def test_convert_array_type_list_to_np_array(self):
"""Test convert_array_type converts a list to a np.ndarray."""
test_scaler = scale.Scaler([1, 2, 3])
output = test_scaler.convert_array_type([1, 2, 3], np.ndarray)
assert isinstance(output, np.ndarray)
def test_convert_array_type_list_to_pd_series(self):
"""Test convert_array_type converts a list to a pd.Series"""
test_scaler = scale.Scaler([1, 2, 3])
output = test_scaler.convert_array_type([1, 2, 3], pd.Series)
assert isinstance(output, pd.Series)
def test_convert_array_type_pd_Series_to_list(self):
"""Test convert_array_type converts a pd.Series to a list."""
test_scaler = scale.Scaler([1, 2, 3])
output = test_scaler.convert_array_type(pd.Series([1, 2, 3]), list)
assert isinstance(output, list)
def test_min_max_values_2(self):
"""Test the min_max values are correct, with negatives."""
test_scaler = scale.Scaler([-2, -5, 10, 3])
test_scaler.get_min_max_values()
assert test_scaler.min_max_val == (-5, 10)
def test_min_max_values_3(self):
"""Test the min_max values are correct, with length 1."""
test_scaler = scale.Scaler([3])
test_scaler.get_min_max_values()
assert test_scaler.min_max_val == (3, 3)
def test_min_max_val_attribute(self):
"""Test min_max_val attribute is assigned."""
test_scaler = scale.Scaler([3, 2, 5, 4])
test_scaler.get_min_max_values()
assert hasattr(test_scaler, "min_max_val")
def test_min_max_values_1(self):
"""Test the min_max values are correct."""
test_scaler = scale.Scaler([1, 2, 3, 4])
test_scaler.get_min_max_values()
assert test_scaler.min_max_val == (1, 4)
def test_array_like_value(self):
"""Test array_like attribute assigned correctly."""
test_scaler = scale.Scaler([1, 2, 3])
assert test_scaler.array_like == [1, 2, 3]
def test_has_attr_array_like(self):
"""Test initialised Scaler object has array_like attribute."""
test_scaler = scale.Scaler([1, 2, 3])
assert hasattr(test_scaler, "array_like")
def test_error_thrown_with_nan(self):
"""Test error is thrown if array_like contains missing values."""
with pytest.raises(ValueError):
scale.Scaler(array_like=[1, 2, np.NaN])
def test_array_like_value_type(self):
"""Test error is thrown if array_like values are not the correct type."""
with pytest.raises(TypeError):
scale.Scaler(array_like=[1, 2, "dummy"])
def test_array_like_type(self):
"""Test error is thrown if array_like is not the correct type."""
with pytest.raises(TypeError):
scale.Scaler(array_like=123)