init

3_train_v2.py

@@ -0,0 +1,51 @@
+import pandas as pd
+import lightgbm as lgb
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error, r2_score, classification_report, balanced_accuracy_score
+import numpy as np
+import os
+import pickle
+from imblearn.over_sampling import SMOTE
+from imblearn.pipeline import Pipeline # 引入 imblearn Pipeline 以防止数据泄露
+
+# ---------------------------------------------------------------------------
+# --- 主流程 ---
+# ---------------------------------------------------------------------------
+df_flash = pd.read_parquet("data/preprocessed_flash/212/flash_212.parquet")
+df_ef = pd.read_parquet("data/preprocessed_ef/212/ef_212.parquet")
+df_ef['time'] = pd.to_datetime(df_ef['time'])
+df_flash['time'] = pd.to_datetime(df_flash['time'])
+merged_df = pd.merge(df_flash, df_ef, on='time', how='inner')
+merged_df = merged_df.drop('time', axis=1)
+
+print("数据框形状:", merged_df.shape)
+print(merged_df.head())
+
+# 分离特征和目标变量
+# 确保列名是字符串，以避免 LightGBM 的问题
+X = merged_df.drop('flash_count', axis=1)
+X.columns = ["".join (c if c.isalnum() else "_" for c in str(x)) for x in X.columns]
+
+y = merged_df['flash_count']
+
+# 划分训练集和测试集
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42, stratify=(y > 0) # 使用stratify保证训练集和测试集中0/1比例相似
+)
+
+
+# 定义回归器
+regressor = lgb.LGBMRegressor(n_estimators=150, learning_rate=0.05, random_state=42)
+
+regressor.fit(X_train, y_train)
+
+
+# ---------------------------------------------------------------------------
+# --- 模型保存 ---
+# ---------------------------------------------------------------------------
+os.makedirs("model", exist_ok=True)
+
+with open("model/212_regressor_model.pkl", "wb") as f:
+    pickle.dump(regressor, f)
+print("回归器模型已保存到 model/212_regressor_model.pkl")
+