[python] minor refactoring of Python code (#4442)

* Update test_sklearn.py

* Update test_basic.py

* Update dask.py

* Update basic.py

* Update basic.py

* Update basic.py

* Update basic.py

* Update callback.py

python-package/lightgbm/basic.py

@@ -649,7 +649,7 @@ class Sequence(abc.ABC):
             elif isinstance(idx, slice):
                 return np.stack(self.__get_one_line__(i) for i in range(idx.start, idx.stop))
             else:
-                raise TypeError(f"Sequence index must be integer or slice, got {type(idx)}")
+                raise TypeError(f"Sequence index must be integer or slice, got {type(idx).__name__}")
 
         Parameters
         ----------
@@ -1436,7 +1436,7 @@ class Dataset:
         args_names = (getattr(self.__class__, '_lazy_init')
                       .__code__
                       .co_varnames[:getattr(self.__class__, '_lazy_init').__code__.co_argcount])
-        for key, _ in params.items():
+        for key in params.keys():
             if key in args_names:
                 _log_warning(f'{key} keyword has been found in `params` and will be ignored.\n'
                              f'Please use {key} argument of the Dataset constructor to pass this parameter.')
@@ -1947,7 +1947,7 @@ class Dataset:
 
         Returns
         -------
-        info : numpy array
+        info : numpy array or None
             A numpy array with information from the Dataset.
         """
         if self.handle is None:
@@ -2042,7 +2042,7 @@ class Dataset:
         self.set_categorical_feature(reference.categorical_feature) \
             .set_feature_name(reference.feature_name) \
             ._set_predictor(reference._predictor)
-        # we're done if self and reference share a common upstrem reference
+        # we're done if self and reference share a common upstream reference
         if self.get_ref_chain().intersection(reference.get_ref_chain()):
             return self
         if self.data is not None:

python-package/lightgbm/callback.py

@@ -129,7 +129,7 @@ def reset_parameter(**kwargs: Union[list, Callable]) -> Callable:
         for key, value in kwargs.items():
             if isinstance(value, list):
                 if len(value) != env.end_iteration - env.begin_iteration:
-                    raise ValueError(f"Length of list {repr(key)} has to equal to 'num_boost_round'.")
+                    raise ValueError(f"Length of list {key!r} has to equal to 'num_boost_round'.")
                 new_param = value[env.iteration - env.begin_iteration]
             else:
                 new_param = value(env.iteration - env.begin_iteration)

python-package/lightgbm/dask.py

@@ -82,7 +82,7 @@ def _concat(seq: List[_DaskPart]) -> _DaskPart:
     elif isinstance(seq[0], ss.spmatrix):
         return ss.vstack(seq, format='csr')
     else:
-        raise TypeError(f'Data must be one of: numpy arrays, pandas dataframes, sparse matrices (from scipy). Got {type(seq[0])}.')
+        raise TypeError(f'Data must be one of: numpy arrays, pandas dataframes, sparse matrices (from scipy). Got {type(seq[0]).__name__}.')
 
 
 def _remove_list_padding(*args: Any) -> List[List[Any]]:
@@ -898,7 +898,7 @@ def _predict(
             **kwargs
         )
     else:
-        raise TypeError(f'Data must be either Dask Array or Dask DataFrame. Got {type(data)}.')
+        raise TypeError(f'Data must be either Dask Array or Dask DataFrame. Got {type(data).__name__}.')
 
 
 class _DaskLGBMModel:

tests/python_package_test/test_basic.py

@@ -106,7 +106,7 @@ class NumpySequence(lgb.Sequence):
                 raise NotImplementedError("No need to implement, caller will not set step by now")
             return self.ndarray[idx.start:idx.stop]
         else:
-            raise TypeError(f"Sequence Index must be an integer/list/slice, got {type(idx)}")
+            raise TypeError(f"Sequence Index must be an integer/list/slice, got {type(idx).__name__}")
 
     def __len__(self):
         return len(self.ndarray)

tests/python_package_test/test_sklearn.py

@@ -33,7 +33,7 @@ decreasing_generator = itertools.count(0, -1)
 
 
 def custom_asymmetric_obj(y_true, y_pred):
-    residual = (y_true - y_pred).astype("float")
+    residual = (y_true - y_pred).astype(np.float64)
     grad = np.where(residual < 0, -2 * 10.0 * residual, -2 * residual)
     hess = np.where(residual < 0, 2 * 10.0, 2.0)
     return grad, hess