def test_encode_features_handles_dictionary_input(pd_es):
f1 = IdentityFeature(pd_es["log"].ww["product_id"])
f2 = IdentityFeature(pd_es["log"].ww["purchased"])
f3 = IdentityFeature(pd_es["log"].ww["session_id"])
features = [f1, f2, f3]
feature_matrix = calculate_feature_matrix(features,
pd_es,
instance_ids=range(16))
feature_matrix_encoded, features_encoded = encode_features(
feature_matrix, features)
true_values = [
'product_id = coke zero', 'product_id = toothpaste',
'product_id = car', 'product_id = brown bag',
'product_id = taco clock',
'product_id = Haribo sugar-free gummy bears', 'product_id is unknown',
'purchased', 'session_id = 0', 'session_id = 1', 'session_id = 4',
'session_id = 3', 'session_id = 5', 'session_id = 2',
'session_id is unknown'
]
assert len(features_encoded) == 15
for col in true_values:
assert col in list(feature_matrix_encoded.columns)
top_n_dict = {}
feature_matrix_encoded, features_encoded = encode_features(
feature_matrix, features, top_n=top_n_dict)
assert len(features_encoded) == 15
for col in true_values:
assert col in list(feature_matrix_encoded.columns)
top_n_dict = {f1.get_name(): 4, f3.get_name(): 3}
feature_matrix_encoded, features_encoded = encode_features(
feature_matrix, features, top_n=top_n_dict)
assert len(features_encoded) == 10
true_values = [
'product_id = coke zero', 'product_id = toothpaste',
'product_id = car', 'product_id = brown bag', 'product_id is unknown',
'purchased', 'session_id = 0', 'session_id = 1', 'session_id = 4',
'session_id is unknown'
]
for col in true_values:
assert col in list(feature_matrix_encoded.columns)
feature_matrix_encoded, features_encoded = encode_features(
feature_matrix, features, top_n=top_n_dict, include_unknown=False)
true_values = [
'product_id = coke zero', 'product_id = toothpaste',
'product_id = car', 'product_id = brown bag', 'purchased',
'session_id = 0', 'session_id = 1', 'session_id = 4'
]
assert len(features_encoded) == 8
for col in true_values:
assert col in list(feature_matrix_encoded.columns)
def test_make_identity(entityset, backend):
f = IdentityFeature(entityset['log']['datetime'])
pandas_backend = backend([f])
df = pandas_backend.calculate_all_features(instance_ids=[0],
time_last=None)
v = df[f.get_name()][0]
assert (v == datetime(2011, 4, 9, 10, 30, 0))
def test_make_identity(es):
f = IdentityFeature(es['log']['datetime'])
feature_set = FeatureSet([f])
calculator = FeatureSetCalculator(es,
time_last=None,
feature_set=feature_set)
df = calculator.run(np.array([0]))
v = df[f.get_name()][0]
assert (v == datetime(2011, 4, 9, 10, 30, 0))
def test_make_identity(es):
f = IdentityFeature(es["log"].ww["datetime"])
feature_set = FeatureSet([f])
calculator = FeatureSetCalculator(es,
time_last=None,
feature_set=feature_set)
df = to_pandas(calculator.run(np.array([0])))
v = df[f.get_name()][0]
assert v == datetime(2011, 4, 9, 10, 30, 0)
