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Merge pull request #488 from sinhrks/pyfeaturenames

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Support feature names in Python package
This commit is contained in:
Tianqi Chen 2015-09-15 09:56:55 -07:00
commit ae43fd7c7a
6 changed files with 214 additions and 43 deletions
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100
python-package/xgboost/core.py
View File

@ -1,5 +1,5 @@
# coding: utf-8
# pylint: disable=too-many-arguments
# pylint: disable=too-many-arguments, too-many-branches
"""Core XGBoost Library."""
from __future__ import absolute_import
@ -23,8 +23,9 @@ class XGBoostError(Exception):
if sys.version_info[0] == 3:
# pylint: disable=invalid-name
# pylint: disable=invalid-name, redefined-builtin
STRING_TYPES = str,
unicode = str
else:
# pylint: disable=invalid-name
STRING_TYPES = basestring,
@ -131,7 +132,11 @@ class DMatrix(object):
which is optimized for both memory efficiency and training speed.
You can construct DMatrix from numpy.arrays
"""
def __init__(self, data, label=None, missing=0.0, weight=None, silent=False):
feature_names = None # for previous version's pickle
def __init__(self, data, label=None, missing=0.0,
weight=None, silent=False, feature_names=None):
"""
Data matrix used in XGBoost.
@ -149,6 +154,8 @@ class DMatrix(object):
Weight for each instance.
silent : boolean, optional
Whether print messages during construction
feature_names : list, optional
Labels for features.
"""
# force into void_p, mac need to pass things in as void_p
if data is None:
@ -176,6 +183,21 @@ class DMatrix(object):
if weight is not None:
self.set_weight(weight)
# validate feature name
if not feature_names is None:
if not isinstance(feature_names, list):
feature_names = list(feature_names)
if len(feature_names) != len(set(feature_names)):
raise ValueError('feature_names must be unique')
if len(feature_names) != self.num_col():
msg = 'feature_names must have the same length as data'
raise ValueError(msg)
# prohibit to use symbols may affect to parse. e.g. ``[]=.``
if not all(isinstance(f, STRING_TYPES) and f.isalnum()
for f in feature_names):
raise ValueError('all feature_names must be alphanumerics')
self.feature_names = feature_names
def _init_from_csr(self, csr):
"""
Initialize data from a CSR matrix.
@ -391,6 +413,18 @@ class DMatrix(object):
ctypes.byref(ret)))
return ret.value
def num_col(self):
"""Get the number of columns (features) in the DMatrix.
Returns
-------
number of columns : int
"""
ret = ctypes.c_uint()
_check_call(_LIB.XGDMatrixNumCol(self.handle,
ctypes.byref(ret)))
return ret.value
def slice(self, rindex):
"""Slice the DMatrix and return a new DMatrix that only contains `rindex`.
@ -404,7 +438,7 @@ class DMatrix(object):
res : DMatrix
A new DMatrix containing only selected indices.
"""
res = DMatrix(None)
res = DMatrix(None, feature_names=self.feature_names)
res.handle = ctypes.c_void_p()
_check_call(_LIB.XGDMatrixSliceDMatrix(self.handle,
c_array(ctypes.c_int, rindex),
@ -419,6 +453,9 @@ class Booster(object):
Booster is the model of xgboost, that contains low level routines for
training, prediction and evaluation.
"""
feature_names = None
def __init__(self, params=None, cache=(), model_file=None):
# pylint: disable=invalid-name
"""Initialize the Booster.
@ -435,6 +472,7 @@ class Booster(object):
for d in cache:
if not isinstance(d, DMatrix):
raise TypeError('invalid cache item: {}'.format(type(d).__name__))
self._validate_feature_names(d)
dmats = c_array(ctypes.c_void_p, [d.handle for d in cache])
self.handle = ctypes.c_void_p()
_check_call(_LIB.XGBoosterCreate(dmats, len(cache), ctypes.byref(self.handle)))
@ -519,6 +557,8 @@ class Booster(object):
"""
if not isinstance(dtrain, DMatrix):
raise TypeError('invalid training matrix: {}'.format(type(dtrain).__name__))
self._validate_feature_names(dtrain)
if fobj is None:
_check_call(_LIB.XGBoosterUpdateOneIter(self.handle, iteration, dtrain.handle))
else:
@ -543,6 +583,8 @@ class Booster(object):
raise ValueError('grad / hess length mismatch: {} / {}'.format(len(grad), len(hess)))
if not isinstance(dtrain, DMatrix):
raise TypeError('invalid training matrix: {}'.format(type(dtrain).__name__))
self._validate_feature_names(dtrain)
_check_call(_LIB.XGBoosterBoostOneIter(self.handle, dtrain.handle,
c_array(ctypes.c_float, grad),
c_array(ctypes.c_float, hess),
@ -572,6 +614,8 @@ class Booster(object):
raise TypeError('expected DMatrix, got {}'.format(type(d[0]).__name__))
if not isinstance(d[1], STRING_TYPES):
raise TypeError('expected string, got {}'.format(type(d[1]).__name__))
self._validate_feature_names(d[0])
dmats = c_array(ctypes.c_void_p, [d[0].handle for d in evals])
evnames = c_array(ctypes.c_char_p, [c_str(d[1]) for d in evals])
msg = ctypes.c_char_p()
@ -605,6 +649,7 @@ class Booster(object):
result: str
Evaluation result string.
"""
self._validate_feature_names(data)
return self.eval_set([(data, name)], iteration)
def predict(self, data, output_margin=False, ntree_limit=0, pred_leaf=False):
@ -642,6 +687,9 @@ class Booster(object):
option_mask |= 0x01
if pred_leaf:
option_mask |= 0x02
self._validate_feature_names(data)
length = ctypes.c_ulong()
preds = ctypes.POINTER(ctypes.c_float)()
_check_call(_LIB.XGBoosterPredict(self.handle, data.handle,
@ -731,16 +779,46 @@ class Booster(object):
"""
Returns the dump the model as a list of strings.
"""
length = ctypes.c_ulong()
sarr = ctypes.POINTER(ctypes.c_char_p)()
if self.feature_names is not None and fmap == '':
flen = int(len(self.feature_names))
fname = (ctypes.c_char_p * flen)()
ftype = (ctypes.c_char_p * flen)()
# supports quantitative type only
# {'q': quantitative, 'i': indicator}
if sys.version_info[0] == 3:
features = [bytes(f, 'utf-8') for f in self.feature_names]
types = [bytes('q', 'utf-8')] * flen
else:
features = [f.encode('utf-8') if isinstance(f, unicode) else f
for f in self.feature_names]
types = ['q'] * flen
fname[:] = features
ftype[:] = types
_check_call(_LIB.XGBoosterDumpModelWithFeatures(self.handle,
flen,
fname,
ftype,
int(with_stats),
ctypes.byref(length),
ctypes.byref(sarr)))
else:
_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):
try:
res.append(str(sarr[i].decode('ascii')))
except UnicodeDecodeError:
res.append(unicode(sarr[i].decode('utf-8')))
return res
def get_fscore(self, fmap=''):
@ -765,3 +843,17 @@ class Booster(object):
else:
fmap[fid] += 1
return fmap
def _validate_feature_names(self, data):
"""
Validate Booster and data's feature_names are identical
"""
if self.feature_names is None:
self.feature_names = data.feature_names
else:
# Booster can't accept data with different feature names
if self.feature_names != data.feature_names:
msg = 'feature_names mismatch: {0} {1}'
raise ValueError(msg.format(self.feature_names,
data.feature_names))

12
scripts/travis_osx_install.sh
View File

@ -5,15 +5,3 @@ if [ ${TRAVIS_OS_NAME} != "osx" ]; then
fi
brew update
if [ ${TASK} == "python-package" ]; then
brew install python git graphviz
easy_install pip
pip install numpy scipy matplotlib nose
fi
if [ ${TASK} == "python-package3" ]; then
brew install python3 git graphviz
sudo pip3 install --upgrade setuptools
pip3 install numpy scipy matplotlib nose graphviz
fi

50
scripts/travis_script.sh
View File

@ -33,30 +33,44 @@ if [ ${TASK} == "R-package" ]; then
scripts/travis_R_script.sh || exit -1
fi
if [ ${TASK} == "python-package" ]; then
sudo apt-get install graphviz
sudo apt-get install python-numpy python-scipy python-matplotlib python-nose
sudo python -m pip install graphviz
make all CXX=${CXX} || exit -1
nosetests tests/python || exit -1
if [ ${TASK} == "python-package" -o ${TASK} == "python-package3" ]; then
if [ ${TRAVIS_OS_NAME} == "osx" ]; then
brew install graphviz
if [ ${TASK} == "python-package3" ]; then
wget -O conda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
else
wget -O conda.sh https://repo.continuum.io/miniconda/Miniconda-latest-MacOSX-x86_64.sh
fi
else
sudo apt-get install graphviz
if [ ${TASK} == "python-package3" ]; then
wget -O conda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
else
wget -O conda.sh https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh
fi
fi
bash conda.sh -b -p $HOME/miniconda
export PATH="$HOME/miniconda/bin:$PATH"
hash -r
conda config --set always_yes yes --set changeps1 no
conda update -q conda
# Useful for debugging any issues with conda
conda info -a
if [ ${TASK} == "python-package3" ]; then
sudo apt-get install graphviz
# python3-matplotlib is unavailale on Ubuntu 12.04
sudo apt-get install python3-dev
sudo apt-get install python3-numpy python3-scipy python3-nose python3-setuptools
conda create -n myenv python=3.4
else
conda create -n myenv python=2.7
fi
source activate myenv
conda install numpy scipy matplotlib nose
python -m pip install graphviz
make all CXX=${CXX} || exit -1
if [ ${TRAVIS_OS_NAME} != "osx" ]; then
sudo easy_install3 pip
sudo easy_install3 -U distribute
sudo pip install graphviz matplotlib
nosetests3 tests/python || exit -1
else
nosetests tests/python || exit -1
fi
python -m nose tests/python || exit -1
python --version
fi
# only test java under linux for now

25
tests/python/test_basic.py
View File

@ -1,3 +1,4 @@
# -*- coding: utf-8 -*-
import numpy as np
import xgboost as xgb
@ -29,6 +30,30 @@ def test_basic():
# assert they are the same
assert np.sum(np.abs(preds2-preds)) == 0
def test_feature_names():
data = np.random.randn(100, 5)
target = np.array([0, 1] * 50)
cases = [['Feature1', 'Feature2', 'Feature3', 'Feature4', 'Feature5'],
[u'要因1', u'要因2', u'要因3', u'要因4', u'要因5']]
for features in cases:
dm = xgb.DMatrix(data, label=target,
feature_names=features)
assert dm.feature_names == features
assert dm.num_row() == 100
assert dm.num_col() == 5
params={'objective': 'multi:softprob',
'eval_metric': 'mlogloss',
'eta': 0.3,
'num_class': 3}
bst = xgb.train(params, dm, num_boost_round=10)
scores = bst.get_fscore()
assert list(sorted(k for k in scores)) == features
def test_plotting():
bst2 = xgb.Booster(model_file='xgb.model')
# plotting

25
wrapper/xgboost_wrapper.cpp
View File

@ -435,6 +435,7 @@ int XGDMatrixGetUIntInfo(const DMatrixHandle handle,
*out_dptr = BeginPtr(vec);
API_END();
}
int XGDMatrixNumRow(const DMatrixHandle handle,
bst_ulong *out) {
API_BEGIN();
@ -442,6 +443,13 @@ int XGDMatrixNumRow(const DMatrixHandle handle,
API_END();
}
int XGDMatrixNumCol(const DMatrixHandle handle,
bst_ulong *out) {
API_BEGIN();
*out = static_cast<size_t>(static_cast<const DataMatrix*>(handle)->info.num_col());
API_END();
}
// xgboost implementation
int XGBoosterCreate(DMatrixHandle dmats[],
bst_ulong len,
@ -572,3 +580,20 @@ int XGBoosterDumpModel(BoosterHandle handle,
featmap, with_stats != 0, len);
API_END();
}
int XGBoosterDumpModelWithFeatures(BoosterHandle handle,
int fnum,
const char **fname,
const char **ftype,
int with_stats,
bst_ulong *len,
const char ***out_models) {
API_BEGIN();
utils::FeatMap featmap;
for (int i = 0; i < fnum; ++i) {
featmap.PushBack(i, fname[i], ftype[i]);
}
*out_models = static_cast<Booster*>(handle)->GetModelDump(
featmap, with_stats != 0, len);
API_END();
}

27
wrapper/xgboost_wrapper.h
View File

@ -184,6 +184,13 @@ XGB_DLL int XGDMatrixGetUIntInfo(const DMatrixHandle handle,
*/
XGB_DLL int XGDMatrixNumRow(DMatrixHandle handle,
bst_ulong *out);
/*!
* \brief get number of columns
* \param handle the handle to the DMatrix
* \return 0 when success, -1 when failure happens
*/
XGB_DLL int XGDMatrixNumCol(DMatrixHandle handle,
bst_ulong *out);
// --- start XGBoost class
/*!
* \brief create xgboost learner
@ -324,4 +331,24 @@ XGB_DLL int XGBoosterDumpModel(BoosterHandle handle,
int with_stats,
bst_ulong *out_len,
const char ***out_dump_array);
/*!
* \brief dump model, return array of strings representing model dump
* \param handle handle
* \param fnum number of features
* \param fnum names of features
* \param fnum types of features
* \param with_stats whether to dump with statistics
* \param out_len length of output array
* \param out_dump_array pointer to hold representing dump of each model
* \return 0 when success, -1 when failure happens
*/
XGB_DLL int XGBoosterDumpModelWithFeatures(BoosterHandle handle,
int fnum,
const char **fname,
const char **ftype,
int with_stats,
bst_ulong *len,
const char ***out_models);
#endif // XGBOOST_WRAPPER_H_