def test_make_trans_feat(es):
f = Hour(es['log']['datetime'])
pandas_backend = PandasBackend(es, [f])
df = pandas_backend.calculate_all_features(instance_ids=[0],
time_last=None)
v = df[f.get_name()][0]
assert v == 10
def test_make_trans_feat(es):
f = Hour(es['log']['datetime'])
pandas_backend = PandasBackend(es, [f])
df = pandas_backend.calculate_all_features(instance_ids=[0],
time_last=None)
v = df[f.get_name()][0]
assert v == 10
def test_seed_features(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'])
dfs_obj = DeepFeatureSynthesis(
target_entity_id='sessions',
entityset=es,
agg_primitives=[Last],
trans_primitives=[],
max_depth=2,
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]
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(Count(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_arithmetic(es):
# P TODO:
return
hour = Hour(es['log']['datetime'])
day = Day(es['log']['datetime'])
to_test = [(Add, [19, 19, 19, 19]), (Subtract, [-1, -1, -1, -1]),
(Multiply, [90, 90, 90, 90]), (Divide, [.9, .9, .9, .9])]
features = []
features.append(day + hour)
features.append(day - hour)
features.append(day * hour)
features.append(day / hour)
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(instance_ids=[0, 3, 5, 7],
time_last=None)
for i, test in enumerate(to_test):
v = df[features[i].get_name()].values.tolist()
assert v == test[1]
def test_overrides(es):
# P TODO:
return
hour = Hour(es['log']['datetime'])
day = Day(es['log']['datetime'])
feats = [Add, Subtract, Multiply, Divide, Mod, And, Or]
compare_ops = [
GreaterThan, LessThan, Equals, NotEquals, GreaterThanEqualTo,
LessThanEqualTo
]
assert Negate(hour).hash() == (-hour).hash()
compares = [(hour, hour), (hour, day), (day, 2)]
overrides = [
hour + hour,
hour - hour,
hour * hour,
hour / hour,
hour % hour,
hour & hour,
hour | hour,
hour > hour,
hour < hour,
hour == hour,
hour != hour,
hour >= hour,
hour <= hour,
hour + day,
hour - day,
hour * day,
hour / day,
hour % day,
hour & day,
hour | day,
hour > day,
hour < day,
hour == day,
hour != day,
hour >= day,
hour <= day,
day + 2,
day - 2,
day * 2,
day / 2,
day % 2,
day & 2,
day | 2,
day > 2,
day < 2,
day == 2,
day != 2,
day >= 2,
day <= 2,
]
i = 0
for left, right in compares:
for feat in feats:
f = feat(left, right)
o = overrides[i]
assert o.hash() == f.hash()
i += 1
for compare_op in compare_ops:
f = compare_op(left, right)
o = overrides[i]
assert o.hash() == f.hash()
i += 1
our_reverse_overrides = [
2 + day, 2 - day, 2 * day, 2 / day, 2 & day, 2 | day
]
i = 0
for feat in feats:
if feat != Mod:
f = feat(2, day)
o = our_reverse_overrides[i]
assert o.hash() == f.hash()
i += 1
python_reverse_overrides = [
2 < day, 2 > day, 2 == day, 2 != day, 2 <= day, 2 >= day
]
i = 0
for compare_op in compare_ops:
f = compare_op(day, 2)
o = python_reverse_overrides[i]
assert o.hash() == f.hash()
i += 1
