{
 "cells": [
  {
   "cell_type": "raw",
   "id": "eccfe49f-de35-4241-90e3-a7095940b61a",
   "metadata": {},
   "source": [
    "设计模式提供高频重复出现需求的最佳解决方案。以下介绍适合词频统计案例的设计模式：策略模式、观察者模式、工厂模式。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c186171f-d1f2-433e-a3eb-b266e2909a2c",
   "metadata": {},
   "source": [
    "##  策略模式（动态选择分词策略）\n",
    "\n",
    "策略模式允许动态切换算法（如分词器），比元编程简单。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97c865cb-0b5a-4fa1-aa74-5ba2e65e7436",
   "metadata": {},
   "outputs": [],
   "source": [
    "from abc import ABC, abstractmethod\n",
    "\n",
    "class Tokenizer(ABC):\n",
    "    \"\"\"分词器接口\"\"\"\n",
    "    @abstractmethod\n",
    "    def tokenize(self, text: str, stop_words: set) -> List[str]:\n",
    "        pass\n",
    "\n",
    "class JiebaTokenizer(Tokenizer):\n",
    "    \"\"\"jieba 分词器\"\"\"\n",
    "    def tokenize(self, text: str, stop_words: set) -> List[str]:\n",
    "        return [w for w in jieba.lcut(text) if w not in stop_words]\n",
    "\n",
    "class SimpleTokenizer(Tokenizer):\n",
    "    \"\"\"简单分词器\"\"\"\n",
    "    def tokenize(self, text: str, stop_words: set) -> List[str]:\n",
    "        return [w for w in text.split() if w not in stop_words]\n",
    "\n",
    "class TextAnalyzer:\n",
    "    def __init__(self, config_path='config.yaml'):\n",
    "        with open(config_path, 'r', encoding='utf-8') as f:\n",
    "            config = yaml.safe_load(f)\n",
    "        self.data_dir = config['data_dir']\n",
    "        self.top_n = config['top_n']\n",
    "        self.stop_words_file = config['stop_words_file']\n",
    "        self.output_file = config['output_file']\n",
    "        self.stop_words = self.load_stop_words()\n",
    "        self.word_count = Counter()\n",
    "        # 动态选择分词器\n",
    "        tokenizer_name = config.get('tokenizer', 'jieba')\n",
    "        self.tokenizer = {'jieba': JiebaTokenizer(), 'simple': SimpleTokenizer()}[tokenizer_name]\n",
    "\n",
    "    def tokenize(self, text: str) -> List[str]:\n",
    "        \"\"\"使用策略分词\"\"\"\n",
    "        return self.tokenizer.tokenize(text, self.stop_words)\n",
    "\n",
    "    # 其余方法同上"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5435ebc3-d3b0-4475-8bd5-cb45fb51638c",
   "metadata": {},
   "source": [
    "工程质量提升：\n",
    "- 可扩展性：添加新分词器只需实现 Tokenizer 接口。\n",
    "- 可维护性：分词逻辑与主类分离，修改更独立。\n",
    "\n",
    "适用场景：适合需要动态切换算法的场景。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbf53455-558c-40fb-8718-446dec989b5d",
   "metadata": {},
   "source": [
    "## 观察者模式（结果输出解耦）\n",
    "\n",
    "观察者模式可用于解耦结果输出逻辑（如打印、保存文件、发送通知）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7a2bd4c-df73-4800-b45b-9b6c73d28d7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class OutputObserver(ABC):\n",
    "    \"\"\"输出观察者接口\"\"\"\n",
    "    @abstractmethod\n",
    "    def update(self, top_words: List[Tuple[str, int]]):\n",
    "        pass\n",
    "\n",
    "class ConsoleOutput(OutputObserver):\n",
    "    \"\"\"控制台输出\"\"\"\n",
    "    def update(self, top_words: List[Tuple[str, int]]):\n",
    "        for word, count in top_words:\n",
    "            print(f\"{word}: {count}\")\n",
    "\n",
    "class FileOutput(OutputObserver):\n",
    "    \"\"\"文件输出\"\"\"\n",
    "    def __init__(self, output_file: str):\n",
    "        self.output_file = output_file\n",
    "\n",
    "    def update(self, top_words: List[Tuple[str, int]]):\n",
    "        with open(self.output_file, 'w', encoding='utf-8') as f:\n",
    "            for word, count in top_words:\n",
    "                f.write(f\"{word}: {count}\\n\")\n",
    "\n",
    "class TextAnalyzer:\n",
    "    def __init__(self, config_path='config.yaml'):\n",
    "        with open(config_path, 'r', encoding='utf-8') as f:\n",
    "            config = yaml.safe_load(f)\n",
    "        self.data_dir = config['data_dir']\n",
    "        self.top_n = config['top_n']\n",
    "        self.stop_words_file = config['stop_words_file']\n",
    "        self.output_file = config['output_file']\n",
    "        self.stop_words = self.load_stop_words()\n",
    "        self.word_count = Counter()\n",
    "        self.observers = [ConsoleOutput(), FileOutput(self.output_file)]\n",
    "\n",
    "    def add_observer(self, observer: OutputObserver):\n",
    "        \"\"\"添加观察者\"\"\"\n",
    "        self.observers.append(observer)\n",
    "\n",
    "    def notify_observers(self, top_words: List[Tuple[str, int]]):\n",
    "        \"\"\"通知所有观察者\"\"\"\n",
    "        for observer in self.observers:\n",
    "            observer.update(top_words)\n",
    "\n",
    "    def run(self):\n",
    "        \"\"\"执行词频统计并通知观察者\"\"\"\n",
    "        self.process_directory()\n",
    "        top_words = self.get_top_words()\n",
    "        self.notify_observers(top_words)\n",
    "\n",
    "    # 其余方法同上"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02b5cfba-431c-4a01-a454-099e4f41922c",
   "metadata": {},
   "source": [
    "### 分析\n",
    "\n",
    "工程质量提升：\n",
    "  - 可扩展性：添加新输出方式只需实现 OutputObserver 接口。\n",
    "  - 解耦性：输出逻辑与统计逻辑分离，修改输出不影响核心功能。\n",
    "\n",
    "适用场景：适合需要多种输出或通知的场景。\n",
    "\n",
    "局限性：观察者模式增加代码复杂性，适合复杂输出需求。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11669305-8cd5-4317-afd5-e85c3f0a5a81",
   "metadata": {},
   "source": [
    "## 工厂模式（动态创建分词器）\n",
    "\n",
    "工厂模式可用于动态创建分词器，简化策略模式中的初始化逻辑。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fa50633-de22-40c8-912d-3ded5ebcedfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TokenizerFactory:\n",
    "    \"\"\"分词器工厂\"\"\"\n",
    "    @staticmethod\n",
    "    def create_tokenizer(name: str) -> Tokenizer:\n",
    "        tokenizers = {\n",
    "            'jieba': JiebaTokenizer(),\n",
    "            'simple': SimpleTokenizer()\n",
    "        }\n",
    "        return tokenizers.get(name, JiebaTokenizer())\n",
    "\n",
    "class TextAnalyzer:\n",
    "    def __init__(self, config_path='config.yaml'):\n",
    "        with open(config_path, 'r', encoding='utf-8') as f:\n",
    "            config = yaml.safe_load(f)\n",
    "        self.data_dir = config['data_dir']\n",
    "        self.top_n = config['top_n']\n",
    "        self.stop_words_file = config['stop_words_file']\n",
    "        self.output_file = config['output_file']\n",
    "        self.stop_words = self.load_stop_words()\n",
    "        self.word_count = Counter()\n",
    "        self.tokenizer = TokenizerFactory.create_tokenizer(config.get('tokenizer', 'jieba'))\n",
    "\n",
    "    # 其余方法同上"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4db7046-dfe2-4bd8-81d1-49a42e2eeb5c",
   "metadata": {},
   "source": [
    "### 分析\n",
    "\n",
    "工程质量提升：\n",
    "   - 可维护性：分词器创建逻辑集中于工厂，易于修改。\n",
    "   - 可扩展性：添加新分词器只需更新工厂方法。\n",
    "\n",
    "适用场景：适合需要动态创建对象的场景。\n",
    "\n",
    "局限性：对于简单场景，工厂模式可能略显冗余。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5f2aef4-a055-43a9-917c-fa183de6db2d",
   "metadata": {},
   "source": [
    "## 综合实现（整合特性与模式）\n",
    "\n",
    "整合上下文管理器、生成器、策略模式和观察者模式的最终实现（部分代码展示）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa7f34e2-d355-4a22-8572-729c49b18605",
   "metadata": {},
   "outputs": [],
   "source": [
    "# text_analyzer.py\n",
    "\n",
    "import os\n",
    "import jieba\n",
    "from collections import Counter\n",
    "import yaml\n",
    "from contextlib import contextmanager\n",
    "from typing import List, Tuple\n",
    "from abc import ABC, abstractmethod\n",
    "\n",
    "@contextmanager\n",
    "def file_reader(file_path: str):\n",
    "    try:\n",
    "        with open(file_path, 'r', encoding='utf-8') as f:\n",
    "            yield f.read()\n",
    "    except Exception as e:\n",
    "        print(f\"Error reading {file_path}: {e}\")\n",
    "        yield \"\"\n",
    "\n",
    "class Tokenizer(ABC):\n",
    "    @abstractmethod\n",
    "    def tokenize(self, text: str, stop_words: set) -> List[str]:\n",
    "        pass\n",
    "\n",
    "class JiebaTokenizer(Tokenizer):\n",
    "    def tokenize(self, text: str, stop_words: set) -> List[str]:\n",
    "        for word in jieba.lcut(text):\n",
    "            if word not in stop_words:\n",
    "                yield word\n",
    "\n",
    "class SimpleTokenizer(Tokenizer):\n",
    "    def tokenize(self, text: str, stop_words: set) -> List[str]:\n",
    "        for word in text.split():\n",
    "            if word not in stop_words:\n",
    "                yield word\n",
    "\n",
    "class TokenizerFactory:\n",
    "    @staticmethod\n",
    "    def create_tokenizer(name: str) -> Tokenizer:\n",
    "        return {'jieba': JiebaTokenizer(), 'simple': SimpleTokenizer()}.get(name, JiebaTokenizer())\n",
    "\n",
    "class OutputObserver(ABC):\n",
    "    @abstractmethod\n",
    "    def update(self, top_words: List[Tuple[str, int]]):\n",
    "        pass\n",
    "\n",
    "class ConsoleOutput(OutputObserver):\n",
    "    def update(self, top_words: List[Tuple[str, int]]):\n",
    "        for word, count in top_words:\n",
    "            print(f\"{word}: {count}\")\n",
    "\n",
    "class FileOutput(OutputObserver):\n",
    "    def __init__(self, output_file: str):\n",
    "        self.output_file = output_file\n",
    "    def update(self, top_words: List[Tuple[str, int]]):\n",
    "        with open(self.output_file, 'w', encoding='utf-8') as f:\n",
    "            for word, count in top_words:\n",
    "                f.write(f\"{word}: {count}\\n\")\n",
    "\n",
    "class TextAnalyzer:\n",
    "    def __init__(self, config_path='config.yaml'):\n",
    "        with open(config_path, 'r', encoding='utf-8') as f:\n",
    "            config = yaml.safe_load(f)\n",
    "        self.data_dir = config['data_dir']\n",
    "        self.top_n = config['top_n']\n",
    "        self.stop_words_file = config['stop_words_file']\n",
    "        self.output_file = config['output_file']\n",
    "        self.stop_words = self.load_stop_words()\n",
    "        self.word_count = Counter()\n",
    "        self.tokenizer = TokenizerFactory.create_tokenizer(config.get('tokenizer', 'jieba'))\n",
    "        self.observers = [ConsoleOutput(), FileOutput(self.output_file)]\n",
    "\n",
    "    def load_stop_words(self) -> set:\n",
    "        with file_reader(self.stop_words_file) as content:\n",
    "            return set(line.strip() for line in content.splitlines() if line.strip())\n",
    "\n",
    "    def process_file(self, file_path: str):\n",
    "        if file_path.endswith('.txt'):\n",
    "            with file_reader(file_path) as text:\n",
    "                words = self.tokenizer.tokenize(text, self.stop_words)\n",
    "                self.word_count.update(words)\n",
    "\n",
    "    def process_directory(self):\n",
    "        for file in os.listdir(self.data_dir):\n",
    "            file_path = os.path.join(self.data_dir, file)\n",
    "            self.process_file(file_path)\n",
    "\n",
    "    def get_top_words(self) -> List[Tuple[str, int]]:\n",
    "        return self.word_count.most_common(self.top_n)\n",
    "\n",
    "    def notify_observers(self, top_words: List[Tuple[str, int]]):\n",
    "        for observer in self.observers:\n",
    "            observer.update(top_words)\n",
    "\n",
    "    def run(self):\n",
    "        self.process_directory()\n",
    "        top_words = self.get_top_words()\n",
    "        self.notify_observers(top_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d130312-b298-4c76-ae09-0fb4bd08b0c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# main.py\n",
    "\n",
    "from text_analyzer import TextAnalyzer\n",
    "\n",
    "def main():\n",
    "    analyzer = TextAnalyzer()\n",
    "    analyzer.run()\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    main()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "770618c9-428e-454a-97de-00e3b49c9d03",
   "metadata": {},
   "source": [
    "## 结论\n",
    "\n",
    "通过引入上下文管理器、生成器、元编程、策略模式、观察者模式和工厂模式，词频统计代码在可扩展性、可维护性和复用性上进一步提升。\n",
    "这些特性和模式使代码更模块化、灵活，适合大型项目，同时保持清晰的工程结构。结合之前的装饰器和函数式编程，代码已达到工程化水平。\n",
    "\n",
    "若需深入，可以进一步考虑其它性能特性."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbeaa07d-272f-465b-a437-9c4b44827d23",
   "metadata": {},
   "source": [
    "## 进一步练习\n",
    "\n",
    "实践练习：\n",
    "- 实现新分词器（如 thulac）并通过策略模式或工厂模式集成。\n",
    "- 添加新观察者（如 JSON 输出）。\n",
    "\n",
    "使用生成器实现流式词频统计，比较内存占用。\n",
    "实现缓存机制，缓存已处理文件的分词结果。\n",
    "\n",
    "添加命令行接口（argparse），动态配置 top_n 和 tokenizer。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a43b53d-1e07-4ebe-a6c8-104353fd5f7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 附：元编程\n",
    "\n",
    "元编程允许动态修改类或函数行为，可用于动态配置分词器或输出格式。案例中，可通过元编程动态注册分词器。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4394008c-88da-44bd-aa0d-f1b7a6dbc7d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TokenizerRegistry(type):\n",
    "    \"\"\"元类：动态注册分词器\"\"\"\n",
    "    tokenizers = {}\n",
    "\n",
    "    def register_tokenizer(cls, name):\n",
    "        def decorator(func):\n",
    "            cls.tokenizers[name] = func\n",
    "            return func\n",
    "        return decorator\n",
    "\n",
    "class TextAnalyzer(metaclass=TokenizerRegistry):\n",
    "    def __init__(self, config_path='config.yaml'):\n",
    "        with open(config_path, 'r', encoding='utf-8') as f:\n",
    "            config = yaml.safe_load(f)\n",
    "        self.data_dir = config['data_dir']\n",
    "        self.top_n = config['top_n']\n",
    "        self.stop_words_file = config['stop_words_file']\n",
    "        self.output_file = config['output_file']\n",
    "        self.stop_words = self.load_stop_words()\n",
    "        self.word_count = Counter()\n",
    "        self.tokenizer_name = config.get('tokenizer', 'jieba')  # 从配置读取分词器\n",
    "\n",
    "    @classmethod\n",
    "    def register_tokenizer(cls, name):\n",
    "        return cls.__class__.register_tokenizer(name)\n",
    "\n",
    "    def tokenize(self, text: str) -> List[str]:\n",
    "        \"\"\"动态调用分词器\"\"\"\n",
    "        tokenizer = self.__class__.tokenizers.get(self.tokenizer_name, self.jieba_tokenizer)\n",
    "        return tokenizer(self, text)\n",
    "\n",
    "    @register_tokenizer('jieba')\n",
    "    def jieba_tokenizer(self, text: str) -> List[str]:\n",
    "        \"\"\"jieba 分词\"\"\"\n",
    "        return [w for w in jieba.lcut(text) if w not in self.stop_words]\n",
    "\n",
    "    @register_tokenizer('simple')\n",
    "    def simple_tokenizer(self, text: str) -> List[str]:\n",
    "        \"\"\"简单分词（按空格）\"\"\"\n",
    "        return [w for w in text.split() if w not in self.stop_words]\n",
    "\n",
    "    # 其余方法（load_stop_words, process_file, etc.）同上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2249f13a-7a3f-4376-ba2a-d92f11658d32",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 分析\n",
    "\n",
    "功能：通过元类和装饰器动态注册分词器，支持配置切换（如 jieba 或 simple）。\n",
    "\n",
    "工程质量提升：\n",
    "    可扩展性：新分词器只需添加新方法并注册，无需修改核心逻辑。\n",
    "    灵活性：通过配置文件动态选择分词器。\n",
    "\n",
    "适用场景：适合需要动态配置或插件化系统的场景。\n",
    "\n",
    "局限性：元编程增加代码复杂性，可能降低可读性，需谨慎使用。"
   ]
  }
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