def test_ancom_tau(self):
exp1 = pd.DataFrame({'W': np.array([8, 7, 3, 3, 7, 3, 3, 3, 3]),
'reject': np.array([True, False, False, False,
False, False, False, False,
False], dtype=bool)})
exp2 = pd.DataFrame({'W': np.array([17, 17, 5, 6, 16, 5, 7, 5,
4, 5, 8, 4, 5, 16, 5, 11, 4, 6]),
'reject': np.array([True, True, False, False,
True, False, False, False,
False, False, False, False,
False, True, False, False,
False, False], dtype=bool)})
exp3 = pd.DataFrame({'W': np.array([16, 16, 17, 10, 17, 16, 16,
15, 15, 15, 13, 10, 10, 10,
9, 9, 9, 9]),
'reject': np.array([True, True, True, False,
True, True, True, True,
True, True, True, False,
False, False, False, False,
False, False], dtype=bool)})
result1 = ancom(self.table4, self.cats4, tau=0.25)
result2 = ancom(self.table9, self.cats9, tau=0.02)
result3 = ancom(self.table10, self.cats10, tau=0.02)
assert_data_frame_almost_equal(result1, exp1)
assert_data_frame_almost_equal(result2, exp2)
assert_data_frame_almost_equal(result3, exp3)
def test_ancom_no_signal(self):
result = ancom(self.table3,
self.cats3,
multiple_comparisons_correction=None)
exp = pd.DataFrame({'W': np.array([0]*7),
'reject': np.array([False]*7, dtype=bool)})
assert_data_frame_almost_equal(result, exp)
def test_ancom_alpha(self):
result = ancom(self.table1, self.cats1, alpha=0.5)
exp = pd.DataFrame({'W': np.array([6, 6, 4, 5, 5, 4, 2]),
'reject': np.array([True, True, False, True,
True, False, False],
dtype=bool)})
assert_data_frame_almost_equal(result, exp)
def test_ancom_basic_counts_swapped(self):
result = ancom(self.table8, self.cats8)
exp = pd.DataFrame({'W': np.array([5, 5, 2, 2, 2, 2, 2]),
'reject': np.array([True, True, False, False,
False, False, False],
dtype=bool)})
assert_data_frame_almost_equal(result, exp)
def test_ancom_multiple_comparisons(self):
result = ancom(self.table1,
self.cats1,
multiple_comparisons_correction='holm-bonferroni',
significance_test=scipy.stats.mannwhitneyu)
exp = pd.DataFrame({'W': np.array([0]*7),
'reject': np.array([False]*7, dtype=bool)})
assert_data_frame_almost_equal(result, exp)
def test_ancom_no_signal(self):
result = ancom(self.table3,
self.cats3,
multiple_comparisons_correction=None)
exp = pd.DataFrame({
'W': np.array([0] * 7),
'reject': np.array([False] * 7, dtype=bool)
})
assert_data_frame_almost_equal(result, exp)
def test_ancom_alpha(self):
result = ancom(self.table1, self.cats1, alpha=0.5)
exp = pd.DataFrame({
'W':
np.array([6, 6, 4, 5, 5, 4, 2]),
'reject':
np.array([True, True, False, True, True, False, False], dtype=bool)
})
assert_data_frame_almost_equal(result, exp)
def test_ancom_letter_categories(self):
result = ancom(self.table7,
self.cats7,
multiple_comparisons_correction=None)
exp = pd.DataFrame({'W': np.array([5, 3, 3, 2, 2, 5, 2]),
'reject': np.array([True, False, False, False,
False, True, False],
dtype=bool)})
assert_data_frame_almost_equal(result, exp)
def test_ancom_noncontiguous(self):
result = ancom(self.table5,
self.cats5,
multiple_comparisons_correction=None)
exp = pd.DataFrame({'W': np.array([6, 2, 2, 2, 2, 6, 2]),
'reject': np.array([True, False, False, False,
False, True, False],
dtype=bool)})
assert_data_frame_almost_equal(result, exp)
def test_ancom_basic_counts_swapped(self):
result = ancom(self.table8, self.cats8)
exp = pd.DataFrame({
'W':
np.array([5, 5, 2, 2, 2, 2, 2]),
'reject':
np.array([True, True, False, False, False, False, False],
dtype=bool)
})
assert_data_frame_almost_equal(result, exp)
def test_ancom_multiple_comparisons(self):
result = ancom(self.table1,
self.cats1,
multiple_comparisons_correction='holm-bonferroni',
significance_test=scipy.stats.mannwhitneyu)
exp = pd.DataFrame({
'W': np.array([0] * 7),
'reject': np.array([False] * 7, dtype=bool)
})
assert_data_frame_almost_equal(result, exp)
def test_ancom_alternative_test(self):
result = ancom(self.table1,
self.cats1,
multiple_comparisons_correction=None,
significance_test=scipy.stats.ttest_ind)
exp = pd.DataFrame({'W': np.array([5, 5, 2, 2, 2, 2, 2]),
'reject': np.array([True, True, False, False,
False, False, False],
dtype=bool)})
assert_data_frame_almost_equal(result, exp)
def test_ancom_tau(self):
exp1 = pd.DataFrame({
'W':
np.array([8, 7, 3, 3, 7, 3, 3, 3, 3]),
'reject':
np.array(
[True, False, False, False, False, False, False, False, False],
dtype=bool)
})
exp2 = pd.DataFrame({
'W':
np.array(
[17, 17, 5, 6, 16, 5, 7, 5, 4, 5, 8, 4, 5, 16, 5, 11, 4, 6]),
'reject':
np.array([
True, True, False, False, True, False, False, False, False,
False, False, False, False, True, False, False, False, False
],
dtype=bool)
})
exp3 = pd.DataFrame({
'W':
np.array([
16, 16, 17, 10, 17, 16, 16, 15, 15, 15, 13, 10, 10, 10, 9, 9,
9, 9
]),
'reject':
np.array([
True, True, True, False, True, True, True, True, True, True,
True, False, False, False, False, False, False, False
],
dtype=bool)
})
result1 = ancom(self.table4, self.cats4, tau=0.25)
result2 = ancom(self.table9, self.cats9, tau=0.02)
result3 = ancom(self.table10, self.cats10, tau=0.02)
assert_data_frame_almost_equal(result1, exp1)
assert_data_frame_almost_equal(result2, exp2)
assert_data_frame_almost_equal(result3, exp3)
def test_ancom_letter_categories(self):
result = ancom(self.table7,
self.cats7,
multiple_comparisons_correction=None)
exp = pd.DataFrame({
'W':
np.array([5, 3, 3, 2, 2, 5, 2]),
'reject':
np.array([True, False, False, False, False, True, False],
dtype=bool)
})
assert_data_frame_almost_equal(result, exp)
def test_ancom_noncontiguous(self):
result = ancom(self.table5,
self.cats5,
multiple_comparisons_correction=None)
exp = pd.DataFrame({
'W':
np.array([6, 2, 2, 2, 2, 6, 2]),
'reject':
np.array([True, False, False, False, False, True, False],
dtype=bool)
})
assert_data_frame_almost_equal(result, exp)
def test_ancom_normal_data(self):
result = ancom(self.table2,
self.cats2,
multiple_comparisons_correction=None,
significance_test=scipy.stats.ttest_ind)
exp = pd.DataFrame({'W': np.array([8, 8, 3, 3,
8, 3, 3, 3, 3]),
'reject': np.array([True, True, False, False,
True, False, False,
False, False],
dtype=bool)})
assert_data_frame_almost_equal(result, exp)
def test_ancom_alternative_test(self):
result = ancom(self.table1,
self.cats1,
multiple_comparisons_correction=None,
significance_test=scipy.stats.ttest_ind)
exp = pd.DataFrame({
'W':
np.array([5, 5, 2, 2, 2, 2, 2]),
'reject':
np.array([True, True, False, False, False, False, False],
dtype=bool)
})
assert_data_frame_almost_equal(result, exp)
def test_ancom_normal_data(self):
result = ancom(self.table2,
self.cats2,
multiple_comparisons_correction=None,
significance_test=scipy.stats.ttest_ind)
exp = pd.DataFrame({
'W':
np.array([8, 8, 3, 3, 8, 3, 3, 3, 3]),
'reject':
np.array(
[True, True, False, False, True, False, False, False, False],
dtype=bool)
})
assert_data_frame_almost_equal(result, exp)
def test_ancom_multiple_groups(self):
test_table = pd.DataFrame(self.table4)
original_table = copy.deepcopy(test_table)
test_cats = pd.Series(self.cats4)
original_cats = copy.deepcopy(test_cats)
result = ancom(test_table, test_cats)
# Test to make sure that the input table hasn't be altered
assert_data_frame_almost_equal(original_table, test_table)
# Test to make sure that the input table hasn't be altered
pdt.assert_series_equal(original_cats, test_cats)
exp = pd.DataFrame({'W': np.array([8, 7, 3, 3, 7, 3, 3, 3, 3]),
'reject': np.array([True, True, False, False,
True, False, False, False,
False], dtype=bool)})
assert_data_frame_almost_equal(result, exp)
def test_permutative_f(self):
test_table = pd.DataFrame(self.table1)
original_table = copy.deepcopy(test_table)
test_cats = pd.Series(self.cats1)
original_cats = copy.deepcopy(test_cats)
result = ancom(test_table, test_cats, significance_test='permutative-anova')
# Test to make sure that the input table hasn't be altered
assert_data_frame_almost_equal(original_table, test_table)
# Test to make sure that the input table hasn't be altered
pdt.assert_series_equal(original_cats, test_cats)
exp = pd.DataFrame({'W': np.array([6, 6, 3, 3, 2, 2, 2]),
'reject': np.array([True, True, False, False,
False, False, False],
dtype=bool)})
assert_data_frame_almost_equal(result, exp)
def test_ancom_basic_counts(self):
test_table = pd.DataFrame(self.table1)
original_table = copy.deepcopy(test_table)
test_cats = pd.Series(self.cats1)
original_cats = copy.deepcopy(test_cats)
result = ancom(test_table,
test_cats,
multiple_comparisons_correction=None)
# Test to make sure that the input table hasn't be altered
assert_data_frame_almost_equal(original_table, test_table)
# Test to make sure that the input table hasn't be altered
pdt.assert_series_equal(original_cats, test_cats)
exp = pd.DataFrame({'W': np.array([5, 5, 2, 2, 2, 2, 2]),
'reject': np.array([True, True, False, False,
False, False, False],
dtype=bool)})
assert_data_frame_almost_equal(result, exp)
def test_ancom_multiple_groups(self):
test_table = pd.DataFrame(self.table4)
original_table = copy.deepcopy(test_table)
test_cats = pd.Series(self.cats4)
original_cats = copy.deepcopy(test_cats)
result = ancom(test_table, test_cats)
# Test to make sure that the input table hasn't be altered
assert_data_frame_almost_equal(original_table, test_table)
# Test to make sure that the input table hasn't be altered
pdt.assert_series_equal(original_cats, test_cats)
exp = pd.DataFrame({
'W':
np.array([8, 7, 3, 3, 7, 3, 3, 3, 3]),
'reject':
np.array(
[True, True, False, False, True, False, False, False, False],
dtype=bool)
})
assert_data_frame_almost_equal(result, exp)
def test_ancom_basic_counts(self):
test_table = pd.DataFrame(self.table1)
original_table = copy.deepcopy(test_table)
test_cats = pd.Series(self.cats1)
original_cats = copy.deepcopy(test_cats)
result = ancom(test_table,
test_cats,
multiple_comparisons_correction=None)
# Test to make sure that the input table hasn't be altered
assert_data_frame_almost_equal(original_table, test_table)
# Test to make sure that the input table hasn't be altered
pdt.assert_series_equal(original_cats, test_cats)
exp = pd.DataFrame({
'W':
np.array([5, 5, 2, 2, 2, 2, 2]),
'reject':
np.array([True, True, False, False, False, False, False],
dtype=bool)
})
assert_data_frame_almost_equal(result, exp)
def test_permutative_f(self):
test_table = pd.DataFrame(self.table1)
original_table = copy.deepcopy(test_table)
test_cats = pd.Series(self.cats1)
original_cats = copy.deepcopy(test_cats)
result = ancom(test_table,
test_cats,
significance_test='permutative-anova')
# Test to make sure that the input table hasn't be altered
assert_data_frame_almost_equal(original_table, test_table)
# Test to make sure that the input table hasn't be altered
pdt.assert_series_equal(original_cats, test_cats)
exp = pd.DataFrame({
'W':
np.array([6, 6, 3, 3, 2, 2, 2]),
'reject':
np.array([True, True, False, False, False, False, False],
dtype=bool)
})
assert_data_frame_almost_equal(result, exp)
def test_ancom_fail_size_mismatch(self):
with self.assertRaises(ValueError):
ancom(self.table1, self.badcats3)
def test_ancom_fail_missing(self):
with self.assertRaises(ValueError):
ancom(self.bad3, self.cats1)
with self.assertRaises(ValueError):
ancom(self.table1, self.badcats1)
def test_ancom_fail_not_implemented_multiple_comparisons_correction(self):
with self.assertRaises(ValueError):
ancom(self.table2,
self.cats2,
multiple_comparisons_correction='fdr')
def test_ancom_fail_negative(self):
with self.assertRaises(ValueError):
ancom(self.bad2, self.cats2, multiple_comparisons_correction=None)
def test_ancom_fail_type(self):
with self.assertRaises(TypeError):
ancom(self.table1.values, self.cats1)
with self.assertRaises(TypeError):
ancom(self.table1, self.cats1.values)
def test_ancom_fail_multiple_groups(self):
with self.assertRaises(TypeError):
ancom(self.table4, self.cats4,
significance_test=scipy.stats.ttest_ind)
def test_ancom_fail_alpha(self):
with self.assertRaises(ValueError):
ancom(self.table1, self.cats1, alpha=-1)
with self.assertRaises(ValueError):
ancom(self.table1, self.cats1, alpha=1.1)
def test_ancom_fail_not_implemented_multiple_comparisons_correction(self):
with self.assertRaises(ValueError):
ancom(self.table2, self.cats2,
multiple_comparisons_correction='fdr')
def test_ancom_fail_missing(self):
with self.assertRaises(ValueError):
ancom(self.bad3, self.cats1)
with self.assertRaises(ValueError):
ancom(self.table1, self.badcats1)
def test_ancom_fail_size_mismatch(self):
with self.assertRaises(ValueError):
ancom(self.table1, self.badcats3)
def test_ancom_fail_1_group(self):
with self.assertRaises(ValueError):
ancom(self.table1, self.badcats5)
def test_ancom_fail_tau(self):
with self.assertRaises(ValueError):
ancom(self.table1, self.cats1, tau=-1)
with self.assertRaises(ValueError):
ancom(self.table1, self.cats1, tau=1.1)
def test_ancom_fail_alpha(self):
with self.assertRaises(ValueError):
ancom(self.table1, self.cats1, alpha=-1)
with self.assertRaises(ValueError):
ancom(self.table1, self.cats1, alpha=1.1)
def test_ancom_fail_1_group(self):
with self.assertRaises(ValueError):
ancom(self.table1, self.badcats5)
def test_ancom_fail_tau(self):
with self.assertRaises(ValueError):
ancom(self.table1, self.cats1, tau=-1)
with self.assertRaises(ValueError):
ancom(self.table1, self.cats1, tau=1.1)
def test_ancom_fail_negative(self):
with self.assertRaises(ValueError):
ancom(self.bad2, self.cats2, multiple_comparisons_correction=None)
def test_ancom_fail_multiple_groups(self):
with self.assertRaises(TypeError):
ancom(self.table4,
self.cats4,
significance_test=scipy.stats.ttest_ind)
def test_ancom_fail_type(self):
with self.assertRaises(TypeError):
ancom(self.table1.values, self.cats1)
with self.assertRaises(TypeError):
ancom(self.table1, self.cats1.values)
