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python package refactor into python-package

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tqchen 2015-07-30 22:04:45 -07:00
parent f6fed76e7e
commit c2fec29bfa
13 changed files with 87 additions and 677 deletions
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2
Makefile
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@ -169,7 +169,7 @@ Rcheck:
# lint requires dmlc to be in current folder # lint requires dmlc to be in current folder
lint: lint:
dmlc-core/scripts/lint.py xgboost $(LINT_LANG) src wrapper R-package dmlc-core/scripts/lint.py xgboost $(LINT_LANG) src wrapper R-package python-package
clean: clean:
$(RM) -rf $(OBJ) $(BIN) $(MPIBIN) $(MPIOBJ) $(SLIB) *.o */*.o */*/*.o *~ */*~ */*/*~ $(RM) -rf $(OBJ) $(BIN) $(MPIBIN) $(MPIOBJ) $(SLIB) *.o */*.o */*/*.o *~ */*~ */*/*~

1
demo/.gitignore vendored
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@ -1 +1,2 @@
*.libsvm *.libsvm
*.pkl

4
demo/README.md
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@ -7,8 +7,8 @@ This folder contains all the code examples using xgboost.
Features Walkthrough Features Walkthrough
==== ====
This is a list of short codes introducing different functionalities of xgboost and its wrapper. This is a list of short codes introducing different functionalities of xgboost packages.
* Basic walkthrough of wrappers * Basic walkthrough of packages
[python](guide-python/basic_walkthrough.py) [python](guide-python/basic_walkthrough.py)
[R](../R-package/demo/basic_walkthrough.R) [R](../R-package/demo/basic_walkthrough.R)
[Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/basic_walkthrough.jl) [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/basic_walkthrough.jl)

10
demo/guide-python/sklearn_examples.py
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@ -75,13 +75,3 @@ clf = xgb.XGBClassifier()
clf.fit(X_train, y_train, early_stopping_rounds=10, eval_metric="auc", clf.fit(X_train, y_train, early_stopping_rounds=10, eval_metric="auc",
eval_set=[(X_test, y_test)]) eval_set=[(X_test, y_test)])
# Custom evaluation function
from sklearn.metrics import log_loss
def log_loss_eval(y_pred, y_true):
return "log-loss", log_loss(y_true.get_label(), y_pred)
clf.fit(X_train, y_train, early_stopping_rounds=10, eval_metric=log_loss_eval,
eval_set=[(X_test, y_test)])

2
doc/python.md
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@ -14,7 +14,7 @@ A [walk through python example](https://github.com/tqchen/xgboost/blob/master/de
= =
#### Install #### Install
To install XGBoost, you need to run `make` in the root directory of the project and then in the `wrappers` directory run To install XGBoost, you need to run `make` in the root directory of the project and then in the `python-package` directory run
```shell ```shell
python setup.py install python setup.py install

3
python-package/.gitignore vendored Normal file
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@ -0,0 +1,3 @@
build
dist
*.egg*

7
python-package/README.md Normal file
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@ -0,0 +1,7 @@
XGBoost Python Package
======================
* To make the python module, type ```./build.sh``` in the root directory of project
* Make sure you have [setuptools](https://pypi.python.org/pypi/setuptools)
* Install with `python setup.py install` from this directory.
* Refer also to the walk through example in [demo folder](../demo/guide-python)
* **NOTE**: if you want to run XGBoost process in parallel using the fork backend for joblib/multiprocessing, you must build XGBoost without support for OpenMP by `make no_omp=1`. Otherwise, use the forkserver (in Python 3.4) or spawn backend. See the sklearn_parallel.py demo.

21
python-package/setup.py Normal file
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@ -0,0 +1,21 @@
# pylint: disable=invalid-name
"""Setup xgboost package."""
from __future__ import absolute_import
import sys
from setuptools import setup
sys.path.insert(0, '.')
import xgboost
LIB_PATH = xgboost.core.find_lib_path()
setup(name='xgboost',
version=xgboost.__version__,
description=xgboost.__doc__,
install_requires=[
'numpy',
'scipy',
],
zip_safe=False,
packages=['xgboost'],
data_files=[('xgboost', [LIB_PATH[0]])],
url='https://github.com/dmlc/xgboost')

630
wrapper/xgboost.py → python-package/xgboost/core.py
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@ -1,17 +1,10 @@
# coding: utf-8 # coding: utf-8
""" # pylint: disable=too-many-arguments
xgboost: eXtreme Gradient Boosting library """Core XGBoost Library."""
Version: 0.40
Authors: Tianqi Chen, Bing Xu
Early stopping by Zygmunt Zając
"""
# pylint: disable=too-many-arguments, too-many-locals, too-many-lines, invalid-name, fixme
from __future__ import absolute_import from __future__ import absolute_import
import os import os
import sys import sys
import re
import ctypes import ctypes
import platform import platform
import collections import collections
@ -19,13 +12,6 @@ import collections
import numpy as np import numpy as np
import scipy.sparse import scipy.sparse
try:
from sklearn.base import BaseEstimator
from sklearn.base import RegressorMixin, ClassifierMixin
from sklearn.preprocessing import LabelEncoder
SKLEARN_INSTALLED = True
except ImportError:
SKLEARN_INSTALLED = False
class XGBoostLibraryNotFound(Exception): class XGBoostLibraryNotFound(Exception):
"""Error throwed by when xgboost is not found""" """Error throwed by when xgboost is not found"""
@ -35,7 +21,6 @@ class XGBoostError(Exception):
"""Error throwed by xgboost trainer.""" """Error throwed by xgboost trainer."""
pass pass
__all__ = ['DMatrix', 'CVPack', 'Booster', 'aggcv', 'cv', 'mknfold', 'train']
if sys.version_info[0] == 3: if sys.version_info[0] == 3:
# pylint: disable=invalid-name # pylint: disable=invalid-name
@ -44,30 +29,43 @@ else:
# pylint: disable=invalid-name # pylint: disable=invalid-name
STRING_TYPES = basestring, STRING_TYPES = basestring,
def load_xglib():
"""Load the xgboost library.""" def find_lib_path():
"""Load find the path to xgboost dynamic library files.
Returns
-------
lib_path: list(string)
List of all found library path to xgboost
"""
curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__))) curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
dll_path = [curr_path] dll_path = [curr_path, os.path.join(curr_path, '../../wrapper/')]
if os.name == 'nt': if os.name == 'nt':
if platform.architecture()[0] == '64bit': if platform.architecture()[0] == '64bit':
dll_path.append(os.path.join(curr_path, '../windows/x64/Release/')) dll_path.append(os.path.join(curr_path, '../../windows/x64/Release/'))
else: else:
dll_path.append(os.path.join(curr_path, '../windows/Release/')) dll_path.append(os.path.join(curr_path, '../../windows/Release/'))
if os.name == 'nt': if os.name == 'nt':
dll_path = [os.path.join(p, 'xgboost_wrapper.dll') for p in dll_path] dll_path = [os.path.join(p, 'xgboost_wrapper.dll') for p in dll_path]
else: else:
dll_path = [os.path.join(p, 'libxgboostwrapper.so') for p in dll_path] dll_path = [os.path.join(p, 'libxgboostwrapper.so') for p in dll_path]
lib_path = [p for p in dll_path if os.path.exists(p) and os.path.isfile(p)] lib_path = [p for p in dll_path if os.path.exists(p) and os.path.isfile(p)]
if len(dll_path) == 0: if len(lib_path) == 0:
raise XGBoostLibraryNotFound( raise XGBoostLibraryNotFound(
'cannot find find the files in the candicate path ' + str(dll_path)) 'Cannot find XGBoost Libarary in the candicate path %s,' +
'Did you run build.sh in root oath?' % str(dll_path))
return lib_path
def _load_lib():
"""Load xgboost Library."""
lib_path = find_lib_path()
lib = ctypes.cdll.LoadLibrary(lib_path[0]) lib = ctypes.cdll.LoadLibrary(lib_path[0])
lib.XGBGetLastError.restype = ctypes.c_char_p lib.XGBGetLastError.restype = ctypes.c_char_p
return lib return lib
# load the XGBoost library globally # load the XGBoost library globally
_LIB = load_xglib() _LIB = _load_lib()
def _check_call(ret): def _check_call(ret):
"""Check the return value of C API call """Check the return value of C API call
@ -117,7 +115,11 @@ def c_array(ctype, values):
class DMatrix(object): class DMatrix(object):
"""Data Matrix used in XGBoost.""" """Data Matrix used in XGBoost.
DMatrix is a internal data structure that used by XGBoost
which is optimized for both memory efficiency and training speed.
"""
def __init__(self, data, label=None, missing=0.0, weight=None, silent=False): def __init__(self, data, label=None, missing=0.0, weight=None, silent=False):
""" """
Data matrix used in XGBoost. Data matrix used in XGBoost.
@ -400,11 +402,14 @@ class DMatrix(object):
class Booster(object): class Booster(object):
""""A Booster of of XGBoost.""" """"A Booster of of XGBoost.
Booster is the model of xgboost, that contains low level routines for
training, prediction and evaluation.
"""
def __init__(self, params=None, cache=(), model_file=None): def __init__(self, params=None, cache=(), model_file=None):
# pylint: disable=invalid-name # pylint: disable=invalid-name
""" """Initialize the Booster.
Learner class.
Parameters Parameters
---------- ----------
@ -735,570 +740,3 @@ class Booster(object):
else: else:
fmap[fid] += 1 fmap[fid] += 1
return fmap return fmap
def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
early_stopping_rounds=None, evals_result=None, verbose_eval=True):
# pylint: disable=too-many-statements,too-many-branches, attribute-defined-outside-init
"""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 (evals): 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.
early_stopping_rounds: int
Activates early stopping. Validation error needs to decrease at least
every <early_stopping_rounds> round(s) to continue training.
Requires at least one item in evals.
If there's more than one, will use the last.
Returns the model from the last iteration (not the best one).
If early stopping occurs, the model will have two additional fields:
bst.best_score and bst.best_iteration.
evals_result: dict
This dictionary stores the evaluation results of all the items in watchlist
verbose_eval : bool
If `verbose_eval` then the evaluation metric on the validation set, if
given, is printed at each boosting stage.
Returns
-------
booster : a trained booster model
"""
evals = list(evals)
bst = Booster(params, [dtrain] + [d[0] for d in evals])
if evals_result is not None:
if not isinstance(evals_result, dict):
raise TypeError('evals_result has to be a dictionary')
else:
evals_name = [d[1] for d in evals]
evals_result.clear()
evals_result.update({key: [] for key in evals_name})
if not early_stopping_rounds:
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):
msg = bst_eval_set
else:
msg = bst_eval_set.decode()
if verbose_eval:
sys.stderr.write(msg + '\n')
if evals_result is not None:
res = re.findall(":-?([0-9.]+).", msg)
for key, val in zip(evals_name, res):
evals_result[key].append(val)
return bst
else:
# early stopping
if len(evals) < 1:
raise ValueError('For early stopping you need at least one set in evals.')
sys.stderr.write("Will train until {} error hasn't decreased in {} rounds.\n".format(\
evals[-1][1], early_stopping_rounds))
# is params a list of tuples? are we using multiple eval metrics?
if isinstance(params, list):
if len(params) != len(dict(params).items()):
raise ValueError('Check your params.'\
'Early stopping works with single eval metric only.')
params = dict(params)
# either minimize loss or maximize AUC/MAP/NDCG
maximize_score = False
if 'eval_metric' in params:
maximize_metrics = ('auc', 'map', 'ndcg')
if any(params['eval_metric'].startswith(x) for x in maximize_metrics):
maximize_score = True
if maximize_score:
best_score = 0.0
else:
best_score = float('inf')
best_msg = ''
best_score_i = 0
for i in range(num_boost_round):
bst.update(dtrain, i, obj)
bst_eval_set = bst.eval_set(evals, i, feval)
if isinstance(bst_eval_set, STRING_TYPES):
msg = bst_eval_set
else:
msg = bst_eval_set.decode()
if verbose_eval:
sys.stderr.write(msg + '\n')
if evals_result is not None:
res = re.findall(":-([0-9.]+).", msg)
for key, val in zip(evals_name, res):
evals_result[key].append(val)
score = float(msg.rsplit(':', 1)[1])
if (maximize_score and score > best_score) or \
(not maximize_score and score < best_score):
best_score = score
best_score_i = i
best_msg = msg
elif i - best_score_i >= early_stopping_rounds:
sys.stderr.write("Stopping. Best iteration:\n{}\n\n".format(best_msg))
bst.best_score = best_score
bst.best_iteration = best_score_i
break
bst.best_score = best_score
bst.best_iteration = best_score_i
return bst
class CVPack(object):
""""Auxiliary datastruct to hold one fold of CV."""
def __init__(self, dtrain, dtest, param):
""""Initialize the CVPack"""
self.dtrain = dtrain
self.dtest = dtest
self.watchlist = [(dtrain, 'train'), (dtest, 'test')]
self.bst = Booster(param, [dtrain, dtest])
def update(self, iteration, fobj):
""""Update the boosters for one iteration"""
self.bst.update(self.dtrain, iteration, fobj)
def eval(self, iteration, feval):
""""Evaluate the CVPack for one iteration."""
return self.bst.eval_set(self.watchlist, iteration, 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):
# pylint: disable=invalid-name
"""
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):
# pylint: disable = invalid-name
"""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 fold in cvfolds:
fold.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
# used for compatiblity without sklearn
XGBModelBase = object
XGBClassifierBase = object
XGBRegressorBase = object
if SKLEARN_INSTALLED:
XGBModelBase = BaseEstimator
XGBRegressorBase = RegressorMixin
XGBClassifierBase = ClassifierMixin
class XGBModel(XGBModelBase):
# pylint: disable=too-many-arguments, too-many-instance-attributes, invalid-name
"""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.
objective : string
Specify the learning task and the corresponding learning objective.
nthread : int
Number of parallel threads used to run xgboost.
gamma : float
Minimum loss reduction required to make a further partition on a leaf node of the tree.
min_child_weight : int
Minimum sum of instance weight(hessian) needed in a child.
max_delta_step : int
Maximum delta step we allow each tree's weight estimation to be.
subsample : float
Subsample ratio of the training instance.
colsample_bytree : float
Subsample ratio of columns when constructing each tree.
base_score:
The initial prediction score of all instances, global bias.
seed : int
Random number seed.
missing : float, optional
Value in the data which needs to be present as a missing value. If
None, defaults to np.nan.
"""
def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100,
silent=True, objective="reg:linear",
nthread=-1, gamma=0, min_child_weight=1, max_delta_step=0,
subsample=1, colsample_bytree=1,
base_score=0.5, seed=0, missing=None):
if not SKLEARN_INSTALLED:
raise XGBoostError('sklearn needs to be installed in order to use this module')
self.max_depth = max_depth
self.learning_rate = learning_rate
self.n_estimators = n_estimators
self.silent = silent
self.objective = objective
self.nthread = nthread
self.gamma = gamma
self.min_child_weight = min_child_weight
self.max_delta_step = max_delta_step
self.subsample = subsample
self.colsample_bytree = colsample_bytree
self.base_score = base_score
self.seed = seed
self.missing = missing if missing is not None else np.nan
self._Booster = None
def __setstate__(self, state):
# backward compatiblity code
# load booster from raw if it is raw
# the booster now support pickle
bst = state["_Booster"]
if bst is not None and not isinstance(bst, Booster):
state["_Booster"] = Booster(model_file=bst)
self.__dict__.update(state)
def booster(self):
"""Get the underlying xgboost Booster of this model.
This will raise an exception when fit was not called
Returns
-------
booster : a xgboost booster of underlying model
"""
if self._Booster is None:
raise XGBoostError('need to call fit beforehand')
return self._Booster
def get_params(self, deep=False):
"""Get parameter.s"""
params = super(XGBModel, self).get_params(deep=deep)
if params['missing'] is np.nan:
params['missing'] = None # sklearn doesn't handle nan. see #4725
if not params.get('eval_metric', True):
del params['eval_metric'] # don't give as None param to Booster
return params
def get_xgb_params(self):
"""Get xgboost type parameters."""
xgb_params = self.get_params()
xgb_params['silent'] = 1 if self.silent else 0
if self.nthread <= 0:
xgb_params.pop('nthread', None)
return xgb_params
def fit(self, X, y, eval_set=None, eval_metric=None,
early_stopping_rounds=None, verbose=True):
# pylint: disable=missing-docstring,invalid-name,attribute-defined-outside-init
"""
Fit the gradient boosting model
Parameters
----------
X : array_like
Feature matrix
y : array_like
Labels
eval_set : list, optional
A list of (X, y) tuple pairs to use as a validation set for
early-stopping
eval_metric : str, callable, optional
If a str, should be a built-in evaluation metric to use. See
doc/parameter.md. If callable, a custom evaluation metric. The call
signature is func(y_predicted, y_true) where y_true will be a
DMatrix object such that you may need to call the get_label
method. It must return a str, value pair where the str is a name
for the evaluation and value is the value of the evaluation
function. This objective is always minimized.
early_stopping_rounds : int
Activates early stopping. Validation error needs to decrease at
least every <early_stopping_rounds> round(s) to continue training.
Requires at least one item in evals. If there's more than one,
will use the last. Returns the model from the last iteration
(not the best one). If early stopping occurs, the model will
have two additional fields: bst.best_score and bst.best_iteration.
verbose : bool
If `verbose` and an evaluation set is used, writes the evaluation
metric measured on the validation set to stderr.
"""
trainDmatrix = DMatrix(X, label=y, missing=self.missing)
eval_results = {}
if eval_set is not None:
evals = list(DMatrix(x[0], label=x[1]) for x in eval_set)
evals = list(zip(evals, ["validation_{}".format(i) for i in
range(len(evals))]))
else:
evals = ()
params = self.get_xgb_params()
feval = eval_metric if callable(eval_metric) else None
if eval_metric is not None:
if callable(eval_metric):
eval_metric = None
else:
params.update({'eval_metric': eval_metric})
self._Booster = train(params, trainDmatrix,
self.n_estimators, evals=evals,
early_stopping_rounds=early_stopping_rounds,
evals_result=eval_results, feval=feval,
verbose_eval=verbose)
if eval_results:
eval_results = {k: np.array(v, dtype=float)
for k, v in eval_results.items()}
eval_results = {k: np.array(v) for k, v in eval_results.items()}
self.eval_results = eval_results
if early_stopping_rounds is not None:
self.best_score = self._Booster.best_score
self.best_iteration = self._Booster.best_iteration
return self
def predict(self, data):
# pylint: disable=missing-docstring,invalid-name
test_dmatrix = DMatrix(data, missing=self.missing)
return self.booster().predict(test_dmatrix)
class XGBClassifier(XGBModel, XGBClassifierBase):
# pylint: disable=missing-docstring,too-many-arguments,invalid-name
__doc__ = """
Implementation of the scikit-learn API for XGBoost classification
""" + "\n".join(XGBModel.__doc__.split('\n')[2:])
def __init__(self, max_depth=3, learning_rate=0.1,
n_estimators=100, silent=True,
objective="binary:logistic",
nthread=-1, gamma=0, min_child_weight=1,
max_delta_step=0, subsample=1, colsample_bytree=1,
base_score=0.5, seed=0, missing=None):
super(XGBClassifier, self).__init__(max_depth, learning_rate,
n_estimators, silent, objective,
nthread, gamma, min_child_weight,
max_delta_step, subsample,
colsample_bytree,
base_score, seed, missing)
def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None,
early_stopping_rounds=None, verbose=True):
# pylint: disable = attribute-defined-outside-init,arguments-differ
"""
Fit gradient boosting classifier
Parameters
----------
X : array_like
Feature matrix
y : array_like
Labels
sample_weight : array_like
Weight for each instance
eval_set : list, optional
A list of (X, y) pairs to use as a validation set for
early-stopping
eval_metric : str, callable, optional
If a str, should be a built-in evaluation metric to use. See
doc/parameter.md. If callable, a custom evaluation metric. The call
signature is func(y_predicted, y_true) where y_true will be a
DMatrix object such that you may need to call the get_label
method. It must return a str, value pair where the str is a name
for the evaluation and value is the value of the evaluation
function. This objective is always minimized.
early_stopping_rounds : int, optional
Activates early stopping. Validation error needs to decrease at
least every <early_stopping_rounds> round(s) to continue training.
Requires at least one item in evals. If there's more than one,
will use the last. Returns the model from the last iteration
(not the best one). If early stopping occurs, the model will
have two additional fields: bst.best_score and bst.best_iteration.
verbose : bool
If `verbose` and an evaluation set is used, writes the evaluation
metric measured on the validation set to stderr.
"""
eval_results = {}
self.classes_ = list(np.unique(y))
self.n_classes_ = len(self.classes_)
if self.n_classes_ > 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'] = self.n_classes_
else:
xgb_options = self.get_xgb_params()
feval = eval_metric if callable(eval_metric) else None
if eval_metric is not None:
if callable(eval_metric):
eval_metric = None
else:
xgb_options.update({"eval_metric": eval_metric})
if eval_set is not None:
# TODO: use sample_weight if given?
evals = list(DMatrix(x[0], label=x[1]) for x in eval_set)
nevals = len(evals)
eval_names = ["validation_{}".format(i) for i in range(nevals)]
evals = list(zip(evals, eval_names))
else:
evals = ()
self._le = LabelEncoder().fit(y)
training_labels = self._le.transform(y)
if sample_weight is not None:
train_dmatrix = DMatrix(X, label=training_labels, weight=sample_weight,
missing=self.missing)
else:
train_dmatrix = DMatrix(X, label=training_labels,
missing=self.missing)
self._Booster = train(xgb_options, train_dmatrix, self.n_estimators,
evals=evals,
early_stopping_rounds=early_stopping_rounds,
evals_result=eval_results, feval=feval,
verbose_eval=verbose)
if eval_results:
eval_results = {k: np.array(v, dtype=float)
for k, v in eval_results.items()}
self.eval_results = eval_results
if early_stopping_rounds is not None:
self.best_score = self._Booster.best_score
self.best_iteration = self._Booster.best_iteration
return self
def predict(self, data):
test_dmatrix = DMatrix(data, missing=self.missing)
class_probs = self.booster().predict(test_dmatrix)
if len(class_probs.shape) > 1:
column_indexes = np.argmax(class_probs, axis=1)
else:
column_indexes = np.repeat(0, data.shape[0])
column_indexes[class_probs > 0.5] = 1
return self._le.inverse_transform(column_indexes)
def predict_proba(self, data):
test_dmatrix = DMatrix(data, missing=self.missing)
class_probs = self.booster().predict(test_dmatrix)
if self.objective == "multi:softprob":
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, XGBRegressorBase):
# pylint: disable=missing-docstring
__doc__ = """
Implementation of the scikit-learn API for XGBoost regression
""" + "\n".join(XGBModel.__doc__.split('\n')[2:])

2
windows/README.md
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@ -11,7 +11,7 @@ This should give you xgboost.exe for CLI version and xgboost_wrapper.dll for pyt
Use Python Module Use Python Module
===== =====
* After you build the dll, you can install the Python package from the [../wrapper](../wrapper) folder * After you build the dll, you can install the Python package from the [../python-package](../python-package) folder
``` ```
python setup.py install python setup.py install

27
wrapper/README.md
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@ -1,20 +1,9 @@
Wrapper of XGBoost XGBoost Wrappers
===== ================
This folder provides wrapper of xgboost to other languages This folder provides wrapper to create xgboost packages to other languages.
Python ***Supported Language Packages***
===== * [Python package](../python-package)
* To make the python module, type ```./build.sh``` in the root directory of project * [R-package](../R-package)
* Make sure you have [setuptools](https://pypi.python.org/pypi/setuptools) * [Java Package](../java)
* Install with `python setup.py install` from this directory. * [Julia Package](https://github.com/antinucleon/XGBoost.jl)
* Refer also to the walk through example in [demo folder](../demo/guide-python)
* **NOTE**: if you want to run XGBoost process in parallel using the fork backend for joblib/multiprocessing, you must build XGBoost without support for OpenMP by `make no_omp=1`. Otherwise, use the forkserver (in Python 3.4) or spawn backend. See the sklearn_parallel.py demo.
R
=====
* See [R-package](../R-package)
Julia
=====
* See [XGBoost.jl](https://github.com/antinucleon/XGBoost.jl)

0
wrapper/__init__.py
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39
wrapper/setup.py
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@ -1,39 +0,0 @@
# pylint: disable=invalid-name
"""Setup xgboost package."""
import os
import platform
from setuptools import setup
class XGBoostLibraryNotFound(Exception):
"""Exception to raise when xgboost library cannot be found."""
pass
curr_dir = os.path.dirname(os.path.abspath(__file__))
dll_path = [curr_dir]
if os.name == 'nt':
if platform.architecture()[0] == '64bit':
dll_path.append(os.path.join(curr_dir, '../windows/x64/Release/'))
else:
dll_path.append(os.path.join(curr_dir, '../windows/Release/'))
if os.name == 'nt':
dll_path = [os.path.join(p, 'xgboost_wrapper.dll') for p in dll_path]
else:
dll_path = [os.path.join(p, 'libxgboostwrapper.so') for p in dll_path]
lib_path = [p for p in dll_path if os.path.exists(p) and os.path.isfile(p)]
if len(lib_path) == 0:
raise XGBoostLibraryNotFound("XGBoost library not found. Did you run "
"../make?")
setup(name="xgboost",
version="0.40",
description="Python wrappers for XGBoost: eXtreme Gradient Boosting",
zip_safe=False,
py_modules=['xgboost'],
data_files=[('.', [lib_path[0]])],
url="https://github.com/dmlc/xgboost")