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']) """
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