replace wildcard imports with explicit import statements; upgrade black to v20.8b1

.pre-commit-config.yaml

@@ -1,5 +1,6 @@
 repos:
-  - repo: https://github.com/ambv/black
-    rev: stable
+  - repo: https://github.com/psf/black
+    rev: 20.8b1
     hooks:
-    - id: black
+      - id: black
+        language_version: python3

environment.yml

@@ -3,7 +3,7 @@ channels:
   - astropy
   - conda-forge
 dependencies:
-  - black = 19.*
+  - black = 20.8b1
   - boruta_py = 0.3.*
   - conda-build
   - conda-verify

src/sklearndf/_sklearndf.py

@@ -4,7 +4,7 @@
 
 import logging
 from abc import ABCMeta, abstractmethod
-from typing import *
+from typing import Any, List, Mapping, Optional, Sequence, Type, TypeVar, Union, cast
 
 import pandas as pd
 from sklearn.base import (
@@ -19,13 +19,7 @@
 
 log = logging.getLogger(__name__)
 
-__all__ = [
-    "EstimatorDF",
-    "LearnerDF",
-    "ClassifierDF",
-    "RegressorDF",
-    "TransformerDF",
-]
+__all__ = ["EstimatorDF", "LearnerDF", "ClassifierDF", "RegressorDF", "TransformerDF"]
 
 #
 # type variables
@@ -401,4 +395,4 @@ def classes_(self) -> Sequence[Any]:
 
         :return: the classes predicted by this classifier
         """
-        pass
+        pass

src/sklearndf/_wrapper.py

@@ -16,7 +16,21 @@
 import re
 from abc import ABCMeta, abstractmethod
 from functools import update_wrapper
-from typing import *
+from typing import (
+    Optional,
+    Iterable,
+    Generic,
+    Mapping,
+    Sequence,
+    Any,
+    Union,
+    Callable,
+    TypeVar,
+    Type,
+    AnyStr,
+    Dict,
+    List,
+)
 
 import numpy as np
 import pandas as pd
@@ -29,7 +43,7 @@
 )
 
 from pytools.api import inheritdoc
-from sklearndf import (ClassifierDF, EstimatorDF, LearnerDF, RegressorDF, TransformerDF)
+from sklearndf import ClassifierDF, EstimatorDF, LearnerDF, RegressorDF, TransformerDF
 
 log = logging.getLogger(__name__)
 

src/sklearndf/classification/_classification.py

@@ -3,7 +3,7 @@
 """
 import logging
 from abc import ABCMeta
-from typing import *
+from typing import Any, List, Optional, Sequence, Union
 
 import numpy as np
 import pandas as pd

src/sklearndf/pipeline/_learner_pipeline.py

@@ -4,7 +4,7 @@
 
 import logging
 from abc import ABCMeta, abstractmethod
-from typing import *
+from typing import Any, Generic, List, Optional, Sequence, TypeVar, Union
 
 import pandas as pd
 from sklearn.base import BaseEstimator

src/sklearndf/pipeline/_pipeline.py

@@ -4,7 +4,7 @@
 
 import logging
 from abc import ABCMeta
-from typing import *
+from typing import Iterator, List, Sequence, Tuple, Union, cast
 
 import numpy as np
 import pandas as pd

src/sklearndf/regression/_regression.py

@@ -3,7 +3,7 @@
 """
 import logging
 from abc import ABCMeta
-from typing import *
+from typing import Any, Generic, Optional, TypeVar, Union
 
 import pandas as pd
 from sklearn.base import RegressorMixin
@@ -56,6 +56,7 @@
 
 from .. import RegressorDF, TransformerDF
 from .._wrapper import _MetaRegressorWrapperDF, _RegressorWrapperDF, df_estimator
+
 # noinspection PyProtectedMember
 from ..transformation._wrapper import _ColumnPreservingTransformerWrapperDF
 

src/sklearndf/regression/_regression_v0_22.py

@@ -3,16 +3,13 @@
 from sklearn 0.22 onwards
 """
 import logging
-from typing import *
+from typing import TypeVar
 
 from sklearn.base import RegressorMixin
 from sklearn.ensemble import StackingRegressor
 
 from .. import RegressorDF
-from .._wrapper import (
-    _StackingRegressorWrapperDF,
-    df_estimator,
-)
+from .._wrapper import _StackingRegressorWrapperDF, df_estimator
 
 # noinspection PyProtectedMember
 

src/sklearndf/regression/_regression_v0_23.py

@@ -4,7 +4,7 @@
 """
 
 import logging
-from typing import *
+from typing import TypeVar
 
 from sklearn.base import RegressorMixin
 from sklearn.linear_model import GammaRegressor, PoissonRegressor, TweedieRegressor

src/sklearndf/transformation/_transformation.py

@@ -19,8 +19,7 @@
 import logging
 from abc import ABCMeta
 from functools import reduce
-from typing import *
-from typing import List
+from typing import Iterable, List, TypeVar
 
 import numpy as np
 import pandas as pd
@@ -228,7 +227,8 @@ def _validate_delegate_estimator(self) -> None:
         if non_compliant_transformers:
             raise ValueError(
                 f"{ColumnTransformerDF.__name__} only accepts strings or "
-                f"instances of " f"{TransformerDF.__name__} as valid transformers, but "
+                f"instances of "
+                f"{TransformerDF.__name__} as valid transformers, but "
                 f'also got: {", ".join(non_compliant_transformers)}'
             )
 

src/sklearndf/transformation/_wrapper.py

@@ -4,7 +4,7 @@
 
 import logging
 from abc import ABCMeta, abstractmethod
-from typing import *
+from typing import Any, Generic, Optional, TypeVar, Union
 
 import pandas as pd
 from sklearn.base import TransformerMixin

src/sklearndf/transformation/extra/_extra.py

@@ -4,16 +4,13 @@
 
 import logging
 from abc import ABCMeta
-from typing import *
+from typing import Any, Mapping, Optional, TypeVar, Union
 
 import pandas as pd
 from boruta import BorutaPy
 from sklearn.base import BaseEstimator
 
-from .._wrapper import (
-    _ColumnSubsetTransformerWrapperDF,
-    _NDArrayTransformerWrapperDF,
-)
+from .._wrapper import _ColumnSubsetTransformerWrapperDF, _NDArrayTransformerWrapperDF
 from ... import TransformerDF
 from ..._wrapper import _MetaEstimatorWrapperDF, df_estimator
 

test/test/sklearndf/__init__.py

@@ -1,8 +1,7 @@
 import re
 import sys
 from distutils import version
-from typing import *
-from typing import Type
+from typing import Any, Iterable, List, Mapping, Optional, Set, Type, Union
 
 import pandas as pd
 import sklearn
@@ -14,13 +13,14 @@
 Module: type = Any
 
 
-def find_all_classes(*modules: Module) -> Set[type]:
+def find_all_classes(*modules: Module) -> Set[Type[_EstimatorWrapperDF]]:
     """ Finds all Class members in given module/modules. """
-    types: Set[type] = set()
+    types: Set[Type[_EstimatorWrapperDF]] = set()
 
     def _add_classes_from_module(_m: Module) -> None:
         for member in vars(module).values():
             if isinstance(member, type):
+                member: Type[_EstimatorWrapperDF]
                 types.add(member)
 
     for module in modules:
@@ -40,8 +40,8 @@ def find_all_submodules(parent_module: Module) -> Set[Module]:
 
 
 def sklearn_delegate_classes(
-    module: Module
-) -> Dict[BaseEstimator, _EstimatorWrapperDF]:
+    module: Module,
+) -> Mapping[Type[BaseEstimator], Type[_EstimatorWrapperDF]]:
     """ Creates a dictionary mapping from sklearndf -> sklearn classes. """
     return {
         df_class.__wrapped__: df_class
@@ -54,7 +54,7 @@ def list_classes(
     from_modules: Union[Module, Iterable[Module]],
     matching: str,
     excluding: Optional[Union[str, Iterable[str]]] = None,
-) -> List[Type]:
+) -> List[Type[_EstimatorWrapperDF]]:
     """ Helper to return all classes with matching name from Python module(s) """
 
     if not isinstance(from_modules, Iterable):
@@ -73,7 +73,7 @@ def list_classes(
 
 def get_sklearndf_wrapper_class(
     to_wrap: Type[BaseEstimator], from_module=None
-) -> _EstimatorWrapperDF:
+) -> Type[_EstimatorWrapperDF]:
     """ Helper to return the wrapped counterpart for a sklearn class """
     try:
         return sklearn_delegate_classes(from_module)[to_wrap]
@@ -92,10 +92,10 @@ def check_expected_not_fitted_error(estimator: Union[LearnerDF, TransformerDF]):
     test_x = pd.DataFrame(data=list(range(10)))
 
     def check_sklearndf_call(
-        func_to_call: str, estimator: Union[LearnerDF, TransformerDF]
+        func_to_call: str, _estimator: Union[LearnerDF, TransformerDF]
     ) -> None:
         try:
-            getattr(estimator, func_to_call)(X=test_x)
+            getattr(_estimator, func_to_call)(X=test_x)
         except sklearn.exceptions.NotFittedError:
             # This is the expected error, that sklearn[df] should raise
             return
@@ -108,16 +108,16 @@ def check_sklearndf_call(
                 else:
                     x = test_x.values.reshape(-1)
 
-                getattr(estimator.native_estimator, func_to_call)(x)
+                getattr(_estimator.native_estimator, func_to_call)(x)
             except sklearn.exceptions.NotFittedError:
                 raise AssertionError(
                     "sklearndf did not return an expected NotFittedError"
-                    f" for {estimator.__class__.__name__}"
+                    f" for {_estimator.__class__.__name__}"
                 )
             except Exception as sklearn_exception:
                 assert repr(sklearndf_exception) == repr(sklearn_exception), (
                     "sklearndf raised a different error as sklearn"
-                    f" for {estimator.__class__.__name__}:"
+                    f" for {_estimator.__class__.__name__}:"
                     f"sklearndf: {repr(sklearndf_exception)} \n"
                     f"sklearn: {repr(sklearn_exception)}"
                 )

test/test/sklearndf/pipeline/test_pipeline_df.py

@@ -6,7 +6,7 @@
 import time
 from distutils.version import LooseVersion
 from tempfile import mkdtemp
-from typing import *
+from typing import Any, Dict, Mapping
 
 import joblib
 import numpy as np

test/test/sklearndf/test_classification.py

@@ -1,9 +1,8 @@
 from itertools import chain
-from typing import *
+from typing import Type
 
 import numpy as np
 import pandas as pd
-# noinspection PyPackageRequirements
 import pytest
 from sklearn.multioutput import ClassifierChain, MultiOutputClassifier
 

test/test/sklearndf/test_regression.py

@@ -1,12 +1,12 @@
-from typing import *
+from typing import List, Type
 
 import pandas as pd
-# noinspection PyPackageRequirements
 import pytest
 from sklearn.multioutput import MultiOutputRegressor, RegressorChain
 
 import sklearndf.regression
 from sklearndf import RegressorDF, TransformerDF
+from sklearndf._wrapper import _EstimatorWrapperDF
 from sklearndf.regression import (
     IsotonicRegressionDF,
     LinearRegressionDF,
@@ -15,7 +15,7 @@
 )
 from test.sklearndf import check_expected_not_fitted_error, list_classes
 
-REGRESSORS_TO_TEST: List[Type] = list_classes(
+REGRESSORS_TO_TEST: List[Type[_EstimatorWrapperDF]] = list_classes(
     from_modules=sklearndf.regression,
     matching=r".*DF",
     excluding=[RegressorDF.__name__, TransformerDF.__name__, r".*WrapperDF"],
@@ -25,7 +25,7 @@
     "MultiOutputRegressorDF": {"estimator": RandomForestRegressorDF()},
     "RegressorChainDF": {"base_estimator": RandomForestRegressorDF()},
     "VotingRegressorDF": {
-        "estimators": [("rfr", RandomForestRegressorDF()), ("svmr", SVRDF())]
+        "estimators": [("rfr", RandomForestRegressorDF()), ("svr", SVRDF())]
     },
     "StackingRegressorDF": {
         "estimators": (

test/test/sklearndf/test_sklearn_coverage.py

@@ -1,6 +1,5 @@
 import itertools
-from typing import *
-from typing import Dict
+from typing import Dict, Iterable, List, Optional, Type, Union
 
 import pytest
 import sklearn
@@ -19,11 +18,7 @@
 from sklearndf import EstimatorDF
 from test import check_sklearn_version
 from test.conftest import UNSUPPORTED_SKLEARN_PACKAGES
-from test.sklearndf import (
-    find_all_submodules,
-    list_classes,
-    sklearn_delegate_classes,
-)
+from test.sklearndf import find_all_submodules, list_classes, sklearn_delegate_classes
 
 Module = type(sklearn)
 

test/test/sklearndf/transformation/test_imputers.py

@@ -1,10 +1,9 @@
 import itertools
 import logging
-from typing import *
+from typing import Type
 
 import numpy as np
 import pandas as pd
-# noinspection PyPackageRequirements
 import pytest
 
 import sklearndf.transformation