# cb.gblinear.history

0th

Percentile

##### Callback closure for collecting the model coefficients history of a gblinear booster during its training.

Callback closure for collecting the model coefficients history of a gblinear booster during its training.

##### Usage
cb.gblinear.history(sparse = FALSE)
##### Arguments
sparse

when set to FALSE/TURE, a dense/sparse matrix is used to store the result. Sparse format is useful when one expects only a subset of coefficients to be non-zero, when using the "thrifty" feature selector with fairly small number of top features selected per iteration.

##### Details

To keep things fast and simple, gblinear booster does not internally store the history of linear model coefficients at each boosting iteration. This callback provides a workaround for storing the coefficients' path, by extracting them after each training iteration.

Callback function expects the following values to be set in its calling frame: bst (or bst_folds).

##### Value

Results are stored in the coefs element of the closure. The xgb.gblinear.history convenience function provides an easy way to access it. With xgb.train, it is either a dense of a sparse matrix. While with xgb.cv, it is a list (an element per each fold) of such matrices.

callbacks, xgb.gblinear.history.

##### Aliases
• cb.gblinear.history
##### Examples
# NOT RUN {
#### Binary classification:
#
# In the iris dataset, it is hard to linearly separate Versicolor class from the rest
# without considering the 2nd order interactions:
require(magrittr)
x <- model.matrix(Species ~ .^2, iris)[,-1]
colnames(x)
dtrain <- xgb.DMatrix(scale(x), label = 1*(iris$Species == "versicolor")) param <- list(booster = "gblinear", objective = "reg:logistic", eval_metric = "auc", lambda = 0.0003, alpha = 0.0003, nthread = 2) # For 'shotgun', which is a default linear updater, using high eta values may result in # unstable behaviour in some datasets. With this simple dataset, however, the high learning # rate does not break the convergence, but allows us to illustrate the typical pattern of # "stochastic explosion" behaviour of this lock-free algorithm at early boosting iterations. bst <- xgb.train(param, dtrain, list(tr=dtrain), nrounds = 200, eta = 1., callbacks = list(cb.gblinear.history())) # Extract the coefficients' path and plot them vs boosting iteration number: coef_path <- xgb.gblinear.history(bst) matplot(coef_path, type = 'l') # With the deterministic coordinate descent updater, it is safer to use higher learning rates. # Will try the classical componentwise boosting which selects a single best feature per round: bst <- xgb.train(param, dtrain, list(tr=dtrain), nrounds = 200, eta = 0.8, updater = 'coord_descent', feature_selector = 'thrifty', top_k = 1, callbacks = list(cb.gblinear.history())) xgb.gblinear.history(bst) %>% matplot(type = 'l') # Componentwise boosting is known to have similar effect to Lasso regularization. # Try experimenting with various values of top_k, eta, nrounds, # as well as different feature_selectors. # For xgb.cv: bst <- xgb.cv(param, dtrain, nfold = 5, nrounds = 100, eta = 0.8, callbacks = list(cb.gblinear.history())) # coefficients in the CV fold #3 xgb.gblinear.history(bst)[[3]] %>% matplot(type = 'l') #### Multiclass classification: # dtrain <- xgb.DMatrix(scale(x), label = as.numeric(iris$Species) - 1)
param <- list(booster = "gblinear", objective = "multi:softprob", num_class = 3,
lambda = 0.0003, alpha = 0.0003, nthread = 2)
# For the default linear updater 'shotgun' it sometimes is helpful
# to use smaller eta to reduce instability
bst <- xgb.train(param, dtrain, list(tr=dtrain), nrounds = 70, eta = 0.5,
callbacks = list(cb.gblinear.history()))
# Will plot the coefficient paths separately for each class:
xgb.gblinear.history(bst, class_index = 0) %>% matplot(type = 'l')
xgb.gblinear.history(bst, class_index = 1) %>% matplot(type = 'l')
xgb.gblinear.history(bst, class_index = 2) %>% matplot(type = 'l')

# CV:
bst <- xgb.cv(param, dtrain, nfold = 5, nrounds = 70, eta = 0.5,
callbacks = list(cb.gblinear.history(FALSE)))
# 1st forld of 1st class
xgb.gblinear.history(bst, class_index = 0)[[1]] %>% matplot(type = 'l')

# }