话不多说，直接上代码
def stacking_first(train, train_y, test):
savepath = './stack_op{}_dt{}_tfidf{}/'.format(args.option, args.data_type, args.tfidf)
os.makedirs(savepath, exist_ok=True) count_kflod = 0
num_folds = 6
kf = KFold(n_splits=num_folds, shuffle=True, random_state=10)
# 测试集上的预测结果
predict = np.zeros((test.shape[0], config.n_class))
# k折交叉验证集的预测结果
oof_predict = np.zeros((train.shape[0], config.n_class))
scores = []
f1s = [] for train_index, test_index in kf.split(train):
# 训练集划分为6折，每一折都要走一遍。那么第一个是5份的训练集索引，第二个是1份的测试集，此处为验证集是索引 kfold_X_train = {}
kfold_X_valid = {} # 取数据的标签
y_train, y_test = train_y[train_index], train_y[test_index]
# 取数据
kfold_X_train, kfold_X_valid = train[train_index], train[test_index] # 模型的前缀
model_prefix = savepath + 'DNN' + str(count_kflod)
if not os.path.exists(model_prefix):
os.mkdir(model_prefix) M = 4 # number of snapshots
alpha_zero = 1e-3 # initial learning rate
snap_epoch = 16
snapshot = SnapshotCallbackBuilder(snap_epoch, M, alpha_zero) # 使用训练集的size设定维度，fit一个模型出来
res_model = get_model(train)
res_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# res_model.fit(train_x, train_y, batch_size=BATCH_SIZE, epochs=EPOCH, verbose=1, class_weight=class_weight)
res_model.fit(kfold_X_train, y_train, batch_size=BATCH_SIZE, epochs=snap_epoch, verbose=1,
validation_data=(kfold_X_valid, y_test),
callbacks=snapshot.get_callbacks(model_save_place=model_prefix)) # 找到这个目录下所有已经训练好的深度学习模型，通过".h5"
evaluations = []
for i in os.listdir(model_prefix):
if '.h5' in i:
evaluations.append(i) # 给测试集和当前的验证集开辟空间，就是当前折的数据预测结果构建出这么多的数据集[数据个数，类别]
preds1 = np.zeros((test.shape[0], config.n_class))
preds2 = np.zeros((len(kfold_X_valid), config.n_class))
# 遍历每一个模型，用他们分别预测当前折数的验证集和测试集，N个模型的结果求平均
for run, i in enumerate(evaluations):
res_model.load_weights(os.path.join(model_prefix, i))
preds1 += res_model.predict(test, verbose=1) / len(evaluations)
preds2 += res_model.predict(kfold_X_valid, batch_size=128) / len(evaluations) # 测试集上预测结果的加权平均
predict += preds1 / num_folds
# 每一折的预测结果放到对应折上的测试集中，用来最后构建训练集
oof_predict[test_index] = preds2 # 计算精度和F1
accuracy = mb.cal_acc(oof_predict[test_index], np.argmax(y_test, axis=1))
f1 = mb.cal_f_alpha(oof_predict[test_index], np.argmax(y_test, axis=1), n_out=config.n_class)
print('the kflod cv is : ', str(accuracy))
print('the kflod f1 is : ', str(f1))
count_kflod += 1 # 模型融合的预测结果，存起来，用以以后求平均值
scores.append(accuracy)
f1s.append(f1)
# 指标均值，最为最后的预测结果
print('total scores is ', np.mean(scores))
print('total f1 is ', np.mean(f1s))
return predict
