sdkagent

xgboost API reference

211 public APIs from xgboost (dmlc/xgboost) — 38 classes, 103 functions, 70 methods. Signatures extracted by static analysis of the actual source.

Repository: dmlc/xgboost

KindCount
Classes38
Functions103
Methods70

API list

funcjvm-packages.create_jni.build(config:dict[str, str], build_dir:Path) -> None
Build the native library.
funcjvm-packages.create_jni.cmake_args(config:dict[str, str]) -> list[str]
Create CMake command line arguments.
funcjvm-packages.create_jni.cmake_config(options:argparse.Namespace) -> dict[str, str]
Create CMake configuration from CLI options.
funcjvm-packages.create_jni.configure(config_args:list[str], build_dir:Path) -> None
Configure the CMake build.
funcjvm-packages.create_jni.copy_file(source:Path, target:Path) -> None
Copy a file to a target path or directory.
funcjvm-packages.create_jni.copy_glob(pattern:str, target:Path) -> None
Copy files matching a glob pattern to a target directory.
funcjvm-packages.create_jni.copy_native_library() -> None
Copy the native library into the JVM package resources.
funcjvm-packages.create_jni.copy_test_resources(*use_cuda:bool) -> None
Copy training data used by JVM package tests.
funcjvm-packages.create_jni.mkdir(path:Path) -> None
Create a directory if it does not already exist.
funcjvm-packages.create_jni.native_build(options:argparse.Namespace) -> None
Build and copy the native JNI library and its test resources.
funcjvm-packages.create_jni.run(command:Sequence[str], *cwd:Path | None=None) -> None
Run a shell command.
funcjvm-packages.create_jni.windows_generators() -> tuple[list[str], ...]
Return CMake generator arguments to try on Windows.
funcops.pipeline.trigger-rtd-impl.trigger_build(token:str) -> None
Trigger RTD build.
methodops.script.lint_cpp.Lint.print_summary(strm:TextIO) -> int
Print summary of lint.
methodops.script.lint_cpp.Lint.process_cpp(path:str, suffix:str) -> None
Process a cpp file.
funcops.script.lint_cpp.filepath_enumerate(paths:list[str]) -> list[str]
Enumerate the file paths of all subfiles of the list of paths
funcops.script.lint_cpp.get_header_guard_dmlc(filename:str) -> str
Get Header Guard Convention for DMLC Projects.
funcops.script.lint_cpp.process(fname:str, allow_type:list[str]) -> None
Process a file.
funcops.script.prepare_jvm_release.cp(source:str, target:str) -> None
Copy a file after normalizing both paths.
funcops.script.prepare_jvm_release.deploy(local:bool, profile:Literal['default', 'gpu'], pl:str | None) -> None
Deploy JVM artifacts with the selected Maven profile.
funcops.script.prepare_jvm_release.deploy_cuda_pkg(local:bool, version:str) -> None
Deploy CUDA JVM packages.
funcops.script.prepare_jvm_release.get_current_commit_hash() -> str
Get the last commit of the current branch.
funcops.script.prepare_jvm_release.get_current_git_branch() -> str
Get the current branch.
funcops.script.prepare_jvm_release.main() -> None
Assemble and deploy the packages.
funcops.script.prepare_jvm_release.maybe_makedirs(path:str) -> None
Create a directory and its parents if needed.
funcops.script.prepare_jvm_release.normpath(path:str) -> str
Normalize UNIX path to a native path.
funcops.script.prepare_jvm_release.parse_args() -> argparse.Namespace
Parse command-line arguments.
funcops.script.prepare_jvm_release.retrieve(url:str, filename:str | None=None) -> None
Download a file from a URL and print the destination.
funcops.script.prepare_jvm_release.run(command:str, **kwargs:Any) -> None
Run a shell command and fail if it exits with an error.
funcops.script.release_artifacts.check_path() -> None
Ensure the script is run from the project root directory.
funcops.script.release_artifacts.download_python_wheels(branch:str, commit_hash:str, outdir:Path) -> None
Download all Python binary wheels for the specified branch.
funcops.script.release_artifacts.latest_hash() -> str
Get latest commit hash.
funcops.script.release_artifacts.make_python_sdist(release:str, rc:Optional[str], rc_ver:Optional[int], outdir:Path) -> None
Make Python source distribution.
funcops.script.release_artifacts.release_note(release:str, artifact_hashes:List[str], r_urls:Dict[str, str], tarball_name:str, outdir:Path) -> None
Generate a note for GitHub release description.
funcops.script.release_artifacts.retrieve(url:str, filename:Optional[Path]=None) -> str
Retrieve a file from a URL with progress indication.
funcops.script.release_artifacts.show_progress(block_num:int, block_size:int, total_size:int) -> None
Show file download progress.
classops.script.type_check_python.TypeCheckPaths
The paths mypy runs on.
classpython-package.xgboost._c_api.XGBoostError
Error thrown by xgboost trainer.
funcpython-package.xgboost._c_api.c_str(string:str) -> ctypes.c_char_p
Convert a python string to cstring.
funcpython-package.xgboost._c_api.from_cstr_to_pystr(data:CStrPptr, length:c_bst_ulong) -> List[str]
Revert C pointer to Python str.
funcpython-package.xgboost._c_api.make_jcargs(**kwargs:Any) -> bytes
Make JSON-based arguments for C functions.
classpython-package.xgboost._data_utils.Array
Wrapper type for communicating with numpy and cupy.
methodpython-package.xgboost._data_utils.Array.shape() -> Tuple[int, ...]
Shape of the input array.
methodpython-package.xgboost._data_utils.Array.size() -> np.signedinteger
Total size of the input array.
classpython-package.xgboost._data_utils.DfCatAccessor
Protocol for pandas cat accessor.
classpython-package.xgboost._data_utils.TransformedDf
Internal class for storing transformed dataframe.
methodpython-package.xgboost._data_utils.TransformedDf.shape() -> Tuple[int, int]
Return the shape of the dataframe.
funcpython-package.xgboost._data_utils.array_hasobject(data:DataType) -> bool
Whether the numpy array has object dtype.
funcpython-package.xgboost._data_utils.array_interface(data:np.ndarray) -> bytes
Make array interface str.
funcpython-package.xgboost._data_utils.array_interface_dict(data:np.ndarray) -> ArrayInf
Returns an array interface from the input.
funcpython-package.xgboost._data_utils.check_cudf_meta(data:_CudaArrayLikeArg, field:str) -> None
Make sure no missing value in meta data.
funcpython-package.xgboost._data_utils.cuda_array_interface(data:_CudaArrayLikeArg) -> bytes
Make cuda array interface str.
funcpython-package.xgboost._data_utils.cuda_array_interface_dict(data:_CudaArrayLikeArg) -> CudaArrayInf
Returns a dictionary storing the CUDA array interface.
funcpython-package.xgboost._data_utils.from_array_interface(interface:ArrayInf, zero_copy:bool=False) -> NumpyOrCupy
Convert array interface to numpy or cupy array
funcpython-package.xgboost._data_utils.is_arrow_dict(data:Any) -> TypeGuard['pa.DictionaryArray']
Is this an arrow dictionary array?
funcpython-package.xgboost._data_utils.npstr_to_arrow_strarr(strarr:Any) -> Tuple[np.ndarray, bytes]
Convert a string-like array to an arrow string array.
classpython-package.xgboost.callback.EarlyStopping
Callback function for early stopping ..
funcpython-package.xgboost.callback.EarlyStopping.get_s(value:_Score) -> float
get score if it's cross validation history.
funcpython-package.xgboost.callback.EarlyStopping.maximize(new:_Score, best:_Score) -> bool
New score should be greater than the old one.
funcpython-package.xgboost.callback.EarlyStopping.minimize(new:_Score, best:_Score) -> bool
New score should be lesser than the old one.
classpython-package.xgboost.callback.EvaluationMonitor
Print the evaluation result at each iteration.
classpython-package.xgboost.callback.LearningRateScheduler
Callback function for scheduling learning rate.
classpython-package.xgboost.callback.TrainingCallback
Interface for training callback.
methodpython-package.xgboost.callback.TrainingCallback.after_iteration(model:_Model, epoch:int, evals_log:EvalsLog) -> bool
Run after each iteration.
methodpython-package.xgboost.callback.TrainingCallback.after_training(model:_Model) -> _Model
Run after training is finished.
methodpython-package.xgboost.callback.TrainingCallback.before_iteration(model:_Model, epoch:int, evals_log:EvalsLog) -> bool
Run before each iteration.
methodpython-package.xgboost.callback.TrainingCallback.before_training(model:_Model) -> _Model
Run before training starts.
classpython-package.xgboost.callback.TrainingCheckPoint
Checkpointing operation.
classpython-package.xgboost.collective.Config
User configuration for the communicator context.
methodpython-package.xgboost.collective.Config.get_comm_config(args:_Conf) -> _Conf
Update the arguments for the communicator.
methodpython-package.xgboost.collective.Config.update_worker_args(args:_Conf) -> _Conf
Worker side arguments resolution.
classpython-package.xgboost.collective.Op
Supported operations for allreduce.
funcpython-package.xgboost.collective.allreduce(data:np.ndarray, op:Op) -> np.ndarray
Perform allreduce, return the result.
funcpython-package.xgboost.collective.broadcast(data:_T, root:int) -> _T
Broadcast object from one node to all other nodes.
funcpython-package.xgboost.collective.communicator_print(msg:Any) -> None
Print message to the communicator.
funcpython-package.xgboost.collective.finalize() -> None
Finalize the communicator.
funcpython-package.xgboost.collective.get_processor_name() -> str
Get the processor name.
funcpython-package.xgboost.collective.get_rank() -> int
Get rank of current process.
funcpython-package.xgboost.collective.get_world_size() -> int
Get total number workers.
funcpython-package.xgboost.collective.init(**args:_ArgVals) -> None
Initialize the collective library with arguments.
funcpython-package.xgboost.collective.is_distributed() -> bool
If the collective communicator is distributed.
funcpython-package.xgboost.collective.signal_error() -> None
Kill the process.
classpython-package.xgboost.compat.XGBClassifierBase
Dummy class for sklearn.base.ClassifierMixin.
classpython-package.xgboost.compat.XGBModelBase
Dummy class for sklearn.base.BaseEstimator.
classpython-package.xgboost.compat.XGBRegressorBase
Dummy class for sklearn.base.RegressorMixin.
funcpython-package.xgboost.compat.concat(value:Sequence[_T]) -> _T
Concatenate row-wise.
funcpython-package.xgboost.compat.import_cupy() -> types.ModuleType
Import cupy.
funcpython-package.xgboost.compat.import_pandas() -> types.ModuleType
Import pandas with memory cache.
funcpython-package.xgboost.compat.import_polars() -> types.ModuleType
Import polars with memory cache.
funcpython-package.xgboost.compat.import_pyarrow() -> types.ModuleType
Import pyarrow with memory cache.
funcpython-package.xgboost.compat.is_cudf_available() -> bool
Check cuDF package available or not
funcpython-package.xgboost.compat.is_cupy_available() -> bool
Check cupy package available or not
funcpython-package.xgboost.compat.is_dataframe(data:DataType) -> bool
Whether the input is a dataframe.
funcpython-package.xgboost.compat.is_pandas_available() -> bool
Check the pandas package is available or not.
funcpython-package.xgboost.compat.is_pyarrow_available() -> bool
Check pyarrow package available or not
funcpython-package.xgboost.compat.lazy_isinstance(instance:Any, module:str, name:str) -> bool
Use string representation to identify a type.
funcpython-package.xgboost.compat.py_str(x:bytes | None) -> str
convert c string back to python string
classpython-package.xgboost.core.Booster
A Booster of XGBoost.
methodpython-package.xgboost.core.Booster.attr(key:str) -> Optional[str]
Get attribute string from the Booster.
methodpython-package.xgboost.core.Booster.best_iteration() -> int
The best iteration during training.
methodpython-package.xgboost.core.Booster.best_score() -> float
The best evaluation score during training.
methodpython-package.xgboost.core.Booster.copy() -> 'Booster'
Copy the booster object.
methodpython-package.xgboost.core.Booster.dump_model(fout:PathLike, fmap:PathLike='', with_stats:bool=False, dump_format:str='text') -> None
Dump model into a text or JSON file.
methodpython-package.xgboost.core.Booster.eval(data:DMatrix, name:str='eval', iteration:int=0) -> str
Evaluate the model on mat.
methodpython-package.xgboost.core.Booster.eval_set(evals:Sequence[Tuple[DMatrix, str]], iteration:int=0, feval:Optional[Metric]=None, output_margin:bool=True) -> str
Evaluate a set of data.
methodpython-package.xgboost.core.Booster.feature_names() -> Optional[FeatureNames]
Feature names for this booster.
methodpython-package.xgboost.core.Booster.feature_types() -> Optional[FeatureTypes]
Feature types for this booster.
methodpython-package.xgboost.core.Booster.get_categories(export_to_arrow:bool=False) -> Categories
Same method as :py:meth:`DMatrix.get_categories`.
methodpython-package.xgboost.core.Booster.get_dump(fmap:PathLike='', with_stats:bool=False, dump_format:str='text') -> List[str]
Returns the model dump as a list of strings.
methodpython-package.xgboost.core.Booster.get_fscore(fmap:PathLike='') -> Dict[str, Union[float, List[float]]]
Get feature importance of each feature.
methodpython-package.xgboost.core.Booster.get_score(fmap:PathLike='', importance_type:str='weight') -> Dict[str, Union[float, List[float]]]
Get feature importance of each feature.
methodpython-package.xgboost.core.Booster.load_config(config:str) -> None
Load configuration returned by `save_config`.
methodpython-package.xgboost.core.Booster.load_model(fname:ModelIn) -> None
Load the model from a file or a bytearray.
methodpython-package.xgboost.core.Booster.num_boosted_rounds() -> int
Get number of boosted rounds.
methodpython-package.xgboost.core.Booster.num_features() -> int
Number of features in booster.
methodpython-package.xgboost.core.Booster.save_model(fname:PathLike) -> None
Save the model to a file.
methodpython-package.xgboost.core.Booster.save_raw(raw_format:str='ubj') -> bytearray
Save the model to a in memory buffer representation instead of file.
methodpython-package.xgboost.core.Booster.set_attr(**kwargs:Optional[Any]) -> None
Set the attribute of the Booster.
methodpython-package.xgboost.core.Booster.set_param(params:Union[Dict, Iterable[Tuple[str, Any]], str], value:Optional[str]=None) -> None
Set parameters into the Booster.
methodpython-package.xgboost.core.Booster.trees_to_dataframe(fmap:PathLike='') -> PdDataFrame
Parse a boosted tree model into a pandas DataFrame.
classpython-package.xgboost.core.DMatrix
Data Matrix used in XGBoost.
methodpython-package.xgboost.core.DMatrix.feature_names() -> Optional[FeatureNames]
Labels for features (column labels).
methodpython-package.xgboost.core.DMatrix.feature_types() -> Optional[FeatureTypes]
Type of features (column types).
methodpython-package.xgboost.core.DMatrix.get_base_margin() -> NumpyOrCupy
Get the base margin of the DMatrix.
methodpython-package.xgboost.core.DMatrix.get_categories(export_to_arrow:bool=False) -> Categories
Get the categories in the dataset.
methodpython-package.xgboost.core.DMatrix.get_data() -> scipy.sparse.csr_matrix
Get the predictors from DMatrix as a CSR matrix.
methodpython-package.xgboost.core.DMatrix.get_float_info(field:str) -> NumpyOrCupy
Get float property from the DMatrix.
methodpython-package.xgboost.core.DMatrix.get_group() -> np.ndarray
Get the group of the DMatrix.
methodpython-package.xgboost.core.DMatrix.get_label() -> NumpyOrCupy
Get the label of the DMatrix.
methodpython-package.xgboost.core.DMatrix.get_quantile_cut() -> Tuple[np.ndarray, np.ndarray]
Get quantile cuts for quantization.
methodpython-package.xgboost.core.DMatrix.get_uint_info(field:str) -> NumpyOrCupy
Get unsigned integer property from the DMatrix.
methodpython-package.xgboost.core.DMatrix.get_weight() -> NumpyOrCupy
Get the weight of the DMatrix.
methodpython-package.xgboost.core.DMatrix.num_col() -> int
Get the number of columns (features) in the DMatrix.
methodpython-package.xgboost.core.DMatrix.num_nonmissing() -> int
Get the number of non-missing values in the DMatrix.
methodpython-package.xgboost.core.DMatrix.num_row() -> int
Get the number of rows in the DMatrix.
methodpython-package.xgboost.core.DMatrix.save_binary(fname:PathLike, silent:bool=True) -> None
Save DMatrix to an XGBoost buffer.
methodpython-package.xgboost.core.DMatrix.set_base_margin(margin:ArrayLike) -> None
Set base margin of booster to start from.
methodpython-package.xgboost.core.DMatrix.set_float_info(field:str, data:ArrayLike) -> None
Set float type property into the DMatrix.
methodpython-package.xgboost.core.DMatrix.set_group(group:ArrayLike) -> None
Set group size of DMatrix (used for ranking).
methodpython-package.xgboost.core.DMatrix.set_uint_info(field:str, data:ArrayLike) -> None
Set uint type property into the DMatrix.
methodpython-package.xgboost.core.DMatrix.set_weight(weight:ArrayLike) -> None
Set weight of each instance.
classpython-package.xgboost.core.DataIter
The interface for user defined data iterator.
methodpython-package.xgboost.core.DataIter.get_callbacks(enable_categorical:bool) -> Tuple[Callable, Callable]
Get callback functions for iterating in C.
methodpython-package.xgboost.core.DataIter.next(input_data:Callable) -> bool
Set the next batch of data.
methodpython-package.xgboost.core.DataIter.proxy() -> '_ProxyDMatrix'
Handle of DMatrix proxy.
methodpython-package.xgboost.core.DataIter.reraise() -> None
Reraise the exception thrown during iteration.
methodpython-package.xgboost.core.DataIter.reset() -> None
Reset the data iterator.
funcpython-package.xgboost.core.build_info() -> dict
Build information of XGBoost.
funcpython-package.xgboost.core.c_array(ctype:Type[CTypeT], values:ArrayLike) -> Union[ctypes.Array, ctypes._Pointer]
Convert a python array to c array.
funcpython-package.xgboost.core.ctypes2buffer(cptr:CStrPtr, length:int) -> bytearray
Convert ctypes pointer to buffer type.
funcpython-package.xgboost.core.ctypes2numpy(cptr:CNumericPtr, length:int, dtype:Type[np.number]) -> np.ndarray
Convert a ctypes pointer array to a numpy array.
classpython-package.xgboost.dask.DaskDMatrix
DMatrix holding on references to Dask DataFrame or Dask Array.
methodpython-package.xgboost.dask.DaskDMatrix.num_col() -> int
Get the number of columns (features) in the DMatrix.
funcpython-package.xgboost.dask.DaskDMatrix.to_futures(d:_DaskCollection) -> List[Future]
Breaking data into partitions.
classpython-package.xgboost.dask.DaskQuantileDMatrix
A dask version of :py:class:`QuantileDMatrix`.
methodpython-package.xgboost.dask.DaskScikitLearnBase.client() -> 'distributed.Client'
The dask client used in this model.
classpython-package.xgboost.dask.data.DaskPartitionIter
A data iterator for the `DaskQuantileDMatrix`.
methodpython-package.xgboost.dask.data.DaskPartitionIter.data() -> Any
Utility function for obtaining current batch of data.
methodpython-package.xgboost.dask.data.DaskPartitionIter.next(input_data:Callable) -> bool
Yield next batch of data
methodpython-package.xgboost.dask.data.DaskPartitionIter.reset() -> None
Reset the iterator
funcpython-package.xgboost.dask.data.get_dict(i:int) -> Dict[str, list]
Return a dictionary containing all the meta info and all partitions.
classpython-package.xgboost.data.ArrowTransformed
A storage class for transformed arrow table.
methodpython-package.xgboost.data.ArrowTransformed.shape() -> Tuple[int, int]
Return shape of the transformed DataFrame.
classpython-package.xgboost.data.CudfTransformed
A storage class for transformed cuDF dataframe.
methodpython-package.xgboost.data.CudfTransformed.shape() -> Tuple[int, int]
Return shape of the transformed DataFrame.
classpython-package.xgboost.data.PandasTransformed
A storage class for transformed pandas DataFrame.
methodpython-package.xgboost.data.PandasTransformed.shape() -> Tuple[int, int]
Return shape of the transformed DataFrame.
funcpython-package.xgboost.data.dispatch_meta_backend(matrix:'DMatrix', data:DataType, name:str, dtype:Optional[NumpyDType]=None) -> None
Dispatch for meta info.
funcpython-package.xgboost.data.dispatch_proxy_set_data(proxy:'_ProxyDMatrix', data:DataType) -> None
Dispatch for QuantileDMatrix.
funcpython-package.xgboost.data.is_nullable_dtype(dtype:PandasDType) -> bool
Whether dtype is a pandas nullable type.
funcpython-package.xgboost.data.is_on_cuda(data:Any) -> bool
Whether the data is a CUDA-based data structure.
funcpython-package.xgboost.data.is_pa_ext_categorical_dtype(dtype:Any) -> bool
Check whether dtype is a dictionary type.
funcpython-package.xgboost.data.is_pa_ext_dtype(dtype:Any) -> bool
Return whether dtype is a pyarrow extension type for pandas
funcpython-package.xgboost.data.is_pd_cat_dtype(dtype:PandasDType) -> bool
Wrapper for testing pandas category type.
funcpython-package.xgboost.data.is_pd_sparse_dtype(dtype:PandasDType) -> bool
Wrapper for testing pandas sparse type.
funcpython-package.xgboost.data.is_scipy_coo(data:DataType) -> bool
Predicate for scipy COO input.
funcpython-package.xgboost.data.is_scipy_csc(data:DataType) -> bool
Predicate for scipy CSC input.
funcpython-package.xgboost.data.is_scipy_csr(data:DataType) -> bool
Predicate for scipy CSR input.
funcpython-package.xgboost.data.pandas_pa_type(ser:Any) -> np.ndarray
Handle pandas pyarrow extension.
classpython-package.xgboost.libpath.XGBoostLibraryNotFound
Error thrown by when xgboost is not found
funcpython-package.xgboost.libpath.find_lib_path() -> List[str]
Find the path to xgboost dynamic library files.
classpython-package.xgboost.objective.Objective
Base class for custom objective functions.
classpython-package.xgboost.objective.TreeObjective
Base class for tree-specific custom objective functions.
funcpython-package.xgboost.plotting.plot_tree(booster:Union[Booster, XGBModel], *fmap:PathLike='', *num_trees:Optional[int]=None, *rankdir:Optional[str]=None, *ax:Optional[Axes]=None, *with_stats:bool=False, *tree_idx:int=0, **kwargs:Any) -> Axes
Plot specified tree.
funcpython-package.xgboost.sklearn.get_doc(item:str) -> str
Return selected item
funcpython-package.xgboost.sklearn.inner(preds:np.ndarray, dmatrix:DMatrix) -> Tuple[np.ndarray, np.ndarray]
Internal function.
funcpython-package.xgboost.sklearn.ltr_metric_decorator(func:Callable, n_jobs:Optional[int]) -> Metric
Decorate a learning to rank metric.
classpython-package.xgboost.spark.core.SparkXGBModelReader
Spark Xgboost model reader.
classpython-package.xgboost.spark.core.SparkXGBModelWriter
Spark Xgboost model writer.
classpython-package.xgboost.spark.core.SparkXGBReader
Spark Xgboost estimator reader.
methodpython-package.xgboost.spark.core.SparkXGBReader.load(path:str) -> '_SparkXGBEstimator'
load model.
classpython-package.xgboost.spark.core.SparkXGBWriter
Spark Xgboost estimator writer.
methodpython-package.xgboost.spark.core.SparkXGBWriter.saveImpl(path:str) -> None
save model.
funcpython-package.xgboost.spark.core._SparkXGBModel.to_gpu_if_possible(data:ArrayLike) -> ArrayLike
Move the data to gpu if possible
classpython-package.xgboost.spark.data.PartIter
Iterator for creating Quantile DMatrix from partitions.
funcpython-package.xgboost.spark.data.cache_partitions(iterator:Iterator[pd.DataFrame], append:Callable[[pd.DataFrame, str, bool], None]) -> None
Extract partitions from pyspark iterator.
funcpython-package.xgboost.spark.data.concat_or_none(seq:Optional[Sequence[np.ndarray]]) -> Optional[np.ndarray]
Concatenate the data if it's not None.
funcpython-package.xgboost.spark.data.stack_series(series:pd.Series) -> np.ndarray
Stack a series of arrays.
classpython-package.xgboost.spark.estimator.SparkXGBClassifier
SparkXGBClassifier is a PySpark ML estimator.
classpython-package.xgboost.spark.estimator.SparkXGBRanker
SparkXGBRanker is a PySpark ML estimator.
classpython-package.xgboost.spark.estimator.SparkXGBRegressor
SparkXGBRegressor is a PySpark ML estimator.
classpython-package.xgboost.spark.params.HasQueryIdCol
Mixin for param qid_col: query id column name.
classpython-package.xgboost.spark.utils.CommunicatorContext
Context with PySpark specific task ID.
funcpython-package.xgboost.spark.utils.get_class_name(cls:Type) -> str
Return the class name.
funcpython-package.xgboost.spark.utils.get_logger_level(name:str) -> Optional[int]
Get the logger level for the given log name
funcpython-package.xgboost.spark.utils.serialize_booster(booster:Booster) -> str
Serialize the input booster to a string.
funcpython-package.xgboost.spark.utils.use_cuda(device:Optional[str]) -> bool
Whether xgboost is using CUDA workers.
funcpython-package.xgboost.tracker.get_family(addr:str) -> int
Get network family from address.
classpython-package.xgboost.training.CVPack
"Auxiliary datastruct to hold one fold of CV.
methodpython-package.xgboost.training.CVPack.eval(iteration:int, feval:Optional[Metric], output_margin:bool) -> str
"Evaluate the CVPack for one iteration.
methodpython-package.xgboost.training.CVPack.update(iteration:int, fobj:Optional[CustomObj]) -> None
"Update the boosters for one iteration

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These signatures were extracted from the public source of dmlc/xgboost using Python's ast module. Argument names, default values, type annotations and return types are taken verbatim from the code. Implementation bodies are never stored. See how it works for details.

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