实验04 交叉验证

实验记录，方便以后查阅

一丶实现代码

# 交叉验证所需的函数
from sklearn.model_selection import train_test_split,cross_val_score,cross_validate
# 交叉验证所需的子集划分方法
from sklearn.model_selection import KFold,LeaveOneOut,LeavePOut,ShuffleSplit
# 分层分割
from sklearn.model_selection import StratifiedKFold,StratifiedShuffleSplit
# 分组分割
from sklearn.model_selection import GroupKFold,LeaveOneGroupOut,LeavePGroupsOut,GroupShuffleSplit
# 时间序列分割
from sklearn.model_selection import TimeSeriesSplit
# 自带数据集
from sklearn import datasets
# SVM算法
from sklearn import svm
# 预处理模块
from sklearn import preprocessing
# 模型度量
from sklearn.metrics import recall_score
# 加载数据集
iris = datasets.load_iris()
print('样本集大小：',iris.data.shape,iris.target.shape)

# ===================================数据集划分,训练模型==========================
# 交叉验证划分训练集和测试集.test_size为测试集所占的比例
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.4, random_state=0)
# 训练集样本大小
print('训练集大小：',X_train.shape,y_train.shape)
# 测试集样本大小
print('测试集大小：',X_test.shape,y_test.shape)
# 使用训练集训练模型
clf = svm.SVC(kernel='linear', C=1).fit(X_train, y_train)
# 计算测试集的度量值（准确率）
print('准确率：',clf.score(X_test, y_test))


#  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。
# 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
scaler = preprocessing.StandardScaler().fit(X_train)
X_train_transformed = scaler.transform(X_train)
# 使用训练集训练模型
clf = svm.SVC(kernel='linear', C=1).fit(X_train_transformed, y_train)
X_test_transformed = scaler.transform(X_test)
# 计算测试集的度量值（准确度）
print(clf.score(X_test_transformed, y_test))

# ===================================直接调用交叉验证评估模型==========================
clf = svm.SVC(kernel='linear', C=1)
#cv为迭代次数。
scores = cross_val_score(clf, iris.data, iris.target, cv=5)
print(scores)  # 打印输出每次迭代的度量值（准确度）
# 获取置信区间。（也就是均值和方差）
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))

# ===================================多种度量结果======================================
# precision_macro为精度，recall_macro为召回率
scoring = ['precision_macro', 'recall_macro']
scores = cross_validate(clf, iris.data, iris.target, scoring=scoring,cv=5, return_train_score=True)
sorted(scores.keys())
# scores类型为字典。包含训练得分，拟合次数， score-times （得分次数）
print('测试结果：',scores)


# ==================================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
# 训练集和测试集属于不同的组
gkf = GroupKFold(n_splits=3)
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))

二丶运行结果