brk-code

xgboost の API リファレンス

xgboost (dmlc/xgboost) の公開 API 211 件 —— クラス 38、関数 103、メソッド 70。実際のソースを静的解析して抽出した正確なシグネチャを掲載しています。

リポジトリ: dmlc/xgboost

種別件数
クラス38
関数103
メソッド70

API 一覧

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

この情報について

掲載しているシグネチャは dmlc/xgboost の公開ソースコードを Python の ast モジュールで静的解析し、引数名・デフォルト値・ 型注釈・戻り値型をそのまま抽出したものです。実装コードは保存していません。 詳しくは仕組みの解説をご覧ください。

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