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feat(training): 实现 DateSplitter 数据划分器

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- 新增 DateSplitter 类，支持基于日期范围的一次性训练/测试划分
- 实现日期格式验证和日期范围逻辑检查
- 支持自定义日期列名参数
- 添加完整的单元测试（12个测试用例）
- 在 components 模块导出 DateSplitter
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liaozhaorun
2026-03-03 22:07:45 +08:00
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commit f48b307ad2
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src/training/components/__init__.py
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# 基础抽象类
from src.training.components.base import BaseModel, BaseProcessor
# 数据划分器
from src.training.components.splitters import DateSplitter
__all__ = [
"BaseModel",
"BaseProcessor",
"DateSplitter",
]

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src/training/components/splitters.py Normal file
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"""数据划分器
提供基于日期范围的数据划分功能，支持一次性训练/测试划分。
"""
from typing import Tuple
import polars as pl
class DateSplitter:
"""基于日期范围的一次性划分
将数据按日期划分为训练集和测试集，不滚动。
示例:
train_start: "20200101", train_end: "20221231" (训练集：3年)
test_start: "20230101", test_end: "20231231" (测试集：1年)
特点:
- 一次性划分，不滚动
- 训练集和测试集互不重叠
- 基于实际日期范围，而非行数
Attributes:
train_start: 训练期开始日期，格式 "YYYYMMDD"
train_end: 训练期结束日期，格式 "YYYYMMDD"
test_start: 测试期开始日期，格式 "YYYYMMDD"
test_end: 测试期结束日期，格式 "YYYYMMDD"
"""
def __init__(
self,
train_start: str,
train_end: str,
test_start: str,
test_end: str,
):
"""初始化日期划分器
Args:
train_start: 训练期开始日期 "YYYYMMDD"
train_end: 训练期结束日期 "YYYYMMDD"
test_start: 测试期开始日期 "YYYYMMDD"
test_end: 测试期结束日期 "YYYYMMDD"
Raises:
ValueError: 日期格式错误或日期范围无效
"""
# 验证日期格式（简单的长度检查）
for name, value in [
("train_start", train_start),
("train_end", train_end),
("test_start", test_start),
("test_end", test_end),
]:
if not isinstance(value, str) or len(value) != 8:
raise ValueError(
f"{name} 必须是格式为 'YYYYMMDD' 的8位字符串，得到: {value}"
)
# 验证日期范围逻辑
if train_start > train_end:
raise ValueError(
f"train_start ({train_start}) 必须早于或等于 train_end ({train_end})"
)
if test_start > test_end:
raise ValueError(
f"test_start ({test_start}) 必须早于或等于 test_end ({test_end})"
)
if test_start <= train_end:
raise ValueError(
f"测试集开始日期 ({test_start}) 必须晚于训练集结束日期 ({train_end})，"
"以确保训练集和测试集不重叠"
)
self.train_start = train_start
self.train_end = train_end
self.test_start = test_start
self.test_end = test_end
def split(
self, data: pl.DataFrame, date_col: str = "trade_date"
) -> Tuple[pl.DataFrame, pl.DataFrame]:
"""划分数据为训练集和测试集
Args:
data: 输入数据，必须包含日期列
date_col: 日期列名，默认为 "trade_date"
Returns:
(train_data, test_data) 元组
Raises:
ValueError: 数据中不包含指定的日期列
"""
if date_col not in data.columns:
raise ValueError(f"数据中不包含列 '{date_col}'，可用列: {data.columns}")
# 筛选训练集数据
train_data = data.filter(
(pl.col(date_col) >= self.train_start)
& (pl.col(date_col) <= self.train_end)
)
# 筛选测试集数据
test_data = data.filter(
(pl.col(date_col) >= self.test_start) & (pl.col(date_col) <= self.test_end)
)
return train_data, test_data
def __repr__(self) -> str:
"""返回划分器的字符串表示"""
return (
f"DateSplitter("
f"train_start='{self.train_start}', "
f"train_end='{self.train_end}', "
f"test_start='{self.test_start}', "
f"test_end='{self.test_end}'"
f")"
)

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tests/training/test_splitters.py Normal file
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"""测试 DateSplitter 数据划分器
验证一次性日期划分功能。
"""
import pytest
import polars as pl
from src.training.components.splitters import DateSplitter
class TestDateSplitter:
"""DateSplitter 测试类"""
def test_initialization_success(self):
"""测试正常初始化"""
splitter = DateSplitter(
train_start="20200101",
train_end="20221231",
test_start="20230101",
test_end="20231231",
)
assert splitter.train_start == "20200101"
assert splitter.train_end == "20221231"
assert splitter.test_start == "20230101"
assert splitter.test_end == "20231231"
def test_invalid_date_format(self):
"""测试无效的日期格式"""
with pytest.raises(ValueError, match="必须是格式为 'YYYYMMDD' 的8位字符串"):
DateSplitter(
train_start="2020-01-01", # 错误格式
train_end="20221231",
test_start="20230101",
test_end="20231231",
)
def test_train_start_after_train_end(self):
"""测试训练集开始日期晚于结束日期"""
with pytest.raises(ValueError, match="train_start.*必须早于或等于 train_end"):
DateSplitter(
train_start="20231231",
train_end="20200101",
test_start="20230101",
test_end="20231231",
)
def test_test_start_after_test_end(self):
"""测试测试集开始日期晚于结束日期"""
with pytest.raises(ValueError, match="test_start.*必须早于或等于 test_end"):
DateSplitter(
train_start="20200101",
train_end="20221231",
test_start="20231231",
test_end="20230101",
)
def test_overlapping_dates(self):
"""测试训练集和测试集日期重叠"""
with pytest.raises(ValueError, match="必须晚于训练集结束日期"):
DateSplitter(
train_start="20200101",
train_end="20221231",
test_start="20220601", # 在训练集范围内
test_end="20231231",
)
def test_split_success(self):
"""测试正常划分数据"""
# 创建测试数据
data = pl.DataFrame(
{
"ts_code": [
"000001.SZ",
"000002.SZ",
"000003.SZ",
"000004.SZ",
"000005.SZ",
"000006.SZ",
],
"trade_date": [
"20200101",
"20211231",
"20221231",
"20230101",
"20230601",
"20231231",
],
"value": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
}
)
splitter = DateSplitter(
train_start="20200101",
train_end="20221231",
test_start="20230101",
test_end="20231231",
)
train_data, test_data = splitter.split(data)
# 验证训练集
assert len(train_data) == 3
assert train_data["trade_date"].to_list() == [
"20200101",
"20211231",
"20221231",
]
# 验证测试集
assert len(test_data) == 3
assert test_data["trade_date"].to_list() == ["20230101", "20230601", "20231231"]
def test_split_no_matching_train_data(self):
"""测试训练集无匹配数据"""
data = pl.DataFrame(
{
"ts_code": ["000001.SZ", "000002.SZ"],
"trade_date": ["20230101", "20231231"],
"value": [1.0, 2.0],
}
)
splitter = DateSplitter(
train_start="20200101",
train_end="20221231",
test_start="20230101",
test_end="20231231",
)
train_data, test_data = splitter.split(data)
# 训练集应该为空
assert len(train_data) == 0
# 测试集应该有数据
assert len(test_data) == 2
def test_split_no_matching_test_data(self):
"""测试测试集无匹配数据"""
data = pl.DataFrame(
{
"ts_code": ["000001.SZ", "000002.SZ"],
"trade_date": ["20200101", "20211231"],
"value": [1.0, 2.0],
}
)
splitter = DateSplitter(
train_start="20200101",
train_end="20221231",
test_start="20230101",
test_end="20231231",
)
train_data, test_data = splitter.split(data)
# 训练集应该有数据
assert len(train_data) == 2
# 测试集应该为空
assert len(test_data) == 0
def test_split_with_custom_date_col(self):
"""测试使用自定义日期列名"""
data = pl.DataFrame(
{
"ts_code": ["000001.SZ", "000002.SZ", "000003.SZ"],
"date": ["20200101", "20211231", "20230101"],
"value": [1.0, 2.0, 3.0],
}
)
splitter = DateSplitter(
train_start="20200101",
train_end="20221231",
test_start="20230101",
test_end="20231231",
)
train_data, test_data = splitter.split(data, date_col="date")
assert len(train_data) == 2
assert len(test_data) == 1
def test_split_missing_date_column(self):
"""测试数据缺少日期列"""
data = pl.DataFrame(
{
"ts_code": ["000001.SZ"],
"value": [1.0],
}
)
splitter = DateSplitter(
train_start="20200101",
train_end="20221231",
test_start="20230101",
test_end="20231231",
)
with pytest.raises(ValueError, match="数据中不包含列 'trade_date'"):
splitter.split(data)
def test_repr(self):
"""测试 __repr__ 方法"""
splitter = DateSplitter(
train_start="20200101",
train_end="20221231",
test_start="20230101",
test_end="20231231",
)
repr_str = repr(splitter)
assert "DateSplitter" in repr_str
assert "20200101" in repr_str
assert "20221231" in repr_str
assert "20230101" in repr_str
assert "20231231" in repr_str
def test_edge_case_same_day_train(self):
"""测试训练集为单日"""
data = pl.DataFrame(
{
"ts_code": ["000001.SZ"],
"trade_date": ["20200101"],
"value": [1.0],
}
)
splitter = DateSplitter(
train_start="20200101",
train_end="20200101",
test_start="20200102",
test_end="20200102",
)
train_data, test_data = splitter.split(data)
assert len(train_data) == 1
assert len(test_data) == 0
if __name__ == "__main__":
pytest.main([__file__, "-v"])