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feat(training): 支持 Label 预处理器

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- DataPipeline 新增 label_processor_configs 参数
- 分离特征与 label 的预处理流程
- regression.py 添加 label 缩尾处理配置
- 调整学习率并更新排除因子列表
This commit is contained in:
liaozhaorun
2026-03-29 02:37:53 +08:00
parent c3d1b157e9
commit 9e0114c745
2 changed files with 140 additions and 86 deletions
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111
src/experiment/regression.py
View File

@@ -51,61 +51,55 @@ TRAINING_TYPE = "regression"
# 排除的因子列表
EXCLUDED_FACTORS = [
"GTJA_alpha062",
"GTJA_alpha060",
"GTJA_alpha058",
"GTJA_alpha056",
"GTJA_alpha053",
"GTJA_alpha040",
"GTJA_alpha043",
"GTJA_alpha027",
"CP",
"max_ret_20",
"debt_to_equity",
"close_vwap_deviation",
"EP",
"BP",
"EP_rank",
"GTJA_alpha044",
"GTJA_alpha036",
"GTJA_alpha010",
"GTJA_alpha005",
"GTJA_alpha001",
"GTJA_alpha002",
"GTJA_alpha007",
"GTJA_alpha016",
"GTJA_alpha073",
"GTJA_alpha133",
"GTJA_alpha131",
"GTJA_alpha117",
"GTJA_alpha124",
"GTJA_alpha120",
"GTJA_alpha119",
"GTJA_alpha103",
"GTJA_alpha099",
"GTJA_alpha105",
"GTJA_alpha104",
"GTJA_alpha090",
"GTJA_alpha085",
"GTJA_alpha083",
"GTJA_alpha084",
"GTJA_alpha087",
"GTJA_alpha092",
"GTJA_alpha074",
"GTJA_alpha089",
"GTJA_alpha173",
"GTJA_alpha157",
"GTJA_alpha139",
"GTJA_alpha162",
"GTJA_alpha163",
"GTJA_alpha177",
"price_to_avg_cost",
"cost_skewness",
"GTJA_alpha191",
"GTJA_alpha180",
"history_position",
"bottom_profit",
"smart_money_accumulation",
'GTJA_alpha036',
'GTJA_alpha032',
'GTJA_alpha010',
'GTJA_alpha005',
'CP',
'BP',
'debt_to_equity',
'current_ratio',
'GTJA_alpha002',
'GTJA_alpha027',
'GTJA_alpha064',
'GTJA_alpha062',
'GTJA_alpha043',
'GTJA_alpha044',
'GTJA_alpha120',
'GTJA_alpha117',
'GTJA_alpha103',
'GTJA_alpha104',
'GTJA_alpha105',
'GTJA_alpha073',
'GTJA_alpha077',
'GTJA_alpha085',
'GTJA_alpha090',
'GTJA_alpha087',
'GTJA_alpha083',
'GTJA_alpha092',
'GTJA_alpha133',
'GTJA_alpha131',
'GTJA_alpha126',
'GTJA_alpha124',
'GTJA_alpha162',
'GTJA_alpha164',
'GTJA_alpha157',
'GTJA_alpha177',
'price_to_avg_cost',
'cost_skewness',
'GTJA_alpha191',
'GTJA_alpha180',
'history_position',
'bottom_profit',
'mean_median_dev',
'smart_money_accumulation',
'GTJA_alpha013',
'GTJA_alpha099',
'GTJA_alpha107',
'GTJA_alpha119',
'GTJA_alpha141',
'GTJA_alpha130',
'GTJA_alpha173',
]
# 模型参数配置
@@ -118,7 +112,7 @@ MODEL_PARAMS = {
"num_leaves": 31, # 【修改】限制为 31（2的5次方-1），确保树是不对称生长的，防止过拟合
"min_data_in_leaf": 512, # 【大幅增加】从256加到1000。训练集有97万条，极大地限制叶子节点样本量能有效抵抗股市噪音
# ==================== 学习参数 ====================
"learning_rate": 0.02, # 【修改】稍微调大一点，帮助模型跳出初始的局部最优（避免十几轮就早停）
"learning_rate": 0.01, # 【修改】稍微调大一点，帮助模型跳出初始的局部最优（避免十几轮就早停）
"n_estimators": 2000,
# ==================== 随机采样与降维 ====================
"subsample": 0.85,
@@ -182,6 +176,11 @@ def main():
(StandardScaler, {}),
# (CrossSectionalStandardScaler, {}),
],
label_processor_configs=[
# 对 label 进行缩尾处理（去除极端收益率）
(Winsorizer, {"lower": 0.05, "upper": 0.95}),
# (StandardScaler, {}),
],
filters=[STFilter(data_router=engine.router)],
stock_pool_filter_func=stock_pool_filter,
stock_pool_required_columns=STOCK_FILTER_REQUIRED_COLUMNS,

67
src/training/pipeline.py
View File

@@ -40,6 +40,9 @@ class DataPipeline:
filters: Optional[List[Any]] = None,
stock_pool_filter_func: Optional[Callable] = None,
stock_pool_required_columns: Optional[List[str]] = None,
label_processor_configs: Optional[
List[Tuple[Type[BaseProcessor], Dict[str, Any]]]
] = None,
):
"""初始化数据流水线
@@ -50,6 +53,8 @@ class DataPipeline:
filters: 类形式的过滤器列表（如 [STFilter]）
stock_pool_filter_func: 函数形式的股票池筛选器
stock_pool_required_columns: 股票池筛选所需的额外列
label_processor_configs: Label 数据处理器配置列表，格式与 processor_configs 相同
例如：[(Winsorizer, {"lower": 0.01, "upper": 0.99})] 用于对 label 进行缩尾处理
"""
self.factor_manager = factor_manager
self.processor_configs = processor_configs or []
@@ -57,6 +62,8 @@ class DataPipeline:
self.stock_pool_filter_func = stock_pool_filter_func
self.stock_pool_required_columns = stock_pool_required_columns or []
self.fitted_processors: List[BaseProcessor] = []
self.label_processor_configs = label_processor_configs or []
self.fitted_label_processors: List[BaseProcessor] = []
def prepare_data(
self,
@@ -250,6 +257,7 @@ class DataPipeline:
"""预处理数据
训练集使用 fit_transform，验证集和测试集使用 transform
同时支持对 label 进行 processor 处理
Args:
split_data: 划分后的数据字典
@@ -259,9 +267,10 @@ class DataPipeline:
Returns:
预处理后的数据字典
"""
if not self.processor_configs:
return split_data
label_name = split_data["train"]["y"].name
# 处理特征
if self.processor_configs:
self.fitted_processors = []
# 实例化 processors（传入 feature_cols）
@@ -272,7 +281,7 @@ class DataPipeline:
# 训练集：fit_transform
if verbose:
print(f" 训练集预处理（fit_transform）...")
print(f" 训练集特征预处理（fit_transform）...")
train_data = split_data["train"]["raw_data"]
for processor in processors:
@@ -282,13 +291,13 @@ class DataPipeline:
# 更新训练集
split_data["train"]["raw_data"] = train_data
split_data["train"]["X"] = train_data.select(feature_cols)
split_data["train"]["y"] = train_data[split_data["train"]["y"].name]
split_data["train"]["y"] = train_data[label_name]
# 验证集和测试集：transform
for split_name in ["val", "test"]:
if split_name in split_data:
if verbose:
print(f" {split_name}集预处理（transform）...")
print(f" {split_name}集特征预处理（transform）...")
split_df = split_data[split_name]["raw_data"]
for processor in self.fitted_processors:
@@ -296,7 +305,45 @@ class DataPipeline:
split_data[split_name]["raw_data"] = split_df
split_data[split_name]["X"] = split_df.select(feature_cols)
split_data[split_name]["y"] = split_df[split_data[split_name]["y"].name]
split_data[split_name]["y"] = split_df[label_name]
# 处理 label
if self.label_processor_configs:
self.fitted_label_processors = []
# 实例化 label processors（传入 label_name 作为 feature_cols）
label_processors = []
for proc_class, proc_kwargs in self.label_processor_configs:
proc_kwargs_with_label = {**proc_kwargs, "feature_cols": [label_name]}
label_processors.append(proc_class(**proc_kwargs_with_label))
# 训练集：fit_transform
if verbose:
print(f" 训练集 Label 预处理（fit_transform）...")
train_data = split_data["train"]["raw_data"]
for processor in label_processors:
train_data = processor.fit_transform(train_data)
self.fitted_label_processors.append(processor)
# 更新训练集
split_data["train"]["raw_data"] = train_data
split_data["train"]["X"] = train_data.select(feature_cols)
split_data["train"]["y"] = train_data[label_name]
# 验证集和测试集：transform
for split_name in ["val", "test"]:
if split_name in split_data:
if verbose:
print(f" {split_name}集 Label 预处理（transform）...")
split_df = split_data[split_name]["raw_data"]
for processor in self.fitted_label_processors:
split_df = processor.transform(split_df)
split_data[split_name]["raw_data"] = split_df
split_data[split_name]["X"] = split_df.select(feature_cols)
split_data[split_name]["y"] = split_df[label_name]
return split_data
@@ -307,3 +354,11 @@ class DataPipeline:
已拟合的处理器列表（用于模型保存）
"""
return self.fitted_processors
def get_fitted_label_processors(self) -> List[BaseProcessor]:
"""获取已拟合的 Label 处理器列表
Returns:
已拟合的 Label 处理器列表（用于模型保存和预测时反转换）
"""
return self.fitted_label_processors