settings['SAE_SVM_RBF_COMBO'] = 1 settings['SVM_POLY'] = 0 settings['DL_S'] = 0 settings['DL_U'] = 0 settings['finetune_lr'] = 1 settings['batch_size'] = 100 settings['pretraining_interations'] = 5004 settings['pretrain_lr'] = 0.001 settings['training_epochs'] = 1503 settings['hidden_layers_sizes'] = [100, 100] settings['corruption_levels'] = [0,0] filename = settings['filename'] file_obj = FileOperator(filename) ddis = file_obj.readStripLines() import logging import time current_date = time.strftime("%m_%d_%Y") logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) logname = 'log_DL_contact_matrix_load' + current_date + '.log' handler = logging.FileHandler(logname) handler.setLevel(logging.DEBUG) # create a logging format formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
settings['SAE_SVM_RBF'] = 1 settings['SAE_SVM_RBF_COMBO'] = 1 settings['SVM_POLY'] = 0 settings['DL_S'] = 1 settings['DL_U'] = 0 settings['finetune_lr'] = 1 settings['batch_size'] = 100 settings['pretraining_interations'] = 5002 settings['pretrain_lr'] = 0.001 settings['training_epochs'] = 20000 # change epochs for split net settings['hidden_layers_sizes'] = [100, 100] settings['corruption_levels'] = [0, 0] filename = settings['filename'] file_obj = FileOperator(filename) ddis = file_obj.readStripLines() import logging import time current_date = time.strftime("%m_%d_%Y") logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) logname = 'log_DL_contact_matrix_load' + current_date + '.log' handler = logging.FileHandler(logname) handler.setLevel(logging.DEBUG) # create a logging format formatter = logging.Formatter(
import sys,os from IO_class import FileOperator import numpy as np #get input file name reportFile=sys.argv[1] reportFileObj=FileOperator(reportFile) lines=reportFileObj.readStripLines() #initiate variables listOfddi=[] listOfAUC=[] listOfsvmRecall=[] listOfsvmPrecision=[] listOfbaselineAUC=[] listOfbaselineRecall=[] listOfbaselinePrecision=[] for line in lines: [ddi, seqNumber, AUC, svmRecall, svmPrecision, baselineAUC, baselineRecall, baselinePrecision]= line.split() listOfddi.append(ddi) listOfAUC.append(float(AUC)) listOfsvmRecall.append(float(svmRecall)) listOfsvmPrecision.append(float(svmPrecision)) listOfbaselineAUC.append(float(baselineAUC)) listOfbaselineRecall.append(float(baselineRecall)) listOfbaselinePrecision.append(float(baselinePrecision)) print 'The total number of DDIs is %d ' % len(set(listOfddi)) print 'The number of training sequence pairs LOO is %d ' % len(lines) print 'The number of average SVM AUC is %f ' % np.mean(listOfAUC)