• 《机器学习》(9)


    from sklearn.model_selection import train_test_split,cross_val_score # 交叉验证所需的函数
    from sklearn.model_selection import KFold,LeaveOneOut,LeavePOut,ShuffleSplit # 交叉验证所需的子集划分方法
    from sklearn.model_selection import StratifiedKFold,StratifiedShuffleSplit # 分层分割
    from sklearn.model_selection import GroupKFold,LeaveOneGroupOut,LeavePGroupsOut,GroupShuffleSplit # 分组分割
    from sklearn.model_selection import TimeSeriesSplit # 时间序列分割
    from sklearn import datasets  # 自带数据集
    from sklearn import svm  # SVM算法
    from sklearn import preprocessing  # 预处理模块
    from sklearn.metrics import recall_score  # 模型度量
    
    iris = datasets.load_iris()  # 加载数据集
    print('样本集大小:',iris.data.shape,iris.target.shape)
    
    # ===================================数据集划分,训练模型==========================
    X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.4, random_state=0)  # 交叉验证划分训练集和测试集.test_size为测试集所占的比例
    print('训练集大小:',X_train.shape,y_train.shape)  # 训练集样本大小
    print('测试集大小:',X_test.shape,y_test.shape)  # 测试集样本大小
    clf = svm.SVC(kernel='linear', C=1).fit(X_train, y_train) # 使用训练集训练模型
    print('准确率:',clf.score(X_test, y_test))  # 计算测试集的度量值(准确率)
    
    
    #  如果涉及到归一化,则在测试集上也要使用训练集模型提取的归一化函数。
    scaler = preprocessing.StandardScaler().fit(X_train)  # 通过训练集获得归一化函数模型。(也就是先减几,再除以几的函数)。在训练集和测试集上都使用这个归一化函数
    X_train_transformed = scaler.transform(X_train)
    clf = svm.SVC(kernel='linear', C=1).fit(X_train_transformed, y_train) # 使用训练集训练模型
    X_test_transformed = scaler.transform(X_test)
    print(clf.score(X_test_transformed, y_test))  # 计算测试集的度量值(准确度)
    
    # ===================================直接调用交叉验证评估模型==========================
    clf = svm.SVC(kernel='linear', C=1)
    scores = cross_val_score(clf, iris.data, iris.target, cv=5)  #cv为迭代次数。
    print(scores)  # 打印输出每次迭代的度量值(准确度)
    print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))  # 获取置信区间。(也就是均值和方差)
    
    # ===================================多种度量结果======================================
    scoring = ['precision_macro', 'recall_macro'] # precision_macro为精度,recall_macro为召回率
    scores = cross_validate(clf, iris.data, iris.target, scoring=scoring,cv=5, return_train_score=True)
    sorted(scores.keys())
    print('测试结果:',scores)  # scores类型为字典。包含训练得分,拟合次数, score-times (得分次数)
    
    
    # ==================================K折交叉验证、留一交叉验证、留p交叉验证、随机排列交叉验证==========================================
    # k折划分子集
    kf = KFold(n_splits=2)
    for train, test in kf.split(iris.data):
        print("k折划分:%s %s" % (train.shape, test.shape))
        break
    
    # 留一划分子集
    loo = LeaveOneOut()
    for train, test in loo.split(iris.data):
        print("留一划分:%s %s" % (train.shape, test.shape))
        break
    
    # 留p划分子集
    lpo = LeavePOut(p=2)
    for train, test in loo.split(iris.data):
        print("留p划分:%s %s" % (train.shape, test.shape))
        break
    
    # 随机排列划分子集
    ss = ShuffleSplit(n_splits=3, test_size=0.25,random_state=0)
    for train_index, test_index in ss.split(iris.data):
        print("随机排列划分:%s %s" % (train.shape, test.shape))
        break
    
    # ==================================分层K折交叉验证、分层随机交叉验证==========================================
    skf = StratifiedKFold(n_splits=3)  #各个类别的比例大致和完整数据集中相同
    for train, test in skf.split(iris.data, iris.target):
        print("分层K折划分:%s %s" % (train.shape, test.shape))
        break
    
    skf = StratifiedShuffleSplit(n_splits=3)  # 划分中每个类的比例和完整数据集中的相同
    for train, test in skf.split(iris.data, iris.target):
        print("分层随机划分:%s %s" % (train.shape, test.shape))
        break
    
    
    # ==================================组 k-fold交叉验证、留一组交叉验证、留 P 组交叉验证、Group Shuffle Split==========================================
    X = [0.1, 0.2, 2.2, 2.4, 2.3, 4.55, 5.8, 8.8, 9, 10]
    y = ["a", "b", "b", "b", "c", "c", "c", "d", "d", "d"]
    groups = [1, 1, 1, 2, 2, 2, 3, 3, 3, 3]
    
    # k折分组
    gkf = GroupKFold(n_splits=3)  # 训练集和测试集属于不同的组
    for train, test in gkf.split(X, y, groups=groups):
        print("组 k-fold分割:%s %s" % (train, test))
    
    # 留一分组
    logo = LeaveOneGroupOut()
    for train, test in logo.split(X, y, groups=groups):
        print("留一组分割:%s %s" % (train, test))
    
    # 留p分组
    lpgo = LeavePGroupsOut(n_groups=2)
    for train, test in lpgo.split(X, y, groups=groups):
        print("留 P 组分割:%s %s" % (train, test))
    
    # 随机分组
    gss = GroupShuffleSplit(n_splits=4, test_size=0.5, random_state=0)
    for train, test in gss.split(X, y, groups=groups):
        print("随机分割:%s %s" % (train, test))
    
    
    # ==================================时间序列分割==========================================
    tscv = TimeSeriesSplit(n_splits=3)
    TimeSeriesSplit(max_train_size=None, n_splits=3)
    for train, test in tscv.split(iris.data):
        print("时间序列分割:%s %s" % (train, test))
    

      

  • 相关阅读:
    编写有效用例_阅读笔记05
    编写有效用例_阅读笔记04
    编写有效用例_阅读笔记03
    编写有效用例_阅读笔记02
    软件需求与分析课堂讨论一
    编写有效用例_阅读笔记01
    问题账户需求分析
    【知识总结】动态 DP
    【知识总结】数论全家桶
    【知识总结】多项式全家桶(三点五)(拆系数解决任意模数多项式卷积)
  • 原文地址:https://www.cnblogs.com/ywqtro/p/14370393.html
Copyright © 2020-2023  润新知