课程完整代码：https://github.com/fengdu78/WZU-machine-learning-course
代码修改并注释：黄海广，haiguang2000@wzu.edu.cn
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
from sklearn.model_selection import train_test_split生成数据
生成12000行的数据，训练集和测试集按照3:1划分
from sklearn.datasets import make_hastie_10_2data, target = make_hastie_10_2()X_train, X_test, y_train, y_test = train_test_split(data, target, random_state=123)
X_train.shape, X_test.shape((9000, 10), (3000, 10))模型对比
对比六大模型，都使用默认参数，因为数据是
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from sklearn.model_selection import cross_val_score
import timeclf1 = LogisticRegression()
clf2 = RandomForestClassifier()
clf3 = AdaBoostClassifier()
clf4 = GradientBoostingClassifier()
clf5 = XGBClassifier()
clf6 = LGBMClassifier()for clf, label in zip([clf1, clf2, clf3, clf4, clf5, clf6], ['Logistic Regression', 'Random Forest', 'AdaBoost', 'GBDT', 'XGBoost','LightGBM'
]):start = time.time()scores = cross_val_score(clf, X_train, y_train, scoring='accuracy', cv=5)end = time.time()running_time = end - startprint("Accuracy: %0.8f (+/- %0.2f),耗时%0.2f秒。模型名称[%s]" %(scores.mean(), scores.std(), running_time, label))Accuracy: 0.47488889 (+/- 0.00),耗时0.04秒。模型名称[Logistic Regression]
Accuracy: 0.88966667 (+/- 0.01),耗时16.34秒。模型名称[Random Forest]
Accuracy: 0.88311111 (+/- 0.00),耗时3.39秒。模型名称[AdaBoost]
Accuracy: 0.91388889 (+/- 0.01),耗时13.14秒。模型名称[GBDT]
Accuracy: 0.92977778 (+/- 0.00),耗时3.60秒。模型名称[XGBoost]
Accuracy: 0.93188889 (+/- 0.01),耗时0.58秒。模型名称[LightGBM]对比了六大模型，可以看出，逻辑回归速度最快，但准确率最低。而LightGBM，速度快，而且准确率最高，所以，现在处理结构化数据的时候，大部分都是用LightGBM算法。
XGBoost的使用
1.原生XGBoost的使用
import xgboost as xgb
#记录程序运行时间
import timestart_time = time.time()#xgb矩阵赋值
xgb_train = xgb.DMatrix(X_train, y_train)
xgb_test = xgb.DMatrix(X_test, label=y_test)
##参数
params = {'booster': 'gbtree',
# 'silent': 1, #设置成1则没有运行信息输出，最好是设置为0.#'nthread':7,# cpu 线程数 默认最大'eta': 0.007, # 如同学习率'min_child_weight': 3,# 这个参数默认是 1，是每个叶子里面 h 的和至少是多少，对正负样本不均衡时的 0-1 分类而言#，假设 h 在 0.01 附近，min_child_weight 为 1 意味着叶子节点中最少需要包含 100 个样本。#这个参数非常影响结果，控制叶子节点中二阶导的和的最小值，该参数值越小，越容易 overfitting。'max_depth': 6, # 构建树的深度，越大越容易过拟合'gamma': 0.1, # 树的叶子节点上作进一步分区所需的最小损失减少,越大越保守，一般0.1、0.2这样子。'subsample': 0.7, # 随机采样训练样本'colsample_bytree': 0.7, # 生成树时进行的列采样 'lambda': 2, # 控制模型复杂度的权重值的L2正则化项参数，参数越大，模型越不容易过拟合。#'alpha':0, # L1 正则项参数#'scale_pos_weight':1, #如果取值大于0的话，在类别样本不平衡的情况下有助于快速收敛。#'objective': 'multi:softmax', #多分类的问题#'num_class':10, # 类别数，多分类与 multisoftmax 并用'seed': 1000, #随机种子#'eval_metric': 'auc'
}
plst = list(params.items())
num_rounds = 500 # 迭代次数
watchlist = [(xgb_train, 'train'), (xgb_test, 'val')]#训练模型并保存
# early_stopping_rounds 当设置的迭代次数较大时，early_stopping_rounds 可在一定的迭代次数内准确率没有提升就停止训练
model = xgb.train(plst,xgb_train,num_rounds,watchlist,early_stopping_rounds=100,
)
#model.save_model('./model/xgb.model') # 用于存储训练出的模型
print("best best_ntree_limit", model.best_ntree_limit)
y_pred = model.predict(xgb_test, ntree_limit=model.best_ntree_limit)
print('error=%f' %(sum(1for i in range(len(y_pred)) if int(y_pred[i] > 0.5) != y_test[i]) /float(len(y_pred))))
# 输出运行时长
cost_time = time.time() - start_time
print("xgboost success!", '\n', "cost time:", cost_time, "(s)......")[0] train-rmse:1.11000 val-rmse:1.10422
[1] train-rmse:1.10734 val-rmse:1.10182
[2] train-rmse:1.10465 val-rmse:1.09932
[3] train-rmse:1.10207 val-rmse:1.09694……
[497] train-rmse:0.62135 val-rmse:0.68680
[498] train-rmse:0.62096 val-rmse:0.68650
[499] train-rmse:0.62056 val-rmse:0.68624
best best_ntree_limit 500
error=0.826667
xgboost success! cost time: 3.5742645263671875 (s)......2.使用scikit-learn接口
会改变的函数名是：
eta -> learning_rate
lambda -> reg_lambda
alpha -> reg_alpha
from sklearn.model_selection import train_test_split
from sklearn import metricsfrom xgboost import XGBClassifierclf = XGBClassifier(# silent=0, #设置成1则没有运行信息输出，最好是设置为0.是否在运行升级时打印消息。#nthread=4,# cpu 线程数 默认最大learning_rate=0.3, # 如同学习率min_child_weight=1,# 这个参数默认是 1，是每个叶子里面 h 的和至少是多少，对正负样本不均衡时的 0-1 分类而言#，假设 h 在 0.01 附近，min_child_weight 为 1 意味着叶子节点中最少需要包含 100 个样本。#这个参数非常影响结果，控制叶子节点中二阶导的和的最小值，该参数值越小，越容易 overfitting。max_depth=6, # 构建树的深度，越大越容易过拟合gamma=0, # 树的叶子节点上作进一步分区所需的最小损失减少,越大越保守，一般0.1、0.2这样子。subsample=1, # 随机采样训练样本 训练实例的子采样比max_delta_step=0, #最大增量步长，我们允许每个树的权重估计。colsample_bytree=1, # 生成树时进行的列采样 reg_lambda=1, # 控制模型复杂度的权重值的L2正则化项参数，参数越大，模型越不容易过拟合。#reg_alpha=0, # L1 正则项参数#scale_pos_weight=1, #如果取值大于0的话，在类别样本不平衡的情况下有助于快速收敛。平衡正负权重#objective= 'multi:softmax', #多分类的问题 指定学习任务和相应的学习目标#num_class=10, # 类别数，多分类与 multisoftmax 并用n_estimators=100, #树的个数seed=1000 #随机种子#eval_metric= 'auc'
)
clf.fit(X_train, y_train)y_true, y_pred = y_test, clf.predict(X_test)
print("Accuracy : %.4g" % metrics.accuracy_score(y_true, y_pred))[20:16:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
Accuracy : 0.936LIghtGBM的使用
1.原生接口
import lightgbm as lgb
from sklearn.metrics import mean_squared_error
# 加载你的数据
# print('Load data...')
# df_train = pd.read_csv('../regression/regression.train', header=None, sep='\t')
# df_test = pd.read_csv('../regression/regression.test', header=None, sep='\t')
#
# y_train = df_train[0].values
# y_test = df_test[0].values
# X_train = df_train.drop(0, axis=1).values
# X_test = df_test.drop(0, axis=1).values# 创建成lgb特征的数据集格式
lgb_train = lgb.Dataset(X_train, y_train) # 将数据保存到LightGBM二进制文件将使加载更快
lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) # 创建验证数据# 将参数写成字典下形式
params = {'task': 'train','boosting_type': 'gbdt', # 设置提升类型'objective': 'regression', # 目标函数'metric': {'l2', 'auc'}, # 评估函数'num_leaves': 31, # 叶子节点数'learning_rate': 0.05, # 学习速率'feature_fraction': 0.9, # 建树的特征选择比例'bagging_fraction': 0.8, # 建树的样本采样比例'bagging_freq': 5, # k 意味着每 k 次迭代执行bagging'verbose': 1 # <0 显示致命的, =0 显示错误 (警告), >0 显示信息
}print('Start training...')
# 训练 cv and train
gbm = lgb.train(params,lgb_train,num_boost_round=500,valid_sets=lgb_eval,early_stopping_rounds=5) # 训练数据需要参数列表和数据集print('Save model...')gbm.save_model('model.txt') # 训练后保存模型到文件print('Start predicting...')
# 预测数据集
y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration) #如果在训练期间启用了早期停止，可以通过best_iteration方式从最佳迭代中获得预测
# 评估模型
print('error=%f' %(sum(1for i in range(len(y_pred)) if int(y_pred[i] > 0.5) != y_test[i]) /float(len(y_pred))))Start training...
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000448 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 9000, number of used features: 10
[LightGBM] [Info] Start training from score 0.012000
[1] valid_0's auc: 0.814399 valid_0's l2: 0.965563
Training until validation scores don't improve for 5 rounds
[2] valid_0's auc: 0.84729 valid_0's l2: 0.934647
……
[193] valid_0's auc: 0.982685 valid_0's l2: 0.320043
Early stopping, best iteration is:
[188] valid_0's auc: 0.982794 valid_0's l2: 0.319746
Save model...
Start predicting...
error=0.6640002.scikit-learn接口
from sklearn import metrics
from lightgbm import LGBMClassifierclf = LGBMClassifier(boosting_type='gbdt', # 提升树的类型 gbdt,dart,goss,rfnum_leaves=31, #树的最大叶子数，对比xgboost一般为2^(max_depth)max_depth=-1, #最大树的深度learning_rate=0.1, #学习率n_estimators=100, # 拟合的树的棵树，相当于训练轮数subsample_for_bin=200000,objective=None,class_weight=None,min_split_gain=0.0, # 最小分割增益min_child_weight=0.001, # 分支结点的最小权重min_child_samples=20,subsample=1.0, # 训练样本采样率 行subsample_freq=0, # 子样本频率colsample_bytree=1.0, # 训练特征采样率 列reg_alpha=0.0, # L1正则化系数reg_lambda=0.0, # L2正则化系数random_state=None,n_jobs=-1,silent=True,
)
clf.fit(X_train, y_train, eval_metric='auc')
#设置验证集合 verbose=False不打印过程
clf.fit(X_train, y_train)y_true, y_pred = y_test, clf.predict(X_test)
print("Accuracy : %.4g" % metrics.accuracy_score(y_true, y_pred))Accuracy : 0.927参考
1.https://xgboost.readthedocs.io/
2.https://lightgbm.readthedocs.io/
3.https://blog.csdn.net/q383700092/article/details/53763328?locationNum=9&fps=1
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