import module.model as model
import featureEngine
from keras.callbacks import ModelCheckpoint
import numpy as np
import pickle
import os
from module.Utils import *
from keras import models

def save(object_to_save, path):
    '''
    保存对象
    @Arugs:
        object_to_save: 需要保存的对象

    @Return:
        保存的路径
    '''
    with open(path, 'wb') as f:
        pickle.dump(object_to_save, f)
def load(path):
    '''
    读取对象
    @Arugs:
        path: 读取的路径

    @Return:
        读取的对象
    '''
    with open(path, 'rb') as f:
        object = pickle.load(f)
        return object


def train():
    pk_file = "iterator/data_28849_16.pk"
    if os.path.exists(pk_file):
        data = load(pk_file)
        #data = featureEngine.paddinig(data, pad=False)
        #data[1] = np.argmax(data[1],-1)
        #print(np.shape(data[0]))
        
        
    else:
        data = featureEngine.getAllData()
        save(data,"data_"+str(len(data[1]))+".pk")
    model1 = model.getBiRNNModel()
    #model1.load_weights("../model_data/ep028-loss0.062-val_loss0.102-f10.9624.h5")
    model_file = "contentExtract.h5"
    log_dir = "log/"
    train_percent = 0.8
    test_percent=0.9
    print(np.shape(data[0]))
    train_len = round(len(data[0])*train_percent)
    test_len = round(len(data[0])*test_percent)
    checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_acc{val_acc:.3f}-val_loss{val_loss:.3f}-f1{val_f1_score:.4f}.h5',
        monitor='loss', save_best_only=True, period=1,mode="min")
    history_model = model1.fit(x=data[0][:train_len],y=data[1][:train_len],validation_data=(data[0][train_len:test_len],data[1][train_len:test_len]),epochs=400,batch_size=256,shuffle=True,callbacks=[checkpoint])
    

def predict(x):
    '''
    model1 = model.getBiRNNModel()
    model1.load_weights("../model_data/ep133-loss-0.991-val_acc0.972-val_loss-0.951-f10.3121.h5")
    '''
    path = "log/ep011-loss0.160-val_acc0.900-val_loss0.156-f10.4536.h5"
    model1 = models.load_model(path, custom_objects={"acc":acc,"precision":precision,"recall":recall,"f1_score":f1_score,"my_loss":my_loss})
    
    return model1.predict(x,batch_size=1)

def test(url):
    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" 
    os.environ["CUDA_VISIBLE_DEVICES"] = ""

    data = featureEngine.getInput_byJS(url)
    if data is not None:
        x,list_inner,list_xpath,_ = data
        print("x:",x)
        p = predict(x)
        print(p)
        print(np.argmax(p,1))
        print(p[0][np.argmax(p,1)[0][1]])
        print(list_inner[np.argmax(p,1)[0][1]])
        #print(list_inner[4])
        print(list_xpath[np.argmax(p,1)[0][1]])
        
def val():
    pk_file = "iterator/data_28849_16.pk"
    data = load(pk_file)
    train_percent = 0.9
    train_len = round(len(data[0])*train_percent)
    #print(np.shape(data))
    predict_y = np.argmax(predict(data[0][train_len:]),1)
    label_y = np.argmax(data[1][train_len:],1)
    list_url = data[2][train_len:]
    size_predict = 0
    size_considence = 0
    dict_root_true_wrong = dict()
    for _predict,_label,_url in zip(predict_y,label_y,list_url):
        root = _url.split("/")[2]
        if root not in dict_root_true_wrong:
            dict_root_true_wrong[root] = [0,0]
        if _predict[1]==_label[1]:
            size_considence += 1
            dict_root_true_wrong[root][0] += 1
        else:
            dict_root_true_wrong[root][1] += 1
            print(_url)
        size_predict += 1
    list_root_true_wrong = []
    for _key in dict_root_true_wrong.keys():
        list_root_true_wrong.append([_key,dict_root_true_wrong[_key]])
    list_root_true_wrong.sort(key=lambda x:x[1][1]/(x[1][0]+x[1][1]))
    print(list_root_true_wrong)
    print(size_considence,size_predict)
    
def iteratorLabel():
    '''
    @summary: 迭代地进行数据的修复
    '''
    data_file = "iterator/data_28849_35.pk"
    threshold = 0.93
    train_epochs = 10
    batch_size=96
    data = load(data_file)
    data_split = round(len(data[1])*0.5)
    last_change_set = set()
    this_change_set = set()
    max_not_change_times = 10
    _not_change_times = 0
    _time = 0
    while(True):
        _time += 1
        #训练模型
        model_1 = model.getBiRNNModel()
        model_2 = model.getBiRNNModel()
        model_1.fit(x=data[0][:data_split],y=data[1][:data_split],epochs=train_epochs,batch_size=batch_size,shuffle=True)
        model_2.fit(x=data[0][data_split:],y=data[1][data_split:],epochs=train_epochs,batch_size=batch_size,shuffle=True)
        
        predict_1 = model_1.predict(data[0])
        predict_2 = model_2.predict(data[0])
        _index = 0
        for _max_1,_max_2,_y1,_y2,Y,_url in zip(np.max(predict_1,1),np.max(predict_2,1),np.argmax(predict_1,1),np.argmax(predict_2,1),np.argmax(data[1],1),data[2]):
            if _y1[1]==_y2[1] and _y1[1]!=Y[1] and _max_1[1]>threshold and _max_2[1]>threshold:
                #修改标注
                data[1][_index][Y[1]] = 0
                data[1][_index][_y1[1]] = 1
                this_change_set.add(_url)
            _index += 1
        if len(this_change_set-last_change_set)<10:
            _not_change_times += 1
        else:
            _not_change_times = 0
        if _not_change_times>=max_not_change_times:
            break
        last_change_set = this_change_set
        this_change_set = set()
        save(data,"iterator/data_"+str(len(data[1]))+"_1.pk")      
        
        
    
    
if __name__=="__main__":
    #train()
    test(url = "https://www.600757.com.cn/show-106-14208-1.html")
    #val()
    #print(2248/2555)
    #iteratorLabel()
    pass