[python-package][PySpark] Expose Training and Validation Metrics

python-package/xgboost/spark/core.py

@@ -100,6 +100,7 @@
     serialize_booster,
     use_cuda,
 )
+from .xgboost_training_summary import _XGBoostTrainingSummary
 
 # Put pyspark specific params here, they won't be passed to XGBoost.
 # like `validationIndicatorCol`, `base_margin_col`
@@ -704,8 +705,10 @@ def _pyspark_model_cls(cls) -> Type["_SparkXGBModel"]:
         """
         raise NotImplementedError()
 
-    def _create_pyspark_model(self, xgb_model: XGBModel) -> "_SparkXGBModel":
-        return self._pyspark_model_cls()(xgb_model)
+    def _create_pyspark_model(
+        self, xgb_model: XGBModel, training_summary: _XGBoostTrainingSummary
+    ) -> "_SparkXGBModel":
+        return self._pyspark_model_cls()(xgb_model, training_summary)
 
     def _convert_to_sklearn_model(self, booster: bytearray, config: str) -> XGBModel:
         xgb_sklearn_params = self._gen_xgb_params_dict(
@@ -1148,7 +1151,7 @@ def _train_booster(
                 if dvalid is not None:
                     dval = [(dtrain, "training"), (dvalid, "validation")]
                 else:
-                    dval = None
+                    dval = [(dtrain, "training")]
                 booster = worker_train(
                     params=booster_params,
                     dtrain=dtrain,
@@ -1159,6 +1162,7 @@ def _train_booster(
             context.barrier()
 
             if context.partitionId() == 0:
+                yield pd.DataFrame({"data": [json.dumps(dict(evals_result))]})
                 config = booster.save_config()
                 yield pd.DataFrame({"data": [config]})
                 booster_json = booster.save_raw("json").decode("utf-8")
@@ -1167,7 +1171,7 @@ def _train_booster(
                     booster_chunk = booster_json[offset : offset + _MODEL_CHUNK_SIZE]
                     yield pd.DataFrame({"data": [booster_chunk]})
 
-        def _run_job() -> Tuple[str, str]:
+        def _run_job() -> Tuple[str, str, str]:
             rdd = (
                 dataset.mapInPandas(
                     _train_booster,  # type: ignore
@@ -1179,7 +1183,7 @@ def _run_job() -> Tuple[str, str]:
             rdd_with_resource = self._try_stage_level_scheduling(rdd)
             ret = rdd_with_resource.collect()
             data = [v[0] for v in ret]
-            return data[0], "".join(data[1:])
+            return data[0], data[1], "".join(data[2:])
 
         get_logger(_LOG_TAG).info(
             "Running xgboost-%s on %s workers with"
@@ -1192,13 +1196,16 @@ def _run_job() -> Tuple[str, str]:
             train_call_kwargs_params,
             dmatrix_kwargs,
         )
-        (config, booster) = _run_job()
+        (evals_result, config, booster) = _run_job()
         get_logger(_LOG_TAG).info("Finished xgboost training!")
 
         result_xgb_model = self._convert_to_sklearn_model(
             bytearray(booster, "utf-8"), config
         )
-        spark_model = self._create_pyspark_model(result_xgb_model)
+        training_summary = _XGBoostTrainingSummary.from_metrics(
+            json.loads(evals_result)
+        )
+        spark_model = self._create_pyspark_model(result_xgb_model, training_summary)
         # According to pyspark ML convention, the model uid should be the same
         # with estimator uid.
         spark_model._resetUid(self.uid)
@@ -1219,9 +1226,14 @@ def read(cls) -> "SparkXGBReader":
 
 
 class _SparkXGBModel(Model, _SparkXGBParams, MLReadable, MLWritable):
-    def __init__(self, xgb_sklearn_model: Optional[XGBModel] = None) -> None:
+    def __init__(
+        self,
+        xgb_sklearn_model: Optional[XGBModel] = None,
+        training_summary: Optional[_XGBoostTrainingSummary] = None,
+    ) -> None:
         super().__init__()
         self._xgb_sklearn_model = xgb_sklearn_model
+        self.training_summary = training_summary
 
     @classmethod
     def _xgb_cls(cls) -> Type[XGBModel]:

python-package/xgboost/spark/xgboost_training_summary.py

@@ -0,0 +1,43 @@
+"""Xgboost training summary integration submodule."""
+
+from dataclasses import dataclass, field
+from typing import Dict, List
+
+
+@dataclass
+class _XGBoostTrainingSummary:
+    """
+    A class that holds the training and validation objective history
+    of an XGBoost model during its training process.
+    """
+
+    train_objective_history: Dict[str, List[float]] = field(default_factory=dict)
+    validation_objective_history: Dict[str, List[float]] = field(default_factory=dict)
+
+    @staticmethod
+    def from_metrics(
+        metrics: Dict[str, Dict[str, List[float]]]
+    ) -> "_XGBoostTrainingSummary":
+        """
+        Create an XGBoostTrainingSummary instance from a nested dictionary of metrics.
+
+        Parameters
+        ----------
+        metrics : dict of str to dict of str to list of float
+            A dictionary containing training and validation metrics.
+            Example format:
+                {
+                    "training": {"logloss": [0.1, 0.08]},
+                    "validation": {"logloss": [0.12, 0.1]}
+                }
+
+        Returns
+        -------
+        A new instance of XGBoostTrainingSummary.
+
+        """
+        train_objective_history = metrics.get("training", {})
+        validation_objective_history = metrics.get("validation", {})
+        return _XGBoostTrainingSummary(
+            train_objective_history, validation_objective_history
+        )