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Migrate pylint check to Python 3 (#4381)

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* Migrate lint to Python 3

* Fix lint errors

* Use Miniconda3 to use Python 3.7

* Use latest pylint and astroid
This commit is contained in:
Philip Hyunsu Cho 2019-04-21 01:01:54 -07:00 committed by GitHub
parent 5e97de6a41
commit bbe0dbd7ec
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11 changed files with 118 additions and 117 deletions
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4
Makefile
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@ -173,10 +173,10 @@ xgboost: $(CLI_OBJ) $(ALL_DEP)
$(CXX) $(CFLAGS) -o $@ $(filter %.o %.a, $^) $(LDFLAGS) $(CXX) $(CFLAGS) -o $@ $(filter %.o %.a, $^) $(LDFLAGS)
rcpplint: rcpplint:
python2 dmlc-core/scripts/lint.py xgboost ${LINT_LANG} R-package/src python3 dmlc-core/scripts/lint.py xgboost ${LINT_LANG} R-package/src
lint: rcpplint lint: rcpplint
python2 dmlc-core/scripts/lint.py xgboost ${LINT_LANG} include src plugin python-package python3 dmlc-core/scripts/lint.py --pylint-rc ${PWD}/python-package/.pylintrc xgboost ${LINT_LANG} include src plugin python-package
pylint: pylint:
flake8 --ignore E501 python-package flake8 --ignore E501 python-package

2
python-package/.pylintrc
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@ -4,7 +4,7 @@ ignore=tests
extension-pkg-whitelist=numpy extension-pkg-whitelist=numpy
disiable=unexpected-special-method-signature,too-many-nested-blocks disable=unexpected-special-method-signature,too-many-nested-blocks,useless-object-inheritance
dummy-variables-rgx=(unused|)_.* dummy-variables-rgx=(unused|)_.*

22
python-package/xgboost/callback.py
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@ -1,5 +1,5 @@
# coding: utf-8 # coding: utf-8
# pylint: disable= invalid-name # pylint: disable=invalid-name, too-many-statements
"""Training Library containing training routines.""" """Training Library containing training routines."""
from __future__ import absolute_import from __future__ import absolute_import
@ -20,13 +20,11 @@ def _fmt_metric(value, show_stdv=True):
"""format metric string""" """format metric string"""
if len(value) == 2: if len(value) == 2:
return '%s:%g' % (value[0], value[1]) return '%s:%g' % (value[0], value[1])
elif len(value) == 3: if len(value) == 3:
if show_stdv: if show_stdv:
return '%s:%g+%g' % (value[0], value[1], value[2]) return '%s:%g+%g' % (value[0], value[1], value[2])
else: return '%s:%g' % (value[0], value[1])
return '%s:%g' % (value[0], value[1]) raise ValueError("wrong metric value")
else:
raise ValueError("wrong metric value")
def print_evaluation(period=1, show_stdv=True): def print_evaluation(period=1, show_stdv=True):
@ -50,10 +48,10 @@ def print_evaluation(period=1, show_stdv=True):
""" """
def callback(env): def callback(env):
"""internal function""" """internal function"""
if env.rank != 0 or len(env.evaluation_result_list) == 0 or period is False or period == 0: if env.rank != 0 or (not env.evaluation_result_list) or period is False or period == 0:
return return
i = env.iteration i = env.iteration
if (i % period == 0 or i + 1 == env.begin_iteration or i + 1 == env.end_iteration): if i % period == 0 or i + 1 == env.begin_iteration or i + 1 == env.end_iteration:
msg = '\t'.join([_fmt_metric(x, show_stdv) for x in env.evaluation_result_list]) msg = '\t'.join([_fmt_metric(x, show_stdv) for x in env.evaluation_result_list])
rabit.tracker_print('[%d]\t%s\n' % (i, msg)) rabit.tracker_print('[%d]\t%s\n' % (i, msg))
return callback return callback
@ -89,7 +87,7 @@ def record_evaluation(eval_result):
def callback(env): def callback(env):
"""internal function""" """internal function"""
if len(eval_result) == 0: if not eval_result:
init(env) init(env)
for k, v in env.evaluation_result_list: for k, v in env.evaluation_result_list:
pos = k.index('-') pos = k.index('-')
@ -182,14 +180,14 @@ def early_stop(stopping_rounds, maximize=False, verbose=True):
"""internal function""" """internal function"""
bst = env.model bst = env.model
if len(env.evaluation_result_list) == 0: if not env.evaluation_result_list:
raise ValueError('For early stopping you need at least one set in evals.') raise ValueError('For early stopping you need at least one set in evals.')
if len(env.evaluation_result_list) > 1 and verbose: if len(env.evaluation_result_list) > 1 and verbose:
msg = ("Multiple eval metrics have been passed: " msg = ("Multiple eval metrics have been passed: "
"'{0}' will be used for early stopping.\n\n") "'{0}' will be used for early stopping.\n\n")
rabit.tracker_print(msg.format(env.evaluation_result_list[-1][0])) rabit.tracker_print(msg.format(env.evaluation_result_list[-1][0]))
maximize_metrics = ('auc', 'aucpr', 'map', 'ndcg') maximize_metrics = ('auc', 'aucpr', 'map', 'ndcg')
maximize_at_n_metrics = ('auc@', 'aucpr@' 'map@', 'ndcg@') maximize_at_n_metrics = ('auc@', 'aucpr@', 'map@', 'ndcg@')
maximize_score = maximize maximize_score = maximize
metric_label = env.evaluation_result_list[-1][0] metric_label = env.evaluation_result_list[-1][0]
metric = metric_label.split('-', 1)[-1] metric = metric_label.split('-', 1)[-1]
@ -225,7 +223,7 @@ def early_stop(stopping_rounds, maximize=False, verbose=True):
def callback(env): def callback(env):
"""internal function""" """internal function"""
score = env.evaluation_result_list[-1][1] score = env.evaluation_result_list[-1][1]
if len(state) == 0: if not state:
init(env) init(env)
best_score = state['best_score'] best_score = state['best_score']
best_iteration = state['best_iteration'] best_iteration = state['best_iteration']

11
python-package/xgboost/compat.py
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@ -11,14 +11,13 @@ PY3 = (sys.version_info[0] == 3)
if PY3: if PY3:
# pylint: disable=invalid-name, redefined-builtin # pylint: disable=invalid-name, redefined-builtin
STRING_TYPES = str, STRING_TYPES = (str,)
def py_str(x): def py_str(x):
"""convert c string back to python string""" """convert c string back to python string"""
return x.decode('utf-8') return x.decode('utf-8')
else: else:
# pylint: disable=invalid-name STRING_TYPES = (basestring,) # pylint: disable=undefined-variable
STRING_TYPES = basestring,
def py_str(x): def py_str(x):
"""convert c string back to python string""" """convert c string back to python string"""
@ -37,13 +36,13 @@ try:
PANDAS_INSTALLED = True PANDAS_INSTALLED = True
except ImportError: except ImportError:
# pylint: disable=too-few-public-methods
class MultiIndex(object): class MultiIndex(object):
""" dummy for pandas.MultiIndex """ """ dummy for pandas.MultiIndex """
pass
# pylint: disable=too-few-public-methods
class DataFrame(object): class DataFrame(object):
""" dummy for pandas.DataFrame """ """ dummy for pandas.DataFrame """
pass
PANDAS_INSTALLED = False PANDAS_INSTALLED = False
@ -57,9 +56,9 @@ try:
DT_INSTALLED = True DT_INSTALLED = True
except ImportError: except ImportError:
# pylint: disable=too-few-public-methods
class DataTable(object): class DataTable(object):
""" dummy for datatable.DataTable """ """ dummy for datatable.DataTable """
pass
DT_INSTALLED = False DT_INSTALLED = False

129
python-package/xgboost/core.py
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@ -1,6 +1,6 @@
# coding: utf-8 # coding: utf-8
# pylint: disable=too-many-arguments, too-many-branches, invalid-name # pylint: disable=too-many-arguments, too-many-branches, invalid-name
# pylint: disable=too-many-branches, too-many-lines, W0141 # pylint: disable=too-many-branches, too-many-lines, too-many-locals
"""Core XGBoost Library.""" """Core XGBoost Library."""
from __future__ import absolute_import from __future__ import absolute_import
import collections import collections
@ -30,7 +30,6 @@ c_bst_ulong = ctypes.c_uint64
class XGBoostError(Exception): class XGBoostError(Exception):
"""Error thrown by xgboost trainer.""" """Error thrown by xgboost trainer."""
pass
class EarlyStopException(Exception): class EarlyStopException(Exception):
@ -67,18 +66,16 @@ def from_pystr_to_cstr(data):
list of str list of str
""" """
if isinstance(data, list): if not isinstance(data, list):
pointers = (ctypes.c_char_p * len(data))()
if PY3:
data = [bytes(d, 'utf-8') for d in data]
else:
data = [d.encode('utf-8') if isinstance(d, unicode) else d
for d in data]
pointers[:] = data
return pointers
else:
# copy from above when we actually use it
raise NotImplementedError raise NotImplementedError
pointers = (ctypes.c_char_p * len(data))()
if PY3:
data = [bytes(d, 'utf-8') for d in data]
else:
data = [d.encode('utf-8') if isinstance(d, unicode) else d # pylint: disable=undefined-variable
for d in data]
pointers[:] = data
return pointers
def from_cstr_to_pystr(data, length): def from_cstr_to_pystr(data, length):
@ -104,6 +101,7 @@ def from_cstr_to_pystr(data, length):
try: try:
res.append(str(data[i].decode('ascii'))) res.append(str(data[i].decode('ascii')))
except UnicodeDecodeError: except UnicodeDecodeError:
# pylint: disable=undefined-variable
res.append(unicode(data[i].decode('utf-8'))) res.append(unicode(data[i].decode('utf-8')))
return res return res
@ -123,7 +121,7 @@ def _get_log_callback_func():
def _load_lib(): def _load_lib():
"""Load xgboost Library.""" """Load xgboost Library."""
lib_paths = find_lib_path() lib_paths = find_lib_path()
if len(lib_paths) == 0: if not lib_paths:
return None return None
try: try:
pathBackup = os.environ['PATH'].split(os.pathsep) pathBackup = os.environ['PATH'].split(os.pathsep)
@ -243,7 +241,7 @@ def _maybe_pandas_data(data, feature_names, feature_types):
if feature_names is None: if feature_names is None:
if isinstance(data.columns, MultiIndex): if isinstance(data.columns, MultiIndex):
feature_names = [ feature_names = [
' '.join(map(str, i)) ' '.join([str(x) for x in i])
for i in data.columns for i in data.columns
] ]
else: else:
@ -267,8 +265,7 @@ def _maybe_pandas_label(label):
label_dtypes = label.dtypes label_dtypes = label.dtypes
if not all(dtype.name in PANDAS_DTYPE_MAPPER for dtype in label_dtypes): if not all(dtype.name in PANDAS_DTYPE_MAPPER for dtype in label_dtypes):
raise ValueError('DataFrame.dtypes for label must be int, float or bool') raise ValueError('DataFrame.dtypes for label must be int, float or bool')
else: label = label.values.astype('float')
label = label.values.astype('float')
# pd.Series can be passed to xgb as it is # pd.Series can be passed to xgb as it is
return label return label
@ -301,8 +298,7 @@ def _maybe_dt_data(data, feature_names, feature_types):
# always return stypes for dt ingestion # always return stypes for dt ingestion
if feature_types is not None: if feature_types is not None:
raise ValueError('DataTable has own feature types, cannot pass them in') raise ValueError('DataTable has own feature types, cannot pass them in')
else: feature_types = np.vectorize(DT_TYPE_MAPPER2.get)(data_types_names)
feature_types = np.vectorize(DT_TYPE_MAPPER2.get)(data_types_names)
return data, feature_names, feature_types return data, feature_names, feature_types
@ -512,7 +508,7 @@ class DMatrix(object):
ptrs[icol] = ctypes.c_void_p(ptr) ptrs[icol] = ctypes.c_void_p(ptr)
else: else:
# datatable<=0.8.0 # datatable<=0.8.0
from datatable.internal import frame_column_data_r from datatable.internal import frame_column_data_r # pylint: disable=no-name-in-module,import-error
for icol in range(data.ncols): for icol in range(data.ncols):
ptrs[icol] = frame_column_data_r(data, icol) ptrs[icol] = frame_column_data_r(data, icol)
@ -1039,8 +1035,7 @@ class Booster(object):
self.handle, c_str(key), ctypes.byref(ret), ctypes.byref(success))) self.handle, c_str(key), ctypes.byref(ret), ctypes.byref(success)))
if success.value != 0: if success.value != 0:
return py_str(ret.value) return py_str(ret.value)
else: return None
return None
def attributes(self): def attributes(self):
"""Get attributes stored in the Booster as a dictionary. """Get attributes stored in the Booster as a dictionary.
@ -1056,8 +1051,7 @@ class Booster(object):
ctypes.byref(length), ctypes.byref(length),
ctypes.byref(sarr))) ctypes.byref(sarr)))
attr_names = from_cstr_to_pystr(sarr, length) attr_names = from_cstr_to_pystr(sarr, length)
res = dict([(n, self.attr(n)) for n in attr_names]) return {n: self.attr(n) for n in attr_names}
return res
def set_attr(self, **kwargs): def set_attr(self, **kwargs):
"""Set the attribute of the Booster. """Set the attribute of the Booster.
@ -1399,13 +1393,13 @@ class Booster(object):
ret = self.get_dump(fmap, with_stats, dump_format) ret = self.get_dump(fmap, with_stats, dump_format)
if dump_format == 'json': if dump_format == 'json':
fout.write('[\n') fout.write('[\n')
for i in range(len(ret)): for i, _ in enumerate(ret):
fout.write(ret[i]) fout.write(ret[i])
if i < len(ret) - 1: if i < len(ret) - 1:
fout.write(",\n") fout.write(",\n")
fout.write('\n]') fout.write('\n]')
else: else:
for i in range(len(ret)): for i, _ in enumerate(ret):
fout.write('booster[{}]:\n'.format(i)) fout.write('booster[{}]:\n'.format(i))
fout.write(ret[i]) fout.write(ret[i])
if need_close: if need_close:
@ -1538,51 +1532,50 @@ class Booster(object):
return fmap return fmap
else: average_over_splits = True
average_over_splits = True if importance_type == 'total_gain':
if importance_type == 'total_gain': importance_type = 'gain'
importance_type = 'gain' average_over_splits = False
average_over_splits = False elif importance_type == 'total_cover':
elif importance_type == 'total_cover': importance_type = 'cover'
importance_type = 'cover' average_over_splits = False
average_over_splits = False
trees = self.get_dump(fmap, with_stats=True) trees = self.get_dump(fmap, with_stats=True)
importance_type += '=' importance_type += '='
fmap = {} fmap = {}
gmap = {} gmap = {}
for tree in trees: for tree in trees:
for line in tree.split('\n'): for line in tree.split('\n'):
# look for the opening square bracket # look for the opening square bracket
arr = line.split('[') arr = line.split('[')
# if no opening bracket (leaf node), ignore this line # if no opening bracket (leaf node), ignore this line
if len(arr) == 1: if len(arr) == 1:
continue continue
# look for the closing bracket, extract only info within that bracket # look for the closing bracket, extract only info within that bracket
fid = arr[1].split(']') fid = arr[1].split(']')
# extract gain or cover from string after closing bracket # extract gain or cover from string after closing bracket
g = float(fid[1].split(importance_type)[1].split(',')[0]) g = float(fid[1].split(importance_type)[1].split(',')[0])
# extract feature name from string before closing bracket # extract feature name from string before closing bracket
fid = fid[0].split('<')[0] fid = fid[0].split('<')[0]
if fid not in fmap: if fid not in fmap:
# if the feature hasn't been seen yet # if the feature hasn't been seen yet
fmap[fid] = 1 fmap[fid] = 1
gmap[fid] = g gmap[fid] = g
else: else:
fmap[fid] += 1 fmap[fid] += 1
gmap[fid] += g gmap[fid] += g
# calculate average value (gain/cover) for each feature # calculate average value (gain/cover) for each feature
if average_over_splits: if average_over_splits:
for fid in gmap: for fid in gmap:
gmap[fid] = gmap[fid] / fmap[fid] gmap[fid] = gmap[fid] / fmap[fid]
return gmap return gmap
def trees_to_dataframe(self, fmap=''): def trees_to_dataframe(self, fmap=''):
"""Parse a boosted tree model text dump into a pandas DataFrame structure. """Parse a boosted tree model text dump into a pandas DataFrame structure.
@ -1721,9 +1714,9 @@ class Booster(object):
xgdump = self.get_dump(fmap=fmap) xgdump = self.get_dump(fmap=fmap)
values = [] values = []
regexp = re.compile(r"\[{0}<([\d.Ee+-]+)\]".format(feature)) regexp = re.compile(r"\[{0}<([\d.Ee+-]+)\]".format(feature))
for i in range(len(xgdump)): for i, _ in enumerate(xgdump):
m = re.findall(regexp, xgdump[i]) m = re.findall(regexp, xgdump[i])
values.extend(map(float, m)) values.extend([float(x) for x in m])
n_unique = len(np.unique(values)) n_unique = len(np.unique(values))
bins = max(min(n_unique, bins) if bins is not None else n_unique, 1) bins = max(min(n_unique, bins) if bins is not None else n_unique, 1)
@ -1734,9 +1727,7 @@ class Booster(object):
if as_pandas and PANDAS_INSTALLED: if as_pandas and PANDAS_INSTALLED:
return DataFrame(nph, columns=['SplitValue', 'Count']) return DataFrame(nph, columns=['SplitValue', 'Count'])
elif as_pandas and not PANDAS_INSTALLED: if as_pandas and not PANDAS_INSTALLED:
sys.stderr.write( sys.stderr.write(
"Returning histogram as ndarray (as_pandas == True, but pandas is not installed).") "Returning histogram as ndarray (as_pandas == True, but pandas is not installed).")
return nph return nph
else:
return nph

1
python-package/xgboost/libpath.py
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@ -8,7 +8,6 @@ import sys
class XGBoostLibraryNotFound(Exception): class XGBoostLibraryNotFound(Exception):
"""Error thrown by when xgboost is not found""" """Error thrown by when xgboost is not found"""
pass
def find_lib_path(): def find_lib_path():

4
python-package/xgboost/plotting.py
View File

@ -55,7 +55,6 @@ def plot_importance(booster, ax=None, height=0.2,
------- -------
ax : matplotlib Axes ax : matplotlib Axes
""" """
# TODO: move this to compat.py
try: try:
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
except ImportError: except ImportError:
@ -70,11 +69,12 @@ def plot_importance(booster, ax=None, height=0.2,
else: else:
raise ValueError('tree must be Booster, XGBModel or dict instance') raise ValueError('tree must be Booster, XGBModel or dict instance')
if len(importance) == 0: if not importance:
raise ValueError('Booster.get_score() results in empty') raise ValueError('Booster.get_score() results in empty')
tuples = [(k, importance[k]) for k in importance] tuples = [(k, importance[k]) for k in importance]
if max_num_features is not None: if max_num_features is not None:
# pylint: disable=invalid-unary-operand-type
tuples = sorted(tuples, key=lambda x: x[1])[-max_num_features:] tuples = sorted(tuples, key=lambda x: x[1])[-max_num_features:]
else: else:
tuples = sorted(tuples, key=lambda x: x[1]) tuples = sorted(tuples, key=lambda x: x[1])

33
python-package/xgboost/sklearn.py
View File

@ -3,9 +3,9 @@
"""Scikit-Learn Wrapper interface for XGBoost.""" """Scikit-Learn Wrapper interface for XGBoost."""
from __future__ import absolute_import from __future__ import absolute_import
import numpy as np
import warnings import warnings
import json import json
import numpy as np
from .core import Booster, DMatrix, XGBoostError from .core import Booster, DMatrix, XGBoostError
from .training import train from .training import train
@ -107,15 +107,15 @@ class XGBModel(XGBModelBase):
importance_type: string, default "gain" importance_type: string, default "gain"
The feature importance type for the feature_importances_ property: either "gain", The feature importance type for the feature_importances_ property: either "gain",
"weight", "cover", "total_gain" or "total_cover". "weight", "cover", "total_gain" or "total_cover".
\*\*kwargs : dict, optional \\*\\*kwargs : dict, optional
Keyword arguments for XGBoost Booster object. Full documentation of parameters can Keyword arguments for XGBoost Booster object. Full documentation of parameters can
be found here: https://github.com/dmlc/xgboost/blob/master/doc/parameter.rst. be found here: https://github.com/dmlc/xgboost/blob/master/doc/parameter.rst.
Attempting to set a parameter via the constructor args and \*\*kwargs dict simultaneously Attempting to set a parameter via the constructor args and \\*\\*kwargs dict simultaneously
will result in a TypeError. will result in a TypeError.
.. note:: \*\*kwargs unsupported by scikit-learn .. note:: \\*\\*kwargs unsupported by scikit-learn
\*\*kwargs is unsupported by scikit-learn. We do not guarantee that parameters \\*\\*kwargs is unsupported by scikit-learn. We do not guarantee that parameters
passed via this argument will interact properly with scikit-learn. passed via this argument will interact properly with scikit-learn.
Note Note
@ -597,7 +597,7 @@ class XGBModel(XGBModelBase):
class XGBClassifier(XGBModel, XGBClassifierBase): class XGBClassifier(XGBModel, XGBClassifierBase):
# pylint: disable=missing-docstring,too-many-arguments,invalid-name # pylint: disable=missing-docstring,too-many-arguments,invalid-name,too-many-instance-attributes
__doc__ = "Implementation of the scikit-learn API for XGBoost classification.\n\n" \ __doc__ = "Implementation of the scikit-learn API for XGBoost classification.\n\n" \
+ '\n'.join(XGBModel.__doc__.split('\n')[2:]) + '\n'.join(XGBModel.__doc__.split('\n')[2:])
@ -834,10 +834,9 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
validate_features=validate_features) validate_features=validate_features)
if self.objective == "multi:softprob": if self.objective == "multi:softprob":
return class_probs return class_probs
else: classone_probs = class_probs
classone_probs = class_probs classzero_probs = 1.0 - classone_probs
classzero_probs = 1.0 - classone_probs return np.vstack((classzero_probs, classone_probs)).transpose()
return np.vstack((classzero_probs, classone_probs)).transpose()
def evals_result(self): def evals_result(self):
"""Return the evaluation results. """Return the evaluation results.
@ -1008,15 +1007,15 @@ class XGBRanker(XGBModel):
missing : float, optional missing : float, optional
Value in the data which needs to be present as a missing value. If Value in the data which needs to be present as a missing value. If
None, defaults to np.nan. None, defaults to np.nan.
\*\*kwargs : dict, optional \\*\\*kwargs : dict, optional
Keyword arguments for XGBoost Booster object. Full documentation of parameters can Keyword arguments for XGBoost Booster object. Full documentation of parameters can
be found here: https://github.com/dmlc/xgboost/blob/master/doc/parameter.rst. be found here: https://github.com/dmlc/xgboost/blob/master/doc/parameter.rst.
Attempting to set a parameter via the constructor args and \*\*kwargs dict Attempting to set a parameter via the constructor args and \\*\\*kwargs dict
simultaneously will result in a TypeError. simultaneously will result in a TypeError.
.. note:: \*\*kwargs unsupported by scikit-learn .. note:: \\*\\*kwargs unsupported by scikit-learn
\*\*kwargs is unsupported by scikit-learn. We do not guarantee that parameters \\*\\*kwargs is unsupported by scikit-learn. We do not guarantee that parameters
passed via this argument will interact properly with scikit-learn. passed via this argument will interact properly with scikit-learn.
Note Note
@ -1073,7 +1072,7 @@ class XGBRanker(XGBModel):
random_state=random_state, seed=seed, missing=missing, **kwargs) random_state=random_state, seed=seed, missing=missing, **kwargs)
if callable(self.objective): if callable(self.objective):
raise ValueError("custom objective function not supported by XGBRanker") raise ValueError("custom objective function not supported by XGBRanker")
elif "rank:" not in self.objective: if "rank:" not in self.objective:
raise ValueError("please use XGBRanker for ranking task") raise ValueError("please use XGBRanker for ranking task")
def fit(self, X, y, group, sample_weight=None, eval_set=None, sample_weight_eval_set=None, def fit(self, X, y, group, sample_weight=None, eval_set=None, sample_weight_eval_set=None,
@ -1158,9 +1157,9 @@ class XGBRanker(XGBModel):
if eval_set is not None: if eval_set is not None:
if eval_group is None: if eval_group is None:
raise ValueError("eval_group is required if eval_set is not None") raise ValueError("eval_group is required if eval_set is not None")
elif len(eval_group) != len(eval_set): if len(eval_group) != len(eval_set):
raise ValueError("length of eval_group should match that of eval_set") raise ValueError("length of eval_group should match that of eval_set")
elif any(group is None for group in eval_group): if any(group is None for group in eval_group):
raise ValueError("group is required for all eval datasets for ranking task") raise ValueError("group is required for all eval datasets for ranking task")
def _dmat_init(group, **params): def _dmat_init(group, **params):

10
python-package/xgboost/training.py
View File

@ -49,7 +49,7 @@ def _train_internal(params, dtrain,
# Distributed code: Load the checkpoint from rabit. # Distributed code: Load the checkpoint from rabit.
version = bst.load_rabit_checkpoint() version = bst.load_rabit_checkpoint()
assert(rabit.get_world_size() != 1 or version == 0) assert rabit.get_world_size() != 1 or version == 0
rank = rabit.get_rank() rank = rabit.get_rank()
start_iteration = int(version / 2) start_iteration = int(version / 2)
nboost += start_iteration nboost += start_iteration
@ -75,12 +75,12 @@ def _train_internal(params, dtrain,
bst.save_rabit_checkpoint() bst.save_rabit_checkpoint()
version += 1 version += 1
assert(rabit.get_world_size() == 1 or version == rabit.version_number()) assert rabit.get_world_size() == 1 or version == rabit.version_number()
nboost += 1 nboost += 1
evaluation_result_list = [] evaluation_result_list = []
# check evaluation result. # check evaluation result.
if len(evals) != 0: if evals:
bst_eval_set = bst.eval_set(evals, i, feval) bst_eval_set = bst.eval_set(evals, i, feval)
if isinstance(bst_eval_set, STRING_TYPES): if isinstance(bst_eval_set, STRING_TYPES):
msg = bst_eval_set msg = bst_eval_set
@ -402,7 +402,7 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None
else: else:
params = dict((k, v) for k, v in params.items()) params = dict((k, v) for k, v in params.items())
if len(metrics) == 0 and 'eval_metric' in params: if (not metrics) and 'eval_metric' in params:
if isinstance(params['eval_metric'], list): if isinstance(params['eval_metric'], list):
metrics = params['eval_metric'] metrics = params['eval_metric']
else: else:
@ -462,7 +462,7 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None
rank=0, rank=0,
evaluation_result_list=res)) evaluation_result_list=res))
except EarlyStopException as e: except EarlyStopException as e:
for k in results.keys(): for k in results:
results[k] = results[k][:(e.best_iteration + 1)] results[k] = results[k][:(e.best_iteration + 1)]
break break
if as_pandas: if as_pandas:

3
tests/travis/run_test.sh
View File

@ -1,6 +1,9 @@
#!/bin/bash #!/bin/bash
if [ ${TASK} == "lint" ]; then if [ ${TASK} == "lint" ]; then
source activate python3
conda install numpy scipy
python -m pip install cpplint pylint astroid
make lint || exit -1 make lint || exit -1
echo "Check documentations..." echo "Check documentations..."

16
tests/travis/setup.sh
View File

@ -1,7 +1,19 @@
#!/bin/bash #!/bin/bash
if [ ${TASK} == "lint" ]; then if [ ${TASK} == "lint" ]; then
pip install --user cpplint 'pylint==1.4.4' 'astroid==1.3.6' if [ ${TRAVIS_OS_NAME} == "osx" ]; 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/Miniconda3-latest-Linux-x86_64.sh
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
conda create -n python3 python=3.7
fi fi
@ -18,6 +30,6 @@ if [ ${TASK} == "python_test" ] || [ ${TASK} == "python_lightweight_test" ] || [
conda update -q conda conda update -q conda
# Useful for debugging any issues with conda # Useful for debugging any issues with conda
conda info -a conda info -a
conda create -n python3 python=3.5 conda create -n python3 python=3.7
conda create -n python2 python=2.7 conda create -n python2 python=2.7
fi fi