pkuseg的混合分词模型源代码

以下是pkuseg混合分词模型的源代码，采用Python语言编写：

import os
import pickle

from pkuseg import hmm
from pkuseg import postag
from pkuseg import trie
from pkuseg.metrics import get_unk_index
from pkuseg.models import HMM, CRF
from pkuseg.utils import (
    load_dict,
    load_model,
    load_model_viterbi,
    load_tag_dict,
    load_word_dict,
    save_model,
    save_model_viterbi,
    save_tag_dict,
    save_word_dict,
)


class MixSeg:
    def __init__(self, model_name=None, user_dict=None, postag=False, train=False):
        """
        模型初始化，加载模型及自定义词典
        :param model_name: 模型名称，包括"ctb8", "pku", "msr"三种
        :param user_dict: 用户自定义词典路径
        :param postag: 是否需要词性标注
        :param train: 是否为训练模式
        """
        if model_name is None:
            model_name = "default"
        self.model_name = model_name
        self.postag = postag
        self.train = train
        self.word_dict = load_word_dict(self.model_name, self.train)
        self.tag_dict = load_tag_dict(self.model_name, self.train)
        self.trie = trie.Trie(self.word_dict.keys())
        self.unk_index = get_unk_index(self.word_dict)
        if train:
            self.crf = CRF(self.word_dict, self.tag_dict, self.unk_index)
            self.hmm = HMM(self.word_dict, self.tag_dict, self.unk_index)
        else:
            self.crf = None
            self.hmm = None
            self.load_model()
            self.load_user_dict(user_dict)

    def load_user_dict(self, user_dict_path):
        """
        加载用户自定义词典
        :param user_dict_path: 用户自定义词典路径
        :return: None
        """
        if user_dict_path is not None and os.path.isfile(user_dict_path):
            with open(user_dict_path, "r", encoding="utf-8") as f:
                for line in f:
                    line = line.strip()
                    if not line:
                        continue
                    word, freq, tag = line.split()
                    freq = int(freq)
                    if word in self.word_dict:
                        continue
                    self.word_dict[word] = freq
                    self.trie.add(word)
                    if self.postag:
                        self.tag_dict[word] = tag

    def train_model(self, train_file_path, model_save_path=None):
        """
        训练模型
        :param train_file_path: 用于训练的语料文件路径
        :param model_save_path: 训练完成后模型保存路径
        :return: None
        """
        if not self.train:
            return
        if self.postag:
            self.crf.train(train_file_path)
        else:
            self.hmm.train(train_file_path)
        self.save_model(model_save_path)

    def save_model(self, model_save_path=None):
        """
        保存模型
        :param model_save_path: 模型保存路径
        :return: None
        """
        if self.postag:
            save_model(self.crf, self.model_name, model_save_path)
            save_tag_dict(self.tag_dict, self.model_name)
        else:
            save_model_viterbi(self.hmm, self.model_name, model_save_path)
        save_word_dict(self.word_dict, self.model_name)

    def load_model(self, model_load_path=None):
        """
        加载模型
        :param model_load_path: 模型加载路径
        :return: None
        """
        if self.postag:
            self.crf = load_model(self.model_name, model_load_path)
            self.tag_dict = load_tag_dict(self.model_name)
        else:
            self.hmm = load_model_viterbi(self.model_name, model_load_path)
        self.word_dict = load_word_dict(self.model_name)

    def cut(self, text):
        """
        分词，返回分词结果
        :param text: 待分词的文本
        :return: 分词结果
        """
        if self.postag:
            return self.crf.cut(text)
        else:
            return self.hmm.cut(text)

    def cut_stanford(self, text):
        """
        可能更好地处理长词，但较慢
        分词，返回分词结果
        :param text: 待分词的文本
        :return: 分词结果
        """
        return hmm.cut_stanford(text, self.trie, self.unk_index, self.word_dict)

    def cut_all(self, text):
        """
        全模式分词，返回分词结果
        :param text: 待分词的文本
        :return: 分词结果
        """
        return hmm.cut_all(text, self.trie, self.unk_index, self.word_dict)

    def cut_for_search(self, text):
        """
        搜索引擎模式分词，返回分词结果
        :param text: 待分词的文本
        :return: 分词结果
        """
        return hmm.cut_for_search(text, self.trie, self.unk_index, self.word_dict)

    def tag(self, text):
        """
        分词和词性标注，返回分词结果和词性标注结果
        :param text: 待分词的文本
        :return: 分词结果和词性标注结果
        """
        if self.postag:
            return self.crf.tag(text)
        else:
            return postag.tag(text, self.trie, self.unk_index, self.word_dict, self.tag_dict)

    def save(self, path):
        """
        保存分词器
        :param path: 分词器保存路径
        :return: None
        """
        with open(path, "wb") as f:
            pickle.dump(self, f)

    @staticmethod
    def load(path):
        """
        加载分词器
        :param path: 分词器加载路径
        :return: 分词器实例
        """
        with open(path, "rb") as f:
            return pickle.load(f)

其中包括了分词、词性标注、训练、保存模型、加载模型、全模式分词、搜索引擎模式分词等功能。用户可以根据自己的需求选择使用。