liaozhaorun
36a3ccbcc8
- 添加 TabMModel、TabPFNModel 深度学习模型实现 - 新增 DataQualityAnalyzer 进行训练前数据质量诊断 - 改进数据处理器 NaN/null 双重处理，增强数据鲁棒性 - 支持 train_skip_days 参数跳过训练初期数据不足期 - Pipeline 自动清理标签为 NaN 的样本
86 lines
2.2 KiB
Python
86 lines
2.2 KiB
Python
"""检查 GTJA_alpha 因子"""
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import polars as pl
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from src.factors import FactorEngine
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from src.training import FactorManager
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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_FACTOR,
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)
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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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]
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def main():
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print("=" * 80)
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print("检查 GTJA_alpha 因子")
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print("=" * 80)
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engine = FactorEngine()
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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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# 注册因子
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feature_cols = factor_manager.register_to_engine(engine, verbose=False)
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# 找出 GTJA_alpha 因子
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gtja_factors = [f for f in feature_cols if f.startswith("GTJA_alpha")]
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print(f"\nGTJA_alpha 因子数量: {len(gtja_factors)}")
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print(f"前10个: {gtja_factors[:10]}")
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# 计算一个小的日期范围
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print("\n计算因子数据...")
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data = engine.compute(
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factor_names=gtja_factors[:10] + ["close"], # 只计算前10个 GTJA_alpha + close
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start_date="20200101",
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end_date="20200110",
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)
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print(f"\n数据形状: {data.shape}")
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print(f"列: {data.columns}")
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# 检查每个 GTJA_alpha 因子的 NaN 情况
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print("\nGTJA_alpha 因子 NaN 统计:")
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for col in gtja_factors[:10]:
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if col in data.columns:
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nan_count = data[col].null_count()
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total = len(data)
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print(f" {col}: {nan_count}/{total} ({nan_count / total * 100:.1f}%)")
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else:
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print(f" {col}: 列不存在！")
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# 检查 close 列作为对比
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print(
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f"\n close: {data['close'].null_count()}/{len(data)} ({data['close'].null_count() / len(data) * 100:.1f}%)"
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)
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# 查看实际数据
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print("\n实际数据预览:")
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print(data.select(["trade_date", "ts_code"] + gtja_factors[:3]).head(10))
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if __name__ == "__main__":
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main()
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