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[dask] [docs] Fix inaccuracies in API docs for Dask module (fixes #3871) #3930

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merged 8 commits into from
Feb 9, 2021
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[dask] [docs] Fix inaccuracies in API docs for Dask module (fixes #3871) #3930

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465874e
got fit() working
jameslamb Feb 9, 2021
12f0006
add predict()
jameslamb Feb 9, 2021
6fa7916
Merge branch 'master' into docs/jlamb
jameslamb Feb 9, 2021
4f47f87
predict_proba()
jameslamb Feb 9, 2021
9f9bb8e
remove custom objective docs
jameslamb Feb 9, 2021
7fdb0bb
Apply suggestions from code review
jameslamb Feb 9, 2021
8cceecf
fix capitalization
jameslamb Feb 9, 2021
56f11c1
Update tests/python_package_test/test_dask.py
jameslamb Feb 9, 2021
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120 changes: 109 additions & 11 deletions python-package/lightgbm/dask.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,14 @@
from .compat import (PANDAS_INSTALLED, pd_DataFrame, pd_Series, concat,
SKLEARN_INSTALLED, LGBMNotFittedError,
DASK_INSTALLED, dask_DataFrame, dask_Array, dask_Series, delayed, Client, default_client, get_worker, wait)
from .sklearn import LGBMClassifier, LGBMModel, LGBMRegressor, LGBMRanker
from .sklearn import (
_lgbmmodel_doc_fit,
_lgbmmodel_doc_predict,
LGBMClassifier,
LGBMModel,
LGBMRegressor,
LGBMRanker
)

_DaskCollection = Union[dask_Array, dask_DataFrame, dask_Series]
_DaskMatrixLike = Union[dask_Array, dask_DataFrame]
Expand Down Expand Up @@ -578,13 +585,17 @@ def __init__(

_base_doc = LGBMClassifier.__init__.__doc__
_before_kwargs, _kwargs, _after_kwargs = _base_doc.partition('**kwargs')
__init__.__doc__ = (
_base_doc = (
_before_kwargs
+ 'client : dask.distributed.Client or None, optional (default=None)\n'
+ ' ' * 12 + 'Dask client. If ``None``, ``distributed.default_client()`` will be used at runtime. The Dask client used by this class will not be saved if the model object is pickled.\n'
+ ' ' * 8 + _kwargs + _after_kwargs
)

# the note on custom objective functions in LGBMModel.__init__ is not
# currently relevant for the Dask estimators
__init__.__doc__ = _base_doc[:_base_doc.find('Note\n')]

def __getstate__(self) -> Dict[Any, Any]:
return self._lgb_getstate()

Expand All @@ -604,7 +615,23 @@ def fit(
**kwargs
)

fit.__doc__ = LGBMClassifier.fit.__doc__
_base_doc = _lgbmmodel_doc_fit.format(
X_shape="dask Array or dask DataFrame of shape = [n_samples, n_features]",
y_shape="dask Array, dask DataFrame or dask Series of shape = [n_samples]",
sample_weight_shape="dask Array, dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None)",
group_shape="dask Array, dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None)"
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)

# DaskLGBMClassifier does not support init_score, evaluation data, or early stopping
_base_doc = (_base_doc[:_base_doc.find('init_score :')]
+ _base_doc[_base_doc.find('verbose :'):])

# DaskLGBMClassifier support for callbacks and init_model is not tested
fit.__doc__ = (
_base_doc[:_base_doc.find('callbacks :')]
+ '**kwargs\n'
+ ' ' * 12 + 'Other parameters passed through to ``LGBMClassifier.fit()``\n'
)

def predict(self, X: _DaskMatrixLike, **kwargs: Any) -> dask_Array:
"""Docstring is inherited from the lightgbm.LGBMClassifier.predict."""
Expand All @@ -615,7 +642,14 @@ def predict(self, X: _DaskMatrixLike, **kwargs: Any) -> dask_Array:
**kwargs
)

predict.__doc__ = LGBMClassifier.predict.__doc__
predict.__doc__ = _lgbmmodel_doc_predict.format(
description="Return the predicted value for each sample.",
X_shape="dask Array or dask DataFrame of shape = [n_samples, n_features]",
output_name="predicted_result",
predicted_result_shape="dask Array of shape = [n_samples] or shape = [n_samples, n_classes]",
X_leaves_shape="dask Array of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]",
X_SHAP_values_shape="dask Array of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes]"
)

def predict_proba(self, X: _DaskMatrixLike, **kwargs: Any) -> dask_Array:
"""Docstring is inherited from the lightgbm.LGBMClassifier.predict_proba."""
Expand All @@ -626,7 +660,14 @@ def predict_proba(self, X: _DaskMatrixLike, **kwargs: Any) -> dask_Array:
**kwargs
)

predict_proba.__doc__ = LGBMClassifier.predict_proba.__doc__
predict_proba.__doc__ = _lgbmmodel_doc_predict.format(
description="Return the predicted probability for each class for each sample.",
X_shape="dask Array or dask DataFrame of shape = [n_samples, n_features]",
output_name="predicted_probability",
predicted_result_shape="dask Array of shape = [n_samples, n_classes]",
X_leaves_shape="dask Array of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]",
X_SHAP_values_shape="dask Array of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes]"
)

def to_local(self) -> LGBMClassifier:
"""Create regular version of lightgbm.LGBMClassifier from the distributed version.
Expand Down Expand Up @@ -695,13 +736,17 @@ def __init__(

_base_doc = LGBMRegressor.__init__.__doc__
_before_kwargs, _kwargs, _after_kwargs = _base_doc.partition('**kwargs')
__init__.__doc__ = (
_base_doc = (
_before_kwargs
+ 'client : dask.distributed.Client or None, optional (default=None)\n'
+ ' ' * 12 + 'Dask client. If ``None``, ``distributed.default_client()`` will be used at runtime. The Dask client used by this class will not be saved if the model object is pickled.\n'
+ ' ' * 8 + _kwargs + _after_kwargs
)

# the note on custom objective functions in LGBMModel.__init__ is not
# currently relevant for the Dask estimators
__init__.__doc__ = _base_doc[:_base_doc.find('Note\n')]

def __getstate__(self) -> Dict[Any, Any]:
return self._lgb_getstate()

Expand All @@ -721,7 +766,23 @@ def fit(
**kwargs
)

fit.__doc__ = LGBMRegressor.fit.__doc__
_base_doc = _lgbmmodel_doc_fit.format(
X_shape="dask Array or dask DataFrame of shape = [n_samples, n_features]",
y_shape="dask Array, dask DataFrame or dask Series of shape = [n_samples]",
sample_weight_shape="dask Array, dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None)",
group_shape="dask Array, dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None)"
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)

# DaskLGBMRegressor does not support init_score, evaluation data, or early stopping
_base_doc = (_base_doc[:_base_doc.find('init_score :')]
+ _base_doc[_base_doc.find('verbose :'):])

# DaskLGBMRegressor support for callbacks and init_model is not tested
fit.__doc__ = (
_base_doc[:_base_doc.find('callbacks :')]
+ '**kwargs\n'
+ ' ' * 12 + 'Other parameters passed through to ``LGBMRegressor.fit()``\n'
)

def predict(self, X: _DaskMatrixLike, **kwargs) -> dask_Array:
"""Docstring is inherited from the lightgbm.LGBMRegressor.predict."""
Expand All @@ -731,7 +792,14 @@ def predict(self, X: _DaskMatrixLike, **kwargs) -> dask_Array:
**kwargs
)

predict.__doc__ = LGBMRegressor.predict.__doc__
predict.__doc__ = _lgbmmodel_doc_predict.format(
description="Return the predicted value for each sample.",
X_shape="dask Array or dask DataFrame of shape = [n_samples, n_features]",
output_name="predicted_result",
predicted_result_shape="dask Array of shape = [n_samples]",
X_leaves_shape="dask Array of shape = [n_samples, n_trees]",
X_SHAP_values_shape="dask Array of shape = [n_samples, n_features + 1]"
)

def to_local(self) -> LGBMRegressor:
"""Create regular version of lightgbm.LGBMRegressor from the distributed version.
Expand Down Expand Up @@ -800,13 +868,17 @@ def __init__(

_base_doc = LGBMRanker.__init__.__doc__
_before_kwargs, _kwargs, _after_kwargs = _base_doc.partition('**kwargs')
__init__.__doc__ = (
_base_doc = (
_before_kwargs
+ 'client : dask.distributed.Client or None, optional (default=None)\n'
+ ' ' * 12 + 'Dask client. If ``None``, ``distributed.default_client()`` will be used at runtime. The Dask client used by this class will not be saved if the model object is pickled.\n'
+ ' ' * 8 + _kwargs + _after_kwargs
)

# the note on custom objective functions in LGBMModel.__init__ is not
# currently relevant for the Dask estimators
__init__.__doc__ = _base_doc[:_base_doc.find('Note\n')]

def __getstate__(self) -> Dict[Any, Any]:
return self._lgb_getstate()

Expand All @@ -832,13 +904,39 @@ def fit(
**kwargs
)

fit.__doc__ = LGBMRanker.fit.__doc__
_base_doc = _lgbmmodel_doc_fit.format(
X_shape="dask Array or dask DataFrame of shape = [n_samples, n_features]",
y_shape="dask Array, dask DataFrame or dask Series of shape = [n_samples]",
sample_weight_shape="dask Array, dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None)",
group_shape="dask Array, dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None)"
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)

# DaskLGBMRanker does not support init_score, evaluation data, or early stopping
_base_doc = (_base_doc[:_base_doc.find('init_score :')]
+ _base_doc[_base_doc.find('init_score :'):])

_base_doc = (_base_doc[:_base_doc.find('eval_set :')]
+ _base_doc[_base_doc.find('verbose :'):])

# DaskLGBMRanker support for callbacks and init_model is not tested
fit.__doc__ = (
_base_doc[:_base_doc.find('callbacks :')]
+ '**kwargs\n'
+ ' ' * 12 + 'Other parameters passed through to ``LGBMRanker.fit()``\n'
)

def predict(self, X: _DaskMatrixLike, **kwargs: Any) -> dask_Array:
"""Docstring is inherited from the lightgbm.LGBMRanker.predict."""
return _predict(self.to_local(), X, **kwargs)

predict.__doc__ = LGBMRanker.predict.__doc__
predict.__doc__ = _lgbmmodel_doc_predict.format(
description="Return the predicted value for each sample.",
X_shape="dask Array or dask DataFrame of shape = [n_samples, n_features]",
output_name="predicted_result",
predicted_result_shape="dask Array of shape = [n_samples]",
X_leaves_shape="dask Array of shape = [n_samples, n_trees]",
X_SHAP_values_shape="dask Array of shape = [n_samples, n_features + 1]"
)

def to_local(self) -> LGBMRanker:
"""Create regular version of lightgbm.LGBMRanker from the distributed version.
Expand Down
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