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Add weights support to xgbTree model #614

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merged 4 commits into from
Mar 15, 2017
Merged

Add weights support to xgbTree model #614

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cd7abc2
Add weights support
schistyakov Mar 9, 2017
c7e15fa
Update NEWS.Rd
schistyakov Mar 15, 2017
71ff4f4
Add test to weights support
schistyakov Mar 15, 2017
677bf22
Merge branch 'master' into xgbTree-weights
topepo Mar 15, 2017
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25 changes: 25 additions & 0 deletions RegressionTests/Code/xgbTree.R
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Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,8 @@ testing <- twoClassSim(500, linearVars = 2)
trainX <- training[, -ncol(training)]
trainY <- training$Class

training_weight <- c(rep(0.1, 10), rep(1, 90))

cctrl1 <- trainControl(method = "cv", number = 3, returnResamp = "all",
classProbs = TRUE,
summaryFunction = twoClassSummary)
Expand All @@ -45,11 +47,34 @@ test_class_cv_form <- train(Class ~ ., data = training,
preProc = c("center", "scale"),
tuneGrid = xgbGrid)

set.seed(849)
test_class_cv_model_weight <- train(trainX, trainY,
weights = training_weight,
method = "xgbTree",
trControl = cctrl1,
metric = "ROC",
preProc = c("center", "scale"),
tuneGrid = xgbGrid)

set.seed(849)
test_class_cv_form_weight <- train(Class ~ ., data = training,
weights = training_weight,
method = "xgbTree",
trControl = cctrl1,
metric = "ROC",
preProc = c("center", "scale"),
tuneGrid = xgbGrid)

test_class_pred <- predict(test_class_cv_model, testing[, -ncol(testing)])
test_class_prob <- predict(test_class_cv_model, testing[, -ncol(testing)], type = "prob")
test_class_pred_form <- predict(test_class_cv_form, testing[, -ncol(testing)])
test_class_prob_form <- predict(test_class_cv_form, testing[, -ncol(testing)], type = "prob")

test_class_pred_weight <- predict(test_class_cv_model_weight, testing[, -ncol(testing)])
test_class_prob_weight <- predict(test_class_cv_model_weight, testing[, -ncol(testing)], type = "prob")
test_class_pred_form_weight <- predict(test_class_cv_form_weight, testing[, -ncol(testing)])
test_class_prob_form_weight <- predict(test_class_cv_form_weight, testing[, -ncol(testing)], type = "prob")

set.seed(849)
test_class_rand <- train(trainX, trainY,
method = "xgbTree",
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8 changes: 7 additions & 1 deletion models/files/xgbTree.R
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Expand Up @@ -57,6 +57,8 @@ modelInfo <- list(label = "eXtreme Gradient Boosting",
if(length(lev) == 2) {
y <- ifelse(y == lev[1], 1, 0)
dat <- xgb.DMatrix(x, label = y, missing = NA)
if (!is.null(wts))
xgboost::setinfo(dat, 'weight', wts)
out <- xgb.train(list(eta = param$eta,
max_depth = param$max_depth,
gamma = param$gamma,
Expand All @@ -70,6 +72,8 @@ modelInfo <- list(label = "eXtreme Gradient Boosting",
} else {
y <- as.numeric(y) - 1
dat <- xgb.DMatrix(x, label = y, missing = NA)
if (!is.null(wts))
xgboost::setinfo(dat, 'weight', wts)
out <- xgb.train(list(eta = param$eta,
max_depth = param$max_depth,
gamma = param$gamma,
Expand All @@ -84,6 +88,8 @@ modelInfo <- list(label = "eXtreme Gradient Boosting",
}
} else {
dat <- xgb.DMatrix(as.matrix(x), label = y, missing = NA)
if (!is.null(wts))
xgboost::setinfo(dat, 'weight', wts)
out <- xgb.train(list(eta = param$eta,
max_depth = param$max_depth,
gamma = param$gamma,
Expand Down Expand Up @@ -193,7 +199,7 @@ modelInfo <- list(label = "eXtreme Gradient Boosting",
imp
},
levels = function(x) x$obsLevels,
tags = c("Tree-Based Model", "Boosting", "Ensemble Model", "Implicit Feature Selection"),
tags = c("Tree-Based Model", "Boosting", "Ensemble Model", "Implicit Feature Selection", "Accepts Case Weights"),
sort = function(x) {
# This is a toss-up, but the # trees probably adds
# complexity faster than number of splits
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5 changes: 3 additions & 2 deletions pkg/caret/inst/NEWS.Rd
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Expand Up @@ -7,13 +7,14 @@
\section{Changes in version 6.0-74}{
\itemize{
\item Monotone multi-layer perceptron neural network models from the \cpkg{monmlp} package were added \issue{489}
\item A new resampling function (\code{groupKFold}) was added \issue(540)
\item The bootstrap optimism estimate was added by Alexis Sarda \issue(544)
\item A new resampling function (\code{groupKFold}) was added \issue{540}
\item The bootstrap optimism estimate was added by Alexis Sarda \issue{544}
\item Bugs in \code{glm}, \code{glm.nb}, and \code{lm} variable importance methods that occur when a single variable is in the model \issue{543}
\item A bug in \code{filterVarImp} was fixed where the ROC curve AUC could be much less than 0.50 because the directionality of the predictor was not taken into account. This will artificially increase the importance of some non-informative predictors. However, the bug might report the AUC for an important predictor to be 0.20 instead of 0.80. \issue{565}
\item \code{multiClassSummary} now reports the average F score \issue{566}
\item The \code{RMSE} and \code{R2} are now (re)exposed to the users \issue{563}
\item A \cpkg{caret} bug was discovered by Jiebiao Wang where \code{glmboost}, \code{gamboost}, and \code{blackboost} models incorrectly reported the class probabilities \issue{560}
\item Training data weights support was added to \code{xgbTree} model by schistyakov
\item Regularised logistic regression through Liblinear (\code{LiblineaR::LiblineaR}) using L1 or L2 regularisation were added
}
}
Expand Down