426 lines
11 KiB
Python
426 lines
11 KiB
Python
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# %% md
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# # TabPFN 回归训练流程
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#
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# 使用 TabPFN (Prior-Data Fitted Network) 进行回归预测。
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# TabPFN 通过上下文学习进行预测，无需传统梯度下降训练过程。
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# %% md
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# ## 1. 导入依赖
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# %%
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import os
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from src.factors import FactorEngine
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from src.training import (
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FactorManager,
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DataPipeline,
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NullFiller,
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Winsorizer,
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StandardScaler,
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CrossSectionalStandardScaler,
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)
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from src.training.core.trainer_v2 import Trainer
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from src.training.components.filters import STFilter
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from src.training.components.models import TabPFNModel
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from src.experiment.common import (
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SELECTED_FACTORS,
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FACTOR_DEFINITIONS,
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LABEL_NAME,
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LABEL_FACTOR,
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TRAIN_START,
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TRAIN_END,
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VAL_START,
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VAL_END,
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TEST_START,
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TEST_END,
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stock_pool_filter,
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STOCK_FILTER_REQUIRED_COLUMNS,
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OUTPUT_DIR,
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SAVE_PREDICTIONS,
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SAVE_MODEL,
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get_model_save_path,
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save_model_with_factors,
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TOP_N,
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)
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# 训练类型标识
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TRAINING_TYPE = "tabpfn"
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# %% md
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# ## 2. 训练特定配置
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# %%
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# Label 配置（从 common.py 统一导入）
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# LABEL_NAME 和 LABEL_FACTOR 已在 common.py 中绑定，只需从 common 导入
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# 排除的因子列表（与 regression.py 保持一致）
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EXCLUDED_FACTORS = [
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"GTJA_alpha001",
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"GTJA_alpha002",
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"GTJA_alpha003",
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"GTJA_alpha004",
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"GTJA_alpha005",
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"GTJA_alpha006",
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"GTJA_alpha007",
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"GTJA_alpha008",
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"GTJA_alpha009",
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"GTJA_alpha010",
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"GTJA_alpha011",
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"GTJA_alpha012",
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"GTJA_alpha013",
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"GTJA_alpha014",
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"GTJA_alpha015",
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"GTJA_alpha016",
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"GTJA_alpha017",
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"GTJA_alpha018",
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"GTJA_alpha019",
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"GTJA_alpha020",
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"GTJA_alpha022",
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"GTJA_alpha023",
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"GTJA_alpha024",
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"GTJA_alpha025",
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"GTJA_alpha026",
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"GTJA_alpha027",
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"GTJA_alpha028",
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"GTJA_alpha029",
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"GTJA_alpha031",
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"GTJA_alpha032",
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"GTJA_alpha033",
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"GTJA_alpha034",
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"GTJA_alpha035",
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"GTJA_alpha036",
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"GTJA_alpha037",
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# "GTJA_alpha038",
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"GTJA_alpha039",
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"GTJA_alpha040",
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"GTJA_alpha041",
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"GTJA_alpha042",
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"GTJA_alpha043",
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"GTJA_alpha044",
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"GTJA_alpha045",
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"GTJA_alpha046",
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"GTJA_alpha047",
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"GTJA_alpha048",
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"GTJA_alpha049",
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"GTJA_alpha050",
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"GTJA_alpha051",
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"GTJA_alpha052",
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"GTJA_alpha053",
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"GTJA_alpha054",
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"GTJA_alpha056",
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"GTJA_alpha057",
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"GTJA_alpha058",
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"GTJA_alpha059",
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"GTJA_alpha060",
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"GTJA_alpha061",
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"GTJA_alpha062",
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"GTJA_alpha063",
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"GTJA_alpha064",
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"GTJA_alpha065",
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"GTJA_alpha066",
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"GTJA_alpha067",
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"GTJA_alpha068",
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"GTJA_alpha070",
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"GTJA_alpha071",
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"GTJA_alpha072",
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"GTJA_alpha073",
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"GTJA_alpha074",
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"GTJA_alpha076",
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"GTJA_alpha077",
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"GTJA_alpha078",
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"GTJA_alpha079",
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"GTJA_alpha080",
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"GTJA_alpha081",
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"GTJA_alpha082",
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"GTJA_alpha083",
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"GTJA_alpha084",
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"GTJA_alpha085",
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"GTJA_alpha086",
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"GTJA_alpha087",
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"GTJA_alpha088",
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"GTJA_alpha089",
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"GTJA_alpha090",
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"GTJA_alpha091",
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"GTJA_alpha092",
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"GTJA_alpha093",
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"GTJA_alpha094",
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"GTJA_alpha095",
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"GTJA_alpha096",
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"GTJA_alpha097",
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"GTJA_alpha098",
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"GTJA_alpha099",
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"GTJA_alpha100",
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"GTJA_alpha101",
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"GTJA_alpha102",
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"GTJA_alpha103",
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"GTJA_alpha104",
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"GTJA_alpha105",
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"GTJA_alpha106",
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"GTJA_alpha107",
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"GTJA_alpha108",
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"GTJA_alpha109",
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"GTJA_alpha110",
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"GTJA_alpha111",
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"GTJA_alpha112",
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# "GTJA_alpha113",
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"GTJA_alpha114",
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"GTJA_alpha115",
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"GTJA_alpha117",
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"GTJA_alpha118",
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"GTJA_alpha119",
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"GTJA_alpha120",
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# "GTJA_alpha121",
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"GTJA_alpha122",
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"GTJA_alpha123",
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"GTJA_alpha124",
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"GTJA_alpha125",
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"GTJA_alpha126",
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"GTJA_alpha127",
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"GTJA_alpha128",
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"GTJA_alpha129",
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"GTJA_alpha130",
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"GTJA_alpha131",
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"GTJA_alpha132",
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"GTJA_alpha133",
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"GTJA_alpha134",
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"GTJA_alpha135",
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"GTJA_alpha136",
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# "GTJA_alpha138",
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"GTJA_alpha139",
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# "GTJA_alpha140",
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"GTJA_alpha141",
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"GTJA_alpha142",
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"GTJA_alpha145",
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# "GTJA_alpha146",
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"GTJA_alpha148",
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"GTJA_alpha150",
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"GTJA_alpha151",
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"GTJA_alpha152",
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"GTJA_alpha153",
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"GTJA_alpha154",
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"GTJA_alpha155",
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"GTJA_alpha156",
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"GTJA_alpha157",
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"GTJA_alpha158",
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"GTJA_alpha159",
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"GTJA_alpha160",
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"GTJA_alpha161",
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"GTJA_alpha162",
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"GTJA_alpha163",
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"GTJA_alpha164",
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# "GTJA_alpha165",
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"GTJA_alpha166",
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"GTJA_alpha167",
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"GTJA_alpha168",
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"GTJA_alpha169",
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"GTJA_alpha170",
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"GTJA_alpha171",
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"GTJA_alpha173",
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"GTJA_alpha174",
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"GTJA_alpha175",
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"GTJA_alpha176",
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"GTJA_alpha177",
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"GTJA_alpha178",
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"GTJA_alpha179",
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"GTJA_alpha180",
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# "GTJA_alpha183",
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"GTJA_alpha184",
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"GTJA_alpha185",
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"GTJA_alpha187",
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"GTJA_alpha188",
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"GTJA_alpha189",
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"GTJA_alpha191",
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"chip_dispersion_90",
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"chip_dispersion_70",
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"cost_skewness",
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"dispersion_change_20",
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"price_to_avg_cost",
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"price_to_median_cost",
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"mean_median_dev",
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"trap_pressure",
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"bottom_profit",
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"history_position",
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"winner_rate_surge_5",
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"winner_rate_cs_rank",
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"winner_rate_dev_20",
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"winner_rate_volatility",
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"smart_money_accumulation",
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"winner_vol_corr_20",
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"cost_base_momentum",
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"bottom_cost_stability",
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"pivot_reversion",
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"chip_transition",
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]
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# 模型参数配置
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MODEL_PARAMS = {
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# ==================== 设备配置 ====================
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"device": "cuda", # 计算设备: "cuda" 或 "cpu"（默认 cuda）
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# ==================== 上下文限制 ====================
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"max_context_size": 100, # 16GB GPU 建议 1000-3000，32GB 可尝试 5000-8000
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}
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# 日期范围配置
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date_range = {
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"train": (TRAIN_START, TRAIN_END),
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"val": (VAL_START, VAL_END),
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"test": (TEST_START, TEST_END),
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}
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# 输出配置
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output_config = {
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"output_dir": OUTPUT_DIR,
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"output_filename": "tabpfn_output.csv",
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"save_predictions": SAVE_PREDICTIONS,
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"save_model": SAVE_MODEL,
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"model_save_path": get_model_save_path(TRAINING_TYPE),
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"top_n": TOP_N,
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}
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# %% md
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# ## 3. 自定义 TabPFN 任务
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# %%
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from src.training.tasks import RegressionTask
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class TabPFNTask(RegressionTask):
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"""TabPFN 回归任务
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继承自 RegressionTask，但使用 TabPFNModel 作为模型。
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TabPFN 不需要传统的训练过程，而是通过上下文学习进行预测。
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"""
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def __init__(self, model_params: dict, label_name: str):
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"""初始化 TabPFN 任务
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Args:
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model_params: TabPFN 参数字典
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label_name: Label 列名称
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"""
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# 不调用父类 __init__，直接初始化以避免创建 LightGBMModel
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from src.training.tasks.base import BaseTask
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BaseTask.__init__(self, model_params, label_name)
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self.evals_result: dict | None = None
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self.model = TabPFNModel(params=model_params)
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def fit(self, train_data: dict, val_data: dict) -> None:
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"""训练 TabPFN 模型
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TabPFN 通过将训练数据加载到模型上下文中进行"训练"，
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不需要传统的梯度下降优化过程。
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Args:
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train_data: 训练数据 {"X": DataFrame, "y": Series}
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val_data: 验证数据，用于评估但不参与训练
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"""
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X_train = train_data["X"]
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y_train = train_data["y"]
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X_val = val_data.get("X")
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y_val = val_data.get("y")
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# TabPFN 使用 eval_set 进行验证
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self.model.fit(
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X_train, y_train, eval_set=(X_val, y_val) if X_val is not None else None
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)
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def get_model(self) -> TabPFNModel:
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"""获取训练好的模型实例"""
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return self.model
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# %% md
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# ## 4. 主函数
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# %%
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def main():
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"""主函数"""
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print("\n" + "=" * 80)
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print("TabPFN 回归模型训练")
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print("=" * 80)
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print("\n[说明] TabPFN 使用上下文学习(In-Context Learning)，")
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print(" 训练过程实际是加载数据到模型上下文。")
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print(" 如果训练数据超过上下文限制，会自动截取最近的数据。")
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# 1. 创建 FactorEngine
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print("\n[1] 创建 FactorEngine")
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engine = FactorEngine()
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# 2. 创建 FactorManager
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print("\n[2] 创建 FactorManager")
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factor_manager = FactorManager(
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selected_factors=SELECTED_FACTORS,
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factor_definitions=FACTOR_DEFINITIONS,
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label_factor=LABEL_FACTOR,
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excluded_factors=EXCLUDED_FACTORS,
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)
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# 3. 创建 DataPipeline
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print("\n[3] 创建 DataPipeline")
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pipeline = DataPipeline(
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factor_manager=factor_manager,
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processor_configs=[
|
|||
|
|
(NullFiller, {"strategy": "mean"}),
|
|||
|
|
(Winsorizer, {"lower": 0.01, "upper": 0.99}),
|
|||
|
|
(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,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# 4. 创建 TabPFNTask
|
|||
|
|
print("\n[4] 创建 TabPFNTask")
|
|||
|
|
task = TabPFNTask(
|
|||
|
|
model_params=MODEL_PARAMS,
|
|||
|
|
label_name=LABEL_NAME,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# 5. 创建 Trainer
|
|||
|
|
print("\n[5] 创建 Trainer")
|
|||
|
|
trainer = Trainer(
|
|||
|
|
data_pipeline=pipeline,
|
|||
|
|
task=task,
|
|||
|
|
output_config=output_config,
|
|||
|
|
verbose=True,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# 6. 执行训练
|
|||
|
|
print("\n[6] 执行训练")
|
|||
|
|
results = trainer.run(engine=engine, date_range=date_range)
|
|||
|
|
|
|||
|
|
# 7. 保存模型和因子信息（如果启用）
|
|||
|
|
if SAVE_MODEL:
|
|||
|
|
print("\n[7] 保存模型和因子信息")
|
|||
|
|
save_model_with_factors(
|
|||
|
|
model=task.get_model(),
|
|||
|
|
model_path=output_config["model_save_path"],
|
|||
|
|
selected_factors=SELECTED_FACTORS,
|
|||
|
|
factor_definitions=FACTOR_DEFINITIONS,
|
|||
|
|
fitted_processors=pipeline.get_fitted_processors(),
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# 8. 输出 TabPFN 特有指标
|
|||
|
|
print("\n" + "=" * 80)
|
|||
|
|
print("TabPFN 训练完成！")
|
|||
|
|
print(f"结果保存路径: {os.path.join(OUTPUT_DIR, 'tabpfn_output.csv')}")
|
|||
|
|
|
|||
|
|
# 显示验证集评估结果（如果可用）
|
|||
|
|
model = task.get_model()
|
|||
|
|
best_score = model.get_best_score()
|
|||
|
|
if best_score:
|
|||
|
|
print("\n[验证集评估指标]")
|
|||
|
|
for metric, value in best_score.get("valid_0", {}).items():
|
|||
|
|
print(f" - {metric}: {value:.6f}")
|
|||
|
|
|
|||
|
|
print("=" * 80)
|
|||
|
|
|
|||
|
|
return results
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
main()
|