sigopt.xgboost.run
xgboost.train
API ; it automatically tracks and records all aspects of the model training process to SigOpt. The sigopt.xgboost.run
API closely matches the xgboost.train
API to minimize any disruption to your workflow, while allowing you to enjoy all the features provided by the SigOpt experiment management platform.xgboost.train
API to our sigopt.xgboost.run
to enable automatic tracking with SigOpt.xgboost.train
APIsigopt.xgboost.run
APIsigopt.xgboost.run
sigopt.xgboost.run
automatically logs the relevant information and metadata of the XGBoost training process and the resulting model. We list all the automated logging functionalities below.sigopt.xgboost.run
automatically logs all XGBoost parameters that you specify via the params
argument. These values are displayed under the Parameter Values section on the Run page. In additional, sigopt.xgboost.run
also records the XGBoost default values for all other parameters.params
argument via eval_metric
, sigopt.xgboost.run
automatically logs every evaluation metric value for each evaluation dataset after the training has completed. The metric values are listed under Metrics on the Run page.sigopt.xgboost.run
logs additional metrics computed using the evaluation datasets. The supported tasks are inferred from the objective
parameter prefix. We list the objective prefixes and the corresponding metrics below.binary:
, multi:
binary:
, multi:
binary:
, multi:
binary:
, multi:
reg:
reg:
xgboost.train
.params
, sigopt.xgboost.run
also automatically tracks the iterative evaluation metric using checkpoints. You can control the frequency of the checkpoint logging using the verbose_eval
argument. If verbose_eval
is an integer, then SigOpt logs the evaluation metric every given verbose_eval
boosting rounds. If verbose_eval
is True
, then SigOpt logs the evaluation metric every boosting round. If it is False
, SigOpt logs the evaluation metric every 5 boosting rounds.sigopt.xgboost.run
will automatically adjust the checkpoint logging frequency accordingly to guarantee that the total number of checkpoints does not exceed this limit.sigopt.xgboost.run
automatically computes and tracks the feature importances of the training dataset. Generally, feature importances indicate how useful each feature is in determining boosted decision tree predictions. Feature importances are computed using the XGBoost get_score
function with importance_type=weight
. The top 50 features are tracked and stored on the Run page.sigopt.xgboost.run
automatically tracks additional metadata associated with the model training process such as dataset dimensions, dataset names, XGBoost version, and more.sigopt.xgboost.run
xgboost.train
are supported by sigopt.xgboost.run
. You can preserve your existing XGBoost code and realize the benefits of tracking the model training as a SigOpt TrainingRun with minimal code change. To better understand the XGBoost Learning API that this integration is based on, you can refer to the XGBoost documentation pages on the Training API and XGBoost Parameters respectively.run_options
sigopt.xgboost.run
call with the run_options
argument. Here, we provide an example of using run_options
argument to create a Run with an assigned name and selectively turn off checkpoint logging and stdout logging.run_options
dict.autolog_checkpoints
Boolean
True
autolog_feature_importances
Boolean
True
autolog_metrics
Boolean
True
autolog_xgboost_defaults
Boolean
True
autolog_stderr
Boolean
True
xgboost.train
call.autolog_stdout
Boolean
True
xgboost.train
call.autolog_sys_info
Boolean
True
name
string
None
run
None
sigopt.xgboost.run
. This is useful for creating a custom Experiment loop.sigopt.xgboost.run
sigopt.xgboost.run
function returns an XGBRun
object which allows you to access the resulting XGBoost Booster object via .model
and the SigOpt RunContext object via .run
. In the following example, we demonstrate how to continue logging information with the XGBRun
object post model training.