Posted in Python onJanuary 20, 2020
1.数据读取
利用原生xgboost库读取libsvm数据
import xgboost as xgb data = xgb.DMatrix(libsvm文件)
使用sklearn读取libsvm数据
from sklearn.datasets import load_svmlight_file X_train,y_train = load_svmlight_file(libsvm文件)
使用pandas读取完数据后在转化为标准形式
2.模型训练过程
1.未调参基线模型
使用xgboost原生库进行训练
import xgboost as xgb from sklearn.metrics import accuracy_score dtrain = xgb.DMatrix(f_train, label = l_train) dtest = xgb.DMatrix(f_test, label = l_test) param = {'max_depth':2, 'eta':1, 'silent':0, 'objective':'binary:logistic' } num_round = 2 bst = xgb.train(param, dtrain, num_round) train_preds = bst.predict(dtrain) train_predictions = [round(value) for value in train_preds] #进行四舍五入的操作--变成0.1(算是设定阈值的符号函数) train_accuracy = accuracy_score(l_train, train_predictions) #使用sklearn进行比较正确率 print ("Train Accuary: %.2f%%" % (train_accuracy * 100.0)) from xgboost import plot_importance #显示特征重要性 plot_importance(bst)#打印重要程度结果。 pyplot.show()
使用XGBClassifier进行训练
# 未设定早停止, 未进行矩阵变换 from xgboost import XGBClassifier from sklearn.datasets import load_svmlight_file #用于直接读取svmlight文件形式, 否则就需要使用xgboost.DMatrix(文件名)来读取这种格式的文件 from sklearn.metrics import accuracy_score from matplotlib import pyplot num_round = 100 bst1 =XGBClassifier(max_depth=2, learning_rate=1, n_estimators=num_round, #弱分类树太少的话取不到更多的特征重要性 silent=True, objective='binary:logistic') bst1.fit(f_train, l_train) train_preds = bst1.predict(f_train) train_accuracy = accuracy_score(l_train, train_preds) print ("Train Accuary: %.2f%%" % (train_accuracy * 100.0)) preds = bst1.predict(f_test) test_accuracy = accuracy_score(l_test, preds) print("Test Accuracy: %.2f%%" % (test_accuracy * 100.0)) from xgboost import plot_importance #显示特征重要性 plot_importance(bst1)#打印重要程度结果。 pyplot.show()
2.两种交叉验证方式
使用cross_val_score进行交叉验证
#利用model_selection进行交叉训练 from xgboost import XGBClassifier from sklearn.model_selection import StratifiedKFold from sklearn.model_selection import cross_val_score from sklearn.metrics import accuracy_score from matplotlib import pyplot param = {'max_depth':2, 'eta':1, 'silent':0, 'objective':'binary:logistic' } num_round = 100 bst2 =XGBClassifier(max_depth=2, learning_rate=0.1,n_estimators=num_round, silent=True, objective='binary:logistic') bst2.fit(f_train, l_train) kfold = StratifiedKFold(n_splits=10, random_state=7) results = cross_val_score(bst2, f_train, l_train, cv=kfold)#对数据进行十折交叉验证--9份训练,一份测试 print(results) print("CV Accuracy: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100)) from xgboost import plot_importance #显示特征重要性 plot_importance(bst2)#打印重要程度结果。 pyplot.show()
使用GridSearchCV进行网格搜索
#使用sklearn中提供的网格搜索进行测试--找出最好参数,并作为默认训练参数 from xgboost import XGBClassifier from sklearn.model_selection import GridSearchCV from sklearn.metrics import accuracy_score from matplotlib import pyplot params = {'max_depth':2, 'eta':0.1, 'silent':0, 'objective':'binary:logistic' } bst =XGBClassifier(max_depth=2, learning_rate=0.1, silent=True, objective='binary:logistic') param_test = { 'n_estimators': range(1, 51, 1) } clf = GridSearchCV(estimator = bst, param_grid = param_test, scoring='accuracy', cv=5)# 5折交叉验证 clf.fit(f_train, l_train) #默认使用最优的参数 preds = clf.predict(f_test) test_accuracy = accuracy_score(l_test, preds) print("Test Accuracy of gridsearchcv: %.2f%%" % (test_accuracy * 100.0)) clf.cv_results_, clf.best_params_, clf.best_score_
3.早停止调参?early_stopping_rounds(查看的是损失是否变化)
#进行提早停止的单独实例 import xgboost as xgb from xgboost import XGBClassifier from sklearn.metrics import accuracy_score from matplotlib import pyplot param = {'max_depth':2, 'eta':1, 'silent':0, 'objective':'binary:logistic' } num_round = 100 bst =XGBClassifier(max_depth=2, learning_rate=0.1, n_estimators=num_round, silent=True, objective='binary:logistic') eval_set =[(f_test, l_test)] bst.fit(f_train, l_train, early_stopping_rounds=10, eval_metric="error",eval_set=eval_set, verbose=True) #early_stopping_rounds--当多少次的效果差不多时停止 eval_set--用于显示损失率的数据 verbose--显示错误率的变化过程 # make prediction preds = bst.predict(f_test) test_accuracy = accuracy_score(l_test, preds) print("Test Accuracy: %.2f%%" % (test_accuracy * 100.0))
4.多数据观察训练损失
#多参数顺 import xgboost as xgb from xgboost import XGBClassifier from sklearn.metrics import accuracy_score from matplotlib import pyplot num_round = 100 bst =XGBClassifier(max_depth=2, learning_rate=0.1, n_estimators=num_round, silent=True, objective='binary:logistic') eval_set = [(f_train, l_train), (f_test, l_test)] bst.fit(f_train, l_train, eval_metric=["error", "logloss"], eval_set=eval_set, verbose=True) # make prediction preds = bst.predict(f_test) test_accuracy = accuracy_score(l_test, preds) print("Test Accuracy: %.2f%%" % (test_accuracy * 100.0))
5.模型保存与读取
#模型保存 bst.save_model('demo.model') #模型读取与预测 modelfile = 'demo.model' # 1 bst = xgb.Booster({'nthread':8}, model_file = modelfile) # 2 f_test1 = xgb.DMatrix(f_test) #尽量使用xgboost的自己的数据矩阵 ypred1 = bst.predict(f_test1) train_predictions = [round(value) for value in ypred1] test_accuracy1 = accuracy_score(l_test, train_predictions) print("Test Accuracy: %.2f%%" % (test_accuracy1 * 100.0))
以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持三水点靠木。
python机器学习库xgboost的使用
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