def test_init_and_name(es):
log = es['log']
features = [Feature(v) for v in log.variables] +\
[GreaterThan(Feature(es["products"]["rating"], es["log"]), 2.5)]
# Add Timedelta feature
features.append(pd.Timestamp.now() - Feature(log['datetime']))
for transform_prim in get_transform_primitives():
if issubclass(transform_prim, Compare):
continue
# use the input_types matching function from DFS
input_types = transform_prim.input_types
if type(input_types[0]) == list:
matching_inputs = [
g for s in input_types for g in match(s, 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 = transform_prim(*s)
# try to get name and calculate
instance.get_name()
instance.head()
def __gt__(self, other_feature_or_val):
"""Compares if greater than other_feature_or_val
See also:
:meth:`PrimitiveBase.GT`
"""
from featuretools.primitives import GreaterThan
return GreaterThan(self, other_feature_or_val)
def test_override_boolean(es):
count = Count(es['log']['value'], es['sessions'])
count_lo = GreaterThan(count, 1)
count_hi = LessThan(count, 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)))
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(instance_ids=[0, 1, 2],
time_last=None)
for i, test in enumerate(to_test):
v = df[features[i].get_name()].values.tolist()
assert v == test
def test_cum_sum_where(es):
log_value_feat = es['log']['value']
compare_feat = GreaterThan(log_value_feat, 3)
dfeat = Feature(es['sessions']['customer_id'], es['log'])
cum_sum = CumSum(log_value_feat, dfeat, where=compare_feat)
features = [cum_sum]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(instance_ids=range(15),
time_last=None)
cvalues = df[cum_sum.get_name()].values
assert len(cvalues) == 15
cum_sum_values = [0, 5, 15, 30, 50, 50, 50, 50, 50, 50, 0, 5, 5, 12, 26]
for i, v in enumerate(cum_sum_values):
if not np.isnan(v):
assert v == cvalues[i]
else:
assert (np.isnan(cvalues[i]))
def test_cum_sum_use_previous_and_where_absolute(es):
log_value_feat = es['log']['value']
compare_feat = GreaterThan(log_value_feat, 3)
dfeat = Feature(es['sessions']['customer_id'], es['log'])
cum_sum = CumSum(log_value_feat, dfeat, es["log"]["datetime"],
where=compare_feat,
use_previous=Timedelta(40, 'seconds'))
features = [cum_sum]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(instance_ids=range(15),
time_last=None)
cum_sum_values = [0, 5, 15, 30, 50, 0, 0, 0, 0, 0,
0, 5, 0, 7, 21]
cvalues = df[cum_sum.get_name()].values
assert len(cvalues) == 15
for i, v in enumerate(cum_sum_values):
assert v == cvalues[i]
def test_cum_mean_use_previous_and_where(es):
log_value_feat = es['log']['value']
compare_feat = GreaterThan(log_value_feat, 3)
# todo should this be cummean?
dfeat = Feature(es['sessions']['customer_id'], es['log'])
cum_mean = CumMean(log_value_feat, dfeat,
where=compare_feat,
use_previous=Timedelta(2, 'observations',
entity=es['log']))
features = [cum_mean]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(instance_ids=range(15),
time_last=None)
cum_mean_values = [0, 5, 7.5, 12.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5,
0, 5, 5, 6, 10.5]
cvalues = df[cum_mean.get_name()].values
assert len(cvalues) == 15
for i, v in enumerate(cum_mean_values):
assert v == cvalues[i]
def test_init_and_name(es):
from featuretools import calculate_feature_matrix
log = es['log']
features = [Feature(v) for v in log.variables] +\
[GreaterThan(Feature(es["products"]["rating"], es["log"]), 2.5)]
# Add Timedelta feature
features.append(pd.Timestamp.now() - Feature(log['datetime']))
for transform_prim in get_transform_primitives().values():
# 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 = transform_prim(*s)
# try to get name and calculate
instance.get_name()
calculate_feature_matrix([instance], entityset=es).head(5)
