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add lightgbm.booster support #270

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add lightgbm.booster support #270

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d369977
add lightgbm.booster support
qh582 May 29, 2018
cc5b7a4
Merge pull request #1 from qh582/qh582-lightgbm-booster
qh582 May 29, 2018
9c2bb8e
fix bug
qh582 May 29, 2018
9c31a99
bug fix
qh582 May 29, 2018
a042650
fix build for typing
qh582 May 29, 2018
72a50a2
add _check_booster_args test
qh582 May 30, 2018
bb19a4f
add explain_weights test for booster
qh582 May 30, 2018
4bf2f6d
Merge pull request #2 from qh582/qh582-lightgbm-booster
qh582 May 30, 2018
ef991cf
correct is_regression
qh582 May 30, 2018
45379f5
add explain_prediction test for regression booster
qh582 May 30, 2018
a35e5c8
Merge pull request #3 from qh582/qh582-test
qh582 May 30, 2018
0bbf3e2
add booster multiclass test
qh582 May 30, 2018
fa1eccd
fix booster binary case
qh582 May 31, 2018
f053399
add test_explain_prediction_booster_binary
qh582 May 31, 2018
87438d4
remove redundant code
qh582 May 31, 2018
3c213e9
add _lgb_n_targets test
qh582 May 31, 2018
866015a
Merge pull request #4 from qh582/qh582-lightgbm-booster
qh582 May 31, 2018
802e543
Update docs
qh582 May 31, 2018
5155979
update docs
qh582 May 31, 2018
6f1ee40
Merge branch 'master' into qh582-lightgbm-booster
qh582 May 31, 2018
0a488e3
update booster is_regression
qh582 May 31, 2018
19e9bf3
Merge branch 'master' into qh582-lightgbm-booster
qh582 Jun 5, 2018
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update booster is_regression
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@qh582
qh582 authored May 31, 2018
commit 0a488e391baf93b3030768a1c976793e271706e6
5 changes: 2 additions & 3 deletions docs/source/libraries/lightgbm.rst
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Expand Up @@ -5,8 +5,7 @@ LightGBM

LightGBM_ is a fast Gradient Boosting framework; it provides a Python
interface. eli5 supports :func:`eli5.explain_weights`
and :func:`eli5.explain_prediction` for ``lightgbm.LGBMClassifer``, ``lightgbm.LGBMRegressor`` and ``lightgbm.Booster`` estimators. It is tested against LightGBM
master branch.
and :func:`eli5.explain_prediction` for ``lightgbm.LGBMClassifer``, ``lightgbm.LGBMRegressor`` and ``lightgbm.Booster`` estimators.

.. _LightGBM: https://github.com/Microsoft/LightGBM

Expand All @@ -22,7 +21,7 @@ arguments for LGBMClassifier , LGBMClassifier and lightgbm.Booster:
- 'weight' - the same as 'split', for better compatibility with
:ref:`library-xgboost`.

``target_names`` and ``target`` arguments are ignored.
``target_names`` arguement is ignored for ``lightgbm.LGBMClassifer`` / ``lightgbm.LGBMRegressor``, but used for ``lightgbm.Booster``. ``target`` argument is ignored.
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@kmike kmike Nov 18, 2018 •
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Suggested change
``target_names`` arguement is ignored for ``lightgbm.LGBMClassifer`` / ``lightgbm.LGBMRegressor``, but used for ``lightgbm.Booster``. ``target`` argument is ignored.
``target_names`` argument is ignored for ``lightgbm.LGBMClassifer`` / ``lightgbm.LGBMRegressor``, but used for ``lightgbm.Booster``. ``targets`` argument is ignored.


.. note::
Top-level :func:`eli5.explain_weights` calls are dispatched
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I think lightgbm.Booster should be mentioned here as well.

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11 changes: 7 additions & 4 deletions eli5/lightgbm.py
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Expand Up @@ -38,7 +38,9 @@ def explain_weights_lightgbm(lgb,
``top``, ``feature_names``,
``feature_re`` and ``feature_filter`` parameters.

``target_names`` and ``targets`` parameters are ignored.
``target_names`` arguement is ignored for ``lightgbm.LGBMClassifer`` / ``lightgbm.LGBMRegressor``,
but used for ``lightgbm.Booster``.
``target`` argument is ignored.
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Suggested change
``target`` argument is ignored.
``targets`` argument is ignored.


Parameters
----------
Expand Down Expand Up @@ -123,12 +125,13 @@ def explain_prediction_lightgbm(
prediction = lgb.predict(X)
n_targets = prediction.shape[-1]
if is_regression is None and target_names is None:
# When n_targets is 1, this can be classification too,
# but it's safer to assume regression.
# When n_targets is 1, this can be classification too.
# It's safer to assume regression in this case,
# unless users set it as a classification problem by assigning 'target_names' input [0,1] etc.
# If n_targets > 1, it must be classification.
is_regression = n_targets == 1
elif is_regression is None:
is_regression = len(target_names) == 1
is_regression = len(target_names) == 1 and n_targets == 1

if is_regression:
proba = None
Expand Down