【機器學習】集成學習代碼練習

課程完整代碼：https://github.com/fengdu78/WZU-machine-learning-course

代碼修改并注釋：黃海廣，[email protected]

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

生成數(shù)據(jù)

生成12000行的數(shù)據(jù)，訓練集和測試集按照3:1劃分

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))

模型對比

對比六大模型，都使用默認參數(shù)，因為數(shù)據(jù)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?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),耗時%0.2f秒。模型名稱[%s]"?%
??????????(scores.mean(),?scores.std(),?running_time,?label))

Accuracy: 0.47488889 (+/- 0.00),耗時0.04秒。模型名稱[Logistic Regression]
Accuracy: 0.88966667 (+/- 0.01),耗時16.34秒。模型名稱[Random Forest]
Accuracy: 0.88311111 (+/- 0.00),耗時3.39秒。模型名稱[AdaBoost]
Accuracy: 0.91388889 (+/- 0.01),耗時13.14秒。模型名稱[GBDT]
Accuracy: 0.92977778 (+/- 0.00),耗時3.60秒。模型名稱[XGBoost]
Accuracy: 0.93188889 (+/- 0.01),耗時0.58秒。模型名稱[LightGBM]

對比了六大模型，可以看出，邏輯回歸速度最快，但準確率最低。而LightGBM，速度快，而且準確率最高，所以，現(xiàn)在處理結構化數(shù)據(jù)的時候，大部分都是用LightGBM算法。

XGBoost的使用

1.原生XGBoost的使用

import?xgboost?as?xgb
#記錄程序運行時間
import?time

start_time?=?time.time()

#xgb矩陣賦值
xgb_train?=?xgb.DMatrix(X_train,?y_train)
xgb_test?=?xgb.DMatrix(X_test,?label=y_test)
##參數(shù)
params?=?{
????'booster':?'gbtree',
#?????'silent':?1,??#設置成1則沒有運行信息輸出，最好是設置為0.
????#'nthread':7,#?cpu?線程數(shù)?默認最大
????'eta':?0.007,??#?如同學習率
????'min_child_weight':?3,
????#?這個參數(shù)默認是?1，是每個葉子里面?h?的和至少是多少，對正負樣本不均衡時的?0-1?分類而言
????#，假設 h 在?0.01 附近，min_child_weight 為 1 意味著葉子節(jié)點中最少需要包含 100?個樣本。
????#這個參數(shù)非常影響結果，控制葉子節(jié)點中二階導的和的最小值，該參數(shù)值越小，越容易 overfitting。
????'max_depth':?6,??#?構建樹的深度，越大越容易過擬合
????'gamma':?0.1,??#?樹的葉子節(jié)點上作進一步分區(qū)所需的最小損失減少,越大越保守，一般0.1、0.2這樣子。
????'subsample':?0.7,??#?隨機采樣訓練樣本
????'colsample_bytree':?0.7,??#?生成樹時進行的列采樣?
????'lambda':?2,??#?控制模型復雜度的權重值的L2正則化項參數(shù)，參數(shù)越大，模型越不容易過擬合。
????#'alpha':0,?#?L1?正則項參數(shù)
????#'scale_pos_weight':1, #如果取值大于0的話，在類別樣本不平衡的情況下有助于快速收斂。
????#'objective':?'multi:softmax',?#多分類的問題
????#'num_class':10,?#?類別數(shù)，多分類與?multisoftmax?并用
????'seed':?1000,??#隨機種子
????#'eval_metric':?'auc'
}
plst?=?list(params.items())
num_rounds?=?500??#?迭代次數(shù)
watchlist?=?[(xgb_train,?'train'),?(xgb_test,?'val')]

#訓練模型并保存
#?early_stopping_rounds?當設置的迭代次數(shù)較大時，early_stopping_rounds?可在一定的迭代次數(shù)內準確率沒有提升就停止訓練
model?=?xgb.train(
????plst,
????xgb_train,
????num_rounds,
????watchlist,
????early_stopping_rounds=100,
)
#model.save_model('./model/xgb.model')?#?用于存儲訓練出的模型
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))))
#?輸出運行時長
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

會改變的函數(shù)名是：

eta -> learning_rate

lambda -> reg_lambda

alpha -> reg_alpha

from?sklearn.model_selection?import?train_test_split
from?sklearn?import?metrics

from?xgboost?import?XGBClassifier

clf?=?XGBClassifier(
????#???? silent=0, ?#設置成1則沒有運行信息輸出，最好是設置為0.是否在運行升級時打印消息。
????#nthread=4,#?cpu?線程數(shù)?默認最大
????learning_rate=0.3,??#?如同學習率
????min_child_weight=1,
????#?這個參數(shù)默認是?1，是每個葉子里面?h?的和至少是多少，對正負樣本不均衡時的?0-1?分類而言
????#，假設 h 在?0.01 附近，min_child_weight 為 1 意味著葉子節(jié)點中最少需要包含 100?個樣本。
????#這個參數(shù)非常影響結果，控制葉子節(jié)點中二階導的和的最小值，該參數(shù)值越小，越容易 overfitting。
????max_depth=6,??#?構建樹的深度，越大越容易過擬合
????gamma=0,??#?樹的葉子節(jié)點上作進一步分區(qū)所需的最小損失減少,越大越保守，一般0.1、0.2這樣子。
????subsample=1,??#?隨機采樣訓練樣本?訓練實例的子采樣比
????max_delta_step=0,??#最大增量步長，我們允許每個樹的權重估計。
????colsample_bytree=1,??#?生成樹時進行的列采樣?
????reg_lambda=1,??#?控制模型復雜度的權重值的L2正則化項參數(shù)，參數(shù)越大，模型越不容易過擬合。
????#reg_alpha=0,?#?L1?正則項參數(shù)
????#scale_pos_weight=1, #如果取值大于0的話，在類別樣本不平衡的情況下有助于快速收斂。平衡正負權重
????#objective=?'multi:softmax',?#多分類的問題?指定學習任務和相應的學習目標
????#num_class=10,?#?類別數(shù)，多分類與?multisoftmax?并用
????n_estimators=100,??#樹的個數(shù)
????seed=1000??#隨機種子
????#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 : 0.936

LIghtGBM的使用

1.原生接口

import?lightgbm?as?lgb
from?sklearn.metrics?import?mean_squared_error
#?加載你的數(shù)據(jù)
#?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

#?創(chuàng)建成lgb特征的數(shù)據(jù)集格式
lgb_train?=?lgb.Dataset(X_train,?y_train)??#?將數(shù)據(jù)保存到LightGBM二進制文件將使加載更快
lgb_eval?=?lgb.Dataset(X_test,?y_test,?reference=lgb_train)??#?創(chuàng)建驗證數(shù)據(jù)

#?將參數(shù)寫成字典下形式
params?=?{
????'task':?'train',
????'boosting_type':?'gbdt',??#?設置提升類型
????'objective':?'regression',??#?目標函數(shù)
????'metric':?{'l2',?'auc'},??#?評估函數(shù)
????'num_leaves':?31,??#?葉子節(jié)點數(shù)
????'learning_rate':?0.05,??#?學習速率
????'feature_fraction':?0.9,??#?建樹的特征選擇比例
????'bagging_fraction':?0.8,??#?建樹的樣本采樣比例
????'bagging_freq':?5,??#?k?意味著每?k?次迭代執(zhí)行bagging
????'verbose':?1??#?<0?顯示致命的,?=0?顯示錯誤?(警告),?>0?顯示信息
}

print('Start?training...')
#?訓練?cv?and?train
gbm?=?lgb.train(params,
????????????????lgb_train,
????????????????num_boost_round=500,
????????????????valid_sets=lgb_eval,
????????????????early_stopping_rounds=5)??#?訓練數(shù)據(jù)需要參數(shù)列表和數(shù)據(jù)集

print('Save?model...')

gbm.save_model('model.txt')??#?訓練后保存模型到文件

print('Start?predicting...')
#?預測數(shù)據(jù)集
y_pred?=?gbm.predict(X_test,?num_iteration=gbm.best_iteration
?????????????????????)??#如果在訓練期間啟用了早期停止，可以通過best_iteration方式從最佳迭代中獲得預測
#?評估模型
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
……
[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

2.scikit-learn接口

from?sklearn?import?metrics
from?lightgbm?import?LGBMClassifier

clf?=?LGBMClassifier(
????boosting_type='gbdt',??#?提升樹的類型?gbdt,dart,goss,rf
????num_leaves=31,??#樹的最大葉子數(shù)，對比xgboost一般為2^(max_depth)
????max_depth=-1,??#最大樹的深度
????learning_rate=0.1,??#學習率
????n_estimators=100,??#?擬合的樹的棵樹，相當于訓練輪數(shù)
????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,??#?分支結點的最小權重
????min_child_samples=20,
????subsample=1.0,??#?訓練樣本采樣率?行
????subsample_freq=0,??#?子樣本頻率
????colsample_bytree=1.0,??#?訓練特征采樣率?列
????reg_alpha=0.0,??#?L1正則化系數(shù)
????reg_lambda=0.0,??#?L2正則化系數(shù)
????random_state=None,
????n_jobs=-1,
????silent=True,
)
clf.fit(X_train,?y_train,?eval_metric='auc')
#設置驗證集合?verbose=False不打印過程
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

參考

1.https://xgboost.readthedocs.io/

2.https://lightgbm.readthedocs.io/

3.https://blog.csdn.net/q383700092/article/details/53763328?locationNum=9&fps=1