Release/2.3.1 (#230)

* Release/2.3.1

* lint fix

* ignore mypy in lgbm learner

* use type-checking

CHANGELOG.md

@@ -1,5 +1,9 @@
 # Changelog
 
+## [2.3.1] - 2023-04-11
+- **Bugfix**
+  - Remove incorrect `lightgbm` import from common paths
+
 ## [2.3.0] - 2023-03-28
 - **Enhacement**
   - Bump maximum allowed `scikit-learn`

src/fklearn/resources/VERSION

@@ -1 +1 @@
-2.3.0
+2.3.1

src/fklearn/training/classification.py

@@ -1,8 +1,7 @@
-from typing import List, Any, Optional, Callable, Tuple, Union
+from typing import List, Any, Optional, Callable, Tuple, Union, TYPE_CHECKING
 
 import numpy as np
 import pandas as pd
-from lightgbm import Booster
 from pathlib import Path
 from toolz import curry, merge, assoc
 from sklearn.feature_extraction.text import TfidfVectorizer
@@ -14,6 +13,10 @@
 from fklearn.training.utils import log_learner_time, expand_features_encoded
 
 
+if TYPE_CHECKING:
+    from lightgbm import Booster
+
+
 @curry
 @log_learner_time(learner_name='logistic_classification_learner')
 def logistic_classification_learner(df: pd.DataFrame,
@@ -513,7 +516,7 @@ def lgbm_classification_learner(df: pd.DataFrame,
                                     Callable[[np.ndarray, pd.DataFrame], Tuple[str, float, bool]],
                                     List[Callable[[np.ndarray, pd.DataFrame], Tuple[str, float, bool]]]]
                                 ] = None,
-                                init_model: Optional[Union[str, Path, Booster]] = None,
+                                init_model: Optional[Union[str, Path, 'Booster']] = None,
                                 feature_name: Union[List[str], str] = 'auto',
                                 categorical_feature: Union[List[str], List[int], str] = 'auto',
                                 keep_training_booster: bool = False,

tests/training/test_classification.py

@@ -1,7 +1,6 @@
 # -*- coding: utf-8 -*-
 from collections import Counter
 
-import lightgbm
 import numpy as np
 import pandas as pd
 
@@ -487,6 +486,7 @@ def test_lgbm_classification_learner():
 
 
 def test_lgbm_classification_learner_params():
+    import lightgbm
     # Test input parameters
 
     df = pd.DataFrame(