adding type check for model_type

evalml/pipelines/utils.py

@@ -25,6 +25,8 @@ def get_pipelines(problem_type, model_types=None):
         pipelines, list of all pipeline
 
     """
+    if model_types is not None and not isinstance(model_types, list):
+        raise TypeError("model_types parameter is not a list.")
 
     problem_pipelines = []
 
@@ -39,7 +41,7 @@ def get_pipelines(problem_type, model_types=None):
     all_model_types = list_model_types(problem_type)
     for model_type in model_types:
         if model_type not in all_model_types:
-            raise RuntimeError("Unrecognized model type for problem type %s: %s f" % (problem_type, model_type))
+            raise RuntimeError("Unrecognized model type for problem type %s: %s" % (problem_type, model_type))
 
     pipelines = []
 

evalml/tests/automl_tests/test_autoclassifier.py

@@ -1,5 +1,6 @@
 import numpy as np
 import pandas as pd
+import pytest
 from sklearn.model_selection import StratifiedKFold, TimeSeriesSplit
 
 from evalml import AutoClassifier
@@ -216,4 +217,10 @@ def test_describe_pipeline_objective_ordered(X_y, capsys):
     assert err == ''
     assert expected_objective_order in out_stripped
 
+
+def test_model_types_as_list():
+    with pytest.raises(TypeError, match="model_types parameter is not a list."):
+        AutoClassifier(objective='AUC', model_types='linear_model', max_pipelines=2)
+
+
 # def test_serialization(trained_model)