add tests for max ani, max contain

src/python/tests/test-data/cluster.pairwise.csv

@@ -1,14 +1,14 @@
 query_name,query_md5,match_name,match_md5,containment,max_containment,jaccard,intersect_hashes,query_ani,match_ani,average_containment_ani,max_containment_ani
 n1,md5q1,n2,md5m1,0.8,0.9,0.55,100,0.9,0.9,0.9,0.9
 n1,md5q1,n3,md5m1,0.8,0.9,0.55,100,0.9,0.9,0.9,0.9
-n1,md5q1,n4,md5m1,0.8,0.9,0.55,100,0.9,0.9,0.45,0.9
-n1,md5q1,n5,md5m1,0.8,0.9,0.55,100,0.9,0.9,0.30,0.9
-n1,md5q1,n6,md5m1,0.8,0.9,0.55,100,0.9,0.9,0.5,0.9
-n2,md5q1,n1,md5m2,0.6,0.7,0.45,150,0.5,0.5,0.5,0.5
-n2,md5q1,n2,md5m2,0.6,0.7,0.45,150,0.5,0.5,0.5,0.5
-n2,md5q1,n3,md5m2,0.6,0.7,0.45,150,0.9,0.9,0.95,0.9
-n2,md5q1,n4,md5m2,0.6,0.7,0.45,150,0.9,0.9,0.95,0.9
-n3,md5q2,n4,md5m3,0.7,0.8,0.6,200,0.9,0.9,0.95,0.9
+n1,md5q1,n4,md5m1,0.8,0.9,0.55,100,0.9,0.9,0.45,0.45
+n1,md5q1,n5,md5m1,0.8,0.9,0.55,100,0.9,0.5,0.3,0.3
+n1,md5q1,n6,md5m1,0.8,0.8,0.55,100,0.9,0.5,0.5,0.5
+n2,md5q1,n1,md5m2,0.6,0.6,0.45,150,0.5,0.5,0.5,0.5
+n2,md5q1,n2,md5m2,0.6,0.6,0.45,150,0.5,0.5,0.5,0.5
+n2,md5q1,n3,md5m2,0.6,0.6,0.45,150,0.9,0.9,0.95,0.95
+n2,md5q1,n4,md5m2,0.6,0.6,0.45,150,0.9,0.9,0.95,0.95
+n3,md5q2,n4,md5m3,0.7,0.8,0.6,200,0.9,0.9,0.95,0.95
 n4,md5q3,n5,md5m4,0.4,0.5,0.65,250,0.9,0.9,0.9,0.9
-n5,md5q3,n6,md5m4,0.4,0.5,0.65,250,0.9,0.9,0.7,0.9
-n6,md5q4,n7,md5m5,0.85,0.95,0.5,300,0.9,0.9,0.92,0.9
+n5,md5q3,n6,md5m4,0.4,0.5,0.65,250,0.9,0.9,0.7,0.7
+n6,md5q4,n7,md5m5,0.85,0.95,0.5,300,0.9,0.9,0.92,0.92

src/python/tests/test_cluster.py

@@ -38,6 +38,73 @@ def test_cluster_containment(runtmp):
     assert rows[0]['count'] == '1'
 
 
+def test_cluster_max_containment_1(runtmp):
+    pairwise_csv = get_test_data('cluster.pairwise.csv')
+    output = runtmp.output('clusters.csv')
+    sizes = runtmp.output('sizes.csv')
+    threshold = '0.7'
+
+    runtmp.sourmash('scripts', 'cluster', pairwise_csv, '-o', output,
+                    '--similarity-column', "max_containment", "--cluster-sizes",
+                    sizes, '--threshold', threshold)
+
+    assert os.path.exists(output)
+
+    # check cluster output
+    with open(output, mode='r', newline='') as csvfile:
+        reader = csv.DictReader(csvfile)
+        rows = [row for row in reader]
+        assert reader.fieldnames == ['cluster','nodes']
+    assert len(rows) == 1, f"Expected 1 data row but found {len(rows)}"
+    assert rows[0]['cluster'] == 'Component_1'
+    expected = set("n2;n3;n7;n1;n6;n5;n4".split(';'))
+    assert set(rows[0]['nodes'].split(';')) == expected
+
+    # check cluster size histogram
+    with open(sizes, mode='r', newline='') as csvfile:
+        reader = csv.DictReader(csvfile)
+        rows = [row for row in reader]
+        assert reader.fieldnames == ['cluster_size','count']
+    assert len(rows) == 1, f"Expected 1 data row but found {len(rows)}"
+    assert rows[0]['cluster_size'] == '7'
+    assert rows[0]['count'] == '1'
+
+
+def test_cluster_max_containment_2(runtmp):
+    pairwise_csv = get_test_data('cluster.pairwise.csv')
+    output = runtmp.output('clusters.csv')
+    sizes = runtmp.output('sizes.csv')
+    threshold = '0.9'
+
+    runtmp.sourmash('scripts', 'cluster', pairwise_csv, '-o', output,
+                    '--similarity-column', "max_containment", "--cluster-sizes",
+                    sizes, '--threshold', threshold)
+
+    assert os.path.exists(output)
+
+    # check cluster output
+    with open(output, mode='r', newline='') as csvfile:
+        reader = csv.DictReader(csvfile)
+        rows = [row for row in reader]
+        assert reader.fieldnames == ['cluster','nodes']
+    assert len(rows) == 2, f"Expected 2 data rows but found {len(rows)}"
+    assert rows[0]['cluster'] == 'Component_1'
+    expected = set("n1;n2;n3;n4;n5".split(';'))
+    assert set(rows[0]['nodes'].split(';')) == expected
+    expected = set("n6;n7".split(';'))
+    assert set(rows[1]['nodes'].split(';')) == expected
+
+    # check cluster size histogram
+    with open(sizes, mode='r', newline='') as csvfile:
+        reader = csv.DictReader(csvfile)
+        rows = [row for row in reader]
+        assert reader.fieldnames == ['cluster_size','count']
+    assert len(rows) == 2, f"Expected 2 data rows but found {len(rows)}"
+    rows_as_tuples = {tuple(row.values()) for row in rows}
+    expected = {('5', '1'), ('2', '1')}
+    assert rows_as_tuples == expected
+
+
 def test_cluster_jaccard(runtmp): 
     pairwise_csv = get_test_data('cluster.pairwise.csv')
     output = runtmp.output('clusters.csv')
@@ -91,6 +158,43 @@ def test_cluster_ani(runtmp):
     assert rows[0]['cluster'] == 'Component_1'
     expected = set("n1;n2;n3;n4;n5".split(';'))
     assert set(rows[0]['nodes'].split(';')) == expected
+    expected = set("n6;n7".split(';'))
+    assert set(rows[1]['nodes'].split(';')) == expected
+
+    # check cluster size histogram
+    with open(sizes, mode='r', newline='') as csvfile:
+        reader = csv.DictReader(csvfile)
+        rows = [row for row in reader]
+        assert reader.fieldnames == ['cluster_size','count']
+    assert len(rows) == 2, f"Expected 2 data rows but found {len(rows)}"
+    rows_as_tuples = {tuple(row.values()) for row in rows}
+    expected = {('5', '1'), ('2', '1')}
+    assert rows_as_tuples == expected
+
+
+def test_cluster_max_ani(runtmp):
+    pairwise_csv = get_test_data('cluster.pairwise.csv')
+    output = runtmp.output('clusters.csv')
+    sizes = runtmp.output('sizes.csv')
+    threshold = '0.9'
+
+    runtmp.sourmash('scripts', 'cluster', pairwise_csv, '-o', output,
+                    '--similarity-column', "max_ani", "--cluster-sizes",
+                    sizes, '--threshold', threshold)
+
+    assert os.path.exists(output)
+
+    # check cluster output
+    with open(output, mode='r', newline='') as csvfile:
+        reader = csv.DictReader(csvfile)
+        rows = [row for row in reader]
+        assert reader.fieldnames == ['cluster','nodes']
+    assert len(rows) == 2, f"Expected 2 data rows but found {len(rows)}"
+    assert rows[0]['cluster'] == 'Component_1'
+    expected = set("n1;n2;n3;n4;n5".split(';'))
+    assert set(rows[0]['nodes'].split(';')) == expected
+    expected = set("n6;n7".split(';'))
+    assert set(rows[1]['nodes'].split(';')) == expected
 
     # check cluster size histogram
     with open(sizes, mode='r', newline='') as csvfile: