0x02 机器学习-交叉检验(附带保存训练好的模型)

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

误差的形式	cv1	cv2	cv3	cv4	cv5	cv6	cv7	cv8	cv9	cv10
10%
25%
50%
75%
100%

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

from sklearn.model_selection import validation_curve # 用来调模型的参数
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
# 在10**-6 到　10**-2.3 之间取５个点
param_range = np.logspace(-6,-2.3,5)
# 进行训练
# －－－－－－－－
# 训练的损失值　测试的损失值
train_loss,test_loss = validation_curve(
    SVC(),X,y,param_name='gamma',param_range=param_range,
   # 用到的方法　　数据　　    要调动的参数　　    调动参数的范围
    scoring='neg_mean_squared_error'
)  # 评分的标准
# －－－－－－－－

gamma	cv1	cv2	cv3
10-6
....
.....
.....
10-2.3

22 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(param_range,train_loss_mean,'o-',color='r',
         label='trainning')
plt.plot(param_range,test_loss_mean,'o-',color='g',
         label='cross-validation')
plt.xlabel('trainning examples')
plt.ylabel('loss')
plt.legend(loc='best')
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]))