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sklearn框架下，自定义作业支持发布保存模型为pickle和joblib格式，并且在发布至模型仓库时需要选择相应的模型文件。使用下面代码进行模型训练时，训练程序可以自行加载数据，训练数据选择空文件夹即可。

pickle格式模型示例代码:

# -*- coding:utf-8 -*- """ sklearn train demo """ import numpy as np from 

sklearn import datasets from sklearn.model_selection import train_test_split from

 sklearn.linear_model import LogisticRegression def load_data(): """ load data "

"" # 共150条数据，训练120条，测试30条，进行2,8分进行模型训练 # 每条数据类型为 

x{nbarray} [6.4, 3.1, 5.5, 1.8] inputdata = datasets.load_iris() # 切分，

测试训练2,8分 x_train, x_test, y_train, y_test = \
        train_test_split(inputdata.data, inputdata.target, test_size = 0.2,

 random_state=0) return x_train, x_test, y_train, y_test def save_model(model)

: """ save model with pickle format """ import pickle with open('output/clf.pickle','wb')

 as f: pickle.dump(model, f) def save_model_joblib(model): """ save model with joblib format 

""" try: import joblib except: from sklearn.externals import joblib
    joblib.dump(model, 'output/clf.pkl') def main(): """ main """ # 训练集x

 ,测试集x,训练集label,测试集label x_train, x_test, y_train, y_test = load_data() 

# l2为正则项 model = LogisticRegression(penalty='l2') model.fit(x_train, y_train) 

save_model(model) print("w: %s" % model.coef_) print("b: %s" % model.intercept_) 

# 准确率 print("precision: %s" % model.score(x_test, y_test)) print("MSE: %s" 

% np.mean((model.predict(x_test) - y_test) ** 2)) if __name__ == '__main__': main()

joblib格式示例代码:

# -*- coding:utf-8 -*- import numpy as np from sklearn import datasets from sklearn.model_

selection import train_test_split from sklearn.linear_model import LogisticRegression def load_data()

: # 共150条数据，训练120条，测试30条，进行2,8分进行模型训练 # 每条数据类型为 x{nbarray}

 [6.4, 3.1, 5.5, 1.8] inputdata = datasets.load_iris() # 切分，测试训练2,8分 x_train, x_test, y_train, y_test = \
        train_test_split(inputdata.data, inputdata.target, test_size = 0.2, random_state=0) return

 x_train, x_test, y_train, y_test def save_model(model): import pickle with open('output/clf.pickle',

'wb') as f: pickle.dump(model, f) def save_model_joblib(model): from sklearn.externals import joblib
    joblib.dump(model, 'output/clf.pkl') def main(): # 训练集x ,测试集x,训练集label,测试集label

 x_train, x_test, y_train, y_test = load_data() # l2为正则项 model = LogisticRegression(penalty='l2') 

model.fit(x_train, y_train) save_model_joblib(model) print("w: %s" % model.coef_) print("b: %s" % model.intercept_)

 # 准确率 print("precision: %s" % model.score(x_test, y_test)) print("MSE: %s" % np.mean

((model.predict(x_test) - y_test) ** 2)) if __name__ == '__main__': main()