65 lines
2.4 KiB
Python
65 lines
2.4 KiB
Python
import pandas as pd
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import lightgbm as lgb
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import mean_squared_error, r2_score, classification_report, balanced_accuracy_score
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import numpy as np
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import os
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import pickle
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from imblearn.over_sampling import SMOTE
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from imblearn.pipeline import Pipeline # 引入 imblearn Pipeline 以防止数据泄露
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# ---------------------------------------------------------------------------
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# --- 主流程 ---
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# ---------------------------------------------------------------------------
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df_flash = pd.read_parquet("data/preprocessed_flash/212/flash_212.parquet")
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df_ef = pd.read_parquet("data/preprocessed_ef/212/ef_212.parquet")
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df_ef['time'] = pd.to_datetime(df_ef['time'])
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df_flash['time'] = pd.to_datetime(df_flash['time'])
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merged_df = pd.merge(df_flash, df_ef, on='time', how='inner')
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merged_df = merged_df.drop('time', axis=1)
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print("数据框形状:", merged_df.shape)
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print(merged_df.head())
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# 分离特征和目标变量
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# 确保列名是字符串，以避免 LightGBM 的问题
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X = merged_df.drop('flash_count', axis=1)
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X.columns = ["".join (c if c.isalnum() else "_" for c in str(x)) for x in X.columns]
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y = merged_df['flash_count']
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# 1. 先训练分类模型，判断flash_count是否为0
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y_cls = (y > 0).astype(int) # 0为无闪电，1为有闪电
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X_train_cls, X_test_cls, y_train_cls, y_test_cls = train_test_split(
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X, y_cls, test_size=0.2, random_state=42, stratify=y_cls
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)
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classifier = lgb.LGBMClassifier(n_estimators=100, learning_rate=0.05, random_state=42)
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classifier.fit(X_train_cls, y_train_cls)
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# 2. 只用flash_count>0的数据训练回归模型
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X_reg = X[y > 0]
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y_reg = y[y > 0]
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X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(
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X_reg, y_reg, test_size=0.2, random_state=42
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)
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regressor = lgb.LGBMRegressor(n_estimators=150, learning_rate=0.05, random_state=42)
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regressor.fit(X_train_reg, y_train_reg)
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# ---------------------------------------------------------------------------
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# --- 模型保存 ---
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# ---------------------------------------------------------------------------
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os.makedirs("model", exist_ok=True)
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with open("model/212_classifier_model.pkl", "wb") as f:
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pickle.dump(classifier, f)
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print("分类器模型已保存到 model/212/212_classifier_model.pkl")
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with open("model/212_regressor_model.pkl", "wb") as f:
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pickle.dump(regressor, f)
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print("回归器模型已保存到 model/212/212_regressor_model.pkl")
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