fix flake8 warnings

examples/wine-quality/wine-quality.py

@@ -24,7 +24,7 @@ result = run_experiment(params,
                         X_train,
                         y_train,
                         X_test,
-                         './wine-quality-{time}',
+                        './wine-quality-{time}',
                         type_of_target='continuous',
                         with_mlflow=True,
                         with_auto_hpo=True)

nyaggle/__init__.py

@@ -1 +1,5 @@
 from nyaggle.version import __version__
+
+__all__ = [
+    "__version__",
+]

nyaggle/ensemble/__init__.py

@@ -1,2 +1,8 @@
 from nyaggle.ensemble.averaging import averaging, averaging_opt
-from nyaggle.ensemble.stacking import stacking
+from nyaggle.ensemble.stacking import stacking
+
+__all__ = [
+    "averaging",
+    "averaging_opt",
+    "stacking",
+]

nyaggle/ensemble/common.py

@@ -1,6 +1,3 @@
 from collections import namedtuple
 
 EnsembleResult = namedtuple('EnsembleResult', ['test_prediction', 'oof_prediction', 'score'])
-
-
-

nyaggle/environment.py

@@ -1,7 +1,7 @@
 # pytorch
 
 try:
-    import torch
+    import torch  # noQA
     _has_torch = True
 except ImportError:
     _has_torch = False
@@ -15,7 +15,7 @@ def requires_torch():
 # mlflow
 
 try:
-    import mlflow
+    import mlflow  # noQA
     _has_mlflow = True
 except ImportError:
     _has_mlflow = False
@@ -30,7 +30,7 @@ def requires_mlflow():
 
 
 try:
-    import lightgbm
+    import lightgbm  # noQA
     _has_lightgbm = True
 except ImportError:
     _has_lightgbm = False
@@ -45,7 +45,7 @@ def requires_lightgbm():
 
 
 try:
-    import catboost
+    import catboost  # noQA
     _has_catboost = True
     # TODO check catboost version >= 0.17
 except ImportError:
@@ -61,7 +61,7 @@ def requires_catboost():
 
 
 try:
-    import xgboost
+    import xgboost  # noQA
     _has_xgboost = True
 except ImportError:
     _has_xgboost = False

nyaggle/experiment/__init__.py

@@ -1,2 +1,10 @@
 from nyaggle.experiment.experiment import Experiment, add_leaderboard_score
 from nyaggle.experiment.run import autoprep_gbdt, run_experiment, find_best_lgbm_parameter
+
+__all__ = [
+    "Experiment",
+    "add_leaderboard_score",
+    "autoprep_gbdt",
+    "run_experiment",
+    "find_best_lgbm_parameter",
+]

nyaggle/feature/category_encoder/__init__.py

@@ -1 +1,6 @@
 from nyaggle.feature.category_encoder.target_encoder import KFoldEncoderWrapper, TargetEncoder
+
+__all__ = [
+    "KFoldEncoderWrapper",
+    "TargetEncoder",
+]

nyaggle/feature/groupby.py

@@ -7,19 +7,19 @@
 # https://github.com/pfnet-research/xfeat/blob/master/xfeat/helper.py
 # -----------------------------------------------------------------------------
 # MIT License
-# 
+#
 # Copyright (c) 2020 Preferred Networks, Inc.
-# 
+#
 # Permission is hereby granted, free of charge, to any person obtaining a copy
 # of this software and associated documentation files (the "Software"), to deal
 # in the Software without restriction, including without limitation the rights
 # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 # copies of the Software, and to permit persons to whom the Software is
 # furnished to do so, subject to the following conditions:
-# 
+#
 # The above copyright notice and this permission notice shall be included in all
 # copies or substantial portions of the Software.
-# 
+#
 # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
@@ -71,7 +71,8 @@ def aggregation(
             Used to aggregate values for the groupby.
         agg_methods:
             List of function or function names, e.g. ['mean', 'max', 'min', numpy.mean].
-            Do not use a lambda function because the name attribute of the lambda function cannot generate a unique string of column names in <lambda>.
+            Do not use a lambda function because the name attribute of the lambda function
+            cannot generate a unique string of column names in <lambda>.
     Returns:
         Tuple of output dataframe and new column names.
     """

nyaggle/feature/nlp/__init__.py

@@ -1 +1,5 @@
 from nyaggle.feature.nlp.bert import BertSentenceVectorizer
+
+__all__ = [
+    "BertSentenceVectorizer",
+]

nyaggle/feature/nlp/bert.py

@@ -58,7 +58,7 @@ class BertSentenceVectorizer(BaseFeaturizer):
             self.tokenizer = transformers.BertJapaneseTokenizer.from_pretrained(pretrained_model_name)
             self.model = transformers.BertModel.from_pretrained(pretrained_model_name)
         else:
-            raise ValueError('Specified language type () is invalid.'.format(lang))
+            raise ValueError(f'Specified language type ({lang}) is invalid.')
 
         self.lang = lang
         self.n_components = n_components

nyaggle/feature_store/__init__.py

@@ -1 +1,8 @@
 from nyaggle.feature_store.feature_store import cached_feature, save_feature, load_feature, load_features
+
+__all__ = [
+    "cached_feature",
+    "save_feature",
+    "load_feature",
+    "load_features"
+]

nyaggle/hyper_parameters/__init__.py

@@ -1 +1,6 @@
 from nyaggle.hyper_parameters.parameters import get_hyperparam_byname, list_hyperparams
+
+__all__ = [
+    "get_hyperparam_byname",
+    "list_hyperparams",
+]

nyaggle/hyper_parameters/catboost.py

@@ -1,7 +1,7 @@
 parameters = [
     {
         "name": "ieee-2019-17th",
-        "url": "https://nbviewer.jupyter.org/github/tmheo/IEEE-Fraud-Detection-17th-Place-Solution/blob/master/notebook/IEEE-17th-Place-Solution-CatBoost-Ensemble.ipynb",
+        "url": "https://nbviewer.jupyter.org/github/tmheo/IEEE-Fraud-Detection-17th-Place-Solution/blob/master/notebook/IEEE-17th-Place-Solution-CatBoost-Ensemble.ipynb",  # noQA
         "competition": "ieee-fraud-detection",
         "rank": 17,
         "metric": "auc",
@@ -42,4 +42,3 @@ parameters = [
         }
     },
 ]
-

nyaggle/hyper_parameters/lightgbm.py

@@ -28,7 +28,7 @@ parameters = [
     },
     {
         "name": "ieee-2019-17th",
-        "url": "https://nbviewer.jupyter.org/github/tmheo/IEEE-Fraud-Detection-17th-Place-Solution/blob/master/notebook/IEEE-17th-Place-Solution-LightGBM.ipynb",
+        "url": "https://nbviewer.jupyter.org/github/tmheo/IEEE-Fraud-Detection-17th-Place-Solution/blob/master/notebook/IEEE-17th-Place-Solution-LightGBM.ipynb",  # noQA
         "competition": "ieee-fraud-detection",
         "rank": 17,
         "metric": "auc",
@@ -98,7 +98,7 @@ parameters = [
     # 2019, Santander Customer Transaction Prediction
     {
         "name": "santander-2019-2nd",
-        "url": "https://github.com/KazukiOnodera/santander-customer-transaction-prediction/blob/master/py/990_2nd_place_solution_golf.py",
+        "url": "https://github.com/KazukiOnodera/santander-customer-transaction-prediction/blob/master/py/990_2nd_place_solution_golf.py",  # noQA
         "competition": "santander-customer-transaction-prediction",
         "rank": 2,
         "metric": "auc",
@@ -120,7 +120,7 @@ parameters = [
     },
     {
         "name": "santander-2019-5th",
-        "url": "https://github.com/tnmichael309/Kaggle-Santander-Customer-Transaction-Prediction-5th-Place-Partial-Solution/blob/master/notebooks/LGB%20Model.ipynb",
+        "url": "https://github.com/tnmichael309/Kaggle-Santander-Customer-Transaction-Prediction-5th-Place-Partial-Solution/blob/master/notebooks/LGB%20Model.ipynb",  # noQA
         "competition": "santander-customer-transaction-prediction",
         "rank": 5,
         "metric": "auc",
@@ -193,7 +193,7 @@ parameters = [
     # 2018, Microsoft Malware Prediction
     {
         "name": "microsoft-2018-2nd",
-        "url": "https://github.com/imor-de/microsoft_malware_prediction_kaggle_2nd/blob/master/code/7_Submission_M2.ipynb",
+        "url": "https://github.com/imor-de/microsoft_malware_prediction_kaggle_2nd/blob/master/code/7_Submission_M2.ipynb",  # noQA
         "competition": "microsoft-malware-prediction",
         "rank": 2,
         "metric": "auc",

nyaggle/testing/__init__.py

@@ -1 +1,7 @@
-from nyaggle.testing.util import *
+from nyaggle.testing.util import make_classification_df, make_regression_df, get_temp_directory
+
+__all__ = [
+    "make_classification_df",
+    "make_regression_df",
+    "get_temp_directory",
+]

nyaggle/testing/util.py

@@ -60,8 +60,6 @@ def make_regression_df(n_samples: int = 1024,
     return X, y
 
 
-
-
 @contextmanager
 def get_temp_directory() -> str:
     path = None
@@ -71,4 +69,3 @@ def get_temp_directory() -> str:
     finally:
         if path:
             shutil.rmtree(path, ignore_errors=True)
-

nyaggle/util/__init__.py

@@ -1,3 +1,10 @@
 from nyaggle.util.plot_importance import plot_importance
 from nyaggle.util.traits import is_instance, is_gbdt_instance
 from nyaggle.util.submission import make_submission_df
+
+__all__ = [
+    "plot_importance",
+    "is_instance",
+    "is_gbdt_instance",
+    "make_submission_df",
+]

nyaggle/util/traits.py

@@ -63,7 +63,7 @@ def is_instance(obj, class_path_str: Union[str, List, Tuple]) -> bool:
         # Check module exists
         try:
             spec = importlib.util.find_spec(module_name)
-        except:
+        except (ValueError, AttributeError, ModuleNotFoundError):
             spec = None
         if spec is None:
             continue

nyaggle/validation/__init__.py

@@ -2,3 +2,15 @@ from nyaggle.validation.cross_validate import cross_validate
 from nyaggle.validation.adversarial_validate import adversarial_validate
 from nyaggle.validation.split import \
     check_cv, TimeSeriesSplit, SlidingWindowSplit, Take, Nth, Skip, StratifiedGroupKFold
+
+__all__ = [
+    "cross_validate",
+    "adversarial_validate",
+    "check_cv",
+    "TimeSeriesSplit",
+    "SlidingWindowSplit",
+    "Take",
+    "Nth",
+    "Skip",
+    "StratifiedGroupKFold",
+]