【机器学习】随机森林、GBDT、XGBoost、LightGBM等集成学习代码练习

import warnings
warnings.filterwarnings("ignore")
import pandas as pd
from sklearn.model_selection import train_test_split  

from sklearn.datasets import make_hastie_10_2

data, 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))

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 time

clf1 = 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 - start
    print("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]

import xgboost as xgb
#记录程序运行时间
import time

start_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(1
           for 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)......

from sklearn.model_selection import train_test_split
from sklearn import metrics

from xgboost import XGBClassifier

clf = 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))

Accuracy : 0.936

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(1
           for 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
[3]	valid_0's auc: 0.872805	valid_0's l2: 0.905265
[4]	valid_0's auc: 0.884117	valid_0's l2: 0.877875
[5]	valid_0's auc: 0.895115	valid_0's l2: 0.852189

[191]	valid_0's auc: 0.982783	valid_0's l2: 0.319851
[192]	valid_0's auc: 0.982751	valid_0's l2: 0.319971
[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=0.664000

from sklearn import metrics
from lightgbm import LGBMClassifier

clf = LGBMClassifier(
    boosting_type='gbdt',  # 提升树的类型 gbdt,dart,goss,rf
    num_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