def test_gradient_missing_samples(self):
from qiime2.plugins.gneiss.methods import gradient_clustering
table = pd.DataFrame({"x": 1, "y": 2}, index=["a", "s1"])
table = qiime2.Artifact.import_data("FeatureTable[Frequency]", table)
metadata = qiime2.Metadata.load(get_data_path("test_metadata.txt"))
with self.assertRaisesRegex(KeyError, "not present.*a"):
gradient_clustering(table, metadata.get_column("x"))
def test_gradient_ignore_missing_samples(self):
from qiime2.plugins.gneiss.methods import gradient_clustering
table = pd.DataFrame({"x": 1, "y": 2}, index=["a", "s1"])
table = qiime2.Artifact.import_data("FeatureTable[Frequency]", table)
metadata = qiime2.Metadata.load(get_data_path("test_metadata.txt"))
gradient_clustering(table,
metadata.get_column("x"),
ignore_missing_samples=True)
# Checkpoint assertion
self.assertTrue(True)
def test_gradient_artifact_weighted(self):
from qiime2.plugins.gneiss.methods import gradient_clustering
table_f = get_data_path("weighted.biom.qza")
metadata_f = get_data_path("test_metadata.txt")
in_table = qiime2.Artifact.load(table_f)
in_metadata = qiime2.Metadata(pd.read_table(metadata_f, index_col=0))
res_uw = gradient_clustering(in_table,
in_metadata.get_category('x'),
weighted=False)
res_w = gradient_clustering(in_table,
in_metadata.get_category('x'),
weighted=True)
res_clust_uw = res_uw.clustering._view(TreeNode)
res_clust_w = res_w.clustering._view(TreeNode)
self.assertNotEqual(str(res_clust_uw), str(res_clust_w))
def test_gradient_artifact(self):
from qiime2.plugins.gneiss.methods import gradient_clustering
table_f = get_data_path("test_gradient.biom.qza")
metadata_f = get_data_path("test_metadata.txt")
in_table = qiime2.Artifact.load(table_f)
in_metadata = qiime2.Metadata(pd.read_table(metadata_f, index_col=0))
res = gradient_clustering(in_table, in_metadata.get_category('x'))
res_clust = res.clustering._view(TreeNode)
exp_str = '((o1:0.5,o2:0.5)y1:0.5,(o3:0.5,o4:0.5)y2:0.5)y0;\n'
self.assertEqual(exp_str, str(res_clust))
def test_gradient_match(self):
# there are extra rows in match that need to be filtered out
from qiime2.plugins.gneiss.methods import gradient_clustering
table_f = get_data_path("test_gradient.biom.qza")
metadata_f = get_data_path("test_metadata2.txt")
in_table = qiime2.Artifact.load(table_f)
in_metadata = qiime2.Metadata.load(metadata_f)
res = gradient_clustering(in_table, in_metadata.get_column('x'))
res_clust = res.clustering._view(TreeNode)
exp_str = '((o1:0.5,o2:0.5)y1:0.5,(o3:0.5,o4:0.5)y2:0.5)y0;\n'
self.assertEqual(exp_str, str(res_clust))