def test_confusion_matrix_class_overlap_error(self):
b = pd.Series([1, 2, 3, 4, 5, 6], name='site',
index=['a1', 'a2', 'b1', 'b2', 'c1', 'c2'])
b.index.name = 'id'
b = qiime2.NumericMetadataColumn(b)
with self.assertRaisesRegex(ValueError, "do not overlap"):
confusion_matrix(self.tmpd, self.a, b)
def test_missing_values(self):
md = qiime2.NumericMetadataColumn(
pd.Series([1.0, 2.0, np.nan, 4.0],
name='number',
index=pd.Index(
['sample1', 'sample2', 'sample3', 'sample4'],
name='id')))
with self.assertRaisesRegex(ValueError, 'missing values'):
distance_matrix(md)
def test_one_sample(self):
md = qiime2.NumericMetadataColumn(
pd.Series([1.5],
name='number',
index=pd.Index(['sample1'], name='id')))
exp = skbio.DistanceMatrix([[0.0]], ids=['sample1'])
obs = distance_matrix(md)
self.assertEqual(exp, obs)
def setUp(self):
super().setUp()
self.beta_correlation = self.plugin.pipelines['beta_correlation']
dm = skbio.DistanceMatrix([[0, 1, 2], [1, 0, 1], [2, 1, 0]],
ids=['sample1', 'sample2', 'sample3'])
self.dm = Artifact.import_data('DistanceMatrix', dm)
self.md = qiime2.NumericMetadataColumn(
pd.Series([1, 2, 3],
name='number',
index=pd.Index(['sample1', 'sample2', 'sample3'],
name='id')))
def test_float_column(self):
md = qiime2.NumericMetadataColumn(
pd.Series([1.5, 2.0, 3.0],
name='number',
index=pd.Index(['sample1', 'sample2', 'sample3'],
name='id')))
exp = skbio.DistanceMatrix(
[[0.0, 0.5, 1.5], [0.5, 0.0, 1.0], [1.5, 1.0, 0.0]],
ids=['sample1', 'sample2', 'sample3'])
obs = distance_matrix(md)
self.assertEqual(exp, obs)
def test_int_column(self):
md = qiime2.NumericMetadataColumn(
pd.Series([1, 2, 3],
name='number',
index=pd.Index(['sample1', 'sample2', 'sample3'],
name='id')))
exp = skbio.DistanceMatrix([[0, 1, 2], [1, 0, 1], [2, 1, 0]],
ids=['sample1', 'sample2', 'sample3'])
obs = distance_matrix(md)
self.assertEqual(exp, obs)
def test_scatterplot(self):
b = qiime2.NumericMetadataColumn(self.c)
scatterplot(self.tmpd, self.c, b)
def _load_nmc(md_fp, column):
md_fp = self.get_data_path(md_fp)
md = pd.read_csv(md_fp, sep='\t', header=0, index_col=0)
md = qiime2.NumericMetadataColumn(md[column])
return md
def _load_nmc(md_fp, column):
md_fp = self.get_data_path(md_fp)
md = pd.DataFrame.from_csv(md_fp, sep='\t')
md = qiime2.NumericMetadataColumn(md[column])
return md
