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# ----------------------------------------------------------------------------
# Copyright (c) 2016-2022, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ----------------------------------------------------------------------------
import unittest
import skbio
import numpy as np
from biom.table import Table
import pandas as pd
import pandas.testing as pdt
from q2_feature_table import merge, merge_seqs, merge_taxa
from q2_feature_table._merge import _merge_feature_data, _get_overlapping
class MergeTableTests(unittest.TestCase):
def test_single_table(self):
t = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
obs = merge([t])
self.assertEqual(t, obs)
def test_valid_overlapping_feature_ids(self):
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]),
['O1', 'O3'],
['S4', 'S5', 'S6'])
obs = merge([t1, t2])
exp = Table(np.array([[0, 1, 3, 0, 2, 6], [1, 1, 2, 0, 0, 0],
[0, 0, 0, 2, 2, 4]]),
['O1', 'O2', 'O3'],
['S1', 'S2', 'S3', 'S4', 'S5', 'S6'])
self.assertEqual(obs, exp)
def test_valid_non_overlapping_feature_ids(self):
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]),
['O3', 'O4'],
['S4', 'S5', 'S6'])
obs = merge([t1, t2])
exp = Table(np.array([[0, 1, 3, 0, 0, 0], [1, 1, 2, 0, 0, 0],
[0, 0, 0, 0, 2, 6], [0, 0, 0, 2, 2, 4]]),
['O1', 'O2', 'O3', 'O4'],
['S1', 'S2', 'S3', 'S4', 'S5', 'S6'])
self.assertEqual(obs, exp)
def test_invalid_overlapping_feature_ids(self):
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]),
['O1', 'O3'],
['S4', 'S5', 'S6'])
with self.assertRaisesRegex(ValueError, 'features are present'):
merge([t1, t2], 'error_on_overlapping_feature')
def test_valid_overlapping_sample_ids(self):
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]),
['O3', 'O4'],
['S1', 'S5', 'S6'])
obs = merge([t1, t2], 'error_on_overlapping_feature')
exp = Table(np.array([[0, 1, 3, 0, 0], [1, 1, 2, 0, 0],
[0, 0, 0, 2, 6], [2, 0, 0, 2, 4]]),
['O1', 'O2', 'O3', 'O4'],
['S1', 'S2', 'S3', 'S5', 'S6'])
self.assertEqual(obs, exp)
def test_invalid_overlapping_sample_ids(self):
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]),
['O1', 'O3'],
['S1', 'S5', 'S6'])
with self.assertRaisesRegex(ValueError, 'samples.*S1'):
merge([t1, t2])
def test_invalid_overlap_method(self):
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]),
['O1', 'O3'],
['S1', 'S5', 'S6'])
with self.assertRaisesRegex(ValueError, 'overlap method'):
merge([t1, t2], 'peanut')
def test_sum_full_overlap(self):
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
obs = merge([t1, t2], 'sum')
exp = Table(np.array([[0, 3, 9], [3, 3, 6]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(obs, exp)
def test_sum_triple_overlap(self):
t1 = Table(np.array([[1, 1, 1], [1, 1, 1]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
obs = merge([t1] * 3, 'sum')
exp = Table(np.array([[3, 3, 3], [3, 3, 3]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(obs, exp)
def test_sum_some_overlap(self):
# Did I stutter?
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]),
['O1', 'O3'],
['S4', 'S2', 'S5'])
obs = merge([t1, t2], 'sum')
exp = Table(np.array([[0, 3, 3, 0, 6], [1, 1, 2, 0, 0],
[0, 2, 0, 2, 4]]),
['O1', 'O2', 'O3'],
['S1', 'S2', 'S3', 'S4', 'S5'])
self.assertEqual(obs, exp)
def test_sum_overlapping_sample_ids(self):
# This should produce the same result as `error_on_overlapping_feature`
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]),
['O3', 'O4'],
['S1', 'S5', 'S6'])
obs = merge([t1, t2], 'sum')
exp = Table(np.array([[0, 1, 3, 0, 0], [1, 1, 2, 0, 0],
[0, 0, 0, 2, 6], [2, 0, 0, 2, 4]]),
['O1', 'O2', 'O3', 'O4'],
['S1', 'S2', 'S3', 'S5', 'S6'])
self.assertEqual(obs, exp)
def test_sum_overlapping_feature_ids(self):
# This should produce the same result as `error_on_overlapping_sample`
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]),
['O1', 'O3'],
['S4', 'S5', 'S6'])
obs = merge([t1, t2], 'sum')
exp = Table(np.array([[0, 1, 3, 0, 2, 6], [1, 1, 2, 0, 0, 0],
[0, 0, 0, 2, 2, 4]]),
['O1', 'O2', 'O3'],
['S1', 'S2', 'S3', 'S4', 'S5', 'S6'])
self.assertEqual(obs, exp)
def test_average(self):
t1 = Table(np.array([[1, 1, 1], [1, 1, 1]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
obs = merge([t1] * 3, 'average')
exp = Table(np.array([[1, 1, 1], [1, 1, 1]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(obs, exp)
def test_average_relative_frequency(self):
t1 = Table(np.array([[0.75, 0.75, 0.75], [0.75, 0.75, 0.75]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
t2 = Table(np.array([[0.25, 0.25, 0.25], [0.25, 0.25, 0.25]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
obs = merge([t1, t2], 'average')
exp = Table(np.array([[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(obs, exp)
class UtilTests(unittest.TestCase):
def test_get_overlapping(self):
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'], ['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]),
['O1', 'O3'], ['S1', 'S5', 'S6'])
# samples
obs = _get_overlapping([t1, t2], 'sample')
self.assertEqual(set(['S1']), obs)
# features
obs = _get_overlapping([t1, t2], 'observation')
self.assertEqual(set(['O1']), obs)
def test_get_overlapping_no_overlap(self):
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'], ['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]),
['O3', 'O4'], ['S4', 'S5', 'S6'])
# samples
obs = _get_overlapping([t1, t2], 'sample')
self.assertEqual(set(), obs)
# features
obs = _get_overlapping([t1, t2], 'observation')
self.assertEqual(set(), obs)
def test_get_overlapping_multiple(self):
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'], ['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]),
['O1', 'O3'], ['S1', 'S5', 'S6'])
t3 = Table(np.array([[3, 3, 1], [0, 2, 1]]),
['O1', 'O2'], ['S1', 'S3', 'S6'])
# samples
obs = _get_overlapping([t1, t2, t3], 'sample')
self.assertEqual({'S1', 'S3', 'S6'}, obs)
# features
obs = _get_overlapping([t1, t2, t3], 'observation')
self.assertEqual({'O1', 'O2'}, obs)
class MergeFeatureDataTests(unittest.TestCase):
def test_merge_single(self):
d = pd.Series(['ACGT', 'ACCT'], index=['f1', 'f2'])
obs = _merge_feature_data([d])
pdt.assert_series_equal(obs, d)
def test_valid_overlapping_feature_ids(self):
d1 = pd.Series(['ACGT', 'ACCT'], index=['f1', 'f2'])
d2 = pd.Series(['ACGT', 'ACCA'], index=['f1', 'f3'])
obs = _merge_feature_data([d1, d2])
exp = pd.Series(['ACGT', 'ACCT', 'ACCA'], index=['f1', 'f2', 'f3'])
pdt.assert_series_equal(obs, exp)
def test_first_feature_data_retained(self):
d1 = pd.Series(['ACGT', 'ACCT'], index=['f1', 'f2'])
d2 = pd.Series(['ACGAAA', 'ACCA'], index=['f1', 'f3'])
obs = _merge_feature_data([d1, d2])
exp = pd.Series(['ACGT', 'ACCT', 'ACCA'], index=['f1', 'f2', 'f3'])
pdt.assert_series_equal(obs, exp)
# swapping input order changes f1 data
obs = _merge_feature_data([d2, d1])
exp = pd.Series(['ACGAAA', 'ACCT', 'ACCA'], index=['f1', 'f2', 'f3'])
pdt.assert_series_equal(obs, exp)
def test_multiple_overlapping_feature_ids_order_maintained(self):
d1 = pd.Series(['ACGT', 'ACCT'], index=['f1', 'f2'])
d2 = pd.Series(['ACGAAA', 'ACCA'], index=['f1', 'f3'])
d3 = pd.Series(['AGGA', 'ATAT'], index=['f3', 'f4'])
obs = _merge_feature_data([d1, d2, d3])
exp = pd.Series(['ACGT', 'ACCT', 'ACCA', 'ATAT'],
index=['f1', 'f2', 'f3', 'f4'])
pdt.assert_series_equal(obs, exp)
# swapping input order changes f1 and f3
obs = _merge_feature_data([d3, d2, d1])
exp = pd.Series(['ACGAAA', 'ACCT', 'AGGA', 'ATAT'],
index=['f1', 'f2', 'f3', 'f4'])
pdt.assert_series_equal(obs, exp)
def test_valid_non_overlapping_feature_ids(self):
d1 = pd.Series(['ACGT', 'ACCT'], index=['f1', 'f2'])
d2 = pd.Series(['ACGT', 'ACCA'], index=['f3', 'f4'])
obs = _merge_feature_data([d1, d2])
exp = pd.Series(['ACGT', 'ACCT', 'ACGT', 'ACCA'],
index=['f1', 'f2', 'f3', 'f4'])
pdt.assert_series_equal(obs, exp)
class MergeFeatureSequenceTests(unittest.TestCase):
# More extensive testing is performed in MergeFeatureDataTests, which
# tests the shared private API.
def test_merge_seqs(self):
d1 = pd.Series([skbio.DNA('ACGT', metadata={'id': 'abc'}),
skbio.DNA('ACCT', metadata={'id': 'xyz'})],
index=['f1', 'f2'])
d2 = pd.Series([skbio.DNA('ACGT', metadata={'id': 'abc'}),
skbio.DNA('ACCA', metadata={'id': 'wxy'})],
index=['f1', 'f3'])
obs = merge_seqs([d1, d2])
exp = pd.Series([skbio.DNA('ACGT', metadata={'id': 'abc'}),
skbio.DNA('ACCT', metadata={'id': 'xyz'}),
skbio.DNA('ACCA', metadata={'id': 'wxy'})],
index=['f1', 'f2', 'f3'])
pdt.assert_series_equal(obs, exp)
class MergeFeatureTaxonomyTests(unittest.TestCase):
# More extensive testing is performed in MergeFeatureDataTests, which
# tests the shared private API.
# This tests a specifically FeatureData[Taxonomy]-like dataframe
# and ensures delivery in valid format (Taxon column first)
def test_merge_taxa(self):
# this test calls the public API directly
d1 = pd.DataFrame([('a;b;c;d', '1.0'), ('a;b;c;f', '0.7')],
index=['f1', 'f2'], columns=['Taxon', 'Confidence'])
d2 = pd.DataFrame([('1.0', 'a;b;c;g'), ('1.0', 'a;b;c;e')],
index=['f1', 'f3'], columns=['Confidence', 'Taxon'])
obs = merge_taxa([d1, d2])
exp = pd.DataFrame(
[('a;b;c;d', '1.0'), ('a;b;c;f', '0.7'), ('a;b;c;e', '1.0')],
index=['f1', 'f2', 'f3'], columns=['Taxon', 'Confidence'])
pdt.assert_frame_equal(obs, exp)
if __name__ == "__main__":
unittest.main()
|
