import jieba
import jieba.posseg as pseg
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np

def get_term_type(term):
    # 使用jieba进行词性标注
    words = pseg.cut(term)
    pos_list = [w.flag for w in words]
    
    # 如果词组只有一个词，直接返回其词性
    if len(pos_list) == 1:
        return pos_list[0]
    
    # 如果是多词组合，返回最后一个词的词性（通常是核心词）
    return pos_list[-1]

def process_file(input_file, output_file):
    with open(input_file, 'r', encoding='utf-8') as f:
        terms = [line.strip() for line in f.readlines()]

    # 创建每个词条的"文档"
    documents = [term.replace(' ', '') for term in terms]

    # 计算TF-IDF
    vectorizer = TfidfVectorizer()
    tfidf_matrix = vectorizer.fit_transform(documents)
    feature_names = vectorizer.get_feature_names_out()
    
    # 获取权重
    weights = {}
    for term_idx, term in enumerate(terms):
        term_without_spaces = term.replace(' ', '')
        if term_without_spaces in feature_names:
            weight = tfidf_matrix[term_idx, feature_names == term_without_spaces].toarray()[0][0]
            weights[term] = weight

    # 将权重归一化到0-100范围
    max_weight = max(weights.values())
    min_weight = min(weights.values())
    weight_range = max_weight - min_weight
    normalized_weights = {term: int((weight - min_weight) / weight_range * 100) if weight_range > 0 else 50
                          for term, weight in weights.items()}

    # 按权重排序词条
    sorted_terms = sorted(normalized_weights.items(), key=lambda x: x[1], reverse=True)

    # 写入输出文件
    cnt = 0
    with open(output_file, 'w', encoding='utf-8') as f:
        if cnt != 10:
            for term, weight in sorted_terms:
                term_type = get_term_type(term.replace(' ', ''))
                f.write(f"{term} {weight} {term_type}\n")
                cnt += 1

    print(f"处理了 {len(sorted_terms)} 个词条。")

# 处理文件
input_file = 'dictionary_reviewed_rows.txt'  # 替换为您的输入文件名
output_file = 'dictioanry_reviewed_rows_weighted.txt'

process_file(input_file, output_file)