enable ensemble when using ray (#583)

* enable ensemble when using ray

* sanitize config

flaml/automl.py

@@ -385,6 +385,15 @@ class AutoMLState:
         tune.report(**result)
         return result
 
+    def sanitize(self, config: dict) -> dict:
+        """Make a config ready for passing to estimator."""
+        config = config.get("ml", config).copy()
+        if "FLAML_sample_size" in config:
+            del config["FLAML_sample_size"]
+        if "learner" in config:
+            del config["learner"]
+        return config
+
     def _train_with_config(
         self,
         estimator,
@@ -395,11 +404,7 @@ class AutoMLState:
             sample_size = config_w_resource.get(
                 "FLAML_sample_size", len(self.y_train_all)
             )
-        config = config_w_resource.get("ml", config_w_resource).copy()
-        if "FLAML_sample_size" in config:
-            del config["FLAML_sample_size"]
-        if "learner" in config:
-            del config["learner"]
+        config = self.sanitize(config_w_resource)
 
         this_estimator_kwargs = self.fit_kwargs_by_estimator.get(
             estimator
@@ -3203,7 +3208,7 @@ class AutoML(BaseEstimator):
                         x[1].learner_class(
                             task=self._state.task,
                             n_jobs=self._state.n_jobs,
-                            **x[1].best_config,
+                            **self._state.sanitize(x[1].best_config),
                         ),
                     )
                     for x in search_states[:2]
@@ -3214,13 +3219,15 @@ class AutoML(BaseEstimator):
                         x[1].learner_class(
                             task=self._state.task,
                             n_jobs=self._state.n_jobs,
-                            **x[1].best_config,
+                            **self._state.sanitize(x[1].best_config),
                         ),
                     )
                     for x in search_states[2:]
                     if x[1].best_loss < 4 * self._selected.best_loss
                 ]
-                logger.info(estimators)
+                logger.info(
+                    [(estimator[0], estimator[1].params) for estimator in estimators]
+                )
             if len(estimators) > 1:
                 if self._state.task in CLASSIFICATION:
                     from sklearn.ensemble import StackingClassifier as Stacker

flaml/version.py

@@ -1 +1 @@
-__version__ = "1.0.6"
+__version__ = "1.0.7"

test/automl/test_classification.py

@@ -256,6 +256,7 @@ class TestClassification(unittest.TestCase):
                 time_budget=10,
                 task="classification",
                 n_concurrent_trials=2,
+                ensemble=True,
             )
         except ImportError:
             return