774 lines
24 KiB
Python
774 lines
24 KiB
Python
"""
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xgboost: eXtreme Gradient Boosting library
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Authors: Tianqi Chen, Bing Xu
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"""
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from __future__ import absolute_import
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import os
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import sys
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import ctypes
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import collections
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import numpy as np
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import scipy.sparse
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try:
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from sklearn.base import BaseEstimator
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from sklearn.base import RegressorMixin, ClassifierMixin
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from sklearn.preprocessing import LabelEncoder
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SKLEARN_INSTALLED = True
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except ImportError:
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SKLEARN_INSTALLED = False
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__all__ = ['DMatrix', 'CVPack', 'Booster', 'aggcv', 'cv', 'mknfold', 'train']
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if sys.version_info[0] == 3:
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string_types = str,
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else:
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string_types = basestring,
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def load_xglib():
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dll_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
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if os.name == 'nt':
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dll_path = os.path.join(dll_path, '../windows/x64/Release/xgboost_wrapper.dll')
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else:
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dll_path = os.path.join(dll_path, 'libxgboostwrapper.so')
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# load the xgboost wrapper library
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lib = ctypes.cdll.LoadLibrary(dll_path)
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# DMatrix functions
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lib.XGDMatrixCreateFromFile.restype = ctypes.c_void_p
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lib.XGDMatrixCreateFromCSR.restype = ctypes.c_void_p
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lib.XGDMatrixCreateFromCSC.restype = ctypes.c_void_p
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lib.XGDMatrixCreateFromMat.restype = ctypes.c_void_p
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lib.XGDMatrixSliceDMatrix.restype = ctypes.c_void_p
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lib.XGDMatrixGetFloatInfo.restype = ctypes.POINTER(ctypes.c_float)
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lib.XGDMatrixGetUIntInfo.restype = ctypes.POINTER(ctypes.c_uint)
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lib.XGDMatrixNumRow.restype = ctypes.c_ulong
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# Booster functions
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lib.XGBoosterCreate.restype = ctypes.c_void_p
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lib.XGBoosterPredict.restype = ctypes.POINTER(ctypes.c_float)
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lib.XGBoosterEvalOneIter.restype = ctypes.c_char_p
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lib.XGBoosterDumpModel.restype = ctypes.POINTER(ctypes.c_char_p)
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return lib
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# load the XGBoost library globally
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xglib = load_xglib()
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def ctypes2numpy(cptr, length, dtype):
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"""
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Convert a ctypes pointer array to a numpy array.
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"""
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if not isinstance(cptr, ctypes.POINTER(ctypes.c_float)):
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raise RuntimeError('expected float pointer')
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res = np.zeros(length, dtype=dtype)
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if not ctypes.memmove(res.ctypes.data, cptr, length * res.strides[0]):
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raise RuntimeError('memmove failed')
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return res
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def c_str(string):
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return ctypes.c_char_p(string.encode('utf-8'))
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def c_array(ctype, values):
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return (ctype * len(values))(*values)
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class DMatrix(object):
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def __init__(self, data, label=None, missing=0.0, weight=None):
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"""
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Data matrix used in XGBoost.
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Parameters
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----------
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data : string/numpy array/scipy.sparse
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Data source, string type is the path of svmlight format txt file or xgb buffer.
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label : list or numpy 1-D array (optional)
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Label of the training data.
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missing : float
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Value in the data which needs to be present as a missing value.
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weight : list or numpy 1-D array (optional)
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Weight for each instance.
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"""
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# force into void_p, mac need to pass things in as void_p
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if data is None:
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self.handle = None
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return
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if isinstance(data, string_types):
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self.handle = ctypes.c_void_p(xglib.XGDMatrixCreateFromFile(c_str(data), 0))
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elif isinstance(data, scipy.sparse.csr_matrix):
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self._init_from_csr(data)
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elif isinstance(data, scipy.sparse.csc_matrix):
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self._init_from_csc(data)
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elif isinstance(data, np.ndarray) and len(data.shape) == 2:
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self._init_from_npy2d(data, missing)
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else:
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try:
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csr = scipy.sparse.csr_matrix(data)
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self._init_from_csr(csr)
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except:
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raise TypeError('can not intialize DMatrix from {}'.format(type(data).__name__))
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if label is not None:
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self.set_label(label)
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if weight is not None:
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self.set_weight(weight)
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def _init_from_csr(self, csr):
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"""
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Initialize data from a CSR matrix.
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"""
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if len(csr.indices) != len(csr.data):
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raise ValueError('length mismatch: {} vs {}'.format(len(csr.indices), len(csr.data)))
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self.handle = ctypes.c_void_p(xglib.XGDMatrixCreateFromCSR(
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c_array(ctypes.c_ulong, csr.indptr),
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c_array(ctypes.c_uint, csr.indices),
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c_array(ctypes.c_float, csr.data),
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len(csr.indptr), len(csr.data)))
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def _init_from_csc(self, csc):
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"""
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Initialize data from a CSC matrix.
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"""
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if len(csc.indices) != len(csc.data):
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raise ValueError('length mismatch: {} vs {}'.format(len(csc.indices), len(csc.data)))
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self.handle = ctypes.c_void_p(xglib.XGDMatrixCreateFromCSC(
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c_array(ctypes.c_ulong, csc.indptr),
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c_array(ctypes.c_uint, csc.indices),
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c_array(ctypes.c_float, csc.data),
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len(csc.indptr), len(csc.data)))
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def _init_from_npy2d(self, mat, missing):
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"""
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Initialize data from a 2-D numpy matrix.
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"""
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data = np.array(mat.reshape(mat.size), dtype=np.float32)
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self.handle = ctypes.c_void_p(xglib.XGDMatrixCreateFromMat(
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data.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
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mat.shape[0], mat.shape[1], ctypes.c_float(missing)))
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def __del__(self):
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xglib.XGDMatrixFree(self.handle)
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def get_float_info(self, field):
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length = ctypes.c_ulong()
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ret = xglib.XGDMatrixGetFloatInfo(self.handle, c_str(field), ctypes.byref(length))
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return ctypes2numpy(ret, length.value, np.float32)
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def get_uint_info(self, field):
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length = ctypes.c_ulong()
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ret = xglib.XGDMatrixGetUIntInfo(self.handle, c_str(field), ctypes.byref(length))
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return ctypes2numpy(ret, length.value, np.uint32)
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def set_float_info(self, field, data):
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xglib.XGDMatrixSetFloatInfo(self.handle, c_str(field),
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c_array(ctypes.c_float, data), len(data))
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def set_uint_info(self, field, data):
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xglib.XGDMatrixSetUIntInfo(self.handle, c_str(field),
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c_array(ctypes.c_uint, data), len(data))
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def save_binary(self, fname, silent=True):
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"""
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Save DMatrix to an XGBoost buffer.
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Parameters
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----------
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fname : string
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Name of the output buffer file.
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silent : bool (optional; default: True)
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If set, the output is suppressed.
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"""
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xglib.XGDMatrixSaveBinary(self.handle, c_str(fname), int(silent))
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def set_label(self, label):
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"""set label of dmatrix
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Args:
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label: list
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label for DMatrix
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Returns:
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None
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"""
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self.set_float_info('label', label)
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def set_weight(self, weight):
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"""
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Set weight of each instance.
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Parameters
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----------
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weight : float
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Weight for positive instance.
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"""
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self.set_float_info('weight', weight)
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def set_base_margin(self, margin):
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"""
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set base margin of booster to start from
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this can be used to specify a prediction value of
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existing model to be base_margin
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However, remember margin is needed, instead of transformed prediction
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e.g. for logistic regression: need to put in value before logistic transformation
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see also example/demo.py
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"""
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self.set_float_info('base_margin', margin)
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def set_group(self, group):
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"""
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Set group size of DMatrix (used for ranking).
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Parameters
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----------
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group : int
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Group size.
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"""
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xglib.XGDMatrixSetGroup(self.handle, c_array(ctypes.c_uint, group), len(group))
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def get_label(self):
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"""
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Get the label of the DMatrix.
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Returns
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-------
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label : list
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"""
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return self.get_float_info('label')
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def get_weight(self):
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"""
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Get the weight of the DMatrix.
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Returns
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-------
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weight : float
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"""
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return self.get_float_info('weight')
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def get_base_margin(self):
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"""
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Get the base margin of the DMatrix.
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Returns
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-------
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base_margin : float
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"""
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return self.get_float_info('base_margin')
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def num_row(self):
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"""
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Get the number of rows in the DMatrix.
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Returns
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-------
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number of rows : int
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"""
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return xglib.XGDMatrixNumRow(self.handle)
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def slice(self, rindex):
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"""
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Slice the DMatrix and return a new DMatrix that only contains `rindex`.
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Parameters
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----------
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rindex : list
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List of indices to be selected.
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Returns
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-------
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res : DMatrix
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A new DMatrix containing only selected indices.
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"""
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res = DMatrix(None)
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res.handle = ctypes.c_void_p(xglib.XGDMatrixSliceDMatrix(
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self.handle, c_array(ctypes.c_int, rindex), len(rindex)))
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return res
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class Booster(object):
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def __init__(self, params=None, cache=(), model_file=None):
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"""
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Learner class.
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Parameters
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----------
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params : dict
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Parameters for boosters.
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cache : list
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List of cache items.
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model_file : string
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Path to the model file.
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"""
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for d in cache:
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if not isinstance(d, DMatrix):
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raise TypeError('invalid cache item: {}'.format(type(d).__name__))
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dmats = c_array(ctypes.c_void_p, [d.handle for d in cache])
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self.handle = ctypes.c_void_p(xglib.XGBoosterCreate(dmats, len(cache)))
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self.set_param({'seed': 0})
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self.set_param(params or {})
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if model_file is not None:
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self.load_model(model_file)
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def __del__(self):
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xglib.XGBoosterFree(self.handle)
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def set_param(self, params, pv=None):
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if isinstance(params, collections.Mapping):
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params = params.items()
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elif isinstance(params, string_types) and pv is not None:
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params = [(params, pv)]
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for k, v in params:
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xglib.XGBoosterSetParam(self.handle, c_str(k), c_str(str(v)))
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def update(self, dtrain, it, fobj=None):
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"""
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Update (one iteration).
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Parameters
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----------
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dtrain : DMatrix
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Training data.
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it : int
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Current iteration number.
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fobj : function
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Customized objective function.
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"""
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if not isinstance(dtrain, DMatrix):
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raise TypeError('invalid training matrix: {}'.format(type(dtrain).__name__))
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if fobj is None:
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xglib.XGBoosterUpdateOneIter(self.handle, it, dtrain.handle)
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else:
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pred = self.predict(dtrain)
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grad, hess = fobj(pred, dtrain)
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self.boost(dtrain, grad, hess)
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def boost(self, dtrain, grad, hess):
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"""
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Update.
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Parameters
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----------
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dtrain : DMatrix
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The training DMatrix.
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grad : list
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The first order of gradient.
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hess : list
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The second order of gradient.
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"""
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if len(grad) != len(hess):
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raise ValueError('grad / hess length mismatch: {} / {}'.format(len(grad), len(hess)))
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if not isinstance(dtrain, DMatrix):
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raise TypeError('invalid training matrix: {}'.format(type(dtrain).__name__))
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xglib.XGBoosterBoostOneIter(self.handle, dtrain.handle,
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c_array(ctypes.c_float, grad),
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c_array(ctypes.c_float, hess),
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len(grad))
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def eval_set(self, evals, it=0, feval=None):
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"""
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Evaluate by a metric.
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Parameters
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----------
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evals : list of tuples (DMatrix, string)
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List of items to be evaluated.
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it : int
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Current iteration.
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feval : function
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Custom evaluation function.
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Returns
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-------
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evaluation result
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"""
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if feval is None:
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for d in evals:
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if not isinstance(d[0], DMatrix):
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raise TypeError('expected DMatrix, got {}'.format(type(d[0]).__name__))
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if not isinstance(d[1], string_types):
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raise TypeError('expected string, got {}'.format(type(d[1]).__name__))
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dmats = c_array(ctypes.c_void_p, [d[0].handle for d in evals])
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evnames = c_array(ctypes.c_char_p, [c_str(d[1]) for d in evals])
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return xglib.XGBoosterEvalOneIter(self.handle, it, dmats, evnames, len(evals))
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else:
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res = '[%d]' % it
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for dm, evname in evals:
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name, val = feval(self.predict(dm), dm)
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res += '\t%s-%s:%f' % (evname, name, val)
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return res
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def eval(self, mat, name='eval', it=0):
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return self.eval_set([(mat, name)], it)
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def predict(self, data, output_margin=False, ntree_limit=0, pred_leaf=False):
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"""
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Predict with data.
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Parameters
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----------
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data : DMatrix
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The dmatrix storing the input.
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output_margin : bool
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Whether to output the raw untransformed margin value.
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ntree_limit : int
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Limit number of trees in the prediction; defaults to 0 (use all trees).
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pred_leaf : bool
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When this option is on, the output will be a matrix of (nsample, ntrees)
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with each record indicating the predicted leaf index of each sample in each tree.
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Note that the leaf index of a tree is unique per tree, so you may find leaf 1
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in both tree 1 and tree 0.
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Returns
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-------
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prediction : numpy array
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"""
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option_mask = 0x00
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if output_margin:
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option_mask |= 0x01
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if pred_leaf:
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option_mask |= 0x02
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length = ctypes.c_ulong()
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preds = xglib.XGBoosterPredict(self.handle, data.handle,
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option_mask, ntree_limit, ctypes.byref(length))
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preds = ctypes2numpy(preds, length.value, np.float32)
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if pred_leaf:
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preds = preds.astype(np.int32)
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nrow = data.num_row()
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if preds.size != nrow and preds.size % nrow == 0:
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preds = preds.reshape(nrow, preds.size / nrow)
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return preds
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def save_model(self, fname):
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"""
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Save the model to a file.
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Parameters
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----------
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fname : string
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Output file name.
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"""
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xglib.XGBoosterSaveModel(self.handle, c_str(fname))
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def load_model(self, fname):
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"""
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Load the model from a file.
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Parameters
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----------
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fname : string
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Input file name.
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"""
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xglib.XGBoosterLoadModel(self.handle, c_str(fname))
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def dump_model(self, fo, fmap='', with_stats=False):
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"""
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Dump model into a text file.
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Parameters
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----------
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fo : string
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Output file name.
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fmap : string, optional
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Name of the file containing feature map names.
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with_stats : bool (optional)
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Controls whether the split statistics are output.
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"""
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if isinstance(fo, string_types):
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fo = open(fo, 'w')
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need_close = True
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else:
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need_close = False
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ret = self.get_dump(fmap, with_stats)
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for i in range(len(ret)):
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fo.write('booster[{}]:\n'.format(i))
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fo.write(ret[i])
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if need_close:
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fo.close()
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def get_dump(self, fmap='', with_stats=False):
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"""
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Returns the dump the model as a list of strings.
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"""
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length = ctypes.c_ulong()
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sarr = xglib.XGBoosterDumpModel(self.handle, c_str(fmap),
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int(with_stats), ctypes.byref(length))
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res = []
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for i in range(length.value):
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res.append(str(sarr[i]))
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return res
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def get_fscore(self, fmap=''):
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"""
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Get feature importance of each feature.
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"""
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trees = self.get_dump(fmap)
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fmap = {}
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for tree in trees:
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sys.stdout.write(str(tree) + '\n')
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for l in tree.split('\n'):
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arr = l.split('[')
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if len(arr) == 1:
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continue
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fid = arr[1].split(']')[0]
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fid = fid.split('<')[0]
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if fid not in fmap:
|
|
fmap[fid] = 1
|
|
else:
|
|
fmap[fid] += 1
|
|
return fmap
|
|
|
|
|
|
def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None):
|
|
"""
|
|
Train a booster with given parameters.
|
|
|
|
Parameters
|
|
----------
|
|
params : dict
|
|
Booster params.
|
|
dtrain : DMatrix
|
|
Data to be trained.
|
|
num_boost_round: int
|
|
Number of boosting iterations.
|
|
watchlist : list of pairs (DMatrix, string)
|
|
List of items to be evaluated during training, this allows user to watch
|
|
performance on the validation set.
|
|
obj : function
|
|
Customized objective function.
|
|
feval : function
|
|
Customized evaluation function.
|
|
|
|
Returns
|
|
-------
|
|
booster : a trained booster model
|
|
"""
|
|
evals = list(evals)
|
|
bst = Booster(params, [dtrain] + [d[0] for d in evals])
|
|
for i in range(num_boost_round):
|
|
bst.update(dtrain, i, obj)
|
|
if len(evals) != 0:
|
|
bst_eval_set = bst.eval_set(evals, i, feval)
|
|
if isinstance(bst_eval_set, string_types):
|
|
sys.stderr.write(bst_eval_set + '\n')
|
|
else:
|
|
sys.stderr.write(bst_eval_set.decode() + '\n')
|
|
return bst
|
|
|
|
|
|
class CVPack(object):
|
|
def __init__(self, dtrain, dtest, param):
|
|
self.dtrain = dtrain
|
|
self.dtest = dtest
|
|
self.watchlist = [(dtrain, 'train'), (dtest, 'test')]
|
|
self.bst = Booster(param, [dtrain, dtest])
|
|
|
|
def update(self, r, fobj):
|
|
self.bst.update(self.dtrain, r, fobj)
|
|
|
|
def eval(self, r, feval):
|
|
return self.bst.eval_set(self.watchlist, r, feval)
|
|
|
|
|
|
def mknfold(dall, nfold, param, seed, evals=(), fpreproc=None):
|
|
"""
|
|
Make an n-fold list of CVPack from random indices.
|
|
"""
|
|
evals = list(evals)
|
|
np.random.seed(seed)
|
|
randidx = np.random.permutation(dall.num_row())
|
|
kstep = len(randidx) / nfold
|
|
idset = [randidx[(i * kstep): min(len(randidx), (i + 1) * kstep)] for i in range(nfold)]
|
|
ret = []
|
|
for k in range(nfold):
|
|
dtrain = dall.slice(np.concatenate([idset[i] for i in range(nfold) if k != i]))
|
|
dtest = dall.slice(idset[k])
|
|
# run preprocessing on the data set if needed
|
|
if fpreproc is not None:
|
|
dtrain, dtest, tparam = fpreproc(dtrain, dtest, param.copy())
|
|
else:
|
|
tparam = param
|
|
plst = list(tparam.items()) + [('eval_metric', itm) for itm in evals]
|
|
ret.append(CVPack(dtrain, dtest, plst))
|
|
return ret
|
|
|
|
|
|
def aggcv(rlist, show_stdv=True):
|
|
"""
|
|
Aggregate cross-validation results.
|
|
"""
|
|
cvmap = {}
|
|
ret = rlist[0].split()[0]
|
|
for line in rlist:
|
|
arr = line.split()
|
|
assert ret == arr[0]
|
|
for it in arr[1:]:
|
|
if not isinstance(it, string_types):
|
|
it = it.decode()
|
|
k, v = it.split(':')
|
|
if k not in cvmap:
|
|
cvmap[k] = []
|
|
cvmap[k].append(float(v))
|
|
for k, v in sorted(cvmap.items(), key=lambda x: x[0]):
|
|
v = np.array(v)
|
|
if not isinstance(ret, string_types):
|
|
ret = ret.decode()
|
|
if show_stdv:
|
|
ret += '\tcv-%s:%f+%f' % (k, np.mean(v), np.std(v))
|
|
else:
|
|
ret += '\tcv-%s:%f' % (k, np.mean(v))
|
|
return ret
|
|
|
|
|
|
def cv(params, dtrain, num_boost_round=10, nfold=3, metrics=(),
|
|
obj=None, feval=None, fpreproc=None, show_stdv=True, seed=0):
|
|
"""
|
|
Cross-validation with given paramaters.
|
|
|
|
Parameters
|
|
----------
|
|
params : dict
|
|
Booster params.
|
|
dtrain : DMatrix
|
|
Data to be trained.
|
|
num_boost_round : int
|
|
Number of boosting iterations.
|
|
nfold : int
|
|
Number of folds in CV.
|
|
metrics : list of strings
|
|
Evaluation metrics to be watched in CV.
|
|
obj : function
|
|
Custom objective function.
|
|
feval : function
|
|
Custom evaluation function.
|
|
fpreproc : function
|
|
Preprocessing function that takes (dtrain, dtest, param) and returns
|
|
transformed versions of those.
|
|
show_stdv : bool
|
|
Whether to display the standard deviation.
|
|
seed : int
|
|
Seed used to generate the folds (passed to numpy.random.seed).
|
|
|
|
Returns
|
|
-------
|
|
evaluation history : list(string)
|
|
"""
|
|
results = []
|
|
cvfolds = mknfold(dtrain, nfold, params, seed, metrics, fpreproc)
|
|
for i in range(num_boost_round):
|
|
for f in cvfolds:
|
|
f.update(i, obj)
|
|
res = aggcv([f.eval(i, feval) for f in cvfolds], show_stdv)
|
|
sys.stderr.write(res + '\n')
|
|
results.append(res)
|
|
return results
|
|
|
|
|
|
XGBModelBase = object
|
|
if SKLEARN_INSTALLED:
|
|
XGBModelBase = BaseEstimator
|
|
|
|
|
|
class XGBModel(BaseEstimator):
|
|
"""
|
|
Implementation of the Scikit-Learn API for XGBoost.
|
|
|
|
Parameters
|
|
----------
|
|
max_depth : int
|
|
Maximum tree depth for base learners.
|
|
learning_rate : float
|
|
Boosting learning rate (xgb's "eta")
|
|
n_estimators : int
|
|
Number of boosted trees to fit.
|
|
silent : boolean
|
|
Whether to print messages while running boosting.
|
|
"""
|
|
def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective="reg:linear"):
|
|
if not SKLEARN_INSTALLED:
|
|
raise Exception('sklearn needs to be installed in order to use this module')
|
|
self.max_depth = max_depth
|
|
self.eta = learning_rate
|
|
self.silent = 1 if silent else 0
|
|
self.n_rounds = n_estimators
|
|
self.objective = objective
|
|
self._Booster = Booster()
|
|
|
|
def get_params(self, deep=True):
|
|
return {'max_depth': self.max_depth,
|
|
'learning_rate': self.eta,
|
|
'n_estimators': self.n_rounds,
|
|
'silent': True if self.silent == 1 else False,
|
|
'objective': self.objective
|
|
}
|
|
def get_xgb_params(self):
|
|
return {'eta': self.eta, 'max_depth': self.max_depth, 'silent': self.silent, 'objective': self.objective}
|
|
|
|
def fit(self, X, y):
|
|
trainDmatrix = DMatrix(X, label=y)
|
|
self._Booster = train(self.get_xgb_params(), trainDmatrix, self.n_rounds)
|
|
return self
|
|
|
|
def predict(self, X):
|
|
testDmatrix = DMatrix(X)
|
|
return self._Booster.predict(testDmatrix)
|
|
|
|
class XGBClassifier(XGBModel, ClassifierMixin):
|
|
def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True):
|
|
super().__init__(max_depth, learning_rate, n_estimators, silent, objective="binary:logistic")
|
|
|
|
def fit(self, X, y, sample_weight=None):
|
|
y_values = list(np.unique(y))
|
|
if len(y_values) > 2:
|
|
# Switch to using a multiclass objective in the underlying XGB instance
|
|
self.objective = "multi:softprob"
|
|
xgb_options = self.get_xgb_params()
|
|
xgb_options['num_class'] = len(y_values)
|
|
else:
|
|
xgb_options = self.get_xgb_params()
|
|
|
|
self._le = LabelEncoder().fit(y)
|
|
training_labels = self._le.transform(y)
|
|
|
|
if sample_weight is not None:
|
|
trainDmatrix = DMatrix(X, label=training_labels, weight=sample_weight)
|
|
else:
|
|
trainDmatrix = DMatrix(X, label=training_labels)
|
|
|
|
self._Booster = train(xgb_options, trainDmatrix, self.n_rounds)
|
|
|
|
return self
|
|
|
|
def predict(self, X):
|
|
testDmatrix = DMatrix(X)
|
|
class_probs = self._Booster.predict(testDmatrix)
|
|
if len(class_probs.shape) > 1:
|
|
column_indexes = np.argmax(class_probs, axis=1)
|
|
else:
|
|
column_indexes = np.repeat(0, X.shape[0])
|
|
column_indexes[class_probs > 0.5] = 1
|
|
return self._le.inverse_transform(column_indexes)
|
|
|
|
def predict_proba(self, X):
|
|
testDmatrix = DMatrix(X)
|
|
class_probs = self._Booster.predict(testDmatrix)
|
|
if self._yspace == "multiclass":
|
|
return class_probs
|
|
else:
|
|
classone_probs = class_probs
|
|
classzero_probs = 1.0 - classone_probs
|
|
return np.vstack((classzero_probs,classone_probs)).transpose()
|
|
|
|
class XGBRegressor(XGBModel, RegressorMixin):
|
|
pass
|
|
|
|
|