def test_ranks_specific(self):
exp_1 = pd.DataFrame([['c', 'sample-1', 1.], ['g', 'sample-1', 2]],
columns=['Taxon', 'Sample ID', 'Rank'])
exp_2 = pd.DataFrame([['c', 'sample-2', 1.]],
columns=['Taxon', 'Sample ID', 'Rank'])
exp_3 = pd.DataFrame([['c', 'sample-3', 2.], ['g', 'sample-3', 1]],
columns=['Taxon', 'Sample ID', 'Rank'])
taxonomy = Taxonomy(self.table, self.taxonomy_df, rank_level=2)
obs_1 = taxonomy.ranks_specific('sample-1')
obs_2 = taxonomy.ranks_specific('sample-2')
obs_3 = taxonomy.ranks_specific('sample-3')
self._clean_sort_df(obs_1, ['Taxon', 'Sample ID'])
self._clean_sort_df(obs_2, ['Taxon', 'Sample ID'])
self._clean_sort_df(obs_3, ['Taxon', 'Sample ID'])
self._clean_sort_df(exp_1, ['Taxon', 'Sample ID'])
self._clean_sort_df(exp_2, ['Taxon', 'Sample ID'])
self._clean_sort_df(exp_3, ['Taxon', 'Sample ID'])
pdt.assert_frame_equal(obs_1, exp_1, check_like=True)
pdt.assert_frame_equal(obs_2, exp_2, check_like=True)
pdt.assert_frame_equal(obs_3, exp_3, check_like=True)
def test_init_disjoint(self):
with self.assertRaisesRegex(DisjointError,
"Table and features are disjoint"):
Taxonomy(self.table, self.taxonomy2_df)
with self.assertRaisesRegex(DisjointError,
"Table and features are disjoint"):
Taxonomy(self.table2, self.taxonomy_df)
def test_get_group(self):
taxonomy = Taxonomy(self.table, self.taxonomy_df)
exp = GroupTaxonomy(name='sample-2',
taxonomy='((((feature-1,((feature-2)e)d)c)b)a);',
features=['feature-1', 'feature-2'],
feature_values=[1. / 5, 4. / 5],
feature_variances=[0.0, 0.0])
obs = taxonomy.get_group(['sample-2'])
self.assertEqual(obs, exp)
def test_get_group_with_variances(self):
taxonomy = Taxonomy(self.table, self.taxonomy_df, self.table_vars)
exp = GroupTaxonomy(
name='sample-1',
taxonomy='((((((feature-2)e)d)c)b,(((feature-3)h)g)f)a);', # noqa
features=['feature-2', 'feature-3'],
feature_values=[2. / 5, 3. / 5],
feature_variances=[2.0, 3.0])
obs = taxonomy.get_group(['sample-1'])
self.assertEqual(obs, exp)
def test_qza_integration(self):
table_qza = Artifact.import_data("FeatureTable[Frequency]", self.table)
taxonomy_qza = Artifact.import_data(
"FeatureData[Taxonomy]",
self.taxonomy_df,
)
table = table_qza.view(biom.Table)
taxonomy_df = taxonomy_qza.view(pd.DataFrame)
taxonomy = Taxonomy(table, taxonomy_df)
taxonomy.get_group(['sample-1', 'sample-2'], 'foo')
def test_get_group_multiple(self):
taxonomy = Taxonomy(self.table, self.taxonomy_df)
exp = GroupTaxonomy(
name='foo',
taxonomy=
'((((feature-1,((feature-2)e)d)c)b,(((feature-3)h)g)f)a);', # noqa
features=['feature-1', 'feature-2', 'feature-3'],
feature_values=[1. / 10, 6. / 10, 3. / 10],
feature_variances=[0.0, 0.0, 0.0])
obs = taxonomy.get_group(['sample-1', 'sample-2'], 'foo')
self.assertEqual(obs.name, exp.name)
self.assertEqual(obs.taxonomy, exp.taxonomy)
self.assertEqual(obs.features, exp.features)
npt.assert_almost_equal(obs.feature_values, exp.feature_values)
self.assertEqual(obs.feature_variances, exp.feature_variances)
def test_init_disjoint_variances(self):
bad = self.table_vars.copy()
bad.update_ids({'sample-1': 'sample-bad'}, inplace=True, strict=False)
with self.assertRaisesRegex(DisjointError,
"Table and variances are disjoint"):
Taxonomy(self.table, self.taxonomy_df, bad)
def test_bp_tree(self):
taxonomy = Taxonomy(self.table, self.taxonomy_df)
bp_tree = taxonomy.bp_tree
exp_parens = [
1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0,
0, 0
]
obs_parens = list(bp_tree.B)
self.assertListEqual(exp_parens, obs_parens)
exp_names = [
'a',
'b',
'c',
'feature-1',
'd',
'e',
'feature-2',
'f',
'g',
'h',
'feature-3',
]
obs_names = []
for i in range(len(bp_tree.B)):
name = bp_tree.name(i)
if name is not None:
obs_names.append(name)
self.assertListEqual(exp_names, obs_names)
def test_ranks_order(self):
taxonomy = Taxonomy(self.table, self.taxonomy_df, rank_level=2)
exp = ['c', 'g']
obs = taxonomy.ranks_order()
self.assertEqual(obs, exp)
exp = ['c', 'g']
obs = taxonomy.ranks_order(['g', 'c'])
self.assertEqual(obs, exp)
exp = ['c']
obs = taxonomy.ranks_order([
'c',
])
self.assertEqual(obs, exp)
def test_rare_unique(self):
# feature 1 is "rare" for samples 2 and 3 at a theshold of <= 50%
# feature 3 is "unique" to sample 1
table = biom.Table(
np.array([[0, 1, 2, 0], [2, 4, 6, 1],
[3, 0, 0, 0]]), ['feature-1', 'feature-2', 'feature-3'],
['sample-1', 'sample-2', 'sample-3', 'sample-4'])
taxonomy_df = pd.DataFrame(
[['feature-1', 'a; b; c', 0.123],
['feature-2', 'a; b; c; d; e', 0.345],
['feature-3', 'a; f; g; h', 0.678]],
columns=['Feature ID', 'Taxon', 'Confidence'])
taxonomy_df.set_index('Feature ID', inplace=True)
tax = Taxonomy(table, taxonomy_df)
exp = {
'sample-1': {
'rare': {
'feature-3': 0.25
},
'unique': [
'feature-3',
]
},
'sample-2': {
'rare': {
'feature-1': 0.5
},
'unique': None
},
'sample-3': {
'rare': {
'feature-1': 0.5
},
'unique': None
},
'sample-4': {
'rare': None,
'unique': None
}
}
for k, e in exp.items():
obs = tax.rare_unique(k, rare_threshold=0.51)
self.assertEqual(obs, e)
def test_init_variances(self):
taxonomy = Taxonomy(self.table, self.taxonomy_df, self.table_vars)
self.assertEqual(taxonomy._table, self.table.copy().norm())
self.assertEqual(taxonomy._variances, self.table_vars)
pdt.assert_frame_equal(taxonomy._features, self.taxonomy_df)
self.assertEqual(list(taxonomy._table.ids(axis='observation')),
list(taxonomy._features.index))
self.assertEqual(list(taxonomy._table.ids(axis='observation')),
list(taxonomy._variances.ids(axis='observation')))
def test_ranks_sample(self):
exp = pd.DataFrame(
[['c', 'sample-1', 1.], ['c', 'sample-2', 1], ['c', 'sample-3', 2],
['g', 'sample-1', 2], ['g', 'sample-3', 1]],
columns=['Taxon', 'Sample ID', 'Rank'])
taxonomy = Taxonomy(self.table, self.taxonomy_df, rank_level=2)
obs = taxonomy.ranks_sample(5)
self._clean_sort_df(obs, ['Taxon', 'Sample ID'])
self._clean_sort_df(exp, ['Taxon', 'Sample ID'])
pdt.assert_frame_equal(obs, exp, check_like=True)
obs = taxonomy.ranks_sample(4)
self.assertIn(sorted(obs['Taxon'].values),
[['c', 'c', 'c', 'g'], ['c', 'c', 'g', 'g']])
obs = taxonomy.ranks_sample(100)
self.assertEqual(sorted(obs['Taxon'].values),
['c', 'c', 'c', 'g', 'g'])
def test_init_rankdata(self):
exp = pd.DataFrame(
[['c', 'sample-1', 1.], ['c', 'sample-2', 1], ['c', 'sample-3', 2],
['g', 'sample-1', 2], ['g', 'sample-3', 1]],
columns=['Taxon', 'Sample ID', 'Rank'])
taxonomy = Taxonomy(self.table, self.taxonomy_df, rank_level=2)
obs = taxonomy._ranked
self._clean_sort_df(obs, ['Taxon', 'Sample ID'])
self._clean_sort_df(exp, ['Taxon', 'Sample ID'])
pdt.assert_frame_equal(obs, exp, check_like=True)
def test_get_counts(self):
taxonomy_df = pd.DataFrame(
[['feature-1', 'k__a; p__b; c__c', 0.123],
['feature-2', 'k__a; p__b; c__c; o__d; f__e', 0.345],
['feature-3', 'k__a; p__f; c__g; o__h', 0.678]],
columns=['Feature ID', 'Taxon', 'Confidence'])
taxonomy_df.set_index('Feature ID', inplace=True)
taxonomy = Taxonomy(self.table, taxonomy_df)
expected = [('Kingdom', {'a': 3}), ('Phylum', {'b': 2, 'f': 1})]
for level, exp in expected:
obs = taxonomy.get_counts(level)
self.assertEqual(obs, exp)
obs = taxonomy.get_counts(
level, samples=['sample-1', 'sample-2', 'sample-3'])
self.assertEqual(obs, exp)
expected_batch = [('sample-1', [('Kingdom', {
'a': 2
}), ('Phylum', {
'b': 1,
'f': 1
})]), ('sample-2', [('Kingdom', {
'a': 2
}), ('Phylum', {
'b': 2
})]),
('sample-3', [('Kingdom', {
'a': 3
}), ('Phylum', {
'b': 2,
'f': 1
})])]
for sample, expected in expected_batch:
for level, exp in expected:
obs = taxonomy.get_counts(level, samples=sample)
self.assertEqual(obs, exp)
def test_ranks_order_unknown(self):
taxonomy = Taxonomy(self.table, self.taxonomy_df, rank_level=2)
with self.assertRaisesRegex(UnknownID, "foobar"):
taxonomy.ranks_order([
"foobar",
])
with self.assertRaisesRegex(UnknownID, "foobar"):
taxonomy.ranks_order([
"c",
"foobar",
])
def test_index_taxa_prevalence(self):
table = biom.Table(
np.array([[0, 1, 2, 0], [2, 4, 6, 1],
[3, 0, 0, 0]]), ['feature-1', 'feature-2', 'feature-3'],
['sample-1', 'sample-2', 'sample-3', 'sample-4'])
taxonomy_df = pd.DataFrame(
[['feature-1', 'a; b; c', 0.123],
['feature-2', 'a; b; c; d; e', 0.345],
['feature-3', 'a; f; g; h', 0.678]],
columns=['Feature ID', 'Taxon', 'Confidence'])
taxonomy_df.set_index('Feature ID', inplace=True)
tax = Taxonomy(table, taxonomy_df)
exp_unique = pd.Series([False, False, True],
index=['feature-1', 'feature-2', 'feature-3'])
exp_prev = pd.Series([0.5, 1., 0.25],
index=['feature-1', 'feature-2', 'feature-3'])
pdt.assert_series_equal(exp_unique, tax.feature_uniques)
pdt.assert_series_equal(exp_prev, tax.feature_prevalence)
def test_init_no_variances(self):
taxonomy = Taxonomy(self.table, self.taxonomy_df)
self.assertEqual(taxonomy._table, self.table.copy().norm())
self.assertEqual(taxonomy._variances, self.no_variances)
pdt.assert_frame_equal(taxonomy._features, self.taxonomy_df)
def test_get_feature_ids(self):
taxonomy = Taxonomy(self.table, self.taxonomy_df)
npt.assert_equal(taxonomy._get_feature_ids(),
['feature-1', 'feature-2', 'feature-3'])
def test_get_sample_ids(self):
taxonomy = Taxonomy(self.table, self.taxonomy_df)
npt.assert_equal(taxonomy._get_sample_ids(),
['sample-1', 'sample-2', 'sample-3'])
def test_presence_data_table(self):
taxonomy = Taxonomy(self.table, self.taxonomy_greengenes_df,
self.table_vars)
obs = taxonomy.presence_data_table(['sample-1', 'sample-2'])
exp_columns = [
'sampleId', 'Kingdom', 'Phylum', 'Class', 'Order', 'Family',
'Genus', 'Species', 'relativeAbundance'
]
DataEntry = create_data_entry(exp_columns)
exp = DataTable(
data=[
DataEntry(
**{
'sampleId': 'sample-1',
'Kingdom': 'a',
'Phylum': 'b',
'Class': None,
'Order': 'c',
'Family': 'd',
'Genus': 'e',
'Species': None,
'relativeAbundance': 2. / 5,
}),
DataEntry(
**{
'sampleId': 'sample-1',
'Kingdom': 'a',
'Phylum': 'f',
'Class': None,
'Order': 'g',
'Family': 'h',
'Genus': None,
'Species': None,
'relativeAbundance': 3. / 5,
}),
DataEntry(
**{
'sampleId': 'sample-2',
'Kingdom': 'a',
'Phylum': 'b',
'Class': None,
'Order': 'c',
'Family': None,
'Genus': None,
'Species': None,
'relativeAbundance': 1. / 5,
}),
DataEntry(
**{
'sampleId': 'sample-2',
'Kingdom': 'a',
'Phylum': 'b',
'Class': None,
'Order': 'c',
'Family': 'd',
'Genus': 'e',
'Species': None,
'relativeAbundance': 4. / 5,
}),
],
columns=exp_columns,
)
self.assertListEqual([{
'data': col
} for col in exp.columns], obs.columns)
# wouldn't want to do this on a huge dataframe..., but it checks if
# there is a row of obs corresponding to each row of exp...
exp_df = pd.DataFrame(exp.data)
obs_df = pd.DataFrame(obs.data)
obs_df_copy = obs_df.copy()
for e_idx, row_exp in exp_df.iterrows():
for o_idx, row_obs in obs_df.iterrows():
if row_exp.eq(row_obs).all():
obs_df_copy.drop(index=o_idx, inplace=True)
break
self.assertTrue(obs_df_copy.empty)
def test_ranks_specific_missing_id(self):
taxonomy = Taxonomy(self.table, self.taxonomy_df, rank_level=2)
with self.assertRaisesRegex(UnknownID, 'foobar'):
taxonomy.ranks_specific('foobar')
def test_init_allow_taxonomy_superset(self):
Taxonomy(self.table, self.taxonomy_superset_df)
def test_init_rankdata_order(self):
exp = ['c', 'g']
taxonomy = Taxonomy(self.table, self.taxonomy_df, rank_level=2)
obs = list(taxonomy._ranked_order.index)
self.assertEqual(obs, exp)
def test_init_disjoint(self):
with self.assertRaisesRegex(SubsetError, "not a subset"):
Taxonomy(self.table, self.taxonomy2_df)
with self.assertRaisesRegex(SubsetError, "not a subset"):
Taxonomy(self.table2, self.taxonomy_df)
def test_get_group_missing(self):
taxonomy = Taxonomy(self.table, self.taxonomy_df)
with self.assertRaisesRegex(UnknownID, "sample-X does not exist"):
taxonomy.get_group(['sample-X'])
