cb.gblinear.history {xgboost} | R Documentation

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

### Description

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.

### See Also

`callbacks`, `xgb.gblinear.history`.

### Examples

    #### 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')

[Package xgboost version 0.90.0.2 Index]