[python-package] stop relying on string concatenation / splitting for cv() eval results

python-package/lightgbm/callback.py

@@ -71,6 +71,14 @@ class CallbackEnv:
     evaluation_result_list: Optional[_ListOfEvalResultTuples]
 
 
+def _using_cv(env: CallbackEnv) -> bool:
+    """Check if model in callback env is a CVBooster"""
+    # this string-matching is used instead of isinstance() to avoid a circular import
+    return env.model.__class__.__name__ == "CVBooster" or any(
+        c.__name__ == "CVBooster" for c in env.model.__class__.__bases__
+    )
+
+
 def _format_eval_result(value: _EvalResultTuple, show_stdv: bool) -> str:
     """Format metric string."""
     dataset_name, metric_name, metric_value, *_ = value
@@ -143,16 +151,13 @@ def _init(self, env: CallbackEnv) -> None:
             )
         self.eval_result.clear()
         for item in env.evaluation_result_list:
-            if len(item) == 4:  # regular train
-                data_name, eval_name = item[:2]
-            else:  # cv
-                data_name, eval_name = item[1].split()
-            self.eval_result.setdefault(data_name, OrderedDict())
+            dataset_name, metric_name, *_ = item
+            self.eval_result.setdefault(dataset_name, OrderedDict())
             if len(item) == 4:
-                self.eval_result[data_name].setdefault(eval_name, [])
+                self.eval_result[dataset_name].setdefault(metric_name, [])
             else:
-                self.eval_result[data_name].setdefault(f"{eval_name}-mean", [])
-                self.eval_result[data_name].setdefault(f"{eval_name}-stdv", [])
+                self.eval_result[dataset_name].setdefault(f"{metric_name}-mean", [])
+                self.eval_result[dataset_name].setdefault(f"{metric_name}-stdv", [])
 
     def __call__(self, env: CallbackEnv) -> None:
         if env.iteration == env.begin_iteration:
@@ -163,15 +168,16 @@ def __call__(self, env: CallbackEnv) -> None:
                 "Please report it at https://github.com/microsoft/LightGBM/issues"
             )
         for item in env.evaluation_result_list:
+            # for cv(), 'metric_value' is actually a mean of metric values over all CV folds
+            dataset_name, metric_name, metric_value, *_ = item
             if len(item) == 4:
-                data_name, eval_name, result = item[:3]
-                self.eval_result[data_name][eval_name].append(result)
+                # train()
+                self.eval_result[dataset_name][metric_name].append(metric_value)
             else:
-                data_name, eval_name = item[1].split()
-                res_mean = item[2]
-                res_stdv = item[4]  # type: ignore[misc]
-                self.eval_result[data_name][f"{eval_name}-mean"].append(res_mean)
-                self.eval_result[data_name][f"{eval_name}-stdv"].append(res_stdv)
+                # cv()
+                metric_std_dev = item[4]  # type: ignore[misc]
+                self.eval_result[dataset_name][f"{metric_name}-mean"].append(metric_value)
+                self.eval_result[dataset_name][f"{metric_name}-stdv"].append(metric_std_dev)
 
 
 def record_evaluation(eval_result: Dict[str, Dict[str, List[Any]]]) -> Callable:
@@ -304,15 +310,15 @@ def _gt_delta(self, curr_score: float, best_score: float, delta: float) -> bool:
     def _lt_delta(self, curr_score: float, best_score: float, delta: float) -> bool:
         return curr_score < best_score - delta
 
-    def _is_train_set(self, ds_name: str, eval_name: str, env: CallbackEnv) -> bool:
+    def _is_train_set(self, dataset_name: str, env: CallbackEnv) -> bool:
         """Check, by name, if a given Dataset is the training data."""
         # for lgb.cv() with eval_train_metric=True, evaluation is also done on the training set
         # and those metrics are considered for early stopping
-        if ds_name == "cv_agg" and eval_name == "train":
+        if _using_cv(env) and dataset_name == "train":
             return True
 
         # for lgb.train(), it's possible to pass the training data via valid_sets with any eval_name
-        if isinstance(env.model, Booster) and ds_name == env.model._train_data_name:
+        if isinstance(env.model, Booster) and dataset_name == env.model._train_data_name:
             return True
 
         return False
@@ -327,11 +333,13 @@ def _init(self, env: CallbackEnv) -> None:
             _log_warning("Early stopping is not available in dart mode")
             return
 
+        # get details of the first dataset
+        first_dataset_name, first_metric_name, *_ = env.evaluation_result_list[0]
+
         # validation sets are guaranteed to not be identical to the training data in cv()
         if isinstance(env.model, Booster):
             only_train_set = len(env.evaluation_result_list) == 1 and self._is_train_set(
-                ds_name=env.evaluation_result_list[0][0],
-                eval_name=env.evaluation_result_list[0][1].split(" ")[0],
+                dataset_name=first_dataset_name,
                 env=env,
             )
             if only_train_set:
@@ -370,8 +378,7 @@ def _init(self, env: CallbackEnv) -> None:
                 _log_info(f"Using {self.min_delta} as min_delta for all metrics.")
             deltas = [self.min_delta] * n_datasets * n_metrics
 
-        # split is needed for "<dataset type> <metric>" case (e.g. "train l1")
-        self.first_metric = env.evaluation_result_list[0][1].split(" ")[-1]
+        self.first_metric = first_metric_name
         for eval_ret, delta in zip(env.evaluation_result_list, deltas):
             self.best_iter.append(0)
             if eval_ret[3]:  # greater is better
@@ -381,15 +388,15 @@ def _init(self, env: CallbackEnv) -> None:
                 self.best_score.append(float("inf"))
                 self.cmp_op.append(partial(self._lt_delta, delta=delta))
 
-    def _final_iteration_check(self, env: CallbackEnv, eval_name_splitted: List[str], i: int) -> None:
+    def _final_iteration_check(self, *, env: CallbackEnv, metric_name: str, i: int) -> None:
         if env.iteration == env.end_iteration - 1:
             if self.verbose:
                 best_score_str = "\t".join([_format_eval_result(x, show_stdv=True) for x in self.best_score_list[i]])
                 _log_info(
                     "Did not meet early stopping. " f"Best iteration is:\n[{self.best_iter[i] + 1}]\t{best_score_str}"
                 )
                 if self.first_metric_only:
-                    _log_info(f"Evaluated only: {eval_name_splitted[-1]}")
+                    _log_info(f"Evaluated only: {metric_name}")
             raise EarlyStopException(self.best_iter[i], self.best_score_list[i])
 
     def __call__(self, env: CallbackEnv) -> None:
@@ -405,21 +412,18 @@ def __call__(self, env: CallbackEnv) -> None:
         # self.best_score_list is initialized to an empty list
         first_time_updating_best_score_list = self.best_score_list == []
         for i in range(len(env.evaluation_result_list)):
-            score = env.evaluation_result_list[i][2]
-            if first_time_updating_best_score_list or self.cmp_op[i](score, self.best_score[i]):
-                self.best_score[i] = score
+            dataset_name, metric_name, metric_value, *_ = env.evaluation_result_list[i]
+            if first_time_updating_best_score_list or self.cmp_op[i](metric_value, self.best_score[i]):
+                self.best_score[i] = metric_value
                 self.best_iter[i] = env.iteration
                 if first_time_updating_best_score_list:
                     self.best_score_list.append(env.evaluation_result_list)
                 else:
                     self.best_score_list[i] = env.evaluation_result_list
-            # split is needed for "<dataset type> <metric>" case (e.g. "train l1")
-            eval_name_splitted = env.evaluation_result_list[i][1].split(" ")
-            if self.first_metric_only and self.first_metric != eval_name_splitted[-1]:
+            if self.first_metric_only and self.first_metric != metric_name:
                 continue  # use only the first metric for early stopping
             if self._is_train_set(
-                ds_name=env.evaluation_result_list[i][0],
-                eval_name=eval_name_splitted[0],
+                dataset_name=dataset_name,
                 env=env,
             ):
                 continue  # train data for lgb.cv or sklearn wrapper (underlying lgb.train)
@@ -430,9 +434,9 @@ def __call__(self, env: CallbackEnv) -> None:
                     )
                     _log_info(f"Early stopping, best iteration is:\n[{self.best_iter[i] + 1}]\t{eval_result_str}")
                     if self.first_metric_only:
-                        _log_info(f"Evaluated only: {eval_name_splitted[-1]}")
+                        _log_info(f"Evaluated only: {metric_name}")
                 raise EarlyStopException(self.best_iter[i], self.best_score_list[i])
-            self._final_iteration_check(env, eval_name_splitted, i)
+            self._final_iteration_check(env=env, metric_name=metric_name, i=i)
 
 
 def _should_enable_early_stopping(stopping_rounds: Any) -> bool:

python-package/lightgbm/engine.py

@@ -581,15 +581,35 @@ def _agg_cv_result(
     raw_results: List[List[_LGBM_BoosterEvalMethodResultType]],
 ) -> List[_LGBM_BoosterEvalMethodResultWithStandardDeviationType]:
     """Aggregate cross-validation results."""
-    cvmap: Dict[str, List[float]] = OrderedDict()
-    metric_type: Dict[str, bool] = {}
+    # build up 2 maps, of the form:
+    #
+    # OrderedDict{
+    #     (<dataset_name>, <metric_name>): <is_higher_better>
+    # }
+    #
+    # OrderedDict{
+    #     (<dataset_name>, <metric_name>): list[<metric_value>]
+    # }
+    #
+    metric_types: Dict[Tuple[str, str], bool] = OrderedDict()
+    metric_values: Dict[Tuple[str, str], List[float]] = OrderedDict()
     for one_result in raw_results:
         for one_line in one_result:
-            key = f"{one_line[0]} {one_line[1]}"
-            metric_type[key] = one_line[3]
-            cvmap.setdefault(key, [])
-            cvmap[key].append(one_line[2])
-    return [("cv_agg", k, float(np.mean(v)), metric_type[k], float(np.std(v))) for k, v in cvmap.items()]
+            dataset_name, metric_name, metric_value, is_higher_better = one_line
+            key = (dataset_name, metric_name)
+            metric_types[key] = is_higher_better
+            metric_values.setdefault(key, [])
+            metric_values[key].append(metric_value)
+
+    # turn that into a list of tuples of the form:
+    #
+    # [
+    #     (<dataset_name>, <metric_name>, mean(<values>), <is_higher_better>, std_dev(<values>))
+    # ]
+    return [
+        (k[0], k[1], float(np.mean(metric_values[k])), metric_types[k], float(np.std(metric_values[k])))
+        for k, v in metric_values.items()
+    ]
 
 
 def cv(
@@ -812,9 +832,9 @@ def cv(
             )
         cvfolds.update(fobj=fobj)  # type: ignore[call-arg]
         res = _agg_cv_result(cvfolds.eval_valid(feval))  # type: ignore[call-arg]
-        for _, key, mean, _, std in res:
-            results[f"{key}-mean"].append(mean)
-            results[f"{key}-stdv"].append(std)
+        for dataset_name, metric_name, metric_mean, _, metric_std_dev in res:
+            results[f"{dataset_name} {metric_name}-mean"].append(metric_mean)
+            results[f"{dataset_name} {metric_name}-stdv"].append(metric_std_dev)
         try:
             for cb in callbacks_after_iter:
                 cb(