评估方法：
人工从文章中提取1-5个关键词，和机器提取的关键词做比较
召回 = 机器提词∩人工提词 / 人工提词
准确 = 机器提词∩人工提词 / 机器提词

TF-IDF

原理参考：http://www.ruanyifeng.com/blog/2013/03/tf-idf.html
实现参考：tf-idf-keyword
其他参考： 使用不同的方法计算TF-IDF值

第一版 标题和正文加权计算tf-idf

主要策略

• 使用nlpc切词服务（可用jieba切词代替）+TF-IDF提取关键词。
• 去除停用词
• 按照体裁+年级分成若干类型，来训练模型，示例用高中+叙事类，取了20000条数据训练
• 对标题进行加权，标题的每个词汇频率+6，再合一起计算tf-idf
• 按照权重取前4个关键词，在这4个关键词中对于权重小于 频率(5)*平均IDF/总词数 的进行过滤
  注：以上数据均为调节后最优解

代码实现

config.py

program = 'composition_term_weight'
logger = logging.getLogger(program)
logging.basicConfig(format='%(asctime)s: %(levelname)s: %(message)s',
                    stream=sys.stderr,
                    datefmt='%a, %d %b %Y %H:%M:%S')
logging.root.setLevel(level=logging.INFO)

IDFLoader.py

class IDFLoader(object):
    """词典加载类"""

    def __init__(self, idf_path):
        self.idf_path = idf_path
        self.idf_freq = {}  # idf
        self.mean_len = 0 #平均长度
        self.mean_idf = 0.0  # 均值
        self.load_idf()

    def load_idf(self):
        """从文件中载入idf"""
        cnt = 0
        with open(self.idf_path, 'rb') as f:
            for line in f:
                try:
                    word, freq = line.strip().decode('utf-8', errors='ignore').split(' ')
                    if word == 'LEN_AVG':
                        self.mean_len = int(freq)
                        break
                    self.idf_freq[word] = float(freq)
                    cnt += 1
                except Exception as e:
                    # logger.error('load_idf error: ' + e.message + ' line: ' + line.decode('utf-8', errors='ignore'))
                    continue

        self.mean_idf = sum(self.idf_freq.values()) / cnt
        logger.info('Vocabularies %s loaded: %d mean_idf: %d' % (self.idf_path, cnt, self.mean_idf))

class TfIdf(object):
    """TF-IDF"""
    # 对正文进行过滤
    p_cut = re.compile(r'[a-zA-Z0-9]', re.VERBOSE)
    # 对标题进行过滤
    p_title = re.compile(r'作文|\d+字|.年级|_', re.VERBOSE)
    # 过滤常用标点符号等，也可以放到停用词表中
    ignored = ['', ' ', '', '。', '：', '，', '）', '（', '！', '?', '”', '“', '＂', '―', '．', '说', '好', '时']
    # 主题最小出现次数，用于过滤权重不达标的关键词
    min_times = 5.0
    # 标题加权次数
    title_add_times = 6.0
    # 取关键词的个数
    words_num = 4

    def __init__(self):
        # 1. 获取停用词库
        my_stop_words_path = 'stop_words.utf8.txt'
        self.stop_words_dict = []
        with open(my_stop_words_path, 'rb') as fr:
            for line in fr.readlines():
                self.stop_words_dict.append(line.strip())

    def my_cut(self, inTxt):
        """切词"""
        inTxt = self.p_cut.sub('', str(inTxt))
        words_list = []
        # 由于性能问题，一句一句的切词
        for l in inTxt.split('。'):
            # NLPC切词服务，可用jieba切词代替
            r = cut(l)
            if r is not None:
                words_list += r
        return [w for w in words_list if w not in self.stop_words_dict and w not in self.ignored and len(w.strip()) > 0]

    def get_tfidf(self, idf_loader, title, content):
        """计算文章tf-idf"""
        filter_title = self.p_title.sub('', title.encode('utf-8', errors='ignore'))
        title_words = self.my_cut(filter_title)

        corpus0 = title_words + self.my_cut(content)

        freq = {}
        for w in corpus0:
            freq[w] = freq.get(w, 0.0) + 1.0
        # 对标题进行加权
        for w in title_words:
            logger.info(freq[w])
            freq[w] = freq.get(w, 0.0) + self.title_add_times
            logger.info(freq[w])
        total = sum(freq.values())

        for k in freq:  # 计算 TF-IDF
            freq[k] *= idf_loader.idf_freq.get(k, idf_loader.mean_idf) / total

        return sorted(freq.items(), key=lambda d: d[1], reverse=True), len(corpus0), title_words

    def get_term_weight(self, idf_loader, title, content):
        """获得term权重"""
        result, words_number, title_words = self.get_tfidf(idf_loader, title, content)
        bound = self.min_times * idf_loader.mean_idf / words_number
        machine_words = [item for item in result[:4] if item[1] > bound]
        # machine_words = [item for item in result[:self.words_num]]
        if len(machine_words) < 1:
            # 如果一个term都没有，则把标题拿出来
            machine_words = [item for item in result if item[1] in title_words]
        data = []
        offset = 0
        for i, word in enumerate(machine_words):
            data.append('%s:%d:%s' % (word[0], offset, str(round(word[1], 4))))
            offset += len(word[0].decode('utf-8', errors='ignore'))
        return data

    def getCorpus(self, data_path):
        """获取词表"""
        count = 0
        corpus_list = []
        with open(data_path, 'rb') as f:
            for line in f:
                info = json.loads(line.decode('utf-8', errors='ignore'))
                sentence = self.p_title.sub('', info.get('title').encode('utf-8', errors='ignore')) + '。' + info.get(
                    '@merge_text').encode('utf-8', errors='ignore')
                r = self.my_cut(sentence)
                if not r:
                    continue
                corpus_list.append(r)
                count += 1
                if count % 1000 == 0:
                    logger.info("processd " + str(count) + " segment_sentence")
        return corpus_list

    def train(self, dir_name, data_path):
        """训练模型"""
        idf_path = 'data/%s/idf.txt' % dir_name
        documents = self.getCorpus(data_path)
        id_freq = {}
        i = 0
        len_sum = 0
        for doc in documents:
            len_sum += len(doc)
            doc = set(doc)
            for x in doc:
                id_freq[x] = id_freq.get(x, 0) + 1
            if i % 1000 == 0:
                logger.info('Documents processed: ' + str(i) + ', time: ' + str(datetime.datetime.now()))
            i += 1

        del documents
        with open(idf_path, 'wb') as f:
            for key, value in id_freq.items():
                f.write(key + ' ' + str(math.log(i / value, 2)) + '\n')
            logger.info(str(i) + ' ' + str(len_sum))
            f.write('LEN_AVG ' + str(len_sum / i))

    def test_one(self, dir_name, method='tfidf'):
        """单个测试"""
        idf_loader = IDFLoader('data/%s/idf.txt' % dir_name)
        for item in sys.stdin:
            info = json.loads(item.decode('utf-8', errors='ignore'))
            title = info['title']
            content = info['@merge_text']
            if method == 'tfidf':
                result, words_number, title_words = self.get_tfidf(idf_loader, title, content)
            else:
                result, words_number, title_words = self.get_bm25(idf_loader, title, content)
            bound = self.min_times * idf_loader.mean_idf / words_number

            print '_____words_number bound_____'
            print words_number, bound
            print '_____tfidf_result_____'
            for item in result[:20]:
                print item[0].encode('utf-8', errors='ignore'), item[1]

经调优，最优解为：min_times=5 title_add_times=6.0 words_num=4

结果

人工抽样评估了100个
TF-IDF召回率：0.2778
TF-IDF准确率：0.2778

BM25

算法参考： 搜索中的权重度量利器: TF-IDF和BM25

第一版

TfIdf.py 增加方法：

    def get_bm25(self, idf_loader, title, content):
        """计算bm25"""
        k = 1.2  # 用来限制TF值的增长极限
        b = 0.75  # b是一个常数，它的作用是规定L对评分的影响有多大。
        # L是文档长度与平均长度的比值
        EPSILON = 0.25  # 如果idf词表中没有，则平均idf*该值

        filter_title = self.p_title.sub('', title.encode('utf-8', errors='ignore'))
        title_words = self.my_cut(filter_title)

        corpus0 = title_words + self.my_cut(content)

        freq = {}
        for w in corpus0:
            freq[w] = freq.get(w, 0.0) + 1.0
        # 对标题进行加权
        for w in title_words:
            freq[w] = freq.get(w, 0.0) + self.title_add_times
        total = sum(freq.values())

        logger.info(str((k, b, total, idf_loader.mean_len)))
        for i in freq:
            tf = freq[i] / total
            idf = idf_loader.idf_freq.get(i, idf_loader.mean_idf * EPSILON)
            freq[i] = idf * ((k + 1) * tf) / (k * (1.0 - b + b * (total / idf_loader.mean_len)) + tf)

        return sorted(freq.items(), key=lambda d: d[1], reverse=True), len(corpus0), title_words

经调优，最优解为：min_times=2.5 title_add_times=6.0 words_num=4 k=1.2 b=0.75 EPSILON=0.25

结果

人工抽样评估了100个
BM25召回率：0.2889
BM25准确率：0.3333