BIDI_ML_INFO_EXTRACTION/BiddingKG/dl/common/Utils.py

'''
Created on 2018年12月20日

@author: User
'''

import numpy as np
import re
import gensim
from keras import backend as K
import os

from threading import RLock

# from pai_tf_predict_proto import tf_predict_pb2
import requests



model_w2v = None
lock_model_w2v = RLock()

USE_PAI_EAS = False

Lazy_load = False

def getw2vfilepath():
    w2vfile = os.path.dirname(__file__)+"/../wiki_128_word_embedding_new.vector"
    if os.path.exists(w2vfile):
        return w2vfile
    return "wiki_128_word_embedding_new.vector"

def getLazyLoad():
    global Lazy_load
    return Lazy_load



model_word_file = os.path.dirname(__file__)+"/../singlew2v_model.vector"
model_word = None
lock_model_word = RLock()

from decimal import Decimal
import logging
logging.basicConfig(level = logging.INFO,format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
import pickle
import os

import json

#自定义jsonEncoder
class MyEncoder(json.JSONEncoder):

    def __init__(self):
        import numpy as np
        global np

    def default(self, obj):
        if isinstance(obj, np.ndarray):
            return obj.tolist()
        elif isinstance(obj, bytes):
            return str(obj, encoding='utf-8')
        elif isinstance(obj, (np.float_, np.float16, np.float32,
                              np.float64)):
            return float(obj)
        elif isinstance(obj,(np.int64,np.int32)):
            return int(obj)
        return json.JSONEncoder.default(self, obj)

vocab_word = None
vocab_words = None

file_vocab_word = "vocab_word.pk"
file_vocab_words = "vocab_words.pk"

selffool_authorization = "NjlhMWFjMjVmNWYyNzI0MjY1OGQ1M2Y0ZmY4ZGY0Mzg3Yjc2MTVjYg=="
selffool_url = "http://pai-eas-vpc.cn-beijing.aliyuncs.com/api/predict/selffool_gpu"
selffool_seg_authorization = "OWUwM2Q0ZmE3YjYxNzU4YzFiMjliNGVkMTA3MzJkNjQ2MzJiYzBhZg=="
selffool_seg_url = "http://pai-eas-vpc.cn-beijing.aliyuncs.com/api/predict/selffool_seg_gpu"
codename_authorization = "Y2M5MDUxMzU1MTU4OGM3ZDk2ZmEzYjkxYmYyYzJiZmUyYTgwYTg5NA=="
codename_url = "http://pai-eas-vpc.cn-beijing.aliyuncs.com/api/predict/codename_gpu"

form_item_authorization = "ODdkZWY1YWY0NmNhNjU2OTI2NWY4YmUyM2ZlMDg1NTZjOWRkYTVjMw=="
form_item_url = "http://pai-eas-vpc.cn-beijing.aliyuncs.com/api/predict/form"
person_authorization = "N2I2MDU2N2Q2MGQ0ZWZlZGM3NDkyNTA1Nzc4YmM5OTlhY2MxZGU1Mw=="
person_url = "http://pai-eas-vpc.cn-beijing.aliyuncs.com/api/predict/person"
role_authorization = "OWM1ZDg5ZDEwYTEwYWI4OGNjYmRlMmQ1NzYwNWNlZGZkZmRmMjE4OQ=="
role_url = "http://pai-eas-vpc.cn-beijing.aliyuncs.com/api/predict/role"
money_authorization = "MDQyNjc2ZDczYjBhYmM4Yzc4ZGI4YjRmMjc3NGI5NTdlNzJiY2IwZA=="
money_url = "http://pai-eas-vpc.cn-beijing.aliyuncs.com/api/predict/money"
codeclasses_authorization = "MmUyNWIxZjQ2NjAzMWJlMGIzYzkxMjMzNWY5OWI3NzJlMWQ1ZjY4Yw=="
codeclasses_url = "http://pai-eas-vpc.cn-beijing.aliyuncs.com/api/predict/codeclasses"

def viterbi_decode(score, transition_params):
    """Decode the highest scoring sequence of tags outside of TensorFlow.

    This should only be used at test time.

    Args:
      score: A [seq_len, num_tags] matrix of unary potentials.
      transition_params: A [num_tags, num_tags] matrix of binary potentials.

    Returns:
      viterbi: A [seq_len] list of integers containing the highest scoring tag
          indices.
      viterbi_score: A float containing the score for the Viterbi sequence.
    """
    trellis = np.zeros_like(score)
    backpointers = np.zeros_like(score, dtype=np.int32)
    trellis[0] = score[0]

    for t in range(1, score.shape[0]):
        v = np.expand_dims(trellis[t - 1], 1) + transition_params
        trellis[t] = score[t] + np.max(v, 0)
        backpointers[t] = np.argmax(v, 0)

    viterbi = [np.argmax(trellis[-1])]
    for bp in reversed(backpointers[1:]):
        viterbi.append(bp[viterbi[-1]])
    viterbi.reverse()

    viterbi_score = np.max(trellis[-1])
    return viterbi, viterbi_score

def limitRun(sess,list_output,feed_dict,MAX_BATCH=1024):
    len_sample = 0
    if len(feed_dict.keys())>0:
        len_sample = len(feed_dict[list(feed_dict.keys())[0]])
    if len_sample>MAX_BATCH:
        list_result = [[] for _ in range(len(list_output))]
        _begin = 0
        while(_begin<len_sample):
            new_dict = dict()
            for _key in feed_dict.keys():
                new_dict[_key] = feed_dict[_key][_begin:_begin+MAX_BATCH]
            _output = sess.run(list_output,feed_dict=new_dict)
            for _index in range(len(list_output)):
                list_result[_index].extend(_output[_index])
            _begin += MAX_BATCH
    else:
        list_result = sess.run(list_output,feed_dict=feed_dict)
    return list_result



def get_values(response,output_name):
        """
        Get the value of a specified output tensor
        :param output_name: name of the output tensor
        :return: the content of the output tensor
        """
        output = response.outputs[output_name]
        if output.dtype == tf_predict_pb2.DT_FLOAT:
            _value = output.float_val
        elif output.dtype == tf_predict_pb2.DT_INT8 or output.dtype == tf_predict_pb2.DT_INT16 or \
                output.dtype == tf_predict_pb2.DT_INT32:
            _value = output.int_val
        elif output.dtype == tf_predict_pb2.DT_INT64:
            _value = output.int64_val
        elif output.dtype == tf_predict_pb2.DT_DOUBLE:
            _value = output.double_val
        elif output.dtype == tf_predict_pb2.DT_STRING:
            _value = output.string_val
        elif output.dtype == tf_predict_pb2.DT_BOOL:
            _value = output.bool_val
        return np.array(_value).reshape(response.outputs[output_name].array_shape.dim)

def vpc_requests(url,authorization,request_data,list_outputs):
    
    
    headers = {"Authorization": authorization}
    dict_outputs = dict()
    
    response = tf_predict_pb2.PredictResponse()
    resp = requests.post(url, data=request_data, headers=headers)
    
    
    if resp.status_code != 200:
        print(resp.status_code,resp.content)
        log("调用pai-eas接口出错,authorization:"+str(authorization))
        return None
    else:
        response = tf_predict_pb2.PredictResponse()
        response.ParseFromString(resp.content)
        for _output in list_outputs:
            dict_outputs[_output] = get_values(response, _output)
        return dict_outputs

def encodeInput(data,word_len,word_flag=True,userFool=False):
    result = []
    out_index = 0
    for item in data:
        if out_index in [0]:
            list_word = item[-word_len:]
        else:
            list_word = item[:word_len]
        temp = []
        if word_flag:
            for word in list_word:
                if userFool:
                    temp.append(getIndexOfWord_fool(word))
                else:
                    temp.append(getIndexOfWord(word))
            list_append = []
            temp_len = len(temp)
            while(temp_len<word_len):
                if userFool:
                    list_append.append(0)
                else:
                    list_append.append(getIndexOfWord("<pad>"))
                temp_len += 1
            if out_index in [0]:
                temp = list_append+temp
            else:
                temp = temp+list_append
        else:
            for words in list_word:
                temp.append(getIndexOfWords(words))
                
            list_append = []
            temp_len = len(temp)
            while(temp_len<word_len):
                list_append.append(getIndexOfWords("<pad>"))
                temp_len += 1
            if out_index in [0,1]:
                temp = list_append+temp
            else:
                temp = temp+list_append
        result.append(temp)
        out_index += 1
    return result

def encodeInput_form(input,MAX_LEN=30):
    x = np.zeros([MAX_LEN])
    for i in range(len(input)):
        if i>=MAX_LEN:
            break
        x[i] = getIndexOfWord(input[i])
    return x
    

def getVocabAndMatrix(model,Embedding_size = 60):
    '''
    @summary:获取子向量的词典和子向量矩阵
    '''
    vocab = ["<pad>"]+model.index2word
    
    embedding_matrix = np.zeros((len(vocab),Embedding_size))
    for i in range(1,len(vocab)):
        embedding_matrix[i] = model[vocab[i]]
    
    return vocab,embedding_matrix

def getIndexOfWord(word):
    global vocab_word,file_vocab_word
    if vocab_word is None:
        if os.path.exists(file_vocab_word):
            vocab = load(file_vocab_word)
            vocab_word = dict((w, i) for i, w in enumerate(np.array(vocab)))
        else:
            model = getModel_word()
            vocab,_ = getVocabAndMatrix(model, Embedding_size=60)
            vocab_word = dict((w, i) for i, w in enumerate(np.array(vocab)))
            save(vocab,file_vocab_word)
    if word in vocab_word.keys():
        return vocab_word[word]
    else:
        return vocab_word['<pad>']

def changeIndexFromWordToWords(tokens,word_index):
    '''
    @summary:转换某个字的字偏移为词偏移
    '''
    before_index = 0
    after_index = 0
    for i in range(len(tokens)):
        after_index = after_index+len(tokens[i])
        if before_index<=word_index and after_index>=word_index:
            return i
        before_index = after_index
        
def getIndexOfWords(words):
    global vocab_words,file_vocab_words
    if vocab_words is None:
        if os.path.exists(file_vocab_words):
            vocab = load(file_vocab_words)
            vocab_words = dict((w, i) for i, w in enumerate(np.array(vocab)))
        else:
            model = getModel_w2v()
            vocab,_ = getVocabAndMatrix(model, Embedding_size=128)
            vocab_words = dict((w, i) for i, w in enumerate(np.array(vocab)))
            save(vocab,file_vocab_words)
    if words in vocab_words.keys():
        return vocab_words[words]
    else:
        return vocab_words["<pad>"]

    
def log(msg):
    '''
    @summary:打印信息
    '''
    logger.info(msg)

def debug(msg):
    '''
    @summary:打印信息
    '''
    logger.debug(msg)


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:
        object1 = pickle.load(f)
        return object1
    
fool_char_to_id = load(os.path.dirname(__file__)+"/fool_char_to_id.pk")

def getIndexOfWord_fool(word):
    
    if word in fool_char_to_id.keys():
        return fool_char_to_id[word]
    else:
        return fool_char_to_id["[UNK]"]


def find_index(list_tofind,text):
    '''
    @summary: 查找所有词汇在字符串中第一次出现的位置
    @param:
        list_tofind:待查找词汇
        text:字符串
    @return: list,每个词汇第一次出现的位置
    
    '''
    result = []
    for item in list_tofind:
        index = text.find(item)
        if index>=0:
            result.append(index)
        else:
            result.append(-1)
    return result


def combine(list1,list2):
    '''
    @summary:将两个list中的字符串两两拼接
    @param:
        list1:字符串list
        list2:字符串list
    @return:拼接结果list
    '''
    result = []
    for item1 in list1:
        for item2 in list2:
            result.append(str(item1)+str(item2))
    return result


def getDigitsDic(unit):
    '''
    @summary:拿到中文对应的数字
    '''
    DigitsDic = {"零":0, "壹":1, "贰":2, "叁":3, "肆":4, "伍":5, "陆":6, "柒":7, "捌":8, "玖":9,
                 "〇":0, "一":1, "二":2, "三":3, "四":4, "五":5, "六":6, "七":7, "八":8, "九":9}
    return DigitsDic.get(unit)

def getMultipleFactor(unit):
    '''
    @summary:拿到单位对应的值
    '''
    MultipleFactor = {"兆":Decimal(1000000000000),"亿":Decimal(100000000),"万":Decimal(10000),"仟":Decimal(1000),"千":Decimal(1000),"佰":Decimal(100),"百":Decimal(100),"拾":Decimal(10),"十":Decimal(10),"元":Decimal(1),"圆":Decimal(1),"角":round(Decimal(0.1),1),"分":round(Decimal(0.01),2)}
    return MultipleFactor.get(unit)

def getUnifyMoney(money):
    '''
    @summary:将中文金额字符串转换为数字金额
    @param:
        money:中文金额字符串
    @return: decimal,数据金额
    '''
    
    MAX_MONEY = 1000000000000
    MAX_NUM = 12
    #去掉逗号
    money = re.sub("[，,]","",money)
    money = re.sub("[^0-9.零壹贰叁肆伍陆柒捌玖拾佰仟萬億圆十百千万亿元角分]","",money)
    result = Decimal(0)
    chnDigits = ["零", "壹", "贰", "叁", "肆", "伍", "陆", "柒", "捌", "玖"]
    chnFactorUnits = ["兆", "亿", "万", "仟", "佰", "拾","圆","元","角","分"]
    
    LowMoneypattern = re.compile("^[\d,]+(\.\d+)?$")
    BigMoneypattern = re.compile("^零?(?P<BigMoney>[%s])$"%("".join(chnDigits)))
    try:
        if re.search(LowMoneypattern,money) is not None:
            return Decimal(money)
        elif re.search(BigMoneypattern,money) is not None:
            return getDigitsDic(re.search(BigMoneypattern,money).group("BigMoney"))
        for factorUnit in chnFactorUnits:
            if re.search(re.compile(".*%s.*"%(factorUnit)),money) is not None:
                subMoneys = re.split(re.compile("%s(?!.*%s.*)"%(factorUnit,factorUnit)),money)
                if re.search(re.compile("^(\d+)(\.\d+)?$"),subMoneys[0]) is not None:
                    if MAX_MONEY/getMultipleFactor(factorUnit)<Decimal(subMoneys[0]):
                        return Decimal(0)
                    result += Decimal(subMoneys[0])*(getMultipleFactor(factorUnit))
                elif len(subMoneys[0])==1:
                    if re.search(re.compile("^[%s]$"%("".join(chnDigits))),subMoneys[0]) is not None:
                        result += Decimal(getDigitsDic(subMoneys[0]))*(getMultipleFactor(factorUnit))
                else:
                    result += Decimal(getUnifyMoney(subMoneys[0]))*(getMultipleFactor(factorUnit))
                if len(subMoneys)>1:
                    if re.search(re.compile("^(\d+(,)?)+(\.\d+)?[百千万亿]?\s?(元)?$"),subMoneys[1]) is not None:
                        result += Decimal(subMoneys[1])
                    elif len(subMoneys[1])==1:
                        if re.search(re.compile("^[%s]$"%("".join(chnDigits))),subMoneys[1]) is not None:
                            result += Decimal(getDigitsDic(subMoneys[1]))
                    else:
                        result += Decimal(getUnifyMoney(subMoneys[1]))
                break
    except Exception as e:
        return Decimal(0)
    return result




def getModel_w2v():
    '''
    @summary:加载词向量
    '''
    global model_w2v,lock_model_w2v
    with lock_model_w2v:
        if model_w2v is None:
            model_w2v = gensim.models.KeyedVectors.load_word2vec_format(getw2vfilepath(),binary=True)
        return model_w2v

def getModel_word():
    '''
    @summary:加载字向量
    '''

    global model_word,lock_model_w2v
    with lock_model_word:
        if model_word is None:
            model_word = gensim.models.KeyedVectors.load_word2vec_format(model_word_file,binary=True)
        return model_word

# getModel_w2v()
# getModel_word()

def findAllIndex(substr,wholestr):
    '''
    @summary: 找到字符串的子串的所有begin_index
    @param:
        substr:子字符串
        wholestr:子串所在完整字符串
    @return: list,字符串的子串的所有begin_index
    '''
    copystr = wholestr
    result = []
    indexappend = 0
    while(True):
        index = copystr.find(substr)
        if index<0:
            break
        else:
            result.append(indexappend+index)
            indexappend += index+len(substr)
            copystr = copystr[index+len(substr):]
    return result
    
  
def spanWindow(tokens,begin_index,end_index,size,center_include=False,word_flag = False,use_text = False,text = None):
    '''
    @summary:取得某个实体的上下文词汇
    @param:
        tokens:句子分词list
        begin_index:实体的开始index
        end_index:实体的结束index
        size:左右两边各取多少个词
        center_include:是否包含实体
        word_flag:词/字,默认是词
    @return: list,实体的上下文词汇
    '''  
    if use_text:
        assert text is not None
    length_tokens = len(tokens)
    if begin_index>size:
        begin = begin_index-size
    else:
        begin = 0
    if end_index+size<length_tokens:
        end = end_index+size+1
    else:
        end = length_tokens
    result = []
    if not word_flag:
        result.append(tokens[begin:begin_index])
        if center_include:
            if use_text:
                result.append(text)
            else:
                result.append(tokens[begin_index:end_index+1])
        result.append(tokens[end_index+1:end])
    else:
        result.append("".join(tokens[begin:begin_index]))
        if center_include:
            if use_text:
                result.append(text)
            else:
                result.append("".join(tokens[begin_index:end_index+1]))
        result.append("".join(tokens[end_index+1:end]))
    #print(result)
    return result

#根据规则补全编号或名称两边的符号
def fitDataByRule(data):
    symbol_dict = {"(":")",
                   "（":"）",
                   "[":"]",
                   "【":"】",
                   ")":"(",
                   "）":"（",
                   "]":"[",
                   "】":"【"}
    leftSymbol_pattern = re.compile("[\(（\[【]")
    rightSymbol_pattern = re.compile("[\)）\]】]")
    leftfinds = re.findall(leftSymbol_pattern,data)
    rightfinds = re.findall(rightSymbol_pattern,data)
    result = data
    if len(leftfinds)+len(rightfinds)==0:
        return data
    elif len(leftfinds)==len(rightfinds):
        return data
    elif abs(len(leftfinds)-len(rightfinds))==1:
        if len(leftfinds)>len(rightfinds):
            if symbol_dict.get(data[0]) is not None:
                result = data[1:]
            else:
                #print(symbol_dict.get(leftfinds[0]))
                result = data+symbol_dict.get(leftfinds[0])
        else:
            if symbol_dict.get(data[-1]) is not None:
                result = data[:-1]
            else:
                result = symbol_dict.get(rightfinds[0])+data
    result = re.sub("[。]","",result)
    return  result

def embedding(datas,shape):
    '''
    @summary:查找词汇对应的词向量
    @param:
        datas:词汇的list
        shape:结果的shape
    @return: array,返回对应shape的词嵌入
    '''
    model_w2v = getModel_w2v()
    embed = np.zeros(shape)
    length = shape[1]
    out_index = 0
    #print(datas)
    for data in datas:
        index = 0
        for item in data:
            item_not_space = re.sub("\s*","",item)
            if index>=length:
                break
            if item_not_space in model_w2v.vocab:
                embed[out_index][index] = model_w2v[item_not_space]
                index += 1
            else:
                #embed[out_index][index] = model_w2v['unk']
                index += 1
        out_index += 1
    return embed

def embedding_word(datas,shape):
    '''
    @summary:查找词汇对应的词向量
    @param:
        datas:词汇的list
        shape:结果的shape
    @return: array,返回对应shape的词嵌入
    '''
    model_w2v = getModel_word()
    embed = np.zeros(shape)
    length = shape[1]
    out_index = 0
    #print(datas)
    for data in datas:
        index = 0
        for item in str(data)[-shape[1]:]:
            if index>=length:
                break
            if item in model_w2v.vocab:
                embed[out_index][index] = model_w2v[item]
                index += 1
            else:
                # embed[out_index][index] = model_w2v['unk']
                index += 1
        out_index += 1
    return embed

def formEncoding(text,shape=(100,60),expand=False):
    embedding = np.zeros(shape)
    word_model = getModel_word()
    for i in range(len(text)):
        if i>=shape[0]:
            break
        if text[i] in word_model.vocab:
            embedding[i] = word_model[text[i]]
    if expand:
        embedding = np.expand_dims(embedding,0)
    return embedding

def partMoney(entity_text,input2_shape = [7]):
    '''
    @summary:对金额分段
    @param:
        entity_text:数值金额
        input2_shape:分类数
    @return: array,分段之后的独热编码
    '''
    money = float(entity_text)
    parts = np.zeros(input2_shape)
    if money<100:
        parts[0] = 1
    elif money<1000:
        parts[1] = 1
    elif money<10000:
        parts[2] = 1
    elif money<100000:
        parts[3] = 1
    elif money<1000000:
        parts[4] = 1
    elif money<10000000:
        parts[5] = 1
    else:
        parts[6] = 1
    return parts

def recall(y_true, y_pred):
    '''
    计算召回率
    @Argus:
        y_true: 正确的标签
        y_pred: 模型预测的标签

    @Return
        召回率
    '''
    c1 = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    c3 = K.sum(K.round(K.clip(y_true, 0, 1)))
    if c3 == 0:
        return 0
    recall = c1 / c3
    return recall


def f1_score(y_true, y_pred):
    '''
    计算F1

    @Argus:
        y_true: 正确的标签
        y_pred: 模型预测的标签

    @Return
        F1值
    '''

    c1 = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    c2 = K.sum(K.round(K.clip(y_pred, 0, 1)))
    c3 = K.sum(K.round(K.clip(y_true, 0, 1)))
    precision = c1 / c2
    if c3 == 0:
        recall = 0
    else:
        recall = c1 / c3
    f1_score = 2 * (precision * recall) / (precision + recall)
    return f1_score


def precision(y_true, y_pred):
    '''
    计算精确率

    @Argus:
        y_true: 正确的标签
        y_pred: 模型预测的标签

    @Return
        精确率
    '''
    c1 = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    c2 = K.sum(K.round(K.clip(y_pred, 0, 1)))
    precision = c1 / c2
    return precision

# def print_metrics(history):
#     '''
#     制作每次迭代的各metrics变化图片
#
#     @Arugs:
#         history: 模型训练迭代的历史记录
#     '''
#     import matplotlib.pyplot as plt
#
#     # loss图
#     loss = history.history['loss']
#     val_loss = history.history['val_loss']
#     epochs = range(1, len(loss) + 1)
#     plt.subplot(2, 2, 1)
#     plt.plot(epochs, loss, 'bo', label='Training loss')
#     plt.plot(epochs, val_loss, 'b', label='Validation loss')
#     plt.title('Training and validation loss')
#     plt.xlabel('Epochs')
#     plt.ylabel('Loss')
#     plt.legend()
#
#     # f1图
#     f1 = history.history['f1_score']
#     val_f1 = history.history['val_f1_score']
#     plt.subplot(2, 2, 2)
#     plt.plot(epochs, f1, 'bo', label='Training f1')
#     plt.plot(epochs, val_f1, 'b', label='Validation f1')
#     plt.title('Training and validation f1')
#     plt.xlabel('Epochs')
#     plt.ylabel('F1')
#     plt.legend()
#
#     # precision图
#     prec = history.history['precision']
#     val_prec = history.history['val_precision']
#     plt.subplot(2, 2, 3)
#     plt.plot(epochs, prec, 'bo', label='Training precision')
#     plt.plot(epochs, val_prec, 'b', label='Validation pecision')
#     plt.title('Training and validation precision')
#     plt.xlabel('Epochs')
#     plt.ylabel('Precision')
#     plt.legend()
#
#     # recall图
#     recall = history.history['recall']
#     val_recall = history.history['val_recall']
#     plt.subplot(2, 2, 4)
#     plt.plot(epochs, recall, 'bo', label='Training recall')
#     plt.plot(epochs, val_recall, 'b', label='Validation recall')
#     plt.title('Training and validation recall')
#     plt.xlabel('Epochs')
#     plt.ylabel('Recall')
#     plt.legend()
#
#     plt.show()


if __name__=="__main__":
    print(fool_char_to_id[">"])
    # model = getModel_w2v()
    # vocab,matrix = getVocabAndMatrix(model, Embedding_size=128)
    # save([vocab,matrix],"vocabMatrix_words.pk")