def test_isin_feat(es):
isin = IsIn(es['log']['product_id'],
list_of_outputs=["toothpaste", "coke zero"])
features = [isin]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(range(8), None)
true = [True, True, True, False, False, True, True, True]
v = df[isin.get_name()].values.tolist()
assert true == v
def test_isin_feat(es):
isin = IsIn(es['log']['product_id'],
list_of_outputs=["toothpaste", "coke zero"])
features = [isin]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(range(8), None)
true = [True, True, True, False, False, True, True, True]
v = df[isin.get_name()].values.tolist()
assert true == v
def test_isin_feat_custom(es):
def pd_is_in(array, list_of_outputs=None):
if list_of_outputs is None:
list_of_outputs = []
return pd.Series(array).isin(list_of_outputs)
def isin_generate_name(self):
return u"%s.isin(%s)" % (self.base_features[0].get_name(),
str(self.kwargs['list_of_outputs']))
IsIn = make_trans_primitive(
pd_is_in,
[Variable],
Boolean,
name="is_in",
description="For each value of the base feature, checks whether it is "
"in a list that is provided.",
cls_attributes={"generate_name": isin_generate_name})
isin = IsIn(es['log']['product_id'],
list_of_outputs=["toothpaste", "coke zero"])
features = [isin]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(range(8), None)
true = [True, True, True, False, False, True, True, True]
v = df[isin.get_name()].values.tolist()
assert true == v
isin = Feature(es['log']['product_id']).isin(["toothpaste", "coke zero"])
features = [isin]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(range(8), None)
true = [True, True, True, False, False, True, True, True]
v = df[isin.get_name()].values.tolist()
assert true == v
isin = Feature(es['log']['value']).isin([5, 10])
features = [isin]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(range(8), None)
true = [False, True, True, False, False, False, False, False]
v = df[isin.get_name()].values.tolist()
assert true == v
def test_isin_feat_custom(es):
def pd_is_in(array, list_of_outputs=None):
if list_of_outputs is None:
list_of_outputs = []
return pd.Series(array).isin(list_of_outputs)
def isin_generate_name(self):
return u"%s.isin(%s)" % (self.base_features[0].get_name(),
str(self.kwargs['list_of_outputs']))
IsIn = make_trans_primitive(
pd_is_in,
[Variable],
Boolean,
name="is_in",
description="For each value of the base feature, checks whether it is "
"in a list that is provided.",
cls_attributes={"generate_name": isin_generate_name})
isin = IsIn(es['log']['product_id'],
list_of_outputs=["toothpaste", "coke zero"])
features = [isin]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(range(8), None)
true = [True, True, True, False, False, True, True, True]
v = df[isin.get_name()].values.tolist()
assert true == v
isin = Feature(es['log']['product_id']).isin(["toothpaste", "coke zero"])
features = [isin]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(range(8), None)
true = [True, True, True, False, False, True, True, True]
v = df[isin.get_name()].values.tolist()
assert true == v
isin = Feature(es['log']['value']).isin([5, 10])
features = [isin]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(range(8), None)
true = [False, True, True, False, False, False, False, False]
v = df[isin.get_name()].values.tolist()
assert true == v
