ml_data_template/sql_ml.py

import json
import re
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import DBSCAN
from sklearn.metrics.pairwise import cosine_similarity
from tqdm import tqdm
import argparse

# --- 规则和辅助函数 (与之前相同) ---
PREDEFINED_RULES = [
    # {'name': 'Transfer', 'pattern': re.compile(r'^Transfer from \d+ to \d+$')},
    # {'name': 'International Remittance', 'pattern': re.compile(r'^International Remittance$')},
    # {'name': 'Bill Payment', 'pattern': re.compile(r'^Bill payment successful for amount \d+$')},
    # {'name': 'New Message', 'pattern': re.compile(r'^You have a new message from \d+\.$')},
    # # 新增规则：匹配类似 "Sent GCash to GoTyme Bank with account ending in 6784"
    # {'name': 'Sent GCash to Account', 'pattern': re.compile(r'^Sent GCash to .+? with account ending in \d+$')}
]

# 占位符到正则表达式的映射
PLACEHOLDER_PATTERNS = {
    '<金额>': r'([\d,.]+)',
    '<付款人名称>': r'(.+?)',
    '<收款人名称>': r'(.+?)',
    '<付款人号码>': r'([\d\w\+\-\(\)]+)',
    '<收款人号码>': r'([\d\w\+\-\(\)]+)',
    '<银行4位数尾号>': r'(\d{4})',
    '<参考号>': r'(.+?)',
    '<交易单号>': r'(.+?)',
    '<日期时间>': r'(.+?)',
    '<日期>': r'(\d{2}-\d{2}-\d{4})',
    '<时间>': r'(\d{1,2}:\d{2}\s[AP]M)',
    '<消息>': r'(.+?)',
    '<流水号>': r'(.+?)',
    '<网络或发票号>': r'(.+?)',
    '<交易类型>': r'(.+?)',
}

def normalize_text(text):
    # 模式 8: 从银行收款 (这条规则必须先运行)
    text = re.sub(
        r'(?i)Received GCash from (.+?) with account ending in (\d+) (via .+|and invno:.+)$',
        r'Received GCash from <付款人名称> with account ending in <银行4位数尾号> via <网络或发票号>',
        text
    )

  # 模式 13: 向未验证账户发送凭证
    # 结构: You have sent <货币> <金额> to an unverified account <手机号> on <日期> <时间> with MSG: <消息>. Your new balance is <货币> <金额>. Ref. No. <流水号>. Go to...
    text = re.sub(
        r'^You have sent PHP [\d,]+\.\d{2} to an unverified account [\d\w\+\-\(\)]+ on \d{2}-\d{2}-\d{4}\s\d{1,2}:\d{2}\s[AP]M with MSG: \..*? Your new balance is PHP [\d,]+\.\d{2}\. Ref\. No\. \d+\. Go to GCash Help Center to know how to secure your transactions\.$',
        r'You have sent PHP <金额> to an unverified account <收款人号码> on <日期> <时间> with MSG: <消息>. Your new balance is PHP <金额>. Ref. No. <流水号>. Go to GCash Help Center to know how to secure your transactions.',
        text
    )

    # 模式 12: 详细发送凭证 (更新后可处理多段姓名)
    # 结构: You have sent <货币> <金额> to <收款人> <手机号> on <日期> <时间> with MSG: <消息>. Your new balance is <货币> <金额>. Ref. No. <流水号>.
    text = re.sub(
        r'^You have sent PHP [\d,]+\.\d{2} to (?:[A-Z\*]+\s)+[A-Z\*]\.\s[\d\w\+\-\(\)]+ on \d{2}-\d{2}-\d{4}\s\d{1,2}:\d{2}\s[AP]M with MSG: \..*? Your new balance is PHP [\d,]+\.\d{2}\. Ref\. No\. \d+\.$',
        r'You have sent PHP <金额> to <收款人名称> <收款人号码> on <日期> <时间> with MSG: <消息>. Your new balance is PHP <金额>. Ref. No. <流水号>.',
        text
    )

    # 模式 11 (机构): 详细收款凭证
    # 结构: You have received ... of GCash from <来源>. Your new balance is ... <日期时间>. Ref. No. <流水号>. Use now to buy load...
    text = re.sub(
        r'^You have received\s+(?:PHP\s+)?[\d,.]+\s+of GCash from\s+.+?\. Your new balance is\s+(?:PHP\s+)?[\d,.]+\s+\d{1,2}-\d{1,2}-\d{2,4}\s+\d{1,2}:\d{1,2}(?::\d{1,2})?\s+[AP]M\. Ref\. No\.\s+.+?\. Use now to buy load, purchase items, send money, pay bills, and a lot more!$',
        r'You have received <金额> of GCash from <付款人名称>. Your new balance is <金额>. <日期时间>. Ref. No. <流水号>. Use now to buy load, purchase items, send money, pay bills, and a lot more!',
        text
    )

    # 模式 11 (个人): 详细收款凭证 (最终修正版，兼容多种手机号/余额/结尾格式)
    text = re.sub(
        r'^You have received PHP [\d,.]+\s+of GCash from .+? w/ MSG: .*\. (?:Your new balance is PHP [\d,.]*\.\s)?Ref\. No\. \d+\.(?: To access your funds,.*)?$',
        r'You have received PHP <金额> of GCash from <付款人名称> w/ MSG: <消息>. Your new balance is PHP <金额>. Ref. No. <参考号>.',
        text
    )
    # 模式 12: 详细发送凭证 (最终修正版，兼容所有已知姓名格式)
    text = re.sub(
        r'^You have sent PHP [\d,]+\.\d{2} to .+? [\d\w\+\-\(\)]+ on \d{2}-\d{2}-\d{4}\s\d{1,2}:\d{2}\s[AP]M with MSG: \..*? Your new balance is PHP [\d,]+\.\d{2}\. Ref\. No\. \d+\.$',
        r'You have sent PHP <金额> to <收款人名称> <收款人号码> on <日期> <时间> with MSG: <消息>. Your new balance is PHP <金额>. Ref. No. <参考号>.',
        text
    )   

      # 模式 10 (来自用户最初的模板列表，这里将其具体化)
    # 结构: You have paid <金额> via GCash to <接收方> on <日期时间>. Ref. No. <参考号>. QRPH Invoice No. <参考号>.
    text = re.sub(
        r'^You have paid P[\d,.]+\s+via GCash to .+? on \d{1,2}-\d{1,2}-\d{2,4}\s\d{1,2}:\d{1,2}:\d{1,2}\s+[AP]M\. Ref\. No\.\s+\d+\. QRPH Invoice No\.\s+\d+\.$',
        r'You have paid P<金额> via GCash to <收款人名称> on <日期时间>. Ref. No. <参考号>. QRPH Invoice No. <参考号>.',
        text
    )

    # 模式 9 (来自用户最初的模板列表，这里将其具体化)
    # 结构: Sent GCash to <机构名> with account ending in <尾号>
    text = re.sub(
        r'Sent GCash to (.+?) with account ending in (\d+)$',
        r'Sent GCash to <收款人名称> with account ending in <银行4位数尾号>',
        text
    )
 



    # 新增规则：模式 7: 从一般来源收款 (这条规则紧随其后)
    # 它只会处理没有被上面那条规则匹配到的 "Received GCash from..."
    text = re.sub(
        r'(?i)^Received GCash from [^<]+$',
        r'Received GCash from <付款人名称>',
        text
    )
    # 模式 6: 带商户交易单号的支付
    # 结构: Payment to <商户名>, Merchant Transaction Number: <交易单号>
    text = re.sub(
        r'Payment to (.+?), Merchant Transaction Number: (.+)$',
        r'Payment to <收款人名称>, Merchant Transaction Number: <交易单号>',
        text
    )

    # 模式 5 (来自用户最初的模板列表，这里将其具体化)
    # 结构: Payment to <商户名>
    text = re.sub(
        r'^Payment to ([^,]+)$',
        r'Payment to <收款人名称>',
        text
    )

    text = re.sub(
        r'^(.+?) with (Ref\. no\.|Parent Ref\.No\.|Reference No\.) (.+)$',
        r'<交易类型> with Ref. no. <参考号>',
        text
    )

    text = re.sub(r'Sent GCash to <收款人名称> with account ending in (\d+)$', r'Sent GCash to <收款人名称> with account ending in <银行4位数尾号>', text)
    text = re.sub(r'^Transfer from \S+ to \S+$', r'Transfer from <付款人号码> to <收款人号码>', text)
         # 模式 8: 从银行收款
    # 结构: Received GCash from <机构名> with account ending in <尾号> via <网络> or with invno:<...>
    text = re.sub(
        r'(?i)Received GCash from (.+?) with account ending in (\d+) (via .+|and invno:.+)$',
        r'Received GCash from <付款人名称> with account ending in <银行4位数尾号> via <流水号>',
        text
    )
    
    # 新增规则：Buy Load Transaction
    text = re.sub(
        r'^Buy Load Transaction for .+$',
        r'Buy Load Transaction for <付款人号码>',
        text
    )
    
    # 新增规则：Refund
    text = re.sub(
        r'^Refund from .+$',
        r'Refund from <收款人名称>',
        text
    )
    
    return  text

def template_to_regex(template):
    """
    将模板转换为可用于提取参数的正则表达式
    """
    # 转义模板中的特殊字符，但保留占位符
    escaped_template = re.escape(template)
    
    # 将占位符映射到对应的正则表达式捕获组
    for placeholder, pattern in PLACEHOLDER_PATTERNS.items():
        escaped_placeholder = re.escape(placeholder)
        # 替换占位符为对应的捕获组
        escaped_template = escaped_template.replace(escaped_placeholder, pattern)
    
    return escaped_template

def extract_parameters(template, message):
    """
    从消息中提取参数值
    """
    # 生成正则表达式
    pattern = template_to_regex(template)
    
    # 匹配消息
    match = re.search(pattern, message)
    
    if match:
        # 获取所有捕获组
        values = match.groups()
        
        # 获取模板中的占位符
        placeholders = re.findall(r'<[^>]+>', template)
        
        # 创建参数字典
        parameters = {}
        for i, placeholder in enumerate(placeholders):
            if i < len(values):
                parameters[placeholder] = values[i]
        
        return parameters
    
    return {}

def run_dbscan_on_corpus(corpus, eps, min_samples, max_samples=10):
    if not corpus: return {}

    processed_corpus = [normalize_text(text) for text in corpus]
    
    try:
        vectorizer = TfidfVectorizer()
        X = vectorizer.fit_transform(processed_corpus)
        
        db = DBSCAN(eps=eps, min_samples=min_samples, metric='cosine', n_jobs=-1).fit(X)
        labels = db.labels_
        
        dbscan_templates = {}
        unique_labels = set(labels)

        for label in unique_labels:
            class_member_indices = np.where(labels == label)[0]
            if label == -1: # 处理噪声点
                for idx in class_member_indices:
                    original = corpus[idx]
                    normalized = processed_corpus[idx]
                    if normalized not in dbscan_templates:
                        dbscan_templates[normalized] = []
                    if len(dbscan_templates[normalized]) < max_samples:
                        dbscan_templates[normalized].append(original)
                continue

            # 处理聚类
            cluster_vectors = X[class_member_indices]
            centroid = np.asarray(cluster_vectors.mean(axis=0))
            similarities = cosine_similarity(cluster_vectors, centroid)
            most_representative_idx_in_cluster = np.argmax(similarities)
            original_corpus_idx = class_member_indices[most_representative_idx_in_cluster]
            most_representative_normalized = processed_corpus[original_corpus_idx]
            cluster_originals = [corpus[idx] for idx in class_member_indices]
            dbscan_templates[most_representative_normalized] = cluster_originals[:max_samples]
            
        return dbscan_templates
    except ValueError:
        # 如果批次中所有词都在停用词表中，TfidfVectorizer会报错
        print("警告: DBSCAN批次处理失败，可能因为内容过于单一或简短。将内容视为独立模板。")
        return {processed_corpus[i]: [corpus[i]][:max_samples] for i in range(len(corpus))}


def extract_templates_iterative(input_file, output_file, rules, batch_size=1000, eps=0.4, min_samples=2, max_samples_per_template=0):
    """
    使用小批量迭代的混合策略来提取模板，并为每个模板收集最多10个原始数据集。
    """
    print("--- 开始迭代式模板提取 ---")
    final_templates = {}  # template -> list of original contents
    unmatched_batch = []
    batch_num = 1

    try:
        print(f"步骤 1: 逐行处理 '{input_file}' 并动态构建模板库...")
        with open(input_file, 'r', encoding='utf-8') as f:
            total_lines = sum(1 for _ in f)
        
        with open(input_file, 'r', encoding='utf-8') as f:
            for line in tqdm(f, total=total_lines, desc="主进程"):
                try:
                    content = json.loads(line).get('content')
                    if not content: continue
                    
                    normalized_content = normalize_text(content)
                    
                    # 1. 检查是否匹配已发现的任何模板
                    if normalized_content in final_templates:
                        if len(final_templates[normalized_content]) < 10:
                            final_templates[normalized_content].append(content)
                        continue
                    
                    # 2. 检查是否匹配预定义规则
                    matched_by_rule = False
                    for rule in rules:
                        if rule['pattern'].match(content):
                            if normalized_content not in final_templates:
                                final_templates[normalized_content] = []
                            if len(final_templates[normalized_content]) < 10:
                                final_templates[normalized_content].append(content)
                            matched_by_rule = True
                            break
                    
                    if matched_by_rule:
                        continue

                    # 3. 如果都未匹配，加入批处理列表
                    unmatched_batch.append(content)

                    # 4. 检查是否触发批处理
                    if len(unmatched_batch) >= batch_size:
                        print(f"\n--- 处理批次 #{batch_num} (大小: {len(unmatched_batch)}) ---")
                        newly_found_templates = run_dbscan_on_corpus(unmatched_batch, eps, min_samples, 10)
                        
                        print(f"批次 #{batch_num}: DBSCAN 发现了 {len(newly_found_templates)} 个潜在模板。")
                        for template, originals in newly_found_templates.items():
                            if template in final_templates:
                                remaining = 10 - len(final_templates[template])
                                final_templates[template].extend(originals[:remaining])
                            else:
                                final_templates[template] = originals[:10]
                        print(f"当前总模板数: {len(final_templates)}")
                        
                        unmatched_batch.clear()
                        batch_num += 1

                except (json.JSONDecodeError, AttributeError):
                    continue

        # --- 收尾处理 ---
        print("\n--- 文件处理完毕，处理最后一批剩余内容 ---")
        if unmatched_batch:
            print(f"处理最后一个批次 (大小: {len(unmatched_batch)})")
            newly_found_templates = run_dbscan_on_corpus(unmatched_batch, eps, min_samples, 10)
            
            print(f"最后一个批次: DBSCAN 发现了 {len(newly_found_templates)} 个潜在模板。")
            for template, originals in newly_found_templates.items():
                if template in final_templates:
                    remaining = 10 - len(final_templates[template])
                    final_templates[template].extend(originals[:remaining])
                else:
                    final_templates[template] = originals[:10]
        else:
            print("没有剩余内容需要处理。")
            
        # --- 输出 ---
        print("\n--- 第 3 部分: 合并结果并保存 ---")
        print(f"总共找到 {len(final_templates)} 个唯一的模板。")
        
        with open(output_file, 'w', encoding='utf-8') as f:
            for template, data_list in sorted(final_templates.items()):
                if max_samples_per_template == 0:
                    json.dump({"content": template}, f, ensure_ascii=False)
                else:
                    json.dump({"content": template, "data": data_list[:max_samples_per_template]}, f, ensure_ascii=False)
                f.write('\n')
                
        print(f"所有模板已成功写入到 '{output_file}'。")

    except FileNotFoundError:
        print(f"错误：找不到输入文件 '{input_file}'。")
        return

def extract_values_with_templates(input_file, template_file, output_file):
    """
    使用DBSCAN生成的模板从原始消息中提取参数值
    """
    print("--- 开始使用模板提取参数值 ---")
    
    # 读取模板
    templates = []
    with open(template_file, 'r', encoding='utf-8') as f:
        for line in f:
            template_data = json.loads(line)
            templates.append(template_data['content'])
    
    print(f"已加载 {len(templates)} 个模板")
    
    # 从原始数据中提取值
    extracted_values = []
    
    with open(input_file, 'r', encoding='utf-8') as f:
        total_lines = sum(1 for _ in f)
    
    with open(input_file, 'r', encoding='utf-8') as f:
        for line in tqdm(f, total=total_lines, desc="提取参数"):
            try:
                data = json.loads(line)
                content = data.get('content', '')
                
                if not content:
                    continue
                
                # 尝试匹配每个模板
                for template in templates:
                    parameters = extract_parameters(template, content)
                    if parameters:
                        extracted_values.append({
                            'template': template,
                            'message': content,
                            'parameters': parameters
                        })
                        # 找到匹配就跳出循环
                        break
                        
            except (json.JSONDecodeError, Exception):
                continue
    
    # 保存提取的值
    with open(output_file, 'w', encoding='utf-8') as f:
        for item in extracted_values:
            json.dump(item, f, ensure_ascii=False)
            f.write('\n')
    
    print(f"成功从 {len(extracted_values)} 条消息中提取参数，并保存到 '{output_file}'")

# --- 使用示例 ---
# 假设您已经运行了上一个脚本，生成了 'content_filtered.jsonl'
input_jsonl_file = 'content_filtered.jsonl'
output_template_file = 'templates_iterative.txt'
BATCH_PROCESSING_SIZE = 10000 # 可以根据你的内存和数据量调整

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='Extract templates from GCash transaction data.')
    parser.add_argument('--input_file', type=str, default=input_jsonl_file, help='Input JSONL file path')
    parser.add_argument('--output_file', type=str, default=output_template_file, help='Output template file path')
    parser.add_argument('--batch_size', type=int, default=BATCH_PROCESSING_SIZE, help='Batch processing size (data volume)')
    parser.add_argument('--eps', type=float, default=0.4, help='DBSCAN eps parameter')
    parser.add_argument('--min_samples', type=int, default=5, help='DBSCAN min_samples parameter')
    parser.add_argument('--extract_values', action='store_true', help='Extract values using generated templates')
    
    args = parser.parse_args()
    
    if args.extract_values:
        # 执行参数提取
        extract_values_with_templates(
            input_file=args.input_file,
            template_file=args.output_file,
            output_file='extracted_parameters.jsonl'
        )
    else:
        # 执行模板提取
        extract_templates_iterative(
            input_file=args.input_file,
            output_file=args.output_file,
            rules=PREDEFINED_RULES,
            batch_size=args.batch_size,
            eps=args.eps,
            min_samples=args.min_samples
        )