0x02　机器学习-交叉检验(附带保存训练好的模型)

淡淡的烟草味﹌ 2021-12-09 15:59 299阅读 0赞

０１机器学习可能出现的误差

1.过拟合　就是机器读死书，死记硬背，对作业掌握的不错，但是测试时就很糟糕

2.欠拟合　就是机器不好好学习，导致结果不令人满意

０２交叉检验－１

平均得分和多次循环求最优解

from sklearn.datasets import load_iris
    from sklearn.model_selection import train_test_split
    from sklearn.neighbors import KNeighborsClassifier
    
    iris = load_iris()
    X = iris.data
    y = iris.target
    '''  级别一
    # TTTTt   T表示训练数据　t表示测试数据
    X_train,X_test,y_train,y_test = train_test_split(X,y,random_state=4)
    knn = KNeighborsClassifier(n_neighbors=5) #取５个点变成一个点
    knn.fit(X_train,y_train)
    print(knn.score(X_test,y_test))
    '''
    '''
     级别二  重复５次ｕ取平均值
    TTTTt
    TTTtT
    TTtTT
    TtTTT
    tTTTT
    from sklearn.model_selection import cross_val_score
    knn = KNeighborsClassifier(n_neighbors=5)
    scores = cross_val_score(knn,X,y,cv=5,scoring='accuracy')
    print(scores.mean())
    '''
    
    from sklearn.model_selection import cross_val_score
    import matplotlib.pyplot as plt
    k_range = range(1,31)
    k_scores = []
    # 通过循环不断的寻找　n_neighbors参数的比较好的解
    for k in k_range:
        knn = KNeighborsClassifier(n_neighbors=k)
        # loss = -cross_val_score(knn,X,y,cv=10,scoring='neg_mean_squared_error') # 注意最前面的负号　一个是误差曲线　下一个是准确度曲线
        scores = cross_val_score(knn,X,y,cv=10,scoring='accuracy')
        k_scores.append(scores.mean())
    # 对结果做可视化
    plt.plot(k_range,k_scores)
    plt.xlabel('Value of K for Knn')
    plt.ylabel('Cross-Validated Accuracy')
    plt.show()

０２交叉检验-２

对结果一步一步的可视化

使用learning\_curve分段评估结果的好坏

from sklearn.model_selection import learning_curve  # 这个是调参用的　评价曲线
    from sklearn.datasets import load_digits
    from sklearn.svm import SVC
    import matplotlib.pyplot as plt
    import numpy as np
    
    digis = load_digits()
    X = digis.data
    y = digis.target
    
    # 进行训练
    # －－－－－－－－
    # 隔多久记录一次　训练的损失值　测试的损失值
    train_sizes,train_loss,test_loss = learning_curve(
        SVC(gamma=0.001),X,y,cv=10,scoring='neg_mean_squared_error',
       # 用到的方法　　数据　　几次交叉验证　　评分的标准
        train_sizes=[0.1,0.25,0.50,0.75,1])　　# 10% 25% 50% 在学习过程中的５个点
    # －－－－－－－－
    # train_loss

<table style="width:622px;"> 
 <tbody> 
  <tr> 
   <td>误差的形式</td> 
   <td>cv1</td> 
   <td>cv2</td> 
   <td>cv3</td> 
   <td>cv4</td> 
   <td>cv5</td> 
   <td>cv6</td> 
   <td>cv7</td> 
   <td>cv8</td> 
   <td>cv9</td> 
   <td>cv10</td> 
  </tr> 
  <tr> 
   <td>10%</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
  </tr> 
  <tr> 
   <td>25%</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
  </tr> 
  <tr> 
   <td>50%</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
  </tr> 
  <tr> 
   <td>75%</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
  </tr> 
  <tr> 
   <td>100%</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
  </tr> 
 </tbody> 
</table>

train_loss_mean = -np.mean(train_loss,axis=1) # 按ｙ轴滑动求平均值
    test_loss_mean = -np.mean(test_loss,axis=1)  # 按ｙ轴滑动求平均值
    # 可视化
    # 红色的线表示，训练完做作业的结果
    # 绿色的线表示训练完考试的结果
    plt.plot(train_sizes,train_loss_mean,'o-',color='r',
             label='trainning')
    plt.plot(train_sizes,test_loss_mean,'o-',color='g',
             label='cross-validation')
    plt.xlabel('trainning examples')
    plt.ylabel('loss')
    plt.legend(loc='best')
    plt.show()

０３交叉验证-３

使用validation\_curve 来调节参数

1 from sklearn.model_selection import validation_curve # 用来调模型的参数
     2 from sklearn.datasets import load_digits
     3 from sklearn.svm import SVC
     4 import matplotlib.pyplot as plt
     5 import numpy as np
     6 
     7 digis = load_digits()
     8 X = digis.data
     9 y = digis.target
    10 # 在10**-6 到　10**-2.3 之间取５个点
    11 param_range = np.logspace(-6,-2.3,5)
    12 # 进行训练
    13 # －－－－－－－－
    14 # 训练的损失值　测试的损失值
    15 train_loss,test_loss = validation_curve(
    16     SVC(),X,y,param_name='gamma',param_range=param_range,
    17    # 用到的方法　　数据　　    要调动的参数　　    调动参数的范围
    18     scoring='neg_mean_squared_error'
    19 )  # 评分的标准
    20 # －－－－－－－－
    21

<table style="width:397px;"> 
 <tbody> 
  <tr> 
   <td>gamma</td> 
   <td>cv1</td> 
   <td>cv2</td> 
   <td>cv3</td> 
  </tr> 
  <tr> 
   <td>10<sup>-6</sup></td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
  </tr> 
  <tr> 
   <td>....</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
  </tr> 
  <tr> 
   <td>.....</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
  </tr> 
  <tr> 
   <td>.....</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
  </tr> 
  <tr> 
   <td>10<sup>-2.3</sup></td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
   <td>&nbsp;</td> 
  </tr> 
 </tbody> 
</table>

22 train_loss_mean = -np.mean(train_loss,axis=1) # 按ｙ轴滑动求平均值
    23 test_loss_mean = -np.mean(test_loss,axis=1)  # 按ｙ轴滑动求平均值
    24 # 可视化
    25 # 红色的线表示，训练完做作业的结果
    26 # 绿色的线表示训练完考试的结果
    27 plt.plot(param_range,train_loss_mean,'o-',color='r',
    28          label='trainning')
    29 plt.plot(param_range,test_loss_mean,'o-',color='g',
    30          label='cross-validation')
    31 plt.xlabel('trainning examples')
    32 plt.ylabel('loss')
    33 plt.legend(loc='best')
    34 plt.show()

０４附带如何保存训练的结果

from sklearn import svm
    from sklearn import datasets
    
    clf = svm.SVC()
    iris = datasets.load_iris()
    X,y = iris.data,iris.target
    # 训练数据
    clf.fit(X,y)
    
    # 法一 当训练出的模型小 : pickle
    import pickle
    with open('save/clf.pickle','wb') as f:
        pickle.dump(clf,f)
    with open('save/clf.pickle','rb') as f:
        clf2 = pickle.load(f)
        print(clf2.predict(X[0:3]))
    
    # 法二　当训练出来的模型大　:joblib #使用多进程存储文件
    from sklearn.externals import joblib
    # Save
    joblib.dump(clf,'save/clf.pkl')
    # Restore
    clf3 = joblib.load('save/clf.pkl')
    print(clf3.predict(X[12:15]))

转载于:https://www.cnblogs.com/liu247/p/10995544.html