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Switch repository licensing to AGPL-3.0

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This commit is contained in:
2569718930@qq.com
2026-03-30 00:46:01 +08:00
parent ee114fb5bf
commit 16404ccf71
11 changed files with 753 additions and 118 deletions
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scripts/train_lgbm_daily_high.py
+33 -23
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@@ -4,20 +4,13 @@ import json
import os
import sys
from datetime import datetime
from typing import Any, Dict, List
from typing import TYPE_CHECKING, Any, Dict, List
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
if ROOT_DIR not in sys.path:
sys.path.insert(0, ROOT_DIR)
import lightgbm as lgb
import numpy as np
from src.models.lgbm_features import (
FEATURE_NAMES,
build_training_samples,
schema_payload,
)
if TYPE_CHECKING:
import lightgbm as lgb
import numpy as np
MODEL_PATH = os.path.join(ROOT_DIR, "artifacts", "models", "lgbm_daily_high.txt")
@@ -58,15 +51,21 @@ def _chronological_split(samples: List[Dict[str, Any]]) -> tuple[List[Dict[str,
return ordered[:-validation_count], ordered[-validation_count:]
def _dataset_from_samples(samples: List[Dict[str, Any]]) -> tuple[np.ndarray, np.ndarray]:
def _dataset_from_samples(samples: List[Dict[str, Any]], np: Any) -> tuple[Any, Any]:
features = np.asarray([row["vector"] for row in samples], dtype=np.float32)
targets = np.asarray([row["target"] for row in samples], dtype=np.float32)
return features, targets
def _train_model(train_samples: List[Dict[str, Any]], valid_samples: List[Dict[str, Any]]) -> lgb.Booster:
train_x, train_y = _dataset_from_samples(train_samples)
train_data = lgb.Dataset(train_x, label=train_y, feature_name=FEATURE_NAMES, free_raw_data=True)
def _train_model(
train_samples: List[Dict[str, Any]],
valid_samples: List[Dict[str, Any]],
lgb: Any,
np: Any,
feature_names: List[str],
) -> Any:
train_x, train_y = _dataset_from_samples(train_samples, np)
train_data = lgb.Dataset(train_x, label=train_y, feature_name=feature_names, free_raw_data=True)
valid_sets = [train_data]
valid_names = ["train"]
@@ -85,8 +84,8 @@ def _train_model(train_samples: List[Dict[str, Any]], valid_samples: List[Dict[s
callbacks = []
if valid_samples:
valid_x, valid_y = _dataset_from_samples(valid_samples)
valid_data = lgb.Dataset(valid_x, label=valid_y, feature_name=FEATURE_NAMES, reference=train_data)
valid_x, valid_y = _dataset_from_samples(valid_samples, np)
valid_data = lgb.Dataset(valid_x, label=valid_y, feature_name=feature_names, reference=train_data)
valid_sets.append(valid_data)
valid_names.append("valid")
callbacks.append(lgb.early_stopping(stopping_rounds=15, verbose=False))
@@ -101,7 +100,7 @@ def _train_model(train_samples: List[Dict[str, Any]], valid_samples: List[Dict[s
)
def _evaluate(booster: lgb.Booster, samples: List[Dict[str, Any]]) -> Dict[str, Any]:
def _evaluate(booster: Any, samples: List[Dict[str, Any]], np: Any) -> Dict[str, Any]:
if not samples:
return {
"sample_count": 0,
@@ -111,7 +110,7 @@ def _evaluate(booster: lgb.Booster, samples: List[Dict[str, Any]]) -> Dict[str,
"median_mae": None,
}
features, _ = _dataset_from_samples(samples)
features, _ = _dataset_from_samples(samples, np)
preds = booster.predict(features, num_iteration=booster.best_iteration)
lgbm_pairs: List[tuple[float, float]] = []
deb_pairs: List[tuple[float, float]] = []
@@ -144,14 +143,25 @@ def _evaluate(booster: lgb.Booster, samples: List[Dict[str, Any]]) -> Dict[str,
def main() -> int:
if ROOT_DIR not in sys.path:
sys.path.insert(0, ROOT_DIR)
import lightgbm as lgb
import numpy as np
from src.models.lgbm_features import (
FEATURE_NAMES,
build_training_samples,
schema_payload,
)
samples = build_training_samples()
if len(samples) < 16:
raise SystemExit(f"Not enough supervised samples for LightGBM training: {len(samples)}")
train_samples, valid_samples = _chronological_split(samples)
booster = _train_model(train_samples, valid_samples)
booster = _train_model(train_samples, valid_samples, lgb, np, FEATURE_NAMES)
all_features, all_targets = _dataset_from_samples(samples)
all_features, all_targets = _dataset_from_samples(samples, np)
final_train = lgb.Dataset(all_features, label=all_targets, feature_name=FEATURE_NAMES, free_raw_data=True)
final_booster = lgb.train(
params={
@@ -174,8 +184,8 @@ def main() -> int:
final_booster.save_model(MODEL_PATH)
metrics = {
"validation": _evaluate(booster, valid_samples),
"full_sample": _evaluate(final_booster, samples),
"validation": _evaluate(booster, valid_samples, np),
"full_sample": _evaluate(final_booster, samples, np),
}
schema = schema_payload(
model_path=os.path.relpath(MODEL_PATH, ROOT_DIR),