diff --git a/expo/experimenter/autosklearn.py b/expo/experimenter/autosklearn.py
index 22aa2a132..602b8385a 100644
--- a/expo/experimenter/autosklearn.py
+++ b/expo/experimenter/autosklearn.py
@@ -9,15 +9,8 @@ def custom_scorer(y_true, y_pred, metric_name):
     return evaluate_score(y_pred, y_true, metric_name)
 
 
-def create_autosklearn_scorer(metric_name):
-    return make_scorer(
-        name=metric_name, score_func=partial(custom_scorer, metric_name=metric_name)
-    )
-
-
 class ASRunner:
     time_limit = 300
-    seed = 42
 
     def __init__(self, state=None):
         self.state = state
@@ -25,12 +18,19 @@ class ASRunner:
         try:
             import autosklearn.classification
             import autosklearn.regression
-            from autosklearn.metrics import make_scorer
+            import autosklearn.metrics
+
+            self.autosklearn = autosklearn
         except ImportError:
             raise ImportError(
                 "autosklearn not found or system not supported, please check it first"
             )
 
+    def create_autosklearn_scorer(self, metric_name):
+        return self.autosklearn.metrics.make_scorer(
+            name=metric_name, score_func=partial(custom_scorer, metric_name=metric_name)
+        )
+
     def run(self):
         train_path = self.datasets["train"]
         dev_wo_target_path = self.datasets["dev_wo_target"]
@@ -45,24 +45,22 @@ class ASRunner:
         y_train = train_data[target_col]
 
         if eval_metric == "rmse":
-            automl = autosklearn.regression.AutoSklearnRegressor(
+            automl = self.autosklearn.regression.AutoSklearnRegressor(
                 time_left_for_this_task=self.time_limit,
                 per_run_time_limit=60,
-                metric=create_autosklearn_scorer(eval_metric),
+                metric=self.create_autosklearn_scorer(eval_metric),
                 memory_limit=8192,
-                seed=self.seed,
                 tmp_folder="AutosklearnModels/as-{}-{}".format(
                     self.state["task"], datetime.now().strftime("%y%m%d_%H%M")
                 ),
                 n_jobs=-1,
             )
         elif eval_metric in ["f1", "f1 weighted"]:
-            automl = autosklearn.classification.AutoSklearnClassifier(
+            automl = self.autosklearn.classification.AutoSklearnClassifier(
                 time_left_for_this_task=self.time_limit,
                 per_run_time_limit=60,
-                metric=create_autosklearn_scorer(eval_metric),
+                metric=self.create_autosklearn_scorer(eval_metric),
                 memory_limit=8192,
-                seed=self.seed,
                 tmp_folder="AutosklearnModels/as-{}-{}".format(
                     self.state["task"], datetime.now().strftime("%y%m%d_%H%M")
                 ),