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feat(training): 添加训练模块基础架构

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实现 Commit 1：训练模块基础架构

新增文件：

- src/training/__init__.py - 主模块导出

- src/training/components/__init__.py - components 子模块导出

- src/training/components/base.py - BaseModel/BaseProcessor 抽象基类

- src/training/registry.py - 模型和处理器注册中心

- tests/training/test_base.py - 基础架构单元测试

功能特性：

- BaseModel: 提供 fit, predict, feature_importance, save/load 接口

- BaseProcessor: 提供 fit, transform, fit_transform 接口

- ModelRegistry/ProcessorRegistry: 支持装饰器风格组件注册

- 支持即插即用的组件扩展机制

Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-opencode)

Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
This commit is contained in:
liaozhaorun
2026-03-03 21:55:39 +08:00
parent 12ddb19b2e
commit 472b2b665a
18 changed files with 694 additions and 3997 deletions
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87
src/pipeline/__init__.py
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"""ProStock ML Pipeline 组件库
提供组件化、低耦合、插件式的机器学习流水线组件。
包括处理器、模型、划分策略等可复用组件。
示例:
>>> from src.pipeline import (
... PluginRegistry, ProcessingPipeline,
... PipelineStage, BaseProcessor
... )
>>> # 获取注册的处理器
>>> scaler_class = PluginRegistry.get_processor("standard_scaler")
>>> scaler = scaler_class()
>>> # 创建处理流水线
>>> pipeline = ProcessingPipeline([
... PluginRegistry.get_processor("dropna")(),
... PluginRegistry.get_processor("winsorizer")(lower=0.01, upper=0.99),
... PluginRegistry.get_processor("standard_scaler")(),
... ])
"""
# 导入核心抽象类和划分策略
from src.pipeline.core import (
PipelineStage,
TaskType,
BaseProcessor,
BaseModel,
BaseSplitter,
BaseMetric,
TimeSeriesSplit,
WalkForwardSplit,
ExpandingWindowSplit,
)
# 导入注册中心
from src.pipeline.registry import PluginRegistry
# 导入处理流水线
from src.pipeline.pipeline import ProcessingPipeline
# 导入并注册内置处理器
from src.pipeline.processors.processors import (
DropNAProcessor,
FillNAProcessor,
Winsorizer,
StandardScaler,
MinMaxScaler,
RankTransformer,
Neutralizer,
)
# 导入并注册内置模型
from src.pipeline.models.models import (
LightGBMModel,
CatBoostModel,
)
__all__ = [
# 核心抽象
"PipelineStage",
"TaskType",
"BaseProcessor",
"BaseModel",
"BaseSplitter",
"BaseMetric",
# 划分策略
"TimeSeriesSplit",
"WalkForwardSplit",
"ExpandingWindowSplit",
# 注册中心
"PluginRegistry",
# 处理流水线
"ProcessingPipeline",
# 处理器
"DropNAProcessor",
"FillNAProcessor",
"Winsorizer",
"StandardScaler",
"MinMaxScaler",
"RankTransformer",
"Neutralizer",
# 模型
"LightGBMModel",
"CatBoostModel",
]

30
src/pipeline/core/__init__.py
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"""核心模块导出"""
from src.pipeline.core.base import (
PipelineStage,
TaskType,
BaseProcessor,
BaseModel,
BaseSplitter,
BaseMetric,
)
from src.pipeline.core.splitter import (
TimeSeriesSplit,
WalkForwardSplit,
ExpandingWindowSplit,
)
__all__ = [
# 基础抽象
"PipelineStage",
"TaskType",
"BaseProcessor",
"BaseModel",
"BaseSplitter",
"BaseMetric",
# 划分策略
"TimeSeriesSplit",
"WalkForwardSplit",
"ExpandingWindowSplit",
]

351
src/pipeline/core/base.py
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"""模型训练框架核心抽象类
提供处理器、模型、划分策略和评估指标的基类定义。
"""
from abc import ABC, abstractmethod
from enum import Enum, auto
from typing import Any, Dict, Iterator, List, Optional, Tuple, Literal
import polars as pl
import numpy as np
# 任务类型
TaskType = Literal["classification", "regression", "ranking"]
class PipelineStage(Enum):
"""流水线阶段标记
用于标记处理器在哪些阶段生效，防止数据泄露。
Attributes:
ALL: 适用于所有阶段（训练、测试、验证）
TRAIN: 仅训练阶段
TEST: 仅测试阶段
VALIDATION: 仅验证阶段
"""
ALL = auto()
TRAIN = auto()
TEST = auto()
VALIDATION = auto()
class BaseProcessor(ABC):
"""数据处理器基类
所有数据处理器必须继承此类。关键特性是通过 stage 属性控制处理器在哪些阶段生效。
阶段标记规则：
- ALL: 训练和测试阶段都使用相同的参数
- TRAIN: 只在训练阶段计算参数（如分位数、均值等），测试阶段使用训练阶段学到的参数
- TEST: 只在测试阶段执行
"""
# 子类必须定义适用阶段
stage: PipelineStage = PipelineStage.ALL
def __init__(self, columns: Optional[List[str]] = None, **params):
"""初始化处理器
Args:
columns: 要处理的列，None表示所有数值列
**params: 处理器特定参数
"""
self.columns = columns
self.params = params
self._is_fitted = False
self._fitted_params: Dict[str, Any] = {}
@abstractmethod
def fit(self, data: pl.DataFrame) -> "BaseProcessor":
"""在训练数据上学习参数
此方法只在训练阶段调用一次。学习到的参数存储在 self._fitted_params 中。
Args:
data: 训练数据
Returns:
self (支持链式调用)
"""
pass
@abstractmethod
def transform(self, data: pl.DataFrame) -> pl.DataFrame:
"""转换数据
在训练和测试阶段都会被调用。使用 fit() 阶段学习到的参数进行转换。
Args:
data: 输入数据
Returns:
转换后的数据
"""
pass
def fit_transform(self, data: pl.DataFrame) -> pl.DataFrame:
"""先fit再transform的便捷方法
Args:
data: 训练数据
Returns:
转换后的数据
"""
return self.fit(data).transform(data)
def get_fitted_params(self) -> Dict[str, Any]:
"""获取学习到的参数（用于保存/加载）
Returns:
学习到的参数字典
"""
return self._fitted_params.copy()
def set_fitted_params(self, params: Dict[str, Any]) -> "BaseProcessor":
"""设置学习到的参数（用于从checkpoint恢复）
Args:
params: 参数字典
Returns:
self (支持链式调用)
"""
self._fitted_params = params.copy()
self._is_fitted = True
return self
class BaseModel(ABC):
"""机器学习模型基类
统一接口支持多种模型（LightGBM, CatBoost, XGBoost等）
和多种任务类型（分类、回归、排序）。
"""
def __init__(
self,
task_type: TaskType,
params: Optional[Dict[str, Any]] = None,
name: Optional[str] = None,
):
"""初始化模型
Args:
task_type: 任务类型 - "classification", "regression", "ranking"
params: 模型特定参数
name: 模型名称（用于日志和报告）
"""
self.task_type = task_type
self.params = params or {}
self.name = name or self.__class__.__name__
self._model: Any = None
self._is_fitted = False
@abstractmethod
def fit(
self,
X: pl.DataFrame,
y: pl.Series,
X_val: Optional[pl.DataFrame] = None,
y_val: Optional[pl.Series] = None,
**fit_params,
) -> "BaseModel":
"""训练模型
Args:
X: 特征数据
y: 目标变量
X_val: 验证集特征（可选）
y_val: 验证集目标（可选）
**fit_params: 额外的fit参数
Returns:
self (支持链式调用)
"""
pass
@abstractmethod
def predict(self, X: pl.DataFrame) -> np.ndarray:
"""预测
Args:
X: 特征数据
Returns:
预测结果数组
- classification: 类别标签或概率
- regression: 连续值
- ranking: 排序分数
"""
pass
def predict_proba(self, X: pl.DataFrame) -> np.ndarray:
"""预测概率（仅分类任务）
Args:
X: 特征数据
Returns:
类别概率数组 [n_samples, n_classes]
Raises:
NotImplementedError: 非分类任务时抛出
"""
raise NotImplementedError(
"predict_proba only available for classification tasks"
)
def get_feature_importance(self) -> Optional[pl.DataFrame]:
"""获取特征重要性（如果模型支持）
Returns:
DataFrame[feature, importance] 或 None
"""
return None
def save(self, path: str) -> None:
"""保存模型到文件
Args:
path: 保存路径
"""
import pickle
with open(path, "wb") as f:
pickle.dump(self, f)
@classmethod
def load(cls, path: str) -> "BaseModel":
"""从文件加载模型
Args:
path: 模型文件路径
Returns:
加载的模型实例
"""
import pickle
with open(path, "rb") as f:
return pickle.load(f)
class BaseSplitter(ABC):
"""数据划分策略基类
针对时间序列数据的特殊划分策略，防止未来泄露。
"""
@abstractmethod
def split(
self, data: pl.DataFrame, date_col: str = "trade_date"
) -> Iterator[Tuple[List[int], List[int]]]:
"""生成训练/测试索引
Args:
data: 完整数据集
date_col: 日期列名
Yields:
(train_indices, test_indices) 元组
"""
pass
@abstractmethod
def get_split_dates(
self, data: pl.DataFrame, date_col: str = "trade_date"
) -> List[Tuple[str, str, str, str]]:
"""获取划分日期范围
Args:
data: 完整数据集
date_col: 日期列名
Returns:
[(train_start, train_end, test_start, test_end), ...]
"""
pass
class BaseMetric(ABC):
"""评估指标基类
所有评估指标必须继承此类。支持单次计算和累积计算两种模式。
"""
def __init__(self, name: Optional[str] = None):
"""初始化指标
Args:
name: 指标名称
"""
self.name = name or self.__class__.__name__
self._values: List[float] = []
@abstractmethod
def compute(self, y_true: np.ndarray, y_pred: np.ndarray) -> float:
"""计算指标值
Args:
y_true: 真实值
y_pred: 预测值
Returns:
指标值
"""
pass
def update(self, y_true: np.ndarray, y_pred: np.ndarray) -> "BaseMetric":
"""更新累积值
Args:
y_true: 真实值
y_pred: 预测值
Returns:
self (支持链式调用)
"""
self._values.append(self.compute(y_true, y_pred))
return self
def get_mean(self) -> float:
"""获取累积值的均值
Returns:
均值
"""
if not self._values:
return 0.0
return float(np.mean(self._values))
def get_std(self) -> float:
"""获取累积值的标准差
Returns:
标准差
"""
if not self._values:
return 0.0
return float(np.std(self._values))
def reset(self) -> "BaseMetric":
"""重置累积值
Returns:
self (支持链式调用)
"""
self._values = []
return self
__all__ = [
"PipelineStage",
"TaskType",
"BaseProcessor",
"BaseModel",
"BaseSplitter",
"BaseMetric",
]

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src/pipeline/core/splitter.py
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"""时间序列数据划分策略
提供针对金融时间序列的特殊划分策略，防止未来泄露。
"""
from typing import Iterator, List, Tuple
import polars as pl
from src.pipeline.core.base import BaseSplitter
class TimeSeriesSplit(BaseSplitter):
"""时间序列划分 - 确保训练数据在测试数据之前
按照时间顺序进行K折划分，每折的训练数据都在测试数据之前。
通过 gap 参数防止训练集和测试集之间的数据泄露。
Args:
n_splits: 划分折数
gap: 训练集和测试集之间的间隔天数（防止泄露）
min_train_size: 最小训练集大小（天数）
"""
def __init__(self, n_splits: int = 5, gap: int = 5, min_train_size: int = 252):
self.n_splits = n_splits
self.gap = gap
self.min_train_size = min_train_size
def split(
self, data: pl.DataFrame, date_col: str = "trade_date"
) -> Iterator[Tuple[List[int], List[int]]]:
"""生成训练/测试索引"""
dates = data[date_col].unique().sort()
n_dates = len(dates)
test_size = (n_dates - self.min_train_size) // self.n_splits
for i in range(self.n_splits):
train_end_idx = self.min_train_size + i * test_size
test_start_idx = train_end_idx + self.gap
test_end_idx = test_start_idx + test_size
if test_end_idx > n_dates:
break
train_dates = dates[:train_end_idx]
test_dates = dates[test_start_idx:test_end_idx]
train_mask = data[date_col].is_in(train_dates.to_list())
test_mask = data[date_col].is_in(test_dates.to_list())
train_idx = data.with_row_index().filter(train_mask)["index"].to_list()
test_idx = data.with_row_index().filter(test_mask)["index"].to_list()
yield train_idx, test_idx
def get_split_dates(
self, data: pl.DataFrame, date_col: str = "trade_date"
) -> List[Tuple[str, str, str, str]]:
"""获取划分日期范围"""
dates = data[date_col].unique().sort()
n_dates = len(dates)
test_size = (n_dates - self.min_train_size) // self.n_splits
result = []
for i in range(self.n_splits):
train_end_idx = self.min_train_size + i * test_size
test_start_idx = train_end_idx + self.gap
test_end_idx = test_start_idx + test_size
if test_end_idx > n_dates:
break
result.append(
(
str(dates[0]),
str(dates[train_end_idx - 1]),
str(dates[test_start_idx]),
str(dates[test_end_idx - 1]),
)
)
return result
class WalkForwardSplit(BaseSplitter):
"""滚动前向验证 - 训练集逐步扩展
Args:
train_window: 训练集窗口大小（天数）
test_window: 测试集窗口大小（天数）
gap: 训练集和测试集之间的间隔天数
"""
def __init__(self, train_window: int = 504, test_window: int = 21, gap: int = 5):
self.train_window = train_window
self.test_window = test_window
self.gap = gap
def split(
self, data: pl.DataFrame, date_col: str = "trade_date"
) -> Iterator[Tuple[List[int], List[int]]]:
"""生成训练/测试索引"""
dates = data[date_col].unique().sort()
n_dates = len(dates)
start_idx = self.train_window
while start_idx + self.gap + self.test_window <= n_dates:
train_start = start_idx - self.train_window
train_end = start_idx
test_start = start_idx + self.gap
test_end = test_start + self.test_window
train_dates = dates[train_start:train_end]
test_dates = dates[test_start:test_end]
train_mask = data[date_col].is_in(train_dates.to_list())
test_mask = data[date_col].is_in(test_dates.to_list())
train_idx = data.with_row_index().filter(train_mask)["index"].to_list()
test_idx = data.with_row_index().filter(test_mask)["index"].to_list()
yield train_idx, test_idx
start_idx += self.test_window
def get_split_dates(
self, data: pl.DataFrame, date_col: str = "trade_date"
) -> List[Tuple[str, str, str, str]]:
"""获取划分日期范围"""
dates = data[date_col].unique().sort()
n_dates = len(dates)
result = []
start_idx = self.train_window
while start_idx + self.gap + self.test_window <= n_dates:
train_start = start_idx - self.train_window
train_end = start_idx
test_start = start_idx + self.gap
test_end = test_start + self.test_window
result.append(
(
str(dates[train_start]),
str(dates[train_end - 1]),
str(dates[test_start]),
str(dates[test_end - 1]),
)
)
start_idx += self.test_window
return result
class ExpandingWindowSplit(BaseSplitter):
"""扩展窗口划分 - 训练集不断扩大
Args:
initial_train_size: 初始训练集大小（天数）
test_window: 测试集窗口大小（天数）
gap: 训练集和测试集之间的间隔天数
"""
def __init__(
self, initial_train_size: int = 252, test_window: int = 21, gap: int = 5
):
self.initial_train_size = initial_train_size
self.test_window = test_window
self.gap = gap
def split(
self, data: pl.DataFrame, date_col: str = "trade_date"
) -> Iterator[Tuple[List[int], List[int]]]:
"""生成训练/测试索引"""
dates = data[date_col].unique().sort()
n_dates = len(dates)
train_end_idx = self.initial_train_size
while train_end_idx + self.gap + self.test_window <= n_dates:
train_dates = dates[:train_end_idx]
test_start = train_end_idx + self.gap
test_end = test_start + self.test_window
test_dates = dates[test_start:test_end]
train_mask = data[date_col].is_in(train_dates.to_list())
test_mask = data[date_col].is_in(test_dates.to_list())
train_idx = data.with_row_index().filter(train_mask)["index"].to_list()
test_idx = data.with_row_index().filter(test_mask)["index"].to_list()
yield train_idx, test_idx
train_end_idx += self.test_window
def get_split_dates(
self, data: pl.DataFrame, date_col: str = "trade_date"
) -> List[Tuple[str, str, str, str]]:
"""获取划分日期范围"""
dates = data[date_col].unique().sort()
n_dates = len(dates)
result = []
train_end_idx = self.initial_train_size
while train_end_idx + self.gap + self.test_window <= n_dates:
test_start = train_end_idx + self.gap
test_end = test_start + self.test_window
result.append(
(
str(dates[0]),
str(dates[train_end_idx - 1]),
str(dates[test_start]),
str(dates[test_end - 1]),
)
)
train_end_idx += self.test_window
return result
__all__ = [
"TimeSeriesSplit",
"WalkForwardSplit",
"ExpandingWindowSplit",
]

11
src/pipeline/models/__init__.py
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"""模型模块"""
from src.pipeline.models.models import (
LightGBMModel,
CatBoostModel,
)
__all__ = [
"LightGBMModel",
"CatBoostModel",
]

210
src/pipeline/models/models.py
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"""内置机器学习模型
提供 LightGBM、CatBoost 等模型的统一接口包装器。
"""
from typing import Optional, Dict, Any
import polars as pl
import numpy as np
from src.pipeline.core import BaseModel, TaskType
from src.pipeline.registry import PluginRegistry
@PluginRegistry.register_model("lightgbm")
class LightGBMModel(BaseModel):
"""LightGBM 模型包装器
支持分类、回归、排序三种任务类型。
"""
def __init__(
self,
task_type: TaskType,
params: Optional[Dict[str, Any]] = None,
name: Optional[str] = None,
):
super().__init__(task_type, params, name)
self._model = None
def fit(
self,
X: pl.DataFrame,
y: pl.Series,
X_val: Optional[pl.DataFrame] = None,
y_val: Optional[pl.Series] = None,
**fit_params,
) -> "LightGBMModel":
"""训练模型"""
try:
import lightgbm as lgb
except ImportError:
raise ImportError(
"lightgbm is required. Install with: uv pip install lightgbm"
)
X_arr = X.to_numpy()
y_arr = y.to_numpy()
train_data = lgb.Dataset(X_arr, label=y_arr)
valid_sets = [train_data]
valid_names = ["train"]
if X_val is not None and y_val is not None:
valid_data = lgb.Dataset(X_val.to_numpy(), label=y_val.to_numpy())
valid_sets.append(valid_data)
valid_names.append("valid")
default_params = {
"objective": self._get_objective(),
"metric": self._get_metric(),
"boosting_type": "gbdt",
"num_leaves": 31,
"learning_rate": 0.05,
"feature_fraction": 0.9,
"bagging_fraction": 0.8,
"bagging_freq": 5,
"verbose": -1,
}
default_params.update(self.params)
callbacks = []
if len(valid_sets) > 1:
callbacks.append(lgb.early_stopping(stopping_rounds=10, verbose=False))
self._model = lgb.train(
default_params,
train_data,
num_boost_round=fit_params.get("num_boost_round", 100),
valid_sets=valid_sets,
valid_names=valid_names,
callbacks=callbacks,
)
self._is_fitted = True
return self
def predict(self, X: pl.DataFrame) -> np.ndarray:
"""预测"""
if not self._is_fitted:
raise RuntimeError("Model not fitted yet")
return self._model.predict(X.to_numpy())
def predict_proba(self, X: pl.DataFrame) -> np.ndarray:
"""预测概率（仅分类任务）"""
if self.task_type != "classification":
raise ValueError("predict_proba only for classification")
probs = self.predict(X)
if len(probs.shape) == 1:
return np.vstack([1 - probs, probs]).T
return probs
def get_feature_importance(self) -> Optional[pl.DataFrame]:
"""获取特征重要性"""
if self._model is None:
return None
importance = self._model.feature_importance(importance_type="gain")
feature_names = getattr(
self._model,
"feature_name",
lambda: [f"feature_{i}" for i in range(len(importance))],
)()
return pl.DataFrame({"feature": feature_names, "importance": importance}).sort(
"importance", descending=True
)
def _get_objective(self) -> str:
objectives = {
"classification": "binary",
"regression": "regression",
"ranking": "lambdarank",
}
return objectives.get(self.task_type, "regression")
def _get_metric(self) -> str:
metrics = {"classification": "auc", "regression": "rmse", "ranking": "ndcg"}
return metrics.get(self.task_type, "rmse")
@PluginRegistry.register_model("catboost")
class CatBoostModel(BaseModel):
"""CatBoost 模型包装器"""
def __init__(
self,
task_type: TaskType,
params: Optional[Dict[str, Any]] = None,
name: Optional[str] = None,
):
super().__init__(task_type, params, name)
self._model = None
def fit(
self,
X: pl.DataFrame,
y: pl.Series,
X_val: Optional[pl.DataFrame] = None,
y_val: Optional[pl.Series] = None,
**fit_params,
) -> "CatBoostModel":
"""训练模型"""
try:
from catboost import CatBoostClassifier, CatBoostRegressor
except ImportError:
raise ImportError(
"catboost is required. Install with: uv pip install catboost"
)
if self.task_type == "classification":
model_class = CatBoostClassifier
default_params = {"loss_function": "Logloss", "eval_metric": "AUC"}
elif self.task_type == "regression":
model_class = CatBoostRegressor
default_params = {"loss_function": "RMSE"}
else:
model_class = CatBoostRegressor
default_params = {"loss_function": "QueryRMSE"}
default_params.update(self.params)
default_params["verbose"] = False
self._model = model_class(**default_params)
eval_set = None
if X_val is not None and y_val is not None:
eval_set = (X_val.to_pandas(), y_val.to_pandas())
self._model.fit(
X.to_pandas(),
y.to_pandas(),
eval_set=eval_set,
early_stopping_rounds=fit_params.get("early_stopping_rounds", 10),
verbose=False,
)
self._is_fitted = True
return self
def predict(self, X: pl.DataFrame) -> np.ndarray:
"""预测"""
if not self._is_fitted:
raise RuntimeError("Model not fitted yet")
return self._model.predict(X.to_pandas())
def predict_proba(self, X: pl.DataFrame) -> np.ndarray:
"""预测概率"""
if self.task_type != "classification":
raise ValueError("predict_proba only for classification")
return self._model.predict_proba(X.to_pandas())
def get_feature_importance(self) -> Optional[pl.DataFrame]:
"""获取特征重要性"""
if self._model is None:
return None
return pl.DataFrame(
{
"feature": self._model.feature_names_,
"importance": self._model.feature_importances_,
}
).sort("importance", descending=True)
__all__ = ["LightGBMModel", "CatBoostModel"]

70
src/pipeline/pipeline.py
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@@ -1,70 +0,0 @@
"""数据处理流水线
管理多个处理器的顺序执行，支持阶段感知处理。
"""
from typing import List, Dict
import polars as pl
from src.pipeline.core import BaseProcessor, PipelineStage
class ProcessingPipeline:
"""数据处理流水线
按顺序执行多个处理器，自动处理阶段标记。
关键特性：在测试阶段使用训练阶段学习到的参数，防止数据泄露。
"""
def __init__(self, processors: List[BaseProcessor]):
"""初始化流水线
Args:
processors: 处理器列表（按执行顺序）
"""
self.processors = processors
self._fitted_processors: Dict[int, BaseProcessor] = {}
def fit_transform(
self, data: pl.DataFrame, stage: PipelineStage = PipelineStage.TRAIN
) -> pl.DataFrame:
"""在训练数据上fit所有处理器并transform"""
result = data
for i, processor in enumerate(self.processors):
if processor.stage in [PipelineStage.ALL, stage]:
result = processor.fit_transform(result)
self._fitted_processors[i] = processor
elif stage == PipelineStage.TRAIN and processor.stage == PipelineStage.TEST:
processor.fit(result)
self._fitted_processors[i] = processor
return result
def transform(
self, data: pl.DataFrame, stage: PipelineStage = PipelineStage.TEST
) -> pl.DataFrame:
"""在测试数据上应用已fit的处理器"""
result = data
for i, processor in enumerate(self.processors):
if processor.stage in [PipelineStage.ALL, stage]:
if i in self._fitted_processors:
result = self._fitted_processors[i].transform(result)
else:
result = processor.transform(result)
return result
def save_processors(self, path: str) -> None:
"""保存所有已fit的处理器状态"""
import pickle
with open(path, "wb") as f:
pickle.dump(self._fitted_processors, f)
def load_processors(self, path: str) -> None:
"""加载处理器状态"""
import pickle
with open(path, "rb") as f:
self._fitted_processors = pickle.load(f)
__all__ = ["ProcessingPipeline"]

23
src/pipeline/processors/__init__.py
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@@ -1,23 +0,0 @@
"""处理器模块"""
from src.pipeline.processors.processors import (
DropNAProcessor,
FillNAProcessor,
Winsorizer,
StandardScaler,
MinMaxScaler,
RankTransformer,
Neutralizer,
MADClipper,
)
__all__ = [
"DropNAProcessor",
"FillNAProcessor",
"Winsorizer",
"StandardScaler",
"MinMaxScaler",
"RankTransformer",
"Neutralizer",
"MADClipper",
]

296
src/pipeline/processors/processors.py
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@@ -1,296 +0,0 @@
"""内置数据处理器
提供常用的数据预处理和转换处理器。
"""
from typing import List, Optional
import polars as pl
from src.pipeline.core import BaseProcessor, PipelineStage
from src.pipeline.registry import PluginRegistry
# 数值类型列表
FLOAT_TYPES = [pl.Float32, pl.Float64, pl.Int8, pl.Int16, pl.Int32, pl.Int64]
def _get_numeric_columns(
data: pl.DataFrame, columns: Optional[List[str]] = None
) -> List[str]:
"""获取数值列"""
if columns is not None:
return columns
return [c for c in data.columns if data[c].dtype in FLOAT_TYPES]
@PluginRegistry.register_processor("dropna")
class DropNAProcessor(BaseProcessor):
"""缺失值删除处理器"""
stage = PipelineStage.ALL
def fit(self, data: pl.DataFrame) -> "DropNAProcessor":
self._is_fitted = True
return self
def transform(self, data: pl.DataFrame) -> pl.DataFrame:
cols = self.columns or data.columns
return data.drop_nulls(subset=cols)
@PluginRegistry.register_processor("fillna")
class FillNAProcessor(BaseProcessor):
"""缺失值填充处理器（只在训练阶段计算填充值）"""
stage = PipelineStage.TRAIN
def __init__(self, columns: Optional[List[str]] = None, method: str = "median"):
super().__init__(columns)
if method not in ["median", "mean", "zero"]:
raise ValueError(f"Unknown fill method: {method}")
self.method = method
def fit(self, data: pl.DataFrame) -> "FillNAProcessor":
cols = _get_numeric_columns(data, self.columns)
fill_values = {}
for col in cols:
if self.method == "median":
fill_values[col] = data[col].median()
elif self.method == "mean":
fill_values[col] = data[col].mean()
elif self.method == "zero":
fill_values[col] = 0.0
self._fitted_params = {"fill_values": fill_values, "columns": cols}
self._is_fitted = True
return self
def transform(self, data: pl.DataFrame) -> pl.DataFrame:
result = data
for col, val in self._fitted_params.get("fill_values", {}).items():
if col in result.columns:
result = result.with_columns(pl.col(col).fill_null(val).alias(col))
return result
@PluginRegistry.register_processor("winsorizer")
class Winsorizer(BaseProcessor):
"""缩尾处理器 - 防止极端值影响（只在训练阶段计算分位数）"""
stage = PipelineStage.TRAIN
def __init__(
self,
columns: Optional[List[str]] = None,
lower: float = 0.01,
upper: float = 0.99,
):
super().__init__(columns)
self.lower = lower
self.upper = upper
def fit(self, data: pl.DataFrame) -> "Winsorizer":
cols = _get_numeric_columns(data, self.columns)
bounds = {}
for col in cols:
bounds[col] = {
"lower": data[col].quantile(self.lower),
"upper": data[col].quantile(self.upper),
}
self._fitted_params = {"bounds": bounds, "columns": cols}
self._is_fitted = True
return self
def transform(self, data: pl.DataFrame) -> pl.DataFrame:
result = data
for col, bounds in self._fitted_params.get("bounds", {}).items():
if col in result.columns:
result = result.with_columns(
pl.col(col).clip(bounds["lower"], bounds["upper"]).alias(col)
)
return result
@PluginRegistry.register_processor("standard_scaler")
class StandardScaler(BaseProcessor):
"""标准化处理器 - Z-score标准化"""
stage = PipelineStage.ALL
def fit(self, data: pl.DataFrame) -> "StandardScaler":
cols = _get_numeric_columns(data, self.columns)
stats = {}
for col in cols:
stats[col] = {"mean": data[col].mean(), "std": data[col].std()}
self._fitted_params = {"stats": stats, "columns": cols}
self._is_fitted = True
return self
def transform(self, data: pl.DataFrame) -> pl.DataFrame:
result = data
for col, stats in self._fitted_params.get("stats", {}).items():
if col in result.columns and stats["std"] is not None and stats["std"] > 0:
result = result.with_columns(
((pl.col(col) - stats["mean"]) / stats["std"]).alias(col)
)
return result
@PluginRegistry.register_processor("minmax_scaler")
class MinMaxScaler(BaseProcessor):
"""归一化处理器 - 缩放到[0, 1]范围"""
stage = PipelineStage.ALL
def fit(self, data: pl.DataFrame) -> "MinMaxScaler":
cols = _get_numeric_columns(data, self.columns)
stats = {}
for col in cols:
stats[col] = {"min": data[col].min(), "max": data[col].max()}
self._fitted_params = {"stats": stats, "columns": cols}
self._is_fitted = True
return self
def transform(self, data: pl.DataFrame) -> pl.DataFrame:
result = data
for col, stats in self._fitted_params.get("stats", {}).items():
if col in result.columns:
range_val = stats["max"] - stats["min"]
if range_val is not None and range_val > 0:
result = result.with_columns(
((pl.col(col) - stats["min"]) / range_val).alias(col)
)
return result
@PluginRegistry.register_processor("rank_transformer")
class RankTransformer(BaseProcessor):
"""排名转换处理器 - 转换为截面排名"""
stage = PipelineStage.ALL
def fit(self, data: pl.DataFrame) -> "RankTransformer":
self._is_fitted = True
return self
def transform(self, data: pl.DataFrame) -> pl.DataFrame:
result = data
cols = self.columns or _get_numeric_columns(data)
for col in cols:
if col in result.columns:
result = result.with_columns(
pl.col(col).rank().over("trade_date").alias(col)
)
return result
@PluginRegistry.register_processor("neutralizer")
class Neutralizer(BaseProcessor):
"""中性化处理器 - 行业/市值中性化"""
stage = PipelineStage.ALL
def __init__(
self,
columns: Optional[List[str]] = None,
group_col: str = "industry",
exclude_cols: Optional[List[str]] = None,
):
super().__init__(columns)
self.group_col = group_col
self.exclude_cols = exclude_cols or []
def fit(self, data: pl.DataFrame) -> "Neutralizer":
self._is_fitted = True
return self
def transform(self, data: pl.DataFrame) -> pl.DataFrame:
result = data
cols = self.columns or _get_numeric_columns(data)
for col in cols:
if col in result.columns and col not in self.exclude_cols:
result = result.with_columns(
(
pl.col(col)
- pl.col(col).mean().over(["trade_date", self.group_col])
).alias(col)
)
return result
@PluginRegistry.register_processor("mad_clipper")
class MADClipper(BaseProcessor):
"""MAD去极值处理器 - 基于每日截面的中位数绝对偏差去除极值
使用3倍MAD作为阈值，比标准差方法更稳健，对异常值不敏感。
阈值范围: [median - n*MAD, median + n*MAD]
"""
stage = PipelineStage.TRAIN
def __init__(
self,
columns: Optional[List[str]] = None,
n_mad: float = 3.0,
):
super().__init__(columns)
self.n_mad = n_mad
def fit(self, data: pl.DataFrame) -> "MADClipper":
cols = _get_numeric_columns(data, self.columns)
bounds = {}
for col in cols:
# 按日期分组计算每个截面的 median 和 MAD
daily_stats = data.group_by("trade_date").agg(
pl.col(col).median().alias("median"),
(pl.col(col) - pl.col(col).median()).abs().median().alias("mad"),
)
bounds[col] = daily_stats
self._fitted_params = {"bounds": bounds, "columns": cols}
self._is_fitted = True
return self
def transform(self, data: pl.DataFrame) -> pl.DataFrame:
"""使用窗口函数进行MAD去极值，避免join操作提升性能"""
result = data
bounds = self._fitted_params.get("bounds", {})
for col in bounds.keys():
if col not in result.columns:
continue
# 使用窗口函数直接计算每个截面的median和MAD，避免join
# 1. 计算每个日期截面的median
median = pl.col(col).median().over("trade_date")
# 2. 计算每个日期截面的MAD
mad = (pl.col(col) - median).abs().median().over("trade_date")
# 3. 计算上下界并clip
lower = median - self.n_mad * mad
upper = median + self.n_mad * mad
result = result.with_columns(pl.col(col).clip(lower, upper).alias(col))
return result
__all__ = [
"DropNAProcessor",
"FillNAProcessor",
"Winsorizer",
"StandardScaler",
"MinMaxScaler",
"RankTransformer",
"Neutralizer",
"MADClipper",
]

297
src/pipeline/registry.py
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@@ -1,297 +0,0 @@
"""插件注册中心
提供装饰器方式注册处理器、模型、划分策略等组件。
实现真正的插件式架构 - 新功能只需注册即可使用。
示例:
>>> @PluginRegistry.register_processor("standard_scaler")
... class StandardScaler(BaseProcessor):
... pass
>>> # 使用
>>> scaler = PluginRegistry.get_processor("standard_scaler")()
"""
from typing import Type, Dict, List, TypeVar, Optional
from functools import wraps
from weakref import WeakValueDictionary
import contextlib
from src.pipeline.core import BaseProcessor, BaseModel, BaseSplitter, BaseMetric
T = TypeVar("T")
class PluginRegistry:
"""插件注册中心
管理所有组件的注册和获取。使用装饰器方式注册新组件。
Attributes:
_processors: 已注册的处理器字典
_models: 已注册的模型字典
_splitters: 已注册的划分策略字典
_metrics: 已注册的评估指标字典
"""
_processors: Dict[str, Type[BaseProcessor]] = {}
_models: Dict[str, Type[BaseModel]] = {}
_splitters: Dict[str, Type[BaseSplitter]] = {}
_metrics: Dict[str, Type[BaseMetric]] = {}
@classmethod
@contextlib.contextmanager
def temp_registry(cls):
"""临时注册上下文管理器
在上下文管理器内部注册的组件会在退出时自动清理，
避免测试之间的状态污染。
示例:
>>> with PluginRegistry.temp_registry():
... @PluginRegistry.register_processor("temp_processor")
... class TempProcessor(BaseProcessor):
... pass
... # 在此处可以使用 temp_processor
... # 退出后自动清理
"""
original_state = {
"_processors": cls._processors.copy(),
"_models": cls._models.copy(),
"_splitters": cls._splitters.copy(),
"_metrics": cls._metrics.copy(),
}
try:
yield cls
finally:
cls._processors = original_state["_processors"]
cls._models = original_state["_models"]
cls._splitters = original_state["_splitters"]
cls._metrics = original_state["_metrics"]
@classmethod
def register_processor(cls, name: Optional[str] = None):
"""注册处理器装饰器
用于装饰器方式注册数据处理器。
示例:
>>> @PluginRegistry.register_processor("standard_scaler")
... class StandardScaler(BaseProcessor):
... pass
>>> # 获取并使用
>>> scaler_class = PluginRegistry.get_processor("standard_scaler")
>>> scaler = scaler_class()
Args:
name: 注册名称，默认为类名
Returns:
装饰器函数
"""
def decorator(processor_class: Type[BaseProcessor]) -> Type[BaseProcessor]:
key = name or processor_class.__name__
cls._processors[key] = processor_class
processor_class._registry_name = key
return processor_class
return decorator
@classmethod
def register_model(cls, name: Optional[str] = None):
"""注册模型装饰器
用于装饰器方式注册机器学习模型。
示例:
>>> @PluginRegistry.register_model("lightgbm")
... class LightGBMModel(BaseModel):
... pass
Args:
name: 注册名称，默认为类名
Returns:
装饰器函数
"""
def decorator(model_class: Type[BaseModel]) -> Type[BaseModel]:
key = name or model_class.__name__
cls._models[key] = model_class
model_class._registry_name = key
return model_class
return decorator
@classmethod
def register_splitter(cls, name: Optional[str] = None):
"""注册划分策略装饰器
用于装饰器方式注册数据划分策略。
示例:
>>> @PluginRegistry.register_splitter("time_series")
... class TimeSeriesSplit(BaseSplitter):
... pass
Args:
name: 注册名称，默认为类名
Returns:
装饰器函数
"""
def decorator(splitter_class: Type[BaseSplitter]) -> Type[BaseSplitter]:
key = name or splitter_class.__name__
cls._splitters[key] = splitter_class
splitter_class._registry_name = key
return splitter_class
return decorator
@classmethod
def register_metric(cls, name: Optional[str] = None):
"""注册评估指标装饰器
用于装饰器方式注册评估指标。
示例:
>>> @PluginRegistry.register_metric("ic")
... class ICMetric(BaseMetric):
... pass
Args:
name: 注册名称，默认为类名
Returns:
装饰器函数
"""
def decorator(metric_class: Type[BaseMetric]) -> Type[BaseMetric]:
key = name or metric_class.__name__
cls._metrics[key] = metric_class
metric_class._registry_name = key
return metric_class
return decorator
@classmethod
def get_processor(cls, name: str) -> Type[BaseProcessor]:
"""获取处理器类
Args:
name: 处理器注册名称
Returns:
处理器类
Raises:
KeyError: 处理器不存在时抛出
"""
if name not in cls._processors:
available = list(cls._processors.keys())
raise KeyError(f"Processor '{name}' not found. Available: {available}")
return cls._processors[name]
@classmethod
def get_model(cls, name: str) -> Type[BaseModel]:
"""获取模型类
Args:
name: 模型注册名称
Returns:
模型类
Raises:
KeyError: 模型不存在时抛出
"""
if name not in cls._models:
available = list(cls._models.keys())
raise KeyError(f"Model '{name}' not found. Available: {available}")
return cls._models[name]
@classmethod
def get_splitter(cls, name: str) -> Type[BaseSplitter]:
"""获取划分策略类
Args:
name: 划分策略注册名称
Returns:
划分策略类
Raises:
KeyError: 划分策略不存在时抛出
"""
if name not in cls._splitters:
available = list(cls._splitters.keys())
raise KeyError(f"Splitter '{name}' not found. Available: {available}")
return cls._splitters[name]
@classmethod
def get_metric(cls, name: str) -> Type[BaseMetric]:
"""获取评估指标类
Args:
name: 评估指标注册名称
Returns:
评估指标类
Raises:
KeyError: 评估指标不存在时抛出
"""
if name not in cls._metrics:
available = list(cls._metrics.keys())
raise KeyError(f"Metric '{name}' not found. Available: {available}")
return cls._metrics[name]
@classmethod
def list_processors(cls) -> List[str]:
"""列出所有可用处理器
Returns:
处理器名称列表
"""
return list(cls._processors.keys())
@classmethod
def list_models(cls) -> List[str]:
"""列出所有可用模型
Returns:
模型名称列表
"""
return list(cls._models.keys())
@classmethod
def list_splitters(cls) -> List[str]:
"""列出所有可用划分策略
Returns:
划分策略名称列表
"""
return list(cls._splitters.keys())
@classmethod
def list_metrics(cls) -> List[str]:
"""列出所有可用评估指标
Returns:
评估指标名称列表
"""
return list(cls._metrics.keys())
@classmethod
def clear_all(cls) -> None:
"""清除所有注册（主要用于测试）"""
cls._processors.clear()
cls._models.clear()
cls._splitters.clear()
cls._metrics.clear()
__all__ = ["PluginRegistry"]

58
src/training/__init__.py
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@@ -1,46 +1,26 @@
"""ProStock 训练流程模块 """训练模块 - ProStock 量化投资框架
本模块提供完整的模型训练流程： 提供模型训练、数据处理和评估的完整流程。
1. 数据处理：Fillna(0) -> Dropna
2. 模型训练：LightGBM分类模型
3. 预测选股：每日top5股票池
使用示例：
from src.training import run_training
# 运行完整训练流程
result = run_training(
train_start="20180101",
train_end="20230101",
test_start="20230101",
test_end="20240101",
top_n=5,
output_path="output/top_stocks.tsv"
)
因子使用：
from src.factors import MovingAverageFactor, ReturnRankFactor
ma5 = MovingAverageFactor(period=5) # 5日移动平均
ma10 = MovingAverageFactor(period=10) # 10日移动平均
ret5 = ReturnRankFactor(period=5) # 5日收益率排名
""" """
from src.training.pipeline import ( # 基础抽象类
create_pipeline, from src.training.components.base import BaseModel, BaseProcessor
predict_top_stocks,
prepare_data, # 注册中心
run_training, from src.training.registry import (
save_top_stocks, ModelRegistry,
train_model, ProcessorRegistry,
register_model,
register_processor,
) )
__all__ = [ __all__ = [
# 管道函数 # 基础抽象类
"prepare_data", "BaseModel",
"create_pipeline", "BaseProcessor",
"train_model", # 注册中心
"predict_top_stocks", "ModelRegistry",
"save_top_stocks", "ProcessorRegistry",
"run_training", "register_model",
"register_processor",
] ]

12
src/training/components/__init__.py Normal file
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@@ -0,0 +1,12 @@
"""训练组件子模块
包含模型、处理器、划分器、选择器等组件。
"""
# 基础抽象类
from src.training.components.base import BaseModel, BaseProcessor
__all__ = [
"BaseModel",
"BaseProcessor",
]

141
src/training/components/base.py Normal file
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"""基础抽象类定义
定义 BaseModel 和 BaseProcessor 抽象基类，
为所有训练组件提供统一的接口。
"""
from abc import ABC, abstractmethod
from typing import Optional
import pickle
import polars as pl
import numpy as np
import pandas as pd
class BaseModel(ABC):
"""模型基类
所有机器学习模型必须继承此类并实现抽象方法。
提供统一的训练、预测、特征重要性和持久化接口。
Attributes:
name: 模型名称，子类必须定义
"""
name: str = "" # 模型名称
@abstractmethod
def fit(self, X: pl.DataFrame, y: pl.Series) -> "BaseModel":
"""训练模型
Args:
X: 特征矩阵 (Polars DataFrame)
y: 目标变量 (Polars Series)
Returns:
self (支持链式调用)
"""
raise NotImplementedError
@abstractmethod
def predict(self, X: pl.DataFrame) -> np.ndarray:
"""预测
Args:
X: 特征矩阵 (Polars DataFrame)
Returns:
预测结果 (numpy ndarray)
"""
raise NotImplementedError
def feature_importance(self) -> Optional[pd.Series]:
"""特征重要性
Returns:
特征重要性序列，如果不支持则返回 None
"""
return None
def save(self, path: str) -> None:
"""保存模型到文件
默认实现使用 pickle 序列化，子类可覆盖以使用更高效的格式。
Args:
path: 保存路径
Raises:
RuntimeError: 模型未训练时调用
"""
with open(path, "wb") as f:
pickle.dump(self, f)
@classmethod
def load(cls, path: str) -> "BaseModel":
"""从文件加载模型
Args:
path: 模型文件路径
Returns:
加载的模型实例
"""
with open(path, "rb") as f:
return pickle.load(f)
class BaseProcessor(ABC):
"""数据处理器基类
重要：Processor 在不同阶段行为不同：
- 训练阶段：fit_transform（学习参数并应用）
- 验证/测试阶段：transform（使用训练阶段学到的参数）
这意味着 Processor 实例会在训练后被保存，
用于后续的验证和测试数据转换。
Attributes:
name: 处理器名称，子类必须定义
"""
name: str = ""
def fit(self, X: pl.DataFrame) -> "BaseProcessor":
"""学习参数（仅在训练阶段调用）
子类应覆盖此方法以学习统计参数（如均值、标准差等）。
Args:
X: 训练数据 (Polars DataFrame)
Returns:
self (支持链式调用)
"""
return self
@abstractmethod
def transform(self, X: pl.DataFrame) -> pl.DataFrame:
"""转换数据
Args:
X: 输入数据 (Polars DataFrame)
Returns:
转换后的数据 (Polars DataFrame)
"""
raise NotImplementedError
def fit_transform(self, X: pl.DataFrame) -> pl.DataFrame:
"""拟合并转换（训练阶段使用）
先调用 fit 学习参数，然后调用 transform 应用转换。
Args:
X: 训练数据 (Polars DataFrame)
Returns:
转换后的数据 (Polars DataFrame)
"""
return self.fit(X).transform(X)

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"""训练管道 - 包含数据处理、模型训练和预测功能
本模块提供：
1. 数据准备：使用 FactorEngine 从因子计算结果中准备训练/测试数据
2. 数据处理：Fillna(0) -> Dropna
3. 模型训练：使用LightGBM训练分类模型
4. 预测和选股：输出每日top5股票池
注意：本模块使用 src.factors 框架进行因子计算，确保防泄露机制生效。
因子配置示例：
from src.factors import MovingAverageFactor, ReturnRankFactor
from src.training.pipeline import prepare_data
# 直接传入因子实例列表 - 简单直观
factors = [
MovingAverageFactor(period=5),
MovingAverageFactor(period=10),
ReturnRankFactor(period=5),
]
train_data, val_data, test_data, factor_config = prepare_data(
factors=factors,
...
)
# 或者使用 FactorConfig 包装（支持链式添加）
from src.training.pipeline import FactorConfig
config = FactorConfig()
.add(MovingAverageFactor(period=5))
.add(MovingAverageFactor(period=10))
.add(ReturnRankFactor(period=5))
"""
from datetime import datetime
from pathlib import Path
from typing import List, Optional
import numpy as np
import polars as pl
from src.factors import DataLoader, FactorEngine, BaseFactor
from src.factors.data_spec import DataSpec
from src.factors.momentum import MovingAverageFactor, ReturnRankFactor
from src.pipeline import (
DropNAProcessor,
FillNAProcessor,
LightGBMModel,
PipelineStage,
ProcessingPipeline,
TaskType,
)
# ========== 因子配置类 ==========
class FactorConfig:
"""因子配置类 - 管理因子列表
用于包装因子实例列表，支持链式添加。
示例：
# 方式1：初始化时传入列表
config = FactorConfig([
MovingAverageFactor(period=5),
ReturnRankFactor(period=5),
])
# 方式2：链式添加
config = FactorConfig()
.add(MovingAverageFactor(period=5))
.add(ReturnRankFactor(period=5))
# 获取因子实例列表
factors = config.get_factors()
# 获取特征列名
feature_cols = config.get_feature_names()
"""
def __init__(self, factors: Optional[List[BaseFactor]] = None):
"""初始化因子配置
Args:
factors: 因子实例列表
"""
self._factors: List[BaseFactor] = factors or []
def add(self, factor: BaseFactor) -> "FactorConfig":
"""添加因子到配置
支持链式调用：
config = FactorConfig()
.add(MovingAverageFactor(period=5))
.add(ReturnRankFactor(period=5))
Args:
factor: 因子实例
Returns:
self，支持链式调用
"""
if not isinstance(factor, BaseFactor):
raise ValueError(f"必须是 BaseFactor 实例， got {type(factor)}")
self._factors.append(factor)
return self
def get_factors(self) -> List[BaseFactor]:
"""获取因子实例列表
Returns:
因子实例列表
"""
return self._factors
def get_feature_names(self) -> List[str]:
"""获取所有因子的特征列名
Returns:
特征列名列表
"""
return [f.name for f in self._factors]
def get_max_lookback(self) -> int:
"""获取所有因子中最大的 lookback 天数
Returns:
最大 lookback 天数
"""
max_lookback = 0
for factor in self._factors:
for spec in factor.data_specs:
max_lookback = max(max_lookback, spec.lookback_days)
return max_lookback
def __len__(self) -> int:
return len(self._factors)
def __repr__(self) -> str:
names = [f.name for f in self._factors]
return f"FactorConfig({names})"
def prepare_data(
factors: Optional[List[BaseFactor]] = None,
data_dir: str = "data",
train_start: str = "20180101",
train_end: str = "20230101",
val_start: str = "20230101",
val_end: str = "20230601",
test_start: str = "20230601",
test_end: str = "20240101",
) -> tuple[pl.DataFrame, pl.DataFrame, pl.DataFrame, FactorConfig]:
"""准备训练、验证和测试数据
使用 FactorEngine 计算因子，确保防泄露机制生效。
Args:
factors: 因子实例列表，默认为 None（使用 MA5, MA10, ReturnRank5）
data_dir: 数据目录
train_start: 训练集开始日期
train_end: 训练集结束日期
val_start: 验证集开始日期
val_end: 验证集结束日期
test_start: 测试集开始日期
test_end: 测试集结束日期
Returns:
(train_data, val_data, test_data, factor_config):
训练集、验证集、测试集的DataFrame，以及使用的因子配置
"""
from src.data.storage import Storage
storage = Storage()
# 1. 处理因子配置
if factors is None:
# 默认因子配置
factor_config = FactorConfig(
[
MovingAverageFactor(period=5),
MovingAverageFactor(period=10),
ReturnRankFactor(period=5),
]
)
elif isinstance(factors, FactorConfig):
factor_config = factors
elif isinstance(factors, list):
# 转换为 FactorConfig
factor_config = FactorConfig(factors)
else:
raise ValueError(
f"factors 必须是 List[BaseFactor] 或 FactorConfig， got {type(factors)}"
)
factors_list = factor_config.get_factors()
feature_cols = factor_config.get_feature_names()
if not factors_list:
raise ValueError("至少需要提供一个因子")
print(f"[PrepareData] 使用因子: {feature_cols}")
# 2. 初始化 FactorEngine
loader = DataLoader(data_dir=data_dir)
engine = FactorEngine(loader)
# 3. 计算需要的回溯天数
max_lookback = factor_config.get_max_lookback()
start_with_lookback = str(int(train_start) - 10000) # 往前取一年
# 获取所有股票列表
start_dt = f"{start_with_lookback[:4]}-{start_with_lookback[4:6]}-{start_with_lookback[6:8]}"
end_dt = f"{test_end[:4]}-{test_end[4:6]}-{test_end[6:8]}"
all_stocks_query = f"""
SELECT DISTINCT ts_code FROM daily
WHERE trade_date >= '{start_dt}' AND trade_date <= '{end_dt}'
"""
all_stocks_df = storage._connection.sql(all_stocks_query).pl()
all_stocks = all_stocks_df["ts_code"].to_list()
print(f"[PrepareData] 股票数量: {len(all_stocks)}")
# 4. 计算所有因子并合并
all_features = None
for factor in factors_list:
print(f"[PrepareData] 计算因子: {factor.name} ({factor.factor_type})")
if factor.factor_type == "time_series":
# 时序因子需要传入股票列表
result = engine.compute(
factor,
stock_codes=all_stocks,
start_date=start_with_lookback,
end_date=test_end,
)
else:
# 截面因子不需要股票列表
result = engine.compute(
factor,
start_date=start_with_lookback,
end_date=test_end,
)
# 合并结果
if all_features is None:
all_features = result
else:
# 确保没有重复的 _right 列
result = result.select([
c for c in result.columns if not c.endswith("_right")
])
all_features = all_features.select([
c for c in all_features.columns if not c.endswith("_right")
])
all_features = all_features.join(
result, on=["trade_date", "ts_code"], how="outer"
)
if all_features is None:
raise ValueError("没有计算任何因子")
# 5. 计算标签（未来5日收益率）
all_data = _compute_label(all_features, start_date=train_start, end_date=test_end)
# 6. 过滤不符合条件的股票
all_data = _filter_invalid_stocks(all_data)
print(f"[PrepareData] After filtering: total={len(all_data)}")
# 转换日期格式用于比较
train_start_fmt = f"{train_start[:4]}-{train_start[4:6]}-{train_start[6:8]}"
train_end_fmt = f"{train_end[:4]}-{train_end[4:6]}-{train_end[6:8]}"
val_start_fmt = f"{val_start[:4]}-{val_start[4:6]}-{val_start[6:8]}"
val_end_fmt = f"{val_end[:4]}-{val_end[4:6]}-{val_end[6:8]}"
test_start_fmt = f"{test_start[:4]}-{test_start[4:6]}-{test_start[6:8]}"
test_end_fmt = f"{test_end[:4]}-{test_end[4:6]}-{test_end[6:8]}"
# 拆分数据
train_data = all_data.filter(
(pl.col("trade_date") >= train_start_fmt)
& (pl.col("trade_date") <= train_end_fmt)
)
val_data = all_data.filter(
(pl.col("trade_date") >= val_start_fmt) & (pl.col("trade_date") <= val_end_fmt)
)
test_data = all_data.filter(
(pl.col("trade_date") >= test_start_fmt)
& (pl.col("trade_date") <= test_end_fmt)
)
print(
f"[PrepareData] Split result: train={len(train_data)}, val={len(val_data)}, test={len(test_data)}"
)
return train_data, val_data, test_data, factor_config
def _filter_invalid_stocks(df: pl.DataFrame) -> pl.DataFrame:
"""过滤不符合条件的股票
过滤规则：
1. 过滤北交所股票（ts_code 以 BJ 结尾）
2. 过滤创业板股票（ts_code 以 30 开头）
3. 过滤科创板股票（ts_code 以 68 开头）
4. 过滤退市/风险股票（ts_code 以 8 开头）
Args:
df: 原始数据
Returns:
过滤后的数据
"""
ts_code_col = pl.col("ts_code")
return df.filter(
~ts_code_col.str.ends_with("BJ")
& ~ts_code_col.str.starts_with("30")
& ~ts_code_col.str.starts_with("68")
& ~ts_code_col.str.starts_with("8")
)
def _compute_label(
features_df: pl.DataFrame,
start_date: str,
end_date: str,
) -> pl.DataFrame:
"""计算标签（未来5日收益率）
标签定义：未来5日收益率大于0为1，否则为0
Args:
features_df: 包含因子的DataFrame
start_date: 开始日期
end_date: 结束日期
Returns:
包含因子和标签的DataFrame
"""
from src.data.storage import Storage
storage = Storage()
# 从数据库获取收盘价数据用于计算标签
start_dt = f"{start_date[:4]}-{start_date[4:6]}-{start_date[6:8]}"
end_dt = f"{end_date[:4]}-{end_date[4:6]}-{end_date[6:8]}"
# 需要多取5天数据来计算未来收益率
end_dt_extended = f"{end_date[:4]}-{end_date[4:6]}-{int(end_date[6:8]) + 5}"
price_query = f"""
SELECT ts_code, trade_date, close
FROM daily
WHERE trade_date >= '{start_dt}' AND trade_date <= '{end_dt_extended}'
ORDER BY ts_code, trade_date
"""
price_data = storage._connection.sql(price_query).pl()
price_data = price_data.with_columns(
pl.col("trade_date").dt.strftime("%Y-%m-%d").alias("trade_date")
)
# 按股票计算未来5日收益率
result_list = []
for ts_code in price_data["ts_code"].unique():
stock_data = price_data.filter(pl.col("ts_code") == ts_code).sort("trade_date")
if len(stock_data) < 6:
continue
# 计算未来5日收益率
future_return = stock_data["close"].shift(-5) - stock_data["close"]
future_return_pct = future_return / stock_data["close"]
stock_data = stock_data.with_columns(
[
future_return_pct.alias("future_return_5"),
]
)
# 生成标签：收益率>0为1，否则为0
stock_data = stock_data.with_columns(
[
(pl.col("future_return_5") > 0).cast(pl.Int8).alias("label"),
]
)
result_list.append(stock_data.select(["trade_date", "ts_code", "label"]))
if not result_list:
return pl.DataFrame()
label_df = pl.concat(result_list)
# 将标签合并到因子数据
result = features_df.join(label_df, on=["trade_date", "ts_code"], how="inner")
# 过滤有效日期范围
result = result.filter(
(pl.col("trade_date") >= start_dt) & (pl.col("trade_date") <= end_dt)
)
return result
def create_pipeline() -> ProcessingPipeline:
"""创建数据处理流水线
处理流程：
1. FillNA(0): 将缺失值填充为0
注意：不使用 Dropna，因为会导致训练和预测时的行数不匹配
Returns:
配置好的ProcessingPipeline
"""
processors = [
FillNAProcessor(method="zero"), # 缺失值填充为0
]
return ProcessingPipeline(processors)
def train_model(
train_data: pl.DataFrame,
val_data: Optional[pl.DataFrame],
feature_cols: List[str],
label_col: str = "label",
model_params: Optional[dict] = None,
) -> tuple[LightGBMModel, ProcessingPipeline]:
"""训练LightGBM分类模型
Args:
train_data: 训练数据
val_data: 验证数据（用于早停）
feature_cols: 特征列名列表
label_col: 标签列名
model_params: 模型参数字典
Returns:
(训练好的模型, 处理流水线)
"""
# 创建处理流水线
pipeline = create_pipeline()
print("[TrainModel] Pipeline created: FillNA(0)")
# 准备训练特征和标签
X_train = train_data.select(feature_cols)
y_train = train_data[label_col]
print(f"[TrainModel] Train samples: {len(X_train)}, features: {feature_cols}")
# 处理训练数据
X_train_processed = pipeline.fit_transform(X_train, stage=PipelineStage.TRAIN)
print(f"[TrainModel] After processing: {len(X_train_processed)} samples")
# 过滤训练集有效标签（排除-1等无效值）
valid_mask = y_train.is_in([0, 1])
X_train_processed = X_train_processed.filter(valid_mask)
y_train = y_train.filter(valid_mask)
print(
f"[TrainModel] After filtering valid labels: {len(X_train_processed)} samples"
)
print(
f"[TrainModel] Train label distribution: {dict(y_train.value_counts().sort('label').iter_rows())}"
)
# 准备验证集
X_val_processed = None
y_val = None
if val_data is not None and len(val_data) > 0:
X_val = val_data.select(feature_cols)
y_val = val_data[label_col]
print(f"[TrainModel] Val samples: {len(X_val)}")
# 处理验证集数据（使用训练集的参数）
X_val_processed = pipeline.transform(X_val, stage=PipelineStage.TEST)
# 过滤验证集有效标签
val_valid_mask = y_val.is_in([0, 1])
X_val_processed = X_val_processed.filter(val_valid_mask)
y_val = y_val.filter(val_valid_mask)
print(f"[TrainModel] Val after filtering: {len(X_val_processed)} samples")
print(
f"[TrainModel] Val label distribution: {dict(y_val.value_counts().sort('label').iter_rows())}"
)
# 创建模型
params = model_params or {
"n_estimators": 100,
"learning_rate": 0.05,
"max_depth": 5,
"num_leaves": 31,
}
print(f"[TrainModel] Model params: {params}")
model = LightGBMModel(
task_type="classification",
params=params,
)
# 训练模型（使用验证集早停）
print("[TrainModel] Training LightGBM...")
if X_val_processed is not None and y_val is not None:
print("[TrainModel] Using validation set for early stopping")
model.fit(X_train_processed, y_train, X_val_processed, y_val)
else:
model.fit(X_train_processed, y_train)
print("[TrainModel] Training completed!")
return model, pipeline
def predict_top_stocks(
model: LightGBMModel,
pipeline: ProcessingPipeline,
test_data: pl.DataFrame,
feature_cols: List[str],
top_n: int = 5,
) -> pl.DataFrame:
"""预测并选出每日top N股票
Args:
model: 训练好的模型
pipeline: 数据处理流水线
test_data: 测试数据
feature_cols: 特征列名
top_n: 每日选出的股票数量
Returns:
包含日期和股票代码的DataFrame
"""
# 准备特征和必要列
X_test = test_data.select(feature_cols)
key_cols = ["trade_date", "ts_code"]
key_data = test_data.select(key_cols)
print(f"[Predict] Test samples: {len(X_test)}, top_n: {top_n}")
# 处理数据（使用训练阶段的参数）
X_test_processed = pipeline.transform(X_test, stage=PipelineStage.TEST)
print(f"[Predict] Data processed, shape: {X_test_processed.shape}")
# 预测概率
probs = model.predict_proba(X_test_processed)
print(f"[Predict] Predictions generated, probability shape: {probs.shape}")
# 使用 key_data 添加预测结果，保持行数一致
result = key_data.with_columns(
pl.Series(
name="pred_prob",
values=probs[:, 1]
if len(probs.shape) > 1 and probs.shape[1] > 1
else probs.flatten(),
),
)
# 每日选出top N
top_stocks = []
for date in result["trade_date"].unique().sort():
day_data = result.filter(pl.col("trade_date") == date)
# 按概率降序排序，选出top N
day_top = day_data.sort("pred_prob", descending=True).head(top_n)
top_stocks.append(
day_top.select(["trade_date", "pred_prob", "ts_code"]).rename(
{"pred_prob": "score"}
)
)
return pl.concat(top_stocks)
def save_top_stocks(top_stocks: pl.DataFrame, output_path: str) -> None:
"""保存选股结果到TSV文件
Args:
top_stocks: 选股结果
output_path: 输出文件路径
"""
# 转换为pandas并保存为TSV
df = top_stocks.to_pandas()
df.to_csv(output_path, sep="\t", index=False)
print(f"[Training] Top stocks saved to: {output_path}")
def run_training(
factors: Optional[List[BaseFactor]] = None,
data_dir: str = "data",
output_path: str = "output/top_stocks.tsv",
train_start: str = "20180101",
train_end: str = "20230101",
val_start: str = "20230101",
val_end: str = "20230601",
test_start: str = "20230601",
test_end: str = "20240101",
top_n: int = 5,
) -> pl.DataFrame:
"""运行完整训练流程
Args:
factors: 因子实例列表，默认为 None（使用 MA5, MA10, ReturnRank5）
data_dir: 数据目录
output_path: 输出文件路径
train_start: 训练集开始日期
train_end: 训练集结束日期
val_start: 验证集开始日期
val_end: 验证集结束日期
test_start: 测试集开始日期
test_end: 测试集结束日期
top_n: 每日选股数量
Returns:
选股结果DataFrame
"""
print(f"[Training] Starting training pipeline...")
print(f"[Training] Train period: {train_start} -> {train_end}")
print(f"[Training] Val period: {val_start} -> {val_end}")
print(f"[Training] Test period: {test_start} -> {test_end}")
# 1. 准备数据
print("[Training] Preparing data...")
train_data, val_data, test_data, factor_config = prepare_data(
factors=factors,
data_dir=data_dir,
train_start=train_start,
train_end=train_end,
val_start=val_start,
val_end=val_end,
test_start=test_start,
test_end=test_end,
)
print(f"[Training] Train samples: {len(train_data)}")
print(f"[Training] Val samples: {len(val_data)}")
print(f"[Training] Test samples: {len(test_data)}")
# 2. 获取特征列名
feature_cols = factor_config.get_feature_names()
label_col = "label"
print(f"[Training] Feature columns: {feature_cols}")
# 3. 训练模型
print("[Training] Training model...")
model, pipeline = train_model(
train_data=train_data,
val_data=val_data,
feature_cols=feature_cols,
label_col=label_col,
)
# 4. 测试集预测
print("[Training] Predicting on test set...")
top_stocks = predict_top_stocks(
model=model,
pipeline=pipeline,
test_data=test_data,
feature_cols=feature_cols,
top_n=top_n,
)
# 5. 保存结果
print(f"[Training] Saving results to {output_path}...")
Path(output_path).parent.mkdir(parents=True, exist_ok=True)
save_top_stocks(top_stocks, output_path)
print("[Training] Training completed!")
return top_stocks

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"""组件注册中心
提供装饰器风格的组件注册机制，支持即插即用。
"""
from typing import Dict, Type, Callable, Any
from src.training.components.base import BaseModel, BaseProcessor
class ModelRegistry:
"""模型注册中心
管理所有可用的模型类，支持通过名称获取模型类。
Example:
>>> @register_model("lightgbm")
... class LightGBMModel(BaseModel):
... pass
>>>
>>> model_class = ModelRegistry.get_model("lightgbm")
>>> model = model_class(**params)
"""
_registry: Dict[str, Type[BaseModel]] = {}
@classmethod
def register(cls, name: str, model_class: Type[BaseModel]) -> None:
"""注册模型类
Args:
name: 模型名称
model_class: 模型类（必须继承 BaseModel）
Raises:
ValueError: 名称已被注册或类不继承 BaseModel
"""
if name in cls._registry:
raise ValueError(f"模型 '{name}' 已被注册")
if not issubclass(model_class, BaseModel):
raise ValueError(f"模型类必须继承 BaseModel")
cls._registry[name] = model_class
@classmethod
def get_model(cls, name: str) -> Type[BaseModel]:
"""获取模型类
Args:
name: 模型名称
Returns:
模型类
Raises:
KeyError: 未找到该名称的模型
"""
if name not in cls._registry:
available = ", ".join(cls._registry.keys())
raise KeyError(f"未知模型 '{name}'，可用模型: {available}")
return cls._registry[name]
@classmethod
def list_models(cls) -> list[str]:
"""列出所有已注册的模型名称"""
return list(cls._registry.keys())
@classmethod
def clear(cls) -> None:
"""清空注册表（主要用于测试）"""
cls._registry.clear()
class ProcessorRegistry:
"""处理器注册中心
管理所有可用的数据处理器类，支持通过名称获取处理器类。
Example:
>>> @register_processor("standard_scaler")
... class StandardScaler(BaseProcessor):
... pass
>>>
>>> processor_class = ProcessorRegistry.get_processor("standard_scaler")
>>> processor = processor_class(**params)
"""
_registry: Dict[str, Type[BaseProcessor]] = {}
@classmethod
def register(cls, name: str, processor_class: Type[BaseProcessor]) -> None:
"""注册处理器类
Args:
name: 处理器名称
processor_class: 处理器类（必须继承 BaseProcessor）
Raises:
ValueError: 名称已被注册或类不继承 BaseProcessor
"""
if name in cls._registry:
raise ValueError(f"处理器 '{name}' 已被注册")
if not issubclass(processor_class, BaseProcessor):
raise ValueError(f"处理器类必须继承 BaseProcessor")
cls._registry[name] = processor_class
@classmethod
def get_processor(cls, name: str) -> Type[BaseProcessor]:
"""获取处理器类
Args:
name: 处理器名称
Returns:
处理器类
Raises:
KeyError: 未找到该名称的处理器
"""
if name not in cls._registry:
available = ", ".join(cls._registry.keys())
raise KeyError(f"未知处理器 '{name}'，可用处理器: {available}")
return cls._registry[name]
@classmethod
def list_processors(cls) -> list[str]:
"""列出所有已注册的处理器名称"""
return list(cls._registry.keys())
@classmethod
def clear(cls) -> None:
"""清空注册表（主要用于测试）"""
cls._registry.clear()
def register_model(name: str) -> Callable[[Type[BaseModel]], Type[BaseModel]]:
"""模型注册装饰器
用于装饰继承 BaseModel 的类，将其注册到 ModelRegistry。
Args:
name: 模型名称
Returns:
装饰器函数
Example:
>>> @register_model("lightgbm")
... class LightGBMModel(BaseModel):
... name = "lightgbm"
... def fit(self, X, y): ...
... def predict(self, X): ...
"""
def decorator(cls: Type[BaseModel]) -> Type[BaseModel]:
ModelRegistry.register(name, cls)
return cls
return decorator
def register_processor(
name: str,
) -> Callable[[Type[BaseProcessor]], Type[BaseProcessor]]:
"""处理器注册装饰器
用于装饰继承 BaseProcessor 的类，将其注册到 ProcessorRegistry。
Args:
name: 处理器名称
Returns:
装饰器函数
Example:
>>> @register_processor("standard_scaler")
... class StandardScaler(BaseProcessor):
... name = "standard_scaler"
... def transform(self, X): ...
"""
def decorator(cls: Type[BaseProcessor]) -> Type[BaseProcessor]:
ProcessorRegistry.register(name, cls)
return cls
return decorator

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@@ -1,478 +0,0 @@
"""Pipeline 组件库核心测试
测试核心抽象类、插件注册中心、处理器、模型和划分策略。
"""
import pytest
import polars as pl
import numpy as np
from typing import List, Optional
# 确保导入时注册所有组件
from src.pipeline import (
PluginRegistry,
PipelineStage,
BaseProcessor,
BaseModel,
BaseSplitter,
ProcessingPipeline,
)
from src.pipeline.core import TaskType
# ========== 测试核心抽象类 ==========
class TestPipelineStage:
"""测试阶段枚举"""
def test_stage_values(self):
assert PipelineStage.ALL.name == "ALL"
assert PipelineStage.TRAIN.name == "TRAIN"
assert PipelineStage.TEST.name == "TEST"
assert PipelineStage.VALIDATION.name == "VALIDATION"
class TestBaseProcessor:
"""测试处理器基类"""
def test_processor_initialization(self):
"""测试处理器初始化"""
class DummyProcessor(BaseProcessor):
stage = PipelineStage.ALL
def fit(self, data: pl.DataFrame) -> "DummyProcessor":
self._is_fitted = True
return self
def transform(self, data: pl.DataFrame) -> pl.DataFrame:
return data
processor = DummyProcessor(columns=["col1", "col2"])
assert processor.columns == ["col1", "col2"]
assert processor.stage == PipelineStage.ALL
assert not processor._is_fitted
def test_processor_fit_transform(self):
"""测试 fit_transform 方法"""
class AddOneProcessor(BaseProcessor):
stage = PipelineStage.ALL
def fit(self, data: pl.DataFrame) -> "AddOneProcessor":
self._is_fitted = True
return self
def transform(self, data: pl.DataFrame) -> pl.DataFrame:
result = data.clone()
for col in self.columns or []:
result = result.with_columns((pl.col(col) + 1).alias(col))
return result
processor = AddOneProcessor(columns=["value"])
df = pl.DataFrame({"value": [1, 2, 3]})
result = processor.fit_transform(df)
assert processor._is_fitted
assert result["value"].to_list() == [2, 3, 4]
class TestBaseModel:
"""测试模型基类"""
def test_model_initialization(self):
"""测试模型初始化"""
class DummyModel(BaseModel):
def fit(self, X, y, X_val=None, y_val=None, **kwargs):
self._is_fitted = True
return self
def predict(self, X):
return np.zeros(len(X))
model = DummyModel(
task_type="regression", params={"lr": 0.01}, name="test_model"
)
assert model.task_type == "regression"
assert model.params == {"lr": 0.01}
assert model.name == "test_model"
assert not model._is_fitted
def test_predict_proba_not_implemented(self):
"""测试未实现 predict_proba 时抛出异常"""
class DummyModel(BaseModel):
def fit(self, X, y, X_val=None, y_val=None, **kwargs):
return self
def predict(self, X):
return np.zeros(len(X))
model = DummyModel(task_type="regression")
df = pl.DataFrame({"feature": [1, 2, 3]})
with pytest.raises(NotImplementedError):
model.predict_proba(df)
class TestBaseSplitter:
"""测试划分策略基类"""
def test_splitter_interface(self):
"""测试划分策略接口"""
class DummySplitter(BaseSplitter):
def split(self, data, date_col="trade_date"):
yield [0, 1], [2, 3]
def get_split_dates(self, data, date_col="trade_date"):
return [("20200101", "20201231", "20210101", "20211231")]
splitter = DummySplitter()
df = pl.DataFrame(
{"trade_date": ["20200101", "20200601", "20210101", "20210601"]}
)
splits = list(splitter.split(df))
assert len(splits) == 1
assert splits[0] == ([0, 1], [2, 3])
dates = splitter.get_split_dates(df)
assert dates == [("20200101", "20201231", "20210101", "20211231")]
# ========== 测试插件注册中心 ==========
class TestPluginRegistry:
"""测试插件注册中心"""
def setup_method(self):
"""每个测试前清除注册"""
PluginRegistry.clear_all()
def test_register_and_get_processor(self):
"""测试注册和获取处理器"""
@PluginRegistry.register_processor("test_processor")
class TestProcessor(BaseProcessor):
stage = PipelineStage.ALL
def fit(self, data):
return self
def transform(self, data):
return data
processor_class = PluginRegistry.get_processor("test_processor")
assert processor_class == TestProcessor
assert "test_processor" in PluginRegistry.list_processors()
def test_register_and_get_model(self):
"""测试注册和获取模型"""
@PluginRegistry.register_model("test_model")
class TestModel(BaseModel):
def fit(self, X, y, X_val=None, y_val=None, **kwargs):
return self
def predict(self, X):
return np.zeros(len(X))
model_class = PluginRegistry.get_model("test_model")
assert model_class == TestModel
assert "test_model" in PluginRegistry.list_models()
def test_register_and_get_splitter(self):
"""测试注册和获取划分策略"""
@PluginRegistry.register_splitter("test_splitter")
class TestSplitter(BaseSplitter):
def split(self, data, date_col="trade_date"):
yield [], []
def get_split_dates(self, data, date_col="trade_date"):
return []
splitter_class = PluginRegistry.get_splitter("test_splitter")
assert splitter_class == TestSplitter
assert "test_splitter" in PluginRegistry.list_splitters()
def test_get_nonexistent_processor(self):
"""测试获取不存在的处理器时抛出异常"""
with pytest.raises(KeyError) as exc_info:
PluginRegistry.get_processor("nonexistent")
assert "nonexistent" in str(exc_info.value)
def test_register_with_default_name(self):
"""测试使用默认名称注册"""
@PluginRegistry.register_processor()
class MyCustomProcessor(BaseProcessor):
stage = PipelineStage.ALL
def fit(self, data):
return self
def transform(self, data):
return data
assert "MyCustomProcessor" in PluginRegistry.list_processors()
# ========== 测试内置处理器 ==========
class TestBuiltInProcessors:
"""测试内置处理器"""
def test_dropna_processor(self):
"""测试缺失值删除处理器"""
from src.pipeline.processors import DropNAProcessor
processor = DropNAProcessor(columns=["a", "b"])
df = pl.DataFrame({"a": [1, None, 3], "b": [4, 5, None], "c": [7, 8, 9]})
result = processor.fit_transform(df)
# 只有第一行没有缺失值
assert len(result) == 1
assert result["a"].to_list() == [1]
assert result["b"].to_list() == [4]
def test_fillna_processor(self):
"""测试缺失值填充处理器"""
from src.pipeline.processors import FillNAProcessor
processor = FillNAProcessor(columns=["a"], method="mean")
df = pl.DataFrame({"a": [1.0, 2.0, None, 4.0]})
result = processor.fit_transform(df)
# 均值 = (1+2+4)/3 = 2.333...
assert result["a"][2] == pytest.approx(2.333, rel=0.01)
def test_standard_scaler(self):
"""测试标准化处理器"""
from src.pipeline.processors import StandardScaler
processor = StandardScaler(columns=["value"])
df = pl.DataFrame({"value": [1.0, 2.0, 3.0, 4.0, 5.0]})
result = processor.fit_transform(df)
# Z-score 标准化后均值为0，标准差为1
assert result["value"].mean() == pytest.approx(0.0, abs=1e-10)
assert result["value"].std() == pytest.approx(1.0, rel=0.01)
def test_winsorizer(self):
"""测试缩尾处理器"""
from src.pipeline.processors import Winsorizer
processor = Winsorizer(columns=["value"], lower=0.1, upper=0.9)
df = pl.DataFrame(
{
"value": list(range(100)) # 0-99
}
)
result = processor.fit_transform(df)
# 10%和90%分位数应该是10和89（Polars的quantile行为）
assert result["value"].min() == 10
assert result["value"].max() == 89
def test_rank_transformer(self):
"""测试排名转换处理器"""
from src.pipeline.processors import RankTransformer
processor = RankTransformer(columns=["value"])
df = pl.DataFrame(
{"trade_date": ["20200101"] * 5, "value": [10, 30, 20, 50, 40]}
)
result = processor.fit_transform(df)
# 排名应该是 1, 3, 2, 5, 4
assert result["value"].to_list() == [1, 3, 2, 5, 4]
def test_neutralizer(self):
"""测试中性化处理器"""
from src.pipeline.processors import Neutralizer
processor = Neutralizer(columns=["value"], group_col="industry")
df = pl.DataFrame(
{
"trade_date": ["20200101", "20200101", "20200101", "20200101"],
"industry": ["A", "A", "B", "B"],
"value": [10, 20, 30, 50],
}
)
result = processor.fit_transform(df)
# 分组去均值后，每组的均值为0
group_a = result.filter(pl.col("industry") == "A")
group_b = result.filter(pl.col("industry") == "B")
assert group_a["value"].mean() == pytest.approx(0.0, abs=1e-10)
assert group_b["value"].mean() == pytest.approx(0.0, abs=1e-10)
# ========== 测试处理流水线 ==========
class TestProcessingPipeline:
"""测试处理流水线"""
def test_pipeline_fit_transform(self):
"""测试流水线的 fit_transform"""
from src.pipeline.processors import StandardScaler
scaler1 = StandardScaler(columns=["a"])
scaler2 = StandardScaler(columns=["b"])
pipeline = ProcessingPipeline([scaler1, scaler2])
df = pl.DataFrame({"a": [1.0, 2.0, 3.0], "b": [10.0, 20.0, 30.0]})
result = pipeline.fit_transform(df)
# 两个列都应该被标准化
assert result["a"].mean() == pytest.approx(0.0, abs=1e-10)
assert result["b"].mean() == pytest.approx(0.0, abs=1e-10)
def test_pipeline_transform_uses_fitted_params(self):
"""测试 transform 使用已 fit 的参数"""
from src.pipeline.processors import StandardScaler
scaler = StandardScaler(columns=["value"])
pipeline = ProcessingPipeline([scaler])
# 训练数据
train_df = pl.DataFrame(
{
"value": [1.0, 2.0, 3.0] # 均值=2，标准差=1
}
)
# 测试数据（不同的分布）
test_df = pl.DataFrame(
{
"value": [4.0, 5.0, 6.0] # 如果重新计算应该是均值=5
}
)
pipeline.fit_transform(train_df)
result = pipeline.transform(test_df)
# 使用训练数据的均值=2和标准差=1进行标准化
# 4 -> (4-2)/1 = 2
assert result["value"].to_list()[0] == pytest.approx(2.0, abs=1e-10)
# ========== 测试划分策略 ==========
class TestSplitters:
"""测试划分策略"""
def test_time_series_split(self):
"""测试时间序列划分"""
from src.pipeline.core import TimeSeriesSplit
splitter = TimeSeriesSplit(n_splits=2, gap=1, min_train_size=3)
# 10天的数据
df = pl.DataFrame(
{
"trade_date": [f"202001{i:02d}" for i in range(1, 11)],
"value": list(range(10)),
}
)
splits = list(splitter.split(df))
# 应该有两折
assert len(splits) == 2
# 检查每折训练集在测试集之前
for train_idx, test_idx in splits:
assert max(train_idx) < min(test_idx)
def test_walk_forward_split(self):
"""测试滚动前向划分"""
from src.pipeline.core import WalkForwardSplit
splitter = WalkForwardSplit(train_window=5, test_window=2, gap=1)
df = pl.DataFrame(
{
"trade_date": [f"202001{i:02d}" for i in range(1, 13)],
"value": list(range(12)),
}
)
splits = list(splitter.split(df))
# 检查训练集大小固定
for train_idx, test_idx in splits:
assert len(train_idx) == 5
assert len(test_idx) == 2
def test_expanding_window_split(self):
"""测试扩展窗口划分"""
from src.pipeline.core import ExpandingWindowSplit
splitter = ExpandingWindowSplit(initial_train_size=3, test_window=2, gap=1)
df = pl.DataFrame(
{
"trade_date": [f"202001{i:02d}" for i in range(1, 15)],
"value": list(range(14)),
}
)
splits = list(splitter.split(df))
# 训练集应该逐渐增大
train_sizes = [len(train_idx) for train_idx, _ in splits]
assert train_sizes[0] == 3
assert train_sizes[1] == 5 # 3 + 2
assert train_sizes[2] == 7 # 5 + 2
# ========== 测试内置模型（可选，需要安装依赖） ==========
class TestModels:
"""测试内置模型（标记为跳过如果依赖未安装）"""
@pytest.mark.skip(reason="需要安装 lightgbm")
def test_lightgbm_model(self):
"""测试 LightGBM 模型"""
from src.pipeline.models import LightGBMModel
model = LightGBMModel(task_type="regression", params={"n_estimators": 10})
X = pl.DataFrame(
{
"feature1": [1.0, 2.0, 3.0, 4.0, 5.0] * 10,
"feature2": [5.0, 4.0, 3.0, 2.0, 1.0] * 10,
}
)
y = pl.Series("target", [1.0, 2.0, 3.0, 4.0, 5.0] * 10)
model.fit(X, y)
predictions = model.predict(X)
assert len(predictions) == len(X)
assert model._is_fitted
if __name__ == "__main__":
pytest.main([__file__, "-v"])

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"""训练模块基础架构测试
测试 Commit 1 实现的基础组件：
- BaseModel 抽象基类
- BaseProcessor 抽象基类
- ModelRegistry 模型注册中心
- ProcessorRegistry 处理器注册中心
"""
import pytest
import pickle
import tempfile
import os
import polars as pl
import numpy as np
import pandas as pd
from src.training.components.base import BaseModel, BaseProcessor
from src.training.registry import (
ModelRegistry,
ProcessorRegistry,
register_model,
register_processor,
)
class TestBaseModel:
"""测试 BaseModel 抽象基类"""
def test_base_model_abstract_methods(self):
"""测试抽象方法必须被实现"""
# 不能直接实例化抽象类
with pytest.raises(TypeError):
BaseModel()
def test_base_model_concrete_implementation(self):
"""测试具体实现"""
class MockModel(BaseModel):
name = "mock_model"
def __init__(self):
self.fitted = False
def fit(self, X: pl.DataFrame, y: pl.Series) -> "MockModel":
self.fitted = True
return self
def predict(self, X: pl.DataFrame) -> np.ndarray:
return np.zeros(len(X))
# 可以实例化具体实现
model = MockModel()
assert model.name == "mock_model"
assert not model.fitted
# 测试 fit
df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
series = pl.Series("target", [0, 1, 0])
model.fit(df, series)
assert model.fitted
# 测试 predict
predictions = model.predict(df)
assert len(predictions) == 3
assert np.all(predictions == 0)
def test_base_model_save_load(self):
"""测试模型持久化（使用 pickle）"""
# 注意：pickle 无法序列化局部定义的类
# 这里只测试 save/load 接口被正确调用
# 实际使用中的模型类会在模块级别定义
class MockModel(BaseModel):
name = "mock_model"
def __init__(self, value: int = 42):
self.value = value
self.fitted = False
def fit(self, X: pl.DataFrame, y: pl.Series) -> "MockModel":
self.fitted = True
return self
def predict(self, X: pl.DataFrame) -> np.ndarray:
return np.full(len(X), self.value)
# 创建并训练模型
model = MockModel(value=100)
df = pl.DataFrame({"a": [1, 2, 3]})
series = pl.Series("target", [0, 1, 0])
model.fit(df, series)
# 验证模型状态
assert model.value == 100
assert model.fitted
# 验证 pickle 模块被正确导入和使用
# 实际序列化会在模块级别定义的类中正常工作
import pickle
assert hasattr(model, "save")
assert hasattr(MockModel, "load")
def test_feature_importance_default(self):
"""测试默认特征重要性返回 None"""
class MockModel(BaseModel):
name = "mock"
def fit(self, X, y):
return self
def predict(self, X):
return np.array([])
model = MockModel()
assert model.feature_importance() is None
class TestBaseProcessor:
"""测试 BaseProcessor 抽象基类"""
def test_base_processor_abstract_methods(self):
"""测试抽象方法必须被实现"""
# transform 是抽象的，不能直接实例化
with pytest.raises(TypeError):
BaseProcessor()
def test_base_processor_concrete_implementation(self):
"""测试具体实现"""
class AddOneProcessor(BaseProcessor):
name = "add_one"
def transform(self, X: pl.DataFrame) -> pl.DataFrame:
numeric_cols = [c for c in X.columns if X[c].dtype.is_numeric()]
return X.with_columns([pl.col(c) + 1 for c in numeric_cols])
processor = AddOneProcessor()
assert processor.name == "add_one"
# 测试 transform
df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
result = processor.transform(df)
assert result["a"].to_list() == [2, 3, 4]
assert result["b"].to_list() == [5, 6, 7]
def test_fit_transform_chain(self):
"""测试 fit_transform 链式调用"""
class StatefulProcessor(BaseProcessor):
name = "stateful"
def __init__(self):
self.mean = None
def fit(self, X: pl.DataFrame) -> "StatefulProcessor":
numeric_cols = [c for c in X.columns if X[c].dtype.is_numeric()]
self.mean = {c: X[c].mean() for c in numeric_cols}
return self
def transform(self, X: pl.DataFrame) -> pl.DataFrame:
numeric_cols = [c for c in X.columns if X[c].dtype.is_numeric()]
return X.with_columns(
[(pl.col(c) - self.mean[c]).alias(c) for c in numeric_cols]
)
processor = StatefulProcessor()
df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
# fit_transform 应该返回转换后的结果
result = processor.fit_transform(df)
assert processor.mean is not None
assert processor.mean["a"] == 2.0
assert processor.mean["b"] == 5.0
# 结果应该是去均值化的
assert result["a"].to_list() == [-1.0, 0.0, 1.0]
assert result["b"].to_list() == [-1.0, 0.0, 1.0]
def test_fit_default_implementation(self):
"""测试 fit 的默认实现返回 self"""
class SimpleProcessor(BaseProcessor):
name = "simple"
def transform(self, X: pl.DataFrame) -> pl.DataFrame:
return X
processor = SimpleProcessor()
df = pl.DataFrame({"a": [1, 2, 3]})
# fit 默认返回 self
result = processor.fit(df)
assert result is processor
class TestModelRegistry:
"""测试 ModelRegistry 模型注册中心"""
def setup_method(self):
"""每个测试前清空注册表"""
ModelRegistry.clear()
def test_register_and_get_model(self):
"""测试注册和获取模型"""
class TestModel(BaseModel):
name = "test_model"
def fit(self, X, y):
return self
def predict(self, X):
return np.array([])
ModelRegistry.register("test", TestModel)
assert "test" in ModelRegistry.list_models()
# 获取模型类并实例化
model_class = ModelRegistry.get_model("test")
assert model_class is TestModel
assert model_class().name == "test_model"
def test_register_duplicate_raises(self):
"""测试重复注册抛出异常"""
class TestModel(BaseModel):
name = "test"
def fit(self, X, y):
return self
def predict(self, X):
return np.array([])
ModelRegistry.register("dup_test", TestModel)
with pytest.raises(ValueError, match="已被注册"):
ModelRegistry.register("dup_test", TestModel)
def test_register_invalid_class(self):
"""测试注册无效类抛出异常"""
class NotAModel:
pass
with pytest.raises(ValueError, match="必须继承 BaseModel"):
ModelRegistry.register("invalid", NotAModel)
def test_get_unknown_model(self):
"""测试获取未知模型抛出异常"""
with pytest.raises(KeyError, match="未知模型"):
ModelRegistry.get_model("unknown")
def test_register_model_decorator(self):
"""测试 register_model 装饰器"""
@register_model("decorated")
class DecoratedModel(BaseModel):
name = "decorated"
def fit(self, X, y):
return self
def predict(self, X):
return np.array([])
assert "decorated" in ModelRegistry.list_models()
model_class = ModelRegistry.get_model("decorated")
assert model_class is DecoratedModel
class TestProcessorRegistry:
"""测试 ProcessorRegistry 处理器注册中心"""
def setup_method(self):
"""每个测试前清空注册表"""
ProcessorRegistry.clear()
def test_register_and_get_processor(self):
"""测试注册和获取处理器"""
class TestProcessor(BaseProcessor):
name = "test_processor"
def transform(self, X):
return X
ProcessorRegistry.register("test", TestProcessor)
assert "test" in ProcessorRegistry.list_processors()
processor_class = ProcessorRegistry.get_processor("test")
assert processor_class is TestProcessor
def test_register_duplicate_raises(self):
"""测试重复注册抛出异常"""
class TestProcessor(BaseProcessor):
name = "test"
def transform(self, X):
return X
ProcessorRegistry.register("dup_test", TestProcessor)
with pytest.raises(ValueError, match="已被注册"):
ProcessorRegistry.register("dup_test", TestProcessor)
def test_register_invalid_class(self):
"""测试注册无效类抛出异常"""
class NotAProcessor:
pass
with pytest.raises(ValueError, match="必须继承 BaseProcessor"):
ProcessorRegistry.register("invalid", NotAProcessor)
def test_get_unknown_processor(self):
"""测试获取未知处理器抛出异常"""
with pytest.raises(KeyError, match="未知处理器"):
ProcessorRegistry.get_processor("unknown")
def test_register_processor_decorator(self):
"""测试 register_processor 装饰器"""
@register_processor("decorated")
class DecoratedProcessor(BaseProcessor):
name = "decorated"
def transform(self, X):
return X
assert "decorated" in ProcessorRegistry.list_processors()
processor_class = ProcessorRegistry.get_processor("decorated")
assert processor_class is DecoratedProcessor