def test_build_arrg_graph_rst_conceptnnet_io(self):
self._setup_link_parse_tree_189()
aspects_per_edu = [
(559, [u'phone']), # test added manually
(560, [u'apple']),
(561, []),
(562, [u'store clerk', u'apple', u'teenager', u'advice']),
(563, []),
]
aspects_graph_builder = Aspect2AspectGraph(aspects_per_edu)
rules_extractor = EDUTreeRulesExtractor()
rules = rules_extractor.extract(self.link_tree, [559, 560, 562], 1)
documents_info = {
189: {
'EDUs': [559, 560, 561, 562, 563],
'accepted_edus': [559, 560, 561, 562, 563],
'aspect_concepts': {
189: {
'conceptnet_io': {
u'apple': [
{
'end': u'apple',
'end-lang': u'en',
'relation': u'IsA',
'start': u'object',
'start-lang': u'en',
'weight': 2.8284271
},
{
'end': u'stuff',
'end-lang': u'en',
'relation': u'Synonym',
'start': u'apple',
'start-lang': u'en',
'weight': 2.8284271
},
]
}
}
},
'sentiment': {
559: 1,
560: -1,
5562: 1
},
# the rest of document info is skipped
}
}
graph, page_rank = aspects_graph_builder.build(rules, documents_info,
True)
self.assertEqual(len(rules), 1)
self.assertGreaterEqual(len(graph.nodes()), 4)
self.assertGreaterEqual(len(graph.edges()), 3)
attrib = nx.get_edge_attributes(graph, 'relation_type')
self.assertEqual(
attrib, {
(u'object', u'apple', 0): u'IsA',
(u'apple', u'stuff', 0): u'Synonym',
(u'apple', u'phone', 0): 'Elaboration'
})
def test_arrg_to_aht(self):
aspects_graph_builder = Aspect2AspectGraph()
graph = nx.Graph()
graph.add_edge('phone', 'screen', gerani_weight=20)
graph.add_edge('phone', 'voicemail', gerani_weight=35)
graph.add_edge('apple', 'voicemail', gerani_weight=22)
graph.add_edge('apple', 'phone', gerani_weight=55)
graph.add_edge('apple', 'screen', gerani_weight=55)
graph.add_edge('apple', 'signal', gerani_weight=20)
graph.add_edge('phone', 'signal', gerani_weight=37)
graph.add_edge('phone', 'camera', gerani_weight=37)
graph.add_edge('sound', 'apple', gerani_weight=7)
graph.add_edge('sound', 'phone', gerani_weight=15)
graph.add_edge('pixel', 'camera', gerani_weight=10)
graph.add_edge('pixel', 'screen', gerani_weight=20)
graph.add_edge('phone', 'screen', gerani_weight=7)
mst_obtained = aspects_graph_builder.arrg_to_aht(
graph, 'gerani_weight')
self.assertEquals(len(mst_obtained.nodes()), len(graph.nodes()))
self.assertLess(len(mst_obtained.edges()), len(graph.edges()))
self.assertEqual(
nx.get_edge_attributes(mst_obtained, name='gerani_weight'), {
('phone', 'voicemail'): 35,
('apple', 'screen'): 55,
('apple', 'phone'): 55,
('signal', 'phone'): 37,
('phone', 'camera'): 37,
('screen', 'pixel'): 20,
('sound', 'phone'): 15,
})
def test_filter_gerani_sum_of_max_weights(self):
rules = {
1: [
(514, 513, 'Elaboration', 0.25),
(514, 513, 'Elaboration', 1.38),
(514, 513, 'Elaboration', 0.38),
],
2: [
(514, 513, 'Elaboration', 0.25),
(514, 513, 'Elaboration', 10.8),
(514, 513, 'Elaboration', 0.38),
],
3: [],
}
aspects_per_edu = [
(513, [u'phone']),
(514, [u'screen', u'voicemail']),
(515, [u'sound']),
(516, [u'speaker']),
(517, [u'screen', u'speaker']),
]
aspects_graph_builder = Aspect2AspectGraph(aspects_per_edu)
rules_obtained = aspects_graph_builder.filter_gerani(rules)
rules_expected = {
-1: [
AspectsRelation(aspect1=u'screen',
aspect2=u'phone',
relation_type='Elaboration',
gerani_weight=12.18),
]
}
self.assertEqual(rules_obtained, rules_expected)
def test_build_exemplary_arrg_graph_sample_tree_189_multiaspects(self):
self._setup_link_parse_tree_189()
aspects_per_edu = [
(559, [u'test', u'test2']), # test added manually
(560, [u'thing', u'test2']),
(561, []),
(562, [u'store clerk', u'apple', u'teenager', u'advice']),
(563, [])
]
aspects_graph_builder = Aspect2AspectGraph(
aspects_per_edu, with_cycles_between_aspects=True)
rules_extractor = EDUTreeRulesExtractor()
rules = rules_extractor.extract(self.link_tree, [559, 560, 562], 1)
graph, page_rank = aspects_graph_builder.build(rules,
conceptnet_io=False)
self.assertEqual(len(rules), 1)
self.assertEqual(len(graph.nodes()), 3)
self.assertEqual(len(graph.edges()), 4)
attrib = nx.get_edge_attributes(graph, 'relation_type')
self.assertEqual(
attrib, {
(u'thing', u'test', 0): 'Elaboration',
(u'test2', u'test2', 0): 'Elaboration',
(u'test2', u'test', 0): 'Elaboration',
(u'thing', u'test2', 0): 'Elaboration'
})
def test_add_aspects_to_graph_equal_aspects(self):
aspects_graph_builder = Aspect2AspectGraph(
with_cycles_between_aspects=False)
graph = nx.Graph()
aspects_graph_builder.add_aspects_to_graph(graph, 'phone', 'phone',
'rel', 55)
self.assertEquals(len(graph.nodes()), 0)
self.assertEquals(len(graph.edges()), 0)
def test_calculate_page_ranks_no_weight(self):
aspects_graph_builder = Aspect2AspectGraph()
self._set_multigraph_arrg()
pagerank = aspects_graph_builder.calculate_page_ranks(
self.arrg_sample_graph)
pagerank_expected = OrderedDict([('phone', 0.3991232045549693),
('cellphone', 0.25),
('screen', 0.17543839772251535),
('voicemail', 0.17543839772251535)])
self.assertEqual(pagerank, pagerank_expected)
def test_calculate_page_ranks(self):
aspects_graph_builder = Aspect2AspectGraph()
self._set_multigraph_arrg()
pagerank = aspects_graph_builder.calculate_page_ranks(
self.arrg_sample_graph, weight='gerani_weight')
pagerank_expected = OrderedDict([('phone', 0.46744998785621517),
('cellphone', 0.18167321669875405),
('screen', 0.17543839772251535),
('voicemail', 0.17543839772251535)])
self.assertEqual(pagerank, pagerank_expected)
def build_aspect_to_aspect_graph(
self,
df: pd.DataFrame,
filter_relation_fn: Callable = None,
aspects_to_skip=None,
):
builder = Aspect2AspectGraph(aspects_to_skip=aspects_to_skip)
graph = builder.build(discourse_tree_df=df,
filter_relation_fn=filter_relation_fn)
serializer.save(graph, self.paths.aspect_to_aspect_graph)
return graph
def test_build_without_conceptnet_multi_rules_filter_confidence_filter(
self):
rules = {
1: [
(514, 513, 'Elaboration', 0.25),
(514, 513, 'Elaboration', 1.38),
(514, 513, 'Elaboration', 0.38),
],
2: [
(514, 513, 'same-unit', 0.25),
(514, 513, 'same-unit', 0.38),
(514, 513, 'Elaboration', 1.38),
(517, 513, 'Elaboration', 94.38),
(516, 515, 'Elaboration', 0.29),
(516, 513, 'Elaboration', 0.2),
],
3: [],
}
aspects_per_edu = [
(513, [u'phone']),
(514, [u'screen', u'voicemail']),
(515, [u'sound']),
(516, [u'speaker']),
(517, [u'screen', u'speaker']),
]
aspects_graph_builder = Aspect2AspectGraph(aspects_per_edu)
graph, pagerank = aspects_graph_builder.build(rules,
docs_info={},
conceptnet_io=False,
filter_gerani=True)
gerani_weight_attrib = nx.get_edge_attributes(graph, 'gerani_weight')
relation_type_weight_attrib = nx.get_edge_attributes(
graph, 'relation_type')
self.assertTrue(isinstance(graph, nx.DiGraph))
self.assertEqual(graph.number_of_nodes(), 4)
gerani_weight_attrib_expected = {
(u'screen', u'phone', 0): 1.38,
(u'speaker', u'sound', 0): 0.29
}
relation_type_weight_attrib_expected = {
(u'screen', u'phone', 0): 'Elaboration',
(u'speaker', u'sound', 0): 'Elaboration'
}
self.assertEqual(gerani_weight_attrib, gerani_weight_attrib_expected)
self.assertEqual(relation_type_weight_attrib,
relation_type_weight_attrib_expected)
def test_merge_multiedges_in_arrg(self):
aspects_graph_builder = Aspect2AspectGraph()
graph = nx.DiGraph()
graph.add_edge('screen',
'phone',
gerani_weight=10,
relation='Elaboration')
graph.add_edge('screen',
'phone',
gerani_weight=10,
relation='same-unit')
graph.add_edge('voicemail',
'phone',
gerani_weight=10,
relation='Elaboration')
graph.add_edge('voicemail',
'phone',
gerani_weight=13,
relation='same-unit')
graph.add_edge('voicemail',
'phone',
gerani_weight=12,
relation='Contrast')
graph.add_edge('signal',
'phone',
gerani_weight=11,
relation='Contrast')
graph.add_edge('signal',
'phone',
gerani_weight=9,
relation='Elaboration')
graph.add_edge('phone',
'signal',
gerani_weight=17,
relation='Elaboration')
graph = aspects_graph_builder.merge_multiedges_in_arrg(graph)
self.assertEqual(
nx.get_edge_attributes(graph, name='gerani_weight'), {
('phone', 'voicemail'): 35,
('phone', 'signal'): 37,
('phone', 'screen'): 20
})
def test_build_without_conceptnet_multi_rules_gerani_moi(self):
self._set_rst_rules_document_info()
aspects_graph_builder = Aspect2AspectGraph(self.aspects_per_edu,
alpha_gerani=0.5)
graph, pagerank = aspects_graph_builder.build(self.rules,
docs_info=self.docs_info,
conceptnet_io=False,
filter_gerani=True,
aht_gerani=False)
self.assertTrue(isinstance(graph, nx.DiGraph))
self.assertEqual(graph.number_of_nodes(), 4)
self.assertEqual(graph.number_of_edges(), 2)
self.assertEqual(
graph['screen']['phone'],
{0: {
'relation_type': 'Elaboration',
'gerani_weight': 1.38
}})
self.assertEqual(
graph['speaker']['sound'],
{0: {
'relation_type': 'Elaboration',
'gerani_weight': 0.29
}})
from aspects.data_io import serializer
from aspects.utilities import settings
REVIEWS_RESULTS = settings.DEFAULT_OUTPUT_PATH.parent.parent / 'results' / 'reviews_Cell_Phones_and_Accessories'
# aspects_per_edu = serializer.load((REVIEWS_RESULTS / 'aspects_per_edu').as_posix())
# edus = serializer.load('results/reviews_Cell_Phones_and_Accessories/raw_edu_list')
# documents_info = serializer.load((REVIEWS_RESULTS / 'documents_info').as_posix())
with open((REVIEWS_RESULTS / 'aspects_per_edu.json').as_posix(), 'r') as f:
aspects_per_edu = json.load(f)
aspect_relations = serializer.load(
(REVIEWS_RESULTS / 'edu_dependency_rules').as_posix())
aspect_graph_builder = Aspect2AspectGraph(aspects_per_edu=aspects_per_edu)
aspect_rules = []
for relation in tqdm(aspect_relations.values()):
for edu_1, edu_2, _, weight in relation:
for aspect_left, aspect_right in aspect_graph_builder.aspects_iterator(
edu_1, edu_2):
if aspect_left != aspect_right:
aspect_rules.append((aspect_left, aspect_right, weight))
df = pd.DataFrame(aspect_rules, columns=['id1', 'id2', 'weight'])
df.to_csv(REVIEWS_RESULTS / 'aspect-rules.csv', index=False)
pass
def test_build_without_conceptnet_multi_rules_no_filter_confidence_filter(
self):
rules = {
1: [
(514, 513, 'Elaboration', 0.25),
(514, 513, 'Elaboration', 1.38),
(514, 513, 'Elaboration', 0.38),
],
2: [
(514, 513, 'same-unit', 0.25),
(514, 513, 'same-unit', 0.38),
(514, 513, 'Elaboration', 1.38),
(517, 513, 'Elaboration', 94.38),
(516, 515, 'Elaboration', 0.29),
(516, 513, 'Elaboration', 0.2),
],
3: [],
}
aspects_per_edu = [
(513, [u'phone']),
(514, [u'screen', u'voicemail']),
(515, [u'sound']),
(516, [u'speaker']),
(517, [u'screen', u'speaker']),
]
aspects_graph_builder = Aspect2AspectGraph(
aspects_per_edu, with_cycles_between_aspects=True)
graph, pagerank = aspects_graph_builder.build(rules,
docs_info={},
conceptnet_io=False,
filter_gerani=False)
self.assertTrue(isinstance(graph, nx.DiGraph))
self.assertEqual(graph.number_of_nodes(), 5)
self.assertEqual(graph.number_of_edges(), 16)
self.assertEqual(
graph['screen']['phone'], {
0: {
'gerani_weight': 0.25,
'relation_type': 'Elaboration'
},
1: {
'gerani_weight': 1.38,
'relation_type': 'Elaboration'
},
2: {
'gerani_weight': 0.38,
'relation_type': 'Elaboration'
},
3: {
'gerani_weight': 0.25,
'relation_type': 'same-unit'
},
4: {
'gerani_weight': 0.38,
'relation_type': 'same-unit'
},
5: {
'gerani_weight': 1.38,
'relation_type': 'Elaboration'
},
6: {
'gerani_weight': 94.38,
'relation_type': 'Elaboration'
},
})
self.assertEqual(
graph['speaker']['sound'],
{0: {
'relation_type': 'Elaboration',
'gerani_weight': 0.29
}})
pagerank_expected = OrderedDict([(u'phone', 0.4698552806383583),
(u'sound', 0.1327942890741717),
(u'screen', 0.1324501434291567),
(u'speaker', 0.1324501434291567),
(u'voicemail', 0.1324501434291567)])
self.assertEqual(pagerank, pagerank_expected)
