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))