def test_get_valid_primitives_custom_primitives(pd_es):
class ThreeMostCommonCat(AggregationPrimitive):
name = "n_most_common_categorical"
input_types = [ColumnSchema(semantic_tags={"category"})]
return_type = ColumnSchema(semantic_tags={"category"})
number_output_features = 3
class AddThree(TransformPrimitive):
name = 'add_three'
input_types = [
ColumnSchema(semantic_tags="numeric"),
ColumnSchema(semantic_tags="numeric"),
ColumnSchema(semantic_tags="numeric")
]
return_type = ColumnSchema(semantic_tags="numeric")
commutative = True
compatibility = [Library.PANDAS, Library.DASK, Library.KOALAS]
agg_prims, trans_prims = get_valid_primitives(pd_es, "log")
assert ThreeMostCommonCat not in agg_prims
assert AddThree not in trans_prims
with pytest.raises(ValueError,
match="'add_three' is not a recognized primitive name"):
agg_prims, trans_prims = get_valid_primitives(
pd_es, "log", 2, [ThreeMostCommonCat, "add_three"])
def test_get_valid_primitives_selected_primitives(es):
agg_prims, trans_prims = get_valid_primitives(
es, "log", selected_primitives=[Hour, Count])
assert set(agg_prims) == set([Count])
assert set(trans_prims) == set([Hour])
agg_prims, trans_prims = get_valid_primitives(es,
"products",
selected_primitives=[Hour],
max_depth=1)
assert set(agg_prims) == set()
assert set(trans_prims) == set()
def test_invalid_primitive(es):
with pytest.raises(ValueError,
match="'foobar' is not a recognized primitive name"):
get_valid_primitives(es,
target_dataframe_name='log',
selected_primitives=['foobar'])
msg = ("Selected primitive <enum 'Library'> "
"is not an AggregationPrimitive, TransformPrimitive, or str")
with pytest.raises(ValueError, match=msg):
get_valid_primitives(es,
target_dataframe_name='log',
selected_primitives=[Library])
def test_get_valid_primitives_single_table(transform_es):
msg = "Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created"
with pytest.warns(UserWarning, match=msg):
agg_prims, trans_prims = get_valid_primitives(transform_es, "first")
assert set(agg_prims) == set()
assert IsIn in trans_prims
def test_primitive_compatibility(es):
_, trans_prims = get_valid_primitives(
es, "customers", selected_primitives=[TimeSincePrevious])
if es.dataframe_type != Library.PANDAS.value:
assert len(trans_prims) == 0
else:
assert len(trans_prims) == 1
def test_get_valid_primitives_with_dfs_kwargs(es):
agg_prims, trans_prims = get_valid_primitives(
es, "customers", selected_primitives=[Hour, Count, Not])
assert set(agg_prims) == set([Count])
assert set(trans_prims) == set([Hour, Not])
# Can use other dfs parameters and they get applied
agg_prims, trans_prims = get_valid_primitives(
es,
"customers",
selected_primitives=[Hour, Count, Not],
ignore_columns={"customers": ["loves_ice_cream"]},
)
assert set(agg_prims) == set([Count])
assert set(trans_prims) == set([Hour])
agg_prims, trans_prims = get_valid_primitives(
es,
"products",
selected_primitives=[Hour, Count],
ignore_dataframes=["log"])
assert set(agg_prims) == set()
assert set(trans_prims) == set()
def test_get_valid_primitives_all_primitives(es):
agg_prims, trans_prims = get_valid_primitives(es, "customers")
assert Count in agg_prims
assert Hour in trans_prims
