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_tau(self):
exp1 = pd.DataFrame(
{'W': np.array([8, 7, 3, 3, 7, 3, 3, 3, 3]),
'Reject null hypothesis': 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 null hypothesis': 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 null hypothesis': 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,
multiple_comparisons_correction=None, tau=0.25)
result2 = ancom(self.table9, self.cats9,
multiple_comparisons_correction=None, tau=0.02)
result3 = ancom(self.table10, self.cats10,
multiple_comparisons_correction=None, tau=0.02)
assert_data_frame_almost_equal(result1[0], exp1)
assert_data_frame_almost_equal(result2[0], exp2)
assert_data_frame_almost_equal(result3[0], exp3)
def test_ancom_tau(self):
exp1 = pd.DataFrame(
{'W': np.array([8, 7, 3, 3, 7, 3, 3, 3, 3]),
'Reject null hypothesis': 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 null hypothesis': 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 null hypothesis': 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,
multiple_comparisons_correction=None, tau=0.25)
result2 = ancom(self.table9, self.cats9,
multiple_comparisons_correction=None, tau=0.02)
result3 = ancom(self.table10, self.cats10,
multiple_comparisons_correction=None, tau=0.02)
assert_data_frame_almost_equal(result1[0], exp1)
assert_data_frame_almost_equal(result2[0], exp2)
assert_data_frame_almost_equal(result3[0], exp3)
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_duplicate_percentiles(self):
table = pd.DataFrame([[12], [9], [1], [22], [20], [23]],
index=['s1', 's2', 's3', 's4', 's5', 's6'],
columns=['b1'])
grouping = pd.Series(['a', 'a', 'a', 'b', 'b', 'b'],
index=['s1', 's2', 's3', 's4', 's5', 's6'])
with self.assertRaises(ValueError):
ancom(table, grouping, percentiles=[10.0, 10.0])
def test_ancom_percentile_order_unimportant(self):
table = pd.DataFrame([[12], [9], [1], [22], [20], [23]],
index=['s1', 's2', 's3', 's4', 's5', 's6'],
columns=['b1'])
grouping = pd.Series(['a', 'a', 'a', 'b', 'b', 'b'],
index=['s1', 's2', 's3', 's4', 's5', 's6'])
# order of percentiles in unimportant after sorting
result1 = ancom(table, grouping, percentiles=[50.0, 42.0])[1]
result2 = ancom(table, grouping, percentiles=[42.0, 50.0])[1]
assert_data_frame_almost_equal(result1.sort_index(axis=1),
result2.sort_index(axis=1))
def test_ancom_duplicate_percentiles(self):
table = pd.DataFrame([[12],
[9],
[1],
[22],
[20],
[23]],
index=['s1', 's2', 's3', 's4', 's5', 's6'],
columns=['b1'])
grouping = pd.Series(['a', 'a', 'a', 'b', 'b', 'b'],
index=['s1', 's2', 's3', 's4', 's5', 's6'])
with self.assertRaises(ValueError):
ancom(table, grouping, percentiles=[10.0, 10.0])
def fit_model(
self,
alpha: float = 0.05,
tau: float = 0.02,
*args,
**kwargs,
):
"""
Parameters
----------
alpha
FDR level for multiplicity correction
tau
cutoff parameter
args
passed to skbio.stats.composition.ancom
kwargs
passed to skbio.stats.composition.ancom
Returns
-------
"""
K = self.y.shape[1]
if self.y.shape[0] == 2:
ancom_out = [False for _ in range(K)]
else:
ancom_out = ancom(self.y, self.x, alpha=alpha, tau=tau, *args, **kwargs)
self.ancom_out = ancom_out
def test_ancom_percentiles(self):
table = pd.DataFrame([[12, 11],
[9, 11],
[1, 11],
[22, 100],
[20, 53],
[23, 1]],
index=['s1', 's2', 's3', 's4', 's5', 's6'],
columns=['b1', 'b2'])
grouping = pd.Series(['a', 'a', 'a', 'b', 'b', 'b'],
index=['s1', 's2', 's3', 's4', 's5', 's6'])
percentiles = [0.0, 25.0, 50.0, 75.0, 100.0]
groups = ['a', 'b']
tuples = [(p, g) for g in groups for p in percentiles]
exp_mi = pd.MultiIndex.from_tuples(tuples,
names=['Percentile', 'Group'])
exp_data = np.array(
[[1.0, 11.0], [5.0, 11.0], [9.0, 11.0], [10.5, 11.0], [12.0, 11.0],
[20.0, 1.0], [21.0, 27.0], [22.0, 53.0], [22.5, 76.5],
[23.0, 100.0]])
exp = pd.DataFrame(exp_data.T, columns=exp_mi, index=['b1', 'b2'])
result = ancom(table, grouping)[1]
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_percentiles_alt_categories(self):
table = pd.DataFrame([[12], [9], [1], [22], [20], [23]],
index=['s1', 's2', 's3', 's4', 's5', 's6'],
columns=['b1'])
grouping = pd.Series(['a', 'a', 'c', 'b', 'b', 'c'],
index=['s1', 's2', 's3', 's4', 's5', 's6'])
percentiles = [0.0, 25.0, 50.0, 75.0, 100.0]
groups = ['a', 'b', 'c']
tuples = [(p, g) for g in groups for p in percentiles]
exp_mi = pd.MultiIndex.from_tuples(tuples,
names=['Percentile', 'Group'])
exp_data = np.array([
[9.0],
[9.75],
[10.5],
[11.25],
[12.0], # a
[20.0],
[20.5],
[21.0],
[21.5],
[22.0], # b
[1.0],
[6.5],
[12.0],
[17.5],
[23.0]
]) # c
exp = pd.DataFrame(exp_data.T, columns=exp_mi, index=['b1'])
result = ancom(table, grouping, percentiles=percentiles)[1]
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_percentile_order_unimportant(self):
table = pd.DataFrame([[12],
[9],
[1],
[22],
[20],
[23]],
index=['s1', 's2', 's3', 's4', 's5', 's6'],
columns=['b1'])
grouping = pd.Series(['a', 'a', 'a', 'b', 'b', 'b'],
index=['s1', 's2', 's3', 's4', 's5', 's6'])
# order of percentiles in unimportant after sorting
result1 = ancom(table, grouping, percentiles=[50.0, 42.0])[1]
result2 = ancom(table, grouping, percentiles=[42.0, 50.0])[1]
assert_data_frame_almost_equal(
result1.sort_index(axis=1), result2.sort_index(axis=1))
def test_ancom_percentiles(self):
table = pd.DataFrame([[12, 11],
[9, 11],
[1, 11],
[22, 100],
[20, 53],
[23, 1]],
index=['s1', 's2', 's3', 's4', 's5', 's6'],
columns=['b1', 'b2'])
grouping = pd.Series(['a', 'a', 'a', 'b', 'b', 'b'],
index=['s1', 's2', 's3', 's4', 's5', 's6'])
percentiles = [0.0, 25.0, 50.0, 75.0, 100.0]
groups = ['a', 'b']
tuples = [(p, g) for g in groups for p in percentiles]
exp_mi = pd.MultiIndex.from_tuples(tuples,
names=['Percentile', 'Group'])
exp_data = np.array(
[[1.0, 11.0], [5.0, 11.0], [9.0, 11.0], [10.5, 11.0], [12.0, 11.0],
[20.0, 1.0], [21.0, 27.0], [22.0, 53.0], [22.5, 76.5],
[23.0, 100.0]])
exp = pd.DataFrame(exp_data.T, columns=exp_mi, index=['b1', 'b2'])
result = ancom(table, grouping)[1]
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_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_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_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_theta(self):
result = ancom(self.table1, self.cats1, theta=0.3)
exp = pd.DataFrame(
{'W': np.array([5, 5, 2, 2, 2, 2, 2]),
'Reject null hypothesis': np.array([True, True, False, False,
False, False, False],
dtype=bool)})
assert_data_frame_almost_equal(result[0], exp)
def test_ancom_theta(self):
result = ancom(self.table1, self.cats1, theta=0.3)
exp = pd.DataFrame(
{'W': np.array([5, 5, 2, 2, 2, 2, 2]),
'Reject null hypothesis': np.array([True, True, False, False,
False, False, False],
dtype=bool)})
assert_data_frame_almost_equal(result[0], exp)
def test_ancom_no_percentiles(self):
table = pd.DataFrame([[12], [9], [1], [22], [20], [23]],
index=['s1', 's2', 's3', 's4', 's5', 's6'],
columns=['b1'])
grouping = pd.Series(['a', 'a', 'a', 'b', 'b', 'b'],
index=['s1', 's2', 's3', 's4', 's5', 's6'])
result = ancom(table, grouping, percentiles=[])[1]
assert_data_frame_almost_equal(result, pd.DataFrame())
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_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_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_alpha(self):
result = ancom(self.table1, self.cats1,
multiple_comparisons_correction=None, alpha=0.5)
exp = pd.DataFrame(
{'W': np.array([6, 6, 4, 5, 5, 4, 2]),
'Reject null hypothesis': np.array([True, True, False, True,
True, False, False],
dtype=bool)})
assert_data_frame_almost_equal(result[0], 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_alpha(self):
result = ancom(self.table1, self.cats1,
multiple_comparisons_correction=None, alpha=0.5)
exp = pd.DataFrame(
{'W': np.array([6, 6, 4, 5, 5, 4, 2]),
'Reject null hypothesis': np.array([True, True, False, True,
True, False, False],
dtype=bool)})
assert_data_frame_almost_equal(result[0], exp)
def test_ancom_unbalanced(self):
result = ancom(self.table6,
self.cats6,
multiple_comparisons_correction=None)
exp = pd.DataFrame(
{'W': np.array([5, 3, 3, 2, 2, 5, 2]),
'Reject null hypothesis': np.array([True, False, False, False,
False, True, False],
dtype=bool)})
assert_data_frame_almost_equal(result[0], exp)
def test_ancom_unbalanced(self):
result = ancom(self.table6,
self.cats6,
multiple_comparisons_correction=None)
exp = pd.DataFrame(
{'W': np.array([5, 3, 3, 2, 2, 5, 2]),
'Reject null hypothesis': np.array([True, False, False, False,
False, True, False],
dtype=bool)})
assert_data_frame_almost_equal(result[0], 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 ancom_cmd(table_file, metadata_file, category, output_file):
metadata = pd.read_table(metadata_file, index_col=0)
table = load_table(table_file)
table = pd.DataFrame(np.array(table.matrix_data.todense()).T,
index=table.ids(axis='sample'),
columns=table.ids(axis='observation'))
res, _ = ancom(table + 1, grouping=metadata[category])
with open(output_file, 'w') as f:
r = res["Reject null hypothesis"]
f.write(','.join(res.loc[r].index.values))
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_multiple_comparisons(self):
significance_test = functools.partial(scipy.stats.mannwhitneyu,
alternative='two-sided')
result = ancom(self.table1,
self.cats1,
multiple_comparisons_correction='holm-bonferroni',
significance_test=significance_test)
exp = pd.DataFrame(
{'W': np.array([0]*7),
'Reject null hypothesis': np.array([False]*7, dtype=bool)})
assert_data_frame_almost_equal(result[0], 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_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 null hypothesis': np.array([True, True, False, False,
True, False, False,
False, False],
dtype=bool)})
assert_data_frame_almost_equal(result[0], exp)
def test_ancom_no_percentiles(self):
table = pd.DataFrame([[12],
[9],
[1],
[22],
[20],
[23]],
index=['s1', 's2', 's3', 's4', 's5', 's6'],
columns=['b1'])
grouping = pd.Series(['a', 'a', 'a', 'b', 'b', 'b'],
index=['s1', 's2', 's3', 's4', 's5', 's6'])
result = ancom(table, grouping, percentiles=[])[1]
assert_data_frame_almost_equal(result, pd.DataFrame())
def test_ancom_multiple_comparisons(self):
significance_test = functools.partial(scipy.stats.mannwhitneyu,
alternative='two-sided')
result = ancom(self.table1,
self.cats1,
multiple_comparisons_correction='holm-bonferroni',
significance_test=significance_test)
exp = pd.DataFrame({
'W':
np.array([0] * 7),
'Reject null hypothesis':
np.array([False] * 7, dtype=bool)
})
assert_data_frame_almost_equal(result[0], 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_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_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_percentiles_iterator(self):
table = pd.DataFrame([[12], [9], [1], [22], [20], [23]],
index=['s1', 's2', 's3', 's4', 's5', 's6'],
columns=['b1'])
grouping = pd.Series(['a', 'a', 'a', 'b', 'b', 'b'],
index=['s1', 's2', 's3', 's4', 's5', 's6'])
percentiles = [42.0, 50.0]
groups = ['a', 'b']
tuples = [(p, g) for g in groups for p in percentiles]
exp_mi = pd.MultiIndex.from_tuples(tuples,
names=['Percentile', 'Group'])
exp_data = np.array([
[7.71999999],
[9.0], # a
[21.68],
[22.0]
]) # b
exp = pd.DataFrame(exp_data.T, columns=exp_mi, index=['b1'])
result = ancom(table, grouping, percentiles=iter(percentiles))[1]
assert_data_frame_almost_equal(result, exp)
def test_ancom_percentiles_iterator(self):
table = pd.DataFrame([[12],
[9],
[1],
[22],
[20],
[23]],
index=['s1', 's2', 's3', 's4', 's5', 's6'],
columns=['b1'])
grouping = pd.Series(['a', 'a', 'a', 'b', 'b', 'b'],
index=['s1', 's2', 's3', 's4', 's5', 's6'])
percentiles = [42.0, 50.0]
groups = ['a', 'b']
tuples = [(p, g) for g in groups for p in percentiles]
exp_mi = pd.MultiIndex.from_tuples(tuples,
names=['Percentile', 'Group'])
exp_data = np.array([[7.71999999], [9.0], # a
[21.68], [22.0]]) # b
exp = pd.DataFrame(exp_data.T, columns=exp_mi, index=['b1'])
result = ancom(table, grouping, percentiles=iter(percentiles))[1]
assert_data_frame_almost_equal(result, exp)
def test_ancom_percentiles_alt_categories(self):
table = pd.DataFrame([[12],
[9],
[1],
[22],
[20],
[23]],
index=['s1', 's2', 's3', 's4', 's5', 's6'],
columns=['b1'])
grouping = pd.Series(['a', 'a', 'c', 'b', 'b', 'c'],
index=['s1', 's2', 's3', 's4', 's5', 's6'])
percentiles = [0.0, 25.0, 50.0, 75.0, 100.0]
groups = ['a', 'b', 'c']
tuples = [(p, g) for g in groups for p in percentiles]
exp_mi = pd.MultiIndex.from_tuples(tuples,
names=['Percentile', 'Group'])
exp_data = np.array([[9.0], [9.75], [10.5], [11.25], [12.0], # a
[20.0], [20.5], [21.0], [21.5], [22.0], # b
[1.0], [6.5], [12.0], [17.5], [23.0]]) # c
exp = pd.DataFrame(exp_data.T, columns=exp_mi, index=['b1'])
result = ancom(table, grouping, percentiles=percentiles)[1]
assert_data_frame_almost_equal(result, exp)
def test_ancom_fail_theta(self):
with self.assertRaises(ValueError):
ancom(self.table1, self.cats1, theta=-1)
with self.assertRaises(ValueError):
ancom(self.table1, self.cats1, theta=1.1)
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_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_negative(self):
with self.assertRaises(ValueError):
ancom(self.bad2, self.cats2, multiple_comparisons_correction=None)
def test_ancom_fail_negative(self):
with self.assertRaises(ValueError):
ancom(self.bad2, self.cats2, multiple_comparisons_correction=None)
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_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_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)
