def test_StandardScaler_output(test_input):
scaler = Scaler()
scaler.execute(params=test_input)
if "Distance" in test_input["train_df"].keys():
assert round(test_input["train_df"]["Distance"][0], 5) == 1.08006
else:
assert round(test_input["train_df"]["Negatives"][0], 5) == 0.57771
def test_BinaryScaler_output(test_input):
scaler = Scaler()
scaler.execute(params=test_input)
assert (test_input["df"]["Negatives"].values.any() == 1
or test_input["df"]["Negatives"].values.any() == 0)
assert not (test_input["df"]["Negatives"].between(0, 1,
inclusive=False).any())
assert test_input["df"]["Negatives"][0] == 1
def test_MinMaxScaler_output(test_input):
scaler = Scaler()
scaler.execute(params=test_input)
if "Distance" in test_input["train_df"].keys():
assert test_input["train_df"]["Distance"].values.all() >= 0
assert test_input["train_df"]["Distance"].values.all() <= 1
else:
assert test_input["train_df"]["Negatives"].values.all() >= 0
assert test_input["train_df"]["Negatives"].values.all() <= 1
def test_BinaryScaler_output(test_input):
scaler = Scaler()
scaler.execute(params=test_input)
assert (test_input["train_df"]["Negatives"].values.any() == 1
or test_input["train_df"]["Negatives"].values.any() == 0)
assert not (test_input["train_df"]["Negatives"].between(
0, 1, inclusive="neither").any())
if test_input["threshold"]["Negatives"] != -1:
assert test_input["train_df"]["Negatives"][0] == 1
else:
assert test_input["train_df"]["Negatives"][0] == 0
def test_incorrect_input_type(test_input, error):
with pytest.raises(error):
scaler = Scaler()
scaler.execute(params=test_input)
def test_MinMaxScaler_output(test_input):
scaler = Scaler()
scaler.execute(params=test_input)
assert test_input["df"]["Distance"].values.all() >= 0
assert test_input["df"]["Distance"].values.all() <= 1