don't init global search with points_to_evaluate unless evaluated_rewards is provided; handle callbacks in fit kwargs (#469)

flaml/model.py

@@ -959,10 +959,16 @@ class LGBMEstimator(BaseEstimator):
                 # when not trained, train at least one iter
                 self.params[self.ITER_HP] = max(max_iter, 1)
         if self.HAS_CALLBACK:
+            kwargs_callbacks = kwargs.get("callbacks")
+            if kwargs_callbacks:
+                callbacks = kwargs_callbacks + self._callbacks(start_time, deadline)
+                kwargs.pop("callbacks")
+            else:
+                callbacks = self._callbacks(start_time, deadline)
             self._fit(
                 X_train,
                 y_train,
-                callbacks=self._callbacks(start_time, deadline),
+                callbacks=callbacks,
                 **kwargs,
             )
             best_iteration = (
@@ -1821,10 +1827,7 @@ class TS_SKLearn(SKLearnEstimator):
                     "low_cost_init_value": False,
                 },
                 "lags": {
-                    "domain": tune.randint(
-                        lower=1, upper=int(np.sqrt(data_size[0]))
-
-                    ),
+                    "domain": tune.randint(lower=1, upper=int(np.sqrt(data_size[0]))),
                     "init_value": 3,
                 },
             }

flaml/searcher/blendsearch.py

@@ -171,6 +171,7 @@ class BlendSearch(Searcher):
             else:
                 sampler = None
             try:
+                assert evaluated_rewards
                 self._gs = GlobalSearch(
                     space=gs_space,
                     metric=metric,
@@ -180,7 +181,7 @@ class BlendSearch(Searcher):
                     points_to_evaluate=points_to_evaluate,
                     evaluated_rewards=evaluated_rewards,
                 )
-            except ValueError:
+            except (AssertionError, ValueError):
                 self._gs = GlobalSearch(
                     space=gs_space,
                     metric=metric,

test/automl/test_classification.py

@@ -214,7 +214,12 @@ class TestClassification(unittest.TestCase):
         }
         X_train = scipy.sparse.eye(900000)
         y_train = np.random.randint(2, size=900000)
-        automl_experiment.fit(X_train=X_train, y_train=y_train, **automl_settings)
+        import xgboost as xgb
+
+        callback = xgb.callback.TrainingCallback()
+        automl_experiment.fit(
+            X_train=X_train, y_train=y_train, callbacks=[callback], **automl_settings
+        )
         print(automl_experiment.predict(X_train))
         print(automl_experiment.model)
         print(automl_experiment.config_history)

test/tune/test_searcher.py

@@ -197,14 +197,21 @@ def test_searcher():
         # sign of metric constraints must be <= or >=.
         pass
     searcher = BlendSearch(
-        metric="m", global_search_alg=searcher, metric_constraints=[("c", "<=", 1)]
+        metric="m",
+        global_search_alg=searcher,
+        metric_constraints=[("c", "<=", 1)],
+        points_to_evaluate=[{"a": 1, "b": 0.01}],
     )
     searcher.set_search_properties(
         metric="m2", config=config, setting={"time_budget_s": 0}
     )
     c = searcher.suggest("t1")
-    searcher.on_trial_complete("t1", {"config": c}, True)
+    print("t1", c)
     c = searcher.suggest("t2")
+    print("t2", c)
+    c = searcher.suggest("t3")
+    print("t3", c)
+    searcher.on_trial_complete("t1", {"config": c}, True)
     searcher.on_trial_complete("t2", {"config": c, "m2": 1, "c": 2, "time_total_s": 1})
     config1 = config.copy()
     config1["_choice_"] = 0