哪里有好看的网站平台补单
文章目录
- 交叉验证
- 划分
- 自定义划分
- 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
- 分层K折交叉验证
- 分层随机交叉验证
- 分割
- 组 k-fold分割
- 留一组分割
- 留 P 组分割
- 随机分割
- 时间序列分割
交叉验证
# 导入相关库# 交叉验证所需函数
from sklearn.model_selection import train_test_split,cross_val_score,cross_validate
# 交叉验证所需子集划分方法
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
划分
# 加载数据集
iris = datasets.load_iris()
print('样本集大小:', iris.data.shape, iris.target.shape)
print('样本:', iris.data, iris.target)
样本集大小: (150, 4) (150,)
样本: [[5.1 3.5 1.4 0.2]
[4.9 3. 1.4 0.2]
[4.7 3.2 1.3 0.2]
[4.6 3.1 1.5 0.2]
[5. 3.6 1.4 0.2]
[5.4 3.9 1.7 0.4]
[4.6 3.4 1.4 0.3]
[5. 3.4 1.5 0.2]
[4.4 2.9 1.4 0.2]
[4.9 3.1 1.5 0.1]
[5.4 3.7 1.5 0.2]
[4.8 3.4 1.6 0.2]
[4.8 3. 1.4 0.1]
[4.3 3. 1.1 0.1]
[5.8 4. 1.2 0.2]
[5.7 4.4 1.5 0.4]
[5.4 3.9 1.3 0.4]
[5.1 3.5 1.4 0.3]
[5.7 3.8 1.7 0.3]
[5.1 3.8 1.5 0.3]
[5.4 3.4 1.7 0.2]
[5.1 3.7 1.5 0.4]
[4.6 3.6 1. 0.2]
[5.1 3.3 1.7 0.5]
[4.8 3.4 1.9 0.2]
[5. 3. 1.6 0.2]
[5. 3.4 1.6 0.4]
[5.2 3.5 1.5 0.2]
[5.2 3.4 1.4 0.2]
[4.7 3.2 1.6 0.2]
[4.8 3.1 1.6 0.2]
[5.4 3.4 1.5 0.4]
[5.2 4.1 1.5 0.1]
[5.5 4.2 1.4 0.2]
[4.9 3.1 1.5 0.1]
[5. 3.2 1.2 0.2]
[5.5 3.5 1.3 0.2]
[4.9 3.1 1.5 0.1]
[4.4 3. 1.3 0.2]
[5.1 3.4 1.5 0.2]
[5. 3.5 1.3 0.3]
[4.5 2.3 1.3 0.3]
[4.4 3.2 1.3 0.2]
[5. 3.5 1.6 0.6]
[5.1 3.8 1.9 0.4]
[4.8 3. 1.4 0.3]
[5.1 3.8 1.6 0.2]
[4.6 3.2 1.4 0.2]
[5.3 3.7 1.5 0.2]
[5. 3.3 1.4 0.2]
[7. 3.2 4.7 1.4]
[6.4 3.2 4.5 1.5]
[6.9 3.1 4.9 1.5]
[5.5 2.3 4. 1.3]
[6.5 2.8 4.6 1.5]
[5.7 2.8 4.5 1.3]
[6.3 3.3 4.7 1.6]
[4.9 2.4 3.3 1. ]
[6.6 2.9 4.6 1.3]
[5.2 2.7 3.9 1.4]
[5. 2. 3.5 1. ]
[5.9 3. 4.2 1.5]
[6. 2.2 4. 1. ]
[6.1 2.9 4.7 1.4]
[5.6 2.9 3.6 1.3]
[6.7 3.1 4.4 1.4]
[5.6 3. 4.5 1.5]
[5.8 2.7 4.1 1. ]
[6.2 2.2 4.5 1.5]
[5.6 2.5 3.9 1.1]
[5.9 3.2 4.8 1.8]
[6.1 2.8 4. 1.3]
[6.3 2.5 4.9 1.5]
[6.1 2.8 4.7 1.2]
[6.4 2.9 4.3 1.3]
[6.6 3. 4.4 1.4]
[6.8 2.8 4.8 1.4]
[6.7 3. 5. 1.7]
[6. 2.9 4.5 1.5]
[5.7 2.6 3.5 1. ]
[5.5 2.4 3.8 1.1]
[5.5 2.4 3.7 1. ]
[5.8 2.7 3.9 1.2]
[6. 2.7 5.1 1.6]
[5.4 3. 4.5 1.5]
[6. 3.4 4.5 1.6]
[6.7 3.1 4.7 1.5]
[6.3 2.3 4.4 1.3]
[5.6 3. 4.1 1.3]
[5.5 2.5 4. 1.3]
[5.5 2.6 4.4 1.2]
[6.1 3. 4.6 1.4]
[5.8 2.6 4. 1.2]
[5. 2.3 3.3 1. ]
[5.6 2.7 4.2 1.3]
[5.7 3. 4.2 1.2]
[5.7 2.9 4.2 1.3]
[6.2 2.9 4.3 1.3]
[5.1 2.5 3. 1.1]
[5.7 2.8 4.1 1.3]
[6.3 3.3 6. 2.5]
[5.8 2.7 5.1 1.9]
[7.1 3. 5.9 2.1]
[6.3 2.9 5.6 1.8]
[6.5 3. 5.8 2.2]
[7.6 3. 6.6 2.1]
[4.9 2.5 4.5 1.7]
[7.3 2.9 6.3 1.8]
[6.7 2.5 5.8 1.8]
[7.2 3.6 6.1 2.5]
[6.5 3.2 5.1 2. ]
[6.4 2.7 5.3 1.9]
[6.8 3. 5.5 2.1]
[5.7 2.5 5. 2. ]
[5.8 2.8 5.1 2.4]
[6.4 3.2 5.3 2.3]
[6.5 3. 5.5 1.8]
[7.7 3.8 6.7 2.2]
[7.7 2.6 6.9 2.3]
[6. 2.2 5. 1.5]
[6.9 3.2 5.7 2.3]
[5.6 2.8 4.9 2. ]
[7.7 2.8 6.7 2. ]
[6.3 2.7 4.9 1.8]
[6.7 3.3 5.7 2.1]
[7.2 3.2 6. 1.8]
[6.2 2.8 4.8 1.8]
[6.1 3. 4.9 1.8]
[6.4 2.8 5.6 2.1]
[7.2 3. 5.8 1.6]
[7.4 2.8 6.1 1.9]
[7.9 3.8 6.4 2. ]
[6.4 2.8 5.6 2.2]
[6.3 2.8 5.1 1.5]
[6.1 2.6 5.6 1.4]
[7.7 3. 6.1 2.3]
[6.3 3.4 5.6 2.4]
[6.4 3.1 5.5 1.8]
[6. 3. 4.8 1.8]
[6.9 3.1 5.4 2.1]
[6.7 3.1 5.6 2.4]
[6.9 3.1 5.1 2.3]
[5.8 2.7 5.1 1.9]
[6.8 3.2 5.9 2.3]
[6.7 3.3 5.7 2.5]
[6.7 3. 5.2 2.3]
[6.3 2.5 5. 1.9]
[6.5 3. 5.2 2. ]
[6.2 3.4 5.4 2.3]
[5.9 3. 5.1 1.8]] [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2]
自定义划分
# 数据集划分
# 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)
print('训练集:', X_train, y_train)
print('测试集:', X_test, y_test)
训练集: [[6. 3.4 4.5 1.6]
[4.8 3.1 1.6 0.2]
[5.8 2.7 5.1 1.9]
[5.6 2.7 4.2 1.3]
[5.6 2.9 3.6 1.3]
[5.5 2.5 4. 1.3]
[6.1 3. 4.6 1.4]
[7.2 3.2 6. 1.8]
[5.3 3.7 1.5 0.2]
[4.3 3. 1.1 0.1]
[6.4 2.7 5.3 1.9]
[5.7 3. 4.2 1.2]
[5.4 3.4 1.7 0.2]
[5.7 4.4 1.5 0.4]
[6.9 3.1 4.9 1.5]
[4.6 3.1 1.5 0.2]
[5.9 3. 5.1 1.8]
[5.1 2.5 3. 1.1]
[4.6 3.4 1.4 0.3]
[6.2 2.2 4.5 1.5]
[7.2 3.6 6.1 2.5]
[5.7 2.9 4.2 1.3]
[4.8 3. 1.4 0.1]
[7.1 3. 5.9 2.1]
[6.9 3.2 5.7 2.3]
[6.5 3. 5.8 2.2]
[6.4 2.8 5.6 2.1]
[5.1 3.8 1.6 0.2]
[4.8 3.4 1.6 0.2]
[6.5 3.2 5.1 2. ]
[6.7 3.3 5.7 2.1]
[4.5 2.3 1.3 0.3]
[6.2 3.4 5.4 2.3]
[4.9 3. 1.4 0.2]
[5.7 2.5 5. 2. ]
[6.9 3.1 5.4 2.1]
[4.4 3.2 1.3 0.2]
[5. 3.6 1.4 0.2]
[7.2 3. 5.8 1.6]
[5.1 3.5 1.4 0.3]
[4.4 3. 1.3 0.2]
[5.4 3.9 1.7 0.4]
[5.5 2.3 4. 1.3]
[6.8 3.2 5.9 2.3]
[7.6 3. 6.6 2.1]
[5.1 3.5 1.4 0.2]
[4.9 3.1 1.5 0.1]
[5.2 3.4 1.4 0.2]
[5.7 2.8 4.5 1.3]
[6.6 3. 4.4 1.4]
[5. 3.2 1.2 0.2]
[5.1 3.3 1.7 0.5]
[6.4 2.9 4.3 1.3]
[5.4 3.4 1.5 0.4]
[7.7 2.6 6.9 2.3]
[4.9 2.4 3.3 1. ]
[7.9 3.8 6.4 2. ]
[6.7 3.1 4.4 1.4]
[5.2 4.1 1.5 0.1]
[6. 3. 4.8 1.8]
[5.8 4. 1.2 0.2]
[7.7 2.8 6.7 2. ]
[5.1 3.8 1.5 0.3]
[4.7 3.2 1.6 0.2]
[7.4 2.8 6.1 1.9]
[5. 3.3 1.4 0.2]
[6.3 3.4 5.6 2.4]
[5.7 2.8 4.1 1.3]
[5.8 2.7 3.9 1.2]
[5.7 2.6 3.5 1. ]
[6.4 3.2 5.3 2.3]
[6.7 3. 5.2 2.3]
[6.3 2.5 4.9 1.5]
[6.7 3. 5. 1.7]
[5. 3. 1.6 0.2]
[5.5 2.4 3.7 1. ]
[6.7 3.1 5.6 2.4]
[5.8 2.7 5.1 1.9]
[5.1 3.4 1.5 0.2]
[6.6 2.9 4.6 1.3]
[5.6 3. 4.1 1.3]
[5.9 3.2 4.8 1.8]
[6.3 2.3 4.4 1.3]
[5.5 3.5 1.3 0.2]
[5.1 3.7 1.5 0.4]
[4.9 3.1 1.5 0.1]
[6.3 2.9 5.6 1.8]
[5.8 2.7 4.1 1. ]
[7.7 3.8 6.7 2.2]
[4.6 3.2 1.4 0.2]] [1 0 2 1 1 1 1 2 0 0 2 1 0 0 1 0 2 1 0 1 2 1 0 2 2 2 2 0 0 2 2 0 2 0 2 2 0
0 2 0 0 0 1 2 2 0 0 0 1 1 0 0 1 0 2 1 2 1 0 2 0 2 0 0 2 0 2 1 1 1 2 2 1 1
0 1 2 2 0 1 1 1 1 0 0 0 2 1 2 0]
测试集: [[5.8 2.8 5.1 2.4]
[6. 2.2 4. 1. ]
[5.5 4.2 1.4 0.2]
[7.3 2.9 6.3 1.8]
[5. 3.4 1.5 0.2]
[6.3 3.3 6. 2.5]
[5. 3.5 1.3 0.3]
[6.7 3.1 4.7 1.5]
[6.8 2.8 4.8 1.4]
[6.1 2.8 4. 1.3]
[6.1 2.6 5.6 1.4]
[6.4 3.2 4.5 1.5]
[6.1 2.8 4.7 1.2]
[6.5 2.8 4.6 1.5]
[6.1 2.9 4.7 1.4]
[4.9 3.1 1.5 0.1]
[6. 2.9 4.5 1.5]
[5.5 2.6 4.4 1.2]
[4.8 3. 1.4 0.3]
[5.4 3.9 1.3 0.4]
[5.6 2.8 4.9 2. ]
[5.6 3. 4.5 1.5]
[4.8 3.4 1.9 0.2]
[4.4 2.9 1.4 0.2]
[6.2 2.8 4.8 1.8]
[4.6 3.6 1. 0.2]
[5.1 3.8 1.9 0.4]
[6.2 2.9 4.3 1.3]
[5. 2.3 3.3 1. ]
[5. 3.4 1.6 0.4]
[6.4 3.1 5.5 1.8]
[5.4 3. 4.5 1.5]
[5.2 3.5 1.5 0.2]
[6.1 3. 4.9 1.8]
[6.4 2.8 5.6 2.2]
[5.2 2.7 3.9 1.4]
[5.7 3.8 1.7 0.3]
[6. 2.7 5.1 1.6]
[5.9 3. 4.2 1.5]
[5.8 2.6 4. 1.2]
[6.8 3. 5.5 2.1]
[4.7 3.2 1.3 0.2]
[6.9 3.1 5.1 2.3]
[5. 3.5 1.6 0.6]
[5.4 3.7 1.5 0.2]
[5. 2. 3.5 1. ]
[6.5 3. 5.5 1.8]
[6.7 3.3 5.7 2.5]
[6. 2.2 5. 1.5]
[6.7 2.5 5.8 1.8]
[5.6 2.5 3.9 1.1]
[7.7 3. 6.1 2.3]
[6.3 3.3 4.7 1.6]
[5.5 2.4 3.8 1.1]
[6.3 2.7 4.9 1.8]
[6.3 2.8 5.1 1.5]
[4.9 2.5 4.5 1.7]
[6.3 2.5 5. 1.9]
[7. 3.2 4.7 1.4]
[6.5 3. 5.2 2. ]] [2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0
1 1 1 2 0 2 0 0 1 2 2 2 2 1 2 1 1 2 2 2 2 1 2]
# 训练模型
clf = svm.SVC(kernel = 'linear', C = 1).fit(X_train, y_train)
# 计算准确率
print('准确率:', clf.score(X_test, y_test))
准确率: 0.9666666666666667
# 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()
X_train_transformed = scaler.fit_transform(X_train)
clf = svm.SVC(kernel = 'linear', C = 1).fit(X_train_transformed, y_train)
X_test_transformed = scaler.fit_transform(X_test)
print('准确率:', clf.score(X_test_transformed, y_test))
准确率: 0.9333333333333333
# 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)
# 打印输出每次迭代的度量值（准确度）
print(scores)
# 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))
[0.96666667 1. 0.96666667 0.96666667 1. ]
Accuracy: 0.98 (+/- 0.03)
# 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)
# scores类型为字典。包含训练得分，拟合次数， score-times （得分次数）
sorted(scores.keys())
print('测试结果:', scores)
测试结果: {'fit_time': array([0.00113702, 0.00095534, 0.0007391 , 0.00055671, 0.0003612 ]), 'score_time': array([0.00205898, 0.00153756, 0.00125694, 0.00080943, 0.00079727]), 'test_precision_macro': array([0.96969697, 1. , 0.96969697, 0.96969697, 1. ]), 'train_precision_macro': array([0.97674419, 0.97674419, 0.99186992, 0.98412698, 0.98333333]), 'test_recall_macro': array([0.96666667, 1. , 0.96666667, 0.96666667, 1. ]), 'train_recall_macro': array([0.975 , 0.975 , 0.99166667, 0.98333333, 0.98333333])}
K折交叉验证
# K折交叉验证
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
k折划分:(75,) (75,)
留一交叉验证
#留一交叉验证
loo = LeaveOneOut()
for train, test in loo.split(iris.data):print("留一划分:%s %s" % (train.shape, test.shape))break
留一划分:(149,) (1,)
留p交叉验证
# 留p交叉验证
lpo = LeavePOut(p=2)
for train, test in loo.split(iris.data):print("留p划分：%s %s" % (train.shape, test.shape))break
留p划分：(149,) (1,)
随机排列交叉验证
# 随机排列交叉验证
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
随机排列划分：(149,) (1,)
分层K折交叉验证
# 分层K折交叉验证
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
分层K折划分：(99,) (51,)
分层随机交叉验证
# 分层随机交叉验证
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
分层随机划分：(135,) (15,)
分割
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]
组 k-fold分割
# 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))
组 k-fold分割：[0 1 2 3 4 5] [6 7 8 9]
组 k-fold分割：[0 1 2 6 7 8 9] [3 4 5]
组 k-fold分割：[3 4 5 6 7 8 9] [0 1 2]
留一组分割
# 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))
留一组分割：[3 4 5 6 7 8 9] [0 1 2]
留一组分割：[0 1 2 6 7 8 9] [3 4 5]
留一组分割：[0 1 2 3 4 5] [6 7 8 9]
留 P 组分割
# 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))
留 P 组分割：[6 7 8 9] [0 1 2 3 4 5]
留 P 组分割：[3 4 5] [0 1 2 6 7 8 9]
留 P 组分割：[0 1 2] [3 4 5 6 7 8 9]
随机分割
# 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))随机分割：[0 1 2] [3 4 5 6 7 8 9]
随机分割：[3 4 5] [0 1 2 6 7 8 9]
随机分割：[3 4 5] [0 1 2 6 7 8 9]
随机分割：[3 4 5] [0 1 2 6 7 8 9]
时间序列分割
# 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))
时间序列分割：[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 2324 25 26 27 28 29 30 31 32 33 34 35 36 37 38] [39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 6263 64 65 66 67 68 69 70 71 72 73 74 75]
时间序列分割：[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 2324 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 4748 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 7172 73 74 75] [ 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 9394 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111112]
时间序列分割：[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1718 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 3536 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 5354 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 7172 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 8990 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107108 109 110 111 112] [113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148149]