc, roc_stackgbm_test$auc)
df$xgboost <- c(roc_xgboost_train$auc, roc_xgboost_test$auc)
df$lightgbm <- c(roc_lightgbm_train$auc, roc_lightgbm_test$auc)
df$catboost <- c(roc_catboost_train$auc, roc_catboost_test$auc)

knitr::kable(
  df,
  digits = 4,
  caption = "AUC values from four models on training and testing set"
)

ROC curves

Plot the ROC curves of all models on the independent test set.

#| roc-curves,
#| message=FALSE,
#| fig.asp=1,
#| fig.width=5,
#| fig.dpi=300,
#| fig.align="center",
#| out.width="65%"
pal <- c("#e15759", "#f28e2c", "#59a14f", "#4e79a7", "#76b7b2")

plot(pROC::smooth(roc_stackgbm_test), col = pal[1], lwd = 1)
plot(pROC::smooth(roc_xgboost_test), col = pal[2], lwd = 1, add = TRUE)
plot(pROC::smooth(roc_lightgbm_test), col = pal[3], lwd = 1, add = TRUE)
plot(pROC::smooth(roc_catboost_test), col = pal[4], lwd = 1, add = TRUE)
legend(
  "bottomright",
  col = pal,
  lwd = 2,
  legend = c("stackgbm", "xgboost", "lightgbm", "catboost")
)

Notes on categorical features

xgboost and lightgbm both prefer the categorical features to be encoded as integers. For catboost, the categorical features can be encoded as character factors.

To avoid possible confusions, if your data has any categorical features, we recommend converting them to integers or use one-hot encoding, and use a numerical matrix as the input.

References

nanxstats/stackgbm documentation built on May 3, 2024, 5:47 p.m.