--- a
+++ b/python-scripts/runCancerCNN.py
@@ -0,0 +1,377 @@
+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 *
+from keras.models import Model
+import keras
+from sklearn.metrics import classification_report
+import os
+# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+# os.environ['CUDA_VISIBLE_DEVICES'] = "0,1"
+from tensorflow.compat.v1 import ConfigProto
+from tensorflow.compat.v1 import InteractiveSession
+config = ConfigProto()
+config.gpu_options.allow_growth = True
+session = InteractiveSession(config=config)
+#训练三个神经网络
+def build_NN_model1(omics,class_num):
+    omics1=omics[0]
+    omics2=omics[1]
+    omics3=omics[2]
+    input1_dim=omics1.shape[1]
+    input2_dim = omics2.shape[1]
+    input3_dim = omics3.shape[1]
+    # class_num = 4
+
+
+    #omics1
+    input_factor1 = Input(shape=(input1_dim,),name='omics1')
+    input_re1 = Reshape((-1, 1))(input_factor1)
+    omics1_cnn = Conv1D(32, (300), activation='relu')(input_re1)
+    omics1_cnn = MaxPool1D(100)(omics1_cnn)
+
+    flatten1 = Flatten()(omics1_cnn)
+
+
+    # omics2
+    input_factor2 = Input(shape=(input2_dim,), name='omics2')
+    input_re2 = Reshape((-1, 1))(input_factor2)
+    omics2_cnn = Conv1D(32, (100), activation='relu' ,name='omics2_cnn_1')(input_re2)
+    omics2_cnn = MaxPool1D(50)(omics2_cnn)
+
+    flatten2 = Flatten(name='flatten2')(omics2_cnn)
+
+    # omics3
+    input_factor3 = Input(shape=(input3_dim,), name='omics3')
+    input_re3 = Reshape((-1, 1))(input_factor3)
+    omics3_cnn = Conv1D(32, (300), activation='relu')(input_re3)
+    omics3_cnn = MaxPool1D(100)(omics3_cnn)
+
+    flatten3 = Flatten()(omics3_cnn)
+
+    mid_concat=concatenate([flatten1, flatten2, flatten3])
+    # classifier
+    nn_classifier = Dense(100, activation='relu')(mid_concat)
+    nn_classifier=Dropout(0.1)(nn_classifier)
+    nn_classifier = Dense(50, activation='relu')(nn_classifier)
+    nn_classifier = Dropout(0.1)(nn_classifier)
+    # nn_classifier = Dense(50, activation='relu')(nn_classifier)
+    # nn_classifier = Dropout(0.1)(nn_classifier)
+    nn_classifier = Dense(10, activation='relu')(nn_classifier)
+    #nn_classifier = Dropout(0.1)(nn_classifier)
+    nn_classifier = Dense(class_num, activation='softmax', name='classifier')(nn_classifier)
+    my_metrics = {
+        'classifier': ['acc']
+    }
+    my_loss = {
+        'classifier': 'categorical_crossentropy', \
+        }
+    adam=keras.optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
+    zlyNN = Model(inputs=[input_factor1,input_factor2,input_factor3], outputs=nn_classifier)
+    zlyNN.compile(optimizer=adam, loss=my_loss, metrics=my_metrics)
+    return zlyNN
+
+
+
+def build_NN_model2(omics,class_num):
+    
+    input_dim=omics.shape[1]
+    
+    #class_num = 5
+
+
+    #omics1
+    input_factor1 = Input(shape=(input_dim,),name='omics')
+    input_re=Reshape((-1,1))(input_factor1)
+    omics1_cnn=Conv1D(32,(1000),activation='relu')(input_re)
+    omics1_cnn=MaxPool1D(100)(omics1_cnn)
+    omics1_cnn = Conv1D(16, (50), activation='relu')(omics1_cnn)
+    omics1_cnn = MaxPool1D(10)(omics1_cnn)
+    flatten=Flatten()(omics1_cnn)
+    # NN
+    # omics1_nn = Dense(500, activation='relu')(input_factor1)
+    # omics1_nn = Dropout(0.1)(omics1_nn)
+    # omics1_nn = Dense(100, activation='relu')(omics1_nn)
+    # omics1_nn = Dropout(0.1)(omics1_nn)
+
+    nn_classifier = Dense(50, activation='relu')(flatten)
+    # nn_classifier = Dropout(0.1)(nn_classifier)
+    if class_num==2:
+        nn_classifier = Dense(1, activation='sigmoid', name='classifier')(nn_classifier)
+    else:
+        nn_classifier = Dense(class_num, activation='softmax', name='classifier')(nn_classifier)
+    my_metrics_multi = {
+        'classifier': ['acc']
+    }
+    my_loss_multi = {
+        'classifier': 'categorical_crossentropy', \
+        }
+    my_metrics_bi = {
+        'classifier': ['acc']
+    }
+    my_loss_bi = {
+        'classifier': 'binary_crossentropy', \
+        }
+    # compile autoencoder
+    # self.autoencoder.compile(optimizer='adam', loss='mse')
+    zlyNN = Model(inputs=[input_factor1], outputs=nn_classifier) 
+    if class_num==2:
+        zlyNN.compile(optimizer='adam', loss=my_loss_bi, metrics=my_metrics_bi)
+    else:
+        zlyNN.compile(optimizer='adam', loss=my_loss_multi, metrics=my_metrics_multi)
+    return zlyNN
+
+
+
+if __name__ == '__main__':
+    files = ['breast2']
+    files = ['gbm']
+    # files = ['sarcoma2']
+    files = ['LUAD2']
+    files = ['STAD2']
+    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)
+        # 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(omics, labels):
+            # select rows
+            train_X, test_X = omics[train_ix], omics[test_ix]
+            train_y, test_y = labels[train_ix], labels[test_ix]
+            # summarize train and test composition
+            unique, count = np.unique(train_y, return_counts=True)
+            train_data_count = dict(zip(unique, count))
+            print('train:' + str(train_data_count))
+            unique, count = np.unique(test_y, return_counts=True)
+            test_data_count = dict(zip(unique, count))
+            print('test:' + str(test_data_count))
+            class_num = 4
+
+            # 多分类的输出
+            train_y = list(np.int_(train_y))
+            # groundtruth = np.int_(groundtruth)
+            y = []
+            num = len(train_y)
+            for i in range(num):
+                tmp = np.zeros(class_num, dtype='uint8')
+                tmp[train_y[i]] = 1
+                y.append(tmp)
+            train_y = np.array(y)
+
+            test_y = list(np.int_(test_y))
+            # groundtruth = np.int_(groundtruth)
+            y = []
+            num = len(test_y)
+            for i in range(num):
+                tmp = np.zeros(class_num, dtype='uint8')
+                tmp[test_y[i]] = 1
+                y.append(tmp)
+            test_y = np.array(y)
+
+            model = build_NN_model2(omics, class_num)
+            model.summary()
+            history = model.fit(train_X, train_y, epochs=50, verbose=2, batch_size=8, shuffle=True,
+                                validation_data=(test_X, test_y))
+            y_true = []
+            for i in range(len(test_y)):
+                y_true.append(np.argmax(test_y[i]))
+            predictions = model.predict(test_X)
+            y_pred = []
+            for i in range(len(predictions)):
+                y_pred.append(np.argmax(predictions[i]))
+            acc = accuracy_score(y_true, y_pred)
+            f1_macro = f1_score(y_true, y_pred, average='macro')
+            # f1_micro=f1_score(y_true, y_pred, average='micro')
+            f1_weighted = f1_score(y_true, y_pred, average='weighted')
+            auc_macro = roc_auc_score(y_true, predictions, multi_class='ovr', average='macro')
+            auc_weighted = roc_auc_score(y_true, predictions, multi_class='ovr', average='weighted')
+            all_acc.append(acc)
+            all_f1_macro.append(f1_macro)
+            all_f1_weighted.append(f1_weighted)
+            all_auc_macro.append(auc_macro)
+            all_auc_weighted.append(auc_weighted)
+
+            print(classification_report(y_true, y_pred))
+            print(acc, f1_macro, f1_weighted, auc_macro, auc_weighted)
+            # print_precison_recall_f1(y_true, y_pred)
+        print('caicai' * 20)
+        print(
+            'acc:{all_acc}\nf1_macro:{all_f1_macro}\nf1_weighted:{all_f1_weighted}\nauc_macro:{all_auc_macro}\nauc_weighted:{all_auc_weighted}'. \
+            format(all_acc=all_acc, all_f1_macro=all_f1_macro, all_f1_weighted=all_f1_weighted,
+                   all_auc_macro=all_auc_macro, all_auc_weighted=all_auc_weighted))
+        avg_acc = np.mean(all_acc)
+        avg_f1_macro = np.mean(all_f1_macro)
+        avg_f1_weighted = np.mean(all_f1_weighted)
+        avg_auc_macro = np.mean(all_auc_macro)
+        avg_auc_weighted = np.mean(all_auc_weighted)
+        print(
+            'acc:{avg_acc}\nf1_macro:{avg_f1_macro}\nf1_weighted:{avg_f1_weighted}\nauc_macro:{avg_auc_macro}\nauc_weighted:{avg_auc_weighted}'. \
+            format(avg_acc=avg_acc, avg_f1_macro=avg_f1_macro, avg_f1_weighted=avg_f1_weighted,
+                   avg_auc_macro=avg_auc_macro, avg_auc_weighted=avg_auc_weighted))
+
+
+
+    # files = ['breast2']
+    # # files = ['gbm']
+    # # files = ['sarcoma2']
+    # files = ['LUAD2']
+    # files = ['STAD2']
+    # 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)
+    #
+    #
+    #     # k折交叉验证
+    #     all_acc = []
+    #     all_f1_macro = []
+    #     all_f1_weighted = []
+    #     all_auc_macro = []
+    #     all_auc_weighted = []
+    #
+    #     omics = np.concatenate((omics1, omics2, omics3), axis=1)
+    #     unique, count = np.unique(labels, return_counts=True)
+    #     all_count = dict(zip(unique, count))
+    #     print(str(all_count))
+    #     # 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]
+    #         # select rows
+    #         train_X, test_X = [train_X_1,train_X_2,train_X_3],[test_X_1,test_X_2,test_X_3]
+    #         #train_X, test_X = (train_X_1,train_X_2,train_X_3),(test_X_1,test_X_2,test_X_3)
+    #         train_y, test_y = labels[train_ix], labels[test_ix]
+    #         # summarize train and test composition
+    #         unique, count = np.unique(train_y, return_counts=True)
+    #         train_data_count = dict(zip(unique, count))
+    #         print('train:' + str(train_data_count))
+    #         unique, count = np.unique(test_y, return_counts=True)
+    #         test_data_count = dict(zip(unique, count))
+    #         print('test:' + str(test_data_count))
+    #         class_num=4
+    #         # 多分类的输出
+    #         train_y = list(np.int_(train_y))
+    #         # groundtruth = np.int_(groundtruth)
+    #         y = []
+    #         num = len(train_y)
+    #         for i in range(num):
+    #             tmp = np.zeros(class_num, dtype='uint8')
+    #             tmp[train_y[i]] = 1
+    #             y.append(tmp)
+    #         train_y = np.array(y)
+    #
+    #         test_y = list(np.int_(test_y))
+    #         # groundtruth = np.int_(groundtruth)
+    #         y = []
+    #         num = len(test_y)
+    #         for i in range(num):
+    #             tmp = np.zeros(class_num, dtype='uint8')
+    #             tmp[test_y[i]] = 1
+    #             y.append(tmp)
+    #         test_y = np.array(y)
+    #
+    #         model = build_NN_model1(omics_tobuild,class_num)
+    #         model.summary()
+    #         history = model.fit(train_X, train_y, epochs=50, verbose=2, batch_size=16, shuffle=True,
+    #                             validation_data=(test_X, test_y))
+    #         y_true = []
+    #         for i in range(len(test_y)):
+    #             y_true.append(np.argmax(test_y[i]))
+    #         predictions = model.predict(test_X)
+    #         y_pred = []
+    #         for i in range(len(predictions)):
+    #             y_pred.append(np.argmax(predictions[i]))
+    #         acc = accuracy_score(y_true, y_pred)
+    #         f1_macro = f1_score(y_true, y_pred, average='macro')
+    #         # f1_micro=f1_score(y_true, y_pred, average='micro')
+    #         f1_weighted = f1_score(y_true, y_pred, average='weighted')
+    #         auc_macro = roc_auc_score(y_true, predictions, multi_class='ovr', average='macro')
+    #         auc_weighted = roc_auc_score(y_true, predictions, multi_class='ovr', average='weighted')
+    #         all_acc.append(acc)
+    #         all_f1_macro.append(f1_macro)
+    #         all_f1_weighted.append(f1_weighted)
+    #         all_auc_macro.append(auc_macro)
+    #         all_auc_weighted.append(auc_weighted)
+    #
+    #         print(classification_report(y_true, y_pred))
+    #         print(acc, f1_macro, f1_weighted, auc_macro, auc_weighted)
+    #         # print_precison_recall_f1(y_true, y_pred)
+    #     print('caicai' * 20)
+    #     print(
+    #         'acc:{all_acc}\nf1_macro:{all_f1_macro}\nf1_weighted:{all_f1_weighted}\nauc_macro:{all_auc_macro}\nauc_weighted:{all_auc_weighted}'. \
+    #         format(all_acc=all_acc, all_f1_macro=all_f1_macro, all_f1_weighted=all_f1_weighted,
+    #                all_auc_macro=all_auc_macro, all_auc_weighted=all_auc_weighted))
+    #     avg_acc = np.mean(all_acc)
+    #     avg_f1_macro = np.mean(all_f1_macro)
+    #     avg_f1_weighted = np.mean(all_f1_weighted)
+    #     avg_auc_macro = np.mean(all_auc_macro)
+    #     avg_auc_weighted = np.mean(all_auc_weighted)
+    #     print(
+    #         'acc:{avg_acc}\nf1_macro:{avg_f1_macro}\nf1_weighted:{avg_f1_weighted}\nauc_macro:{avg_auc_macro}\nauc_weighted:{avg_auc_weighted}'. \
+    #         format(avg_acc=avg_acc, avg_f1_macro=avg_f1_macro, avg_f1_weighted=avg_f1_weighted,
+    #                avg_auc_macro=avg_auc_macro, avg_auc_weighted=avg_auc_weighted))
+
+