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xgboost.py
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xgboost.py
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# Author: Tianqi Chen, Bing Xu
# module for xgboost
import ctypes
import os
# optinally have scipy sparse, though not necessary
import numpy
import numpy.ctypeslib
import scipy.sparse as scp
# set this line correctly
XGBOOST_PATH = os.path.dirname(__file__)+'/libxgboostpy.so'
# entry type of sparse matrix
class REntry(ctypes.Structure):
_fields_ = [("findex", ctypes.c_uint), ("fvalue", ctypes.c_float) ]
# load in xgboost library
xglib = ctypes.cdll.LoadLibrary(XGBOOST_PATH)
xglib.XGDMatrixCreate.restype = ctypes.c_void_p
xglib.XGDMatrixNumRow.restype = ctypes.c_ulong
xglib.XGDMatrixGetLabel.restype = ctypes.POINTER( ctypes.c_float )
xglib.XGDMatrixGetWeight.restype = ctypes.POINTER( ctypes.c_float )
xglib.XGDMatrixGetRow.restype = ctypes.POINTER( REntry )
xglib.XGBoosterCreate.restype = ctypes.c_void_p
xglib.XGBoosterPredict.restype = ctypes.POINTER( ctypes.c_float )
def ctypes2numpy( cptr, length ):
# convert a ctypes pointer array to numpy
assert isinstance( cptr, ctypes.POINTER( ctypes.c_float ) )
res = numpy.zeros( length, dtype='float32' )
assert ctypes.memmove( res.ctypes.data, cptr, length * res.strides[0] )
return res
# data matrix used in xgboost
class DMatrix:
# constructor
def __init__(self, data=None, label=None, missing=0.0, weight = None):
self.handle = xglib.XGDMatrixCreate()
if data == None:
return
if isinstance(data,str):
xglib.XGDMatrixLoad(self.handle, ctypes.c_char_p(data), 1)
elif isinstance(data,scp.csr_matrix):
self.__init_from_csr(data)
elif isinstance(data, numpy.ndarray) and len(data.shape) == 2:
self.__init_from_npy2d(data, missing)
else:
try:
csr = scp.csr_matrix(data)
self.__init_from_csr(csr)
except:
raise Exception, "can not intialize DMatrix from"+str(type(data))
if label != None:
self.set_label(label)
if weight !=None:
self.set_weight(weight)
# convert data from csr matrix
def __init_from_csr(self,csr):
assert len(csr.indices) == len(csr.data)
xglib.XGDMatrixParseCSR( self.handle,
( ctypes.c_ulong * len(csr.indptr) )(*csr.indptr),
( ctypes.c_uint * len(csr.indices) )(*csr.indices),
( ctypes.c_float * len(csr.data) )(*csr.data),
len(csr.indptr), len(csr.data) )
# convert data from numpy matrix
def __init_from_npy2d(self,mat,missing):
data = numpy.array( mat.reshape(mat.size), dtype='float32' )
xglib.XGDMatrixParseMat( self.handle,
data.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
mat.shape[0], mat.shape[1], ctypes.c_float(missing) )
# destructor
def __del__(self):
xglib.XGDMatrixFree(self.handle)
# load data from file
def load(self, fname, silent=True):
xglib.XGDMatrixLoad(self.handle, ctypes.c_char_p(fname), int(silent))
# load data from file
def save_binary(self, fname, silent=True):
xglib.XGDMatrixSaveBinary(self.handle, ctypes.c_char_p(fname), int(silent))
# set label of dmatrix
def set_label(self, label):
xglib.XGDMatrixSetLabel(self.handle, (ctypes.c_float*len(label))(*label), len(label) )
# set group size of dmatrix, used for rank
def set_group(self, group):
xglib.XGDMatrixSetGroup(self.handle, (ctypes.c_uint*len(group))(*group), len(group) )
# set weight of each instances
def set_weight(self, weight):
xglib.XGDMatrixSetWeight(self.handle, (ctypes.c_float*len(weight))(*weight), len(weight) )
# get label from dmatrix
def get_label(self):
length = ctypes.c_ulong()
labels = xglib.XGDMatrixGetLabel(self.handle, ctypes.byref(length))
return ctypes2numpy( labels, length.value );
# get weight from dmatrix
def get_weight(self):
length = ctypes.c_ulong()
weights = xglib.XGDMatrixGetWeight(self.handle, ctypes.byref(length))
return ctypes2numpy( weights, length.value );
# clear everything
def clear(self):
xglib.XGDMatrixClear(self.handle)
def num_row(self):
return xglib.XGDMatrixNumRow(self.handle)
# append a row to DMatrix
def add_row(self, row):
xglib.XGDMatrixAddRow(self.handle, (REntry*len(row))(*row), len(row) )
# get n-throw from DMatrix
def __getitem__(self, ridx):
length = ctypes.c_ulong()
row = xglib.XGDMatrixGetRow(self.handle, ridx, ctypes.byref(length) );
return [ (int(row[i].findex),row[i].fvalue) for i in xrange(length.value) ]
class Booster:
"""learner class """
def __init__(self, params={}, cache=[]):
""" constructor, param: """
for d in cache:
assert isinstance(d,DMatrix)
dmats = ( ctypes.c_void_p * len(cache) )(*[ ctypes.c_void_p(d.handle) for d in cache])
self.handle = xglib.XGBoosterCreate( dmats, len(cache) )
self.set_param( params )
def __del__(self):
xglib.XGBoosterFree(self.handle)
def set_param(self, params,pv=None):
if isinstance(params,dict):
for k, v in params.iteritems():
xglib.XGBoosterSetParam( self.handle, ctypes.c_char_p(k), ctypes.c_char_p(str(v)) )
elif isinstance(params,str) and pv != None:
xglib.XGBoosterSetParam( self.handle, ctypes.c_char_p(params), ctypes.c_char_p(str(pv)) )
else:
for k, v in params:
xglib.XGBoosterSetParam( self.handle, ctypes.c_char_p(k), ctypes.c_char_p(str(v)) )
def update(self, dtrain):
""" update """
assert isinstance(dtrain, DMatrix)
xglib.XGBoosterUpdateOneIter( self.handle, dtrain.handle )
def boost(self, dtrain, grad, hess, bst_group = -1):
""" update """
assert len(grad) == len(hess)
assert isinstance(dtrain, DMatrix)
xglib.XGBoosterBoostOneIter( self.handle, dtrain.handle,
(ctypes.c_float*len(grad))(*grad),
(ctypes.c_float*len(hess))(*hess),
len(grad), bst_group )
def update_interact(self, dtrain, action, booster_index=None):
""" beta: update with specified action"""
assert isinstance(dtrain, DMatrix)
if booster_index != None:
self.set_param('interact:booster_index', str(booster_index))
xglib.XGBoosterUpdateInteract( self.handle, dtrain.handle, ctypes.c_char_p(str(action)) )
def eval_set(self, evals, it = 0):
for d in evals:
assert isinstance(d[0], DMatrix)
assert isinstance(d[1], str)
dmats = ( ctypes.c_void_p * len(evals) )(*[ ctypes.c_void_p(d[0].handle) for d in evals])
evnames = ( ctypes.c_char_p * len(evals) )(*[ ctypes.c_char_p(d[1]) for d in evals])
xglib.XGBoosterEvalOneIter( self.handle, it, dmats, evnames, len(evals) )
def eval(self, mat, name = 'eval', it = 0 ):
self.eval_set( [(mat,name)], it)
def predict(self, data, bst_group = -1):
length = ctypes.c_ulong()
preds = xglib.XGBoosterPredict( self.handle, data.handle, ctypes.byref(length), bst_group)
return ctypes2numpy( preds, length.value )
def save_model(self, fname):
""" save model to file """
xglib.XGBoosterSaveModel( self.handle, ctypes.c_char_p(fname) )
def load_model(self, fname):
"""load model from file"""
xglib.XGBoosterLoadModel( self.handle, ctypes.c_char_p(fname) )
def dump_model(self, fname, fmap=''):
"""dump model into text file"""
xglib.XGBoosterDumpModel( self.handle, ctypes.c_char_p(fname), ctypes.c_char_p(fmap) )
def train(params, dtrain, num_boost_round = 10, evals = [], obj=None):
""" train a booster with given paramaters """
bst = Booster(params, [dtrain] )
if obj == None:
for i in xrange(num_boost_round):
bst.update( dtrain )
if len(evals) != 0:
bst.eval_set( evals, i )
else:
# try customized objective function
for i in xrange(num_boost_round):
pred = bst.predict( dtrain )
grad, hess = obj( pred, dtrain )
bst.boost( dtrain, grad, hess )
if len(evals) != 0:
bst.eval_set( evals, i )
return bst