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[python] improved sklearn interface #870

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merged 15 commits into from
Sep 5, 2017
Merged

[python] improved sklearn interface #870

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6cb1675
improved sklearn interface; added sklearns' tests
Aug 28, 2017
62a0ac0
moved best_score into the if statement
Aug 30, 2017
1b9d998
improved docstrings; simplified LGBMCheckConsistentLength
Aug 30, 2017
2e63c64
fixed typo
Aug 30, 2017
be18b2b
pylint
Aug 31, 2017
86f7e0b
updated example
Aug 31, 2017
04e7d23
fixed Ranker interface
Aug 31, 2017
a3b6bd8
added missed boosting_type
Sep 2, 2017
1ea5d2d
fixed more comfortable autocomplete without unused objects
Sep 2, 2017
5732811
removed check for None of eval_at
Sep 3, 2017
d571945
fixed according to review
Sep 3, 2017
7873e61
fixed typo
Sep 4, 2017
cff8b65
added description of fit return type
Sep 4, 2017
a24dd78
dictionary->dict for short
Sep 4, 2017
fae2014
markdown cleanup
Sep 5, 2017
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2 changes: 1 addition & 1 deletion .travis/test.sh
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Expand Up @@ -40,7 +40,7 @@ if [[ ${TASK} == "if-else" ]]; then
exit 0
fi

conda install --yes numpy scipy scikit-learn pandas matplotlib
conda install --yes numpy nose scipy scikit-learn pandas matplotlib
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why do you install nose?

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Sklearn's checks require nose. Without it new integration test fails.

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Sklearn's checks require nose

oh..., I got it.

pip install pytest

if [[ ${TASK} == "sdist" ]]; then
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2 changes: 1 addition & 1 deletion appveyor.yml
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Expand Up @@ -21,7 +21,7 @@ test_script:
- conda config --set always_yes yes --set changeps1 no
- conda update -q conda
- conda info -a
- conda install --yes numpy scipy scikit-learn pandas matplotlib
- conda install --yes numpy nose scipy scikit-learn pandas matplotlib
- pip install pep8 pytest
- pytest tests/c_api_test/test_.py
- "set /p LGB_VER=< VERSION.txt"
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15 changes: 15 additions & 0 deletions python-package/lightgbm/compat.py
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Expand Up @@ -64,23 +64,38 @@ class DataFrame(object):
from sklearn.base import RegressorMixin, ClassifierMixin
from sklearn.preprocessing import LabelEncoder
from sklearn.utils import deprecated
from sklearn.utils.multiclass import check_classification_targets
from sklearn.utils.validation import check_X_y, check_array, check_consistent_length
try:
from sklearn.model_selection import StratifiedKFold, GroupKFold
from sklearn.exceptions import NotFittedError
except ImportError:
from sklearn.cross_validation import StratifiedKFold, GroupKFold
from sklearn.utils.validation import NotFittedError
SKLEARN_INSTALLED = True
LGBMModelBase = BaseEstimator
LGBMRegressorBase = RegressorMixin
LGBMClassifierBase = ClassifierMixin
LGBMLabelEncoder = LabelEncoder
LGBMDeprecated = deprecated
LGBMNotFittedError = NotFittedError
LGBMStratifiedKFold = StratifiedKFold
LGBMGroupKFold = GroupKFold
LGBMCheckXY = check_X_y
LGBMCheckArray = check_array
LGBMCheckConsistentLength = check_consistent_length
LGBMCheckClassificationTargets = check_classification_targets
except ImportError:
SKLEARN_INSTALLED = False
LGBMModelBase = object
LGBMClassifierBase = object
LGBMRegressorBase = object
LGBMLabelEncoder = None
LGBMDeprecated = None
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@wxchan wxchan Sep 3, 2017 •
edited
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I remember this will raise an error if sklearn not installed, check #221. You can uninstall scikit-learn and take a try.

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I thought that it's just forgotten object. Sorry.
btw, in two words why these dummy variables are needed?

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Maybe some of these I don't need to specify too?

LGBMNotFittedError = ValueError 
_LGBMCheckXY = None
_LGBMCheckArray = None
_LGBMCheckConsistentLength = None
_LGBMCheckClassificationTargets = None

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I uninstalled skikit-learn, removed the line
LGBMDeprecated = None
and
import lightgbm as lgb
didn't raise an error. Does it indicate that everything is OK?

LGBMNotFittedError = ValueError
LGBMStratifiedKFold = None
LGBMGroupKFold = None
LGBMCheckXY = None
LGBMCheckArray = None
LGBMCheckConsistentLength = None
LGBMCheckClassificationTargets = None
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