def test_meta_knowledge_graph_of_complex_graph_data(): """ Test generate meta knowledge graph operation. """ input_args = { "filename": [ os.path.join(RESOURCE_DIR, "complex_graph_nodes.tsv"), os.path.join(RESOURCE_DIR, "complex_graph_edges.tsv"), ], "format": "tsv", } transformer = Transformer() transformer.transform(input_args) output_filename = os.path.join(TARGET_DIR, "test_meta_knowledge_graph-1.json") generate_meta_knowledge_graph( graph=transformer.store.graph, name="Complex Test Graph", filename=output_filename, edge_facet_properties=["aggregator_knowledge_source"]) data = json.load(open(output_filename)) assert data["name"] == "Complex Test Graph" print(f"\n{json.dumps(data, indent=4)}")
def test_clique_merge(): """ Test for clique merge. """ input_args = { "filename": [ os.path.join(RESOURCE_DIR, "cm_nodes.csv"), os.path.join(RESOURCE_DIR, "cm_edges.csv"), ], "format": "csv", } t = Transformer() t.transform(input_args) updated_graph, clique_graph = clique_merge( target_graph=t.store.graph, prefix_prioritization_map=prefix_prioritization_map ) leaders = NxGraph.get_node_attributes(updated_graph, "clique_leader") leader_list = list(leaders.keys()) leader_list.sort() assert len(leader_list) == 2 n1 = updated_graph.nodes()[leader_list[0]] assert n1["election_strategy"] == "PREFIX_PRIORITIZATION" assert "NCBIGene:100302240" in n1["same_as"] assert "ENSEMBL:ENSG00000284458" in n1["same_as"] n2 = updated_graph.nodes()[leader_list[1]] assert n2["election_strategy"] == "PREFIX_PRIORITIZATION" assert "NCBIGene:8202" in n2["same_as"] assert "OMIM:601937" in n2["same_as"] assert "ENSEMBL:ENSG00000124151" not in n2["same_as"]
def test_validate_by_stream_inspector(): """ Test generate the validate function by streaming graph data through a graph Transformer.process() Inspector """ input_args = { "filename": [ os.path.join(RESOURCE_DIR, "graph_nodes.tsv"), os.path.join(RESOURCE_DIR, "graph_edges.tsv"), ], "format": "tsv", "aggregator_knowledge_source": True, } Validator.set_biolink_model("1.8.2") # Validator assumes the currently set Biolink Release validator = Validator() transformer = Transformer(stream=True) transformer.transform( input_args=input_args, output_args={ "format": "null" }, # streaming processing throws the graph data away # ... Second, we inject the Inspector into the transform() call, # for the underlying Transformer.process() to use... inspector=validator, ) validator.write_report() e = validator.get_errors() assert len(e) == 0
def test_transformer_infores_basic_formatting(): input_args = { "filename": [ os.path.join(RESOURCE_DIR, "test_infores_coercion_nodes.tsv"), os.path.join(RESOURCE_DIR, "test_infores_coercion_edges.tsv"), ], "format": "tsv", "provided_by": True, "aggregator_knowledge_source": "true", } t = Transformer() t.transform(input_args=input_args) n1 = t.store.graph.nodes()["FlyBase:FBgn0000008"] assert "provided_by" in n1 assert len(n1["provided_by"]) == 1 assert "infores:flybase-monarch-version-202012" in n1["provided_by"] n2 = t.store.graph.nodes()["GO:0005912"] assert "provided_by" in n2 assert len(n2["provided_by"]) == 1 assert "infores:gene-ontology-monarch-version-202012" in n2["provided_by"] et = list( t.store.graph.get_edge("FlyBase:FBgn0000008", "GO:0005912").values())[0] assert ("infores:gene-ontology-monarch-version-202012" in et["aggregator_knowledge_source"])
def test_generate_classical_meta_knowledge_graph(): """ Test generate meta knowledge graph operation. """ input_args = { 'filename': [ os.path.join(RESOURCE_DIR, 'graph_nodes.tsv'), os.path.join(RESOURCE_DIR, 'graph_edges.tsv'), ], 'format': 'tsv', } transformer = Transformer() transformer.transform(input_args) output_filename = os.path.join(TARGET_DIR, 'test_meta_knowledge_graph-1.json') generate_meta_knowledge_graph(transformer.store.graph, 'Test Graph', output_filename) data = json.load(open(output_filename)) assert data['name'] == 'Test Graph' assert 'NCBIGene' in data['nodes']['biolink:Gene']['id_prefixes'] assert 'REACT' in data['nodes']['biolink:Pathway']['id_prefixes'] assert 'HP' in data['nodes']['biolink:PhenotypicFeature']['id_prefixes'] assert data['nodes']['biolink:Gene']['count'] == 178 assert len(data['nodes']) == 8 assert len(data['edges']) == 13
def test_summarize_graph_inspector(): """ Test for Inspector sourced graph stats, and comparing the resulting stats. """ input_args = { 'filename': [ os.path.join(RESOURCE_DIR, 'graph_nodes.tsv'), os.path.join(RESOURCE_DIR, 'graph_edges.tsv'), ], 'format': 'tsv', } transformer = Transformer(stream=True) inspector = GraphSummary('Test Graph Summary - Streamed') transformer.transform(input_args=input_args, inspector=inspector) output_filename = os.path.join(TARGET_DIR, 'test_graph-summary-from-inspection.json') with open(output_filename, 'w') as gsh: inspector.save(output_filename) data = json.load(open(output_filename)) assert data['name'] == 'Test Graph Summary - Streamed' assert 'NCBIGene' in data['nodes']['biolink:Gene']['id_prefixes'] assert 'REACT' in data['nodes']['biolink:Pathway']['id_prefixes'] assert 'HP' in data['nodes']['biolink:PhenotypicFeature']['id_prefixes'] assert data['nodes']['biolink:Gene']['count'] == 178 assert len(data['nodes']) == 8 assert len(data['edges']) == 13
def test_generate_streaming_meta_knowledge_graph_direct(): """ Test generate meta knowledge graph operation... MetaKnowledgeGraph as direct Transformer.transform Inspector """ input_args = { 'filename': [ os.path.join(RESOURCE_DIR, 'graph_nodes.tsv'), os.path.join(RESOURCE_DIR, 'graph_edges.tsv'), ], 'format': 'tsv', } transformer = Transformer(stream=True) mkg = MetaKnowledgeGraph('Test Graph - Streamed') transformer.transform(input_args=input_args, inspector=mkg) assert mkg.get_name() == 'Test Graph - Streamed' assert mkg.get_total_nodes_count() == 512 assert mkg.get_number_of_categories() == 8 assert mkg.get_total_edges_count() == 540 assert mkg.get_edge_mapping_count() == 13 assert 'NCBIGene' in mkg.get_category('biolink:Gene').get_id_prefixes() assert 'REACT' in mkg.get_category('biolink:Pathway').get_id_prefixes() assert 'HP' in mkg.get_category( 'biolink:PhenotypicFeature').get_id_prefixes() assert mkg.get_category('biolink:Gene').get_count() == 178
def test_clique_merge(): """ Test for clique merge. """ input_args = { 'filename': [ os.path.join(RESOURCE_DIR, 'cm_nodes.csv'), os.path.join(RESOURCE_DIR, 'cm_edges.csv'), ], 'format': 'csv', } t = Transformer() t.transform(input_args) updated_graph, clique_graph = clique_merge( target_graph=t.store.graph, prefix_prioritization_map=prefix_prioritization_map) leaders = NxGraph.get_node_attributes(updated_graph, 'clique_leader') leader_list = list(leaders.keys()) leader_list.sort() assert len(leader_list) == 2 n1 = updated_graph.nodes()[leader_list[0]] assert n1['election_strategy'] == 'PREFIX_PRIORITIZATION' assert 'NCBIGene:100302240' in n1['same_as'] assert 'ENSEMBL:ENSG00000284458' in n1['same_as'] n2 = updated_graph.nodes()[leader_list[1]] assert n2['election_strategy'] == 'PREFIX_PRIORITIZATION' assert 'NCBIGene:8202' in n2['same_as'] assert 'OMIM:601937' in n2['same_as'] assert 'ENSEMBL:ENSG00000124151' not in n2['same_as']
def test_transformer_infores_parser_prefix_rewrite(): input_args = { "filename": [ os.path.join(RESOURCE_DIR, "test_infores_coercion_nodes.tsv"), os.path.join(RESOURCE_DIR, "test_infores_coercion_edges.tsv"), ], "format": "tsv", "provided_by": (r"\(.+\)", "", "Monarch"), "aggregator_knowledge_source": (r"\(.+\)", "", "Monarch"), } t = Transformer() t.transform(input_args=input_args) n1 = t.store.graph.nodes()["FlyBase:FBgn0000008"] assert "provided_by" in n1 assert len(n1["provided_by"]) == 1 assert "infores:monarch-flybase" in n1["provided_by"] n2 = t.store.graph.nodes()["GO:0005912"] assert "provided_by" in n2 assert len(n2["provided_by"]) == 1 assert "infores:monarch-gene-ontology" in n2["provided_by"] et = list( t.store.graph.get_edge("FlyBase:FBgn0000008", "GO:0005912").values())[0] assert "infores:monarch-gene-ontology" in et["aggregator_knowledge_source"] irc = t.get_infores_catalog() assert len(irc) == 2 assert "Gene Ontology (Monarch version 202012)" in irc assert ("infores:monarch-gene-ontology" in irc["Gene Ontology (Monarch version 202012)"])
def test_transformer_infores_suppression(): input_args = { "filename": [ os.path.join(RESOURCE_DIR, "test_infores_coercion_nodes.tsv"), os.path.join(RESOURCE_DIR, "test_infores_coercion_edges.tsv"), ], "format": "tsv", "provided_by": "False", "aggregator_knowledge_source": False, } t = Transformer() t.transform(input_args=input_args) n1 = t.store.graph.nodes()["FlyBase:FBgn0000008"] assert "provided_by" not in n1 n2 = t.store.graph.nodes()["GO:0005912"] assert "provided_by" not in n2 et = list( t.store.graph.get_edge("FlyBase:FBgn0000008", "GO:0005912").values())[0] assert "aggregator_knowledge_source" not in et
def test_generate_classical_meta_knowledge_graph(): """ Test generate meta knowledge graph operation. """ input_args = { "filename": [ os.path.join(RESOURCE_DIR, "graph_nodes.tsv"), os.path.join(RESOURCE_DIR, "graph_edges.tsv"), ], "format": "tsv", } transformer = Transformer() transformer.transform(input_args) output_filename = os.path.join(TARGET_DIR, "test_meta_knowledge_graph-1.json") generate_meta_knowledge_graph( graph=transformer.store.graph, name="Test Graph", filename=output_filename, edge_facet_properties=["aggregator_knowledge_source"]) data = json.load(open(output_filename)) assert data["name"] == "Test Graph" _check_mkg_json_contents(data)
def test_validate_incorrect_edge(edge): """ Test basic validation of an edge, where the edge is invalid. """ t = Transformer() s = Source(t) assert not s.validate_edge(edge) assert len(t.get_errors()) > 0 t.write_report()
def test_validate_correct_edge(edge): """ Test basic validation of an edge, where the edge is valid. """ t = Transformer() s = Source(t) e = s.validate_edge(edge) assert e is not None assert len(t.get_errors()) == 0 t.write_report()
def test_incorrect_edge_filters(edge): """ Test filtering of an edge """ t = Transformer() s = Source(t) filters = {"some_field": {"bad_edge_filter": 1}} s.set_edge_filters(filters) s.check_edge_filter(edge) assert len(t.get_errors("Error")) > 0 t.write_report()
def test_validate_incorrect_node(node): """ Test basic validation of a node, where the node is invalid. """ t = Transformer() s = Source(t) result = s.validate_node(node) if len(t.get_errors("Error")) > 0: assert result is None else: assert result is not None t.write_report()
def test_validate_json(): """ Validate against a valid representative Biolink Model compliant JSON. """ input_args = { "filename": [os.path.join(RESOURCE_DIR, "valid.json")], "format": "json", } t = Transformer() t.transform(input_args) validator = Validator() validator.validate(t.store.graph) assert len(validator.get_errors()) == 0
def test_incorrect_nodes(): """ Test basic validation of a node, where the node is invalid. """ t = Transformer() s = TsvSource(t) g = s.parse(filename=os.path.join(RESOURCE_DIR, "incomplete_nodes.tsv"), format="tsv") nodes = [] for rec in g: if rec: nodes.append(rec) t.write_report()
def test_validate_json(): """ Validate against a valid representative Biolink Model compliant JSON. """ input_args = { 'filename': [os.path.join(RESOURCE_DIR, 'valid.json')], 'format': 'json' } t = Transformer() t.transform(input_args) validator = Validator() e = validator.validate(t.store.graph) assert len(e) == 0
def test_meta_knowledge_graph_multiple_category_and_predicate_parsing(): """ Test meta knowledge graph parsing multiple categories """ input_args = { 'filename': [ os.path.join(RESOURCE_DIR, 'graph_multi_category_nodes.tsv'), os.path.join(RESOURCE_DIR, 'graph_multi_category_edges.tsv'), ], 'format': 'tsv', } t = Transformer(stream=True) mkg = MetaKnowledgeGraph(name='Test Graph - Multiple Node Categories') t.transform(input_args=input_args, inspector=mkg) assert mkg.get_name() == 'Test Graph - Multiple Node Categories' assert mkg.get_total_nodes_count() == 10 # unique set, including (shared) parent # classes (not including category 'unknown' ) assert mkg.get_number_of_categories() == 7 assert mkg.get_node_count_by_category("biolink:Disease") == 1 assert mkg.get_node_count_by_category("biolink:BiologicalEntity") == 5 assert mkg.get_node_count_by_category( "biolink:AnatomicalEntityEntity") == 0 # sums up all the counts of node mappings across # all categories (not including category 'unknown') assert mkg.get_total_node_counts_across_categories() == 35 # only counts 'valid' edges for which # subject and object nodes are in the nodes file assert mkg.get_total_edges_count() == 8 # total number of distinct predicates assert mkg.get_predicate_count() == 2 # counts edges with a given predicate # (ignoring edges with unknown subject or object identifiers) assert mkg.get_edge_count_by_predicate("biolink:has_phenotype") == 4 assert mkg.get_edge_count_by_predicate("biolink:involved_in") == 0 assert mkg.get_edge_mapping_count() == 25 assert mkg.get_total_edge_counts_across_mappings() == 100
def test_rdf_transform3(): """ Test parsing an RDF N-triple and round-trip. """ input_args1 = { "filename": [os.path.join(RESOURCE_DIR, "rdf", "test1.nt")], "format": "nt", } t1 = Transformer() t1.transform(input_args1) assert t1.store.graph.number_of_nodes() == 2 assert t1.store.graph.number_of_edges() == 1 output_args1 = { "filename": os.path.join(TARGET_DIR, "test1-export.nt"), "format": "nt", } t1.save(output_args1) input_args2 = { "filename": [os.path.join(TARGET_DIR, "test1-export.nt")], "format": "nt", } t2 = Transformer() t2.transform(input_args2) assert t2.store.graph.number_of_nodes() == 2 assert t2.store.graph.number_of_edges() == 1 n1t1 = t1.store.graph.nodes()["ENSEMBL:ENSG0000000000001"] n1t2 = t2.store.graph.nodes()["ENSEMBL:ENSG0000000000001"] n1t3 = t2.store.graph.nodes()["ENSEMBL:ENSG0000000000001"] assert n1t1["type"] == n1t2["type"] == n1t3["type"] == "SO:0000704" assert len(n1t1["category"]) == len(n1t2["category"]) == len( n1t3["category"]) == 4 assert ("biolink:Gene" in n1t1["category"] and "biolink:Gene" in n1t2["category"] and "biolink:Gene" in n1t3["category"]) assert ("biolink:GenomicEntity" in n1t1["category"] and "biolink:GenomicEntity" in n1t2["category"] and "biolink:GenomicEntity" in n1t3["category"]) assert ("biolink:NamedThing" in n1t1["category"] and "biolink:NamedThing" in n1t2["category"] and "biolink:NamedThing" in n1t3["category"]) assert n1t1["name"] == n1t2["name"] == n1t3["name"] == "Test Gene 123" assert (n1t1["description"] == n1t2["description"] == n1t3["description"] == "This is a Test Gene 123") assert ("Test Dataset" in n1t1["provided_by"] and "Test Dataset" in n1t2["provided_by"] and "Test Dataset" in n1t3["provided_by"])
def _stream_transform(query): """ Transform an input to an output via Transformer where streaming is enabled. """ t1 = Transformer(stream=True) t1.transform(query[0], query[1]) output = query[1] if output["format"] in {"tsv", "csv", "jsonl"}: input_args = { "filename": [ f"{output['filename']}_nodes.{output['format']}", f"{output['filename']}_edges.{output['format']}", ], "format": output["format"], } elif output["format"] in {"neo4j"}: input_args = { "uri": DEFAULT_NEO4J_URL, "username": DEFAULT_NEO4J_USERNAME, "password": DEFAULT_NEO4J_PASSWORD, "format": "neo4j", } else: input_args = { "filename": [f"{output['filename']}"], "format": output["format"] } t2 = Transformer() t2.transform(input_args) assert t2.store.graph.number_of_nodes() == query[2] assert t2.store.graph.number_of_edges() == query[3]
def _stream_transform(query): """ Transform an input to an output via Transformer where streaming is enabled. """ t1 = Transformer(stream=True) t1.transform(query[0], query[1]) output = query[1] if output['format'] in {'tsv', 'csv', 'jsonl'}: input_args = { 'filename': [ f"{output['filename']}_nodes.{output['format']}", f"{output['filename']}_edges.{output['format']}", ], 'format': output['format'], } elif output['format'] in {'neo4j'}: input_args = { 'uri': DEFAULT_NEO4J_URL, 'username': DEFAULT_NEO4J_USERNAME, 'password': DEFAULT_NEO4J_PASSWORD, 'format': 'neo4j', } else: input_args = { 'filename': [f"{output['filename']}"], 'format': output['format'] } t2 = Transformer() t2.transform(input_args) assert t2.store.graph.number_of_nodes() == query[2] assert t2.store.graph.number_of_edges() == query[3]
def test_rdf_transform3(): """ Test parsing an RDF N-triple and round-trip. """ input_args1 = { 'filename': [os.path.join(RESOURCE_DIR, 'rdf', 'test1.nt')], 'format': 'nt' } t1 = Transformer() t1.transform(input_args1) assert t1.store.graph.number_of_nodes() == 2 assert t1.store.graph.number_of_edges() == 1 output_args1 = { 'filename': os.path.join(TARGET_DIR, 'test1-export.nt'), 'format': 'nt' } t1.save(output_args1) input_args2 = { 'filename': [os.path.join(TARGET_DIR, 'test1-export.nt')], 'format': 'nt' } t2 = Transformer() t2.transform(input_args2) assert t2.store.graph.number_of_nodes() == 2 assert t2.store.graph.number_of_edges() == 1 n1t1 = t1.store.graph.nodes()['ENSEMBL:ENSG0000000000001'] n1t2 = t2.store.graph.nodes()['ENSEMBL:ENSG0000000000001'] n1t3 = t2.store.graph.nodes()['ENSEMBL:ENSG0000000000001'] assert n1t1['type'] == n1t2['type'] == n1t3['type'] == 'SO:0000704' assert len(n1t1['category']) == len(n1t2['category']) == len( n1t3['category']) == 4 assert ('biolink:Gene' in n1t1['category'] and 'biolink:Gene' in n1t2['category'] and 'biolink:Gene' in n1t3['category']) assert ('biolink:GenomicEntity' in n1t1['category'] and 'biolink:GenomicEntity' in n1t2['category'] and 'biolink:GenomicEntity' in n1t3['category']) assert ('biolink:NamedThing' in n1t1['category'] and 'biolink:NamedThing' in n1t2['category'] and 'biolink:NamedThing' in n1t3['category']) assert n1t1['name'] == n1t2['name'] == n1t3['name'] == 'Test Gene 123' assert (n1t1['description'] == n1t2['description'] == n1t3['description'] == 'This is a Test Gene 123') assert ('Test Dataset' in n1t1['provided_by'] and 'Test Dataset' in n1t2['provided_by'] and 'Test Dataset' in n1t3['provided_by'])
def test_rdf_transform_with_filters1(query): """ Test RDF transform with filters. """ input_args = { "filename": [os.path.join(RESOURCE_DIR, "rdf", "test3.nt")], "format": "nt", "node_filters": query[0], "edge_filters": query[1], } t = Transformer() t.transform(input_args) assert t.store.graph.number_of_edges() == query[3]
def test_rdf_transform_with_filters1(query): """ Test RDF transform with filters. """ input_args = { 'filename': [os.path.join(RESOURCE_DIR, 'rdf', 'test3.nt')], 'format': 'nt', 'node_filters': query[0], 'edge_filters': query[1], } t = Transformer() t.transform(input_args) assert t.store.graph.number_of_nodes() == query[2] assert t.store.graph.number_of_edges() == query[3]
def test_read_trapi_json1(): """ Read from a JSON using TrapiSource. """ t = Transformer() s = TrapiSource(t) g = s.parse(os.path.join(RESOURCE_DIR, "rsa_sample.json")) nodes = {} edges = {} for rec in g: if rec: if len(rec) == 4: edges[(rec[0], rec[1])] = rec[3] else: nodes[rec[0]] = rec[1] assert len(nodes.keys()) == 4 assert len(edges.keys()) == 3 n = nodes["HGNC:11603"] assert n["id"] == "HGNC:11603" assert n["name"] == "TBX4" assert n["category"] == ["biolink:Gene"] e = edges["HGNC:11603", "MONDO:0005002"] assert e["subject"] == "HGNC:11603" assert e["object"] == "MONDO:0005002" assert e["predicate"] == "biolink:related_to"
def test_read_tsv(): """ Read a TSV using TsvSource. """ t = Transformer() s = TsvSource(t) g = s.parse(filename=os.path.join(RESOURCE_DIR, "test_nodes.tsv"), format="tsv") nodes = [] for rec in g: if rec: nodes.append(rec) assert len(nodes) == 3 nodes.sort() n1 = nodes.pop()[-1] assert n1["id"] == "CURIE:456" assert n1["name"] == "Disease 456" assert "biolink:Disease" in n1["category"] assert "biolink:NamedThing" in n1["category"] assert n1["description"] == '"Node of type Disease, CURIE:456"' g = s.parse(filename=os.path.join(RESOURCE_DIR, "test_edges.tsv"), format="tsv") edges = [] for rec in g: if rec: edges.append(rec) e1 = edges.pop()[-1] assert "id" in e1 assert e1["subject"] == "CURIE:123" assert e1["object"] == "CURIE:456" assert e1["predicate"] == "biolink:related_to" assert e1["relation"] == "biolink:related_to" assert "PMID:1" in e1["publications"]
def test_read_jsonl1(): """ Read from JSON Lines using JsonlSource. """ t = Transformer() s = JsonlSource(t) g = s.parse(os.path.join(RESOURCE_DIR, "valid_nodes.jsonl")) nodes = {} for rec in g: if rec: nodes[rec[0]] = rec[1] g = s.parse(os.path.join(RESOURCE_DIR, "valid_edges.jsonl")) edges = {} for rec in g: if rec: edges[(rec[0], rec[1])] = rec[3] assert len(nodes.keys()) == 7 assert len(edges.keys()) == 5 n = nodes["MONDO:0017148"] assert "id" in n and n["id"] == "MONDO:0017148" assert n["name"] == "heritable pulmonary arterial hypertension" assert n["category"][0] == "biolink:Disease" n2 = nodes["PUBCHEM.COMPOUND:10429502"] assert "id" in n2 and n2["id"] == "PUBCHEM.COMPOUND:10429502" assert n2["name"] == "16|A-Methyl Prednisolone" e = edges[("HGNC:11603", "MONDO:0017148")] assert e["subject"] == "HGNC:11603" assert e["object"] == "MONDO:0017148" assert e["predicate"] == "biolink:related_to" assert e["relation"] == "RO:0004013"
def test_write_graph_no_edge_identifier(): """ Write a graph via GraphSink. """ t = Transformer() s = GraphSink(t) s.write_node({ "id": "A", "name": "Node A", "category": ["biolink:NamedThing"] }) s.write_node({ "id": "B", "name": "Node B", "category": ["biolink:NamedThing"] }) s.write_node({ "id": "C", "name": "Node C", "category": ["biolink:NamedThing"] }) s.write_edge({ "subject": "A", "predicate": "biolink:related_to", "object": "B", "relation": "biolink:related_to", }) assert s.graph.number_of_nodes() == 3 assert s.graph.number_of_edges() == 1
def test_write_neo2(clean_database, query): """ Test writing a graph to a Neo4j instance. """ graph = query[0] t = Transformer() sink = NeoSink( owner=t, uri=DEFAULT_NEO4J_URL, username=DEFAULT_NEO4J_USERNAME, password=DEFAULT_NEO4J_PASSWORD, ) for n, data in graph.nodes(data=True): sink.write_node(data) for u, v, k, data in graph.edges(data=True, keys=True): sink.write_edge(data) sink.finalize() nr = sink.session.run("MATCH (n) RETURN count(n)") [node_counts] = [x for x in nr][0] assert node_counts >= query[1] er = sink.session.run("MATCH ()-[p]->() RETURN count(p)") [edge_counts] = [x for x in er][0] assert edge_counts >= query[2]