fix model_type parameter pass to generate response pattern

adaptivetesting/implementations/__test_assembler.py

@@ -175,6 +175,7 @@ def __init__(self,
                          initial_ability_level,
                          simulation,
                          debug,
+                         model_type=model_type,
                          **kwargs)
 
     def estimate_ability_level(self):

adaptivetesting/models/__adaptive_test.py

@@ -64,7 +64,8 @@ def __init__(self, item_pool: ItemPool,
                     self.item_pool.simulated_responses = generate_response_pattern(
                         ability=self.true_ability_level,
                         items=self.item_pool.test_items,
-                        seed=kwargs["seed"] if "seed" in kwargs.keys() else None
+                        seed=kwargs["seed"] if "seed" in kwargs.keys() else None,
+                        model=kwargs["model_type"] if "model_type" in kwargs.keys() else None
                     )
 
     def get_answered_items(self) -> List[TestItem]:

adaptivetesting/tests/test_generate_response_pattern.py

@@ -4,6 +4,7 @@
 from adaptivetesting.models import ItemPool, TestItem
 from adaptivetesting.math.estimators import MLEstimator
 from adaptivetesting.math import generate_response_pattern
+from adaptivetesting.implementations import TestAssembler
 
 
 source_dictionary = {"a": [1.0507,
@@ -135,3 +136,14 @@ def test_polytomous_one_hot_and_reproducible(self):
 
         for a, b in zip(r1, r2):
             self.assertTrue(np.array_equal(np.asarray(a), np.asarray(b)))
+
+
+    def test_poly_items_from_adt_test_class(self):
+        # run this without any errors
+        items = ItemPool([make_polyt_item() for _ in range(3)])
+        TestAssembler(item_pool=items,
+                             simulation_id="test",
+                             participant_id="test",
+                             ability_estimator=MLEstimator,
+                             model_type="GPCM",
+                             simulation=True)