达观杯--模型构建（lightgbm）

最新推荐文章于 2022-03-21 23:36:40 发布

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最新推荐文章于 2022-03-21 23:36:40 发布

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分类专栏：机器学习达观杯特征工程文章标签：达观杯 lightgbm

本文链接：https://blog.youkuaiyun.com/linichengpps/article/details/83898415

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本文详细介绍使用LightGBM进行多分类任务的过程，包括自定义评价函数、数据预处理、模型训练及预测，并最终将结果保存至本地。

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使用lightgbm作为模型对数据进行训练。

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score
import time
import pickle
import lightgbm as lgb

t_start = time.time()

"""=====================================================================================================================
0 自定义验证集的评价函数
"""
def f1_score_vali(preds, data_vali):
    
    labels = data_vali.get_label()
    preds = np.argmax(preds.reshape(20, -1), axis=0)
    score_vali = f1_score(y_true=labels, y_pred=preds, average='macro')
    return 'f1_score', score_vali, True

"""=====================================================================================================================
1 读取数据,并转换到lgb的标准数据格式
"""
features_path = '../features/data_ensemble.pkl'
data_fp = open(features_path, 'rb')
x_train, y_train, x_test = pickle.load(data_fp)
data_fp.close()

"""划分训练集和验证集，验证集比例为test_size"""
x_train, x_vali, y_train, y_vali = train_test_split(x_train, y_train, test_size=0.1, random_state=0)
d_train = lgb.Dataset(data=x_train, label=y_train)
d_vali = lgb.Dataset(data=x_vali, label=y_vali)

"""=====================================================================================================================
2 训练lgb分类器
"""
params = {
        'boosting': 'gbdt',
        'application': 'multiclass',
        'num_class': 20,
        'learning_rate': 0.1,
        'num_leaves':31,
        'max_depth':-1,
        'lambda_l1': 0,
        'lambda_l2': 0.5,
        'bagging_fraction' :1.0,
        'feature_fraction': 1.0
        }

bst = lgb.train(params, d_train, num_boost_round=800, valid_sets=d_vali,feval=f1_score_vali, early_stopping_rounds=None,
                verbose_eval=True)

 
"""=====================================================================================================================
3 对测试集进行预测;将预测结果转换为官方标准格式；并将结果保存至本地
"""
y_test = np.argmax(bst.predict(x_test), axis=1) + 1

df_result = pd.DataFrame(data={'id':range(102277), 'class': y_test.tolist()})
result_path = '../results/' + features_path.split('/')[-1] + '_lgb' + '.csv'
df_result.to_csv(result_path, index=False)

t_end = time.time()
print("训练结束，耗时:{}min".format((t_end - t_start) / 60))