def test_diff(es):
value = IdentityFeature(es['log']['value'])
customer_id_feat = \
DirectFeature(es['sessions']['customer_id'],
child_entity=es['log'])
diff1 = Diff(value, es['log']['session_id'])
diff2 = Diff(value, customer_id_feat)
pandas_backend = PandasBackend(es, [diff1, diff2])
df = pandas_backend.calculate_all_features(instance_ids=range(15),
time_last=None)
val1 = df[diff1.get_name()].values.tolist()
val2 = df[diff2.get_name()].values.tolist()
correct_vals1 = [
np.nan, 5, 5, 5, 5, np.nan, 1, 1, 1, np.nan, np.nan, 5, np.nan, 7, 7
]
correct_vals2 = [np.nan, 5, 5, 5, 5, -20, 1, 1, 1, -3, np.nan, 5, -5, 7, 7]
for i, v in enumerate(val1):
v1 = val1[i]
if np.isnan(v1):
assert (np.isnan(correct_vals1[i]))
else:
assert v1 == correct_vals1[i]
v2 = val2[i]
if np.isnan(v2):
assert (np.isnan(correct_vals2[i]))
else:
assert v2 == correct_vals2[i]
def test_diff_single_value(es):
diff = Diff(es['stores']['num_square_feet'], es['stores'][u'région_id'])
pandas_backend = PandasBackend(es, [diff])
df = pandas_backend.calculate_all_features(instance_ids=[5],
time_last=None)
assert df.shape[0] == 1
assert df[diff.get_name()].dropna().shape[0] == 0
def test_isnull_feat(es):
value = IdentityFeature(es['log']['value'])
diff = Diff(value, es['log']['session_id'])
isnull = IsNull(diff)
features = [isnull]
pandas_backend = PandasBackend(es, features)
df = pandas_backend.calculate_all_features(range(15), None)
# correct_vals_diff = [
# np.nan, 5, 5, 5, 5, np.nan, 1, 1, 1, np.nan, np.nan, 5, np.nan, 7, 7]
correct_vals = [
True, False, False, False, False, True, False, False, False, True,
True, False, True, False, False
]
values = df[isnull.get_name()].values.tolist()
assert correct_vals == values
