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Compatibility with both Python 2(.7) and 3

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This commit is contained in:
Joerg Rings
2014-05-19 11:23:53 -05:00
parent 991634a58e
commit 93d83ca077
12 changed files with 93 additions and 67 deletions
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10
python/example/demo.py
View File

@@ -22,7 +22,7 @@ bst = xgb.train( param, dtrain, num_round, evallist )
# this is prediction
preds = bst.predict( dtest )
labels = dtest.get_label()
print 'error=%f' % ( sum(1 for i in xrange(len(preds)) if int(preds[i]>0.5)!=labels[i]) /float(len(preds)))
print ('error=%f' % ( sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) /float(len(preds))))
bst.save_model('0001.model')
# dump model
bst.dump_model('dump.raw.txt')
@@ -32,7 +32,7 @@ bst.dump_model('dump.raw.txt','featmap.txt')
###
# build dmatrix in python iteratively
#
print 'start running example of build DMatrix in python'
print ('start running example of build DMatrix in python')
dtrain = xgb.DMatrix()
labels = []
for l in open('agaricus.txt.train'):
@@ -50,7 +50,7 @@ bst = xgb.train( param, dtrain, num_round, evallist )
###
# build dmatrix from scipy.sparse
print 'start running example of build DMatrix from scipy.sparse'
print ('start running example of build DMatrix from scipy.sparse')
labels = []
row = []; col = []; dat = []
i = 0
@@ -68,7 +68,7 @@ dtrain.set_label(labels)
evallist = [(dtest,'eval'), (dtrain,'train')]
bst = xgb.train( param, dtrain, num_round, evallist )
print 'start running example of build DMatrix from numpy array'
print ('start running example of build DMatrix from numpy array')
# NOTE: npymat is numpy array, we will convert it into scipy.sparse.csr_matrix in internal implementation,then convert to DMatrix
npymat = csr.todense()
dtrain = xgb.DMatrix( npymat )
@@ -79,7 +79,7 @@ bst = xgb.train( param, dtrain, num_round, evallist )
###
# advanced: cutomsized loss function, set loss_type to 0, so that predict get untransformed score
#
print 'start running example to used cutomized objective function'
print ('start running example to used cutomized objective function')
# note: set loss_type properly, loss_type=2 means the prediction will get logistic transformed
# in most case, we may want to set loss_type = 0, to get untransformed score to compute gradient

44
python/xgboost.py
View File

@@ -41,7 +41,7 @@ class DMatrix:
if data == None:
return
if isinstance(data,str):
xglib.XGDMatrixLoad(self.handle, ctypes.c_char_p(data), 1)
xglib.XGDMatrixLoad(self.handle, ctypes.c_char_p(data.encode('utf-8')), 1)
elif isinstance(data,scp.csr_matrix):
self.__init_from_csr(data)
elif isinstance(data, numpy.ndarray) and len(data.shape) == 2:
@@ -51,7 +51,7 @@ class DMatrix:
csr = scp.csr_matrix(data)
self.__init_from_csr(csr)
except:
raise Exception, "can not intialize DMatrix from"+str(type(data))
raise Exception("can not intialize DMatrix from"+str(type(data)))
if label != None:
self.set_label(label)
if weight !=None:
@@ -76,10 +76,10 @@ class DMatrix:
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))
xglib.XGDMatrixLoad(self.handle, ctypes.c_char_p(fname.encode('utf-8')), int(silent))
# load data from file
def save_binary(self, fname, silent=True):
xglib.XGDMatrixSaveBinary(self.handle, ctypes.c_char_p(fname), int(silent))
xglib.XGDMatrixSaveBinary(self.handle, ctypes.c_char_p(fname.encode('utf-8')), int(silent))
# set label of dmatrix
def set_label(self, label):
xglib.XGDMatrixSetLabel(self.handle, (ctypes.c_float*len(label))(*label), len(label) )
@@ -111,7 +111,7 @@ class 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) ]
return [ (int(row[i].findex),row[i].fvalue) for i in range(length.value) ]
class Booster:
"""learner class """
@@ -124,15 +124,21 @@ class Booster:
self.set_param( params )
def __del__(self):
xglib.XGBoosterFree(self.handle)
def set_param(self, params,pv=None):
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)) )
for k, v in params.items():
xglib.XGBoosterSetParam(
self.handle, ctypes.c_char_p(k.encode('utf-8')),
ctypes.c_char_p(str(v).encode('utf-8')))
elif isinstance(params,str) and pv != None:
xglib.XGBoosterSetParam( self.handle, ctypes.c_char_p(params), ctypes.c_char_p(str(pv)) )
xglib.XGBoosterSetParam(
self.handle, ctypes.c_char_p(params.encode('utf-8')),
ctypes.c_char_p(str(pv).encode('utf-8')) )
else:
for k, v in params:
xglib.XGBoosterSetParam( self.handle, ctypes.c_char_p(k), ctypes.c_char_p(str(v)) )
xglib.XGBoosterSetParam(
self.handle, ctypes.c_char_p(k.encode('utf-8')),
ctypes.c_char_p(str(v).encode('utf-8')) )
def update(self, dtrain):
""" update """
assert isinstance(dtrain, DMatrix)
@@ -150,13 +156,15 @@ class Booster:
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)) )
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) )(*[ d[0].handle for d in evals])
evnames = ( ctypes.c_char_p * len(evals) )(*[ ctypes.c_char_p(d[1]) for d in evals])
evnames = ( ctypes.c_char_p * len(evals) )(
*[ctypes.c_char_p(d[1].encode('utf-8')) 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)
@@ -166,25 +174,27 @@ class Booster:
return ctypes2numpy( preds, length.value )
def save_model(self, fname):
""" save model to file """
xglib.XGBoosterSaveModel( self.handle, ctypes.c_char_p(fname) )
xglib.XGBoosterSaveModel(self.handle, ctypes.c_char_p(fname.encode('utf-8')))
def load_model(self, fname):
"""load model from file"""
xglib.XGBoosterLoadModel( self.handle, ctypes.c_char_p(fname) )
xglib.XGBoosterLoadModel( self.handle, ctypes.c_char_p(fname.encode('utf-8')) )
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) )
xglib.XGBoosterDumpModel(
self.handle, ctypes.c_char_p(fname.encode('utf-8')),
ctypes.c_char_p(fmap.encode('utf-8')))
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):
for i in range(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):
for i in range(num_boost_round):
pred = bst.predict( dtrain )
grad, hess = obj( pred, dtrain )
bst.boost( dtrain, grad, hess )