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

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(8, (10), activation='relu')(input_re1)

    omics1_cnn = MaxPool1D(2)(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(8, (10), activation='relu', name='omics2_cnn_1')(input_re2)

    omics2_cnn = MaxPool1D(2)(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(8, (10), activation='relu')(input_re3)

    omics3_cnn = MaxPool1D(2)(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(16, (10), activation='relu')(input_re)

    omics1_cnn = MaxPool1D(10)(omics1_cnn)

    omics1_cnn = Conv1D(8, (5), activation='relu')(omics1_cnn)

    omics1_cnn = MaxPool1D(2)(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']

    # 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))

    #         # 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(4, 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(4, dtype='uint8')

    #             tmp[test_y[i]] = 1

    #             y.append(tmp)

    #         test_y = np.array(y)

    #         model = build_NN_model2(omics, 4)

    #         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))

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

    # typenums=[5,10,15]

    # noise_factor=0.5

    # savepath='./result/simulations/lfcnn_res1.txt'

    # with open(savepath, 'w') as f2:

    #     for datatype in datatypes:

    #         f2.write(datatype+'\n')

    #         for typenum in typenums:

    #             f2.write(str(typenum)+'\n')

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

    #             resultpath='result/simulations/{}/{}'.format(datatype, typenum)

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

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

    #

    #             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)

    #

    #             # k折交叉验证

    #             all_acc = []

    #             all_f1_macro = []

    #             all_f1_weighted = []

    #

    #

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

    #             for train_ix, test_ix in kfold.split(omics, 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=typenum

    #                 # 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')

    #                 all_acc.append(acc)

    #                 all_f1_macro.append(f1_macro)

    #                 all_f1_weighted.append(f1_weighted)

    #

    #

    #                 print(classification_report(y_true, y_pred))

    #                 break

    #                 # 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}\n'. \

    #                 format(all_acc=all_acc, all_f1_macro=all_f1_macro, all_f1_weighted=all_f1_weighted))

    #             avg_acc = np.mean(all_acc)

    #             avg_f1_macro = np.mean(all_f1_macro)

    #             avg_f1_weighted = np.mean(all_f1_weighted)

    #

    #             print(

    #                 'acc:{avg_acc}\nf1_macro:{avg_f1_macro}\nf1_weighted:{avg_f1_weighted}\n'. \

    #                 format(avg_acc=avg_acc, avg_f1_macro=avg_f1_macro, avg_f1_weighted=avg_f1_weighted))

    #             f2.write('acc:{avg_acc}\nf1_macro:{avg_f1_macro}\nf1_weighted:{avg_f1_weighted}\n'. \

    #                 format(avg_acc=avg_acc, avg_f1_macro=avg_f1_macro, avg_f1_weighted=avg_f1_weighted))

    #         f2.write('*'*20)

    datatypes=["equal","heterogeneous"]

    typenums=[5,10,15]

    noise_factor=0.5

    savepath='./result/simulations/efcnn_res1.txt'

    with open(savepath, 'w') as f2:

        for datatype in datatypes:

            f2.write(datatype+'\n')

            for typenum in typenums:

                f2.write(str(typenum)+'\n')

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

                resultpath='result/simulations/{}/{}'.format(datatype, typenum)

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

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

                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)

                # k折交叉验证

                all_acc = []

                all_f1_macro = []

                all_f1_weighted = []

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

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

                    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=typenum

                    # 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)

                    history = model.fit(train_X, train_y, epochs=20, 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')

                    all_acc.append(acc)

                    all_f1_macro.append(f1_macro)

                    all_f1_weighted.append(f1_weighted)

                    print(classification_report(y_true, y_pred))

                    break

                    # 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}\n'. \

                    format(all_acc=all_acc, all_f1_macro=all_f1_macro, all_f1_weighted=all_f1_weighted))

                avg_acc = np.mean(all_acc)

                avg_f1_macro = np.mean(all_f1_macro)

                avg_f1_weighted = np.mean(all_f1_weighted)

                print(

                    'acc:{avg_acc}\nf1_macro:{avg_f1_macro}\nf1_weighted:{avg_f1_weighted}\n'. \

                    format(avg_acc=avg_acc, avg_f1_macro=avg_f1_macro, avg_f1_weighted=avg_f1_weighted))

                f2.write('acc:{avg_acc}\nf1_macro:{avg_f1_macro}\nf1_weighted:{avg_f1_weighted}\n'. \

                    format(avg_acc=avg_acc, avg_f1_macro=avg_f1_macro, avg_f1_weighted=avg_f1_weighted))

            f2.write('*'*20)