Beispiel #1
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def open_ontology(path=None):
    """ builds a networkx graph from obo parsed data
    
    Returns:
        networkx graph object, alt IDs and obsolete IDs
    """

    header, entries = load_hpo_database(path)
    graph = ICSimilarity()

    # track alternate HPO IDs (since we use HPO IDs as node IDs)
    alt_ids = {}
    obsolete_ids = set()

    # add the hpo header values as attributes for the graph
    for header_id in header:
        graph.graph[header_id] = header[header_id]

    for entry in entries:
        add_entry(graph, entry, alt_ids, obsolete_ids)

    return graph, alt_ids, obsolete_ids
 def setUp(self):
     """ construct a ICSimilarity object for unit tests
     """
     
     path = os.path.join(os.path.dirname(__file__), "data", "obo.txt")
     ontology = Ontology(path)
     graph = ontology.get_graph()
     
     self.hpo_terms = {
         "person_01": ["HP:0000924"],
         "person_02": ["HP:0000118", "HP:0002011"],
         "person_03": ["HP:0000707", "HP:0002011"]
     }
     
     self.hpo_graph = ICSimilarity(self.hpo_terms, graph)
Beispiel #3
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    def get_graph(self):
        """ builds a networkx graph from obo parsed data
        
        Returns:
            networkx graph object
        """

        graph = ICSimilarity()

        # add the hpo header values as attributes for the graph
        for header_id in self.hpo_header:
            graph.graph[header_id] = self.hpo_header[header_id]

        for entry in self.hpo_list:
            tags = entry.tags
            # ignore obsolete HPO entries
            if self.is_obsolete(tags):
                self.obsolete_ids.add(str(tags["id"][0]))
                continue

            node_id = str(tags["id"][0])
            graph.add_node(node_id)

            # make sure we can convert between HPO ID and their alternate IDs
            self.track_alt_ids(tags, node_id)

            # include the attribute data for the node
            self.add_hpo_attributes_to_node(graph, node_id, tags)

            # add the predecessors to the node
            if "is_a" in tags:
                for predecessor in tags["is_a"]:
                    predecessor = str(predecessor)
                    graph.add_edge(predecessor, node_id)

        return graph
class TestICSimilarityPy(unittest.TestCase):
    """ class to test ICSimilarity
    """
    
    def setUp(self):
        """ construct a ICSimilarity object for unit tests
        """
        
        path = os.path.join(os.path.dirname(__file__), "data", "obo.txt")
        ontology = Ontology(path)
        graph = ontology.get_graph()
        
        self.hpo_terms = {
            "person_01": ["HP:0000924"],
            "person_02": ["HP:0000118", "HP:0002011"],
            "person_03": ["HP:0000707", "HP:0002011"]
        }
        
        self.hpo_graph = ICSimilarity(self.hpo_terms, graph)
    
    def test_get_term_count(self):
        """ check that get_term_count works correctly
        
        All of the counts here are derived from their usage in self.hpo_terms
        """
        
        # check that we count the term usage (and subterms correctly)
        self.assertEqual(self.hpo_graph.get_term_count("HP:0000118"), 5)
        self.assertEqual(self.hpo_graph.get_term_count("HP:0000707"), 3)
        self.assertEqual(self.hpo_graph.get_term_count("HP:0002011"), 2)
        
        # check that a terminal node, only used once in the probands, has a
        # count of 1
        self.assertEqual(self.hpo_graph.get_term_count("HP:0000924"), 1)
        
        # check the term/subterm count for a term that isn't used within any of
        # he probands, but which all of the used terms descend from.
        self.assertEqual(self.hpo_graph.get_term_count("HP:0000001"), 5)
    
    def test_calculate_information_content(self):
        """ check that calculate_information_content works correctly
        """
        
        # check that the top node has an information content of 0
        self.assertEqual(self.hpo_graph.calculate_information_content("HP:0000001"), \
            0)
        
        # check the information content for a terminal node
        self.assertAlmostEqual(self.hpo_graph.calculate_information_content("HP:0000924"), \
            1.6094379)
        
        # check the information content for a node that is somewhat distant, but
        # which has some descendant nodes that need to be included in the term
        # count
        self.assertAlmostEqual(self.hpo_graph.calculate_information_content("HP:0000707"), \
            0.5108256)
        
    def test_get_most_informative_ic(self):
        """ check that get_most_informative_ic works correctly
        """
        
        # check the most informative information content for two nodes where
        # every common ancestor is the ancestor of all terms used in the probands
        self.assertAlmostEqual(self.hpo_graph.get_most_informative_ic("HP:0000707", \
            "HP:0000924"), 0)
        
        # check the most informative information content for two nodes where
        # both nodes are somewhat down the HPO graph
        self.assertAlmostEqual(self.hpo_graph.get_most_informative_ic("HP:0000707", \
            "HP:0002011"), 0.5108256)
            
        # check the most informative information content for two identical nodes
        self.assertAlmostEqual(self.hpo_graph.get_most_informative_ic("HP:0000924", \
            "HP:0000924"), 1.6094379)