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PyDTI.py
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PyDTI.py
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import os
import sys
import logging
import time
import getopt
import cv_eval
from functions import *
from nrlmf import NRLMF
from netlaprls import NetLapRLS
from blm import BLMNII
from wnngip import WNNGIP
#from kbmf import KBMF
from cmf import CMF
from new_pairs import novel_prediction_analysis
def main(argv):
try:
opts, args = getopt.getopt(argv, "m:d:f:c:s:o:n:p:g:r:i:w", ["method=","dataset=","data-dir=","cvs=","specify-arg=","method-options=","predict-num=","gpmi=","output-dir=","seed=","job-id=","workdir="])
except getopt.GetoptError:
sys.exit()
# data_dir = os.path.join(os.path.pardir, 'data')
# output_dir = os.path.join(os.path.pardir, 'output')
data_dir = '.'
output_dir = '.'
cvs, sp_arg, model_settings, predict_num = 1, 1, [], 0
gpmi = None
random_seed = None
workdir = "./"
# seeds = [7771, 8367, 22, 1812, 4659]
# seeds = np.random.choice(10000, 5, replace=False)
for opt, arg in opts:
if opt == "--method":
method = arg
if opt == "--dataset":
dataset = arg
if opt == "--data-dir":
data_dir = arg
if opt == "--output-dir":
output_dir = arg
if opt == "--cvs":
cvs = int(arg)
if opt == "--specify-arg":
sp_arg = int(arg)
if opt == "--method-options":
model_settings = [s.split('=') for s in str(arg).split()]
if opt == "--predict-num":
predict_num = int(arg)
if opt == "--gpmi":
gpmi = dict()
for s in str(arg).split():
key, val = s.split('=')
gpmi[key] = float(val)
if opt == "--seed":
random_seed = int(arg)
if opt == "--job-id":
job_id = int(arg)
if opt == "--workdir":
workdir = str(arg)
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
if random_seed is not None:
seeds = [int(random_seed)]
else:
seeds = [7771, 8367, 22, 1812, 4659]
# set logger
logger = logging.getLogger("logger")
logger.setLevel(logging.INFO)
filename = "job"+str(job_id)+".log"
fh = logging.FileHandler(workdir+"/"+filename)
fh.name = filename
logger.addHandler(fh)
# default parameters for each methods
if method == 'nrlmf':
args = {'c': 5, 'K1': 5, 'K2': 5, 'r': 50, 'lambda_d': 0.125, 'lambda_t': 0.125, 'alpha': 0.25, 'beta': 0.125, 'theta': 0.5, 'max_iter': 100}
if method == 'netlaprls':
args = {'gamma_d': 10, 'gamma_t': 10, 'beta_d': 1e-5, 'beta_t': 1e-5}
if method == 'blmnii':
args = {'alpha': 0.7, 'gamma': 1.0, 'sigma': 1.0, 'avg': False}
if method == 'wnngip':
args = {'T': 0.8, 'sigma': 1.0, 'alpha': 0.8}
if method == 'kbmf':
args = {'R': 50}
if method == 'cmf':
args = {'K': 50, 'lambda_l': 0.5, 'lambda_d': 0.125, 'lambda_t': 0.125, 'max_iter': 30}
for key, val in model_settings:
args[key] = val
intMat, drugMat, targetMat = load_data_from_file(dataset, os.path.join(data_dir, 'dataset'))
drug_names, target_names = get_drugs_targets_names(dataset, os.path.join(data_dir, 'dataset'))
if predict_num == 0:
if cvs == 1: # CV setting CVS1
X, D, T, cv = intMat, drugMat, targetMat, 1
if cvs == 2: # CV setting CVS2
X, D, T, cv = intMat, drugMat, targetMat, 0
if cvs == 3: # CV setting CVS3
X, D, T, cv = intMat.T, targetMat, drugMat, 0
cv_data = cross_validation(X, seeds, cv)
if sp_arg == 0 and predict_num == 0:
if method == 'nrlmf':
cv_eval.nrlmf_cv_eval(method,dataset,cv_data,X,D,T,cvs,args,logger,scoring='auc',gpmi=gpmi)
if method == 'netlaprls':
cv_eval.netlaprls_cv_eval(method, dataset, cv_data, X, D, T, cvs, args)
if method == 'blmnii':
cv_eval.blmnii_cv_eval(method, dataset, cv_data, X, D, T, cvs, args)
if method == 'wnngip':
cv_eval.wnngip_cv_eval(method, dataset, cv_data, X, D, T, cvs, args)
if method == 'kbmf':
cv_eval.kbmf_cv_eval(method, dataset, cv_data, X, D, T, cvs, args)
if method == 'cmf':
cv_eval.cmf_cv_eval(method, dataset, cv_data, X, D, T, cvs, args)
if sp_arg == 1 or predict_num > 0:
tic = time.clock()
if method == 'nrlmf':
model = NRLMF(cfix=args['c'], K1=args['K1'], K2=args['K2'], num_factors=args['r'], lambda_d=args['lambda_d'], lambda_t=args['lambda_t'], alpha=args['alpha'], beta=args['beta'], theta=args['theta'], max_iter=args['max_iter'])
if method == 'netlaprls':
model = NetLapRLS(gamma_d=args['gamma_d'], gamma_t=args['gamma_t'], beta_d=args['beta_t'], beta_t=args['beta_t'])
if method == 'blmnii':
model = BLMNII(alpha=args['alpha'], gamma=args['gamma'], sigma=args['sigma'], avg=args['avg'])
if method == 'wnngip':
model = WNNGIP(T=args['T'], sigma=args['sigma'], alpha=args['alpha'])
if method == 'kbmf':
model = KBMF(num_factors=args['R'])
if method == 'cmf':
model = CMF(K=args['K'], lambda_l=args['lambda_l'], lambda_d=args['lambda_d'], lambda_t=args['lambda_t'], max_iter=args['max_iter'])
cmd = str(model)
if predict_num == 0:
print("Dataset:"+dataset+" CVS:"+str(cvs)+"\n"+cmd)
aupr_vec, auc_vec = train(model, cv_data, X, D, T)
aupr_avg, aupr_conf = mean_confidence_interval(aupr_vec)
auc_avg, auc_conf = mean_confidence_interval(auc_vec)
print("auc:%.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f, Time:%.6f" % (auc_avg, aupr_avg, auc_conf, aupr_conf, time.clock()-tic))
write_metric_vector_to_file(auc_vec, os.path.join(output_dir, method+"_auc_cvs"+str(cvs)+"_"+dataset+".txt"))
write_metric_vector_to_file(aupr_vec, os.path.join(output_dir, method+"_aupr_cvs"+str(cvs)+"_"+dataset+".txt"))
logger.info(cmd+', '+"auc:%.6f, aupr: %.6f, auc_conf:%.6f, aupr_conf:%.6f, Time:%.6f" % (auc_avg, aupr_avg, auc_conf, aupr_conf, time.clock()-tic))
elif predict_num > 0:
print("Dataset:"+dataset+"\n"+cmd)
seed = 7771 if method == 'cmf' else 22
model.fix_model(intMat, intMat, drugMat, targetMat, seed)
x, y = np.where(intMat == 0)
scores = model.predict_scores(zip(x, y), 5)
ii = np.argsort(scores)[::-1]
predict_pairs = [(drug_names[x[i]], target_names[y[i]], scores[i]) for i in ii[:predict_num]]
new_dti_file = os.path.join(output_dir, "_".join([method, dataset, "new_dti.txt"]))
novel_prediction_analysis(predict_pairs, new_dti_file, os.path.join(data_dir, 'biodb'))
if __name__ == "__main__":
main(sys.argv[1:])