Remove unused type error suppressions (landing group 94) (#352)

src/mcgrad/_compat.py

@@ -13,8 +13,7 @@
 _PANDAS_GROUPBY_INCLUDE_GROUPS: bool
 try:
     _pandas_version: tuple[int, ...] = tuple(
-        int(x)
-        for x in pd.__version__.split(".")[:2]  # pyre-ignore[16]
+        int(x) for x in pd.__version__.split(".")[:2]
     )
     _PANDAS_GROUPBY_INCLUDE_GROUPS = _pandas_version >= (2, 2)
 except (ValueError, AttributeError):

src/mcgrad/methods.py

@@ -471,7 +471,6 @@ def feature_importance(self) -> pd.DataFrame:
             {
                 # Ordering of features here relies on two things 1) that MCGrad.extract_features returns first categoricals then
                 # numericals and 2) that .fit method concatenates logits to the end of the feature matrix
-                # pyre-ignore[58] if either feature_names attribute is None an error is raised above
                 "feature": self.categorical_feature_names
                 + self.numerical_feature_names
                 + [self._PREDICTION_FEATURE_NAME],

src/mcgrad/metrics.py

@@ -1320,7 +1320,6 @@ def _rank_calibration_error(
     :return: tuple (RCE, label_cdfs, prediction_cdfs)
     """
     # break ties
-    # pyrefly: ignore [bad-argument-type]
     rng = np.random.RandomState(42) if rng is None else rng
     eps = rng.uniform(0, 1, labels.shape[0]) * CALIBRATION_ERROR_EPSILON
     labels = labels + eps
@@ -2128,7 +2127,6 @@ def soft_label_log_loss(
     return float(np.mean(loss))
 
 
-# pyre-ignore[16]: Setting __name__ so wrap_sklearn_metric_func
 # identifies this metric as "log_loss" for early-stopping heuristics.
 soft_label_log_loss.__name__ = "log_loss"
 

src/mcgrad/tutorials/helpers.py

@@ -42,7 +42,6 @@ def format(self, record: logging.LogRecord) -> str:
             hours = total_seconds // 3600
             minutes = (total_seconds % 3600) // 60
             seconds = total_seconds % 60
-            # pyre-ignore[16]: Dynamic attribute for elapsed time formatting
             record.elapsed = f"[{hours:02d}:{minutes:02d}:{seconds:02d}]"
         else:
             record.elapsed = ""