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@@ -110,14 +110,14 @@ class LightGBM(object):
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"objective": "binary", # 指定二分类任务
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"metric": "binary_logloss", # 评估指标为二分类的log损失
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"num_leaves": 31, # 叶子节点数
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- "learning_rate": 0.01, # 学习率
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+ "learning_rate": 0.005, # 学习率
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"bagging_fraction": 0.9, # 建树的样本采样比例
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"feature_fraction": 0.8, # 建树的特征选择比例
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"bagging_freq": 5, # k 意味着每 k 次迭代执行bagging
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"num_threads": 4, # 线程数量
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}
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# 训练模型
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- num_round = 1000
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+ num_round = 2000
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print("开始训练......")
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bst = lgb.train(params, train_data, num_round, valid_sets=[test_data])
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bst.save_model(self.model)
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@@ -128,7 +128,7 @@ class LightGBM(object):
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评估模型性能
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:return:
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"""
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- fw = open("summary_tag_02.txt", "a+", encoding="utf-8")
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+ fw = open("summary_tag_03.txt", "a+", encoding="utf-8")
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# 测试数据
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with open("produce_data/x_data_total_return_predict.json") as f1:
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x_list = json.loads(f1.read())
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@@ -181,6 +181,6 @@ class LightGBM(object):
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if __name__ == "__main__":
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L = LightGBM()
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- # L.train_model()
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+ L.train_model()
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L.evaluate_model()
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- # L.feature_importance()
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+ L.feature_importance()
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