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tianlh
LightGBM-DCU
Commits
9503d3f9
"...git@developer.sourcefind.cn:tianlh/lightgbm-dcu.git" did not exist on "bf1a604a5b5106d6b7f2aa07ea02be12115dcabc"
Unverified
Commit
9503d3f9
authored
Aug 24, 2020
by
Nikita Titov
Committed by
GitHub
Aug 24, 2020
Browse files
[doc] remove excess fit arguments from docstrings (#3330)
parent
8ab35255
Changes
1
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11 additions
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3 deletions
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-3
python-package/lightgbm/sklearn.py
python-package/lightgbm/sklearn.py
+11
-3
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python-package/lightgbm/sklearn.py
View file @
9503d3f9
...
@@ -761,8 +761,12 @@ class LGBMRegressor(LGBMModel, _LGBMRegressorBase):
...
@@ -761,8 +761,12 @@ class LGBMRegressor(LGBMModel, _LGBMRegressorBase):
return
self
return
self
_base_doc
=
LGBMModel
.
fit
.
__doc__
_base_doc
=
LGBMModel
.
fit
.
__doc__
fit
.
__doc__
=
(
_base_doc
[:
_base_doc
.
find
(
'eval_class_weight :'
)]
_base_doc
=
(
_base_doc
[:
_base_doc
.
find
(
'group :'
)]
+
_base_doc
[
_base_doc
.
find
(
'eval_init_score :'
):])
+
_base_doc
[
_base_doc
.
find
(
'eval_set :'
):])
_base_doc
=
(
_base_doc
[:
_base_doc
.
find
(
'eval_class_weight :'
)]
+
_base_doc
[
_base_doc
.
find
(
'eval_init_score :'
):])
fit
.
__doc__
=
(
_base_doc
[:
_base_doc
.
find
(
'eval_group :'
)]
+
_base_doc
[
_base_doc
.
find
(
'eval_metric :'
):])
class
LGBMClassifier
(
LGBMModel
,
_LGBMClassifierBase
):
class
LGBMClassifier
(
LGBMModel
,
_LGBMClassifierBase
):
...
@@ -834,7 +838,11 @@ class LGBMClassifier(LGBMModel, _LGBMClassifierBase):
...
@@ -834,7 +838,11 @@ class LGBMClassifier(LGBMModel, _LGBMClassifierBase):
callbacks
=
callbacks
,
init_model
=
init_model
)
callbacks
=
callbacks
,
init_model
=
init_model
)
return
self
return
self
fit
.
__doc__
=
LGBMModel
.
fit
.
__doc__
_base_doc
=
LGBMModel
.
fit
.
__doc__
_base_doc
=
(
_base_doc
[:
_base_doc
.
find
(
'group :'
)]
+
_base_doc
[
_base_doc
.
find
(
'eval_set :'
):])
fit
.
__doc__
=
(
_base_doc
[:
_base_doc
.
find
(
'eval_group :'
)]
+
_base_doc
[
_base_doc
.
find
(
'eval_metric :'
):])
def
predict
(
self
,
X
,
raw_score
=
False
,
start_iteration
=
0
,
num_iteration
=
None
,
def
predict
(
self
,
X
,
raw_score
=
False
,
start_iteration
=
0
,
num_iteration
=
None
,
pred_leaf
=
False
,
pred_contrib
=
False
,
**
kwargs
):
pred_leaf
=
False
,
pred_contrib
=
False
,
**
kwargs
):
...
...
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