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DLBCLB.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Aug 28 17:50:35 2019
@author: tiger
"""
from __future__ import print_function
import numpy as np
import random
#from utils import *
from mgcn import *
# 'breastA', breastB, DLBCLA, DLBCLB, DLBCLC, DLBCLD, MultiA, MultiB
# 'breastB', 'DLBCLA', 'DLBCLB', 'DLBCLC',
DATASET = 'DLBCLB' #,'breastB'] #, 'DLBCLA', 'DLBCLB', 'DLBCLC', 'DLBCLD']
nFiles = 6
FilePath="data/"
DROUPOUT = 0.2
'''
TUNNING_LIST = [0.001, 0.002, 0.005] # [0.0001] #,0.0001,0.00001] #,0.0001,0.00001] #,0.00005]
LEARNING_RATE_LIST = [0.01, 0.001, 0.0005] # [0.001,0.0005,0.0001] #[0.0005, 0.0001]
NB_EPOCH_LIST = [100, 200, 300]
'''
'''
2019-9-12 settings
TUNNING_LIST = [0.001,0.002,0.005]
LEARNING_RATE_LIST = [0.01,0.001,0.0005]
NB_EPOCH_LIST = [200,300,400]
'''
#2019-9-16 settings
TUNNING_LIST = [0.001]#,0.002,0.005]
LEARNING_RATE_LIST = [0.001,0.0005]
NB_EPOCH_LIST = [80,120,160,200,300]
DATE = "2019-9-16-8"
OUTPATH = "{}{}/".format(FilePath, DATASET)
#OUTPATH_WEIGHT = "{}{}{}".format("H:/networks/weights/", DATASET, "/")
inputs_list,y = load_data_sets(FilePath,DATASET,nFiles)
MDGCNN1_outlist = list()
DNN1_outlist = list()
for i in range(1,500):
random.seed(i)
randomset = np.random.rand(len(y))
training_mask = randomset < 0.65
test_mask = (randomset >= 0.65)
out_array = np.zeros(6)
MGCN_array = np.zeros(nFiles)
DNN_array = np.zeros(nFiles)
###################################
MODEL_TYPE = 'MDGCNN1'
print(y.shape)
print(inputs_list[0].shape)
print(len(inputs_list))
out_array = MDGCCN_Exp(inputs_list, y, training_mask, test_mask
,DROUPOUT, LEARNING_RATE_LIST, TUNNING_LIST, NB_EPOCH_LIST, MODEL_TYPE=MODEL_TYPE)
MGCN_array[0] = out_array[0]
for j in range(1,nFiles):
out_array = MDGCCN_Exp([inputs_list[0],inputs_list[j]], y, training_mask, test_mask
,DROUPOUT, LEARNING_RATE_LIST, TUNNING_LIST, NB_EPOCH_LIST, MODEL_TYPE=MODEL_TYPE)
MGCN_array[j] = out_array[0]
MDGCNN1_outlist.append(MGCN_array)
#######################################################
MODEL_TYPE = 'DNN2'
out_array = MDGCCN_Exp(inputs_list[0], y, training_mask, test_mask
,DROUPOUT, LEARNING_RATE_LIST, TUNNING_LIST, NB_EPOCH_LIST, MODEL_TYPE=MODEL_TYPE)
DNN_array[0] = out_array[0]
for j in range(1,nFiles):
out_array = MDGCCN_Exp(inputs_list[j], y, training_mask, test_mask
,DROUPOUT, LEARNING_RATE_LIST, TUNNING_LIST, NB_EPOCH_LIST, MODEL_TYPE=MODEL_TYPE)
DNN_array[j] = out_array[0]
DNN1_outlist.append(DNN_array)
###########################################
np.savetxt( "{}{}-MDGCNN.csv".format(OUTPATH,DATE), MDGCNN1_outlist, delimiter=',') # X is an array
np.savetxt( "{}{}-DNN.csv".format(OUTPATH,DATE), DNN1_outlist, delimiter=',') # X is an array