def identify_interaction(self,path_to_test_file):
document_data=open(path_to_test_file,'rb')
xml_data=parse(document_data)
sentences = xml_data.getElementsByTagName("sentence")
result={}
for sentence in sentences:
entity_collection={}
sentence_attrs = dict(sentence.attributes.items())
sentence_text=sentence_attrs["text"]
sentence_id=sentence_attrs["id"]
entities = sentence.getElementsByTagName("entity")
for entity in entities:
entity_attrs = dict(entity.attributes.items())
id=entity_attrs["id"]
text=(entity_attrs["text"]).lower()
type=(entity_attrs["type"]).lower()
entity_collection[id]={}
entity_collection[id]={"text":text,"type":type}
words=utils.tokenize_string_without_punctuations(sentence_text)
bigrams=utils.generate_bigrams(words)
trigrams=utils.generate_trigrams(words)
result_found=0
for bigram in bigrams:
if bigram in self.bigrams_based_on_score.keys():
result[sentence_id]={}
result[sentence_id]=self.bigrams_based_on_score[bigram]
result_found=1
print "bigram as a result of which classification is done:"+bigram
break
if result_found!=1:
result[sentence_id]="false"
#pprint.pprint(self.bigrams_based_on_score)
document_data.close()
return result
def parse_ddi_corpus(self,file):
#print file
document_data=open(file,'r')
xml_data=parse(document_data)
sentences = xml_data.getElementsByTagName("sentence")
for sentence in sentences:
self.total_sentence_count+=1
sentence_attrs = dict(sentence.attributes.items())
text=sentence_attrs["text"]
sentence_id=sentence_attrs["id"]
words=utils.tokenize_string_without_punctuations(text)
entities = sentence.getElementsByTagName("entity")
for entity in entities:
entity_attrs = dict(entity.attributes.items())
id=entity_attrs["id"]
text=(entity_attrs["text"]).lower()
type=(entity_attrs["type"]).lower()
self.entity_collection[id]={}
self.entity_collection[id]={"text":text,"type":type}
interacting_pairs = sentence.getElementsByTagName("pair")
# removing drugs
for ids in self.entity_collection:
if self.entity_collection[ids]["text"] in words:
words.remove(self.entity_collection[ids]["text"])
for pair in interacting_pairs:
pair_attrs = dict(pair.attributes.items())
type="null"
ddi="0"
if "ddi" in pair_attrs.keys():
ddi=pair_attrs["ddi"]
if ddi=="false":
ddi="0"
elif ddi=="true":
ddi="1"
if "type" in pair_attrs.keys():
type=pair_attrs["type"]
#self.pairs_collection[pair_attrs["id"]]={}
pair_id=pair_attrs["id"]
self.pairs_collection[pair_id]=ddi
entity_1=self.entity_collection[pair_attrs["e1"]]["text"]
entity_2=self.entity_collection[pair_attrs["e2"]]["text"]
if entity_1 in self.interaction_collection.keys():
if entity_2 not in self.interaction_collection[entity_1].keys():
self.interaction_collection[entity_1][entity_2]=ddi
elif entity_2 in self.interaction_collection.keys():
if entity_1 not in self.interaction_collection[entity_2]:
self.interaction_collection[entity_2][entity_1]=ddi
else:
self.interaction_collection[entity_2]={}
self.interaction_collection[entity_2][entity_1]=ddi
self.interaction_collection[entity_2][entity_1]=ddi
document_data.close()
def parse_ddi_corpus(self,file):
#print file
document_data=open(file,'r')
xml_data=parse(document_data)
sentences = xml_data.getElementsByTagName("sentence")
for sentence in sentences:
# setting flag to zero at the beginning of sentence
is_there_a_positive_interaction_for_sentence=0
is_there_a_negative_interaction_for_sentence=0
#counting the total sentences
self.total_sentence_count+=1
sentence_attrs = dict(sentence.attributes.items())
text=sentence_attrs["text"]
sentence_id=sentence_attrs["id"]
words=utils.tokenize_string_without_punctuations(text)
entities = sentence.getElementsByTagName("entity")
for entity in entities:
entity_attrs = dict(entity.attributes.items())
id=entity_attrs["id"]
text=(entity_attrs["text"]).lower()
type=(entity_attrs["type"]).lower()
self.entity_collection[id]={}
self.entity_collection[id]={"text":text,"type":type}
interacting_pairs = sentence.getElementsByTagName("pair")
# removing drugs
for ids in self.entity_collection:
if self.entity_collection[ids]["text"] in words:
words.remove(self.entity_collection[ids]["text"])
for pair in interacting_pairs:
pair_attrs = dict(pair.attributes.items())
type="null"
if "type" in pair_attrs.keys():
type=pair_attrs["type"]
if pair_attrs["ddi"]=="true":
is_there_a_positive_interaction_for_sentence=1
entity_1=self.entity_collection[pair_attrs["e1"]]["text"]
entity_2=self.entity_collection[pair_attrs["e2"]]["text"]
if entity_1 in self.interaction_collection.keys():
if entity_2 not in self.interaction_collection[entity_1].keys():
self.interaction_collection[entity_1][entity_2]=type
elif entity_2 in self.interaction_collection.keys():
if entity_1 not in self.interaction_collection[entity_2]:
self.interaction_collection[entity_2][entity_1]=type
else:
self.interaction_collection[entity_2]={}
self.interaction_collection[entity_2][entity_1]=type
elif pair_attrs["ddi"]=="false" and is_there_a_positive_interaction_for_sentence!=1:
is_there_a_negative_interaction_for_sentence=1
if is_there_a_positive_interaction_for_sentence ==1:
self.sentence_interaction_information[sentence_id]={}
self.sentence_interaction_information[sentence_id]="true"
self.positive_sentence_count=self.positive_sentence_count+1
self.populate_unigram_hashes(words,self.interaction_true_word_count)
self.update_bigram_hash(words,self.interaction_true_bigram_count)
self.update_trigram_hash(words,self.interaction_true_trigram_count)
elif is_there_a_negative_interaction_for_sentence==1:
self.negative_sentence_count=self.negative_sentence_count+1
self.populate_unigram_hashes(words,self.interaction_false_word_count)
self.update_bigram_hash(words,self.interaction_false_bigram_count)
self.update_trigram_hash(words,self.interaction_false_trigram_count)
self.sentence_interaction_information[sentence_id]={}
self.sentence_interaction_information[sentence_id]="false"
else:
self.sentence_interaction_information[sentence_id]={}
self.sentence_interaction_information[sentence_id]="false"
document_data.close()
