зеркало из https://github.com/microsoft/LightGBM.git
[ci][python] run isort in CI linting job (#3990)
* run isort in CI linting job * workaround conda compatibility issues
This commit is contained in:
Родитель
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Коммит
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@ -10,6 +10,7 @@ import json
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from os import environ
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from os import environ
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from sys import argv, exit
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from sys import argv, exit
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from time import sleep
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from time import sleep
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try:
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try:
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from urllib import request
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from urllib import request
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except ImportError:
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except ImportError:
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@ -20,8 +20,8 @@ cd $BUILD_DIRECTORY
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if [[ $TASK == "check-docs" ]] || [[ $TASK == "check-links" ]]; then
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if [[ $TASK == "check-docs" ]] || [[ $TASK == "check-links" ]]; then
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cd $BUILD_DIRECTORY/docs
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cd $BUILD_DIRECTORY/docs
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conda install -q -y -n $CONDA_ENV -c conda-forge doxygen
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conda install -q -y -n $CONDA_ENV -c conda-forge doxygen rstcheck
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pip install --user -r requirements.txt rstcheck
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pip install --user -r requirements.txt
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# check reStructuredText formatting
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# check reStructuredText formatting
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cd $BUILD_DIRECTORY/python-package
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cd $BUILD_DIRECTORY/python-package
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rstcheck --report warning `find . -type f -name "*.rst"` || exit -1
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rstcheck --report warning `find . -type f -name "*.rst"` || exit -1
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@ -55,10 +55,11 @@ if [[ $TASK == "lint" ]]; then
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libxml2 \
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libxml2 \
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"r-xfun>=0.19" \
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"r-xfun>=0.19" \
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"r-lintr>=2.0"
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"r-lintr>=2.0"
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pip install --user cpplint mypy
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pip install --user cpplint isort mypy
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echo "Linting Python code"
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echo "Linting Python code"
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pycodestyle --ignore=E501,W503 --exclude=./.nuget,./external_libs . || exit -1
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pycodestyle --ignore=E501,W503 --exclude=./.nuget,./external_libs . || exit -1
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pydocstyle --convention=numpy --add-ignore=D105 --match-dir="^(?!^external_libs|test|example).*" --match="(?!^test_|setup).*\.py" . || exit -1
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pydocstyle --convention=numpy --add-ignore=D105 --match-dir="^(?!^external_libs|test|example).*" --match="(?!^test_|setup).*\.py" . || exit -1
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isort . --check-only || exit -1
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mypy --ignore-missing-imports python-package/ || true
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mypy --ignore-missing-imports python-package/ || true
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echo "Linting R code"
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echo "Linting R code"
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Rscript ${BUILD_DIRECTORY}/.ci/lint_r_code.R ${BUILD_DIRECTORY} || exit -1
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Rscript ${BUILD_DIRECTORY}/.ci/lint_r_code.R ${BUILD_DIRECTORY} || exit -1
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@ -9,6 +9,9 @@ indent_size = 2
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[*.{py,sh,js}]
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[*.{py,sh,js}]
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indent_size = 4
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indent_size = 4
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line_length = 120
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skip = external_libs
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known_first_party = lightgbm
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# Placeholder files
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# Placeholder files
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[{*.gitkeep,__init__.py}]
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[{*.gitkeep,__init__.py}]
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@ -3,10 +3,8 @@
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import datetime
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import datetime
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import os
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import os
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import sys
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import sys
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from distutils.file_util import copy_file
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from distutils.file_util import copy_file
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if __name__ == "__main__":
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if __name__ == "__main__":
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source = sys.argv[1]
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source = sys.argv[1]
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current_dir = os.path.abspath(os.path.dirname(__file__))
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current_dir = os.path.abspath(os.path.dirname(__file__))
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@ -6,8 +6,7 @@ Contributors: https://github.com/microsoft/LightGBM/graphs/contributors.
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import os
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import os
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from .basic import Booster, Dataset, register_logger
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from .basic import Booster, Dataset, register_logger
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from .callback import (early_stopping, print_evaluation, record_evaluation,
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from .callback import early_stopping, print_evaluation, record_evaluation, reset_parameter
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reset_parameter)
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from .engine import CVBooster, cv, train
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from .engine import CVBooster, cv, train
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try:
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try:
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@ -15,8 +14,7 @@ try:
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except ImportError:
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except ImportError:
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pass
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pass
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try:
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try:
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from .plotting import (create_tree_digraph, plot_importance, plot_metric,
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from .plotting import create_tree_digraph, plot_importance, plot_metric, plot_split_value_histogram, plot_tree
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plot_split_value_histogram, plot_tree)
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except ImportError:
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except ImportError:
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pass
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pass
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try:
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try:
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@ -14,8 +14,7 @@ from typing import Any, Dict
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import numpy as np
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import numpy as np
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import scipy.sparse
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import scipy.sparse
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from .compat import (PANDAS_INSTALLED, concat, dt_DataTable, is_dtype_sparse,
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from .compat import PANDAS_INSTALLED, concat, dt_DataTable, is_dtype_sparse, pd_DataFrame, pd_Series
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pd_DataFrame, pd_Series)
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from .libpath import find_lib_path
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from .libpath import find_lib_path
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@ -61,8 +61,7 @@ try:
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from sklearn.preprocessing import LabelEncoder
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from sklearn.preprocessing import LabelEncoder
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from sklearn.utils.class_weight import compute_sample_weight
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from sklearn.utils.class_weight import compute_sample_weight
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from sklearn.utils.multiclass import check_classification_targets
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from sklearn.utils.multiclass import check_classification_targets
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from sklearn.utils.validation import (assert_all_finite, check_array,
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from sklearn.utils.validation import assert_all_finite, check_array, check_X_y
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check_X_y)
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try:
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try:
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from sklearn.exceptions import NotFittedError
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from sklearn.exceptions import NotFittedError
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from sklearn.model_selection import GroupKFold, StratifiedKFold
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from sklearn.model_selection import GroupKFold, StratifiedKFold
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@ -9,20 +9,17 @@ It is based on dask-lightgbm, which was based on dask-xgboost.
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import socket
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import socket
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from collections import defaultdict
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from collections import defaultdict
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from copy import deepcopy
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from copy import deepcopy
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from typing import Any, Callable, Dict, Iterable, List, Optional, Type, Union, Set
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from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Type, Union
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from urllib.parse import urlparse
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from urllib.parse import urlparse
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import numpy as np
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import numpy as np
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import scipy.sparse as ss
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import scipy.sparse as ss
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from .basic import (_LIB, LightGBMError, _choose_param_value, _ConfigAliases,
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from .basic import _LIB, LightGBMError, _choose_param_value, _ConfigAliases, _log_warning, _safe_call
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_log_warning, _safe_call)
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from .compat import (DASK_INSTALLED, PANDAS_INSTALLED, SKLEARN_INSTALLED, Client, LGBMNotFittedError, concat,
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from .compat import (DASK_INSTALLED, PANDAS_INSTALLED, SKLEARN_INSTALLED,
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dask_Array, dask_DataFrame, dask_Series, default_client, delayed, get_worker, pd_DataFrame,
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Client, LGBMNotFittedError, concat, dask_Array,
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pd_Series, wait)
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dask_DataFrame, dask_Series, default_client, delayed,
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from .sklearn import LGBMClassifier, LGBMModel, LGBMRanker, LGBMRegressor, _lgbmmodel_doc_fit, _lgbmmodel_doc_predict
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get_worker, pd_DataFrame, pd_Series, wait)
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from .sklearn import (LGBMClassifier, LGBMModel, LGBMRanker, LGBMRegressor,
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_lgbmmodel_doc_fit, _lgbmmodel_doc_predict)
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_DaskCollection = Union[dask_Array, dask_DataFrame, dask_Series]
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_DaskCollection = Union[dask_Array, dask_DataFrame, dask_Series]
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_DaskMatrixLike = Union[dask_Array, dask_DataFrame]
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_DaskMatrixLike = Union[dask_Array, dask_DataFrame]
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@ -7,8 +7,7 @@ from operator import attrgetter
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import numpy as np
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import numpy as np
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from . import callback
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from . import callback
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from .basic import (Booster, Dataset, LightGBMError, _ConfigAliases,
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from .basic import Booster, Dataset, LightGBMError, _ConfigAliases, _InnerPredictor, _log_warning
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_InnerPredictor, _log_warning)
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from .compat import SKLEARN_INSTALLED, _LGBMGroupKFold, _LGBMStratifiedKFold
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from .compat import SKLEARN_INSTALLED, _LGBMGroupKFold, _LGBMStratifiedKFold
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@ -5,14 +5,10 @@ from inspect import signature
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import numpy as np
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import numpy as np
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from .basic import (Dataset, LightGBMError, _choose_param_value,
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from .basic import Dataset, LightGBMError, _choose_param_value, _ConfigAliases, _log_warning
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_ConfigAliases, _log_warning)
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from .compat import (SKLEARN_INSTALLED, LGBMNotFittedError, _LGBMAssertAllFinite, _LGBMCheckArray,
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from .compat import (SKLEARN_INSTALLED, LGBMNotFittedError,
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_LGBMCheckClassificationTargets, _LGBMCheckSampleWeight, _LGBMCheckXY, _LGBMClassifierBase,
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_LGBMAssertAllFinite, _LGBMCheckArray,
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_LGBMComputeSampleWeight, _LGBMLabelEncoder, _LGBMModelBase, _LGBMRegressorBase, dt_DataTable,
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_LGBMCheckClassificationTargets, _LGBMCheckSampleWeight,
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_LGBMCheckXY, _LGBMClassifierBase,
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_LGBMComputeSampleWeight, _LGBMLabelEncoder,
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_LGBMModelBase, _LGBMRegressorBase, dt_DataTable,
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pd_DataFrame)
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pd_DataFrame)
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from .engine import train
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from .engine import train
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@ -11,11 +11,8 @@ import psutil
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import pytest
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import pytest
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from scipy.sparse import csr_matrix, isspmatrix_csc, isspmatrix_csr
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from scipy.sparse import csr_matrix, isspmatrix_csc, isspmatrix_csr
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from sklearn.datasets import load_svmlight_file, make_multilabel_classification
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from sklearn.datasets import load_svmlight_file, make_multilabel_classification
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from sklearn.metrics import (average_precision_score, log_loss,
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from sklearn.metrics import average_precision_score, log_loss, mean_absolute_error, mean_squared_error, roc_auc_score
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mean_absolute_error, mean_squared_error,
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from sklearn.model_selection import GroupKFold, TimeSeriesSplit, train_test_split
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roc_auc_score)
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from sklearn.model_selection import (GroupKFold, TimeSeriesSplit,
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train_test_split)
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import lightgbm as lgb
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import lightgbm as lgb
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@ -1,30 +1,31 @@
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# coding: utf-8
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# coding: utf-8
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import itertools
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import itertools
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import joblib
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import math
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import math
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import os
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import os
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import lightgbm as lgb
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import joblib
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import numpy as np
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import numpy as np
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import pytest
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import pytest
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from pkg_resources import parse_version
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from pkg_resources import parse_version
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from sklearn import __version__ as sk_version
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from sklearn import __version__ as sk_version
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from sklearn.base import clone
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from sklearn.base import clone
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from sklearn.datasets import load_svmlight_file, make_multilabel_classification
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from sklearn.datasets import load_svmlight_file, make_multilabel_classification
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from sklearn.utils.estimator_checks import check_parameters_default_constructible
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from sklearn.metrics import log_loss, mean_squared_error
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from sklearn.metrics import log_loss, mean_squared_error
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from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, train_test_split
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from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, train_test_split
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from sklearn.multioutput import (MultiOutputClassifier, ClassifierChain, MultiOutputRegressor,
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from sklearn.multioutput import ClassifierChain, MultiOutputClassifier, MultiOutputRegressor, RegressorChain
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RegressorChain)
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from sklearn.utils.estimator_checks import check_parameters_default_constructible
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from sklearn.utils.validation import check_is_fitted
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from sklearn.utils.validation import check_is_fitted
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import lightgbm as lgb
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from .utils import load_boston, load_breast_cancer, load_digits, load_iris, load_linnerud
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from .utils import load_boston, load_breast_cancer, load_digits, load_iris, load_linnerud
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sk_version = parse_version(sk_version)
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sk_version = parse_version(sk_version)
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if sk_version < parse_version("0.23"):
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if sk_version < parse_version("0.23"):
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import warnings
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import warnings
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from sklearn.exceptions import SkipTestWarning
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from sklearn.exceptions import SkipTestWarning
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from sklearn.utils.estimator_checks import _yield_all_checks, SkipTest
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from sklearn.utils.estimator_checks import SkipTest, _yield_all_checks
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else:
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else:
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from sklearn.utils.estimator_checks import parametrize_with_checks
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from sklearn.utils.estimator_checks import parametrize_with_checks
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