cac2cd2e94
- Restrict the number of threads in IO. - Specify the number of threads in demos and tests. - Add helper scripts for checks.
365 lines
18 KiB
R
365 lines
18 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/xgb.train.R, R/xgboost.R
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\name{xgb.train}
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\alias{xgb.train}
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\alias{xgboost}
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\title{eXtreme Gradient Boosting Training}
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\usage{
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xgb.train(
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params = list(),
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data,
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nrounds,
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watchlist = list(),
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obj = NULL,
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feval = NULL,
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verbose = 1,
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print_every_n = 1L,
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early_stopping_rounds = NULL,
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maximize = NULL,
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save_period = NULL,
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save_name = "xgboost.model",
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xgb_model = NULL,
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callbacks = list(),
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...
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)
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xgboost(
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data = NULL,
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label = NULL,
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missing = NA,
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weight = NULL,
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params = list(),
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nrounds,
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verbose = 1,
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print_every_n = 1L,
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early_stopping_rounds = NULL,
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maximize = NULL,
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save_period = NULL,
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save_name = "xgboost.model",
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xgb_model = NULL,
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callbacks = list(),
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...
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)
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}
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\arguments{
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\item{params}{the list of parameters. The complete list of parameters is
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available in the \href{http://xgboost.readthedocs.io/en/latest/parameter.html}{online documentation}. Below
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is a shorter summary:
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1. General Parameters
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\itemize{
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\item \code{booster} which booster to use, can be \code{gbtree} or \code{gblinear}. Default: \code{gbtree}.
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}
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2. Booster Parameters
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2.1. Parameters for Tree Booster
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\itemize{
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\item{ \code{eta} control the learning rate: scale the contribution of each tree by a factor of \code{0 < eta < 1}
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when it is added to the current approximation.
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Used to prevent overfitting by making the boosting process more conservative.
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Lower value for \code{eta} implies larger value for \code{nrounds}: low \code{eta} value means model
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more robust to overfitting but slower to compute. Default: 0.3}
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\item{ \code{gamma} minimum loss reduction required to make a further partition on a leaf node of the tree.
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the larger, the more conservative the algorithm will be.}
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\item \code{max_depth} maximum depth of a tree. Default: 6
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\item{\code{min_child_weight} minimum sum of instance weight (hessian) needed in a child.
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If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight,
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then the building process will give up further partitioning.
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In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node.
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The larger, the more conservative the algorithm will be. Default: 1}
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\item{ \code{subsample} subsample ratio of the training instance.
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Setting it to 0.5 means that xgboost randomly collected half of the data instances to grow trees
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and this will prevent overfitting. It makes computation shorter (because less data to analyse).
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It is advised to use this parameter with \code{eta} and increase \code{nrounds}. Default: 1}
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\item \code{colsample_bytree} subsample ratio of columns when constructing each tree. Default: 1
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\item \code{lambda} L2 regularization term on weights. Default: 1
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\item \code{alpha} L1 regularization term on weights. (there is no L1 reg on bias because it is not important). Default: 0
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\item{ \code{num_parallel_tree} Experimental parameter. number of trees to grow per round.
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Useful to test Random Forest through XGBoost
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(set \code{colsample_bytree < 1}, \code{subsample < 1} and \code{round = 1}) accordingly.
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Default: 1}
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\item{ \code{monotone_constraints} A numerical vector consists of \code{1}, \code{0} and \code{-1} with its length
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equals to the number of features in the training data.
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\code{1} is increasing, \code{-1} is decreasing and \code{0} is no constraint.}
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\item{ \code{interaction_constraints} A list of vectors specifying feature indices of permitted interactions.
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Each item of the list represents one permitted interaction where specified features are allowed to interact with each other.
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Feature index values should start from \code{0} (\code{0} references the first column).
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Leave argument unspecified for no interaction constraints.}
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}
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2.2. Parameters for Linear Booster
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\itemize{
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\item \code{lambda} L2 regularization term on weights. Default: 0
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\item \code{lambda_bias} L2 regularization term on bias. Default: 0
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\item \code{alpha} L1 regularization term on weights. (there is no L1 reg on bias because it is not important). Default: 0
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}
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3. Task Parameters
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\itemize{
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\item{ \code{objective} specify the learning task and the corresponding learning objective, users can pass a self-defined function to it.
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The default objective options are below:
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\itemize{
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\item \code{reg:squarederror} Regression with squared loss (Default).
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\item{ \code{reg:squaredlogerror}: regression with squared log loss \eqn{1/2 * (log(pred + 1) - log(label + 1))^2}.
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All inputs are required to be greater than -1.
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Also, see metric rmsle for possible issue with this objective.}
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\item \code{reg:logistic} logistic regression.
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\item \code{reg:pseudohubererror}: regression with Pseudo Huber loss, a twice differentiable alternative to absolute loss.
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\item \code{binary:logistic} logistic regression for binary classification. Output probability.
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\item \code{binary:logitraw} logistic regression for binary classification, output score before logistic transformation.
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\item \code{binary:hinge}: hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.
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\item{ \code{count:poisson}: Poisson regression for count data, output mean of Poisson distribution.
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\code{max_delta_step} is set to 0.7 by default in poisson regression (used to safeguard optimization).}
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\item{ \code{survival:cox}: Cox regression for right censored survival time data (negative values are considered right censored).
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Note that predictions are returned on the hazard ratio scale (i.e., as HR = exp(marginal_prediction) in the proportional
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hazard function \code{h(t) = h0(t) * HR)}.}
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\item{ \code{survival:aft}: Accelerated failure time model for censored survival time data. See
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\href{https://xgboost.readthedocs.io/en/latest/tutorials/aft_survival_analysis.html}{Survival Analysis with Accelerated Failure Time}
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for details.}
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\item \code{aft_loss_distribution}: Probability Density Function used by \code{survival:aft} and \code{aft-nloglik} metric.
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\item{ \code{multi:softmax} set xgboost to do multiclass classification using the softmax objective.
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Class is represented by a number and should be from 0 to \code{num_class - 1}.}
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\item{ \code{multi:softprob} same as softmax, but prediction outputs a vector of ndata * nclass elements, which can be
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further reshaped to ndata, nclass matrix. The result contains predicted probabilities of each data point belonging
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to each class.}
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\item \code{rank:pairwise} set xgboost to do ranking task by minimizing the pairwise loss.
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\item{ \code{rank:ndcg}: Use LambdaMART to perform list-wise ranking where
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\href{https://en.wikipedia.org/wiki/Discounted_cumulative_gain}{Normalized Discounted Cumulative Gain (NDCG)} is maximized.}
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\item{ \code{rank:map}: Use LambdaMART to perform list-wise ranking where
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\href{https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Mean_average_precision}{Mean Average Precision (MAP)}
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is maximized.}
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\item{ \code{reg:gamma}: gamma regression with log-link.
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Output is a mean of gamma distribution.
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It might be useful, e.g., for modeling insurance claims severity, or for any outcome that might be
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\href{https://en.wikipedia.org/wiki/Gamma_distribution#Applications}{gamma-distributed}.}
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\item{ \code{reg:tweedie}: Tweedie regression with log-link.
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It might be useful, e.g., for modeling total loss in insurance, or for any outcome that might be
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\href{https://en.wikipedia.org/wiki/Tweedie_distribution#Applications}{Tweedie-distributed}.}
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}
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}
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\item \code{base_score} the initial prediction score of all instances, global bias. Default: 0.5
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\item{ \code{eval_metric} evaluation metrics for validation data.
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Users can pass a self-defined function to it.
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Default: metric will be assigned according to objective
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(rmse for regression, and error for classification, mean average precision for ranking).
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List is provided in detail section.}
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}}
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\item{data}{training dataset. \code{xgb.train} accepts only an \code{xgb.DMatrix} as the input.
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\code{xgboost}, in addition, also accepts \code{matrix}, \code{dgCMatrix}, or name of a local data file.}
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\item{nrounds}{max number of boosting iterations.}
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\item{watchlist}{named list of xgb.DMatrix datasets to use for evaluating model performance.
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Metrics specified in either \code{eval_metric} or \code{feval} will be computed for each
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of these datasets during each boosting iteration, and stored in the end as a field named
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\code{evaluation_log} in the resulting object. When either \code{verbose>=1} or
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\code{\link{cb.print.evaluation}} callback is engaged, the performance results are continuously
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printed out during the training.
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E.g., specifying \code{watchlist=list(validation1=mat1, validation2=mat2)} allows to track
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the performance of each round's model on mat1 and mat2.}
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\item{obj}{customized objective function. Returns gradient and second order
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gradient with given prediction and dtrain.}
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\item{feval}{customized evaluation function. Returns
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\code{list(metric='metric-name', value='metric-value')} with given
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prediction and dtrain.}
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\item{verbose}{If 0, xgboost will stay silent. If 1, it will print information about performance.
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If 2, some additional information will be printed out.
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Note that setting \code{verbose > 0} automatically engages the
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\code{cb.print.evaluation(period=1)} callback function.}
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\item{print_every_n}{Print each n-th iteration evaluation messages when \code{verbose>0}.
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Default is 1 which means all messages are printed. This parameter is passed to the
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\code{\link{cb.print.evaluation}} callback.}
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\item{early_stopping_rounds}{If \code{NULL}, the early stopping function is not triggered.
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If set to an integer \code{k}, training with a validation set will stop if the performance
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doesn't improve for \code{k} rounds.
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Setting this parameter engages the \code{\link{cb.early.stop}} callback.}
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\item{maximize}{If \code{feval} and \code{early_stopping_rounds} are set,
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then this parameter must be set as well.
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When it is \code{TRUE}, it means the larger the evaluation score the better.
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This parameter is passed to the \code{\link{cb.early.stop}} callback.}
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\item{save_period}{when it is non-NULL, model is saved to disk after every \code{save_period} rounds,
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0 means save at the end. The saving is handled by the \code{\link{cb.save.model}} callback.}
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\item{save_name}{the name or path for periodically saved model file.}
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\item{xgb_model}{a previously built model to continue the training from.
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Could be either an object of class \code{xgb.Booster}, or its raw data, or the name of a
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file with a previously saved model.}
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\item{callbacks}{a list of callback functions to perform various task during boosting.
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See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the
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parameters' values. User can provide either existing or their own callback methods in order
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to customize the training process.}
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\item{...}{other parameters to pass to \code{params}.}
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\item{label}{vector of response values. Should not be provided when data is
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a local data file name or an \code{xgb.DMatrix}.}
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\item{missing}{by default is set to NA, which means that NA values should be considered as 'missing'
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by the algorithm. Sometimes, 0 or other extreme value might be used to represent missing values.
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This parameter is only used when input is a dense matrix.}
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\item{weight}{a vector indicating the weight for each row of the input.}
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}
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\value{
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An object of class \code{xgb.Booster} with the following elements:
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\itemize{
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\item \code{handle} a handle (pointer) to the xgboost model in memory.
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\item \code{raw} a cached memory dump of the xgboost model saved as R's \code{raw} type.
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\item \code{niter} number of boosting iterations.
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\item \code{evaluation_log} evaluation history stored as a \code{data.table} with the
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first column corresponding to iteration number and the rest corresponding to evaluation
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metrics' values. It is created by the \code{\link{cb.evaluation.log}} callback.
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\item \code{call} a function call.
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\item \code{params} parameters that were passed to the xgboost library. Note that it does not
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capture parameters changed by the \code{\link{cb.reset.parameters}} callback.
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\item \code{callbacks} callback functions that were either automatically assigned or
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explicitly passed.
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\item \code{best_iteration} iteration number with the best evaluation metric value
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(only available with early stopping).
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\item \code{best_score} the best evaluation metric value during early stopping.
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(only available with early stopping).
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\item \code{feature_names} names of the training dataset features
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(only when column names were defined in training data).
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\item \code{nfeatures} number of features in training data.
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}
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}
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\description{
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\code{xgb.train} is an advanced interface for training an xgboost model.
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The \code{xgboost} function is a simpler wrapper for \code{xgb.train}.
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}
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\details{
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These are the training functions for \code{xgboost}.
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The \code{xgb.train} interface supports advanced features such as \code{watchlist},
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customized objective and evaluation metric functions, therefore it is more flexible
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than the \code{xgboost} interface.
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Parallelization is automatically enabled if \code{OpenMP} is present.
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Number of threads can also be manually specified via the \code{nthread}
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parameter.
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The evaluation metric is chosen automatically by XGBoost (according to the objective)
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when the \code{eval_metric} parameter is not provided.
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User may set one or several \code{eval_metric} parameters.
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Note that when using a customized metric, only this single metric can be used.
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The following is the list of built-in metrics for which XGBoost provides optimized implementation:
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\itemize{
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\item \code{rmse} root mean square error. \url{https://en.wikipedia.org/wiki/Root_mean_square_error}
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\item \code{logloss} negative log-likelihood. \url{https://en.wikipedia.org/wiki/Log-likelihood}
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\item \code{mlogloss} multiclass logloss. \url{https://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html}
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\item \code{error} Binary classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
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By default, it uses the 0.5 threshold for predicted values to define negative and positive instances.
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Different threshold (e.g., 0.) could be specified as "error@0."
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\item \code{merror} Multiclass classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
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\item \code{mae} Mean absolute error
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\item \code{mape} Mean absolute percentage error
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\item{ \code{auc} Area under the curve.
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\url{https://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.}
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\item \code{aucpr} Area under the PR curve. \url{https://en.wikipedia.org/wiki/Precision_and_recall} for ranking evaluation.
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\item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{https://en.wikipedia.org/wiki/NDCG}
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}
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The following callbacks are automatically created when certain parameters are set:
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\itemize{
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\item \code{cb.print.evaluation} is turned on when \code{verbose > 0};
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and the \code{print_every_n} parameter is passed to it.
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\item \code{cb.evaluation.log} is on when \code{watchlist} is present.
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\item \code{cb.early.stop}: when \code{early_stopping_rounds} is set.
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\item \code{cb.save.model}: when \code{save_period > 0} is set.
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}
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}
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\examples{
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data(agaricus.train, package='xgboost')
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data(agaricus.test, package='xgboost')
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## Keep the number of threads to 1 for examples
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nthread <- 1
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data.table::setDTthreads(nthread)
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dtrain <- with(
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agaricus.train, xgb.DMatrix(data, label = label, nthread = nthread)
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)
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dtest <- with(
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agaricus.test, xgb.DMatrix(data, label = label, nthread = nthread)
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)
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watchlist <- list(train = dtrain, eval = dtest)
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## A simple xgb.train example:
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param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = nthread,
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objective = "binary:logistic", eval_metric = "auc")
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bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
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## An xgb.train example where custom objective and evaluation metric are
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## used:
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logregobj <- function(preds, dtrain) {
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labels <- getinfo(dtrain, "label")
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preds <- 1/(1 + exp(-preds))
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grad <- preds - labels
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hess <- preds * (1 - preds)
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return(list(grad = grad, hess = hess))
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}
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evalerror <- function(preds, dtrain) {
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labels <- getinfo(dtrain, "label")
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err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
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return(list(metric = "error", value = err))
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}
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# These functions could be used by passing them either:
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# as 'objective' and 'eval_metric' parameters in the params list:
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param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = nthread,
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objective = logregobj, eval_metric = evalerror)
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bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
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# or through the ... arguments:
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param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = nthread)
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bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
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objective = logregobj, eval_metric = evalerror)
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# or as dedicated 'obj' and 'feval' parameters of xgb.train:
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bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
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obj = logregobj, feval = evalerror)
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## An xgb.train example of using variable learning rates at each iteration:
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param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = nthread,
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objective = "binary:logistic", eval_metric = "auc")
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my_etas <- list(eta = c(0.5, 0.1))
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bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
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callbacks = list(cb.reset.parameters(my_etas)))
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## Early stopping:
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bst <- xgb.train(param, dtrain, nrounds = 25, watchlist,
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early_stopping_rounds = 3)
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## An 'xgboost' interface example:
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bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label,
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max_depth = 2, eta = 1, nthread = nthread, nrounds = 2,
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objective = "binary:logistic")
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pred <- predict(bst, agaricus.test$data)
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}
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\references{
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Tianqi Chen and Carlos Guestrin, "XGBoost: A Scalable Tree Boosting System",
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22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016, \url{https://arxiv.org/abs/1603.02754}
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}
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\seealso{
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\code{\link{callbacks}},
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\code{\link{predict.xgb.Booster}},
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\code{\link{xgb.cv}}
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}
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