def test_compare_all_nans(es):
nan_feat = Mode(es['log']['product_id'], es['sessions'])
compare = nan_feat == 'brown bag'
# before all data
time_last = pd.Timestamp('1/1/1993')
pandas_backend = PandasBackend(es, [nan_feat, compare])
df = pandas_backend.calculate_all_features(instance_ids=[0, 1, 2],
time_last=time_last)
assert df[nan_feat.get_name()].dropna().shape[0] == 0
assert not df[compare.get_name()].any()
def test_compare_all_nans(es):
nan_feat = Mode(es['log']['product_id'], es['sessions'])
compare = nan_feat == 'brown bag'
# before all data
time_last = pd.Timestamp('1/1/1993')
pandas_backend = PandasBackend(es, [nan_feat, compare])
df = pandas_backend.calculate_all_features(instance_ids=[0, 1, 2],
time_last=time_last)
assert df[nan_feat.get_name()].dropna().shape[0] == 0
assert not df[compare.get_name()].any()
def test_make_agg_feat_multiple_dtypes(entityset, backend):
compare_prod = IdentityFeature(entityset['log']['product_id']) == 'coke zero'
agg_feat = Count(entityset['log']['id'],
parent_entity=entityset['sessions'],
where=compare_prod)
agg_feat2 = Mode(entityset['log']['product_id'],
parent_entity=entityset['sessions'],
where=compare_prod)
pandas_backend = backend([agg_feat, agg_feat2])
df = pandas_backend.calculate_all_features(instance_ids=[0],
time_last=None)
v = df[agg_feat.get_name()][0]
v2 = df[agg_feat2.get_name()][0]
assert (v == 3)
assert (v2 == 'coke zero')
def test_make_agg_feat_multiple_dtypes(entityset, backend):
compare_prod = IdentityFeature(entityset['log']['product_id']) == 'coke zero'
agg_feat = Count(entityset['log']['id'],
parent_entity=entityset['sessions'],
where=compare_prod)
agg_feat2 = Mode(entityset['log']['product_id'],
parent_entity=entityset['sessions'],
where=compare_prod)
pandas_backend = backend([agg_feat, agg_feat2])
df = pandas_backend.calculate_all_features(instance_ids=[0],
time_last=None)
v = df[agg_feat.get_name()][0]
v2 = df[agg_feat2.get_name()][0]
assert (v == 3)
assert (v2 == 'coke zero')
def test_return_type_inference_id(es):
# direct features should keep Id variable type
direct_id_feature = Feature(es["sessions"]["customer_id"], es["log"])
assert direct_id_feature.variable_type == Id
# aggregations of Id variable types should get converted
mode = Mode(es["log"]["session_id"], es["customers"])
assert mode.variable_type == Categorical
# also test direct feature of aggregation
mode_direct = Feature(mode, es["sessions"])
assert mode_direct.variable_type == Categorical
def test_limit_mode_uniques(es, session_id_feat, product_id_feat,
datetime_feat):
mode_feat = Mode(product_id_feat, parent_entity=es['sessions'])
mode_filter = filt.LimitModeUniques()
assert mode_filter.is_valid(feature=mode_feat,
entity=es['sessions'],
target_entity_id='customers')
# percent_unique is 6/15
mode_filter = filt.LimitModeUniques(threshold=.3)
assert not mode_filter.is_valid(
feature=mode_feat, entity=es['sessions'], target_entity_id='customers')
def test_dfs_builds_on_seed_features_more_than_max_depth(es):
seed_feature_sessions = Count(es['log']["id"], es['sessions']) > 2
seed_feature_log = Hour(es['log']['datetime'])
session_agg = Last(seed_feature_log, es['sessions'])
# Depth of this feat is 2 relative to session_agg, the seed feature,
# which is greater than max_depth so it shouldn't be built
session_agg_trans = DirectFeature(Mode(session_agg, es['customers']),
es['sessions'])
dfs_obj = DeepFeatureSynthesis(
target_entity_id='sessions',
entityset=es,
agg_primitives=[Last, Count],
trans_primitives=[],
max_depth=1,
seed_features=[seed_feature_sessions, seed_feature_log])
features = dfs_obj.build_features()
assert seed_feature_sessions.get_name() in [f.get_name() for f in features]
assert session_agg.get_name() in [f.get_name() for f in features]
assert session_agg_trans.get_name() not in [f.get_name() for f in features]
def test_return_type_inference_direct_feature(es):
mode = Mode(es["log"]["priority_level"], es["customers"])
mode_session = Feature(mode, es["sessions"])
assert mode_session.variable_type == es["log"]["priority_level"].__class__
def test_return_type_inference(es):
mode = Mode(es["log"]["priority_level"], es["customers"])
assert mode.variable_type == es["log"]["priority_level"].__class__
