Datasets:
jblitzar
/
github-python-corpus

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# Select template type
template_type = prompt_until_valid('Template type (HTTP/Network/File/DNS): ', ['HTTP', 'Network', 'File', 'DNS'])
try:
if template_type == 'HTTP':
template['requests'] = add_http_requests(unsafe)
elif template_type == 'Network':
template['network'] = add_network_requests(unsafe)
elif template_type == 'File':
template['file'] = add_file_requests(unsafe)
elif template_type == 'DNS':
template['dns'] = add_dns_requests(unsafe)
except Exception as e:
print(f"An error occurred: {e}")
# Output template
print(yaml.dump(template))
if __name__ == "__main__":
main()
# <FILESEP>
#!/usr/bin/env python
import numpy as np
import pandas as pd
import click as ck
from sklearn.metrics import classification_report
from sklearn.metrics.pairwise import cosine_similarity
import sys
from collections import deque
import time
import logging
from sklearn.metrics import roc_curve, auc, matthews_corrcoef
from scipy.spatial import distance
from scipy import sparse
import math
from utils import FUNC_DICT, Ontology, NAMESPACES
from matplotlib import pyplot as plt
logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.INFO)
@ck.command()
@ck.option(
'--train-data-file', '-trdf', default='data/train_data_train.pkl',
help='Data file with training features')
@ck.option(
'--valid-data-file', '-trdf', default='data/train_data_valid.pkl',
help='Data file with training features')
@ck.option(
'--terms-file', '-tf', default='data/terms.pkl',
help='Data file with sequences and complete set of annotations')
@ck.option(
'--diamond-scores-file', '-dsf', default='data/valid_diamond.res',
help='Diamond output')
@ck.option(
'--ont', '-o', default='mf',
help='GO subontology (bp, mf, cc)')
def main(train_data_file, valid_data_file, terms_file, diamond_scores_file, ont):
go_rels = Ontology('data/go.obo', with_rels=True)
terms_df = pd.read_pickle(terms_file)
terms = terms_df['terms'].values.flatten()
terms_dict = {v: i for i, v in enumerate(terms)}
train_df = pd.read_pickle(train_data_file)
valid_df = pd.read_pickle(valid_data_file)
annotations = train_df['annotations'].values
annotations = list(map(lambda x: set(x), annotations))
valid_annotations = valid_df['annotations'].values
valid_annotations = list(map(lambda x: set(x), valid_annotations))
go_rels.calculate_ic(annotations + valid_annotations)
# Print IC values of terms
ics = {}
for term in terms:
ics[term] = go_rels.get_ic(term)
prot_index = {}
for i, row in enumerate(train_df.itertuples()):
prot_index[row.proteins] = i
# BLAST Similarity (Diamond)
diamond_scores = {}
with open(diamond_scores_file) as f:
for line in f:
it = line.strip().split()
if it[0] not in diamond_scores:
diamond_scores[it[0]] = {}
diamond_scores[it[0]][it[1]] = float(it[2])
blast_preds = []
for i, row in enumerate(valid_df.itertuples()):
annots = {}
prot_id = row.proteins
# BlastKNN
if prot_id in diamond_scores:
sim_prots = diamond_scores[prot_id]