226 lines
8.3 KiB
Python
226 lines
8.3 KiB
Python
"""
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grid_seeker v6.7r3 三级模型推理管道
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Rank → Top → Stacking → stacking_probability (最终排序)
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"""
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import pickle
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import numpy as np
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import pandas as pd
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from pathlib import Path
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from datetime import date
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from core.scoring.config import (
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get_model_path, RANK_MODEL, TOP_MODEL, STACKING_MODEL,
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STACKING_THRESHOLD,
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)
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from core.scoring.features.pipeline import FeaturePipeline
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from core.scoring.models import ScoringResult
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from core.database import db
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from core.logger import LogLevel, PrintLog
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# ============================================================
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# Rank 模型输入特征 (56维 v6.7/v3.4, 同时支持 feat_names 和 selected_features)
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# ============================================================
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def _get_rank_features() -> list:
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import pickle
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from core.scoring.config import get_model_path
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path = get_model_path(RANK_MODEL)
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with open(path, 'rb') as f:
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obj = pickle.load(f)
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if isinstance(obj, dict):
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# v6.7r3 使用 feat_names, v6.6 使用 selected_features
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sf = obj.get('feat_names', []) or obj.get('selected_features', [])
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if sf:
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return sf
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raise RuntimeError("无法从 rank.pkl 读取 feat_names 或 selected_features")
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RANK_FEATURE_COLS = _get_rank_features()
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# Top/Stacking 模型只用 52 维基础特征（不含 v6.7 新增的4维）
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# v6.7 新增: vol_decay_5d, grid_touch_relative_10d, vol_decay_x_grid_balance, vol_decay_x_dist_lower
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_V67_NEW_FEATS = {
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'vol_decay_5d', 'grid_touch_relative_10d',
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'vol_decay_x_grid_balance', 'vol_decay_x_dist_lower'
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}
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BASE_52_COLS = [f for f in RANK_FEATURE_COLS if f not in _V67_NEW_FEATS]
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class GridSeekerPipeline:
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"""
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grid_seeker v6.7r3 三级模型评分管道。
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Usage:
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engine = GridSeekerPipeline()
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rankings = engine.run(trade_date=date.today())
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# 返回 DataFrame: stock_code, stacking_probability, rank 等
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"""
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def __init__(self, model_dir: Path = None):
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self._rank_model = None
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self._top_model = None
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self._stacking_model = None
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# ---- 模型加载 ----
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def _load_model(self, name: str):
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"""加载单个 .pkl 模型"""
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path = get_model_path(name)
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if not path.exists():
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raise FileNotFoundError(f'模型文件不存在: {path}')
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with open(path, 'rb') as f:
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obj = pickle.load(f)
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# 支持 dict 格式 {"model": lgbm_model, ...} 或直接返回模型对象
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if isinstance(obj, dict):
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return obj.get('model', obj)
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return obj
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@property
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def rank_model(self):
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if self._rank_model is None:
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self._rank_model = self._load_model(RANK_MODEL)
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return self._rank_model
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@property
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def top_model(self):
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if self._top_model is None:
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self._top_model = self._load_model(TOP_MODEL)
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return self._top_model
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@property
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def stacking_model(self):
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if self._stacking_model is None:
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self._stacking_model = self._load_model(STACKING_MODEL)
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return self._stacking_model
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# ---- 预测 ----
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def _predict_with_model(self, model, X: pd.DataFrame, feature_cols: list) -> np.ndarray:
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"""
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使用模型预测。自动选择特征子集，兼容 sklearn API (predict/predict_proba)。
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"""
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available = [c for c in feature_cols if c in X.columns]
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missing = set(feature_cols) - set(available)
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if missing:
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PrintLog(LogLevel.WARNING,
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f'[scorer] 缺少特征列 ({len(missing)}): {list(missing)[:5]}...')
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X_sub = X[available].fillna(0).values
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try:
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if hasattr(model, 'predict_proba'):
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proba = model.predict_proba(X_sub)
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if proba.shape[1] >= 2:
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return proba[:, 1]
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return proba[:, 0]
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elif hasattr(model, 'predict'):
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return model.predict(X_sub)
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else:
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return model.predict(X_sub)
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except Exception as e:
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PrintLog(LogLevel.ERROR, f'[scorer] 模型预测失败: {e}')
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raise
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# ---- 主流程 ----
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def run(self, trade_date: date) -> pd.DataFrame:
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"""
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执行完整的 3 级评分管道。
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Returns:
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DataFrame indexed by stock_code, 含 stacking_probability / rank 等列,
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按 stacking_probability 降序排列
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"""
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PrintLog(LogLevel.INFO, f'[scorer] ===== grid_seeker v6.7r3 评分开始 ({trade_date}) =====')
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# 1. 特征工程
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pipeline = FeaturePipeline(trade_date)
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feature_df = pipeline.run()
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if feature_df.empty:
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PrintLog(LogLevel.WARNING, '[scorer] 无股票通过特征工程, 终止')
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return pd.DataFrame()
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PrintLog(LogLevel.INFO,
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f'[scorer] 特征工程完成: {len(feature_df)} stocks, '
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f'{len(feature_df.columns)} dims')
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# 2. Stage 1: Rank 模型 → rank_predicted_rounds (52维)
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PrintLog(LogLevel.INFO, '[scorer] Stage 1/3: Rank 模型...')
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feature_df['rank_predicted_rounds'] = self._predict_with_model(
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self.rank_model, feature_df, RANK_FEATURE_COLS
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)
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# 3. Stage 2: Top 模型 → top_elite_prob (53维 = 52基础 + rank)
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PrintLog(LogLevel.INFO, '[scorer] Stage 2/3: Top 模型...')
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top_cols = BASE_52_COLS + ['rank_predicted_rounds']
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feature_df['top_elite_prob'] = self._predict_with_model(
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self.top_model, feature_df, top_cols
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)
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# 4. Stage 3: Stacking 模型 → stacking_probability (55维 = 52基础 + rank + top)
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PrintLog(LogLevel.INFO, '[scorer] Stage 3/3: Stacking 模型...')
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stk_cols = BASE_52_COLS + ['rank_predicted_rounds', 'top_elite_prob']
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feature_df['stacking_probability'] = self._predict_with_model(
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self.stacking_model, feature_df, stk_cols
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)
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# 5. 排序（直接用 stacking_probability）
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feature_df['score_rank'] = feature_df['stacking_probability'].rank(
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ascending=False, method='min'
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).astype(int)
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feature_df['candidate_count'] = len(feature_df)
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feature_df = feature_df.sort_values('score_rank')
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n_above = (feature_df['stacking_probability'] >= STACKING_THRESHOLD).sum()
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PrintLog(LogLevel.INFO,
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f'[scorer] 评分完成: {len(feature_df)} 只候选, '
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f'{n_above} 只高于阈值 {STACKING_THRESHOLD}')
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PrintLog(LogLevel.INFO,
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f'[scorer] Top-5: '
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f'{feature_df.head(5)[["stacking_probability", "rank_predicted_rounds"]].to_dict("index")}')
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return feature_df
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def persist(self, rankings: pd.DataFrame, trade_date: date):
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"""将评分结果持久化到 ScoringResult 表"""
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if rankings.empty:
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return
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records = []
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for code, row in rankings.iterrows():
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records.append({
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'stock_code': str(code),
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'trade_date': trade_date,
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'predicted_profit': float(row.get('stacking_probability', 0)),
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'rank_predicted_rounds': float(row.get('rank_predicted_rounds', 0))
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if 'rank_predicted_rounds' in row else None,
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'top_elite_prob': float(row.get('top_elite_prob', 0))
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if 'top_elite_prob' in row else None,
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'stacking_probability': float(row.get('stacking_probability', 0))
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if 'stacking_probability' in row else None,
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'score_rank': int(row.get('score_rank', 0)),
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'candidate_count': int(row.get('candidate_count', 0)),
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})
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with db.atomic():
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for batch in _chunked(records, 500):
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ScoringResult.insert_many(batch).on_conflict_replace().execute()
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PrintLog(LogLevel.INFO,
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f'[scorer] 评分结果已持久化: {len(records)} 条')
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def get_top_n(self, trade_date: date, n: int = 50) -> list[dict]:
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"""查询历史评分 Top-N"""
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rows = (ScoringResult
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.select()
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.where(
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(ScoringResult.trade_date == trade_date) &
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(ScoringResult.score_rank <= n)
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)
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.order_by(ScoringResult.score_rank)
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.dicts())
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return list(rows)
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def _chunked(lst: list, n: int):
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for i in range(0, len(lst), n):
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yield lst[i:i + n] |