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eval_BRDTI.py
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eval_BRDTI.py
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import os
import sys
import time
import getopt
import cv_eval
from functions import *
from netlaprls import NetLapRLS
from blmnii import BLMNII
from wnngip import WNNGIP
from cmf import CMF
from brdti import BRDTI
from eval_new_DTI_prediction import *
def main(argv):
try:
opts, args = getopt.getopt(argv, "m:d:f:c:s:o:n:p", ["method=", "dataset=", "data-dir=", "cvs=", "specify-arg=", "method-options=", "predict-num=", "output-dir=", ])
except getopt.GetoptError:
sys.exit()
data_dir = 'data'
output_dir = 'output'
cvs, sp_arg, model_settings, predict_num = 1, 1, [], 0
seeds = [7771, 8367, 22, 1812, 4659]
seedsOptPar = [156]
# 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 not os.path.isdir(output_dir):
os.makedirs(output_dir)
if not os.path.isdir(os.path.join(output_dir,"optPar")):
os.makedirs(os.path.join(output_dir,"optPar"))
# default parameters for each methods
if (method == 'brdti') | (method == 'inv_brdti') :
args = {
'D':100,
'learning_rate':0.1,
'max_iters' : 100,
'simple_predict' :False,
'bias_regularization':1,
'global_regularization':10**(-2),
"cbSim": "knn",
'cb_alignment_regularization_user' :1,
'cb_alignment_regularization_item' :1}
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 == 'cmf':
args = {'K': 100, 'lambda_l': 0.5, 'lambda_d': 0.125, 'lambda_t': 0.125, 'max_iter': 100}
#print(model_settings)
for key, val in model_settings:
args[key] = float(eval(val))
intMat, drugMat, targetMat = load_data_from_file(dataset, os.path.join(data_dir, 'datasets'))
drug_names, target_names = get_drugs_targets_names(dataset, os.path.join(data_dir, 'datasets'))
invert = 0
if (method == 'inv_brdti') :
invert = 1
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, invert)
cv_data_optimize_params = cross_validation(X, seedsOptPar, cv, invert, num=5)
if sp_arg == 0 and predict_num == 0:
if (method == 'brdti'):
cv_eval.brdti_cv_eval(method, dataset,output_dir, cv_data_optimize_params, X, D, T, cvs, args)
if (method == 'inv_brdti'):
cv_eval.brdti_cv_eval(method, dataset,output_dir, cv_data_optimize_params, X.T, T, D, cvs, args)
if method == 'netlaprls':
cv_eval.netlaprls_cv_eval(method, dataset,output_dir, cv_data_optimize_params, X, D, T, cvs, args)
if method == 'blmnii':
cv_eval.blmnii_cv_eval(method, dataset,output_dir, cv_data_optimize_params, X, D, T, cvs, args)
if method == 'wnngip':
cv_eval.wnngip_cv_eval(method, dataset,output_dir, cv_data_optimize_params, X, D, T, cvs, args)
if method == 'cmf':
cv_eval.cmf_cv_eval(method, dataset,output_dir, cv_data_optimize_params, X, D, T, cvs, args)
if sp_arg == 1 or predict_num > 0:
tic = time.clock()
if (method == 'brdti')|(method == 'inv_brdti'):
model = BRDTI(args)
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 == '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)
#predict hidden part of the current datasets
if predict_num == 0:
print "Dataset:"+dataset+" CVS:"+str(cvs)+"\n"+cmd
if (method == 'inv_brdti') :
aupr_vec, auc_vec, ndcg_inv_vec, ndcg_vec, results = train(model, cv_data, X.T, T, D)
else:
aupr_vec, auc_vec, ndcg_vec, ndcg_inv_vec, results = 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)
ndcg_avg, ndcg_conf = mean_confidence_interval(ndcg_vec)
ndcg_inv_avg, ndcg_inv_conf = mean_confidence_interval(ndcg_inv_vec)
resfile = os.path.join('output','rawResults', method+"_res_"+str(cvs)+"_"+dataset+".csv")
outd = open(resfile, "w")
outd.write(('drug;target;true;predict\n'))
for r in results:
outd.write('%s;%s;%s;%s\n' % (r[0],r[1],r[2],r[3]) )
print "auc:%.6f, aupr: %.6f, ndcg: %.6f, ndcg_inv: %.6f, auc_conf:%.6f, aupr_conf:%.6f, ndcg_conf:%.6f, ndcg_inv_conf:%.6f, Time:%.6f" % (auc_avg, aupr_avg, ndcg_avg, ndcg_inv_avg, auc_conf, aupr_conf, ndcg_conf, ndcg_inv_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"))
write_metric_vector_to_file(ndcg_vec, os.path.join(output_dir, method+"_ndcg_cvs"+str(cvs)+"_"+dataset+".txt"))
write_metric_vector_to_file(ndcg_inv_vec, os.path.join(output_dir, method+"_ndcg_inv_cvs"+str(cvs)+"_"+dataset+".txt"))
#predict novel DTIs
elif predict_num > 0:
print "Dataset:"+dataset+"\n"+cmd
seed = 376
if invert: #predicting drugs for targets
model.fix_model(intMat.T, intMat.T, targetMat, drugMat, seed)
npa = newDTIPrediction()
x, y = np.where(intMat == 0)
scores = model.predict_scores(zip(y, x), 1)
sz = np.array(zip(x,y,scores))
else: #predicting targets for drugs
model.fix_model(intMat, intMat, drugMat, targetMat, seed)
npa = newDTIPrediction()
x, y = np.where(intMat == 0)
scores = model.predict_scores(zip(x, y), 1)
sz = np.array(zip(x,y,scores))
ndcg_d, ndcg_t, recall_d, recall_t = npa.verify_novel_interactions(method, dataset, sz, predict_num, drug_names, target_names)
st_file= os.path.join('output/newDTI', "_".join([dataset,str(predict_num), "stats.csv"]))
out = open(st_file, "a")
out.write(('%s;%f;%f;%f;%f\n' % (method,ndcg_d, ndcg_t, recall_d, recall_t)))
if __name__ == "__main__":
"""
main(['--method=blmnii', '--dataset=davis', '--cvs=1', '--specify-arg=1', '--method-opt=alpha=0.6' ])
main(['--method=brdti', '--dataset=gpcr', '--cvs=1', '--specify-arg=0'])
"""