Fixed all lint errors

python-package/.pylintrc

@@ -2,7 +2,7 @@
 
 ignore=tests
 
-unexpected-special-method-signature,too-many-nested-blocks,consider-using-enumerate
+unexpected-special-method-signature,too-many-nested-blocks
 
 dummy-variables-rgx=(unused|)_.*
 

python-package/setup_pip.py

@@ -11,7 +11,7 @@ sys.path.insert(0, '.')
 #it builds xgboost code on the fly and packs for pip
 #please don't use this file for installing from github
 
-if not os.name == 'nt': #if not windows, compile and install
+if os.name != 'nt': #if not windows, compile and install
     os.system('sh ./xgboost/build-python.sh')
 else:
     print('Windows users please use github installation.')

python-package/xgboost/compat.py

@@ -1,5 +1,5 @@
 # coding: utf-8
-# pylint: disable=unused-import, invalid-name
+# pylint: disable=unused-import, invalid-name, wrong-import-position
 """For compatibility"""
 
 from __future__ import absolute_import

python-package/xgboost/core.py

@@ -890,6 +890,7 @@ class Booster(object):
             _check_call(_LIB.XGBoosterLoadModelFromBuffer(self.handle, ptr, length))
 
     def dump_model(self, fout, fmap='', with_stats=False):
+        # pylint: disable=consider-using-enumerate
         """
         Dump model into a text file.
 

python-package/xgboost/plotting.py

@@ -5,12 +5,11 @@
 from __future__ import absolute_import
 
 import re
+from io import BytesIO
 import numpy as np
 from .core import Booster
 from .sklearn import XGBModel
 
-from io import BytesIO
-
 def plot_importance(booster, ax=None, height=0.2,
                     xlim=None, ylim=None, title='Feature importance',
                     xlabel='F score', ylabel='Features',

python-package/xgboost/sklearn.py

@@ -130,7 +130,7 @@ class XGBModel(XGBModelBase):
 
     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
+        # pylint: disable=missing-docstring,invalid-name,attribute-defined-outside-init, redefined-variable-type
         """
         Fit the gradient boosting model
 
@@ -265,7 +265,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
 
     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
+        # pylint: disable = attribute-defined-outside-init,arguments-differ, redefined-variable-type
         """
         Fit gradient boosting classifier