[Tune] Add repr for ResultGrid class

python/ray/air/result.py

@@ -44,7 +44,7 @@ class Result:
     log_dir: Optional[Path]
     metrics_dataframe: Optional["pd.DataFrame"]
     best_checkpoints: Optional[List[Tuple[Checkpoint, Dict[str, Any]]]]
-    _items_to_repr = ["metrics", "error", "log_dir"]
+    _items_to_repr = ["error", "metrics", "log_dir", "checkpoint"]
 
     @property
     def config(self) -> Optional[Dict[str, Any]]:
@@ -57,10 +57,16 @@ def __repr__(self):
         from ray.tune.result import AUTO_RESULT_KEYS
 
         shown_attributes = {k: self.__dict__[k] for k in self._items_to_repr}
+        if self.error:
+            shown_attributes["error"] = type(self.error).__name__
+        else:
+            shown_attributes.pop("error")
 
         if self.metrics:
             shown_attributes["metrics"] = {
                 k: v for k, v in self.metrics.items() if k not in AUTO_RESULT_KEYS
             }
+
         kws = [f"{key}={value!r}" for key, value in shown_attributes.items()]
-        return "{}({})".format(type(self).__name__, ", ".join(kws))
+        kws_repr = "  " + ",\n  ".join(kws)
+        return "{}(\n{}\n)".format(type(self).__name__, kws_repr)

python/ray/tune/result_grid.py

@@ -240,3 +240,10 @@ def _trial_to_result(self, trial: Trial) -> Result:
             best_checkpoints=best_checkpoints,
         )
         return result
+
+    def __repr__(self) -> str:
+        all_results_repr = ""
+        for result in self:
+            result_repr = "  " + result.__repr__().replace("\n", "\n  ")
+            all_results_repr += f"{result_repr},\n"
+        return f"[\n{all_results_repr}]"

python/ray/tune/tests/test_result_grid.py

@@ -224,6 +224,39 @@ def f(config):
     assert not any(key in representation for key in AUTO_RESULT_KEYS)
 
 
+def test_result_grid_repr(ray_start_2_cpus, tmpdir):
+    from ray.air import session
+
+    def f(config):
+        if config["x"] == 1:
+            raise RuntimeError
+
+        metrics = {"loss": 1}
+        session.report(metrics, checkpoint=Checkpoint.from_dict(metrics))
+
+    tuner = tune.Tuner(
+        f,
+        run_config=air.RunConfig(
+            name="test_result_grid_repr",
+            local_dir=str(tmpdir / "test_result_grid_repr_results"),
+        ),
+        param_space={"x": tune.grid_search([1, 2])},
+    )
+    result_grid = tuner.fit()
+
+    representation = result_grid.__repr__()
+
+    from ray.tune.result import AUTO_RESULT_KEYS
+
+    assert not any(key in representation for key in AUTO_RESULT_KEYS)
+
+    assert len(result_grid) == 2
+    assert representation.count("metrics=") == 2
+    assert representation.count("log_dir=") == 2
+    assert representation.count("checkpoint=") == 2
+    assert representation.count("error=") == 1 and "RuntimeError" in representation
+
+
 def test_no_metric_mode(ray_start_2_cpus):
     def f(config):
         tune.report(x=1)