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一，sklearn模型的保存和加载API

from sklearn.externals import joblib

• 保存：joblib.dump(rf,“test.pkl”)
• 加载：estimator = joblib.load(‘test.pkl’)

二，线性回归模型的保存加载案例

1,保存模型

import pandas as pdimport numpy as npfrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScalerfrom sklearn.linear_model import LogisticRegressionfrom sklearn.metrics import classification_report,roc_auc_scorefrom sklearn.externals import joblibdef logisticregression():    '''逻辑回归癌症预测'''    # 确定数据columns数值    columns = ["Sample code number","Clump Thickness","Uniformity of Cell Size","Uniformity of Cell Shape","Marginal Adhesion","Single Epithelial Cell Size","Bare Nuclei","Bland Chromatin","Normal Nucleoli","Mitoses","Class"]    data = pd.read_csv("breast-cancer-wisconsin.data",names=columns)    # 去掉缺失值    data.replace(to_replace="?",value=np.nan,inplace=True)    data.dropna(axis=0,inplace=True,how="any")    # 提取目标值    target = data["Class"]    # 提取特征值    data = data.drop(["Sample code number"],axis=1).iloc[:,:-1]    # 切割训练集和测试集    x_train,x_test,y_train,y_test = train_test_split(data,target,test_size=0.3)    # 进行标准化    std = StandardScaler()    x_train = std.fit_transform(x_train)    x_test = std.fit_transform(x_test)    # 逻辑回归进行训练和    lr = LogisticRegression()    lr.fit(x_train,y_train)    # 得到训练集返回数据    # print("逻辑回归权重：",lr.coef_)    # print("逻辑回归偏置：",lr.intercept_)    # 保存训练模型    joblib.dump(lr, "test.pkl")if __name__ == '__main__':    logisticregression()

2，加载模型

import pandas as pdimport numpy as npfrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScalerfrom sklearn.linear_model import LogisticRegressionfrom sklearn.metrics import classification_report,roc_auc_scorefrom sklearn.externals import joblibdef logisticregression():    '''逻辑回归癌症预测'''    # 确定数据columns数值    columns = ["Sample code number","Clump Thickness","Uniformity of Cell Size","Uniformity of Cell Shape","Marginal Adhesion","Single Epithelial Cell Size","Bare Nuclei","Bland Chromatin","Normal Nucleoli","Mitoses","Class"]    data = pd.read_csv("breast-cancer-wisconsin.data",names=columns)    # 去掉缺失值    data.replace(to_replace="?",value=np.nan,inplace=True)    data.dropna(axis=0,inplace=True,how="any")    # 提取目标值    target = data["Class"]    # 提取特征值    data = data.drop(["Sample code number"],axis=1).iloc[:,:-1]    # 切割训练集和测试集    x_train,x_test,y_train,y_test = train_test_split(data,target,test_size=0.3)    # 进行标准化    std = StandardScaler()    x_train = std.fit_transform(x_train)    x_test = std.fit_transform(x_test)    lr = joblib.load("test.pkl")    # 逻辑回归测试集预测结果    pre_result = lr.predict(x_test)    # print(pre_result)    # 逻辑回归预测准确率    sore = lr.score(x_test,y_test)    print(sore)    # 精确率(Precision)与召回率(Recall)    report = classification_report(y_test,pre_result,target_names=["良性","恶性"])    print(report)    # 查看AUC指标    y_test = np.where(y_test>2.5,1,0)    print(y_test)    auc_score = roc_auc_score(y_test,pre_result)    print(auc_score)if __name__ == '__main__':    logisticregression()

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