relation.add_feature("GENE_2_NGRAM_RIGHT_1_[" +
sentence.words[gene_2_end + 1].lemma + "]")
if __name__ == "__main__":
# Process input
with fileinput.input() as input_files:
for line in input_files:
# Parse the TSV line
line_dict = get_dict_from_TSVline(line, [
"doc_id", "sent_id", "wordidxs", "words", "poses", "ners",
"lemmas", "dep_paths", "dep_parents", "bounding_boxes",
"gene_1_entity", "gene_1_wordidxs", "gene_1_is_correct",
"gene_1_type", "gene_2_entity", "gene_2_wordidxs",
"gene_2_is_correct", "gene_2_type"
], [
no_op, int, lambda x: TSVstring2list(x, int), TSVstring2list,
TSVstring2list, TSVstring2list, TSVstring2list, TSVstring2list,
lambda x: TSVstring2list(x, int), TSVstring2list, no_op,
lambda x: TSVstring2list(x, int), TSVstring2bool, no_op, no_op,
lambda x: TSVstring2list(x, int), TSVstring2bool, no_op
])
# Create the sentence object where the two mentions appear
sentence = Sentence(line_dict["doc_id"], line_dict["sent_id"],
line_dict["wordidxs"], line_dict["words"],
line_dict["poses"], line_dict["ners"],
line_dict["lemmas"], line_dict["dep_paths"],
line_dict["dep_parents"],
line_dict["bounding_boxes"])
# Create the mentions
gene_1_mention = Mention(
# There are many PERSONs/ORGANIZATIONs/LOCATIONs in the sentence
# for ner in ["PERSON", "ORGANIZATION", "LOCATION"]:
# if [x.ner for x in sentence.words].count(ner) > 4:
# print_feature(
# sentence.doc_id, mention_id, "MANY_{}_IN_SENTENCE".format(ner))
if __name__ == "__main__":
# Process the input
with fileinput.input() as input_files:
for line in input_files:
# Parse the TSV line
line_dict = get_dict_from_TSVline(
line, ["doc_id", "sent_id", "wordidxs", "words", "poses",
"ners", "lemmas", "dep_paths", "dep_parents",
"mention_id", "mention_wordidxs"],
[no_op, int, lambda x: TSVstring2list(x, int), TSVstring2list,
TSVstring2list, TSVstring2list, TSVstring2list,
TSVstring2list, lambda x: TSVstring2list(x, int),
no_op, lambda x: TSVstring2list(x, int)])
# Create the sentence object
null_list = [None, ] * len(line_dict["wordidxs"])
sentence = Sentence(
line_dict["doc_id"], line_dict["sent_id"],
line_dict["wordidxs"], line_dict["words"], line_dict["poses"],
line_dict["ners"], line_dict["lemmas"], line_dict["dep_paths"],
line_dict["dep_parents"], null_list)
if sentence.is_weird():
continue
mention_words = []
for mention_wordidx in line_dict["mention_wordidxs"]:
mention_words.append(sentence.words[mention_wordidx])
import fileinput
from dstruct.Sentence import Sentence
from extract_gene_mentions import extract
from helper.easierlife import get_dict_from_TSVline, TSVstring2list, no_op
from helper.dictionaries import load_dict
if __name__ == "__main__":
# Load the merged genes dictionary
merged_genes_dict = load_dict("merged_genes")
# Process the input
with fileinput.input() as input_files:
for line in input_files:
# Parse the TSV line
line_dict = get_dict_from_TSVline(
line, ["doc_id", "sent_id", "wordidxs", "words", "gene"],
[no_op, int, lambda x: TSVstring2list(x, int), TSVstring2list,
no_op])
# Create the Sentence object
null_list = [None, ] * len(line_dict["wordidxs"])
sentence = Sentence(
line_dict["doc_id"], line_dict["sent_id"],
line_dict["wordidxs"], line_dict["words"], null_list,
null_list, null_list, null_list, null_list, null_list)
# This is the 'labelled' gene that we know is in the sentence
gene = line_dict["gene"]
# Get the main symbol (or list of symbols) for the labelled gene
if gene in merged_genes_dict:
gene = merged_genes_dict[gene]
else:
gene = [gene, ]
# Skip sentences that are "( GENE )", as they give no info about
# for ner in ["PERSON", "ORGANIZATION", "LOCATION"]:
# if [x.ner for x in sentence.words].count(ner) > 4:
# print_feature(
# sentence.doc_id, mention_id, "MANY_{}_IN_SENTENCE".format(ner))
if __name__ == "__main__":
# Process the input
with fileinput.input() as input_files:
for line in input_files:
# Parse the TSV line
line_dict = get_dict_from_TSVline(line, [
"doc_id", "sent_id", "wordidxs", "words", "poses", "ners",
"lemmas", "dep_paths", "dep_parents", "mention_id",
"mention_wordidxs"
], [
no_op, int, lambda x: TSVstring2list(x, int), TSVstring2list,
TSVstring2list, TSVstring2list, TSVstring2list, TSVstring2list,
lambda x: TSVstring2list(x, int), no_op,
lambda x: TSVstring2list(x, int)
])
# Create the sentence object
null_list = [
None,
] * len(line_dict["wordidxs"])
sentence = Sentence(line_dict["doc_id"], line_dict["sent_id"],
line_dict["wordidxs"], line_dict["words"],
line_dict["poses"], line_dict["ners"],
line_dict["lemmas"], line_dict["dep_paths"],
line_dict["dep_parents"], null_list)
if sentence.is_weird():
continue
from dstruct.Sentence import Sentence
from extract_gene_mentions import extract, add_features
from helper.easierlife import get_dict_from_TSVline, TSVstring2list, no_op
from helper.dictionaries import load_dict
if __name__ == "__main__":
# Load the merged genes dictionary
merged_genes_dict = load_dict("merged_genes")
# Process the input
with fileinput.input() as input_files:
for line in input_files:
# Parse the TSV line
line_dict = get_dict_from_TSVline(
line, ["doc_id", "sent_id", "wordidxs", "words", "poses",
"ners", "lemmas", "dep_paths", "dep_parents",
"bounding_boxes", "gene"],
[no_op, int, lambda x: TSVstring2list(x, int), TSVstring2list,
TSVstring2list, TSVstring2list, TSVstring2list,
TSVstring2list, lambda x: TSVstring2list(x, int),
TSVstring2list, no_op])
# Create the Sentence object
sentence = Sentence(
line_dict["doc_id"], line_dict["sent_id"],
line_dict["wordidxs"], line_dict["words"], line_dict["poses"],
line_dict["ners"], line_dict["lemmas"], line_dict["dep_paths"],
line_dict["dep_parents"], line_dict["bounding_boxes"])
# This is the 'labelled' gene that we know is in the sentence
gene = line_dict["gene"]
# Get the main symbol (or list of symbols) for the labelled gene
if gene in merged_genes_dict:
gene = merged_genes_dict[gene]
else:
relation.add_feature("GENE_2_NGRAM_RIGHT_1_[" +
sentence.words[gene_2_end+1].lemma + "]")
if __name__ == "__main__":
# Process input
with fileinput.input() as input_files:
for line in input_files:
# Parse the TSV line
line_dict = get_dict_from_TSVline(
line, ["doc_id", "sent_id", "wordidxs", "words", "poses",
"ners", "lemmas", "dep_paths", "dep_parents",
"bounding_boxes", "gene_1_entity", "gene_1_wordidxs",
"gene_1_is_correct", "gene_1_type",
"gene_2_entity", "gene_2_wordidxs",
"gene_2_is_correct", "gene_2_type"],
[no_op, int, lambda x: TSVstring2list(x, int), TSVstring2list,
TSVstring2list, TSVstring2list, TSVstring2list,
TSVstring2list, lambda x: TSVstring2list(x, int),
TSVstring2list, no_op, lambda x: TSVstring2list(x, int),
TSVstring2bool, no_op, no_op, lambda x: TSVstring2list(x,
int), TSVstring2bool, no_op])
# Create the sentence object where the two mentions appear
sentence = Sentence(
line_dict["doc_id"], line_dict["sent_id"],
line_dict["wordidxs"], line_dict["words"], line_dict["poses"],
line_dict["ners"], line_dict["lemmas"], line_dict["dep_paths"],
line_dict["dep_parents"], line_dict["bounding_boxes"])
# Create the mentions
gene_1_mention = Mention(
"GENE", line_dict["gene_1_entity"],
# Load the gene<->hpoterm dictionary
genehpoterms_dict = load_dict("genehpoterms")
if __name__ == "__main__":
# Process input
with fileinput.input() as input_files:
for line in input_files:
# Parse the TSV line
line_dict = get_dict_from_TSVline(
line, ["doc_id", "sent_id", "wordidxs", "words", "poses",
"ners", "lemmas", "dep_paths", "dep_parents",
"bounding_boxes", "gene_entities", "gene_wordidxss",
"gene_is_corrects", "gene_types",
"hpoterm_entities", "hpoterm_wordidxss",
"hpoterm_is_corrects", "hpoterm_types"],
[no_op, int, lambda x: TSVstring2list(x, int), TSVstring2list,
TSVstring2list, TSVstring2list, TSVstring2list,
TSVstring2list, lambda x: TSVstring2list(x, int),
TSVstring2list, # these are for the sentence
TSVstring2list, lambda x: TSVstring2list(x, sep="!~!"),
TSVstring2list, TSVstring2list, # these are for the genes
TSVstring2list, lambda x: TSVstring2list(x, sep="!~!"),
TSVstring2list, TSVstring2list, # these are for the HPO
])
# Remove the genes that are unsupervised copies or duplicates
supervised_idxs = set()
unsupervised_idxs = set()
for i in range(len(line_dict["gene_is_corrects"])):
if line_dict["gene_is_corrects"][i] == "n":
unsupervised_idxs.add(i)
else:
if line_dict["gene_types"][i] != "GENE_SUP_contr_2":
# Load the genes dictionary
merged_genes_dict = load_dict("merged_genes")
inverted_long_names = load_dict("inverted_long_names")
if __name__ == "__main__":
# Process the input
with fileinput.input() as input_files:
for line in input_files:
# Parse the TSV line
line_dict = get_dict_from_TSVline(
line,
["doc_id", "sent_ids", "wordidxss", "wordss", "posess",
"nerss", "lemmass", "dep_pathss", "dep_parentss",
"bounding_boxess"],
[no_op, lambda x: TSVstring2list(x, int),
lambda x: TSVstring2list(x,sep='!~!'),
lambda x: TSVstring2list(x,sep='!~!'),
lambda x: TSVstring2list(x,sep='!~!'),
lambda x: TSVstring2list(x,sep='!~!'),
lambda x: TSVstring2list(x,sep='!~!'),
lambda x: TSVstring2list(x,sep='!~!'),
lambda x: TSVstring2list(x,sep='!~!'),
lambda x: TSVstring2list(x,sep='!~!')])
# Acronyms defined in the document
acronyms = dict()
for idx in range(len(line_dict["sent_ids"])):
wordidxs = TSVstring2list(line_dict["wordidxss"][idx], int)
words = TSVstring2list(line_dict["wordss"][idx])
poses = TSVstring2list(line_dict["posess"][idx])
ners = TSVstring2list(line_dict["nerss"][idx])
lemmas = TSVstring2list(line_dict["lemmass"][idx])
dep_paths = TSVstring2list(line_dict["dep_pathss"][idx])
# Parse the TSV line
line_dict = get_dict_from_TSVline(
line,
[
"doc_id", "sent_id", "wordidxs", "words", "poses", "ners",
"lemmas", "dep_paths", "dep_parents", "bounding_boxes",
"gene_entities", "gene_wordidxss", "gene_is_corrects",
"gene_types", "hpoterm_entities", "hpoterm_wordidxss",
"hpoterm_is_corrects", "hpoterm_types"
],
[
no_op,
int,
lambda x: TSVstring2list(x, int),
TSVstring2list,
TSVstring2list,
TSVstring2list,
TSVstring2list,
TSVstring2list,
lambda x: TSVstring2list(x, int),
TSVstring2list, # these are for the sentence
TSVstring2list,
lambda x: TSVstring2list(x, sep="!~!"),
TSVstring2list,
TSVstring2list, # these are for the genes
TSVstring2list,
lambda x: TSVstring2list(x, sep="!~!"),
TSVstring2list,
TSVstring2list, # these are for the HPO
])
# Remove the genes that are unsupervised copies or duplicates
from dstruct.Sentence import Sentence
from extract_gene_mentions import extract
from helper.easierlife import get_dict_from_TSVline, TSVstring2list, no_op
from helper.dictionaries import load_dict
if __name__ == "__main__":
# Load the merged genes dictionary
merged_genes_dict = load_dict("merged_genes")
# Process the input
with fileinput.input() as input_files:
for line in input_files:
# Parse the TSV line
line_dict = get_dict_from_TSVline(
line, ["doc_id", "sent_id", "wordidxs", "words", "gene"], [
no_op, int, lambda x: TSVstring2list(x, int),
TSVstring2list, no_op
])
# Create the Sentence object
null_list = [
None,
] * len(line_dict["wordidxs"])
sentence = Sentence(line_dict["doc_id"], line_dict["sent_id"],
line_dict["wordidxs"], line_dict["words"],
null_list, null_list, null_list, null_list,
null_list, null_list)
# This is the 'labelled' gene that we know is in the sentence
gene = line_dict["gene"]
# Get the main symbol (or list of symbols) for the labelled gene
if gene in merged_genes_dict:
gene = merged_genes_dict[gene]
mentions.append(mention)
# Add indexes to history so that they are not used for another
# mention
for i in range(start, end):
history.add(i)
return mentions
if __name__ == "__main__":
# Process the input
with fileinput.input() as input_files:
for line in input_files:
# Parse the TSV line
line_dict = get_dict_from_TSVline(
line,
["doc_id", "sent_id", "wordidxs", "words", "poses", "ners",
"lemmas"],
[no_op, int, lambda x: TSVstring2list(x, int), TSVstring2list,
TSVstring2list, TSVstring2list, TSVstring2list])
# Create the sentence object
null_list = [None, ] * len(line_dict["wordidxs"])
sentence = Sentence(
line_dict["doc_id"], line_dict["sent_id"],
line_dict["wordidxs"], line_dict["words"], line_dict["poses"],
line_dict["ners"], line_dict["lemmas"], null_list, null_list,
null_list)
# Skip weird sentences
if sentence.is_weird():
continue
# Get list of mentions candidates in this sentence
mentions = extract(sentence)
# Supervise them
# Load the genes dictionary
merged_genes_dict = load_dict("merged_genes")
inverted_long_names = load_dict("inverted_long_names")
if __name__ == "__main__":
# Process the input
with fileinput.input() as input_files:
for line in input_files:
# Parse the TSV line
line_dict = get_dict_from_TSVline(line, [
"doc_id", "sent_ids", "wordidxss", "wordss", "posess", "nerss",
"lemmass", "dep_pathss", "dep_parentss", "bounding_boxess"
], [
no_op, lambda x: TSVstring2list(x, int),
lambda x: TSVstring2list(x, sep='!~!'),
lambda x: TSVstring2list(x, sep='!~!'),
lambda x: TSVstring2list(x, sep='!~!'),
lambda x: TSVstring2list(x, sep='!~!'),
lambda x: TSVstring2list(x, sep='!~!'),
lambda x: TSVstring2list(x, sep='!~!'),
lambda x: TSVstring2list(x, sep='!~!'),
lambda x: TSVstring2list(x, sep='!~!')
])
# Acronyms defined in the document
acronyms = dict()
for idx in range(len(line_dict["sent_ids"])):
wordidxs = TSVstring2list(line_dict["wordidxss"][idx], int)
words = TSVstring2list(line_dict["wordss"][idx])
poses = TSVstring2list(line_dict["posess"][idx])
ners = TSVstring2list(line_dict["nerss"][idx])
lemmas = TSVstring2list(line_dict["lemmass"][idx])
dep_paths = TSVstring2list(line_dict["dep_pathss"][idx])
