为什么评分都是1.0呢，从哪里得到的？歌单例根本没有啊

问一下pkl文件是怎么得到的，爬虫爬的吗？

为什么我这个部分只有buy，会不会和我不再国内有关系

大家注意这个b64_code，人人车好像每隔一段时间会换一下这个b64_code，然后根据下面这个代码就可以获得车辆价格了

import base64
from fontTools.ttLib import TTFont


def create_ttf():
    b64_code = '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'

    with open('./font.ttf','wb') as f:
        f.write(base64.decodebytes(b64_code.encode()))

def decode_ttf(ttf_path):
    ttf = TTFont(ttf_path)
   # 输出数字与uni编码之间的关系 元素下标就是数字，元素内容是uni编码
    li = ttf.getGlyphOrder()[1:]
    # 输出uni编码与数字之间的关系，输出类型为字典，特殊字符的acill值:uni编码
    dic = ttf.getBestCmap()

    new_dict = {}
    for k, v in dic.items():
        # chr(k) 转成特殊字符   li.index(v) 找到uni编码所对应的下标(即对应数字)
        new_dict[chr(k)] = li.index(v)
    return new_dict
 
if __name__ == '__main__':
    # 生成ttf文件
    create_ttf()
    # 进行内容解析测试
    ttf_path = './font.ttf'  # 人人车加密价格的TTF字体文件路径
    encrypted_text = '+¥.起'  # 需要解密的加密文本
    decrypted_text = decode_ttf(ttf_path)
    # 输出：人人车价格解密结果
    ans = ''
    for char in encrypted_text:
        if char == '.':
            ans += '.'
        else:
            ans += str(decrypted_text[char])
    print(ans)

人人车的网页更新了，静态网页数据还能爬取；价格文本进行了加密，破解方式就在下节视频的问答部分，目前我是能爬取到数据的。

加密的密钥或者字体加密的辅助内容如下，个人认为有用的信息是base64 后面那串很长的字符串：

人人车网页更新升级了，不过静态网页还是能爬取到数据的，但是他汽车价格的文本进行加密，我的破解方式也发了，目前我是能爬取到数据的，价格的加密文本也破解了。

这是人人车的价格加密后，进行破解的方法：

import base64
from fontTools.ttLib import TTFont
'''
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'''

def create_ttf():
    b64_code = 'AAEAAAAOAIAAAwBgT1MvMi9w4T0AAAFoAAAAYGNtYXDE6zKeAAAB6AAAAHRjdnQgAKcBHgAABFQAAAAQZnBnbQZZnDcAAAJcAAABc2dhc3AAAAAQAAAURAAAAAhnbHlmIUeQjQAABHwAAAwcaGVhZBTOym0AAADsAAAANmhoZWEGTAETAAABJAAAACRobXR4B7QBAAAAAcgAAAAebG9jYQ7eEbIAAARkAAAAGG1heHACHAGOAAABSAAAACBuYW1lkYFW/gAAEJgAAAMkcG9zdE5werQAABO8AAAAiHByZXBKotU/AAAD0AAAAIQAAQAAAAEAgzau5zxfDzz1ABsD6AAAAADYv5kdAAAAANnn7fAAAP8vAvgCywAAAAYAAgAAAAAAAAABAAAC7v8GAGQCCAAAAAAC+AABAAAAAAAAAAAAAAAAAAAABAABAAAACwBpAAYAAAAAAAEAAAAAAAoAAAIAASQAAAAAAAQCLgGQAAUABAKKAlgAAABLAooCWAAAAV4AMgAyAAAAAAAAAAAAAAAAAAAAAwiAAAAAAAAAAAAAADU4ICAAwAAAl2IC7v8GAGQC7gD6AAAAAQAAAAAB9AK8AAAAIAACAggAAAHgACkBRgAxAeAAKgApACoAKgApACoAKQApAAAAAAABAAMAAQAAAAwABABoAAAAFgAQAAMABgAlACsALQAvAKVOB1FDYphl9o13//8AAAAlACsALQAvAKVOB1FDYphl9o13////3v/a/9f/2/9jsfuuvp1vmhBykgABAAAAAAAAAAAAAAAAAAAAAAAAAAAAALgAACxLuAAJUFixAQGOWbgB/4W4AEQduQAJAANfXi24AAEsICBFaUSwAWAtuAACLLgAASohLbgAAywgRrADJUZSWCNZIIogiklkiiBGIGhhZLAEJUYgaGFkUlgjZYpZLyCwAFNYaSCwAFRYIbBAWRtpILAAVFghsEBlWVk6LbgABCwgRrAEJUZSWCOKWSBGIGphZLAEJUYgamFkUlgjilkv/S24AAUsSyCwAyZQWFFYsIBEG7BARFkbISEgRbDAUFiwwEQbIVlZLbgABiwgIEVpRLABYCAgRX1pGESwAWAtuAAHLLgABiotuAAILEsgsAMmU1iwQBuwAFmKiiCwAyZTWCMhsICKihuKI1kgsAMmU1gjIbgAwIqKG4ojWSCwAyZTWCMhuAEAioobiiNZILADJlNYIyG4AUCKihuKI1kguAADJlNYsAMlRbgBgFBYIyG4AYAjIRuwAyVFIyEjIVkbIVlELbgACSxLU1hFRBshIVktALgAACsAugABAAIAAisBugADAAMAAisBvwADADcAMQAmABwAEQAAAAgrvwAEAFsASgA6ACkAGQAAAAgrvwAFAEAAMQAmABwAEQAAAAgrAL8AAQA3ADEAJgAcABEAAAAIK78AAgBAADEAJgAcABEAAAAIKwC6AAYAAQAHK7gAACBFfWkYRAAUAGQAVgBkAD0AVgAAAAAAAAAAAOoBFgGoAngCtgOKBGYEjAUwBg4AAgAp/5wBtwK8AB8APQEkuAAAKwC6AC4AAAADK7oAEAAgAAMrAbgAPi+4ADYvuAA+ELgACNC4AAgvuAAF0LgABS+4AAgQuAAL0LgACy9BBQDaADYA6gA2AAJdQRsACQA2ABkANgApADYAOQA2AEkANgBZADYAaQA2AHkANgCJADYAmQA2AKkANgC5ADYAyQA2AA1duAA2ELgAGNy4ABXQuAAVL7gAGBC4ABvQuAAbL7gACBC4ACbcQRsABgAmABYAJgAmACYANgAmAEYAJgBWACYAZgAmAHYAJgCGACYAlgAmAKYAJgC2ACYAxgAmAA1dQQUA1QAmAOUAJgACXbgAI9C4ACMvuAAmELgAKdC4ACkvuAA2ELgAM9C4ADMvuAA2ELgAOdC4ADkvuAAYELgAP9wwMRciLgInJjQnNDY3PgMzMh4CFxYUFRwBBw4DAyIGBw4BFRYUFxQeAjMyPgI1NjQ1PAEnNC4C8ShHNh8BAgEBAQEfNkcoKEc2HwECAgEfNUYqKDcCAQEBAhAaJBMUIhsPAgIQGiNkHjRHKDRnNDRnNClGNB4eNEYpNGc0NGc0KEc0HgK8OCczZzMzZzMTIxoPDxojEzNnMzNnMxQjGQ8AAAAAAQAx/6wA9wKrAAYANLgAACsAugAAAAEAAyu6AAMAAQAAERI5ugAEAAEAABESOQG6AAEAAgADK7gAARC4AAjcMDETESMRBzU392RiaQKr/QECg0B3RQAAAAEAKv+sAbgCvAAvAKC4AAArALoALQAAAAMrugAeAA8AAysBuAAwL7gADC+4ADAQuAAX0LgAFy+4AADQQQUA2gAMAOoADAACXUEbAAkADAAZAAwAKQAMADkADABJAAwAWQAMAGkADAB5AAwAiQAMAJkADACpAAwAuQAMAMkADAANXbgAFxC4ABbcuAAMELgAI9y4AC7QuAAuL7oALQAXAC4REjm4ACMQuAAx3DAxFzU3PgU3PgE1NCYrASIOAh0BIzU0PgI7ATIeAhUUBgcOBQchFSoJASE0PzswCwgNOSoCFCMaD2QfNUgoAilJNh8SEAkjKzEwKw8BFVRmDQEwS1xYSxQQJRIqOQ8aIhQdHSlHNB8fNkkpID0bETZCSUY+F2QAAAABACn/nAG4ArsATgDOuAAAKwC6ABgACwADK7oAPgAzAAMrugApACYAAysBugATABAAAyu6AEQALwADK7oAIAAvAEQREjm4ACAvQQUA2gAgAOoAIAACXUEbAAkAIAAZACAAKQAgADkAIABJACAAWQAgAGkAIAB5ACAAiQAgAJkAIACpACAAuQAgAMkAIAANXbgAA9y4AADQuAAAL7gAAxC4AAbQuAAGL7gAIBC4AB3QuAAdL7gAIBC4ACPQuAAjL7oAOQAQABMREjm4ADkvuAA23LgAAxC4AFDcMDElFhQVHAEHFA4CIyIuAjUnNxcUHgIzMj4CNTY0NTwBJzQmKwE1MzI+Aj0CNCYjIgYVByM3ND4CMzIeAh0BFA4CBx4BFx4BAbcBAR82SCkoSTYgAWQBEBskFBQjGxABATkpMzISHxcOMiMjMwFkAR4zQyYmQzMdAwwWEwIGAh0esAsTCgoTCihINh8fNUgpJwEnFCQbDxAbIxQJEwoKEwkpOGQNFx8SHR4jMTAkJygmQzIcHTFDJh8ZKickEwIFAxxHAAAAAAEAKv+sAbcCqwAOAEq4AAArALoABwAIAAMruAAHELgAAtC4AAgQuAAM0AG6AAYAAwADK7oAAQADAAYREjm4AAYQuAAJ0LgAAxC4AAvQuAAGELgAENwwMQEzAzM1MxUzFSMVIzUjNQEHbNiGZDIyZPcCq/3curpkd3dVAAABACr/nAG2AqwARQDquAAAKwC6ACAADAADK7oAOgA7AAMrugBBADEAAyu6AD0AMQBBERI5AboAPQA4AAMrugAEACgAAyu4AAQQuAAA0LgAAC+4AAQQuAAH0LgABy+6ABgAOAA9ERI5uAAYL7gAF9y4ABHQuAARL7gAFxC4ABTQuAAUL7gAGBC4ABvQuAAbL0EFANoAKADqACgAAl1BGwAJACgAGQAoACkAKAA5ACgASQAoAFkAKABpACgAeQAoAIkAKACZACgAqQAoALkAKADJACgADV24ACgQuAAl0LgAJS+4ACgQuAAs0LgALC+4AAQQuABH3DAxARYUHQEcAQcOAyMiLgInNCY1JjQ1MxwBFxQeAjMyPgI3NjQ9ATwBJy4DKwEiBgcGBycRIRUjFT4BOwEyHgIBtAICAiE1RScnRTQhAwEBZAENGCQYEyIaEAECAgEQGiITARIfCw4LVwFb9xElEgEnRTUhAQ4XLhcBFy8YJ0IyHBwyQicHCAMICBAOBQUWJx4SDhghExYqFwEXKhUTIBgOCwgICyIBT2SQBwYcMUMAAAAAAgAp/5wBtwKrACoARwDsuAAAKwC6AEMACwADK7oAJgA0AAMrugAjADQAJhESOQG4AEgvuAAsL7gAA9y4AADQuAAAL7gASBC4ABPQuAATL7gAENC4ABAvuAATELgAFdC4ABUvugAcACwAAxESObgALBC4AB/QuAAfL7oAIwATAAMREjm4ACwQuAAv0LgALy+4ABMQuAA73EEbAAYAOwAWADsAJgA7ADYAOwBGADsAVgA7AGYAOwB2ADsAhgA7AJYAOwCmADsAtgA7AMYAOwANXUEFANUAOwDlADsAAl24ADjQuAA4L7gAOxC4AD7QuAA+L7gAAxC4AEncMDElFhQVFA4CBw4BIyIuAicmNDU0Nz4DPwEzDgEPAQ4BBz4BMzIeAgc1PAEnNC4CKwEiBgcOARUcARcUHgIzMj4CAbYBBAwYFBxHJyhINiABAQMDGS9GMA9vBQcFHyU5FQ4jFCU9LRliAQ4YHxEKJzcFAQEBEBskFBQjGhC5DRoNGy8rKBMcHR41RygKEwoaGyBQbZNiHwgSCUBLeTAFBxgsPn4lDBkMEh4WDTInCxULCRIJFCQaDw8aIwAAAAABACr/rAG3AqoACAAiuAAAKwC6AAAABAADKwG6AAYABwADK7oAAwAHAAYREjkwMQEVAyMTIxUjNQG35GrnwmQCqmz9bgKaXsIAAAAAAwAp/5sBtwK8AC4APQBSAFy4AAArALoATgAGAAMrugAeADsAAyu6ADQARAADKwG6AEsACwADK7oAJAA3AAMrugA+ADcAJBESObgAPi+4AADcugAWAAsASxESObgAFi+4ADDcuABLELgASdAwMSUUBgcOASMiLgInNTQ+Ajc2Ny4BNTQ+Ajc+ATMyHgIXFQ4BBx4BFx4DAR0BHgEzMjY3NS4BIyIGEzU0LgIjIgYHBh0CHgEzMj4CAbcUKRxHJylHNh8CAwwYFQMIJhMEDBYTGkIkJUEyHwECHBoCBgIUGA0E/uQCMCMiMAICMCIjMLUQGyMUFCMOHgI4KRQjGxCHNFgnHB0eNUcoKhsvLCgUBAYkTTIZKygkEhkbHDBBJEsjQRkCBQIUKSwwAWUpJCIvLyJOIi8w/jdXFCMbDw8OHCgvKCk3DxojAAIAKf+tAbcCvAAqAEcA8LgAACsAugALAEMAAyu6ADQAJgADK7oAIwAmADQREjkBuABIL7gAOy+4AEgQuAAD0LgAAy+4AADQuAAAL0EFANoAOwDqADsAAl1BGwAJADsAGQA7ACkAOwA5ADsASQA7AFkAOwBpADsAeQA7AIkAOwCZADsAqQA7ALkAOwDJADsADV24ADsQuAAT3LgAENC4ABAvuAATELgAFdC4ABUvuAADELgALNy6ABwAAwAsERI5uAAf0LgAHy+6ACMAAwATERI5uAAsELgAL9C4AC8vuAA7ELgAONC4ADgvuAA7ELgAPtC4AD4vuAATELgASdwwMRMmNDU0PgI3PgEzMh4CFxYUFRQHDgMPASM+AT8BPgE3DgEjIi4CNxUcARcUHgI7ATI2Nz4BNTwBJzQuAiMiDgIqAQQMGBQcRycoSDYgAQEDAxkvRjAPbwUHBR8lORUOIxQlPS0ZYgEOGB8RCic3BQEBARAbJBQUIxoQAZ8NGg0bLysoExwdHjVGKQoTChobIFBtk2IfCBIJQEt5MAUHGCw+fiUMGQwSHhYNMicLFQsJEgkUJBoPDxojAAAAAAAAEwDqAAEAAAAAAAAAOwAAAAEAAAAAAAEABgA7AAEAAAAAAAIABwBBAAEAAAAAAAMAKQBIAAEAAAAAAAQADQBxAAEAAAAAAAUACwB+AAEAAAAAAAYADACJAAEAAAAAAAkACwCVAAEAAAAAAAwAGgCgAAMAAQQJAAAAdgC6AAMAAQQJAAEADAEwAAMAAQQJAAIADgE8AAMAAQQJAAMAUgFKAAMAAQQJAAQAGgGcAAMAAQQJAAUAFgG2AAMAAQQJAAYAGAHMAAMAAQQJAAkAFgHkAAMAAQQJAAwANAH6AAMAAQQJABEADAIuZG9uIDU4IEZvbnQgRGVzaWduIGJ5IEtpZG9vb20gWHUuIDIwMTmpQWxsIFJpZ2h0cyBSZXNlcnZlZC5kb24gNThSZWd1bGFyVmVyc2lvbiAxLjI7NTg7ZG9uNTgtTWVkaXVtOzIwMTk7RkxWSS02MTNkb24gNTggTWVkaXVtVmVyc2lvbiAxLjJkb241OC1NZWRpdW1LaWRvb29tIFh1Lmh0dHA6Ly9raWRvb29tLmxvZnRlci5jb20vAGQAbwBuACAANQA4ACAARgBvAG4AdAAgAEQAZQBzAGkAZwBuACAAYgB5ACAASwBpAGQAbwBvAG8AbQAgAFgAdQAuACAAMgAwADEAOQCpAEEAbABsACAAUgBpAGcAaAB0AHMAIABSAGUAcwBlAHIAdgBlAGQALgBkAG8AbgAgADUAOABSAGUAZwB1AGwAYQByAFYAZQByAHMAaQBvAG4AIAAxAC4AMgA7ADUAOAA7AGQAbwBuADUAOAAtAE0AZQBkAGkAdQBtADsAMgAwADEAOQA7AEYATABWAEkALQA2ADEAMwBkAG8AbgAgADUAOAAgAE0AZQBkAGkAdQBtAFYAZQByAHMAaQBvAG4AIAAxAC4AMgBkAG8AbgA1ADgALQBNAGUAZABpAHUAbQBLAGkAZABvAG8AbwBtACAAWAB1AC4AaAB0AHQAcAA6AC8ALwBrAGkAZABvAG8AbwBtAC4AbABvAGYAdABlAHIALgBjAG8AbQAvAE0AZQBkAGkAdQBtAAIAAAAAAAD/nAAyAAAAAAAAAAAAAAAAAAAAAAAAAAAACwAAAQIBAwEEAQUBBgEHAQgBCQEKAQsHdW5pNTE0Mwd1bmk0RTA3B3VuaTAwMjUHdW5pMDAyRAd1bmkwMDJCB3VuaTY1RjYHdW5pNjI5OAd1bmkwMEE1B3VuaThENzcHdW5pMDAyRgABAAH//wAP'

    with open('./font.ttf','wb') as f:
        f.write(base64.decodebytes(b64_code.encode()))

def decode_ttf(ttf_path):
    ttf = TTFont(ttf_path)
   # 输出数字与uni编码之间的关系 元素下标就是数字，元素内容是uni编码
    li = ttf.getGlyphOrder()[1:]
    # 输出uni编码与数字之间的关系，输出类型为字典，特殊字符的acill值:uni编码
    dic = ttf.getBestCmap()

    new_dict = {}
    for k, v in dic.items():
        # chr(k) 转成特殊字符   li.index(v) 找到uni编码所对应的下标(即对应数字)
        new_dict[chr(k)] = li.index(v)
    return new_dict
 
if __name__ == '__main__':
    # 生成ttf文件
    create_ttf()
    # 进行内容解析测试
    ttf_path = './font.ttf'  # 人人车加密价格的TTF字体文件路径
    encrypted_text = '-.%起'  # 需要解密的加密文本
    decrypted_text = decode_ttf(ttf_path)
    # 输出：人人车价格解密结果
    ans = ''
    for char in encrypted_text:
        if char == '.':
            ans += '.'
        else:
            ans += str(decrypted_text[char])
    print(ans)

class TextClassfier:
    def __init__(self):
        self.classfier=MultinomialNB(),
        self.cv=CountVectorizer(max_features=4000)
    def features(self,x):
        return self.cv.transform(x)
    def fit(self,x,y):
        self.cv.fit(x)
        self.classfier.fit(self.features(x),y)
    def score(self,x,y):
        return self.model.score(self.features(x),y)
    def predict(self,x,y):
        return self.model.predict(self.features([x]))

model=TextClassfier()
model.fit(x_train,y_train)
display(model.socre(x_test,y_test))
display(model.predict(['中旬 备战 冲刺']))

老师 分类器为什么会是tuple类型呢？

老师 数据集的处理与划分，模型的选择和您的一样 为什么最后model.score()得到的评估分数和您的不一样呢，而且很低，是什么原因呢？

cv=CountVectorizer(max_features=4000)
model.fit(cv.fit_transform(x_train),y_train)
model.fit(cv.transform(x_train),y_train)

老师　cv.fit_transform与cv.transform有什么区别吗？

cv=CountVectorizer(max_features=4000)

老师 max_features是指挑选4000个词汇并指定唯一的ID吗

课程过时了，现在跟着课程实操完全没法下手

dictionary=corpora.Dictionary(segments)

老师 通过corproa.Dictionary生成的是语料库吗？

for line in data.content.values:
    segs=jieba.lcut(line)
    segs=filter(lambda x: len(x.strip())>1,segs)

老师 filter是对传入的列表对象中的每一个元素使用匿名函数吗？