def test_pearson_correlation():
expected_shifts = [randint(2, 9) * 2 for _ in range(3)]
df = make_df_from_expected_shifts(expected_shifts)
spc = ShiftedPearsonCorrelation(target_col="A", max_shift=20)
spc.fit(df)
shifts = spc.best_shifts_["A"][1:].values
np.testing.assert_array_equal(shifts, expected_shifts)
def test_linear_coefficient():
expected_shifts = [randint(2, 9) * 2 for _ in range(3)]
df = make_df_from_expected_shifts(expected_shifts)
slc = ShiftedLinearCoefficient(target_col="A", max_shift=20)
slc.fit(df)
shifts = slc.best_shifts_["A"][1:].values
np.testing.assert_array_equal(shifts, expected_shifts)
def test_pearson_permutation_p_values():
expected_shifts = [randint(2, 9) * 2 for _ in range(3)]
df = make_df_from_expected_shifts(expected_shifts)
shifted_test = ShiftedPearsonCorrelation(
target_col="A", max_shift=5, permutation_iterations=50,
)
shifted_test.fit(df)
pearson_p_values = shifted_test.permutation_p_values_
for col_index in range(len(pearson_p_values.columns)):
assert pearson_p_values.iloc[col_index, col_index] == 0
def test_linear_permutation_p_values():
expected_shifts = [randint(2, 9) * 2 for _ in range(3)]
df = make_df_from_expected_shifts(expected_shifts)
shifted_test = ShiftedLinearCoefficient(
target_col="A",
max_shift=5,
permutation_iterations=50,
)
shifted_test.fit(df)
linear_p_values = shifted_test.permutation_p_values_
for col_index in range(len(linear_p_values.columns)):
assert linear_p_values.iloc[col_index, col_index] == 0
def test_linear_bootstrap_p_values():
# This test and the next one just test if the p_values on the diagonal are equal
# to 0. Is hard to implement other unittest, since the bootstrapping always
# gives different result. However, other properties could be tested
expected_shifts = [randint(2, 9) * 2 for _ in range(3)]
df = make_df_from_expected_shifts(expected_shifts)
shifted_test = ShiftedLinearCoefficient(
target_col="A",
max_shift=5,
bootstrap_iterations=500,
)
shifted_test.fit(df)
linear_p_values = shifted_test.bootstrap_p_values_
for col_index in range(len(linear_p_values.columns)):
assert linear_p_values.iloc[col_index, col_index] == 0