init

3_train_v3.py

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+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']
+
+# 1. 先训练分类模型，判断flash_count是否为0
+y_cls = (y > 0).astype(int)  # 0为无闪电，1为有闪电
+
+X_train_cls, X_test_cls, y_train_cls, y_test_cls = train_test_split(
+    X, y_cls, test_size=0.2, random_state=42, stratify=y_cls
+)
+
+classifier = lgb.LGBMClassifier(n_estimators=100, learning_rate=0.05, random_state=42)
+classifier.fit(X_train_cls, y_train_cls)
+
+# 2. 只用flash_count>0的数据训练回归模型
+X_reg = X[y > 0]
+y_reg = y[y > 0]
+
+X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(
+    X_reg, y_reg, test_size=0.2, random_state=42
+)
+
+regressor = lgb.LGBMRegressor(n_estimators=150, learning_rate=0.05, random_state=42)
+regressor.fit(X_train_reg, y_train_reg)
+
+# ---------------------------------------------------------------------------
+# --- 模型保存 ---
+# ---------------------------------------------------------------------------
+os.makedirs("model", exist_ok=True)
+
+with open("model/212_classifier_model.pkl", "wb") as f:
+    pickle.dump(classifier, f)
+print("分类器模型已保存到 model/212/212_classifier_model.pkl")
+
+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/212/212_regressor_model.pkl")
+