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1261 lines
65 KiB
1261 lines
65 KiB
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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表达式验证器 - 使用抽象语法树验证字符串表达式格式是否正确
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本模块实现了一个能够检测字符串表达式格式是否正确的系统，基于PLY(Python Lex-Yacc)
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构建词法分析器和语法分析器，识别表达式中的操作符、函数和字段，并验证其格式正确性。
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"""
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import re
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import sys
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import json
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import os
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from typing import List, Dict, Any, Optional, Tuple
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# 尝试导入PLY库，如果不存在则提供安装提示
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try:
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import ply.lex as lex
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import ply.yacc as yacc
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except ImportError:
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print("错误: 需要安装PLY库。请运行 'pip install ply' 来安装。")
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sys.exit(1)
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# 1. 定义支持的操作符和函数
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supported_functions = {
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# Group 类别函数
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'group_min': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'category']},
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# group_mean(x, w, group)
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'group_mean': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'category']},
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'group_median': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'category']},
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'group_max': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'category']},
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'group_rank': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'category']},
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'group_vector_proj': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'category']},
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'group_normalize': {'min_args': 2, 'max_args': 5, 'arg_types': ['expression', 'category', 'expression', 'expression', 'expression']},
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'group_extra': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'category']},
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'group_backfill': {'min_args': 3, 'max_args': 4, 'arg_types': ['expression', 'expression', 'expression', 'expression'], 'param_names': ['x', 'cat', 'days', 'std']},
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'group_scale': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'category']},
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'group_count': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'category']},
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'group_zscore': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'category']},
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'group_std_dev': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'category']},
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'group_sum': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'category']},
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'group_neutralize': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'category']},
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'group_multi_regression': {'min_args': 4, 'max_args': 9, 'arg_types': ['expression'] * 9},
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'group_cartesian_product': {'min_args': 2, 'max_args': 2, 'arg_types': ['category', 'category']},
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'combo_a': {'min_args': 1, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression']},
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# Transformational 类别函数
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'right_tail': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression']},
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'bucket': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression']}, # 第二个参数可以是string类型的range参数
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'tail': {'min_args': 1, 'max_args': 4, 'arg_types': ['expression', 'expression', 'expression', 'expression']},
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'left_tail': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression']},
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'trade_when': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression']},
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'generate_stats': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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# Cross Sectional 类别函数
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'winsorize': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['x', 'std']},
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'rank': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression']},
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'regression_proj': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
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'vector_neut': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
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'regression_neut': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
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'multi_regression': {'min_args': 2, 'max_args': 100, 'arg_types': ['expression'] * 100}, # 支持多个自变量
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# Time Series 类别函数
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'ts_std_dev': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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'ts_mean': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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'ts_delay': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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'ts_corr': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'number']},
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'ts_zscore': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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'ts_returns': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'number', 'number'], 'param_names': ['x', 'd', 'mode'], 'keyword_only': True},
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'ts_product': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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# Platform: ts_backfill(x, d)
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'ts_backfill': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number'], 'param_names': ['x', 'd']},
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'days_from_last_change': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'last_diff_value': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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# Platform: ts_scale(x, d, constant=0)
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'ts_scale': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'number', 'number'], 'param_names': ['x', 'd', 'constant'], 'keyword_only': True},
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# Platform: ts_entropy(x, d)
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'ts_entropy': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number'], 'param_names': ['x', 'd']},
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'ts_step': {'min_args': 1, 'max_args': 1, 'arg_types': ['number']},
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'ts_sum': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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'ts_co_kurtosis': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'number']},
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'inst_tvr': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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'ts_decay_exp_window': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'number', 'number'], 'param_names': ['x', 'd', 'factor'], 'keyword_only': True},
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'ts_av_diff': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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'ts_kurtosis': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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# Platform: ts_min_max_diff(x, d, f=0.5)
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'ts_min_max_diff': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'number', 'number'], 'param_names': ['x', 'd', 'f'], 'keyword_only': True},
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'ts_arg_max': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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'ts_max': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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# Platform: ts_min_max_cps(x, d, f=2)
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'ts_min_max_cps': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'number', 'number'], 'param_names': ['x', 'd', 'f'], 'keyword_only': True},
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# Platform: ts_rank(x, d, constant=0)
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'ts_rank': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'number', 'number'], 'param_names': ['x', 'd', 'constant'], 'keyword_only': True},
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'ts_ir': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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'ts_theilsen': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'number']},
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# Platform: hump_decay(x, p=0)
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'hump_decay': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'number'], 'param_names': ['x', 'p'], 'keyword_only': True},
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# Platform: ts_weighted_decay(x, k=0.5)
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'ts_weighted_decay': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'number'], 'param_names': ['x', 'k'], 'keyword_only': True},
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# Platform: ts_quantile(x, d, driver="gaussian")
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'ts_quantile': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'number', 'string'], 'param_names': ['x', 'd', 'driver'], 'keyword_only': True},
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'ts_min': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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'ts_count_nans': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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'ts_covariance': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'number']},
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'ts_co_skewness': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'number']},
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'ts_min_diff': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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# Platform: ts_decay_linear(x, d, dense=false)
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'ts_decay_linear': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'number', 'boolean'], 'param_names': ['x', 'd', 'dense'], 'keyword_only': True},
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# Platform: jump_decay(x, d, sensitivity=0.5, force=0.1)
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'jump_decay': {'min_args': 2, 'max_args': 4, 'arg_types': ['expression', 'number', 'number', 'number'], 'param_names': ['x', 'd', 'sensitivity', 'force'], 'keyword_only': True},
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# Platform: ts_moment(x, d, k=0)
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'ts_moment': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'number', 'number'], 'param_names': ['x', 'd', 'k'], 'keyword_only': True},
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'ts_arg_min': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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'ts_regression': {'min_args': 3, 'max_args': 5, 'arg_types': ['expression', 'expression', 'number', 'number', 'number'], 'param_names': ['y', 'x', 'd', 'lag', 'rettype'], 'keyword_only': True},
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'ts_skewness': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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'ts_max_diff': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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'kth_element': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'number', 'number']},
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'hump': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'number'], 'param_names': ['x', 'hump']},
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'ts_median': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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'ts_delta': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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# Platform: ts_poly_regression(y, x, d, k=1) and k must be keyword if provided
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'ts_poly_regression': {'min_args': 3, 'max_args': 4, 'arg_types': ['expression', 'expression', 'number', 'number'], 'param_names': ['y', 'x', 'd', 'k'], 'keyword_only': True, 'keyword_only_from': 3},
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'ts_target_tvr_decay': {'min_args': 1, 'max_args': 4, 'arg_types': ['expression', 'number', 'number', 'number'], 'param_names': ['x', 'lambda_min', 'lambda_max', 'target_tvr'], 'keyword_only': True},
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'ts_target_tvr_delta_limit': {'min_args': 2, 'max_args': 5, 'arg_types': ['expression', 'expression', 'number', 'number', 'number'], 'param_names': ['x', 'y', 'lambda_min', 'lambda_max', 'target_tvr'], 'keyword_only': True},
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'ts_target_tvr_hump': {'min_args': 1, 'max_args': 4, 'arg_types': ['expression', 'number', 'number', 'number'], 'param_names': ['x', 'lambda_min', 'lambda_max', 'target_tvr'], 'keyword_only': True},
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# Platform: ts_delta_limit(x, y, limit_volume=0.1)
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'ts_delta_limit': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'expression', 'number'], 'param_names': ['x', 'y', 'limit_volume'], 'keyword_only': True},
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# Special 类别函数
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'inst_pnl': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'self_corr': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'in': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']}, # 注意：这是关键字
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'universe_size': {'min_args': 0, 'max_args': 0, 'arg_types': []},
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# Missing functions from operators.py
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'quantile': {'min_args': 1, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression'], 'param_names': ['x', 'driver', 'sigma']}, # quantile(x, driver = gaussian, sigma = 1.0)
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'normalize': {'min_args': 1, 'max_args': 3, 'arg_types': ['expression', 'boolean', 'number']}, # normalize(x, useStd = false, limit = 0.0)
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'zscore': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']}, # zscore(x)
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# Logical 类别函数
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'or': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']}, # 注意：这是关键字
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'and': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']}, # 注意：这是关键字
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'not': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']}, # 注意：这是关键字
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'is_nan': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'is_not_nan': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'less': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
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'equal': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
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'greater': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
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'is_finite': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'if_else': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression']},
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'not_equal': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
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'less_equal': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
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'greater_equal': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
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# Vector 类别函数
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'vec_kurtosis': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'vec_min': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'vec_count': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'vec_sum': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'vec_skewness': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'vec_max': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'vec_avg': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'vec_range': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'vec_choose': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number'], 'param_names': ['x', 'nth']},
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'vec_powersum': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'number'], 'param_names': ['x', 'constant']},
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'vec_stddev': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'vec_percentage': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'number'], 'param_names': ['x', 'percentage']},
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'vec_ir': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'vec_norm': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'ts_percentage': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'number', 'number'], 'param_names': ['x', 'd', 'percentage']},
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'signed_power': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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'ts_product': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number']},
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# Additional functions from test cases
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'rank_by_side': {'min_args': 1, 'max_args': 3, 'arg_types': ['expression', 'number', 'number'], 'param_names': ['x', 'rate', 'scale']},
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'log_diff': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'nan_mask': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']},
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'ts_partial_corr': {'min_args': 4, 'max_args': 4, 'arg_types': ['expression', 'expression', 'expression', 'number']},
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'ts_triple_corr': {'min_args': 4, 'max_args': 4, 'arg_types': ['expression', 'expression', 'expression', 'number']},
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'clamp': {'min_args': 1, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression'], 'param_names': ['x', 'lower', 'upper']},
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'keep': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'expression', 'number'], 'param_names': ['x', 'condition', 'period']},
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'replace': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression'], 'param_names': ['x', 'target', 'dest']},
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'filter': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression'], 'param_names': ['x', 'h', 't']},
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'one_side': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'string'], 'param_names': ['x', 'side']},
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'scale_down': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'number'], 'param_names': ['x', 'constant']},
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# Arithmetic 类别函数
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# add(x, y, ..., filter=false)
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# NOTE: add() is variadic (>=2 terms) with an optional boolean filter flag.
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# We validate it with custom logic in validate_function().
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'add': {'min_args': 2, 'max_args': 101, 'arg_types': ['expression'] * 101},
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'multiply': {'min_args': 2, 'max_args': 100, 'arg_types': ['expression'] * 99 + ['boolean'], 'param_names': ['x', 'y', 'filter']}, # multiply(x, y, ..., filter=false)
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'sign': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'subtract': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'expression', 'boolean']}, # subtract(x, y, filter=false)
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'pasteurize': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'log': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'purify': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'arc_tan': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'max': {'min_args': 2, 'max_args': 100, 'arg_types': ['expression'] * 100}, # max(x, y, ...)
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'to_nan': {'min_args': 1, 'max_args': 3, 'arg_types': ['expression', 'expression', 'boolean']}, # to_nan(x, value=0, reverse=false)
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'abs': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'sigmoid': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'divide': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']}, # divide(x, y)
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'min': {'min_args': 2, 'max_args': 100, 'arg_types': ['expression'] * 100}, # min(x, y, ...)
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'tanh': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'nan_out': {'min_args': 1, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression'], 'param_names': ['x', 'lower', 'upper']}, # nan_out(x, lower=0, upper=0)
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'signed_power': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']}, # signed_power(x, y)
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'inverse': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'round': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'sqrt': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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's_log_1p': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'reverse': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']}, # -x
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'power': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression']}, # power(x, y)
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'densify': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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'floor': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression']},
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# Appended missing operators
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'arc_cos': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['x']},
|
|
'arc_sin': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['x']},
|
|
'ceiling': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['x']},
|
|
'exp': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['x']},
|
|
'fraction': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['x']},
|
|
'round_down': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['x', 'f']},
|
|
'is_not_finite': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['input']},
|
|
'negate': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['input']},
|
|
'ts_rank_gmean_amean_diff': {'min_args': 5, 'max_args': 5, 'arg_types': ['expression', 'expression', 'expression', 'expression', 'number'], 'param_names': ['input1', 'input2', 'input3', '...', 'd']},
|
|
'ts_vector_neut': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'number'], 'param_names': ['x', 'y', 'd']},
|
|
'ts_vector_proj': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'number'], 'param_names': ['x', 'y', 'd']},
|
|
'scale': {'min_args': 1, 'max_args': 4, 'arg_types': ['expression', 'expression', 'expression', 'expression'], 'param_names': ['x', 'scale', 'longscale', 'shortscale']},
|
|
'generalized_rank': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['open', 'm']},
|
|
'rank_gmean_amean_diff': {'min_args': 4, 'max_args': 4, 'arg_types': ['expression', 'expression', 'expression', 'expression'], 'param_names': ['input1', 'input2', 'input3', '...']},
|
|
'truncate': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['x', 'maxPercent']},
|
|
'vector_proj': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['x', 'y']},
|
|
'vec_filter': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['vec', 'value']},
|
|
'group_coalesce': {'min_args': 4, 'max_args': 4, 'arg_types': ['expression', 'expression', 'expression', 'expression'], 'param_names': ['original_group', 'group2', 'group3', '…']},
|
|
'group_percentage': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'category', 'expression'], 'param_names': ['x', 'group', 'percentage']},
|
|
'group_vector_neut': {'min_args': 3, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression'], 'param_names': ['x', 'y', 'g']},
|
|
'convert': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['x', 'mode']},
|
|
'reduce_avg': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['input', 'threshold']},
|
|
'reduce_choose': {'min_args': 2, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression'], 'param_names': ['input', 'nth', 'ignoreNan']},
|
|
'reduce_count': {'min_args': 2, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['input', 'threshold']},
|
|
'reduce_ir': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['input']},
|
|
'reduce_kurtosis': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['input']},
|
|
'reduce_max': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['input']},
|
|
'reduce_min': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['input']},
|
|
'reduce_norm': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['input']},
|
|
'reduce_percentage': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['input', 'percentage']},
|
|
'reduce_powersum': {'min_args': 1, 'max_args': 3, 'arg_types': ['expression', 'expression', 'expression'], 'param_names': ['input', 'constant', 'precise']},
|
|
'reduce_range': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['input']},
|
|
'reduce_skewness': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['input']},
|
|
'reduce_stddev': {'min_args': 1, 'max_args': 2, 'arg_types': ['expression', 'expression'], 'param_names': ['input', 'threshold']},
|
|
'reduce_sum': {'min_args': 1, 'max_args': 1, 'arg_types': ['expression'], 'param_names': ['input']},
|
|
}
|
|
|
|
# 2. 定义group类型字段
|
|
group_fields = {
|
|
'sector', 'subindustry', 'industry', 'exchange', 'country', 'market'
|
|
}
|
|
|
|
# 3. 有效类别集合
|
|
valid_categories = group_fields
|
|
|
|
# 4. 字段命名模式 - 只校验字段是不是数字字母下划线组成
|
|
field_patterns = [
|
|
re.compile(r'^[a-zA-Z0-9_]+$'), # 只允许数字、字母和下划线组成的字段名
|
|
]
|
|
|
|
# 4. 抽象语法树节点类型
|
|
class ASTNode:
|
|
"""抽象语法树节点基类"""
|
|
def __init__(self, node_type: str, children: Optional[List['ASTNode']] = None,
|
|
value: Optional[Any] = None, line: Optional[int] = None):
|
|
self.node_type = node_type # 'function', 'operator', 'field', 'number', 'expression'
|
|
self.children = children or []
|
|
self.value = value
|
|
self.line = line
|
|
|
|
def __str__(self) -> str:
|
|
return f"ASTNode({self.node_type}, {self.value}, line={self.line})"
|
|
|
|
def __repr__(self) -> str:
|
|
return self.__str__()
|
|
|
|
class ExpressionValidator:
|
|
"""表达式验证器类"""
|
|
|
|
def __init__(self):
|
|
"""初始化词法分析器和语法分析器"""
|
|
# 构建词法分析器
|
|
self.lexer = lex.lex(module=self, debug=False)
|
|
# 构建语法分析器
|
|
self.parser = yacc.yacc(module=self, debug=False)
|
|
# 错误信息存储
|
|
self.errors = []
|
|
# Cache for unit inference (unit/scalar/category)
|
|
self._unit_cache: Dict[int, str] = {}
|
|
# Cache for derived category detection (bucket/group_cartesian_product outputs)
|
|
self._derived_category_cache: Dict[int, bool] = {}
|
|
|
|
# 词法分析器规则
|
|
tokens = ('FUNCTION', 'FIELD', 'NUMBER', 'LPAREN', 'RPAREN',
|
|
'PLUS', 'MINUS', 'TIMES', 'DIVIDE', 'COMMA', 'CATEGORY',
|
|
'EQUAL', 'ASSIGN', 'IDENTIFIER', 'STRING', 'GREATER', 'LESS', 'GREATEREQUAL', 'LESSEQUAL', 'NOTEQUAL', 'BOOLEAN')
|
|
|
|
# 忽略空白字符
|
|
t_ignore = ' \t\n'
|
|
|
|
# 操作符 - 注意顺序很重要，长的操作符要放在前面
|
|
t_PLUS = r'\+'
|
|
t_MINUS = r'-'
|
|
t_TIMES = r'\*'
|
|
t_DIVIDE = r'/'
|
|
t_LPAREN = r'\('
|
|
t_RPAREN = r'\)'
|
|
t_COMMA = r','
|
|
t_EQUAL = r'=='
|
|
t_NOTEQUAL = r'!='
|
|
t_GREATEREQUAL = r'>='
|
|
t_LESSEQUAL = r'<='
|
|
t_GREATER = r'>'
|
|
t_LESS = r'<'
|
|
t_ASSIGN = r'='
|
|
|
|
# 数字（整数和浮点数）
|
|
def t_NUMBER(self, t):
|
|
r'\d+\.?\d*'
|
|
if '.' in t.value:
|
|
t.value = float(t.value)
|
|
else:
|
|
t.value = int(t.value)
|
|
return t
|
|
|
|
# 字符串 - 需要放在所有其他标识符规则之前
|
|
def t_STRING(self, t):
|
|
r"'[^']*'|\"[^\"]*\""
|
|
# 去除引号
|
|
t.value = t.value[1:-1]
|
|
return t
|
|
|
|
# 函数和字段名
|
|
def t_IDENTIFIER(self, t):
|
|
r'[a-zA-Z_][a-zA-Z0-9_]*'
|
|
# 检查是否为布尔值
|
|
if t.value.lower() in {'true', 'false'}:
|
|
t.type = 'BOOLEAN'
|
|
t.value = t.value.lower() # 转换为小写以保持一致性
|
|
else:
|
|
# 查看当前token后面的字符，判断是否为参数名（后面跟着'='）
|
|
lexpos = t.lexpos
|
|
next_chars = ''
|
|
if lexpos + len(t.value) < len(t.lexer.lexdata):
|
|
# 查看当前token后面的字符，跳过空格
|
|
next_pos = lexpos + len(t.value)
|
|
while next_pos < len(t.lexer.lexdata) and t.lexer.lexdata[next_pos].isspace():
|
|
next_pos += 1
|
|
if next_pos < len(t.lexer.lexdata):
|
|
next_chars = t.lexer.lexdata[next_pos:next_pos+1]
|
|
|
|
# 如果后面跟着'='，则为参数名
|
|
if next_chars == '=':
|
|
t.type = 'IDENTIFIER'
|
|
# 如果后面跟着'('，则为函数名
|
|
elif next_chars == '(':
|
|
t.type = 'FUNCTION'
|
|
t.value = t.value.lower() # 转换为小写以保持一致性
|
|
# 检查是否为参数名（支持更多参数名）
|
|
elif t.value in {'std', 'k', 'lambda_min', 'lambda_max', 'target_tvr', 'range', 'buckets', 'lag', 'rettype', 'mode', 'nth', 'constant', 'percentage', 'driver', 'sigma', 'rate', 'scale', 'filter', 'lower', 'upper', 'target', 'dest', 'event', 'sensitivity', 'force', 'h', 't', 'period', 'stddev', 'factor', 'k', 'useStd', 'limit', 'gaussian', 'uniform', 'cauchy'}:
|
|
t.type = 'IDENTIFIER'
|
|
# 检查是否为函数名（不区分大小写）
|
|
elif t.value.lower() in supported_functions:
|
|
t.type = 'FUNCTION'
|
|
t.value = t.value.lower() # 转换为小写以保持一致性
|
|
# 检查是否为有效类别
|
|
elif t.value in valid_categories:
|
|
t.type = 'CATEGORY'
|
|
# 检查是否为字段名
|
|
elif self._is_valid_field(t.value):
|
|
t.type = 'FIELD'
|
|
else:
|
|
# 其他标识符，保留为IDENTIFIER类型
|
|
t.type = 'IDENTIFIER'
|
|
return t
|
|
|
|
# 行号跟踪
|
|
def t_newline(self, t):
|
|
r'\n+'
|
|
t.lexer.lineno += len(t.value)
|
|
|
|
# 错误处理
|
|
def t_error(self, t):
|
|
if t:
|
|
# 检查是否为非法字符
|
|
if not re.match(r'[a-zA-Z0-9_\+\-\*/\(\)\,\s=<>!]', t.value[0]):
|
|
# 这是一个非法字符
|
|
self.errors.append(f"非法字符 '{t.value[0]}' (行 {t.lexer.lineno})")
|
|
else:
|
|
# 这是一个非法标记
|
|
self.errors.append(f"非法标记 '{t.value}' (行 {t.lexer.lineno})")
|
|
# 跳过这个字符，继续处理
|
|
t.lexer.skip(1)
|
|
else:
|
|
self.errors.append("词法分析器到达文件末尾")
|
|
|
|
# 语法分析器规则
|
|
def p_expression(self, p):
|
|
"""expression : comparison
|
|
| expression EQUAL comparison
|
|
| expression NOTEQUAL comparison
|
|
| expression GREATER comparison
|
|
| expression LESS comparison
|
|
| expression GREATEREQUAL comparison
|
|
| expression LESSEQUAL comparison"""
|
|
if len(p) == 2:
|
|
p[0] = p[1]
|
|
else:
|
|
p[0] = ASTNode('binop', [p[1], p[3]], {'op': p[2]})
|
|
|
|
def p_comparison(self, p):
|
|
"""comparison : term
|
|
| comparison PLUS term
|
|
| comparison MINUS term"""
|
|
if len(p) == 2:
|
|
p[0] = p[1]
|
|
else:
|
|
p[0] = ASTNode('binop', [p[1], p[3]], {'op': p[2]})
|
|
|
|
def p_term(self, p):
|
|
"""term : factor
|
|
| term TIMES factor
|
|
| term DIVIDE factor"""
|
|
if len(p) == 2:
|
|
p[0] = p[1]
|
|
else:
|
|
p[0] = ASTNode('binop', [p[1], p[3]], {'op': p[2]})
|
|
|
|
def p_factor(self, p):
|
|
"""factor : NUMBER
|
|
| STRING
|
|
| FIELD
|
|
| CATEGORY
|
|
| IDENTIFIER
|
|
| BOOLEAN
|
|
| MINUS factor
|
|
| LPAREN expression RPAREN
|
|
| function_call"""
|
|
if len(p) == 2:
|
|
# 数字、字符串、字段、类别或标识符
|
|
if p.slice[1].type == 'NUMBER':
|
|
p[0] = ASTNode('number', value=p[1])
|
|
elif p.slice[1].type == 'STRING':
|
|
p[0] = ASTNode('string', value=p[1])
|
|
elif p.slice[1].type == 'FIELD':
|
|
p[0] = ASTNode('field', value=p[1])
|
|
elif p.slice[1].type == 'CATEGORY':
|
|
p[0] = ASTNode('category', value=p[1])
|
|
elif p.slice[1].type == 'BOOLEAN':
|
|
p[0] = ASTNode('boolean', value=p[1])
|
|
elif p.slice[1].type == 'IDENTIFIER':
|
|
p[0] = ASTNode('identifier', value=p[1])
|
|
else:
|
|
p[0] = p[1]
|
|
elif len(p) == 3:
|
|
# 一元负号
|
|
p[0] = ASTNode('unop', [p[2]], {'op': p[1]})
|
|
elif len(p) == 4:
|
|
# 括号表达式
|
|
p[0] = p[2]
|
|
else:
|
|
# 函数调用
|
|
p[0] = p[1]
|
|
|
|
def p_function_call(self, p):
|
|
'''function_call : FUNCTION LPAREN args RPAREN'''
|
|
p[0] = ASTNode('function', p[3], p[1])
|
|
|
|
def p_args(self, p):
|
|
'''args : arg_list
|
|
| empty'''
|
|
if len(p) == 2 and p[1] is not None:
|
|
p[0] = p[1]
|
|
else:
|
|
p[0] = []
|
|
|
|
def p_arg_list(self, p):
|
|
'''arg_list : arg
|
|
| arg_list COMMA arg'''
|
|
if len(p) == 2:
|
|
p[0] = [p[1]]
|
|
else:
|
|
p[0] = p[1] + [p[3]]
|
|
|
|
def p_arg(self, p):
|
|
'''arg : expression
|
|
| IDENTIFIER ASSIGN expression'''
|
|
if len(p) == 2:
|
|
p[0] = {'type': 'positional', 'value': p[1]}
|
|
else:
|
|
p[0] = {'type': 'named', 'name': p[1], 'value': p[3]}
|
|
|
|
def p_empty(self, p):
|
|
'''empty :'''
|
|
p[0] = None
|
|
|
|
# 语法错误处理
|
|
def p_error(self, p):
|
|
if p:
|
|
self.errors.append(f"语法错误在位置 {p.lexpos}: 非法标记 '{p.value}'")
|
|
else:
|
|
self.errors.append("语法错误: 表达式不完整")
|
|
|
|
def _is_valid_field(self, field_name: str) -> bool:
|
|
"""检查字段名是否符合模式"""
|
|
for pattern in field_patterns:
|
|
if pattern.match(field_name):
|
|
return True
|
|
return False
|
|
|
|
def validate_function(self, node: ASTNode, is_in_group_arg: bool = False) -> List[str]:
|
|
"""验证函数调用的参数数量和类型"""
|
|
function_name = node.value
|
|
args = node.children
|
|
function_info = supported_functions.get(function_name)
|
|
|
|
if not function_info:
|
|
return [f"未知函数: {function_name}"]
|
|
|
|
# Custom validation for variadic functions with optional flags
|
|
if function_name == 'add':
|
|
return self._validate_add(args, is_in_group_arg)
|
|
|
|
errors = []
|
|
|
|
# Keyword-only enforcement for optional parameters.
|
|
# If enabled, only the required leading arguments can be positional.
|
|
keyword_only_from = function_info.get('keyword_only_from')
|
|
if keyword_only_from is None and function_info.get('keyword_only'):
|
|
keyword_only_from = function_info.get('min_args', 0)
|
|
|
|
# 检查参数数量
|
|
if len(args) < function_info['min_args']:
|
|
errors.append(f"函数 {function_name} 需要至少 {function_info['min_args']} 个参数，但只提供了 {len(args)}")
|
|
elif len(args) > function_info['max_args']:
|
|
errors.append(f"函数 {function_name} 最多接受 {function_info['max_args']} 个参数，但提供了 {len(args)}")
|
|
|
|
# 处理参数验证
|
|
# 跟踪已使用的位置参数索引
|
|
positional_index = 0
|
|
|
|
# 对于所有函数，支持命名参数
|
|
for arg in args:
|
|
if isinstance(arg, dict):
|
|
if arg['type'] == 'named':
|
|
# 命名参数
|
|
if 'param_names' in function_info and arg['name'] in function_info['param_names']:
|
|
# 查找参数在param_names中的索引
|
|
param_index = function_info['param_names'].index(arg['name'])
|
|
if param_index < len(function_info['arg_types']):
|
|
expected_type = function_info['arg_types'][param_index]
|
|
arg_errors = self._validate_arg_type(arg['value'], expected_type, param_index, function_name, is_in_group_arg)
|
|
errors.extend(arg_errors)
|
|
# 对于winsorize函数，支持std和clip参数
|
|
elif function_name == 'winsorize' and arg['name'] in ['std', 'clip']:
|
|
arg_errors = self._validate_arg_type(arg['value'], 'number', 0, function_name, is_in_group_arg)
|
|
errors.extend(arg_errors)
|
|
# 对于bucket函数，支持'range'和'buckets'参数
|
|
elif function_name == 'bucket' and arg['name'] in ['range', 'buckets']:
|
|
# range和buckets参数应该是string类型
|
|
arg_errors = self._validate_arg_type(arg['value'], 'string', 1, function_name, is_in_group_arg)
|
|
errors.extend(arg_errors)
|
|
else:
|
|
errors.append(f"函数 {function_name} 不存在参数 '{arg['name']}'")
|
|
elif arg['type'] == 'positional':
|
|
# 位置参数（字典形式）
|
|
if keyword_only_from is not None and positional_index >= keyword_only_from:
|
|
param_name = None
|
|
if 'param_names' in function_info and positional_index < len(function_info['param_names']):
|
|
param_name = function_info['param_names'][positional_index]
|
|
if param_name:
|
|
errors.append(f"函数 {function_name} 的第{positional_index+1}个参数必须使用命名参数 '{param_name}='")
|
|
else:
|
|
errors.append(f"函数 {function_name} 的第{positional_index+1}个参数必须使用命名参数")
|
|
else:
|
|
# 验证位置参数的类型
|
|
if positional_index < len(function_info['arg_types']):
|
|
expected_type = function_info['arg_types'][positional_index]
|
|
arg_errors = self._validate_arg_type(arg['value'], expected_type, positional_index, function_name, is_in_group_arg)
|
|
errors.extend(arg_errors)
|
|
positional_index += 1
|
|
else:
|
|
# 其他字典类型参数
|
|
errors.append(f"参数 {positional_index+1} 格式错误")
|
|
positional_index += 1
|
|
else:
|
|
# 位置参数（直接ASTNode形式）
|
|
if keyword_only_from is not None and positional_index >= keyword_only_from:
|
|
param_name = None
|
|
if 'param_names' in function_info and positional_index < len(function_info['param_names']):
|
|
param_name = function_info['param_names'][positional_index]
|
|
if param_name:
|
|
errors.append(f"函数 {function_name} 的第{positional_index+1}个参数必须使用命名参数 '{param_name}='")
|
|
else:
|
|
errors.append(f"函数 {function_name} 的第{positional_index+1}个参数必须使用命名参数")
|
|
else:
|
|
# 验证位置参数的类型
|
|
if positional_index < len(function_info['arg_types']):
|
|
expected_type = function_info['arg_types'][positional_index]
|
|
arg_errors = self._validate_arg_type(arg, expected_type, positional_index, function_name, is_in_group_arg)
|
|
errors.extend(arg_errors)
|
|
positional_index += 1
|
|
|
|
return errors
|
|
|
|
def _validate_arg_type(self, arg: ASTNode, expected_type: str, arg_index: int, function_name: str, is_in_group_arg: bool = False) -> List[str]:
|
|
"""验证参数类型是否符合预期"""
|
|
errors = []
|
|
|
|
def _is_number_like(node: ASTNode) -> bool:
|
|
if node is None:
|
|
return False
|
|
if node.node_type == 'number':
|
|
return True
|
|
if node.node_type == 'unop' and isinstance(node.value, dict) and node.value.get('op') in {'-', '+'}:
|
|
if node.children and hasattr(node.children[0], 'node_type'):
|
|
return _is_number_like(node.children[0])
|
|
return False
|
|
|
|
# Unit compatibility check
|
|
# bucket()/group_cartesian_product() output a derived category (grouping key).
|
|
# It can only be consumed where a category/grouping key is expected.
|
|
if self._is_derived_category(arg) and expected_type != 'category':
|
|
errors.append(
|
|
f"Incompatible unit for input of \"{function_name}\" at index {arg_index}, expected \"Unit[]\", found \"Unit[Group:1]\""
|
|
)
|
|
return errors
|
|
|
|
# 首先检查是否是group类型字段，如果是则只能用于Group类型函数
|
|
# 但是如果当前函数是group_xxx或在group函数的参数链中，则允许使用
|
|
if arg.node_type == 'category' and arg.value in group_fields:
|
|
if not (function_name.startswith('group_') or is_in_group_arg):
|
|
errors.append(f"Group类型字段 '{arg.value}' 只能用于Group类型函数的参数中")
|
|
|
|
# 然后验证参数类型是否符合预期
|
|
if expected_type == 'expression':
|
|
# 表达式可以是任何有效的AST节点
|
|
pass
|
|
elif expected_type == 'number':
|
|
# 允许 -1 这类一元负号数字常量（解析为 unop(number)）
|
|
if not _is_number_like(arg):
|
|
errors.append(f"参数 {arg_index+1} 应该是一个数字，但得到 {arg.node_type}")
|
|
elif expected_type == 'boolean':
|
|
# 布尔值可以是 true/false 或数字（0/1）
|
|
if arg.node_type not in {'boolean', 'number'}:
|
|
errors.append(f"参数 {arg_index+1} 应该是一个布尔值（true/false 或 0/1），但得到 {arg.node_type}")
|
|
elif expected_type == 'field':
|
|
if arg.node_type != 'field' and arg.node_type != 'category':
|
|
# 允许field或category作为字段参数
|
|
errors.append(f"参数 {arg_index+1} 应该是一个字段，但得到 {arg.node_type}")
|
|
elif arg.node_type == 'field' and not self._is_valid_field(arg.value):
|
|
errors.append(f"无效的字段名: {arg.value}")
|
|
elif expected_type == 'category':
|
|
if not function_name.startswith('group_'):
|
|
# 非group函数的category参数必须是category类型且在valid_categories中
|
|
if arg.node_type != 'category':
|
|
errors.append(f"参数 {arg_index+1} 应该是一个类别，但得到 {arg.node_type}")
|
|
elif arg.value not in valid_categories:
|
|
errors.append(f"无效的类别: {arg.value}")
|
|
# group函数的category参数可以是任何类型（field、category等），不进行类型校验
|
|
|
|
return errors
|
|
|
|
def _infer_unit(self, node: ASTNode) -> str:
|
|
"""Infer the Unit kind of an AST node.
|
|
|
|
Returns:
|
|
'unit' - regular numeric time-series Unit[]
|
|
'scalar' - literals (numbers/booleans/strings)
|
|
'category' - category/grouping keys (industry/sector or derived via bucket/cartesian)
|
|
"""
|
|
if node is None:
|
|
return 'unit'
|
|
|
|
cache_key = id(node)
|
|
cached = self._unit_cache.get(cache_key)
|
|
if cached is not None:
|
|
return cached
|
|
|
|
unit = 'unit'
|
|
|
|
if node.node_type in {'number', 'boolean', 'string'}:
|
|
unit = 'scalar'
|
|
elif node.node_type in {'field', 'identifier'}:
|
|
unit = 'unit'
|
|
elif node.node_type == 'category':
|
|
unit = 'category'
|
|
elif node.node_type in {'unop', 'binop'}:
|
|
child_units = [self._infer_unit(child) for child in node.children if hasattr(child, 'node_type')]
|
|
unit = 'category' if 'category' in child_units else 'unit'
|
|
elif node.node_type == 'function':
|
|
fname = node.value
|
|
if fname in {'bucket', 'group_cartesian_product'}:
|
|
unit = 'category'
|
|
else:
|
|
first_arg = None
|
|
for child in node.children:
|
|
if isinstance(child, dict):
|
|
if child.get('type') == 'positional':
|
|
first_arg = child.get('value')
|
|
break
|
|
else:
|
|
first_arg = child
|
|
break
|
|
if hasattr(first_arg, 'node_type'):
|
|
unit = self._infer_unit(first_arg)
|
|
else:
|
|
unit = 'unit'
|
|
|
|
self._unit_cache[cache_key] = unit
|
|
return unit
|
|
|
|
def _is_derived_category(self, node: ASTNode) -> bool:
|
|
"""Return True if node is a derived category/grouping key (e.g., bucket/cartesian output)."""
|
|
if node is None:
|
|
return False
|
|
|
|
cache_key = id(node)
|
|
cached = self._derived_category_cache.get(cache_key)
|
|
if cached is not None:
|
|
return cached
|
|
|
|
derived = False
|
|
if node.node_type == 'function':
|
|
if node.value in {'bucket', 'group_cartesian_product'}:
|
|
derived = True
|
|
else:
|
|
function_info = supported_functions.get(node.value, {})
|
|
arg_types = function_info.get('arg_types', [])
|
|
param_names = function_info.get('param_names', [])
|
|
|
|
positional_index = 0
|
|
for child in node.children:
|
|
if isinstance(child, dict):
|
|
if child.get('type') == 'named':
|
|
name = child.get('name')
|
|
value = child.get('value')
|
|
|
|
expected_type = None
|
|
if name in param_names:
|
|
param_index = param_names.index(name)
|
|
if param_index < len(arg_types):
|
|
expected_type = arg_types[param_index]
|
|
|
|
if expected_type == 'category':
|
|
continue
|
|
|
|
if self._is_derived_category(value):
|
|
derived = True
|
|
break
|
|
elif child.get('type') == 'positional':
|
|
value = child.get('value')
|
|
expected_type = arg_types[positional_index] if positional_index < len(arg_types) else None
|
|
|
|
if expected_type != 'category' and self._is_derived_category(value):
|
|
derived = True
|
|
break
|
|
positional_index += 1
|
|
else:
|
|
expected_type = arg_types[positional_index] if positional_index < len(arg_types) else None
|
|
if expected_type != 'category' and self._is_derived_category(child):
|
|
derived = True
|
|
break
|
|
positional_index += 1
|
|
elif node.node_type in {'unop', 'binop'}:
|
|
derived = any(
|
|
self._is_derived_category(child)
|
|
for child in node.children
|
|
if hasattr(child, 'node_type')
|
|
)
|
|
|
|
self._derived_category_cache[cache_key] = derived
|
|
return derived
|
|
|
|
def _validate_add(self, args: List[Any], is_in_group_arg: bool = False) -> List[str]:
|
|
"""Validate add(x, y, ..., filter=false).
|
|
|
|
Rules:
|
|
- At least 2 positional expression terms.
|
|
- Optional filter flag can be provided as:
|
|
- named argument: filter=<boolean>
|
|
- last positional argument: <boolean> or 0/1
|
|
"""
|
|
errors: List[str] = []
|
|
|
|
if len(args) < 2:
|
|
return [f"函数 add 需要至少 2 个参数，但只提供了 {len(args)}"]
|
|
|
|
named_filter_nodes: List[ASTNode] = []
|
|
positional_nodes: List[ASTNode] = []
|
|
|
|
for arg in args:
|
|
if isinstance(arg, dict) and arg.get('type') == 'named':
|
|
name = arg.get('name')
|
|
value = arg.get('value')
|
|
if name != 'filter':
|
|
errors.append(f"函数 add 不存在参数 '{name}'")
|
|
continue
|
|
if not hasattr(value, 'node_type'):
|
|
errors.append("函数 add 的参数 filter 格式错误")
|
|
continue
|
|
named_filter_nodes.append(value)
|
|
elif isinstance(arg, dict) and arg.get('type') == 'positional':
|
|
value = arg.get('value')
|
|
if hasattr(value, 'node_type'):
|
|
positional_nodes.append(value)
|
|
else:
|
|
errors.append("函数 add 的位置参数格式错误")
|
|
elif hasattr(arg, 'node_type'):
|
|
positional_nodes.append(arg)
|
|
else:
|
|
errors.append("函数 add 的参数格式错误")
|
|
|
|
if len(named_filter_nodes) > 1:
|
|
errors.append("函数 add 的参数 'filter' 只能出现一次")
|
|
|
|
positional_filter_node: Optional[ASTNode] = None
|
|
# Only infer a positional filter flag when:
|
|
# - no named filter is provided
|
|
# - there are at least 3 positional args (x, y, filter)
|
|
# - the last arg is boolean or numeric 0/1
|
|
if not named_filter_nodes and len(positional_nodes) >= 3:
|
|
last = positional_nodes[-1]
|
|
if last.node_type == 'boolean' or (last.node_type == 'number' and last.value in {0, 1}):
|
|
positional_filter_node = positional_nodes.pop()
|
|
|
|
if len(positional_nodes) < 2:
|
|
errors.append(f"函数 add 需要至少 2 个输入项（不含filter），但只提供了 {len(positional_nodes)}")
|
|
|
|
for idx, node in enumerate(positional_nodes):
|
|
errors.extend(self._validate_arg_type(node, 'expression', idx, 'add', is_in_group_arg))
|
|
|
|
if positional_filter_node is not None and named_filter_nodes:
|
|
errors.append("函数 add 的 filter 不能同时用位置参数和命名参数传递")
|
|
if positional_filter_node is not None:
|
|
errors.extend(self._validate_arg_type(positional_filter_node, 'boolean', len(positional_nodes), 'add', is_in_group_arg))
|
|
if named_filter_nodes:
|
|
errors.extend(self._validate_arg_type(named_filter_nodes[0], 'boolean', len(positional_nodes), 'add', is_in_group_arg))
|
|
|
|
return errors
|
|
|
|
def validate_ast(self, ast: Optional[ASTNode], is_in_group_arg: bool = False) -> List[str]:
|
|
"""递归验证抽象语法树"""
|
|
if not ast:
|
|
return ["无法解析表达式"]
|
|
|
|
errors = []
|
|
|
|
# 根据节点类型进行验证
|
|
if ast.node_type == 'function':
|
|
# 检查当前函数是否是group函数
|
|
is_group_function = ast.value.startswith('group_')
|
|
# 确定当前是否在group函数的参数链中
|
|
current_in_group_arg = is_in_group_arg or is_group_function
|
|
# 验证函数
|
|
function_errors = self.validate_function(ast, current_in_group_arg)
|
|
errors.extend(function_errors)
|
|
|
|
# 递归验证子节点时使用current_in_group_arg
|
|
for child in ast.children:
|
|
if isinstance(child, dict):
|
|
# 命名参数，验证其值
|
|
if 'value' in child and hasattr(child['value'], 'node_type'):
|
|
child_errors = self.validate_ast(child['value'], current_in_group_arg)
|
|
errors.extend(child_errors)
|
|
elif hasattr(child, 'node_type'):
|
|
child_errors = self.validate_ast(child, current_in_group_arg)
|
|
errors.extend(child_errors)
|
|
elif ast.node_type in ['unop', 'binop']:
|
|
# 对操作符的子节点进行验证
|
|
for child in ast.children:
|
|
if hasattr(child, 'node_type'):
|
|
child_errors = self.validate_ast(child, is_in_group_arg)
|
|
errors.extend(child_errors)
|
|
elif ast.node_type == 'field':
|
|
# 验证字段名
|
|
if not self._is_valid_field(ast.value):
|
|
errors.append(f"无效的字段名: {ast.value}")
|
|
else:
|
|
# 递归验证子节点
|
|
for child in ast.children:
|
|
if isinstance(child, dict):
|
|
# 命名参数，验证其值
|
|
if 'value' in child and hasattr(child['value'], 'node_type'):
|
|
child_errors = self.validate_ast(child['value'], is_in_group_arg)
|
|
errors.extend(child_errors)
|
|
elif hasattr(child, 'node_type'):
|
|
child_errors = self.validate_ast(child, is_in_group_arg)
|
|
errors.extend(child_errors)
|
|
|
|
return errors
|
|
|
|
def _process_semicolon_expression(self, expression: str) -> Tuple[bool, str]:
|
|
"""处理带有分号的表达式，将其转换为不带分号的简化形式
|
|
|
|
Args:
|
|
expression: 要处理的表达式字符串
|
|
|
|
Returns:
|
|
Tuple[bool, str]: (是否成功, 转换后的表达式或错误信息)
|
|
"""
|
|
def _top_level_equals_positions(stmt: str) -> List[int]:
|
|
"""返回所有“顶层赋值”等号位置。
|
|
|
|
仅统计括号外（()[]{})、引号外、且不属于比较操作符(==,!=,<=,>=)的 '='。
|
|
这样可以避免把关键字参数（如 rettype=0）误判为赋值语句。
|
|
"""
|
|
positions: List[int] = []
|
|
paren_depth = 0
|
|
bracket_depth = 0
|
|
brace_depth = 0
|
|
in_single_quote = False
|
|
in_double_quote = False
|
|
escape = False
|
|
|
|
for i, ch in enumerate(stmt):
|
|
if escape:
|
|
escape = False
|
|
continue
|
|
if ch == '\\':
|
|
escape = True
|
|
continue
|
|
|
|
if in_single_quote:
|
|
if ch == "'":
|
|
in_single_quote = False
|
|
continue
|
|
if in_double_quote:
|
|
if ch == '"':
|
|
in_double_quote = False
|
|
continue
|
|
|
|
if ch == "'":
|
|
in_single_quote = True
|
|
continue
|
|
if ch == '"':
|
|
in_double_quote = True
|
|
continue
|
|
|
|
if ch == '(':
|
|
paren_depth += 1
|
|
continue
|
|
if ch == ')':
|
|
paren_depth = max(0, paren_depth - 1)
|
|
continue
|
|
if ch == '[':
|
|
bracket_depth += 1
|
|
continue
|
|
if ch == ']':
|
|
bracket_depth = max(0, bracket_depth - 1)
|
|
continue
|
|
if ch == '{':
|
|
brace_depth += 1
|
|
continue
|
|
if ch == '}':
|
|
brace_depth = max(0, brace_depth - 1)
|
|
continue
|
|
|
|
if paren_depth or bracket_depth or brace_depth:
|
|
continue
|
|
|
|
if ch != '=':
|
|
continue
|
|
|
|
prev_ch = stmt[i - 1] if i > 0 else ''
|
|
next_ch = stmt[i + 1] if i + 1 < len(stmt) else ''
|
|
if prev_ch in ['=', '!', '<', '>'] or next_ch == '=':
|
|
continue
|
|
|
|
positions.append(i)
|
|
|
|
return positions
|
|
|
|
def _keyword_arg_names(stmt: str):
|
|
"""提取函数调用中的命名参数名（如 rettype=0 中的 rettype）。
|
|
|
|
只收集括号/中括号/大括号内部出现的 name= 形式，避免把脚本级赋值误当作命名参数。
|
|
"""
|
|
names = set()
|
|
paren_depth = 0
|
|
bracket_depth = 0
|
|
brace_depth = 0
|
|
in_single_quote = False
|
|
in_double_quote = False
|
|
escape = False
|
|
|
|
i = 0
|
|
while i < len(stmt):
|
|
ch = stmt[i]
|
|
|
|
if escape:
|
|
escape = False
|
|
i += 1
|
|
continue
|
|
if ch == '\\':
|
|
escape = True
|
|
i += 1
|
|
continue
|
|
|
|
if in_single_quote:
|
|
if ch == "'":
|
|
in_single_quote = False
|
|
i += 1
|
|
continue
|
|
if in_double_quote:
|
|
if ch == '"':
|
|
in_double_quote = False
|
|
i += 1
|
|
continue
|
|
|
|
if ch == "'":
|
|
in_single_quote = True
|
|
i += 1
|
|
continue
|
|
if ch == '"':
|
|
in_double_quote = True
|
|
i += 1
|
|
continue
|
|
|
|
if ch == '(':
|
|
paren_depth += 1
|
|
i += 1
|
|
continue
|
|
if ch == ')':
|
|
paren_depth = max(0, paren_depth - 1)
|
|
i += 1
|
|
continue
|
|
if ch == '[':
|
|
bracket_depth += 1
|
|
i += 1
|
|
continue
|
|
if ch == ']':
|
|
bracket_depth = max(0, bracket_depth - 1)
|
|
i += 1
|
|
continue
|
|
if ch == '{':
|
|
brace_depth += 1
|
|
i += 1
|
|
continue
|
|
if ch == '}':
|
|
brace_depth = max(0, brace_depth - 1)
|
|
i += 1
|
|
continue
|
|
|
|
inside_container = bool(paren_depth or bracket_depth or brace_depth)
|
|
|
|
if inside_container and (ch.isalpha() or ch == '_'):
|
|
start = i
|
|
i += 1
|
|
while i < len(stmt) and (stmt[i].isalnum() or stmt[i] == '_'):
|
|
i += 1
|
|
name = stmt[start:i]
|
|
|
|
j = i
|
|
while j < len(stmt) and stmt[j].isspace():
|
|
j += 1
|
|
|
|
if j < len(stmt) and stmt[j] == '=':
|
|
next_ch = stmt[j + 1] if j + 1 < len(stmt) else ''
|
|
if next_ch != '=':
|
|
names.add(name.lower())
|
|
continue
|
|
|
|
i += 1
|
|
|
|
return names
|
|
|
|
# 检查表达式是否以分号结尾
|
|
if expression.strip().endswith(';'):
|
|
return False, "表达式不能以分号结尾"
|
|
|
|
# 分割表达式为语句列表
|
|
statements = [stmt.strip() for stmt in expression.split(';') if stmt.strip()]
|
|
if not statements:
|
|
return False, "表达式不能为空"
|
|
|
|
# 存储变量赋值
|
|
variables = {}
|
|
|
|
# 处理每个赋值语句（除了最后一个）
|
|
for i, stmt in enumerate(statements[:-1]):
|
|
eq_positions = _top_level_equals_positions(stmt)
|
|
if not eq_positions:
|
|
return False, f"第{i+1}个语句必须是赋值语句（使用=符号）"
|
|
if len(eq_positions) > 1:
|
|
return False, f"第{i+1}个语句只能包含一个赋值符号（=）"
|
|
|
|
real_equals_pos = eq_positions[0]
|
|
|
|
# 分割变量名和值
|
|
var_name = stmt[:real_equals_pos].strip()
|
|
var_value = stmt[real_equals_pos + 1:].strip()
|
|
|
|
# 检查变量名是否有效
|
|
if not re.match(r'^[a-zA-Z_][a-zA-Z0-9_]*$', var_name):
|
|
return False, f"第{i+1}个语句的变量名'{var_name}'无效，只能包含字母、数字和下划线，且不能以数字开头"
|
|
|
|
var_name_lower = var_name.lower() # 变量名不区分大小写
|
|
|
|
# 检查变量名是否在后续表达式中使用
|
|
# 这里不需要，因为后面的表达式会检查
|
|
|
|
# 检查变量值中使用的变量是否已经定义
|
|
# 简单检查：提取所有可能的变量名
|
|
kw_names = _keyword_arg_names(var_value)
|
|
used_vars = re.findall(r'\b[a-zA-Z_][a-zA-Z0-9_]*\b', var_value)
|
|
for used_var in used_vars:
|
|
used_var_lower = used_var.lower()
|
|
if used_var_lower in kw_names:
|
|
continue
|
|
if used_var_lower not in variables:
|
|
# 检查是否是函数名
|
|
if used_var not in supported_functions:
|
|
# 对于单个字母或简单单词，不自动视为字段名，要求先定义
|
|
if len(used_var) <= 2:
|
|
return False, f"第{i+1}个语句中使用的变量'{used_var}'未在之前定义"
|
|
# 对于较长的字段名，仍然允许作为字段名
|
|
elif not self._is_valid_field(used_var):
|
|
return False, f"第{i+1}个语句中使用的变量'{used_var}'未在之前定义"
|
|
|
|
# 将之前定义的变量替换到当前值中
|
|
for existing_var, existing_val in variables.items():
|
|
# 使用单词边界匹配，避免替换到其他单词的一部分
|
|
var_value = re.sub(rf'\b{existing_var}\b', existing_val, var_value)
|
|
|
|
# 存储变量
|
|
variables[var_name_lower] = var_value
|
|
|
|
# 处理最后一个语句（实际的表达式）
|
|
final_stmt = statements[-1]
|
|
|
|
# 检查最后一个语句是否是赋值语句
|
|
if _top_level_equals_positions(final_stmt):
|
|
return False, "最后一个语句不能是赋值语句"
|
|
|
|
# 检查最后一个语句中使用的变量是否已经定义
|
|
kw_names = _keyword_arg_names(final_stmt)
|
|
used_vars = re.findall(r'\b[a-zA-Z_][a-zA-Z0-9_]*\b', final_stmt)
|
|
for used_var in used_vars:
|
|
used_var_lower = used_var.lower()
|
|
if used_var_lower in kw_names:
|
|
continue
|
|
if used_var_lower not in variables:
|
|
# 检查是否是函数名
|
|
if used_var not in supported_functions:
|
|
# 对于单个字母或简单单词，不自动视为字段名，要求先定义
|
|
if len(used_var) <= 2:
|
|
return False, f"最后一个语句中使用的变量'{used_var}'未在之前定义"
|
|
|
|
# 允许直接使用字段名/类别名（如 close/industry）
|
|
if self._is_valid_field(used_var) or used_var_lower in valid_categories or used_var_lower in group_fields:
|
|
continue
|
|
|
|
return False, f"最后一个语句中使用的变量'{used_var}'未在之前定义"
|
|
|
|
# 将变量替换到最后一个表达式中
|
|
final_expr = final_stmt
|
|
for var_name, var_value in variables.items():
|
|
final_expr = re.sub(rf'\b{var_name}\b', var_value, final_expr)
|
|
|
|
return True, final_expr
|
|
|
|
def check_expression(self, expression: str) -> Dict[str, Any]:
|
|
"""
|
|
检查表达式格式是否正确
|
|
|
|
Args:
|
|
expression: 要验证的表达式字符串
|
|
|
|
Returns:
|
|
包含验证结果的字典
|
|
"""
|
|
# 重置错误列表
|
|
self.errors = []
|
|
# Reset unit inference cache for this expression
|
|
self._unit_cache = {}
|
|
self._derived_category_cache = {}
|
|
|
|
try:
|
|
expression = expression.strip()
|
|
if not expression:
|
|
return {
|
|
'valid': False,
|
|
'errors': ['表达式不能为空'],
|
|
'tokens': [],
|
|
'ast': None
|
|
}
|
|
|
|
# 处理带有分号的表达式
|
|
if ';' in expression:
|
|
success, result = self._process_semicolon_expression(expression)
|
|
if not success:
|
|
return {
|
|
'valid': False,
|
|
'errors': [result],
|
|
'tokens': [],
|
|
'ast': None
|
|
}
|
|
expression = result
|
|
|
|
# 重置词法分析器的行号
|
|
self.lexer.lineno = 1
|
|
|
|
# 词法分析（用于调试）
|
|
self.lexer.input(expression)
|
|
tokens = []
|
|
# 调试：打印识别的标记
|
|
# print(f"\n调试 - 表达式: {expression}")
|
|
# print("识别的标记:")
|
|
for token in self.lexer:
|
|
# print(f" - 类型: {token.type}, 值: '{token.value}', 位置: {token.lexpos}")
|
|
tokens.append(token)
|
|
|
|
# 重新设置词法分析器的输入，以便语法分析器使用
|
|
self.lexer.input(expression)
|
|
self.lexer.lineno = 1
|
|
|
|
# 语法分析
|
|
ast = self.parser.parse(expression, lexer=self.lexer)
|
|
|
|
# 验证AST
|
|
validation_errors = self.validate_ast(ast)
|
|
|
|
# 合并所有错误
|
|
all_errors = self.errors + validation_errors
|
|
|
|
# 检查括号是否匹配
|
|
bracket_count = 0
|
|
for char in expression:
|
|
if char == '(':
|
|
bracket_count += 1
|
|
elif char == ')':
|
|
bracket_count -= 1
|
|
if bracket_count < 0:
|
|
all_errors.append("括号不匹配: 右括号过多")
|
|
break
|
|
if bracket_count > 0:
|
|
all_errors.append("括号不匹配: 左括号过多")
|
|
|
|
return {
|
|
'valid': len(all_errors) == 0,
|
|
'errors': all_errors,
|
|
'tokens': tokens,
|
|
'ast': ast
|
|
}
|
|
except Exception as e:
|
|
return {
|
|
'valid': False,
|
|
'errors': [f"解析错误: {str(e)}"],
|
|
'tokens': [],
|
|
'ast': None
|
|
}
|
|
|
|
|
|
|
|
|