线性回归10-模型保存和加载

1 sklearn模型的保存和加载API

• from sklearn.externals import joblib
  • 保存：joblib.dump(estimator, 'test.pkl')
  • 加载：estimator = joblib.load('test.pkl')

2 线性回归的模型保存加载案例

from sklearn.datasets import load_boston
from sklearn.model_selection import  train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import  LinearRegression,SGDRegressor,RidgeCV,Ridge
from sklearn.metrics import mean_squared_error
#from sklearn.externals import joblib
import joblib


def dump_load() :
    """
    模型保存和加载
    :return:
    """
    # 1.获取数据
    boston = load_boston()
    # 2.数据处理
    # 2.1 分割数据
    x_train, x_test, y_train, y_test = train_test_split(boston.data, boston.target,random_state=22,test_size=0.2)
    # 3.特征工程-数据标准化
    transfer = StandardScaler()
    x_train = transfer.fit_transform(x_train)
    x_test = transfer.fit_transform(x_test)

    # # 4.机器学习-线性回归(梯度下降)
    # # 4.1 模型训练
    # estimator = Ridge(alpha=1.0)
    # estimator.fit(x_train, y_train)
    #
    # # 4.2 模型保存
    # joblib.dump(estimator,"./data/test1.pkl")

     # # 4.3 模型加载
    estimator=joblib.load("./data/test1.pkl")
    # 5.模型评估
    y_predict = estimator.predict(x_test)
    print("预测值为:\n", y_predict)
    print("模型中的系数为:\n", estimator.coef_)
    print("模型中的偏置为:\n", estimator.intercept_)
    # 评价指标 均方误差
    error = mean_squared_error(y_test, y_predict)
    print("均方误差：\n", error)

    return None
#调用函数
if __name__=='__main__':
  dump_load()

5.4 结果

直接调用模型和原本模型中的结果是一样的

注:保证结果一致，需要参数一样，同时运行时需要运行当前的代码