269 lines
13 KiB
Python
269 lines
13 KiB
Python
import json
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import re
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import numpy as np
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.cluster import DBSCAN
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from sklearn.metrics.pairwise import cosine_similarity
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from tqdm import tqdm
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# --- 规则和辅助函数 (与之前相同) ---
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PREDEFINED_RULES = [
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# {'name': 'Transfer', 'pattern': re.compile(r'^Transfer from \d+ to \d+$')},
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# {'name': 'International Remittance', 'pattern': re.compile(r'^International Remittance$')},
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# {'name': 'Bill Payment', 'pattern': re.compile(r'^Bill payment successful for amount \d+$')},
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# {'name': 'New Message', 'pattern': re.compile(r'^You have a new message from \d+\.$')},
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# # 新增规则：匹配类似 "Sent GCash to GoTyme Bank with account ending in 6784"
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# {'name': 'Sent GCash to Account', 'pattern': re.compile(r'^Sent GCash to .+? with account ending in \d+$')}
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]
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def normalize_text(text):
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# 模式 8: 从银行收款 (这条规则必须先运行)
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text = re.sub(
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r'Received GCash from (.+?) with account ending in (\d+) (via .+|and invno:.+)$',
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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>',
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text
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)
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# 模式 13: 向未验证账户发送凭证
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# 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...
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text = re.sub(
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r'^You have sent PHP [\d,]+\.\d{2} to an unverified account \d{11} 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\.$',
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r'You have sent PHP <金额> to an unverified account <收款人号码> on <日期> <时间> with MSG: <消息>. Your new balance is PHP <金额>. Ref. No. <流水号>. Go to GCash Help Center to know how to secure your transactions.',
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text
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)
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# 模式 12: 详细发送凭证 (更新后可处理多段姓名)
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# 结构: You have sent <货币> <金额> to <收款人> <手机号> on <日期> <时间> with MSG: <消息>. Your new balance is <货币> <金额>. Ref. No. <流水号>.
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text = re.sub(
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r'^You have sent PHP [\d,]+\.\d{2} to (?:[A-Z\*]+\s)+[A-Z\*]\.\s\d{11} on \d{2}-\d{2}-\d{4}\s\d{1,2}:\d{2}\s[AP]M with MSG: \..*? Your new balance is PHP [\d,]+\.\d{2}\. Ref\. No\. \d+\.$',
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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>.',
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text
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)
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# 模式 11 (机构): 详细收款凭证
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# 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...
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text = re.sub(
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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!$',
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r'You have received <金额> of GCash from <付款人名称>. Your new balance is <金额>. <日期时间>. Ref. No. <流水号>. Use now to buy load, purchase items, send money, pay bills, and a lot more!',
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text
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)
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# 模式 11 (个人): 详细收款凭证 (最终修正版，兼容多种手机号/余额/结尾格式)
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text = re.sub(
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r'^You have received PHP [\d,.]+\s+of GCash from .+? w/ MSG: .*\. (?:Your new balance is PHP [\d,.]*\.\s)?Ref\. No\. \d+\.(?: To access your funds,.*)?$',
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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>.',
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text
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)
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# 模式 12: 详细发送凭证 (最终修正版，兼容所有已知姓名格式)
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text = re.sub(
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r'^You have sent PHP [\d,]+\.\d{2} to .+? \d{11} 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+\.$',
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r'You have sent PHP <金额> to <收款人名称> <收款人号码> on <日期> <时间> with MSG: <消息>. Your new balance is PHP <金额>. Ref. No. <参考号>.',
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text
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)
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# 模式 10 (来自用户最初的模板列表，这里将其具体化)
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# 结构: You have paid <金额> via GCash to <接收方> on <日期时间>. Ref. No. <参考号>. QRPH Invoice No. <参考号>.
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text = re.sub(
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r'^You have paid P[\d,.]+\s+via GCash to .+? on \d{1,2}-\d{1,2}-\d{2,4}\s\d{1,2}:\d{1,2}:\d{1,2}\s+[AP]M\. Ref\. No\.\s+\d+\. QRPH Invoice No\.\s+\d+\.$',
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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>.',
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text
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)
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# 模式 9 (来自用户最初的模板列表，这里将其具体化)
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# 结构: Sent GCash to <机构名> with account ending in <尾号>
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text = re.sub(
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r'Sent GCash to (.+?) with account ending in (\d+)$',
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r'Sent GCash to <收款人名称> with account ending in <银行4位数尾号>',
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text
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)
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# 新增规则：模式 7: 从一般来源收款 (这条规则紧随其后)
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# 它只会处理没有被上面那条规则匹配到的 "Received GCash from..."
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text = re.sub(
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r'(?i)^Received GCash from .+$',
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r'Received GCash from <付款人名称>',
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text
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)
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# 模式 6: 带商户交易单号的支付
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# 结构: Payment to <商户名>, Merchant Transaction Number: <交易单号>
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text = re.sub(
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r'Payment to (.+?), Merchant Transaction Number: (.+)$',
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r'Payment to <收款人名称>, Merchant Transaction Number: <交易单号>',
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text
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)
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# 模式 5 (来自用户最初的模板列表，这里将其具体化)
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# 结构: Payment to <商户名>
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text = re.sub(
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r'^Payment to (.+)$',
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r'Payment to <收款人名称>',
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text
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)
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text = re.sub(
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r'^(.+?) with (Ref\. no\.|Parent Ref\.No\.|Reference No\.) (.+)$',
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r'<交易类型> with <参考号类型> <参考号>',
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text
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)
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text = re.sub(r'Sent GCash to <收款人名称> with account ending in (\d+)$', r'Sent GCash to <收款人名称> with account ending in <银行4位数尾号>', text)
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text = re.sub(r'^Transfer from \S+ to \S+$', r'Transfer from <付款人号码> to <收款人号码>', text)
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# 模式 8: 从银行收款
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# 结构: Received GCash from <机构名> with account ending in <尾号> via <网络> or with invno:<...>
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text = re.sub(
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r'Received GCash from (.+?) with account ending in (\d+) (via .+|and invno:.+)$',
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r'Received GCash from <付款人名称> with account ending in <银行4位数尾号> via <流水号>',
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text
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)
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# 新增规则：Buy Load Transaction
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text = re.sub(
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r'^Buy Load Transaction for .+$',
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r'Buy Load Transaction for <付款人号码>',
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text
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)
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# 新增规则：Refund
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text = re.sub(
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r'^Refund from .+$',
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r'Refund from <收款人名称>',
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text
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)
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return text
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def run_dbscan_on_corpus(corpus, eps, min_samples):
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if not corpus: return set()
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processed_corpus = [normalize_text(text) for text in corpus]
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try:
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vectorizer = TfidfVectorizer()
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X = vectorizer.fit_transform(processed_corpus)
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db = DBSCAN(eps=eps, min_samples=min_samples, metric='cosine', n_jobs=-1).fit(X)
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labels = db.labels_
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dbscan_templates = set()
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unique_labels = set(labels)
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for label in unique_labels:
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class_member_indices = np.where(labels == label)[0]
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if label == -1: # 处理噪声点
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for idx in class_member_indices:
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dbscan_templates.add(processed_corpus[idx])
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continue
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# 处理聚类
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cluster_vectors = X[class_member_indices]
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centroid = np.asarray(cluster_vectors.mean(axis=0))
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similarities = cosine_similarity(cluster_vectors, centroid)
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most_representative_idx_in_cluster = np.argmax(similarities)
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original_corpus_idx = class_member_indices[most_representative_idx_in_cluster]
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dbscan_templates.add(processed_corpus[original_corpus_idx])
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return dbscan_templates
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except ValueError:
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# 如果批次中所有词都在停用词表中，TfidfVectorizer会报错
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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。")
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return set(processed_corpus)
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def extract_templates_iterative(input_file, output_file, rules, batch_size=1000, eps=0.4, min_samples=2):
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"""
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使用小批量迭代的混合策略来提取模板。
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"""
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print("--- 开始迭代式模板提取 ---")
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final_templates = set()
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unmatched_batch = []
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batch_num = 1
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try:
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print(f"步骤 1: 逐行处理 '{input_file}' 并动态构建模板库...")
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with open(input_file, 'r', encoding='utf-8') as f:
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total_lines = sum(1 for _ in f)
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with open(input_file, 'r', encoding='utf-8') as f:
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for line in tqdm(f, total=total_lines, desc="主进程"):
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try:
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content = json.loads(line).get('content')
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if not content: continue
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normalized_content = normalize_text(content)
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# 1. 检查是否匹配已发现的任何模板
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if normalized_content in final_templates:
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continue
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# 2. 检查是否匹配预定义规则
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matched_by_rule = False
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for rule in rules:
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if rule['pattern'].match(content):
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final_templates.add(normalized_content)
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matched_by_rule = True
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break
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if matched_by_rule:
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continue
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# 3. 如果都未匹配，加入批处理列表
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unmatched_batch.append(content)
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# 4. 检查是否触发批处理
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if len(unmatched_batch) >= batch_size:
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print(f"\n--- 处理批次 #{batch_num} (大小: {len(unmatched_batch)}) ---")
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newly_found_templates = run_dbscan_on_corpus(unmatched_batch, eps, min_samples)
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print(f"批次 #{batch_num}: DBSCAN 发现了 {len(newly_found_templates)} 个潜在模板。")
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final_templates.update(newly_found_templates)
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print(f"当前总模板数: {len(final_templates)}")
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unmatched_batch.clear()
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batch_num += 1
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except (json.JSONDecodeError, AttributeError):
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continue
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# --- 收尾处理 ---
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print("\n--- 文件处理完毕，处理最后一批剩余内容 ---")
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if unmatched_batch:
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print(f"处理最后一个批次 (大小: {len(unmatched_batch)})")
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newly_found_templates = run_dbscan_on_corpus(unmatched_batch, eps, min_samples)
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print(f"最后一个批次: DBSCAN 发现了 {len(newly_found_templates)} 个潜在模板。")
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final_templates.update(newly_found_templates)
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else:
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print("没有剩余内容需要处理。")
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# --- 输出 ---
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print("\n--- 第 3 部分: 合并结果并保存 ---")
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print(f"总共找到 {len(final_templates)} 个唯一的模板。")
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with open(output_file, 'w', encoding='utf-8') as f:
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for template in sorted(list(final_templates)):
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json.dump({"content": template}, f, ensure_ascii=False)
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f.write('\n')
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print(f"所有模板已成功写入到 '{output_file}'。")
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except FileNotFoundError:
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print(f"错误：找不到输入文件 '{input_file}'。")
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return
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# --- 使用示例 ---
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# 假设您已经运行了上一个脚本，生成了 'content_filtered.jsonl'
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input_jsonl_file = 'content_filtered.jsonl'
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output_template_file = 'templates_iterative.txt'
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BATCH_PROCESSING_SIZE = 10000 # 可以根据你的内存和数据量调整
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extract_templates_iterative(
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input_file=input_jsonl_file,
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output_file=output_template_file,
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rules=PREDEFINED_RULES,
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batch_size=BATCH_PROCESSING_SIZE,
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eps=0.4,
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min_samples=5 # 稍微提高min_samples可以得到更可靠的模板
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) |