def test_copy(games_es):
home_games = next(r for r in games_es.relationships
if r.child_variable.id == 'home_team_id')
path = RelationshipPath([(False, home_games)])
feat = ft.AggregationFeature(games_es['games']['home_team_score'],
games_es['teams'],
relationship_path=path,
primitive=ft.primitives.Mean)
copied = feat.copy()
assert copied.entity == feat.entity
assert copied.base_features == feat.base_features
assert copied.relationship_path == feat.relationship_path
assert copied.primitive == feat.primitive
def test_diamond_entityset(diamond_es):
es = diamond_es
amount = ft.IdentityFeature(es['transactions']['amount'])
path = backward_path(es, ['regions', 'customers', 'transactions'])
through_customers = ft.AggregationFeature(amount,
es['regions'],
primitive=ft.primitives.Sum,
relationship_path=path)
path = backward_path(es, ['regions', 'stores', 'transactions'])
through_stores = ft.AggregationFeature(amount,
es['regions'],
primitive=ft.primitives.Sum,
relationship_path=path)
feature_set = FeatureSet([through_customers, through_stores])
calculator = FeatureSetCalculator(es,
time_last=datetime(2011, 4, 8),
feature_set=feature_set)
df = calculator.run(np.array([0, 1, 2]))
assert (df['SUM(stores.transactions.amount)'] == [94, 261, 128]).all()
assert (df['SUM(customers.transactions.amount)'] == [72, 411, 0]).all()
def test_feature_use_previous_pd_dateoffset(es):
value = ft.IdentityFeature(es['log']['id'])
do = pd.DateOffset(months=3)
count_feature = ft.AggregationFeature(value, es['customers'], ft.primitives.Count, use_previous=do)
features = [count_feature, value]
serializer = FeaturesSerializer(features)
expected = {
'ft_version': ft.__version__,
'schema_version': SCHEMA_VERSION,
'entityset': es.to_dictionary(),
'feature_list': [count_feature.unique_name(), value.unique_name()],
'feature_definitions': {
count_feature.unique_name(): count_feature.to_dictionary(),
value.unique_name(): value.to_dictionary(),
}
}
_compare_feature_dicts(expected, serializer.to_dict())
value = ft.IdentityFeature(es['log']['id'])
do = pd.DateOffset(months=3, days=2, minutes=30)
count_feature = ft.AggregationFeature(value, es['customers'], ft.primitives.Count, use_previous=do)
features = [count_feature, value]
serializer = FeaturesSerializer(features)
expected = {
'ft_version': ft.__version__,
'schema_version': SCHEMA_VERSION,
'entityset': es.to_dictionary(),
'feature_list': [count_feature.unique_name(), value.unique_name()],
'feature_definitions': {
count_feature.unique_name(): count_feature.to_dictionary(),
value.unique_name(): value.to_dictionary(),
}
}
_compare_feature_dicts(expected, serializer.to_dict())
def test_name_with_multiple_possible_paths(diamond_es):
transaction_relationships = diamond_es.get_forward_relationships(
'transactions')
transaction_to_customer = next(r for r in transaction_relationships
if r.parent_entity.id == 'customers')
customer_to_region = diamond_es.get_forward_relationships('customers')[0]
# Does not raise if path specified.
feat = ft.AggregationFeature(
diamond_es['transactions']['amount'],
diamond_es['regions'],
ft.primitives.Mean,
relationship_path=[customer_to_region, transaction_to_customer])
assert feat.get_name() == "MEAN(customers.transactions.amount)"
def test_copy(games_es):
home_games = next(r for r in games_es.relationships
if r._child_column_name == 'home_team_id')
path = RelationshipPath([(False, home_games)])
feat = ft.AggregationFeature(ft.IdentityFeature(
games_es['games'].ww['home_team_score']),
'teams',
relationship_path=path,
primitive=ft.primitives.Mean)
copied = feat.copy()
assert copied.dataframe_name == feat.dataframe_name
assert copied.base_features == feat.base_features
assert copied.relationship_path == feat.relationship_path
assert copied.primitive == feat.primitive
def test_diamond_entityset(diamond_es):
es = diamond_es
amount = ft.IdentityFeature(es["transactions"].ww["amount"])
path = backward_path(es, ["regions", "customers", "transactions"])
through_customers = ft.AggregationFeature(amount,
"regions",
primitive=ft.primitives.Sum,
relationship_path=path)
path = backward_path(es, ["regions", "stores", "transactions"])
through_stores = ft.AggregationFeature(amount,
"regions",
primitive=ft.primitives.Sum,
relationship_path=path)
feature_set = FeatureSet([through_customers, through_stores])
calculator = FeatureSetCalculator(es,
time_last=datetime(2011, 4, 8),
feature_set=feature_set)
df = calculator.run(np.array([0, 1, 2]))
df = to_pandas(df, index="id", sort_index=True)
assert (df["SUM(stores.transactions.amount)"] == [94, 261, 128]).all()
assert (df["SUM(customers.transactions.amount)"] == [72, 411, 0]).all()
def test_base_features_in_list(es):
value = ft.IdentityFeature(es['log']['value'])
max_feat = ft.AggregationFeature(value, es['sessions'], ft.primitives.Max)
dictionary = {
'ft_version': ft.__version__,
'schema_version': SCHEMA_VERSION,
'entityset': es.to_dictionary(),
'feature_list': [max_feat.unique_name(), value.unique_name()],
'feature_definitions': {
max_feat.unique_name(): max_feat.to_dictionary(),
value.unique_name(): value.to_dictionary(),
}
}
deserializer = FeaturesDeserializer(dictionary)
expected = [max_feat, value]
assert expected == deserializer.to_list()
def test_serialized_renamed_features(es):
def serialize_name_unchanged(original):
new_name = 'MyFeature'
original_names = original.get_feature_names()
renamed = original.rename(new_name)
new_names = [new_name] if len(original_names) == 1 else [new_name + '[{}]'.format(i) for i in range(len(original_names))]
check_names(renamed, new_name, new_names)
serializer = FeaturesSerializer([renamed])
serialized = serializer.to_dict()
deserializer = FeaturesDeserializer(serialized)
deserialized = deserializer.to_list()[0]
check_names(deserialized, new_name, new_names)
identity_original = ft.IdentityFeature(es['log']['value'])
assert identity_original.get_name() == 'value'
value = ft.IdentityFeature(es['log']['value'])
primitive = ft.primitives.Max()
agg_original = ft.AggregationFeature(value, es['customers'], primitive)
assert agg_original.get_name() == 'MAX(log.value)'
direct_original = ft.DirectFeature(es['customers']['age'], es['sessions'])
assert direct_original.get_name() == 'customers.age'
primitive = ft.primitives.MultiplyNumericScalar(value=2)
transform_original = ft.TransformFeature(value, primitive)
assert transform_original.get_name() == 'value * 2'
zipcode = ft.IdentityFeature(es['log']['zipcode'])
primitive = CumSum()
groupby_original = ft.feature_base.GroupByTransformFeature(value, primitive, zipcode)
assert groupby_original.get_name() == 'CUM_SUM(value) by zipcode'
multioutput_original = ft.Feature(es['log']['product_id'], parent_entity=es['customers'], primitive=NMostCommon(n=2))
assert multioutput_original.get_name() == 'N_MOST_COMMON(log.product_id, n=2)'
featureslice_original = ft.feature_base.FeatureOutputSlice(multioutput_original, 0)
assert featureslice_original.get_name() == 'N_MOST_COMMON(log.product_id, n=2)[0]'
feature_type_list = [identity_original, agg_original, direct_original, transform_original, groupby_original, multioutput_original, featureslice_original]
for feature_type in feature_type_list:
serialize_name_unchanged(feature_type)
def test_base_features_in_list(es):
value = ft.IdentityFeature(es['log']['value'])
max_feature = ft.AggregationFeature(value, es['sessions'], ft.primitives.Max)
features = [max_feature, value]
serializer = FeaturesSerializer(features)
expected = {
'ft_version': ft.__version__,
'schema_version': SCHEMA_VERSION,
'entityset': es.to_dictionary(),
'feature_list': [max_feature.unique_name(), value.unique_name()],
'feature_definitions': {
max_feature.unique_name(): max_feature.to_dictionary(),
value.unique_name(): value.to_dictionary(),
}
}
_compare_feature_dicts(expected, serializer.to_dict())
def test_feature_use_previous_pd_timedelta(es):
value = ft.IdentityFeature(es['log']['id'])
td = pd.Timedelta(3, "M")
count_feature = ft.AggregationFeature(value, es['customers'], ft.primitives.Count, use_previous=td)
dictionary = {
'ft_version': ft.__version__,
'schema_version': SCHEMA_VERSION,
'entityset': es.to_dictionary(),
'feature_list': [count_feature.unique_name(), value.unique_name()],
'feature_definitions': {
count_feature.unique_name(): count_feature.to_dictionary(),
value.unique_name(): value.to_dictionary(),
}
}
deserializer = FeaturesDeserializer(dictionary)
expected = [count_feature, value]
assert expected == deserializer.to_list()
def test_base_features_in_list(es):
value = ft.IdentityFeature(es["log"].ww["value"])
max_feat = ft.AggregationFeature(value, "sessions", ft.primitives.Max)
dictionary = {
"ft_version": ft.__version__,
"schema_version": SCHEMA_VERSION,
"entityset": es.to_dictionary(),
"feature_list": [max_feat.unique_name(),
value.unique_name()],
"feature_definitions": {
max_feat.unique_name(): max_feat.to_dictionary(),
value.unique_name(): value.to_dictionary(),
},
}
deserializer = FeaturesDeserializer(dictionary)
expected = [max_feat, value]
assert expected == deserializer.to_list()
def test_base_features_in_list(es):
value = ft.IdentityFeature(es["log"].ww["value"])
max_feature = ft.AggregationFeature(value, "sessions", ft.primitives.Max)
features = [max_feature, value]
serializer = FeaturesSerializer(features)
expected = {
"ft_version": ft.__version__,
"schema_version": SCHEMA_VERSION,
"entityset": es.to_dictionary(),
"feature_list": [max_feature.unique_name(),
value.unique_name()],
"feature_definitions": {
max_feature.unique_name(): max_feature.to_dictionary(),
value.unique_name(): value.to_dictionary(),
},
}
_compare_feature_dicts(expected, serializer.to_dict())
def test_serialized_renamed_features(es):
def serialize_name_unchanged(original):
renamed = original.rename('MyFeature')
assert renamed.get_name() == 'MyFeature'
serializer = FeaturesSerializer([renamed])
serialized = serializer.to_dict()
deserializer = FeaturesDeserializer(serialized)
deserialized = deserializer.to_list()[0]
assert deserialized.get_name() == 'MyFeature'
identity_original = ft.IdentityFeature(es['log']['value'])
assert identity_original.get_name() == 'value'
value = ft.IdentityFeature(es['log']['value'])
primitive = ft.primitives.Max()
agg_original = ft.AggregationFeature(value, es['customers'], primitive)
assert agg_original.get_name() == 'MAX(log.value)'
direct_original = ft.DirectFeature(es['customers']['age'], es['sessions'])
assert direct_original.get_name() == 'customers.age'
primitive = ft.primitives.MultiplyNumericScalar(value=2)
transform_original = ft.TransformFeature(value, primitive)
assert transform_original.get_name() == 'value * 2'
zipcode = ft.IdentityFeature(es['log']['zipcode'])
primitive = CumSum()
groupby_original = ft.feature_base.GroupByTransformFeature(
value, primitive, zipcode)
assert groupby_original.get_name() == 'CUM_SUM(value) by zipcode'
feature_type_list = [
identity_original, agg_original, direct_original, transform_original,
groupby_original
]
for feature_type in feature_type_list:
serialize_name_unchanged(feature_type)
def test_unknown_primitive_module(es):
value = ft.IdentityFeature(es['log']['value'])
max_feat = ft.AggregationFeature(value, es['sessions'], ft.primitives.Max)
max_dict = max_feat.to_dictionary()
max_dict['arguments']['primitive']['module'] = 'fake.module'
dictionary = {
'ft_version': ft.__version__,
'schema_version': SCHEMA_VERSION,
'entityset': es.to_dictionary(),
'feature_list': [max_feat.unique_name(), value.unique_name()],
'feature_definitions': {
max_feat.unique_name(): max_dict,
value.unique_name(): value.to_dictionary(),
}
}
deserializer = FeaturesDeserializer(dictionary)
with pytest.raises(RuntimeError) as excinfo:
deserializer.to_list()
error_text = 'Primitive "Max" in module "fake.module" not found'
assert error_text == str(excinfo.value)
def test_base_features_not_in_list(es):
value = ft.IdentityFeature(es['log'].ww['value'])
value_x2 = ft.TransformFeature(
value, ft.primitives.MultiplyNumericScalar(value=2))
max_feature = ft.AggregationFeature(value_x2, 'sessions',
ft.primitives.Max)
features = [max_feature]
serializer = FeaturesSerializer(features)
expected = {
'ft_version': ft.__version__,
'schema_version': SCHEMA_VERSION,
'entityset': es.to_dictionary(),
'feature_list': [max_feature.unique_name()],
'feature_definitions': {
max_feature.unique_name(): max_feature.to_dictionary(),
value_x2.unique_name(): value_x2.to_dictionary(),
value.unique_name(): value.to_dictionary(),
}
}
_compare_feature_dicts(expected, serializer.to_dict())
def test_feature_use_previous_pd_timedelta(es):
value = ft.IdentityFeature(es["log"].ww["id"])
td = pd.Timedelta(12, "W")
count_feature = ft.AggregationFeature(value,
"customers",
ft.primitives.Count,
use_previous=td)
dictionary = {
"ft_version": ft.__version__,
"schema_version": SCHEMA_VERSION,
"entityset": es.to_dictionary(),
"feature_list": [count_feature.unique_name(),
value.unique_name()],
"feature_definitions": {
count_feature.unique_name(): count_feature.to_dictionary(),
value.unique_name(): value.to_dictionary(),
},
}
deserializer = FeaturesDeserializer(dictionary)
expected = [count_feature, value]
assert expected == deserializer.to_list()
def test_where_feature_dependency(es):
value = ft.IdentityFeature(es['log'].ww['value'])
is_purchased = ft.IdentityFeature(es['log'].ww['purchased'])
max_feature = ft.AggregationFeature(value,
'sessions',
ft.primitives.Max,
where=is_purchased)
features = [max_feature]
serializer = FeaturesSerializer(features)
expected = {
'ft_version': ft.__version__,
'schema_version': SCHEMA_VERSION,
'entityset': es.to_dictionary(),
'feature_list': [max_feature.unique_name()],
'feature_definitions': {
max_feature.unique_name(): max_feature.to_dictionary(),
value.unique_name(): value.to_dictionary(),
is_purchased.unique_name(): is_purchased.to_dictionary(),
}
}
_compare_feature_dicts(expected, serializer.to_dict())
def test_unknown_primitive_module(es):
value = ft.IdentityFeature(es["log"].ww["value"])
max_feat = ft.AggregationFeature(value, "sessions", ft.primitives.Max)
max_dict = max_feat.to_dictionary()
max_dict["arguments"]["primitive"]["module"] = "fake.module"
dictionary = {
"ft_version": ft.__version__,
"schema_version": SCHEMA_VERSION,
"entityset": es.to_dictionary(),
"feature_list": [max_feat.unique_name(),
value.unique_name()],
"feature_definitions": {
max_feat.unique_name(): max_dict,
value.unique_name(): value.to_dictionary(),
},
}
deserializer = FeaturesDeserializer(dictionary)
with pytest.raises(RuntimeError) as excinfo:
deserializer.to_list()
error_text = 'Primitive "Max" in module "fake.module" not found'
assert error_text == str(excinfo.value)
def test_feature_use_previous_pd_timedelta(es):
value = ft.IdentityFeature(es['log'].ww['id'])
td = pd.Timedelta(12, "W")
count_feature = ft.AggregationFeature(value,
'customers',
ft.primitives.Count,
use_previous=td)
features = [count_feature, value]
serializer = FeaturesSerializer(features)
expected = {
'ft_version': ft.__version__,
'schema_version': SCHEMA_VERSION,
'entityset': es.to_dictionary(),
'feature_list': [count_feature.unique_name(),
value.unique_name()],
'feature_definitions': {
count_feature.unique_name(): count_feature.to_dictionary(),
value.unique_name(): value.to_dictionary(),
}
}
_compare_feature_dicts(expected, serializer.to_dict())
def test_feature_trie_without_needs_full_dataframe(diamond_es):
es = diamond_es
country_name = ft.IdentityFeature(es["countries"].ww["name"])
direct_name = ft.DirectFeature(country_name, "regions")
amount = ft.IdentityFeature(es["transactions"].ww["amount"])
path_through_customers = backward_path(es, ["regions", "customers", "transactions"])
through_customers = ft.AggregationFeature(
amount,
"regions",
primitive=ft.primitives.Mean,
relationship_path=path_through_customers,
)
path_through_stores = backward_path(es, ["regions", "stores", "transactions"])
through_stores = ft.AggregationFeature(
amount,
"regions",
primitive=ft.primitives.Mean,
relationship_path=path_through_stores,
)
customers_to_transactions = backward_path(es, ["customers", "transactions"])
customers_mean = ft.AggregationFeature(
amount,
"customers",
primitive=ft.primitives.Mean,
relationship_path=customers_to_transactions,
)
negation = ft.TransformFeature(customers_mean, ft.primitives.Negate)
regions_to_customers = backward_path(es, ["regions", "customers"])
mean_of_mean = ft.AggregationFeature(
negation,
"regions",
primitive=ft.primitives.Mean,
relationship_path=regions_to_customers,
)
features = [direct_name, through_customers, through_stores, mean_of_mean]
feature_set = FeatureSet(features)
trie = feature_set.feature_trie
assert trie.value == (False, set(), {f.unique_name() for f in features})
assert trie.get_node(direct_name.relationship_path).value == (
False,
set(),
{country_name.unique_name()},
)
assert trie.get_node(regions_to_customers).value == (
False,
set(),
{negation.unique_name(), customers_mean.unique_name()},
)
regions_to_stores = backward_path(es, ["regions", "stores"])
assert trie.get_node(regions_to_stores).value == (False, set(), set())
assert trie.get_node(path_through_customers).value == (
False,
set(),
{amount.unique_name()},
)
assert trie.get_node(path_through_stores).value == (
False,
set(),
{amount.unique_name()},
)
def test_serialized_renamed_features(es):
def serialize_name_unchanged(original):
new_name = "MyFeature"
original_names = original.get_feature_names()
renamed = original.rename(new_name)
new_names = (
[new_name]
if len(original_names) == 1
else [new_name + "[{}]".format(i) for i in range(len(original_names))]
)
check_names(renamed, new_name, new_names)
serializer = FeaturesSerializer([renamed])
serialized = serializer.to_dict()
deserializer = FeaturesDeserializer(serialized)
deserialized = deserializer.to_list()[0]
check_names(deserialized, new_name, new_names)
identity_original = ft.IdentityFeature(es["log"].ww["value"])
assert identity_original.get_name() == "value"
value = ft.IdentityFeature(es["log"].ww["value"])
primitive = ft.primitives.Max()
agg_original = ft.AggregationFeature(value, "customers", primitive)
assert agg_original.get_name() == "MAX(log.value)"
direct_original = ft.DirectFeature(
ft.IdentityFeature(es["customers"].ww["age"]), "sessions"
)
assert direct_original.get_name() == "customers.age"
primitive = ft.primitives.MultiplyNumericScalar(value=2)
transform_original = ft.TransformFeature(value, primitive)
assert transform_original.get_name() == "value * 2"
zipcode = ft.IdentityFeature(es["log"].ww["zipcode"])
primitive = CumSum()
groupby_original = ft.feature_base.GroupByTransformFeature(
value, primitive, zipcode
)
assert groupby_original.get_name() == "CUM_SUM(value) by zipcode"
multioutput_original = ft.Feature(
es["log"].ww["product_id"],
parent_dataframe_name="customers",
primitive=NMostCommon(n=2),
)
assert multioutput_original.get_name() == "N_MOST_COMMON(log.product_id, n=2)"
featureslice_original = ft.feature_base.FeatureOutputSlice(multioutput_original, 0)
assert featureslice_original.get_name() == "N_MOST_COMMON(log.product_id, n=2)[0]"
feature_type_list = [
identity_original,
agg_original,
direct_original,
transform_original,
groupby_original,
multioutput_original,
featureslice_original,
]
for feature_type in feature_type_list:
serialize_name_unchanged(feature_type)
