[python] added f-strings to python-package/lightgbm/dask.py (#4144)

* added f-string to dask.py

* Update python-package/lightgbm/dask.py
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update python-package/lightgbm/dask.py
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Updated branch

* Updated file as per specifications

* Removed warning as per specification

* update other places

* Apply suggestions from code review
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* revert unnecessary change

* Apply suggestions from code review
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
Co-authored-by: James Lamb <jaylamb20@gmail.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

python-package/lightgbm/dask.py

@@ -67,7 +67,7 @@ def _concat(seq: List[_DaskPart]) -> _DaskPart:
     elif isinstance(seq[0], ss.spmatrix):
         return ss.vstack(seq, format='csr')
     else:
-        raise TypeError('Data must be one of: numpy arrays, pandas dataframes, sparse matrices (from scipy). Got %s.' % str(type(seq[0])))
+        raise TypeError(f'Data must be one of: numpy arrays, pandas dataframes, sparse matrices (from scipy). Got {type(seq[0])}.')
 
 
 def _train_part(
@@ -306,7 +306,7 @@ def _train(
         'voting_parallel'
     }
     if params["tree_learner"] not in allowed_tree_learners:
-        _log_warning('Parameter tree_learner set to %s, which is not allowed. Using "data" as default' % params['tree_learner'])
+        _log_warning(f'Parameter tree_learner set to {params["tree_learner"]}, which is not allowed. Using "data" as default')
         params['tree_learner'] = 'data'
 
     # Some passed-in parameters can be removed:
@@ -413,7 +413,7 @@ def _train(
             )
 
         machines = ','.join([
-            '%s:%d' % (urlparse(worker_address).hostname, port)
+            f'{urlparse(worker_address).hostname}:{port}'
             for worker_address, port
             in worker_address_to_port.items()
         ])
@@ -572,7 +572,7 @@ def _predict(
             drop_axis=1
         )
     else:
-        raise TypeError('Data must be either Dask Array or Dask DataFrame. Got %s.' % str(type(data)))
+        raise TypeError(f'Data must be either Dask Array or Dask DataFrame. Got {type(data)}.')
 
 
 class _DaskLGBMModel:
@@ -704,12 +704,11 @@ class DaskLGBMClassifier(LGBMClassifier, _DaskLGBMModel):
 
     _base_doc = LGBMClassifier.__init__.__doc__
     _before_kwargs, _kwargs, _after_kwargs = _base_doc.partition('**kwargs')
-    _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
-    )
+    _base_doc = f"""
+        {_before_kwargs}client : dask.distributed.Client or None, optional (default=None)
+        {' ':4}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.
+        {_kwargs}{_after_kwargs}
+        """
 
     # the note on custom objective functions in LGBMModel.__init__ is not
     # currently relevant for the Dask estimators
@@ -749,11 +748,9 @@ class DaskLGBMClassifier(LGBMClassifier, _DaskLGBMModel):
                  + _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'
-    )
+    fit.__doc__ = f"""{_base_doc[:_base_doc.find('callbacks :')]}**kwargs
+        Other parameters passed through to ``LGBMClassifier.fit()``.
+        """
 
     def predict(self, X: _DaskMatrixLike, **kwargs: Any) -> dask_Array:
         """Docstring is inherited from the lightgbm.LGBMClassifier.predict."""
@@ -858,13 +855,11 @@ class DaskLGBMRegressor(LGBMRegressor, _DaskLGBMModel):
 
     _base_doc = LGBMRegressor.__init__.__doc__
     _before_kwargs, _kwargs, _after_kwargs = _base_doc.partition('**kwargs')
-    _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
-    )
-
+    _base_doc = f"""
+        {_before_kwargs}client : dask.distributed.Client or None, optional (default=None)
+        {' ':4}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.
+        {_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')]
@@ -903,11 +898,9 @@ class DaskLGBMRegressor(LGBMRegressor, _DaskLGBMModel):
                  + _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'
-    )
+    fit.__doc__ = f"""{_base_doc[:_base_doc.find('callbacks :')]}**kwargs
+        Other parameters passed through to ``LGBMRegressor.fit()``.
+        """
 
     def predict(self, X: _DaskMatrixLike, **kwargs) -> dask_Array:
         """Docstring is inherited from the lightgbm.LGBMRegressor.predict."""
@@ -993,12 +986,11 @@ class DaskLGBMRanker(LGBMRanker, _DaskLGBMModel):
 
     _base_doc = LGBMRanker.__init__.__doc__
     _before_kwargs, _kwargs, _after_kwargs = _base_doc.partition('**kwargs')
-    _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
-    )
+    _base_doc = f"""
+        {_before_kwargs}client : dask.distributed.Client or None, optional (default=None)
+        {' ':4}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.
+        {_kwargs}{_after_kwargs}
+        """
 
     # the note on custom objective functions in LGBMModel.__init__ is not
     # currently relevant for the Dask estimators
@@ -1040,11 +1032,9 @@ class DaskLGBMRanker(LGBMRanker, _DaskLGBMModel):
                  + _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'
-    )
+    fit.__doc__ = f"""{_base_doc[:_base_doc.find('callbacks :')]}**kwargs
+        Other parameters passed through to ``LGBMRanker.fit()``.
+        """
 
     def predict(self, X: _DaskMatrixLike, **kwargs: Any) -> dask_Array:
         """Docstring is inherited from the lightgbm.LGBMRanker.predict."""