def test_init_and_name(es):
log = es['log']
rating = ft.Feature(ft.IdentityFeature(es["products"].ww["rating"]), "log")
log_features = [ft.Feature(es['log'].ww[col]) for col in log.columns] +\
[ft.Feature(rating, primitive=GreaterThanScalar(2.5)),
ft.Feature(rating, primitive=GreaterThanScalar(3.5))]
# Add Timedelta feature
# features.append(pd.Timestamp.now() - ft.Feature(log['datetime']))
customers_features = [
ft.Feature(es["customers"].ww[col]) for col in es["customers"].columns
]
# check all transform primitives have a name
for attribute_string in dir(ft.primitives):
attr = getattr(ft.primitives, attribute_string)
if isclass(attr):
if issubclass(attr,
TransformPrimitive) and attr != TransformPrimitive:
assert getattr(attr, "name") is not None
trans_primitives = get_transform_primitives().values()
# If Dask EntitySet use only Dask compatible primitives
if es.dataframe_type == Library.DASK.value:
trans_primitives = [
prim for prim in trans_primitives
if Library.DASK in prim.compatibility
]
if es.dataframe_type == Library.KOALAS.value:
trans_primitives = [
prim for prim in trans_primitives
if Library.KOALAS in prim.compatibility
]
for transform_prim in trans_primitives:
# skip automated testing if a few special cases
features_to_use = log_features
if transform_prim in [NotEqual, Equal]:
continue
if transform_prim in [Age]:
features_to_use = customers_features
# use the input_types matching function from DFS
input_types = transform_prim.input_types
if type(input_types[0]) == list:
matching_inputs = match(input_types[0], features_to_use)
else:
matching_inputs = match(input_types, features_to_use)
if len(matching_inputs) == 0:
raise Exception("Transform Primitive %s not tested" %
transform_prim.name)
for prim in matching_inputs:
instance = ft.Feature(prim, primitive=transform_prim)
# try to get name and calculate
instance.get_name()
ft.calculate_feature_matrix([instance], entityset=es)
def test_init_and_name(es):
log = es['log']
rating = ft.Feature(es["products"]["rating"], es["log"])
features = [ft.Feature(v) for v in log.variables] +\
[ft.Feature(rating, primitive=GreaterThanScalar(2.5))]
# Add Timedelta feature
# features.append(pd.Timestamp.now() - ft.Feature(log['datetime']))
for transform_prim in get_transform_primitives().values():
# skip automated testing if a few special cases
if transform_prim in [NotEqual, Equal]:
continue
# use the input_types matching function from DFS
input_types = transform_prim.input_types
if type(input_types[0]) == list:
matching_inputs = match(input_types[0], features)
else:
matching_inputs = match(input_types, features)
if len(matching_inputs) == 0:
raise Exception("Transform Primitive %s not tested" %
transform_prim.name)
for s in matching_inputs:
instance = ft.Feature(s, primitive=transform_prim)
# try to get name and calculate
instance.get_name()
ft.calculate_feature_matrix([instance], entityset=es).head(5)
def test_does_not_warn_with_stacking_feature(pd_es):
with pytest.warns(None) as record:
dfs(
entityset=pd_es,
target_dataframe_name="régions",
agg_primitives=["percent_true"],
trans_primitives=[GreaterThanScalar(5)],
primitive_options={
"greater_than_scalar": {"include_dataframes": ["stores"]}
},
features_only=True,
)
assert not record
def test_does_not_warn_with_stacking_feature(pd_es):
with pytest.warns(None) as record:
dfs(entityset=pd_es,
target_entity='régions',
agg_primitives=['percent_true'],
trans_primitives=[GreaterThanScalar(5)],
primitive_options={
'greater_than_scalar': {
'include_entities': ['stores']
}
},
features_only=True)
assert not record
def test_override_boolean(es):
count = ft.Feature(es['log']['id'], parent_entity=es['sessions'], primitive=Count)
count_lo = ft.Feature(count, primitive=GreaterThanScalar(1))
count_hi = ft.Feature(count, primitive=LessThanScalar(10))
to_test = [[True, True, True],
[True, True, False],
[False, False, True]]
features = []
features.append(count_lo.OR(count_hi))
features.append(count_lo.AND(count_hi))
features.append(~(count_lo.AND(count_hi)))
df = ft.calculate_feature_matrix(entityset=es, features=features, instance_ids=[0, 1, 2])
for i, test in enumerate(to_test):
v = df[features[i].get_name()].values.tolist()
assert v == test
def test_init_and_name(es):
log = es['log']
rating = ft.Feature(es["products"]["rating"], es["log"])
log_features = [ft.Feature(v) for v in log.variables] +\
[ft.Feature(rating, primitive=GreaterThanScalar(2.5))]
# Add Timedelta feature
# features.append(pd.Timestamp.now() - ft.Feature(log['datetime']))
customers_features = [ft.Feature(v) for v in es["customers"].variables]
trans_primitives = get_transform_primitives().values()
# If Dask EntitySet use only Dask compatible primitives
if isinstance(es['log'].df, dd.DataFrame):
trans_primitives = [
prim for prim in trans_primitives
if Library.DASK in prim.compatibility
]
if ks and isinstance(es['log'].df, ks.DataFrame):
trans_primitives = [
prim for prim in trans_primitives
if Library.KOALAS in prim.compatibility
]
for transform_prim in trans_primitives:
# skip automated testing if a few special cases
features_to_use = log_features
if transform_prim in [NotEqual, Equal]:
continue
if transform_prim in [Age]:
features_to_use = customers_features
# use the input_types matching function from DFS
input_types = transform_prim.input_types
if type(input_types[0]) == list:
matching_inputs = match(input_types[0], features_to_use)
else:
matching_inputs = match(input_types, features_to_use)
if len(matching_inputs) == 0:
raise Exception("Transform Primitive %s not tested" %
transform_prim.name)
for prim in matching_inputs:
instance = ft.Feature(prim, primitive=transform_prim)
# try to get name and calculate
instance.get_name()
ft.calculate_feature_matrix([instance], entityset=es)
def test_override_boolean(es):
count = ft.Feature(es["log"].ww["id"],
parent_dataframe_name="sessions",
primitive=Count)
count_lo = ft.Feature(count, primitive=GreaterThanScalar(1))
count_hi = ft.Feature(count, primitive=LessThanScalar(10))
to_test = [[True, True, True], [True, True, False], [False, False, True]]
features = []
features.append(count_lo.OR(count_hi))
features.append(count_lo.AND(count_hi))
features.append(~(count_lo.AND(count_hi)))
df = ft.calculate_feature_matrix(entityset=es,
features=features,
instance_ids=[0, 1, 2])
df = to_pandas(df, index="id", sort_index=True)
for i, test in enumerate(to_test):
v = df[features[i].get_name()].tolist()
assert v == test
