def test_clear_sentences3(self):
s = [
Sentence(
'snowboarding is harder to learn but easier to master than skiing',
20, '', '')
]
s = clear_sentences(s, self.objA, self.objB)
self.assertFalse(s)
def test_clear_sentences2(self):
s = [
Sentence('Dog is worse than cat', 20, '', ''),
Sentence('Dog didn\'t look better than cat', 20, '', '')
]
s = clear_sentences(s, self.objA, self.objB)
self.assertTrue('Dog didn\'t look better than cat' in
[sentence.text for sentence in s])
def test_clear_sentences9(self):
obj_a = Argument('coca-cola light')
obj_b = Argument('pepsi light')
s = [
Sentence('Coca Cola™ light tastes better than Pepsi™ light', 20,
'', '')
]
s = clear_sentences(s, obj_a, obj_b)
self.assertTrue(s)
def test_clear_sentences5(self):
s = [
Sentence('Cat and dog work well together', 10, '', ''),
Sentence('Cat are worse than dog', 20, '', '')
]
s = clear_sentences(s, self.objA, self.objB)
self.assertFalse('Cats and dogs work well together' in
[sentence.text for sentence in s])
self.assertTrue(s)
def test_clear_sentences4(self):
s = [
Sentence('A better cat is still no dog', 20, '', ''),
Sentence('Cat are worse than dog', 20, '', '')
]
s = clear_sentences(s, self.objA, self.objB)
self.assertFalse('A better cat is still no dog' in
[sentence.text for sentence in s])
self.assertTrue(s)
def test_clear_sentences6(self):
s = [
Sentence('Cat is worse than dog', 10, '', ''),
Sentence(
'Cat are more beautiful, but are harder to raise than dog', 20,
'', '')
]
s = clear_sentences(s, self.objA, self.objB)
self.assertFalse('Cat are wiser, but are harder to raise than dog' in
[sentence.text for sentence in s])
self.assertTrue(s)
def cam():
'''
to be visited after a user clicked the 'compare' button.
'''
load_config()
fast_search = request.args.get('fs')
obj_a = Argument(request.args.get('objectA').lower().strip())
obj_b = Argument(request.args.get('objectB').lower().strip())
aspects = extract_aspects(request)
model = request.args.get('model')
statusID = request.args.get('statusID')
if model == 'default' or model is None:
# json obj with all ES hits containing obj_a, obj_b and a marker.
setStatus(statusID, 'Request ES')
json_compl = request_es(fast_search, obj_a, obj_b)
# list of all sentences containing obj_a, obj_b and a marker.
setStatus(statusID, 'Extract sentences')
all_sentences = extract_sentences(json_compl)
# removing sentences that can't be properly analyzed
setStatus(statusID, 'Clear sentences')
all_sentences = clear_sentences(all_sentences, obj_a, obj_b)
# find the winner of the two objects
setStatus(statusID, 'Find winner')
return jsonify(find_winner(all_sentences, obj_a, obj_b, aspects))
else:
setStatus(statusID, 'Request all sentences containing the objects')
if aspects:
json_compl_triples = request_es_triple(obj_a, obj_b, aspects)
json_compl = request_es_ML(fast_search, obj_a, obj_b)
setStatus(statusID, 'Extract sentences')
if aspects:
all_sentences = extract_sentences(json_compl_triples)
all_sentences.extend([
sentence for sentence in extract_sentences(json_compl)
if sentence.text not in
[sentence.text for sentence in all_sentences]
])
else:
all_sentences = extract_sentences(json_compl)
if len(all_sentences) == 0:
return jsonify(find_winner(all_sentences, obj_a, obj_b, aspects))
remove_questions(all_sentences)
setStatus(statusID, 'Prepare sentences for classification')
prepared_sentences = prepare_sentence_DF(all_sentences, obj_a, obj_b)
setStatus(statusID, 'Classify sentences')
classification_results = classify_sentences(prepared_sentences, model)
setStatus(statusID, 'Evaluate classified sentences; Find winner')
final_dict = evaluate(all_sentences, prepared_sentences,
classification_results, obj_a, obj_b, aspects)
return jsonify(final_dict)
def test_clear_sentences1(self):
s = [Sentence('Dog is worse than cat?', 20, '', '')]
s = clear_sentences(s, self.objA, self.objB)
self.assertFalse(s)