methods.py code quality review - Rename upper case class attributes -> lower case class attributes - EARLY_STOPPING

src/multicalibration/methods.py

@@ -31,6 +31,8 @@
 
 logger: logging.Logger = logging.getLogger(__name__)
 
+# @oss-disable[end= ]: from multicalibration._compat import DeprecatedAttributesMixin
+
 
 @dataclass(frozen=True, slots=True)
 class MCBoostProcessedData:
@@ -60,7 +62,11 @@ class EstimationMethod(Enum):
     AUTO = 3
 
 
-class BaseMCBoost(BaseCalibrator, ABC):
+class BaseMCBoost(
+    # @oss-disable[end= ]: DeprecatedAttributesMixin,
+    BaseCalibrator,
+    ABC,
+):
     """
     Abstract base class for MCBoost models. This class hosts the common functionality for all MCBoost models and defines
     an abstract interface that all MCBoost models must implement.
@@ -218,17 +224,17 @@ def __init__(
         self._set_lightgbm_params(lightgbm_params)
 
         self.encode_categorical_variables = encode_categorical_variables
-        self.MONOTONE_T: bool = (
+        self.monotone_t: bool = (
             self.DEFAULT_HYPERPARAMS["monotone_t"] if monotone_t is None else monotone_t
         )
 
-        self.EARLY_STOPPING: bool = (
+        self.early_stopping: bool = (
             self.DEFAULT_HYPERPARAMS["early_stopping"]
             if early_stopping is None
             else early_stopping
         )
 
-        if not self.EARLY_STOPPING:
+        if not self.early_stopping:
             if patience is not None:
                 raise ValueError(
                     "`patience` must be None when argument `early_stopping` is disabled."
@@ -263,7 +269,7 @@ def __init__(
                 )
 
         if num_rounds is None:
-            if self.EARLY_STOPPING:
+            if self.early_stopping:
                 num_rounds = self.MAX_NUM_ROUNDS_EARLY_STOPPING
             else:
                 num_rounds = self.NUM_ROUNDS_DEFAULT_NO_EARLY_STOPPING
@@ -575,7 +581,7 @@ def fit(
         preprocessed_val_data = None
 
         num_rounds = self.NUM_ROUNDS
-        if self.EARLY_STOPPING:
+        if self.early_stopping:
             timeout_msg = (
                 f" (timeout: {self.EARLY_STOPPING_TIMEOUT}s)"
                 if self.EARLY_STOPPING_TIMEOUT
@@ -797,7 +803,7 @@ def _predict(
 
     def _get_lgbm_params(self, x: npt.NDArray) -> dict[str, Any]:
         lgb_params = self.lightgbm_params.copy()
-        if self.MONOTONE_T:
+        if self.monotone_t:
             score_constraint = [1]
             segment_feature_constraints = [0] * (x.shape[1] - 1)
             lgb_params["monotone_constraints"] = (
@@ -955,7 +961,7 @@ def _determine_best_num_rounds(
                     if fold_num not in mcboost_per_fold:
                         mcboost = self._create_instance_for_cv(
                             encode_categorical_variables=self.encode_categorical_variables,
-                            monotone_t=self.MONOTONE_T,
+                            monotone_t=self.monotone_t,
                             lightgbm_params=self.lightgbm_params,
                             early_stopping=False,
                             num_rounds=0,

src/multicalibration/plotting.py

@@ -695,7 +695,7 @@ def plot_learning_curve(
     :param show_all: Whether to show all metrics in the learning curve. If False, only the metric specified in MCBoost's early_stopping_score_func is shown.
     :returns: A Plotly Figure object representing the learning curve.
     """
-    if not mcboost_model.EARLY_STOPPING:
+    if not mcboost_model.early_stopping:
         raise ValueError(
             "Learning curve can only be plotted for models that have been trained with EARLY_STOPPING=True."
         )
@@ -704,7 +704,7 @@ def plot_learning_curve(
     extra_evaluation_due_to_early_stopping = (
         1
         if (
-            mcboost_model.EARLY_STOPPING
+            mcboost_model.early_stopping
             and len(mcboost_model.mr) < mcboost_model.NUM_ROUNDS
         )
         else 0

tests/test_methods.py

@@ -1516,7 +1516,7 @@ def dummy_score_func(
     effective_num_rounds = len(mcboost.mr)
     extra_evaluation_due_to_early_stopping = (
         1
-        if (mcboost.EARLY_STOPPING and effective_num_rounds < mcboost.NUM_ROUNDS)
+        if (mcboost.early_stopping and effective_num_rounds < mcboost.NUM_ROUNDS)
         else 0
     )