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test_lexmap_local.py
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test_lexmap_local.py
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from ontobio import OntologyFactory
from ontobio import Synonym, Ontology
from ontobio.lexmap import LexicalMapEngine
import networkx as nx
import logging
# TODO move this
def test_merge():
factory = OntologyFactory()
print("Creating ont")
ont = factory.create('tests/resources/lexmap_test.json')
ont2 = Ontology()
ont2.merge([ont])
assert ont2.xref_graph is not None
def test_lexmap_basic():
"""
Text lexical mapping
"""
factory = OntologyFactory()
print("Creating ont")
ont = factory.create('tests/resources/lexmap_test.json')
lexmap = LexicalMapEngine()
lexmap.index_ontology(ont)
print(lexmap.lmap)
print(ont.all_synonyms())
g = lexmap.get_xref_graph()
for (x,y,d) in g.edges(data=True):
print("{}<->{} :: {}".format(x,y,d))
for x in g.nodes():
print("{} --> {}".format(x,lexmap.grouped_mappings(x)))
assert g.has_edge('Z:2','ZZ:2') # roman numerals
assert g.has_edge('Z:2','Y:2') # case insensitivity
assert g.has_edge('A:1','B:1') # synonyms
assert g.has_edge('B:1','A:1') # bidirectional
for (x,y,d) in g.edges(data=True):
print("{}<->{} :: {}".format(x,y,d))
cpr = d[lexmap.CONDITIONAL_PR]
assert cpr > 0 and cpr <= 1.0
df = lexmap.as_dataframe(g)
print(df.to_csv(sep="\t"))
lexmap = LexicalMapEngine(config=dict(synsets=[dict(word="",
synonym="ignoreme",
weight=-2.0)],
normalized_form_confidence=0.25,
abbreviation_confidence=0.5,
meaningful_ids=True,
ontology_configurations=[dict(prefix='AA',
normalized_form_confidence=-1000)]))
assert len(lexmap._get_config_val('NULL','synsets')) == 1
assert lexmap._normalize_label('ignoreme foo', {'ignoreme':''}) == 'foo'
assert lexmap._normalize_label('replaceme foo', {'replaceme':'zz'}) == 'foo zz'
ont.add_node('TEST:1', 'foo bar')
ont.add_node('TEST:2', 'bar foo')
ont.add_node('TEST:3', 'foo bar')
ont.add_node('TEST:4', 'wiz')
syn = Synonym('TEST:4', val='bar foo', pred='hasRelatedSynonym')
ont.add_synonym(syn)
ont.add_node('http://x.org/wiz#FooBar')
ont.add_node('TEST:6', '123')
ont.add_node('TEST:7', '123')
ont.add_node('TEST:8', 'bar ignoreme foo')
ont.add_node('AA:1', 'foo bar')
ont.add_node('AA:2', 'bar foo')
ont.add_node('ABBREV:1', 'ABCD')
ont.add_node('ABBREV:2', 'ABCD')
for s in ont.synonyms('TEST:4'):
print('S={}'.format(s))
lexmap.index_ontology(ont)
g = lexmap.get_xref_graph()
for x,d in g['TEST:1'].items():
print('XREF: {} = {}'.format(x,d))
assert g.has_edge('TEST:1','TEST:2') # normalized
logging.info('E 1-2 = {}'.format(g['TEST:1']['TEST:2']))
assert int(g['TEST:1']['TEST:2']['score']) == 25
assert int(g['TEST:1']['TEST:3']['score']) == 100
assert int(g['TEST:1']['TEST:4']['score']) < 25
assert g.has_edge('TEST:3','http://x.org/wiz#FooBar') # IDs and CamelCase
assert not g.has_edge('TEST:6','TEST:7') # should omit syns with no alphanumeric
# test exclude normalized form
assert not g.has_edge('AA:1','AA:2')
# test custom synsets are used
assert g.has_edge('TEST:8','TEST:2')
assert g.has_edge('TEST:8','AA:2')
assert not g.has_edge('TEST:8','AA:1') # do not normalize AAs
assert lexmap.smap['ABBREV:1'][0].is_abbreviation()
assert lexmap.smap['ABBREV:2'][0].is_abbreviation()
assert g.has_edge('ABBREV:1','ABBREV:2')
assert int(g['ABBREV:1']['ABBREV:2']['score']) == 25
df = lexmap.unmapped_dataframe(g)
print(df.to_csv())
def test_lexmap_multi():
"""
Text lexical mapping
"""
factory = OntologyFactory()
print("Creating ont")
files = ['x','m','h','bto']
onts = [factory.create('tests/resources/autopod-{}.json'.format(f)) for f in files]
lexmap = LexicalMapEngine()
lexmap.index_ontologies(onts)
#print(lexmap.lmap)
#print(ont.all_synonyms())
g = lexmap.get_xref_graph()
for x in g.nodes():
print("{} --> {}".format(x,lexmap.grouped_mappings(x)))
for (x,y,d) in g.edges(data=True):
cl = nx.ancestors(g,x)
print("{} '{}' <-> {} '{}' :: {} CLOSURE={}".format(x,lexmap.label(x),y,lexmap.label(y),d,len(cl)))
cpr = d[lexmap.CONDITIONAL_PR]
assert cpr > 0 and cpr <= 1.0
unmapped = lexmap.unmapped_nodes(g)
print('U: {}'.format(len(unmapped)))
unmapped = lexmap.unmapped_nodes(g, rs_threshold=4)
print('U4: {}'.format(len(unmapped)))
cliques = lexmap.cliques(g)
maxc = max(cliques, key=len)
print('CLIQUES: {}'.format(cliques))
print('MAX CLIQUES: {}'.format(maxc))
df = lexmap.as_dataframe(g)
print(df.to_csv(sep="\t"))
def test_awe_1_to_many_flat():
"""
Text axiom weight estimation, for a 1-to-many situation, where the many are not inter-related
"""
ont = Ontology()
lexmap = LexicalMapEngine(config={'cardinality_weights': [
{'prefix1':'X',
'prefix2':'Y',
'cardinality':'1m',
'weights':[-1.0, 1.0, -2.0, 0.0]
}
]})
ont.add_node('X:1', 'foo 1')
ont.add_node('Y:1a', 'foo 1a')
ont.add_synonym(Synonym('Y:1a', val='foo 1', pred='hasRelatedSynonym'))
ont.add_node('Y:1b', 'foo 1b')
ont.add_synonym(Synonym('Y:1b', val='foo 1', pred='hasExactSynonym'))
lexmap.index_ontology(ont)
xg = lexmap.get_xref_graph()
df = lexmap.as_dataframe(xg)
print(df.to_csv(sep="\t"))
P = lexmap.weighted_axioms('X:1','Y:1a', xg)
logging.info('P={}'.format(P))
assert P[0] < P[1]
assert P[1] > P[2]
def test_awe_1_to_many_inverse():
"""
As previous test, but checking to ensure directionality in config is reversed
"""
ont = Ontology()
lexmap = LexicalMapEngine(config={'cardinality_weights': [
{'prefix1':'Y',
'prefix2':'X',
'cardinality':'m1',
'weights':[1.0, -1.0, -2.0, 0.0]
}
]})
ont.add_node('X:1', 'foo 1')
ont.add_node('Y:1a', 'foo 1a')
ont.add_synonym(Synonym('Y:1a', val='foo 1', pred='hasRelatedSynonym'))
ont.add_node('Y:1b', 'foo 1b')
ont.add_synonym(Synonym('Y:1b', val='foo 1', pred='hasExactSynonym'))
lexmap.index_ontology(ont)
xg = lexmap.get_xref_graph()
df = lexmap.as_dataframe(xg)
print(df.to_csv(sep="\t"))
P = lexmap.weighted_axioms('X:1','Y:1a', xg)
logging.info('P={}'.format(P))
assert P[0] < P[1]
assert P[1] > P[2]
def test_awe_1_to_many_default():
"""
As previous test, but with defaults
"""
ont = Ontology()
lexmap = LexicalMapEngine(config={'cardinality_weights': [
{'cardinality':'m1',
'weights':[1.0, -1.0, -2.0, 0.0]
}
]})
ont.add_node('X:1', 'foo 1')
ont.add_node('Y:1a', 'foo 1a')
ont.add_synonym(Synonym('Y:1a', val='foo 1', pred='hasRelatedSynonym'))
ont.add_node('Y:1b', 'foo 1b')
ont.add_synonym(Synonym('Y:1b', val='foo 1', pred='hasExactSynonym'))
lexmap.index_ontology(ont)
xg = lexmap.get_xref_graph()
df = lexmap.as_dataframe(xg)
print(df.to_csv(sep="\t"))
P = lexmap.weighted_axioms('X:1','Y:1a', xg)
logging.info('P={}'.format(P))
assert P[0] < P[1]
assert P[1] > P[2]
def test_awe_1_to_many_hier():
"""
Text axiom weight estimation
"""
ont = Ontology()
assert isinstance(ont.nodes(), nx.classes.reportviews.NodeView)
lexmap = LexicalMapEngine()
ont.add_node('X:1', 'foo 1')
ont.add_node('Z:1a', 'foo 1')
ont.add_node('Z:1b', 'foo 1')
ont.add_parent('Z:1b','Z:1a')
lexmap.index_ontology(ont)
xg = lexmap.get_xref_graph()
df = lexmap.as_dataframe(xg)
print(df.to_csv(sep="\t"))
P_a = lexmap.weighted_axioms('X:1','Z:1a', xg)
P_b = lexmap.weighted_axioms('X:1','Z:1b', xg)
logging.info('P_a={} P_b={}'.format(P_a, P_b))
assert P_a[0] > P_a[1]
assert P_b[0] < P_b[1]
assert P_a[0] > P_b[0]
def test_awe_1_to_1():
"""
Text axiom weight estimation
"""
ont = Ontology()
assert isinstance(ont.nodes(), nx.classes.reportviews.NodeView)
lexmap = LexicalMapEngine(config={'cardinality_weights': [
{'prefix1':'X',
'prefix2':'Y',
'cardinality':'11',
'weights':[-1.0, -1.0, 2.0, 0.0]
}
]})
ont.add_node('X:1', 'foo 1')
ont.add_node('Y:1', 'foo 1')
ont.add_node('Z:1a', 'foo 1')
ont.add_node('Z:1b', 'foo 1')
lexmap.index_ontology(ont)
xg = lexmap.get_xref_graph()
df = lexmap.as_dataframe(xg)
print(df.to_csv(sep="\t"))
P_XY = lexmap.weighted_axioms('X:1','Y:1', xg)
P_XZ = lexmap.weighted_axioms('X:1','Z:1a', xg)
logging.info('P_XY={} P_XZ={}'.format(P_XY, P_XZ))
assert P_XY[2] > P_XZ[2]
def test_awe_match_pairs():
"""
Text axiom weight estimation
"""
ont = Ontology()
assert isinstance(ont.nodes(), nx.classes.reportviews.NodeView)
lexmap = LexicalMapEngine(config={'match_weights': [
{'prefix1':'X',
'prefix2':'Y',
'weights':[1.0, -1.0, 2.0, 0.0]
}
]})
ont.add_node('X:1', 'foo 1')
ont.add_node('Y:1', 'foo 1')
lexmap.index_ontology(ont)
xg = lexmap.get_xref_graph()
df = lexmap.as_dataframe(xg)
print(df.to_csv(sep="\t"))
P_XY = lexmap.weighted_axioms('X:1','Y:1', xg)
P_YX = lexmap.weighted_axioms('Y:1','X:1', xg)
logging.info('P_XY={} P_YX={}'.format(P_XY, P_YX))
assert P_XY[0] > P_XY[1]
assert P_XY[0] == P_YX[1]
def test_awe_scope_map():
"""
Text axiom weight estimation, syn scopes
"""
ont = Ontology()
assert isinstance(ont.nodes(), nx.classes.reportviews.NodeView)
lexmap = LexicalMapEngine()
ont.add_node('X:1', 'x1')
ont.add_node('Y:1', 'y1')
ont.add_node('Z:1', 'z1')
ont.add_synonym(Synonym('X:1', val='related', pred='hasRelatedSynonym'))
ont.add_synonym(Synonym('Y:1', val='related', pred='hasRelatedSynonym'))
ont.add_synonym(Synonym('Y:1', val='exact', pred='hasExactSynonym'))
ont.add_synonym(Synonym('Z:1', val='exact', pred='hasExactSynonym'))
lexmap.index_ontology(ont)
xg = lexmap.get_xref_graph()
df = lexmap.as_dataframe(xg)
print(df.to_csv(sep="\t"))
P_XY = lexmap.weighted_axioms('X:1','Y:1', xg)
P_YZ = lexmap.weighted_axioms('Y:1','Z:1', xg)
logging.info('P_XY={} P_XZ={}'.format(P_XY, P_YZ))
assert P_XY[2] > P_XY[0]
assert P_XY[2] > P_XY[1]
assert P_XY[2] > P_XY[3]
assert P_XY[2] < P_YZ[2]
def test_awe_xref_weights():
"""
Text axiom weight estimation, when provided with defaults
"""
ont = Ontology()
assert isinstance(ont.nodes(), nx.classes.reportviews.NodeView)
lexmap = LexicalMapEngine(config={'xref_weights':[
{'left':'X:1',
'right':'Y:1',
'weights':[100.0, 0.0 ,0.0 ,0.0]},
{'left':'Z:1',
'right':'Y:1',
'weights':[0.0, 100.0, 0.0, 0.0]},
]})
ont.add_node('X:1', 'foo')
ont.add_node('Y:1', 'foo')
ont.add_node('Z:1', 'foo')
lexmap.index_ontology(ont)
xg = lexmap.get_xref_graph()
df = lexmap.as_dataframe(xg)
print(df.to_csv(sep="\t"))
P_XY = lexmap.weighted_axioms('X:1','Y:1', xg)
P_YZ = lexmap.weighted_axioms('Y:1','Z:1', xg)
logging.info('P_XY={} P_XZ={}'.format(P_XY, P_YZ))
assert P_XY[0] > P_XY[1]
assert P_XY[0] > P_XY[2]
assert P_XY[0] > P_XY[3]
assert P_YZ[0] > P_YZ[1]
assert P_YZ[0] > P_YZ[2]
assert P_YZ[0] > P_YZ[3]