def remove_wrong_structures(sentences, obj_a, obj_b):
'''
Removes sentences 1: in which no marker is between the objects
2: in which there are both positive and negative markers between the objects
sentences: dictionary
a dictionary of sentences
obj_a: Argument
the first object to be compared
obj_b: Argument
the second object to be compared
'''
sentences_to_delete = []
for sentence in sentences:
a_pos = find_pos_in_sentence(obj_a.name, sentence.text)
b_pos = find_pos_in_sentence(obj_b.name, sentence.text)
if a_pos == -1 and b_pos == -1:
sentences_to_delete.append(sentence)
continue
pos_first = min(a_pos, b_pos)
pos_second = max(a_pos, b_pos)
has_pos_marker = has_marker(sentence.text, pos_first, pos_second,
POSITIVE_MARKERS)
has_neg_marker = has_marker(sentence.text, pos_first, pos_second,
NEGATIVE_MARKERS)
if (has_pos_marker and has_neg_marker) or (not has_pos_marker
and not has_neg_marker):
sentences_to_delete.append(sentence)
for sentence in sentences_to_delete:
del sentences[sentences.index(sentence)]
return sentences
def prepare_sentence_DF(sentences, obj_a, obj_b):
index = 0
temp_list = []
for sentence in sentences:
pos_a = find_pos_in_sentence(obj_a.name, sentence.text)
pos_b = find_pos_in_sentence(obj_b.name, sentence.text)
if pos_a < pos_b:
temp_list.append([obj_a.name, obj_b.name, sentence.text])
else:
temp_list.append([obj_b.name, obj_a.name, sentence.text])
index += 1
sentence_df = pd.DataFrame.from_records(temp_list, columns=['object_a', 'object_b', 'sentence'])
return sentence_df
def prepare_sentence_DF(sentences, obj_a, obj_b):
index = 0
temp_list = []
for sentence in sentences:
pos_a = find_pos_in_sentence(obj_a, sentence)
pos_b = find_pos_in_sentence(obj_b, sentence)
if (pos_a == -1 or pos_b == -1):
pos_a = 0
pos_b = len(sentence) - 1
if pos_a < pos_b:
temp_list.append([obj_a, obj_b, sentence])
else:
temp_list.append([obj_b, obj_a, sentence])
print (pos_a, pos_b)
index += 1
sentence_df = pd.DataFrame.from_records(temp_list, columns=['object_a', 'object_b', 'sentence'])
return sentence_df
def marker_pos(sentence, pos_first, pos_second, marker):
'''
Checks if the given marker is contained in the sentence and between the given positions,
if yes the position is returned, else -1 is returned
'''
pos_marker = find_pos_in_sentence(marker, sentence)
if pos_marker != -1 and pos_first < pos_marker < pos_second:
return pos_marker # found a marker between the objects
return -1
def transform(self, dataframe):
results = []
for index, row in dataframe.iterrows():
a, b, text = row['object_a'], row['object_b'], row['sentence']
a_index, b_index = find_pos_in_sentence(
a, text), find_pos_in_sentence(b, text)
if a_index < b_index:
begin, end = a_index, b_index + len(b)
else:
begin, end = b_index, a_index + len(a)
res = process(text[begin:end],
a,
b,
self.processing,
rep_a=self.rep_a,
rep_b=self.rep_b)
results.append(res)
return results
def what_is_better(sentence, obj_a, obj_b):
'''
Analyzes a sentence that contains two given objects. Returns object containing winner
and a boolean marking multiple markers.
Currently only sentences are supported that are built in the form of
... object ... marker ... object ...
sentence: String
the sentence to analyze. Has to contain obj_a and obj_b.
obj_a: Argument
the first object to be compared to the second.
obj_b: Argument
the second object to be compared to the first.
'''
sentence = sentence.lower()
result = {}
a_pos = find_pos_in_sentence(obj_a.name, sentence)
b_pos = find_pos_in_sentence(obj_b.name, sentence)
first_pos = min(a_pos, b_pos)
second_pos = max(a_pos, b_pos)
opp_pos = get_marker_pos(sentence, first_pos, second_pos, OPPOSITE_MARKERS)
neg_pos = get_marker_pos(sentence, first_pos, second_pos, NEGATIONS)
positive_pos = get_marker_pos(sentence, first_pos, second_pos,
POSITIVE_MARKERS)
if positive_pos != -1: # there's a positive marker, check if a won
result['marker_cnt'] = get_marker_count(sentence, first_pos,
second_pos, POSITIVE_MARKERS)
result['winner'] = obj_a if obj_a_wins_sentence(
first_pos, a_pos, opp_pos, neg_pos, positive_pos) else obj_b
return result
# we can return because there's never both markers in a sentence
negative_pos = get_marker_pos(sentence, first_pos, second_pos,
NEGATIVE_MARKERS)
result['marker_cnt'] = get_marker_count(sentence, first_pos, second_pos,
NEGATIVE_MARKERS)
# we're only here if there's no positive marker, so there must be negative one
result['winner'] = obj_b if obj_a_wins_sentence(
first_pos, a_pos, opp_pos, neg_pos, negative_pos) else obj_a
return result
