# coding: utf-8 # pylint: disable=too-many-arguments, too-many-branches, invalid-name # pylint: disable=too-many-lines, too-many-locals, no-self-use """Core XGBoost Library.""" import collections # pylint: disable=no-name-in-module,import-error from collections.abc import Mapping from typing import List, Optional, Any, Union, Dict # pylint: enable=no-name-in-module,import-error from typing import Callable, Tuple import ctypes import os import re import sys import json import warnings from functools import wraps from inspect import signature, Parameter import numpy as np import scipy.sparse from .compat import (STRING_TYPES, DataFrame, py_str, PANDAS_INSTALLED, lazy_isinstance) from .libpath import find_lib_path # c_bst_ulong corresponds to bst_ulong defined in xgboost/c_api.h c_bst_ulong = ctypes.c_uint64 class XGBoostError(ValueError): """Error thrown by xgboost trainer.""" class EarlyStopException(Exception): """Exception to signal early stopping. Parameters ---------- best_iteration : int The best iteration stopped. """ def __init__(self, best_iteration): super().__init__() self.best_iteration = best_iteration # Callback environment used by callbacks CallbackEnv = collections.namedtuple( "XGBoostCallbackEnv", ["model", "cvfolds", "iteration", "begin_iteration", "end_iteration", "rank", "evaluation_result_list"]) def from_pystr_to_cstr(data): """Convert a list of Python str to C pointer Parameters ---------- data : list list of str """ if not isinstance(data, list): raise NotImplementedError pointers = (ctypes.c_char_p * len(data))() data = [bytes(d, 'utf-8') for d in data] pointers[:] = data return pointers def from_cstr_to_pystr(data, length): """Revert C pointer to Python str Parameters ---------- data : ctypes pointer pointer to data length : ctypes pointer pointer to length of data """ res = [] for i in range(length.value): try: res.append(str(data[i].decode('ascii'))) except UnicodeDecodeError: res.append(str(data[i].decode('utf-8'))) return res def _expect(expectations, got): """Translate input error into string. Parameters ---------- expectations: sequence a list of expected value. got: actual input Returns ------- msg: str """ msg = 'Expecting ' for t in range(len(expectations) - 1): msg += str(expectations[t]) msg += ' or ' msg += str(expectations[-1]) msg += '. Got ' + str(got) return msg def _log_callback(msg): """Redirect logs from native library into Python console""" print("{0:s}".format(py_str(msg))) def _get_log_callback_func(): """Wrap log_callback() method in ctypes callback type""" # pylint: disable=invalid-name CALLBACK = ctypes.CFUNCTYPE(None, ctypes.c_char_p) return CALLBACK(_log_callback) def _load_lib(): """Load xgboost Library.""" lib_paths = find_lib_path() if not lib_paths: return None try: pathBackup = os.environ['PATH'].split(os.pathsep) except KeyError: pathBackup = [] lib_success = False os_error_list = [] for lib_path in lib_paths: try: # needed when the lib is linked with non-system-available # dependencies os.environ['PATH'] = os.pathsep.join( pathBackup + [os.path.dirname(lib_path)]) lib = ctypes.cdll.LoadLibrary(lib_path) lib_success = True except OSError as e: os_error_list.append(str(e)) continue finally: os.environ['PATH'] = os.pathsep.join(pathBackup) if not lib_success: libname = os.path.basename(lib_paths[0]) raise XGBoostError( 'XGBoost Library ({}) could not be loaded.\n'.format(libname) + 'Likely causes:\n' + ' * OpenMP runtime is not installed ' + '(vcomp140.dll or libgomp-1.dll for Windows, libomp.dylib for Mac OSX, ' + 'libgomp.so for Linux and other UNIX-like OSes). Mac OSX users: Run ' + '`brew install libomp` to install OpenMP runtime.\n' + ' * You are running 32-bit Python on a 64-bit OS\n' + 'Error message(s): {}\n'.format(os_error_list)) lib.XGBGetLastError.restype = ctypes.c_char_p lib.callback = _get_log_callback_func() if lib.XGBRegisterLogCallback(lib.callback) != 0: raise XGBoostError(lib.XGBGetLastError()) return lib # load the XGBoost library globally _LIB = _load_lib() def _check_call(ret): """Check the return value of C API call This function will raise exception when error occurs. Wrap every API call with this function Parameters ---------- ret : int return value from API calls """ if ret != 0: raise XGBoostError(py_str(_LIB.XGBGetLastError())) def ctypes2numpy(cptr, length, dtype): """Convert a ctypes pointer array to a numpy array.""" NUMPY_TO_CTYPES_MAPPING = { np.float32: ctypes.c_float, np.uint32: ctypes.c_uint, } if dtype not in NUMPY_TO_CTYPES_MAPPING: raise RuntimeError('Supported types: {}'.format( NUMPY_TO_CTYPES_MAPPING.keys())) ctype = NUMPY_TO_CTYPES_MAPPING[dtype] if not isinstance(cptr, ctypes.POINTER(ctype)): raise RuntimeError('expected {} pointer'.format(ctype)) res = np.zeros(length, dtype=dtype) if not ctypes.memmove(res.ctypes.data, cptr, length * res.strides[0]): raise RuntimeError('memmove failed') return res def ctypes2cupy(cptr, length, dtype): """Convert a ctypes pointer array to a cupy array.""" # pylint: disable=import-error import cupy from cupy.cuda.memory import MemoryPointer from cupy.cuda.memory import UnownedMemory CUPY_TO_CTYPES_MAPPING = { cupy.float32: ctypes.c_float, cupy.uint32: ctypes.c_uint } if dtype not in CUPY_TO_CTYPES_MAPPING.keys(): raise RuntimeError('Supported types: {}'.format( CUPY_TO_CTYPES_MAPPING.keys() )) addr = ctypes.cast(cptr, ctypes.c_void_p).value # pylint: disable=c-extension-no-member,no-member device = cupy.cuda.runtime.pointerGetAttributes(addr).device # The owner field is just used to keep the memory alive with ref count. As # unowned's life time is scoped within this function we don't need that. unownd = UnownedMemory( addr, length.value * ctypes.sizeof(CUPY_TO_CTYPES_MAPPING[dtype]), owner=None) memptr = MemoryPointer(unownd, 0) # pylint: disable=unexpected-keyword-arg mem = cupy.ndarray((length.value, ), dtype=dtype, memptr=memptr) assert mem.device.id == device arr = cupy.array(mem, copy=True) return arr def ctypes2buffer(cptr, length): """Convert ctypes pointer to buffer type.""" if not isinstance(cptr, ctypes.POINTER(ctypes.c_char)): raise RuntimeError('expected char pointer') res = bytearray(length) rptr = (ctypes.c_char * length).from_buffer(res) if not ctypes.memmove(rptr, cptr, length): raise RuntimeError('memmove failed') return res def c_str(string): """Convert a python string to cstring.""" return ctypes.c_char_p(string.encode('utf-8')) def c_array(ctype, values): """Convert a python string to c array.""" if (isinstance(values, np.ndarray) and values.dtype.itemsize == ctypes.sizeof(ctype)): return (ctype * len(values)).from_buffer_copy(values) return (ctype * len(values))(*values) class DataIter: '''The interface for user defined data iterator. Currently is only supported by Device DMatrix. Parameters ---------- rows : int Total number of rows combining all batches. cols : int Number of columns for each batch. ''' def __init__(self): proxy_handle = ctypes.c_void_p() _check_call(_LIB.XGProxyDMatrixCreate(ctypes.byref(proxy_handle))) self._handle = DeviceQuantileDMatrix(proxy_handle) self.exception = None @property def proxy(self): '''Handler of DMatrix proxy.''' return self._handle def reset_wrapper(self, this): # pylint: disable=unused-argument '''A wrapper for user defined `reset` function.''' self.reset() def next_wrapper(self, this): # pylint: disable=unused-argument '''A wrapper for user defined `next` function. `this` is not used in Python. ctypes can handle `self` of a Python member function automatically when converting it to c function pointer. ''' if self.exception is not None: return 0 def data_handle(data, label=None, weight=None, base_margin=None, group=None, qid=None, label_lower_bound=None, label_upper_bound=None, feature_names=None, feature_types=None, feature_weights=None): from .data import dispatch_device_quantile_dmatrix_set_data from .data import _device_quantile_transform data, feature_names, feature_types = _device_quantile_transform( data, feature_names, feature_types ) dispatch_device_quantile_dmatrix_set_data(self.proxy, data) self.proxy.set_info( label=label, weight=weight, base_margin=base_margin, group=group, qid=qid, label_lower_bound=label_lower_bound, label_upper_bound=label_upper_bound, feature_names=feature_names, feature_types=feature_types, feature_weights=feature_weights ) try: # Differ the exception in order to return 0 and stop the iteration. # Exception inside a ctype callback function has no effect except # for printing to stderr (doesn't stop the execution). ret = self.next(data_handle) # pylint: disable=not-callable except Exception as e: # pylint: disable=broad-except tb = sys.exc_info()[2] # On dask the worker is restarted and somehow the information is # lost. self.exception = e.with_traceback(tb) return 0 return ret def reset(self): '''Reset the data iterator. Prototype for user defined function.''' raise NotImplementedError() def next(self, input_data): '''Set the next batch of data. Parameters ---------- data_handle: callable A function with same data fields like `data`, `label` with `xgboost.DMatrix`. Returns ------- 0 if there's no more batch, otherwise 1. ''' raise NotImplementedError() # Notice for `_deprecate_positional_args` # Authors: Olivier Grisel # Gael Varoquaux # Andreas Mueller # Lars Buitinck # Alexandre Gramfort # Nicolas Tresegnie # Sylvain Marie # License: BSD 3 clause def _deprecate_positional_args(f): """Decorator for methods that issues warnings for positional arguments Using the keyword-only argument syntax in pep 3102, arguments after the * will issue a warning when passed as a positional argument. Modifed from sklearn utils.validation. Parameters ---------- f : function function to check arguments on """ sig = signature(f) kwonly_args = [] all_args = [] for name, param in sig.parameters.items(): if param.kind == Parameter.POSITIONAL_OR_KEYWORD: all_args.append(name) elif param.kind == Parameter.KEYWORD_ONLY: kwonly_args.append(name) @wraps(f) def inner_f(*args, **kwargs): extra_args = len(args) - len(all_args) if extra_args > 0: # ignore first 'self' argument for instance methods args_msg = [ '{}'.format(name) for name, _ in zip( kwonly_args[:extra_args], args[-extra_args:]) ] warnings.warn( "Pass `{}` as keyword args. Passing these as positional " "arguments will be considered as error in future releases.". format(", ".join(args_msg)), FutureWarning) for k, arg in zip(sig.parameters, args): kwargs[k] = arg return f(**kwargs) return inner_f class DMatrix: # pylint: disable=too-many-instance-attributes """Data Matrix used in XGBoost. DMatrix is an internal data structure that is used by XGBoost, which is optimized for both memory efficiency and training speed. You can construct DMatrix from multiple different sources of data. """ @_deprecate_positional_args def __init__( self, data, label=None, *, weight=None, base_margin=None, missing: Optional[float] = None, silent=False, feature_names=None, feature_types=None, nthread: Optional[int] = None, group=None, qid=None, label_lower_bound=None, label_upper_bound=None, feature_weights=None, enable_categorical: bool = False, ) -> None: """Parameters ---------- data : os.PathLike/string/numpy.array/scipy.sparse/pd.DataFrame/ dt.Frame/cudf.DataFrame/cupy.array/dlpack Data source of DMatrix. When data is string or os.PathLike type, it represents the path libsvm format txt file, csv file (by specifying uri parameter 'path_to_csv?format=csv'), or binary file that xgboost can read from. label : array_like Label of the training data. weight : array_like Weight for each instance. .. note:: For ranking task, weights are per-group. In ranking task, one weight is assigned to each group (not each data point). This is because we only care about the relative ordering of data points within each group, so it doesn't make sense to assign weights to individual data points. base_margin: array_like Base margin used for boosting from existing model. missing : float, optional Value in the input data which needs to be present as a missing value. If None, defaults to np.nan. silent : boolean, optional Whether print messages during construction feature_names : list, optional Set names for features. feature_types : list, optional Set types for features. nthread : integer, optional Number of threads to use for loading data when parallelization is applicable. If -1, uses maximum threads available on the system. group : array_like Group size for all ranking group. qid : array_like Query ID for data samples, used for ranking. label_lower_bound : array_like Lower bound for survival training. label_upper_bound : array_like Upper bound for survival training. feature_weights : array_like, optional Set feature weights for column sampling. enable_categorical: boolean, optional .. versionadded:: 1.3.0 Experimental support of specializing for categorical features. Do not set to True unless you are interested in development. Currently it's only available for `gpu_hist` tree method with 1 vs rest (one hot) categorical split. Also, JSON serialization format, `gpu_predictor` and pandas input are required. """ if isinstance(data, list): raise TypeError("Input data can not be a list.") if group is not None and qid is not None: raise ValueError("Either one of `group` or `qid` should be None.") self.missing = missing if missing is not None else np.nan self.nthread = nthread if nthread is not None else -1 self.silent = silent # force into void_p, mac need to pass things in as void_p if data is None: self.handle = None return from .data import dispatch_data_backend handle, feature_names, feature_types = dispatch_data_backend( data, missing=self.missing, threads=self.nthread, feature_names=feature_names, feature_types=feature_types, enable_categorical=enable_categorical, ) assert handle is not None self.handle = handle self.set_info( label=label, weight=weight, base_margin=base_margin, group=group, qid=qid, label_lower_bound=label_lower_bound, label_upper_bound=label_upper_bound, feature_weights=feature_weights, ) if feature_names is not None: self.feature_names = feature_names if feature_types is not None: self.feature_types = feature_types def __del__(self): if hasattr(self, "handle") and self.handle: _check_call(_LIB.XGDMatrixFree(self.handle)) self.handle = None @_deprecate_positional_args def set_info( self, *, label=None, weight=None, base_margin=None, group=None, qid=None, label_lower_bound=None, label_upper_bound=None, feature_names=None, feature_types=None, feature_weights=None ) -> None: """Set meta info for DMatrix. See doc string for DMatrix constructor.""" from .data import dispatch_meta_backend if label is not None: self.set_label(label) if weight is not None: self.set_weight(weight) if base_margin is not None: self.set_base_margin(base_margin) if group is not None: self.set_group(group) if qid is not None: self.set_uint_info('qid', qid) if label_lower_bound is not None: self.set_float_info('label_lower_bound', label_lower_bound) if label_upper_bound is not None: self.set_float_info('label_upper_bound', label_upper_bound) if feature_names is not None: self.feature_names = feature_names if feature_types is not None: self.feature_types = feature_types if feature_weights is not None: dispatch_meta_backend(matrix=self, data=feature_weights, name='feature_weights') def get_float_info(self, field): """Get float property from the DMatrix. Parameters ---------- field: str The field name of the information Returns ------- info : array a numpy array of float information of the data """ length = c_bst_ulong() ret = ctypes.POINTER(ctypes.c_float)() _check_call(_LIB.XGDMatrixGetFloatInfo(self.handle, c_str(field), ctypes.byref(length), ctypes.byref(ret))) return ctypes2numpy(ret, length.value, np.float32) def get_uint_info(self, field): """Get unsigned integer property from the DMatrix. Parameters ---------- field: str The field name of the information Returns ------- info : array a numpy array of unsigned integer information of the data """ length = c_bst_ulong() ret = ctypes.POINTER(ctypes.c_uint)() _check_call(_LIB.XGDMatrixGetUIntInfo(self.handle, c_str(field), ctypes.byref(length), ctypes.byref(ret))) return ctypes2numpy(ret, length.value, np.uint32) def set_float_info(self, field, data): """Set float type property into the DMatrix. Parameters ---------- field: str The field name of the information data: numpy array The array of data to be set """ from .data import dispatch_meta_backend dispatch_meta_backend(self, data, field, 'float') def set_float_info_npy2d(self, field, data): """Set float type property into the DMatrix for numpy 2d array input Parameters ---------- field: str The field name of the information data: numpy array The array of data to be set """ from .data import dispatch_meta_backend dispatch_meta_backend(self, data, field, 'float') def set_uint_info(self, field, data): """Set uint type property into the DMatrix. Parameters ---------- field: str The field name of the information data: numpy array The array of data to be set """ from .data import dispatch_meta_backend dispatch_meta_backend(self, data, field, 'uint32') def save_binary(self, fname, silent=True): """Save DMatrix to an XGBoost buffer. Saved binary can be later loaded by providing the path to :py:func:`xgboost.DMatrix` as input. Parameters ---------- fname : string or os.PathLike Name of the output buffer file. silent : bool (optional; default: True) If set, the output is suppressed. """ fname = os.fspath(os.path.expanduser(fname)) _check_call(_LIB.XGDMatrixSaveBinary(self.handle, c_str(fname), ctypes.c_int(silent))) def set_label(self, label): """Set label of dmatrix Parameters ---------- label: array like The label information to be set into DMatrix """ from .data import dispatch_meta_backend dispatch_meta_backend(self, label, 'label', 'float') def set_weight(self, weight): """Set weight of each instance. Parameters ---------- weight : array like Weight for each data point .. note:: For ranking task, weights are per-group. In ranking task, one weight is assigned to each group (not each data point). This is because we only care about the relative ordering of data points within each group, so it doesn't make sense to assign weights to individual data points. """ from .data import dispatch_meta_backend dispatch_meta_backend(self, weight, 'weight', 'float') def set_base_margin(self, margin): """Set base margin of booster to start from. This can be used to specify a prediction value of existing model to be base_margin However, remember margin is needed, instead of transformed prediction e.g. for logistic regression: need to put in value before logistic transformation see also example/demo.py Parameters ---------- margin: array like Prediction margin of each datapoint """ from .data import dispatch_meta_backend dispatch_meta_backend(self, margin, 'base_margin', 'float') def set_group(self, group): """Set group size of DMatrix (used for ranking). Parameters ---------- group : array like Group size of each group """ from .data import dispatch_meta_backend dispatch_meta_backend(self, group, 'group', 'uint32') def get_label(self): """Get the label of the DMatrix. Returns ------- label : array """ return self.get_float_info('label') def get_weight(self): """Get the weight of the DMatrix. Returns ------- weight : array """ return self.get_float_info('weight') def get_base_margin(self): """Get the base margin of the DMatrix. Returns ------- base_margin : float """ return self.get_float_info('base_margin') def num_row(self): """Get the number of rows in the DMatrix. Returns ------- number of rows : int """ ret = c_bst_ulong() _check_call(_LIB.XGDMatrixNumRow(self.handle, ctypes.byref(ret))) return ret.value def num_col(self): """Get the number of columns (features) in the DMatrix. Returns ------- number of columns : int """ ret = c_bst_ulong() _check_call(_LIB.XGDMatrixNumCol(self.handle, ctypes.byref(ret))) return ret.value def slice( self, rindex: Union[List[int], np.ndarray], allow_groups: bool = False ) -> "DMatrix": """Slice the DMatrix and return a new DMatrix that only contains `rindex`. Parameters ---------- rindex List of indices to be selected. allow_groups Allow slicing of a matrix with a groups attribute Returns ------- res A new DMatrix containing only selected indices. """ from .data import _maybe_np_slice res = DMatrix(None) res.handle = ctypes.c_void_p() rindex = _maybe_np_slice(rindex, dtype=np.int32) _check_call( _LIB.XGDMatrixSliceDMatrixEx( self.handle, c_array(ctypes.c_int, rindex), c_bst_ulong(len(rindex)), ctypes.byref(res.handle), ctypes.c_int(1 if allow_groups else 0), ) ) return res @property def feature_names(self) -> List[str]: """Get feature names (column labels). Returns ------- feature_names : list or None """ length = c_bst_ulong() sarr = ctypes.POINTER(ctypes.c_char_p)() _check_call( _LIB.XGDMatrixGetStrFeatureInfo( self.handle, c_str("feature_name"), ctypes.byref(length), ctypes.byref(sarr), ) ) feature_names = from_cstr_to_pystr(sarr, length) if not feature_names: feature_names = ["f{0}".format(i) for i in range(self.num_col())] return feature_names @feature_names.setter def feature_names(self, feature_names: Optional[Union[List[str], str]]) -> None: """Set feature names (column labels). Parameters ---------- feature_names : list or None Labels for features. None will reset existing feature names """ if feature_names is not None: # validate feature name try: if not isinstance(feature_names, str): feature_names = list(feature_names) else: feature_names = [feature_names] except TypeError: feature_names = [feature_names] if len(feature_names) != len(set(feature_names)): raise ValueError('feature_names must be unique') if len(feature_names) != self.num_col() and self.num_col() != 0: msg = 'feature_names must have the same length as data' raise ValueError(msg) # prohibit to use symbols may affect to parse. e.g. []< if not all(isinstance(f, str) and not any(x in f for x in set(('[', ']', '<'))) for f in feature_names): raise ValueError('feature_names must be string, and may not contain [, ] or <') c_feature_names = [bytes(f, encoding='utf-8') for f in feature_names] c_feature_names = (ctypes.c_char_p * len(c_feature_names))(*c_feature_names) _check_call(_LIB.XGDMatrixSetStrFeatureInfo( self.handle, c_str('feature_name'), c_feature_names, c_bst_ulong(len(feature_names)))) else: # reset feature_types also _check_call(_LIB.XGDMatrixSetStrFeatureInfo( self.handle, c_str('feature_name'), None, c_bst_ulong(0))) self.feature_types = None @property def feature_types(self) -> Optional[List[str]]: """Get feature types (column types). Returns ------- feature_types : list or None """ length = c_bst_ulong() sarr = ctypes.POINTER(ctypes.c_char_p)() _check_call(_LIB.XGDMatrixGetStrFeatureInfo(self.handle, c_str('feature_type'), ctypes.byref(length), ctypes.byref(sarr))) res = from_cstr_to_pystr(sarr, length) if not res: return None return res @feature_types.setter def feature_types(self, feature_types: Optional[Union[List[Any], Any]]) -> None: """Set feature types (column types). This is for displaying the results and unrelated to the learning process. Parameters ---------- feature_types : list or None Labels for features. None will reset existing feature names """ if feature_types is not None: if not isinstance(feature_types, (list, str)): raise TypeError( 'feature_types must be string or list of strings') if isinstance(feature_types, str): # single string will be applied to all columns feature_types = [feature_types] * self.num_col() try: if not isinstance(feature_types, str): feature_types = list(feature_types) else: feature_types = [feature_types] except TypeError: feature_types = [feature_types] c_feature_types = [bytes(f, encoding='utf-8') for f in feature_types] c_feature_types = (ctypes.c_char_p * len(c_feature_types))(*c_feature_types) _check_call(_LIB.XGDMatrixSetStrFeatureInfo( self.handle, c_str('feature_type'), c_feature_types, c_bst_ulong(len(feature_types)))) if len(feature_types) != self.num_col(): msg = 'feature_types must have the same length as data' raise ValueError(msg) else: # Reset. _check_call(_LIB.XGDMatrixSetStrFeatureInfo( self.handle, c_str('feature_type'), None, c_bst_ulong(0))) class DeviceQuantileDMatrix(DMatrix): """Device memory Data Matrix used in XGBoost for training with tree_method='gpu_hist'. Do not use this for test/validation tasks as some information may be lost in quantisation. This DMatrix is primarily designed to save memory in training from device memory inputs by avoiding intermediate storage. Set max_bin to control the number of bins during quantisation. See doc string in `DMatrix` for documents on meta info. You can construct DeviceQuantileDMatrix from cupy/cudf/dlpack. .. versionadded:: 1.1.0 """ @_deprecate_positional_args def __init__( # pylint: disable=super-init-not-called self, data, label=None, *, weight=None, base_margin=None, missing=None, silent=False, feature_names=None, feature_types=None, nthread: Optional[int] = None, max_bin: int = 256, group=None, qid=None, label_lower_bound=None, label_upper_bound=None, feature_weights=None, enable_categorical: bool = False, ): self.max_bin = max_bin self.missing = missing if missing is not None else np.nan self.nthread = nthread if nthread is not None else 1 self._silent = silent # unused, kept for compatibility if isinstance(data, ctypes.c_void_p): self.handle = data return from .data import init_device_quantile_dmatrix handle, feature_names, feature_types = init_device_quantile_dmatrix( data, label=label, weight=weight, base_margin=base_margin, group=group, qid=qid, missing=self.missing, label_lower_bound=label_lower_bound, label_upper_bound=label_upper_bound, feature_weights=feature_weights, feature_names=feature_names, feature_types=feature_types, threads=self.nthread, max_bin=self.max_bin, ) if enable_categorical: raise NotImplementedError( 'categorical support is not enabled on DeviceQuantileDMatrix.' ) self.handle = handle if qid is not None and group is not None: raise ValueError( 'Only one of the eval_qid or eval_group for each evaluation ' 'dataset should be provided.' ) self.feature_names = feature_names self.feature_types = feature_types def _set_data_from_cuda_interface(self, data): '''Set data from CUDA array interface.''' interface = data.__cuda_array_interface__ interface_str = bytes(json.dumps(interface, indent=2), 'utf-8') _check_call( _LIB.XGDeviceQuantileDMatrixSetDataCudaArrayInterface( self.handle, interface_str ) ) def _set_data_from_cuda_columnar(self, data): '''Set data from CUDA columnar format.1''' from .data import _cudf_array_interfaces interfaces_str = _cudf_array_interfaces(data) _check_call( _LIB.XGDeviceQuantileDMatrixSetDataCudaColumnar( self.handle, interfaces_str ) ) Objective = Callable[[np.ndarray, DMatrix], Tuple[np.ndarray, np.ndarray]] Metric = Callable[[np.ndarray, DMatrix], Tuple[str, float]] class Booster(object): # pylint: disable=too-many-public-methods """A Booster of XGBoost. Booster is the model of xgboost, that contains low level routines for training, prediction and evaluation. """ feature_names = None def __init__(self, params=None, cache=(), model_file=None): # pylint: disable=invalid-name """ Parameters ---------- params : dict Parameters for boosters. cache : list List of cache items. model_file : string/os.PathLike/Booster/bytearray Path to the model file if it's string or PathLike. """ for d in cache: if not isinstance(d, DMatrix): raise TypeError('invalid cache item: {}'.format(type(d).__name__), cache) self._validate_features(d) dmats = c_array(ctypes.c_void_p, [d.handle for d in cache]) self.handle = ctypes.c_void_p() _check_call(_LIB.XGBoosterCreate(dmats, c_bst_ulong(len(cache)), ctypes.byref(self.handle))) params = params or {} params = self._configure_metrics(params.copy()) if isinstance(params, list): params.append(('validate_parameters', True)) else: params['validate_parameters'] = True self.set_param(params or {}) if (params is not None) and ('booster' in params): self.booster = params['booster'] else: self.booster = 'gbtree' if isinstance(model_file, Booster): assert self.handle is not None # We use the pickle interface for getting memory snapshot from # another model, and load the snapshot with this booster. state = model_file.__getstate__() handle = state['handle'] del state['handle'] ptr = (ctypes.c_char * len(handle)).from_buffer(handle) length = c_bst_ulong(len(handle)) _check_call( _LIB.XGBoosterUnserializeFromBuffer(self.handle, ptr, length)) self.__dict__.update(state) elif isinstance(model_file, (STRING_TYPES, os.PathLike, bytearray)): self.load_model(model_file) elif model_file is None: pass else: raise TypeError('Unknown type:', model_file) def _configure_metrics(self, params: Union[Dict, List]) -> Union[Dict, List]: if isinstance(params, dict) and 'eval_metric' in params \ and isinstance(params['eval_metric'], list): params = dict((k, v) for k, v in params.items()) eval_metrics = params['eval_metric'] params.pop("eval_metric", None) params = list(params.items()) for eval_metric in eval_metrics: params += [('eval_metric', eval_metric)] return params def __del__(self): if hasattr(self, 'handle') and self.handle is not None: _check_call(_LIB.XGBoosterFree(self.handle)) self.handle = None def __getstate__(self): # can't pickle ctypes pointers, put model content in bytearray this = self.__dict__.copy() handle = this['handle'] if handle is not None: length = c_bst_ulong() cptr = ctypes.POINTER(ctypes.c_char)() _check_call(_LIB.XGBoosterSerializeToBuffer(self.handle, ctypes.byref(length), ctypes.byref(cptr))) buf = ctypes2buffer(cptr, length.value) this["handle"] = buf return this def __setstate__(self, state): # reconstruct handle from raw data handle = state['handle'] if handle is not None: buf = handle dmats = c_array(ctypes.c_void_p, []) handle = ctypes.c_void_p() _check_call(_LIB.XGBoosterCreate( dmats, c_bst_ulong(0), ctypes.byref(handle))) length = c_bst_ulong(len(buf)) ptr = (ctypes.c_char * len(buf)).from_buffer(buf) _check_call( _LIB.XGBoosterUnserializeFromBuffer(handle, ptr, length)) state['handle'] = handle self.__dict__.update(state) def __getitem__(self, val): if isinstance(val, int): val = slice(val, val+1) if isinstance(val, tuple): raise ValueError('Only supports slicing through 1 dimension.') if not isinstance(val, slice): msg = _expect((int, slice), type(val)) raise TypeError(msg) if isinstance(val.start, type(Ellipsis)) or val.start is None: start = 0 else: start = val.start if isinstance(val.stop, type(Ellipsis)) or val.stop is None: stop = 0 else: stop = val.stop if stop < start: raise ValueError('Invalid slice', val) step = val.step if val.step is not None else 1 start = ctypes.c_int(start) stop = ctypes.c_int(stop) step = ctypes.c_int(step) sliced_handle = ctypes.c_void_p() status = _LIB.XGBoosterSlice(self.handle, start, stop, step, ctypes.byref(sliced_handle)) if status == -2: raise IndexError('Layer index out of range') _check_call(status) sliced = Booster() _check_call(_LIB.XGBoosterFree(sliced.handle)) sliced.handle = sliced_handle return sliced def save_config(self): '''Output internal parameter configuration of Booster as a JSON string. .. versionadded:: 1.0.0 ''' json_string = ctypes.c_char_p() length = c_bst_ulong() _check_call(_LIB.XGBoosterSaveJsonConfig( self.handle, ctypes.byref(length), ctypes.byref(json_string))) json_string = json_string.value.decode() return json_string def load_config(self, config): '''Load configuration returned by `save_config`. .. versionadded:: 1.0.0 ''' assert isinstance(config, str) _check_call(_LIB.XGBoosterLoadJsonConfig( self.handle, c_str(config))) def __copy__(self): return self.__deepcopy__(None) def __deepcopy__(self, _): '''Return a copy of booster.''' return Booster(model_file=self) def copy(self): """Copy the booster object. Returns ------- booster: `Booster` a copied booster model """ return self.__copy__() def attr(self, key): """Get attribute string from the Booster. Parameters ---------- key : str The key to get attribute from. Returns ------- value : str The attribute value of the key, returns None if attribute do not exist. """ ret = ctypes.c_char_p() success = ctypes.c_int() _check_call(_LIB.XGBoosterGetAttr( self.handle, c_str(key), ctypes.byref(ret), ctypes.byref(success))) if success.value != 0: return py_str(ret.value) return None def attributes(self): """Get attributes stored in the Booster as a dictionary. Returns ------- result : dictionary of attribute_name: attribute_value pairs of strings. Returns an empty dict if there's no attributes. """ length = c_bst_ulong() sarr = ctypes.POINTER(ctypes.c_char_p)() _check_call(_LIB.XGBoosterGetAttrNames(self.handle, ctypes.byref(length), ctypes.byref(sarr))) attr_names = from_cstr_to_pystr(sarr, length) return {n: self.attr(n) for n in attr_names} def set_attr(self, **kwargs): """Set the attribute of the Booster. Parameters ---------- **kwargs The attributes to set. Setting a value to None deletes an attribute. """ for key, value in kwargs.items(): if value is not None: if not isinstance(value, STRING_TYPES): raise ValueError("Set Attr only accepts string values") value = c_str(str(value)) _check_call(_LIB.XGBoosterSetAttr( self.handle, c_str(key), value)) def set_param(self, params, value=None): """Set parameters into the Booster. Parameters ---------- params: dict/list/str list of key,value pairs, dict of key to value or simply str key value: optional value of the specified parameter, when params is str key """ if isinstance(params, Mapping): params = params.items() elif isinstance(params, STRING_TYPES) and value is not None: params = [(params, value)] for key, val in params: if val is not None: _check_call(_LIB.XGBoosterSetParam(self.handle, c_str(key), c_str(str(val)))) def update(self, dtrain, iteration, fobj=None): """Update for one iteration, with objective function calculated internally. This function should not be called directly by users. Parameters ---------- dtrain : DMatrix Training data. iteration : int Current iteration number. fobj : function Customized objective function. """ if not isinstance(dtrain, DMatrix): raise TypeError('invalid training matrix: {}'.format( type(dtrain).__name__)) self._validate_features(dtrain) if fobj is None: _check_call(_LIB.XGBoosterUpdateOneIter(self.handle, ctypes.c_int(iteration), dtrain.handle)) else: pred = self.predict(dtrain, output_margin=True, training=True) grad, hess = fobj(pred, dtrain) self.boost(dtrain, grad, hess) def boost(self, dtrain, grad, hess): """Boost the booster for one iteration, with customized gradient statistics. Like :func:`xgboost.core.Booster.update`, this function should not be called directly by users. Parameters ---------- dtrain : DMatrix The training DMatrix. grad : list The first order of gradient. hess : list The second order of gradient. """ if len(grad) != len(hess): raise ValueError('grad / hess length mismatch: {} / {}'.format(len(grad), len(hess))) if not isinstance(dtrain, DMatrix): raise TypeError('invalid training matrix: {}'.format(type(dtrain).__name__)) self._validate_features(dtrain) _check_call(_LIB.XGBoosterBoostOneIter(self.handle, dtrain.handle, c_array(ctypes.c_float, grad), c_array(ctypes.c_float, hess), c_bst_ulong(len(grad)))) def eval_set(self, evals, iteration=0, feval=None): # pylint: disable=invalid-name """Evaluate a set of data. Parameters ---------- evals : list of tuples (DMatrix, string) List of items to be evaluated. iteration : int Current iteration. feval : function Custom evaluation function. Returns ------- result: str Evaluation result string. """ for d in evals: if not isinstance(d[0], DMatrix): raise TypeError('expected DMatrix, got {}'.format( type(d[0]).__name__)) if not isinstance(d[1], STRING_TYPES): raise TypeError('expected string, got {}'.format( type(d[1]).__name__)) self._validate_features(d[0]) dmats = c_array(ctypes.c_void_p, [d[0].handle for d in evals]) evnames = c_array(ctypes.c_char_p, [c_str(d[1]) for d in evals]) msg = ctypes.c_char_p() _check_call(_LIB.XGBoosterEvalOneIter(self.handle, ctypes.c_int(iteration), dmats, evnames, c_bst_ulong(len(evals)), ctypes.byref(msg))) res = msg.value.decode() if feval is not None: for dmat, evname in evals: feval_ret = feval(self.predict(dmat, training=False, output_margin=True), dmat) if isinstance(feval_ret, list): for name, val in feval_ret: res += '\t%s-%s:%f' % (evname, name, val) else: name, val = feval_ret res += '\t%s-%s:%f' % (evname, name, val) return res def eval(self, data, name='eval', iteration=0): """Evaluate the model on mat. Parameters ---------- data : DMatrix The dmatrix storing the input. name : str, optional The name of the dataset. iteration : int, optional The current iteration number. Returns ------- result: str Evaluation result string. """ self._validate_features(data) return self.eval_set([(data, name)], iteration) # pylint: disable=too-many-function-args def predict(self, data, output_margin=False, ntree_limit=0, pred_leaf=False, pred_contribs=False, approx_contribs=False, pred_interactions=False, validate_features=True, training=False): """Predict with data. .. note:: This function is not thread safe except for ``gbtree`` booster. For ``gbtree`` booster, the thread safety is guaranteed by locks. For lock free prediction use ``inplace_predict`` instead. Also, the safety does not hold when used in conjunction with other methods. When using booster other than ``gbtree``, predict can only be called from one thread. If you want to run prediction using multiple thread, call ``bst.copy()`` to make copies of model object and then call ``predict()``. Parameters ---------- data : DMatrix The dmatrix storing the input. output_margin : bool Whether to output the raw untransformed margin value. ntree_limit : int Limit number of trees in the prediction; defaults to 0 (use all trees). pred_leaf : bool When this option is on, the output will be a matrix of (nsample, ntrees) with each record indicating the predicted leaf index of each sample in each tree. Note that the leaf index of a tree is unique per tree, so you may find leaf 1 in both tree 1 and tree 0. pred_contribs : bool When this is True the output will be a matrix of size (nsample, nfeats + 1) with each record indicating the feature contributions (SHAP values) for that prediction. The sum of all feature contributions is equal to the raw untransformed margin value of the prediction. Note the final column is the bias term. approx_contribs : bool Approximate the contributions of each feature pred_interactions : bool When this is True the output will be a matrix of size (nsample, nfeats + 1, nfeats + 1) indicating the SHAP interaction values for each pair of features. The sum of each row (or column) of the interaction values equals the corresponding SHAP value (from pred_contribs), and the sum of the entire matrix equals the raw untransformed margin value of the prediction. Note the last row and column correspond to the bias term. validate_features : bool When this is True, validate that the Booster's and data's feature_names are identical. Otherwise, it is assumed that the feature_names are the same. training : bool Whether the prediction value is used for training. This can effect `dart` booster, which performs dropouts during training iterations. .. versionadded:: 1.0.0 .. note:: Using ``predict()`` with DART booster If the booster object is DART type, ``predict()`` will not perform dropouts, i.e. all the trees will be evaluated. If you want to obtain result with dropouts, provide `training=True`. Returns ------- prediction : numpy array """ option_mask = 0x00 if output_margin: option_mask |= 0x01 if pred_leaf: option_mask |= 0x02 if pred_contribs: option_mask |= 0x04 if approx_contribs: option_mask |= 0x08 if pred_interactions: option_mask |= 0x10 if not isinstance(data, DMatrix): raise TypeError('Expecting data to be a DMatrix object, got: ', type(data)) if validate_features: self._validate_features(data) length = c_bst_ulong() preds = ctypes.POINTER(ctypes.c_float)() _check_call(_LIB.XGBoosterPredict(self.handle, data.handle, ctypes.c_int(option_mask), ctypes.c_uint(ntree_limit), ctypes.c_int(training), ctypes.byref(length), ctypes.byref(preds))) preds = ctypes2numpy(preds, length.value, np.float32) if pred_leaf: preds = preds.astype(np.int32) nrow = data.num_row() if preds.size != nrow and preds.size % nrow == 0: chunk_size = int(preds.size / nrow) if pred_interactions: ngroup = int(chunk_size / ((data.num_col() + 1) * (data.num_col() + 1))) if ngroup == 1: preds = preds.reshape(nrow, data.num_col() + 1, data.num_col() + 1) else: preds = preds.reshape(nrow, ngroup, data.num_col() + 1, data.num_col() + 1) elif pred_contribs: ngroup = int(chunk_size / (data.num_col() + 1)) if ngroup == 1: preds = preds.reshape(nrow, data.num_col() + 1) else: preds = preds.reshape(nrow, ngroup, data.num_col() + 1) else: preds = preds.reshape(nrow, chunk_size) return preds def inplace_predict(self, data, iteration_range=(0, 0), predict_type='value', missing=np.nan): '''Run prediction in-place, Unlike ``predict`` method, inplace prediction does not cache the prediction result. Calling only ``inplace_predict`` in multiple threads is safe and lock free. But the safety does not hold when used in conjunction with other methods. E.g. you can't train the booster in one thread and perform prediction in the other. .. code-block:: python booster.set_param({'predictor': 'gpu_predictor'}) booster.inplace_predict(cupy_array) booster.set_param({'predictor': 'cpu_predictor}) booster.inplace_predict(numpy_array) .. versionadded:: 1.1.0 Parameters ---------- data : numpy.ndarray/scipy.sparse.csr_matrix/cupy.ndarray/ cudf.DataFrame/pd.DataFrame The input data, must not be a view for numpy array. Set ``predictor`` to ``gpu_predictor`` for running prediction on CuPy array or CuDF DataFrame. iteration_range : tuple Specifies which layer of trees are used in prediction. For example, if a random forest is trained with 100 rounds. Specifying `iteration_range=(10, 20)`, then only the forests built during [10, 20) (open set) rounds are used in this prediction. predict_type : str * `value` Output model prediction values. * `margin` Output the raw untransformed margin value. missing : float Value in the input data which needs to be present as a missing value. Returns ------- prediction : numpy.ndarray/cupy.ndarray The prediction result. When input data is on GPU, prediction result is stored in a cupy array. ''' def reshape_output(predt, rows): '''Reshape for multi-output prediction.''' if predt.size != rows and predt.size % rows == 0: cols = int(predt.size / rows) predt = predt.reshape(rows, cols) return predt return predt length = c_bst_ulong() preds = ctypes.POINTER(ctypes.c_float)() iteration_range = (ctypes.c_uint(iteration_range[0]), ctypes.c_uint(iteration_range[1])) # once caching is supported, we can pass id(data) as cache id. try: import pandas as pd if isinstance(data, pd.DataFrame): data = data.values except ImportError: pass if isinstance(data, np.ndarray): assert data.flags.c_contiguous arr = np.array(data.reshape(data.size), copy=False, dtype=np.float32) _check_call(_LIB.XGBoosterPredictFromDense( self.handle, arr.ctypes.data_as(ctypes.POINTER(ctypes.c_float)), c_bst_ulong(data.shape[0]), c_bst_ulong(data.shape[1]), ctypes.c_float(missing), iteration_range[0], iteration_range[1], c_str(predict_type), c_bst_ulong(0), ctypes.byref(length), ctypes.byref(preds) )) preds = ctypes2numpy(preds, length.value, np.float32) rows = data.shape[0] return reshape_output(preds, rows) if isinstance(data, scipy.sparse.csr_matrix): csr = data _check_call(_LIB.XGBoosterPredictFromCSR( self.handle, c_array(ctypes.c_size_t, csr.indptr), c_array(ctypes.c_uint, csr.indices), c_array(ctypes.c_float, csr.data), ctypes.c_size_t(len(csr.indptr)), ctypes.c_size_t(len(csr.data)), ctypes.c_size_t(csr.shape[1]), ctypes.c_float(missing), iteration_range[0], iteration_range[1], c_str(predict_type), c_bst_ulong(0), ctypes.byref(length), ctypes.byref(preds))) preds = ctypes2numpy(preds, length.value, np.float32) rows = data.shape[0] return reshape_output(preds, rows) if lazy_isinstance(data, 'cupy.core.core', 'ndarray'): assert data.flags.c_contiguous interface = data.__cuda_array_interface__ if 'mask' in interface: interface['mask'] = interface['mask'].__cuda_array_interface__ interface_str = bytes(json.dumps(interface, indent=2), 'utf-8') _check_call(_LIB.XGBoosterPredictFromArrayInterface( self.handle, interface_str, ctypes.c_float(missing), iteration_range[0], iteration_range[1], c_str(predict_type), c_bst_ulong(0), ctypes.byref(length), ctypes.byref(preds))) mem = ctypes2cupy(preds, length, np.float32) rows = data.shape[0] return reshape_output(mem, rows) if lazy_isinstance(data, 'cudf.core.dataframe', 'DataFrame'): from .data import _cudf_array_interfaces interfaces_str = _cudf_array_interfaces(data) _check_call(_LIB.XGBoosterPredictFromArrayInterfaceColumns( self.handle, interfaces_str, ctypes.c_float(missing), iteration_range[0], iteration_range[1], c_str(predict_type), c_bst_ulong(0), ctypes.byref(length), ctypes.byref(preds))) mem = ctypes2cupy(preds, length, np.float32) rows = data.shape[0] predt = reshape_output(mem, rows) return predt raise TypeError('Data type:' + str(type(data)) + ' not supported by inplace prediction.') def save_model(self, fname): """Save the model to a file. The model is saved in an XGBoost internal format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be saved. See: https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html for more info. Parameters ---------- fname : string or os.PathLike Output file name """ if isinstance(fname, (STRING_TYPES, os.PathLike)): # assume file name fname = os.fspath(os.path.expanduser(fname)) _check_call(_LIB.XGBoosterSaveModel( self.handle, c_str(fname))) else: raise TypeError("fname must be a string or os PathLike") def save_raw(self): """Save the model to a in memory buffer representation instead of file. Returns ------- a in memory buffer representation of the model """ length = c_bst_ulong() cptr = ctypes.POINTER(ctypes.c_char)() _check_call(_LIB.XGBoosterGetModelRaw(self.handle, ctypes.byref(length), ctypes.byref(cptr))) return ctypes2buffer(cptr, length.value) def load_model(self, fname): """Load the model from a file or bytearray. Path to file can be local or as an URI. The model is loaded from XGBoost format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be loaded. See: https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html for more info. Parameters ---------- fname : string, os.PathLike, or a memory buffer Input file name or memory buffer(see also save_raw) """ if isinstance(fname, (STRING_TYPES, os.PathLike)): # assume file name, cannot use os.path.exist to check, file can be # from URL. fname = os.fspath(os.path.expanduser(fname)) _check_call(_LIB.XGBoosterLoadModel( self.handle, c_str(fname))) elif isinstance(fname, bytearray): buf = fname length = c_bst_ulong(len(buf)) ptr = (ctypes.c_char * len(buf)).from_buffer(buf) _check_call(_LIB.XGBoosterLoadModelFromBuffer(self.handle, ptr, length)) else: raise TypeError('Unknown file type: ', fname) if self.attr("best_iteration") is not None: self.best_iteration = int(self.attr("best_iteration")) if self.attr("best_score") is not None: self.best_score = float(self.attr("best_score")) if self.attr("best_ntree_limit") is not None: self.best_ntree_limit = int(self.attr("best_ntree_limit")) def num_boosted_rounds(self) -> int: '''Get number of boosted rounds. For gblinear this is reset to 0 after serializing the model. ''' rounds = ctypes.c_int() assert self.handle is not None _check_call(_LIB.XGBoosterBoostedRounds(self.handle, ctypes.byref(rounds))) return rounds.value def num_features(self) -> int: '''Number of features in booster.''' features = ctypes.c_int() assert self.handle is not None _check_call(_LIB.XGBoosterGetNumFeature(self.handle, ctypes.byref(features))) return features.value def dump_model(self, fout, fmap='', with_stats=False, dump_format="text"): """Dump model into a text or JSON file. Unlike `save_model`, the output format is primarily used for visualization or interpretation, hence it's more human readable but cannot be loaded back to XGBoost. Parameters ---------- fout : string or os.PathLike Output file name. fmap : string or os.PathLike, optional Name of the file containing feature map names. with_stats : bool, optional Controls whether the split statistics are output. dump_format : string, optional Format of model dump file. Can be 'text' or 'json'. """ if isinstance(fout, (STRING_TYPES, os.PathLike)): fout = os.fspath(os.path.expanduser(fout)) fout = open(fout, 'w') need_close = True else: need_close = False ret = self.get_dump(fmap, with_stats, dump_format) if dump_format == 'json': fout.write('[\n') for i, _ in enumerate(ret): fout.write(ret[i]) if i < len(ret) - 1: fout.write(",\n") fout.write('\n]') else: for i, _ in enumerate(ret): fout.write('booster[{}]:\n'.format(i)) fout.write(ret[i]) if need_close: fout.close() def get_dump(self, fmap='', with_stats=False, dump_format="text"): """Returns the model dump as a list of strings. Unlike `save_model`, the output format is primarily used for visualization or interpretation, hence it's more human readable but cannot be loaded back to XGBoost. Parameters ---------- fmap : string or os.PathLike, optional Name of the file containing feature map names. with_stats : bool, optional Controls whether the split statistics are output. dump_format : string, optional Format of model dump. Can be 'text', 'json' or 'dot'. """ fmap = os.fspath(os.path.expanduser(fmap)) length = c_bst_ulong() sarr = ctypes.POINTER(ctypes.c_char_p)() if self.feature_names is not None and fmap == '': flen = len(self.feature_names) fname = from_pystr_to_cstr(self.feature_names) if self.feature_types is None: # use quantitative as default # {'q': quantitative, 'i': indicator} ftype = from_pystr_to_cstr(['q'] * flen) else: ftype = from_pystr_to_cstr(self.feature_types) _check_call(_LIB.XGBoosterDumpModelExWithFeatures( self.handle, ctypes.c_int(flen), fname, ftype, ctypes.c_int(with_stats), c_str(dump_format), ctypes.byref(length), ctypes.byref(sarr))) else: if fmap != '' and not os.path.exists(fmap): raise ValueError("No such file: {0}".format(fmap)) _check_call(_LIB.XGBoosterDumpModelEx(self.handle, c_str(fmap), ctypes.c_int(with_stats), c_str(dump_format), ctypes.byref(length), ctypes.byref(sarr))) res = from_cstr_to_pystr(sarr, length) return res def get_fscore(self, fmap=''): """Get feature importance of each feature. .. note:: Feature importance is defined only for tree boosters Feature importance is only defined when the decision tree model is chosen as base learner (`booster=gbtree`). It is not defined for other base learner types, such as linear learners (`booster=gblinear`). .. note:: Zero-importance features will not be included Keep in mind that this function does not include zero-importance feature, i.e. those features that have not been used in any split conditions. Parameters ---------- fmap: str or os.PathLike (optional) The name of feature map file """ return self.get_score(fmap, importance_type='weight') def get_score(self, fmap='', importance_type='weight'): """Get feature importance of each feature. Importance type can be defined as: * 'weight': the number of times a feature is used to split the data across all trees. * 'gain': the average gain across all splits the feature is used in. * 'cover': the average coverage across all splits the feature is used in. * 'total_gain': the total gain across all splits the feature is used in. * 'total_cover': the total coverage across all splits the feature is used in. .. note:: Feature importance is defined only for tree boosters Feature importance is only defined when the decision tree model is chosen as base learner (`booster=gbtree`). It is not defined for other base learner types, such as linear learners (`booster=gblinear`). Parameters ---------- fmap: str or os.PathLike (optional) The name of feature map file. importance_type: str, default 'weight' One of the importance types defined above. """ fmap = os.fspath(os.path.expanduser(fmap)) if getattr(self, 'booster', None) is not None and self.booster not in {'gbtree', 'dart'}: raise ValueError('Feature importance is not defined for Booster type {}' .format(self.booster)) allowed_importance_types = ['weight', 'gain', 'cover', 'total_gain', 'total_cover'] if importance_type not in allowed_importance_types: msg = ("importance_type mismatch, got '{}', expected one of " + repr(allowed_importance_types)) raise ValueError(msg.format(importance_type)) # if it's weight, then omap stores the number of missing values if importance_type == 'weight': # do a simpler tree dump to save time trees = self.get_dump(fmap, with_stats=False) fmap = {} for tree in trees: for line in tree.split('\n'): # look for the opening square bracket arr = line.split('[') # if no opening bracket (leaf node), ignore this line if len(arr) == 1: continue # extract feature name from string between [] fid = arr[1].split(']')[0].split('<')[0] if fid not in fmap: # if the feature hasn't been seen yet fmap[fid] = 1 else: fmap[fid] += 1 return fmap average_over_splits = True if importance_type == 'total_gain': importance_type = 'gain' average_over_splits = False elif importance_type == 'total_cover': importance_type = 'cover' average_over_splits = False trees = self.get_dump(fmap, with_stats=True) importance_type += '=' fmap = {} gmap = {} for tree in trees: for line in tree.split('\n'): # look for the opening square bracket arr = line.split('[') # if no opening bracket (leaf node), ignore this line if len(arr) == 1: continue # look for the closing bracket, extract only info within that bracket fid = arr[1].split(']') # extract gain or cover from string after closing bracket g = float(fid[1].split(importance_type)[1].split(',')[0]) # extract feature name from string before closing bracket fid = fid[0].split('<')[0] if fid not in fmap: # if the feature hasn't been seen yet fmap[fid] = 1 gmap[fid] = g else: fmap[fid] += 1 gmap[fid] += g # calculate average value (gain/cover) for each feature if average_over_splits: for fid in gmap: gmap[fid] = gmap[fid] / fmap[fid] return gmap def trees_to_dataframe(self, fmap=''): """Parse a boosted tree model text dump into a pandas DataFrame structure. This feature is only defined when the decision tree model is chosen as base learner (`booster in {gbtree, dart}`). It is not defined for other base learner types, such as linear learners (`booster=gblinear`). Parameters ---------- fmap: str or os.PathLike (optional) The name of feature map file. """ # pylint: disable=too-many-locals fmap = os.fspath(os.path.expanduser(fmap)) if not PANDAS_INSTALLED: raise Exception(('pandas must be available to use this method.' 'Install pandas before calling again.')) if getattr(self, 'booster', None) is not None and self.booster not in {'gbtree', 'dart'}: raise ValueError('This method is not defined for Booster type {}' .format(self.booster)) tree_ids = [] node_ids = [] fids = [] splits = [] y_directs = [] n_directs = [] missings = [] gains = [] covers = [] trees = self.get_dump(fmap, with_stats=True) for i, tree in enumerate(trees): for line in tree.split('\n'): arr = line.split('[') # Leaf node if len(arr) == 1: # Last element of line.split is an empy string if arr == ['']: continue # parse string parse = arr[0].split(':') stats = re.split('=|,', parse[1]) # append to lists tree_ids.append(i) node_ids.append(int(re.findall(r'\b\d+\b', parse[0])[0])) fids.append('Leaf') splits.append(float('NAN')) y_directs.append(float('NAN')) n_directs.append(float('NAN')) missings.append(float('NAN')) gains.append(float(stats[1])) covers.append(float(stats[3])) # Not a Leaf Node else: # parse string fid = arr[1].split(']') parse = fid[0].split('<') stats = re.split('=|,', fid[1]) # append to lists tree_ids.append(i) node_ids.append(int(re.findall(r'\b\d+\b', arr[0])[0])) fids.append(parse[0]) splits.append(float(parse[1])) str_i = str(i) y_directs.append(str_i + '-' + stats[1]) n_directs.append(str_i + '-' + stats[3]) missings.append(str_i + '-' + stats[5]) gains.append(float(stats[7])) covers.append(float(stats[9])) ids = [str(t_id) + '-' + str(n_id) for t_id, n_id in zip(tree_ids, node_ids)] df = DataFrame({'Tree': tree_ids, 'Node': node_ids, 'ID': ids, 'Feature': fids, 'Split': splits, 'Yes': y_directs, 'No': n_directs, 'Missing': missings, 'Gain': gains, 'Cover': covers}) if callable(getattr(df, 'sort_values', None)): # pylint: disable=no-member return df.sort_values(['Tree', 'Node']).reset_index(drop=True) # pylint: disable=no-member return df.sort(['Tree', 'Node']).reset_index(drop=True) def _validate_features(self, data): """ Validate Booster and data's feature_names are identical. Set feature_names and feature_types from DMatrix """ if data.num_row() == 0: return if self.feature_names is None: self.feature_names = data.feature_names self.feature_types = data.feature_types else: # Booster can't accept data with different feature names if self.feature_names != data.feature_names: dat_missing = set(self.feature_names) - set(data.feature_names) my_missing = set(data.feature_names) - set(self.feature_names) msg = 'feature_names mismatch: {0} {1}' if dat_missing: msg += ('\nexpected ' + ', '.join( str(s) for s in dat_missing) + ' in input data') if my_missing: msg += ('\ntraining data did not have the following fields: ' + ', '.join(str(s) for s in my_missing)) raise ValueError(msg.format(self.feature_names, data.feature_names)) def get_split_value_histogram(self, feature, fmap='', bins=None, as_pandas=True): """Get split value histogram of a feature Parameters ---------- feature: str The name of the feature. fmap: str or os.PathLike (optional) The name of feature map file. bin: int, default None The maximum number of bins. Number of bins equals number of unique split values n_unique, if bins == None or bins > n_unique. as_pandas: bool, default True Return pd.DataFrame when pandas is installed. If False or pandas is not installed, return numpy ndarray. Returns ------- a histogram of used splitting values for the specified feature either as numpy array or pandas DataFrame. """ xgdump = self.get_dump(fmap=fmap) values = [] regexp = re.compile(r"\[{0}<([\d.Ee+-]+)\]".format(feature)) for i, _ in enumerate(xgdump): m = re.findall(regexp, xgdump[i]) values.extend([float(x) for x in m]) n_unique = len(np.unique(values)) bins = max(min(n_unique, bins) if bins is not None else n_unique, 1) nph = np.histogram(values, bins=bins) nph = np.column_stack((nph[1][1:], nph[0])) nph = nph[nph[:, 1] > 0] if as_pandas and PANDAS_INSTALLED: return DataFrame(nph, columns=['SplitValue', 'Count']) if as_pandas and not PANDAS_INSTALLED: sys.stderr.write( "Returning histogram as ndarray (as_pandas == True, but pandas is not installed).") return nph