ace4016c36
* modify test_helper.R * fix noLD * update desc * fix solaris test * fix desc * improve fix * fix url * change Matrix cBind to cbind * fix * fix error in demo * fix examples
96 lines
4.4 KiB
R
96 lines
4.4 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/callbacks.R
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\name{cb.gblinear.history}
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\alias{cb.gblinear.history}
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\title{Callback closure for collecting the model coefficients history of a gblinear booster
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during its training.}
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\usage{
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cb.gblinear.history(sparse = FALSE)
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}
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\arguments{
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\item{sparse}{when set to FALSE/TURE, a dense/sparse matrix is used to store the result.
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Sparse format is useful when one expects only a subset of coefficients to be non-zero,
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when using the "thrifty" feature selector with fairly small number of top features
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selected per iteration.}
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}
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\value{
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Results are stored in the \code{coefs} element of the closure.
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The \code{\link{xgb.gblinear.history}} convenience function provides an easy way to access it.
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With \code{xgb.train}, it is either a dense of a sparse matrix.
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While with \code{xgb.cv}, it is a list (an element per each fold) of such matrices.
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}
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\description{
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Callback closure for collecting the model coefficients history of a gblinear booster
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during its training.
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}
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\details{
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To keep things fast and simple, gblinear booster does not internally store the history of linear
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model coefficients at each boosting iteration. This callback provides a workaround for storing
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the coefficients' path, by extracting them after each training iteration.
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Callback function expects the following values to be set in its calling frame:
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\code{bst} (or \code{bst_folds}).
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}
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\examples{
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#### Binary classification:
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#
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# In the iris dataset, it is hard to linearly separate Versicolor class from the rest
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# without considering the 2nd order interactions:
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require(magrittr)
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x <- model.matrix(Species ~ .^2, iris)[,-1]
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colnames(x)
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dtrain <- xgb.DMatrix(scale(x), label = 1*(iris$Species == "versicolor"))
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param <- list(booster = "gblinear", objective = "reg:logistic", eval_metric = "auc",
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lambda = 0.0003, alpha = 0.0003, nthread = 2)
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# For 'shotgun', which is a default linear updater, using high eta values may result in
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# unstable behaviour in some datasets. With this simple dataset, however, the high learning
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# rate does not break the convergence, but allows us to illustrate the typical pattern of
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# "stochastic explosion" behaviour of this lock-free algorithm at early boosting iterations.
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bst <- xgb.train(param, dtrain, list(tr=dtrain), nrounds = 200, eta = 1.,
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callbacks = list(cb.gblinear.history()))
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# Extract the coefficients' path and plot them vs boosting iteration number:
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coef_path <- xgb.gblinear.history(bst)
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matplot(coef_path, type = 'l')
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# With the deterministic coordinate descent updater, it is safer to use higher learning rates.
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# Will try the classical componentwise boosting which selects a single best feature per round:
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bst <- xgb.train(param, dtrain, list(tr=dtrain), nrounds = 200, eta = 0.8,
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updater = 'coord_descent', feature_selector = 'thrifty', top_k = 1,
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callbacks = list(cb.gblinear.history()))
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xgb.gblinear.history(bst) \%>\% matplot(type = 'l')
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# Componentwise boosting is known to have similar effect to Lasso regularization.
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# Try experimenting with various values of top_k, eta, nrounds,
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# as well as different feature_selectors.
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# For xgb.cv:
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bst <- xgb.cv(param, dtrain, nfold = 5, nrounds = 100, eta = 0.8,
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callbacks = list(cb.gblinear.history()))
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# coefficients in the CV fold #3
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xgb.gblinear.history(bst)[[3]] \%>\% matplot(type = 'l')
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#### Multiclass classification:
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#
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dtrain <- xgb.DMatrix(scale(x), label = as.numeric(iris$Species) - 1)
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param <- list(booster = "gblinear", objective = "multi:softprob", num_class = 3,
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lambda = 0.0003, alpha = 0.0003, nthread = 2)
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# For the default linear updater 'shotgun' it sometimes is helpful
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# to use smaller eta to reduce instability
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bst <- xgb.train(param, dtrain, list(tr=dtrain), nrounds = 70, eta = 0.5,
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callbacks = list(cb.gblinear.history()))
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# Will plot the coefficient paths separately for each class:
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xgb.gblinear.history(bst, class_index = 0) \%>\% matplot(type = 'l')
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xgb.gblinear.history(bst, class_index = 1) \%>\% matplot(type = 'l')
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xgb.gblinear.history(bst, class_index = 2) \%>\% matplot(type = 'l')
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# CV:
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bst <- xgb.cv(param, dtrain, nfold = 5, nrounds = 70, eta = 0.5,
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callbacks = list(cb.gblinear.history(FALSE)))
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# 1st forld of 1st class
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xgb.gblinear.history(bst, class_index = 0)[[1]] \%>\% matplot(type = 'l')
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}
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\seealso{
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\code{\link{callbacks}}, \code{\link{xgb.gblinear.history}}.
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}
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