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LightGBM|LightGBM baseline

【LightGBM|LightGBM baseline】LightGBM是一种使用基于树的学习算法的梯度提升框架。相比XGBoost速度更快，结果也相近。
使用交叉验证，以f1为评价方法的baseline:

#!/usr/bin/env python # _*_ coding:utf-8 _*_import matplotlib.pyplot as plt import pandas as pd import numpy as np from sklearn.externals import joblib from sklearn.model_selection import StratifiedKFold from sklearn.metrics import f1_score import lightgbm as lgb import warnings warnings.filterwarnings('ignore')pd.set_option('display.max_columns', 500, 'display.width', 1000)# 设置显示宽度 # data_train = pd.read_csv("train/train.csv") data_train = pd.read_csv("train_3.csv")# 训练集 data_test = pd.read_csv("test_3.csv")# 测试集 # 对current_service映射编码，对要分类的label不是从0开始的一般操作 label2current_service = dict(zip(range(0, len(set(data_train['current_service']))), sorted(list(set(data_train['current_service']))))) current_service2label = dict(zip(sorted(list(set(data_train['current_service']))), range(0, len(set(data_train['current_service']))))) data_train['current_service'] = data_train['current_service'].map(current_service2label) # print(len(set(data_train['current_service'])))# 15种类型 y = data_train.pop('current_service') user_id = data_train.pop('user_id') x_train = data_train test_user_id = data_test.pop('user_id') x_test = data_test print(x_train.info()) X, y, X_test = x_train.values, y.values, x_test.values# 转为np.array类型 n_splits = 5# 分为5折 seed = 2333# 随机种子 # lgb 参数 lgb_params = { "learning_rate": 0.05, "lambda_l1": 0.1, "lambda_l2": 0.2, "max_depth": 7, "num_leaves": 120, "objective": "multiclass", "num_class": 15, "verbose": -1, 'feature_fraction': 0.8, "min_split_gain": 0.1, "boosting_type": "gbdt", "subsample": 0.8, "min_data_in_leaf": 50, "colsample_bytree": 0.7, "colsample_bylevel": 0.7, "tree_method": 'exact' }# 采取k折模型方案 # 自定义F1评价函数 def f1_score_vail(pred, data_vail): labels = data_vail.get_label() pred = np.argmax(pred.reshape(15, -1), axis=0)# lgb的predict输出为各类型概率值 score_vail = f1_score(y_true=labels, y_pred=pred, average='macro') return 'f1_score', score_vail, Truex_score = []# 交叉验证各折的f1值 cv_pred = []# 各折的预测值 skf = StratifiedKFold(n_splits=n_splits, random_state=seed, shuffle=True) # 交叉验证 for index, (train_index, test_index) in enumerate(skf.split(X, y)): print(index) X_train, X_valid, y_train, y_valid = X[train_index], X[test_index], y[train_index], y[test_index] train_data = https://www.it610.com/article/lgb.Dataset(X_train, label=y_train)# 训练数据 validation_data = lgb.Dataset(X_valid, label=y_valid)# 验证数据 clf = lgb.train(lgb_params, train_data, num_boost_round=150000, valid_sets=[validation_data], early_stopping_rounds=100, feval=f1_score_vail, verbose_eval=1)# 训练 # clf = joblib.load("model/lgb_{}.m".format(index))# 保存模型 # joblib.dump(clf, "model/lgb_{}.m".format(index))# 加载模型 x_pred = clf.predict(X_valid, num_iteration=clf.best_iteration) x_pred = [np.argmax(x) for x in x_pred] x_score.append(f1_score(y_valid, x_pred, average='macro'))# 计算f1值 y_test = clf.predict(X_test, num_iteration=clf.best_iteration)# 预测 y_test = [np.argmax(x) for x in y_test] if index == 0: cv_pred = np.array(y_test).reshape(-1, 1) else: cv_pred = np.hstack((cv_pred, np.array(y_test).reshape(-1, 1))) if index == 4: lgb.plot_importance(clf, figsize=(50, 50))# 画出重要特征 plt.title("Feature_importance") plt.show() # 投票 submit = [] for line in cv_pred: submit.append(np.argmax(np.bincount(line))) # 保存结果 df_test = pd.DataFrame() df_test['id'] = list(test_user_id.unique()) df_test['predict'] = submit df_test['predict'] = df_test['predict'].map(label2current_service) df_test.to_csv('output/lgb4.csv', index=False) print(x_score, np.mean(x_score))

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