1.训练好一个Model以后需要保存和再次预测

2.有两个模块用来保存模型 : pickle和joblib

3.Sklearn的模型导出本质上是利用Python的Pickle机制。对Python的函数进行序列化，也就是把训练好的Transformer函数序列化并存为文件。

代码流程：

1.保存Model(注:save文件夹要预先建立，否则会报错)

joblib.dump(clf, ‘save/clf.pkl’)

2.读取Model

clf2 = joblib.load(‘save/clf.pkl’)

3.测试读取后的Model

print(clf2.predict(X[0:1]))**

实例一，SVM分类模型

import pickle

from sklearn.externals import joblib

from sklearn.svm import SVC

from sklearn import datasets

#定义一个分类器

svm = SVC()

iris = datasets.load_iris()

X = iris.data

y = iris.target

#训练模型

svm.fit(X,y)

#1.保存成Python支持的文件格式Pickle

#在当前目录下可以看到svm.pickle

with open('svm.pickle','wb') as fw:

pickle.dump(svm,fw)

#加载svm.pickle

with open('svm.pickle','rb') as fr:

new_svm1 = pickle.load(fr)

# print (new_svm1.predict(X[0:1]))

#2.保存成sklearn自带的文件格式Joblib

joblib.dump(svm,'svm.pkl')

#加载svm.pkl

new_svm2 = joblib.load('svm.pkl')

print (new_svm2.predict(X[0:1]))

运行结果

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补充：

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实例二，随机森林模型

#随机森林

from sklearn.ensemble import RandomForestClassifier

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from sklearn.metrics import classification_report

from sklearn.externals import joblib

#1.保存模型

data = load_iris()

X,y = data["data"],data["target"]

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.33,random_state = 42)

clf = RandomForestClassifier(max_depth = 2,random_state = 0)

clf.fit(X_train,y_train)

#print (clf.feature_importances_)

"""

print (

classification_report(y_test,clf.predict(X_test),target_names = data["target_names"])

)

"""

joblib.dump(clf,"classification.pkl")

#2.加载模型

clf2 = joblib.load('classification.pkl')

print (clf2.feature_importances_)

print (

classification_report(y,clf.predict(X),target_names = data["target_names"])

)

运行结果1

运行结果2