"""正则表达式筛选器.
按特征名称正则表达式筛选特征。
**参考样例**
>>> from hscredit.core.selectors import RegexSelector
>>> import pandas as pd
>>> X = pd.DataFrame({
... 'income_1': [1, 2, 3],
... 'income_2': [4, 5, 6],
... 'age': [1, 2, 3]
... })
>>> # 选择以income开头的特征
>>> selector = RegexSelector(pattern='^income')
>>> selector.fit(X)
"""
import re
from typing import Union, List, Optional, Dict, Any
import numpy as np
import pandas as pd
from .base import BaseFeatureSelector
from ._statistical_utils import record_conditions
def _matches_regex_feature(task):
"""判断单个特征名是否匹配正则表达式。"""
feature, pattern, flags = task
return feature, re.search(pattern, str(feature), flags=flags) is not None
[文档]
class RegexSelector(BaseFeatureSelector):
"""正则表达式筛选器.
按特征名称的正则表达式匹配筛选特征。
**参数**
:param pattern: 正则表达式模式
:param invert: 是否反转匹配（True 表示排除匹配的特征，保留不匹配的），默认为 False
:param flags: 正则表达式标志，默认为 0
**参考样例**
::
>>> from hscredit.core.selectors import RegexSelector
>>> import pandas as pd
>>> X = pd.DataFrame({
... 'income_1': [1, 2, 3],
... 'income_2': [4, 5, 6],
... 'age': [1, 2, 3]
... })
>>> # 选择以income开头的特征
>>> selector = RegexSelector(pattern='^income')
>>> selector.fit(X)
"""
method_name = "正则筛选"
def __init__(
self,
pattern: str,
invert: bool = False,
flags: int = 0,
target: str = "target",
include: Optional[List[str]] = None,
exclude: Optional[List[str]] = None,
force_drop: Optional[List[str]] = None,
n_jobs: Optional[Union[int, float]] = -1,
binner: Optional[Any] = None,
binning_params: Optional[Dict[str, Any]] = None,
parallel_backend: Optional[str] = None,
parallel_config: Optional[Dict[str, Any]] = None,
target_rm: bool = False,
):
"""初始化筛选器；默认透传已有目标列，仅target_rm=True移除。"""
super().__init__(
target=target,
include=include,
exclude=exclude,
force_drop=force_drop,
n_jobs=n_jobs,
binner=binner,
binning_params=binning_params,
parallel_backend=parallel_backend,
parallel_config=parallel_config,
target_rm=target_rm,
)
self.pattern = pattern
self.invert = invert
self.flags = flags
def _check_input(self, X, y=None):
if not isinstance(self.invert, (bool, np.bool_)):
raise ValueError("invert 必须是布尔值")
try:
re.compile(self.pattern, flags=self.flags)
except (TypeError, ValueError, re.error) as exc:
raise ValueError(f"正则表达式配置无效: {exc}") from exc
return super()._check_input(X, y)
def _fit_impl(
self,
X: pd.DataFrame,
y: Optional[Union[pd.Series, np.ndarray]],
) -> None:
"""拟合正则筛选器。
:param X: 输入特征DataFrame
:param y: 目标变量（此筛选器不需要）
"""
self._get_feature_names(X)
self._validate_parallel_configuration()
# 字段名匹配不需要访问样本值，避免额外全表缺失扫描。
self.total_counts_ = pd.Series(len(X), index=X.columns, dtype=np.int64)
self.threshold_ = None
self.score_name_, self.score_direction_ = "正则匹配", "满足条件"
matches = np.array(
[_matches_regex_feature((col, self.pattern, self.flags))[1] for col in X.columns],
dtype=bool,
)
if self.invert:
selected_cols = X.columns[~matches].tolist()
self.scores_ = pd.Series((~matches).astype(int), index=X.columns)
else:
selected_cols = X.columns[matches].tolist()
self.scores_ = pd.Series(matches.astype(int), index=X.columns)
self.selected_features_ = selected_cols
self.matches_ = pd.Series(matches, index=X.columns)
record_conditions(self, X.columns, 正则策略达标=self.scores_.astype(bool))
self._drop_reason = f"特征名{'匹配排除' if self.invert else '不匹配'}正则表达式: {self.pattern}"