test the util.clone() method

tests/test_bias.py

@@ -69,6 +69,22 @@ def test_bias_clone():
     assert getattr(a2, "user_offsets_", None) is None
 
 
+def test_bias_clone_damping():
+    algo = Bias(damping=(10, 5))
+    algo.fit(simple_df)
+
+    params = algo.get_params()
+    assert sorted(params.keys()) == ["damping", "items", "users"]
+
+    a2 = lku.clone(algo)
+    assert a2 is not algo
+    assert getattr(a2, "item_damping", None) == 5
+    assert getattr(a2, "user_damping", None) == 10
+    assert getattr(a2, "mean_", None) is None
+    assert getattr(a2, "item_offsets_", None) is None
+    assert getattr(a2, "user_offsets_", None) is None
+
+
 def test_bias_global_only():
     algo = Bias(users=False, items=False)
     algo.fit(simple_df)

tests/test_util.py

@@ -10,6 +10,8 @@
 
 import numpy as np
 import pandas as pd
+from hypothesis import given
+from hypothesis import strategies as st
 
 from lenskit import util as lku
 
@@ -80,3 +82,18 @@ def func(foo):
     assert len(history) == 1
     cache("bar")
     assert len(history) == 2
+
+
+@given(
+    st.one_of(
+        st.lists(st.floats(allow_nan=False)),
+        st.tuples(st.floats(allow_nan=False)),
+        st.integers(),
+        st.floats(allow_nan=False),
+        st.emails(),
+    )
+)
+def test_clone_core_obj(obj):
+    o2 = lku.clone(obj)
+    assert o2 == obj
+    assert type(o2) == type(obj)