模型的保存和加载
文章源代码下载地址：波士顿房价岭回归正则化预测代码实现
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')
注意：保存的后缀名是.pkl
2.线性回归的模型保存加载案例
注意：
-
要保存训练后的模型才有意义！！！
-
保存后的用文本打开是错误的，只能加载！！
-
加载的模型在使用之前加载，不需要训练数据了！！
2.1保存模型
# 1.获取数据 laod_boston bunch字典
data = load_boston()
# 2.数据集划分 数据的基本处理
x_train, x_test, y_train, y_test = train_test_split(data.data, data.target, random_state=22)
# 3.特征工程-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(岭回归)【重要】
# 4.1 创建模型 实例化估计器
estimator = Ridge(alpha=1)#(alpha表示正则的程度）
# 4.2 训练模型 fit 正规方程计算得到最优可训练参数
estimator.fit(x_train, y_train)
#保存模型
joblib.dump(estimator,'./test.pkl')#注意用文本打开是错误的，只能加载！！
# 5.模型评估
# 5.1 获取系数等值
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_)
# 5.2 评价
# 均方误差
error = mean_squared_error(y_test, y_predict)
print("误差为:\n", error)
2.2加载模型
# 1.获取数据 laod_boston bunch字典
data = load_boston()
# 2.数据集划分 数据的基本处理
x_train, x_test, y_train, y_test = train_test_split(data.data, data.target, random_state=22)
# 3.特征工程-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(岭回归)【重要】
# # 4.1 创建模型 实例化估计器
# estimator = Ridge(alpha=1)#(alpha表示正则的程度）
# # 4.2 训练模型 fit 正规方程计算得到最优可训练参数
# estimator.fit(x_train, y_train)
# #保存模型
# joblib.dump(estimator,'./test.pkl')#注意用文本打开是错误的，只能加载！！
## 加载模型
estimator = joblib.load('./test.pkl')
# 5.模型评估
# 5.1 获取系数等值
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_)
# 5.2 评价
# 均方误差
error = mean_squared_error(y_test, y_predict)
print("误差为:\n", error)
两个结果都一样
2.3结果
预测值为:
[28.14336439 31.29120593 20.54384341 31.45949883 19.05713232 18.25154031
20.59333004 18.46668579 18.49439324 32.90278303 20.39074387 27.19391547
14.82896742 19.22647169 36.99680592 18.30216415 7.77234952 17.59204777
30.20233488 23.61819202 18.13165677 33.80976641 28.45514573 16.97450477
34.72448519 26.19876013 34.77528305 26.63056236 18.62636595 13.34630747
30.34386216 14.5911294 37.18589518 8.96603866 15.1046276 16.0870778
7.2410686 19.13817477 39.5390249 28.27770546 24.63218813 16.74118324
37.8401846 5.70041018 21.17142785 24.60567485 18.90535427 19.95506965
15.19437924 26.28324334 7.54840338 27.10725806 29.18271353 16.27866225
7.9813597 35.42054763 32.2845617 20.95634259 16.43407021 20.88411873
22.93442975 23.58724813 19.3655118 38.2810092 23.98858525 18.95166781
12.62360991 6.12834839 41.45200493 21.09795707 16.19808353 21.5210458
40.71914496 20.54014744 36.78495192 27.02863306 19.9217193 19.64062326
24.60418297 21.26677099 30.94032672 19.33770303 22.30888436 31.07881055
26.39477737 20.24104002 28.79548502 20.86317185 26.04545844 19.2573741
24.92683599 22.29008698 18.92825484 18.92207977 14.04840276 17.41630198
24.16632188 15.83303972 20.04416558 26.5192807 20.10159263 17.02240369
23.84898152 22.82854834 20.89047727 36.1141591 14.72135442 20.67674724
32.4387071 33.1767914 19.81979219 26.46158288 20.97213033 16.46431333
20.7661367 20.59296518 26.86196155 24.18675233 23.22897169 13.78214313
15.38170591 2.77742469 28.88657667 19.78630135 21.50773167 27.54387951
28.49827366]
模型中的系数为:
[-0.62113007 1.11962804 -0.09020315 0.74692857 -1.92185544 2.71649332
-0.08404963 -3.25764933 2.40502586 -1.76845144 -1.7441452 0.88008135
-3.904193 ]
模型中的偏置为:
22.62137203166228
误差为:
20.06442562822488
