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Alyssa Salazar
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DL-mo
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[0b32b6]: / python-scripts / runToGetMOG.py

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import numpy as np

from sklearn.preprocessing import normalize

from keras.layers import Input, Dense,concatenate,Dropout,average

from keras.models import Model

from keras import backend as K

from sklearn.metrics import roc_auc_score, f1_score, accuracy_score

import numpy as np

from sklearn.model_selection import StratifiedKFold

from keras.layers import Input, Dense,concatenate,Dropout,average

from keras.models import Model

import keras

from sklearn.metrics import classification_report



    

    



#cancer数据

if __name__ == '__main__':

    # files = ['breast2']

    files = ['gbm','breast2']

    for f in files:

        datapath='./data/cancer_d2d/{f}'.format(f=f)

        omics1 = np.loadtxt('{}/after_log_exp.txt'.format(datapath),str)

        omics1 = np.delete(omics1, 0, axis=1)

        #omics1 = np.transpose(omics1)

        omics1 = omics1.astype(np.float)

        omics1 = normalize(omics1, axis=0, norm='max')

        print(omics1.shape)



        omics2 = np.loadtxt('{}/after_log_mirna.txt'.format(datapath),str)

        omics2= np.delete(omics2, 0, axis=1)

        #omics2 = np.transpose(omics2)

        omics2 = omics2.astype(np.float)

        omics2 = normalize(omics2, axis=0, norm='max')

        print(omics2.shape)



        omics3 = np.loadtxt('{}/after_methy.txt'.format(datapath),str)

        omics3= np.delete(omics3,0,axis=1)

        #omics3 = np.transpose(omics3)

        omics3 = omics3.astype(np.float)

        omics3 = normalize(omics3, axis=0, norm='max')

        print(omics3.shape)



        labels = np.loadtxt('{datapath}/after_labels.txt'.format(datapath=datapath), str)

        labels = np.delete(labels, 0, axis=1)

        labels = labels.astype(np.int)

        labels = np.squeeze(labels,axis=1)

        # datapath = 'data/BRCA'

        # omics1 = np.loadtxt('{}/1_all.csv'.format(datapath),delimiter=',')

        # #omics1 = np.transpose(omics1)

        # omics1 = normalize(omics1, axis=0, norm='max')



        # omics2 = np.loadtxt('{}/2_all.csv'.format(datapath),delimiter=',')

        # #omics2 = np.transpose(omics2)

        # omics2 = normalize(omics2, axis=0, norm='max')



        # omics3 = np.loadtxt('{}/3_all.csv'.format(datapath),delimiter=',')

        # #omics3 = np.transpose(omics3)

        # omics3 = normalize(omics3, axis=0, norm='max')

        

        # k折交叉验证

        all_acc = []

        all_f1_macro = []

        all_f1_weighted = []

        all_auc_macro = []

        all_auc_weighted = []

        #omics = np.loadtxt('./result/nmf/mf_em.txt')

        omics = np.concatenate((omics1, omics2, omics3), axis=1)

        

        # labels = np.loadtxt('./data/BRCA/labels_all.csv', delimiter=',')

        # data=np.concatenate([])

        kfold = StratifiedKFold(n_splits=4, shuffle=True, random_state=1)

        for train_ix, test_ix in kfold.split(omics1, labels):

            omics_tobuild=[omics1,omics2,omics3]

            train_X_1=omics1[train_ix]

            train_X_2=omics2[train_ix]

            train_X_3=omics3[train_ix]



            test_X_1=omics1[test_ix]

            test_X_2=omics2[test_ix]

            test_X_3=omics3[test_ix]

            

            train_y, test_y = labels[train_ix], labels[test_ix]

            

            np.savetxt('{}/1_tr.csv'.format(datapath), train_X_1, delimiter=',')

            np.savetxt('{}/2_tr.csv'.format(datapath), train_X_2, delimiter=',')

            np.savetxt('{}/3_tr.csv'.format(datapath), train_X_3, delimiter=',')

            np.savetxt('{}/1_te.csv'.format(datapath), test_X_1, delimiter=',')

            np.savetxt('{}/2_te.csv'.format(datapath), test_X_2, delimiter=',')

            np.savetxt('{}/3_te.csv'.format(datapath), test_X_3, delimiter=',')

            np.savetxt('{}/labels_tr.csv'.format(datapath), train_y, delimiter=',')

            np.savetxt('{}/labels_te.csv'.format(datapath), test_y, delimiter=',')

            break





#simulations数据

# if __name__ == '__main__':

#     datatypes=["equal","heterogeneous"]

#     typenums=[5,10,15]

#     for datatype in datatypes:

#         for typenum in typenums:

#             datapath='data/simulations/{}/{}'.format(datatype, typenum)

    

#             labels = np.loadtxt('{}/c.txt'.format(datapath))

                



#             omics1 = np.loadtxt('{}/o1.txt'.format(datapath))

#             omics1 = np.transpose(omics1)

#             omics1 = normalize(omics1, axis=0, norm='max')



#             omics2 = np.loadtxt('{}/o2.txt'.format(datapath))

#             omics2 = np.transpose(omics2)

#             omics2 = normalize(omics2, axis=0, norm='max')



#             omics3 = np.loadtxt('{}/o3.txt'.format(datapath))

#             omics3 = np.transpose(omics3)

#             omics3 = normalize(omics3, axis=0, norm='max')



#             omics = np.concatenate((omics1, omics2, omics3), axis=1)



#             kfold = StratifiedKFold(n_splits=4, shuffle=True, random_state=1)

#             for train_ix, test_ix in kfold.split(omics1, labels):

#                 omics_tobuild=[omics1,omics2,omics3]

#                 train_X_1=omics1[train_ix]

#                 train_X_2=omics2[train_ix]

#                 train_X_3=omics3[train_ix]



#                 test_X_1=omics1[test_ix]

#                 test_X_2=omics2[test_ix]

#                 test_X_3=omics3[test_ix]

                

#                 train_y, test_y = labels[train_ix], labels[test_ix]

                

#                 np.savetxt('{}/1_tr.csv'.format(datapath), train_X_1, delimiter=',')

#                 np.savetxt('{}/2_tr.csv'.format(datapath), train_X_2, delimiter=',')

#                 np.savetxt('{}/3_tr.csv'.format(datapath), train_X_3, delimiter=',')

#                 np.savetxt('{}/1_te.csv'.format(datapath), test_X_1, delimiter=',')

#                 np.savetxt('{}/2_te.csv'.format(datapath), test_X_2, delimiter=',')

#                 np.savetxt('{}/3_te.csv'.format(datapath), test_X_3, delimiter=',')

#                 np.savetxt('{}/labels_tr.csv'.format(datapath), train_y, delimiter=',')

#                 np.savetxt('{}/labels_te.csv'.format(datapath), test_y, delimiter=',')

#                 break



#single数据

if __name__ == '__main__':

    

    datapath = 'data/single-cell/'

    resultpath = 'result/single-cell/'

    labels = np.loadtxt('{}/c.txt'.format(datapath))

    # groundtruth = list(np.int_(groundtruth))



    omics = np.loadtxt('{}/omics.txt'.format(datapath))

    omics = np.transpose(omics)

    omics1=omics[0:206]

    omics2=omics[206:412]

    omics1 = normalize(omics1, axis=0, norm='max')

    omics2 = normalize(omics2, axis=0, norm='max')

    omics = np.concatenate((omics1, omics2), axis=1)

    



    kfold = StratifiedKFold(n_splits=4, shuffle=True, random_state=1)

    for train_ix, test_ix in kfold.split(omics1, labels):

        omics_tobuild=[omics1,omics2]

        train_X_1=omics1[train_ix]

        train_X_2=omics2[train_ix]





        test_X_1=omics1[test_ix]

        test_X_2=omics2[test_ix]



        

        train_y, test_y = labels[train_ix], labels[test_ix]

        

        np.savetxt('{}/1_tr.csv'.format(datapath), train_X_1, delimiter=',')

        np.savetxt('{}/2_tr.csv'.format(datapath), train_X_2, delimiter=',')

        np.savetxt('{}/1_te.csv'.format(datapath), test_X_1, delimiter=',')

        np.savetxt('{}/2_te.csv'.format(datapath), test_X_2, delimiter=',')

        np.savetxt('{}/labels_tr.csv'.format(datapath), train_y, delimiter=',')

        np.savetxt('{}/labels_te.csv'.format(datapath), test_y, delimiter=',')

        break