def test_percentage_when_non_directional():
""" Test the percentage function behaves as expected """
ndarr = np.array([[10, 2, 3, 4, 5, 6], [6, 5, 4, 3, 8, 1],
[1, 1, 9, 1, 1, 1], [9, 2, 2, 2, 2,
2], [3, 3, 3, 9, 3, 3],
[1, 2, 2, 9, 1, 4], [1, 2, 2, 9, 1, 4],
[1, 2, 2, 9, 1, 4]])
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])
percentage = Kruskals.Kruskals(ndarr, arr).driver_score(percentage=True)
assert (np.round(percentage, decimals=4) == [
18.7523, 13.8413, 15.4078, 21.5111, 23.4954, 6.9921
]).all()
def test_that_direction_is_applied_on_directional_drivers_analysis():
""" Test whether some driver scores are negative """
ndarr = np.array([[10, 2, 3, 4, 5, 6], [6, 5, 4, 3, 8, 1],
[1, 1, 9, 1, 1, 1], [9, 2, 2, 2, 2,
2], [3, 3, 3, 9, 3, 3],
[1, 2, 2, 9, 1, 4], [1, 2, 2, 9, 1, 4],
[1, 2, 2, 9, 1, 4]])
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])
series = Kruskals.Kruskals(ndarr, arr).driver_score_to_series(True)
assert (series.values < 0).any()
def test_percentage():
""" Test percentage is calculated correctly """
ndarr = np.array([[1, 2, 3, 4, 5, 6], [6, 5, 4, 3, 8,
1], [1, 1, 9, 1, 1, 1],
[9, 2, 2, 2, 2, 2], [3, 3, 3, 9, 3, 3],
[1, 2, 2, 9, 1, 4]])
arr = np.array([1, 2, 3, 4, 5, 6])
exp_driver_score = np.array(
[5.90856, 17.81959, 9.62429, 25.08222, 28.85722, 12.70813])
driver_score = np.round(Kruskals.Kruskals(ndarr, arr).percentage(),
decimals=5)
assert np.array_equal(driver_score, exp_driver_score)
def test_return_error_if_i_vars_not_sufficient():
""" Test that error raised when columns insufficient length """
ndarr = np.array([[1, 2, 3, 4, 5, 6, 1], [6, 5, 4, 3, 8, 1, 2],
[1, 1, 9, 1, 1, 1, 3], [9, 2, 2, 2, 2, 2, 4],
[3, 3, 3, 9, 3, 3, 5], [1, 2, 2, 9, 1, 4, 6]])
exp_driver_score = np.array(
[0.14721, 0.44398, 0.23979, 0.62493, 0.71898, 0.31662])
i_vars = ['a', 'b', 'c', 'd', 'e', 'f']
with pytest.raises(ValueError) as e:
Kruskals.Kruskals(ndarr, exp_driver_score,
i_vars=i_vars).driver_score_to_series()
assert 'driver labels: {}, not sufficient for ndarray of shape {}'.format(
i_vars, ndarr.shape) in str(e.value)
def test_can_handle_numpy_arrays_for_col_names():
""" Test that df.columns can be passed into __init__ """
ndarr = np.array([[1, 2, 3, 4, 5, 6, 1], [6, 5, 4, 3, 8, 1, 2],
[1, 1, 9, 1, 1, 1, 3], [9, 2, 2, 2, 2, 2, 4],
[3, 3, 3, 9, 3, 3, 5], [1, 2, 2, 9, 1, 4, 6]])
exp_driver_score = np.array(
[0.14721, 0.44398, 0.23979, 0.62493, 0.71898, 0.31662])
df = pd.DataFrame(ndarr)
df.columns = ['a', 'b', 'c', 'd', 'e', 'f', 'g']
driver_score = Kruskals.Kruskals(
ndarr, exp_driver_score, i_vars=df.columns).driver_score_to_series()
assert np.array_equal(driver_score.index.values,
['a', 'b', 'c', 'd', 'e', 'f', 'g'])
def test_driver_score():
""" Test driver_score is calculated correctly """
ndarr = np.array([[1, 2, 3, 4, 5, 6], [6, 5, 4, 3, 8,
1], [1, 1, 9, 1, 1, 1],
[9, 2, 2, 2, 2, 2], [3, 3, 3, 9, 3, 3],
[1, 2, 2, 9, 1, 4]])
arr = np.array([1, 2, 3, 4, 5, 6])
exp_driver_score = np.array(
[0.14721, 0.44398, 0.23979, 0.62493, 0.71898, 0.31662])
driver_score = np.round(Kruskals.Kruskals(ndarr, arr).driver_score(),
decimals=5)
assert np.array_equal(driver_score, exp_driver_score)
def test_series_output():
""" Test percentage is calculated correctly """
ndarr = np.array([[1, 2, 3, 4, 5, 6], [6, 5, 4, 3, 8,
1], [1, 1, 9, 1, 1, 1],
[9, 2, 2, 2, 2, 2], [3, 3, 3, 9, 3, 3],
[1, 2, 2, 9, 1, 4]])
arr = np.array([1, 2, 3, 4, 5, 6])
exp_driver_score = np.array(
[0.14721, 0.44398, 0.23979, 0.62493, 0.71898, 0.31662])
series = Kruskals.Kruskals(ndarr, arr).driver_score_to_series()
assert np.array_equal(np.round(series.values, decimals=5),
exp_driver_score)
assert series.name == 'score'
assert series.index.name == 'driver'
def test_ability_to_handle_all_same_type():
"""
Test to make sure that kruskals can handle data
when all the values for and independent set are 0
"""
ndarr = np.array([[10, 0, 3, 4, 5, 6], [6, 0, 4, 3, 5, 1],
[1, 0, 9, 1, 5, 1], [9, 0, 2, 2, 5,
2], [3, 0, 3, 9, 5, 3],
[1, 0, 2, 9, 5, 4], [1, 0, 2, 9, 5, 4],
[1, 0, 2, 9, 5, 4]])
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])
series = Kruskals.Kruskals(ndarr, arr).driver_score()
assert series[1] == 0.0
assert series[4] == 0.0