[python-package] use dataclass for CallbackEnv (#6048)

.ci/test-python-oldest.sh

@@ -7,6 +7,7 @@
 #
 echo "installing lightgbm's dependencies"
 pip install \
+  'dataclasses' \
   'numpy==1.12.0' \
   'pandas==0.24.0' \
   'scikit-learn==0.18.2' \

python-package/lightgbm/callback.py

@@ -1,10 +1,14 @@
 # coding: utf-8
 """Callbacks library."""
-import collections
+from collections import OrderedDict
+from dataclasses import dataclass
 from functools import partial
-from typing import Any, Callable, Dict, List, Tuple, Union
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
 
-from .basic import _ConfigAliases, _LGBM_BoosterEvalMethodResultType, _log_info, _log_warning
+from .basic import Booster, _ConfigAliases, _LGBM_BoosterEvalMethodResultType, _log_info, _log_warning
+
+if TYPE_CHECKING:
+    from .engine import CVBooster
 
 __all__ = [
     'early_stopping',
@@ -43,14 +47,14 @@ def __init__(self, best_iteration: int, best_score: _ListOfEvalResultTuples) ->
 
 
 # Callback environment used by callbacks
-CallbackEnv = collections.namedtuple(
-    "CallbackEnv",
-    ["model",
-     "params",
-     "iteration",
-     "begin_iteration",
-     "end_iteration",
-     "evaluation_result_list"])
+@dataclass
+class CallbackEnv:
+    model: Union[Booster, "CVBooster"]
+    params: Dict[str, Any]
+    iteration: int
+    begin_iteration: int
+    end_iteration: int
+    evaluation_result_list: Optional[List[_LGBM_BoosterEvalMethodResultType]]
 
 
 def _format_eval_result(value: _EvalResultTuple, show_stdv: bool) -> str:
@@ -126,7 +130,7 @@ def _init(self, env: CallbackEnv) -> None:
                 data_name, eval_name = item[:2]
             else:  # cv
                 data_name, eval_name = item[1].split()
-            self.eval_result.setdefault(data_name, collections.OrderedDict())
+            self.eval_result.setdefault(data_name, OrderedDict())
             if len(item) == 4:
                 self.eval_result[data_name].setdefault(eval_name, [])
             else:

python-package/lightgbm/engine.py

@@ -1,8 +1,8 @@
 # coding: utf-8
 """Library with training routines of LightGBM."""
-import collections
 import copy
 import json
+from collections import OrderedDict, defaultdict
 from operator import attrgetter
 from pathlib import Path
 from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
@@ -293,7 +293,7 @@ def train(
             booster.best_iteration = earlyStopException.best_iteration + 1
             evaluation_result_list = earlyStopException.best_score
             break
-    booster.best_score = collections.defaultdict(collections.OrderedDict)
+    booster.best_score = defaultdict(OrderedDict)
     for dataset_name, eval_name, score, _ in evaluation_result_list:
         booster.best_score[dataset_name][eval_name] = score
     if not keep_training_booster:
@@ -526,7 +526,7 @@ def _agg_cv_result(
     raw_results: List[List[Tuple[str, str, float, bool]]]
 ) -> List[Tuple[str, str, float, bool, float]]:
     """Aggregate cross-validation results."""
-    cvmap: Dict[str, List[float]] = collections.OrderedDict()
+    cvmap: Dict[str, List[float]] = OrderedDict()
     metric_type: Dict[str, bool] = {}
     for one_result in raw_results:
         for one_line in one_result:
@@ -717,7 +717,7 @@ def cv(
              .set_feature_name(feature_name) \
              .set_categorical_feature(categorical_feature)
 
-    results = collections.defaultdict(list)
+    results = defaultdict(list)
     cvfolds = _make_n_folds(full_data=train_set, folds=folds, nfold=nfold,
                             params=params, seed=seed, fpreproc=fpreproc,
                             stratified=stratified, shuffle=shuffle,

python-package/pyproject.toml

@@ -18,6 +18,7 @@ classifiers = [
     "Topic :: Scientific/Engineering :: Artificial Intelligence"
 ]
 dependencies = [
+    "dataclasses ; python_version < '3.7'",
     "numpy",
     "scipy"
 ]