def test_two_kinds_of_dependents(es):
v = Feature(es['log']['value'])
product = Feature(es['log']['product_id'])
agg = Sum(v, es['customers'], where=product == 'coke zero')
p = Percentile(agg)
g = Absolute(agg)
agg2 = Sum(v, es['sessions'], where=product == 'coke zero')
# Adding this feature in tests line 218 in pandas_backend
# where we remove columns in result_frame that already exist
# in the output entity_frames in preparation for pd.concat
# In a prior version, this failed because we changed the result_frame
# variable itself, rather than making a new variable _result_frame.
# When len(output_frames) > 1, the second iteration won't have
# all the necessary columns because they were removed in the first
agg3 = Sum(agg2, es['customers'])
pandas_backend = PandasBackend(es, [p, g, agg3])
df = pandas_backend.calculate_all_features([0, 1], None)
assert df[p.get_name()].tolist() == [0.5, 1.0]
assert df[g.get_name()].tolist() == [15, 26]
def test_two_kinds_of_dependents(es):
v = Feature(es['log']['value'])
product = Feature(es['log']['product_id'])
agg = Sum(v, es['customers'], where=product == 'coke zero')
p = Percentile(agg)
g = Absolute(agg)
agg2 = Sum(v, es['sessions'], where=product == 'coke zero')
# Adding this feature in tests line 218 in pandas_backend
# where we remove columns in result_frame that already exist
# in the output entity_frames in preparation for pd.concat
# In a prior version, this failed because we changed the result_frame
# variable itself, rather than making a new variable _result_frame.
# When len(output_frames) > 1, the second iteration won't have
# all the necessary columns because they were removed in the first
agg3 = Sum(agg2, es['customers'])
pandas_backend = PandasBackend(es, [p, g, agg3])
df = pandas_backend.calculate_all_features([0, 1], None)
assert df[p.get_name()].tolist() == [2. / 3, 1.0]
assert df[g.get_name()].tolist() == [15, 26]
def __abs__(self):
from featuretools.primitives import Absolute
return Absolute(self)