764 lines
25 KiB
Python
764 lines
25 KiB
Python
# coding: utf-8
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# pylint: disable=too-many-arguments
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"""Core XGBoost Library."""
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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 platform
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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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class XGBoostLibraryNotFound(Exception):
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"""Error throwed by when xgboost is not found"""
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pass
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class XGBoostError(Exception):
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"""Error throwed by xgboost trainer."""
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pass
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if sys.version_info[0] == 3:
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# pylint: disable=invalid-name
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STRING_TYPES = str,
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else:
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# pylint: disable=invalid-name
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STRING_TYPES = basestring,
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def find_lib_path():
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"""Load find the path to xgboost dynamic library files.
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Returns
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-------
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lib_path: list(string)
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List of all found library path to xgboost
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"""
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curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
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#make pythonpack hack: copy this directory one level upper for setup.py
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dll_path = [curr_path, os.path.join(curr_path, '../../wrapper/')
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, os.path.join(curr_path, './wrapper/')]
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if os.name == 'nt':
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if platform.architecture()[0] == '64bit':
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dll_path.append(os.path.join(curr_path, '../../windows/x64/Release/'))
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else:
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dll_path.append(os.path.join(curr_path, '../../windows/Release/'))
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if os.name == 'nt':
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dll_path = [os.path.join(p, 'xgboost_wrapper.dll') for p in dll_path]
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else:
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dll_path = [os.path.join(p, 'libxgboostwrapper.so') for p in dll_path]
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lib_path = [p for p in dll_path if os.path.exists(p) and os.path.isfile(p)]
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if len(lib_path) == 0 and not os.environ.get('XGBOOST_BUILD_DOC', False):
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raise XGBoostLibraryNotFound(
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'Cannot find XGBoost Libarary in the candicate path, ' +
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'did you run build.sh in root path?\n'
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'List of candidates:\n' + ('\n'.join(dll_path)))
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return lib_path
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def _load_lib():
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"""Load xgboost Library."""
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lib_path = find_lib_path()
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if len(lib_path) == 0:
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return None
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lib = ctypes.cdll.LoadLibrary(lib_path[0])
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lib.XGBGetLastError.restype = ctypes.c_char_p
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return lib
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# load the XGBoost library globally
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_LIB = _load_lib()
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def _check_call(ret):
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"""Check the return value of C API call
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This function will raise exception when error occurs.
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Wrap every API call with this function
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Parameters
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----------
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ret : int
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return value from API calls
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"""
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if ret != 0:
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raise XGBoostError(_LIB.XGBGetLastError())
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def ctypes2numpy(cptr, length, dtype):
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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 ctypes2buffer(cptr, length):
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"""Convert ctypes pointer to buffer type."""
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if not isinstance(cptr, ctypes.POINTER(ctypes.c_char)):
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raise RuntimeError('expected char pointer')
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res = bytearray(length)
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rptr = (ctypes.c_char * length).from_buffer(res)
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if not ctypes.memmove(rptr, cptr, length):
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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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"""Convert a python string to cstring."""
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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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"""Convert a python string to c array."""
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return (ctype * len(values))(*values)
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class DMatrix(object):
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"""Data Matrix used in XGBoost.
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DMatrix is a internal data structure that used by XGBoost
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which is optimized for both memory efficiency and training speed.
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You can construct DMatrix from numpy.arrays
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"""
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def __init__(self, data, label=None, missing=0.0, weight=None, silent=False):
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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 of DMatrix.
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When data is string type, it represents the path libsvm format txt file,
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or binary file that xgboost can read from.
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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, optional
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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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silent : boolean, optional
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Whether print messages during construction
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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()
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_check_call(_LIB.XGDMatrixCreateFromFile(c_str(data),
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int(silent),
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ctypes.byref(self.handle)))
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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()
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_check_call(_LIB.XGDMatrixCreateFromCSR(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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ctypes.byref(self.handle)))
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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()
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_check_call(_LIB.XGDMatrixCreateFromCSC(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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ctypes.byref(self.handle)))
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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()
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_check_call(_LIB.XGDMatrixCreateFromMat(data.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
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mat.shape[0], mat.shape[1],
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ctypes.c_float(missing),
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ctypes.byref(self.handle)))
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def __del__(self):
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_check_call(_LIB.XGDMatrixFree(self.handle))
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def get_float_info(self, field):
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"""Get float property from the DMatrix.
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Parameters
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----------
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field: str
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The field name of the information
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Returns
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-------
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info : array
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a numpy array of float information of the data
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"""
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length = ctypes.c_ulong()
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ret = ctypes.POINTER(ctypes.c_float)()
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_check_call(_LIB.XGDMatrixGetFloatInfo(self.handle,
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c_str(field),
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ctypes.byref(length),
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ctypes.byref(ret)))
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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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"""Get unsigned integer property from the DMatrix.
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Parameters
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----------
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field: str
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The field name of the information
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Returns
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-------
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info : array
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a numpy array of float information of the data
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"""
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length = ctypes.c_ulong()
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ret = ctypes.POINTER(ctypes.c_uint)()
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_check_call(_LIB.XGDMatrixGetUIntInfo(self.handle,
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c_str(field),
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ctypes.byref(length),
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ctypes.byref(ret)))
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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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"""Set float type property into the DMatrix.
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Parameters
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----------
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field: str
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The field name of the information
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data: numpy array
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The array ofdata to be set
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"""
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_check_call(_LIB.XGDMatrixSetFloatInfo(self.handle,
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c_str(field),
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c_array(ctypes.c_float, data),
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len(data)))
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def set_uint_info(self, field, data):
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"""Set uint type property into the DMatrix.
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Parameters
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----------
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field: str
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The field name of the information
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data: numpy array
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The array ofdata to be set
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"""
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_check_call(_LIB.XGDMatrixSetUIntInfo(self.handle,
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c_str(field),
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c_array(ctypes.c_uint, data),
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len(data)))
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def save_binary(self, fname, silent=True):
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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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_check_call(_LIB.XGDMatrixSaveBinary(self.handle,
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c_str(fname),
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int(silent)))
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def set_label(self, label):
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"""Set label of dmatrix
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Parameters
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----------
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label: array like
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The label information to be set into DMatrix
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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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""" Set weight of each instance.
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Parameters
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----------
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weight : array like
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Weight for each data point
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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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""" 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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Parameters
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----------
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margin: array like
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Prediction margin of each datapoint
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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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"""Set group size of DMatrix (used for ranking).
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Parameters
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----------
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group : array like
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Group size of each group
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"""
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_check_call(_LIB.XGDMatrixSetGroup(self.handle,
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c_array(ctypes.c_uint, group),
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len(group)))
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def get_label(self):
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"""Get the label of the DMatrix.
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Returns
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-------
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label : array
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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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"""Get the weight of the DMatrix.
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Returns
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-------
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weight : array
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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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"""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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"""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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ret = ctypes.c_ulong()
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_check_call(_LIB.XGDMatrixNumRow(self.handle,
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ctypes.byref(ret)))
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return ret.value
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def slice(self, rindex):
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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()
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_check_call(_LIB.XGDMatrixSliceDMatrix(self.handle,
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c_array(ctypes.c_int, rindex),
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len(rindex),
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ctypes.byref(res.handle)))
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return res
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class Booster(object):
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""""A Booster of of XGBoost.
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Booster is the model of xgboost, that contains low level routines for
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training, prediction and evaluation.
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"""
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def __init__(self, params=None, cache=(), model_file=None):
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# pylint: disable=invalid-name
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"""Initialize the Booster.
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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()
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_check_call(_LIB.XGBoosterCreate(dmats, len(cache), ctypes.byref(self.handle)))
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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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_LIB.XGBoosterFree(self.handle)
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def __getstate__(self):
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# can't pickle ctypes pointers
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# put model content in bytearray
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this = self.__dict__.copy()
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handle = this['handle']
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if handle is not None:
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raw = self.save_raw()
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this["handle"] = raw
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return this
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def __setstate__(self, state):
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# reconstruct handle from raw data
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handle = state['handle']
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if handle is not None:
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buf = handle
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dmats = c_array(ctypes.c_void_p, [])
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handle = ctypes.c_void_p()
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_check_call(_LIB.XGBoosterCreate(dmats, 0, ctypes.byref(handle)))
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length = ctypes.c_ulong(len(buf))
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ptr = (ctypes.c_char * len(buf)).from_buffer(buf)
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_check_call(_LIB.XGBoosterLoadModelFromBuffer(handle, ptr, length))
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state['handle'] = handle
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self.__dict__.update(state)
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self.set_param({'seed': 0})
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def __copy__(self):
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return self.__deepcopy__()
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def __deepcopy__(self):
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return Booster(model_file=self.save_raw())
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def copy(self):
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"""Copy the booster object.
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Returns
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-------
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booster: `Booster`
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a copied booster model
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"""
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return self.__copy__()
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def set_param(self, params, value=None):
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"""Set parameters into the Booster.
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Parameters
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----------
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params: dict/list/str
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list of key,value paris, dict of key to value or simply str key
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value: optional
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value of the specified parameter, when params is str key
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"""
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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 value is not None:
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params = [(params, value)]
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for key, val in params:
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_check_call(_LIB.XGBoosterSetParam(self.handle, c_str(key), c_str(str(val))))
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def update(self, dtrain, iteration, fobj=None):
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"""
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Update for one iteration, with objective function calculated internally.
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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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iteration : 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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_check_call(_LIB.XGBoosterUpdateOneIter(self.handle, iteration, 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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Boost the booster for one iteration, with customized gradient statistics.
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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):
|
|
raise TypeError('invalid training matrix: {}'.format(type(dtrain).__name__))
|
|
_check_call(_LIB.XGBoosterBoostOneIter(self.handle, dtrain.handle,
|
|
c_array(ctypes.c_float, grad),
|
|
c_array(ctypes.c_float, hess),
|
|
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.
|
|
"""
|
|
if feval is None:
|
|
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__))
|
|
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, iteration,
|
|
dmats, evnames, len(evals),
|
|
ctypes.byref(msg)))
|
|
return msg.value
|
|
else:
|
|
res = '[%d]' % iteration
|
|
for dmat, evname in evals:
|
|
name, val = feval(self.predict(dmat), dmat)
|
|
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.
|
|
"""
|
|
return self.eval_set([(data, name)], iteration)
|
|
|
|
def predict(self, data, output_margin=False, ntree_limit=0, pred_leaf=False):
|
|
"""
|
|
Predict with data.
|
|
|
|
NOTE: This function is not thread safe.
|
|
For each booster object, 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.
|
|
|
|
Returns
|
|
-------
|
|
prediction : numpy array
|
|
"""
|
|
option_mask = 0x00
|
|
if output_margin:
|
|
option_mask |= 0x01
|
|
if pred_leaf:
|
|
option_mask |= 0x02
|
|
length = ctypes.c_ulong()
|
|
preds = ctypes.POINTER(ctypes.c_float)()
|
|
_check_call(_LIB.XGBoosterPredict(self.handle, data.handle,
|
|
option_mask, ntree_limit,
|
|
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:
|
|
preds = preds.reshape(nrow, preds.size / nrow)
|
|
return preds
|
|
|
|
def save_model(self, fname):
|
|
"""
|
|
Save the model to a file.
|
|
|
|
Parameters
|
|
----------
|
|
fname : string
|
|
Output file name
|
|
"""
|
|
if isinstance(fname, STRING_TYPES): # assume file name
|
|
_check_call(_LIB.XGBoosterSaveModel(self.handle, c_str(fname)))
|
|
else:
|
|
raise TypeError("fname must be a string")
|
|
|
|
def save_raw(self):
|
|
"""
|
|
Save the model to a in memory buffer represetation
|
|
|
|
Returns
|
|
-------
|
|
a in memory buffer represetation of the model
|
|
"""
|
|
length = ctypes.c_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.
|
|
|
|
Parameters
|
|
----------
|
|
fname : string or a memory buffer
|
|
Input file name or memory buffer(see also save_raw)
|
|
"""
|
|
if isinstance(fname, str): # assume file name
|
|
_LIB.XGBoosterLoadModel(self.handle, c_str(fname))
|
|
else:
|
|
buf = fname
|
|
length = ctypes.c_ulong(len(buf))
|
|
ptr = (ctypes.c_char * len(buf)).from_buffer(buf)
|
|
_check_call(_LIB.XGBoosterLoadModelFromBuffer(self.handle, ptr, length))
|
|
|
|
def dump_model(self, fout, fmap='', with_stats=False):
|
|
"""
|
|
Dump model into a text file.
|
|
|
|
Parameters
|
|
----------
|
|
foout : string
|
|
Output file name.
|
|
fmap : string, optional
|
|
Name of the file containing feature map names.
|
|
with_stats : bool (optional)
|
|
Controls whether the split statistics are output.
|
|
"""
|
|
if isinstance(fout, STRING_TYPES):
|
|
fout = open(fout, 'w')
|
|
need_close = True
|
|
else:
|
|
need_close = False
|
|
ret = self.get_dump(fmap, with_stats)
|
|
for i in range(len(ret)):
|
|
fout.write('booster[{}]:\n'.format(i))
|
|
fout.write(ret[i])
|
|
if need_close:
|
|
fout.close()
|
|
|
|
def get_dump(self, fmap='', with_stats=False):
|
|
"""
|
|
Returns the dump the model as a list of strings.
|
|
"""
|
|
length = ctypes.c_ulong()
|
|
sarr = ctypes.POINTER(ctypes.c_char_p)()
|
|
_check_call(_LIB.XGBoosterDumpModel(self.handle,
|
|
c_str(fmap),
|
|
int(with_stats),
|
|
ctypes.byref(length),
|
|
ctypes.byref(sarr)))
|
|
res = []
|
|
for i in range(length.value):
|
|
res.append(str(sarr[i].decode('ascii')))
|
|
return res
|
|
|
|
def get_fscore(self, fmap=''):
|
|
"""Get feature importance of each feature.
|
|
|
|
Parameters
|
|
----------
|
|
fmap: str (optional)
|
|
The name of feature map file
|
|
"""
|
|
trees = self.get_dump(fmap)
|
|
fmap = {}
|
|
for tree in trees:
|
|
for line in tree.split('\n'):
|
|
arr = line.split('[')
|
|
if len(arr) == 1:
|
|
continue
|
|
fid = arr[1].split(']')[0]
|
|
fid = fid.split('<')[0]
|
|
if fid not in fmap:
|
|
fmap[fid] = 1
|
|
else:
|
|
fmap[fid] += 1
|
|
return fmap
|