保存模型
from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression,SGDRegressor,Ridge
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error
import joblib
def mylinear():
"""
线性回归直接预测房子价格
:return:
"""
# 获取数据
lb = load_boston()
# 分割数据到训练集和测试集
x_train,x_test,y_train,y_test = train_test_split(lb.data,lb.target,test_size=0.25)
print(y_train,x_test)
# 进行标准化处理
# 特征值和目标值都必须进行标准化处理(实例化2个标准化API)
# 特征值标准化处理
std_x = StandardScaler()
x_train = std_x.fit_transform(x_train)
x_test = std_x.transform(x_test)
# 目标值标准化处理
std_y = StandardScaler()
y_train = std_y.fit_transform(y_train.reshape(-1,1)) # 要求数据必须是二维我们需要使用reshape(-1,1)进行转换
y_test = std_y.transform(y_test.reshape(-1,1))
# estimator预测
# 正规方程求解预测结果
lr = LinearRegression()
lr.fit(x_train,y_train)
print(lr.coef_)
# 保存训练好的模型
joblib.dump(lr,"test.pkl")
# 预测测试集的房子价格
y_lr_predict = lr.predict(x_test)
y_lr_predict = std_y.inverse_transform(y_lr_predict)# 将降维后的数据转换成原始数据
print("正规方程测试集里面每个房子的预测价格:",y_lr_predict)
print("正规方程的均方误差:",mean_squared_error(std_y.inverse_transform(y_test),y_lr_predict))
return None
if __name__=="__main__":
mylinear()
调用模型预测的结果
from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression,SGDRegressor,Ridge
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error
import joblib
def mylinear():
"""
线性回归直接预测房子价格
:return:
"""
# 获取数据
lb = load_boston()
# 分割数据到训练集和测试集
x_train,x_test,y_train,y_test = train_test_split(lb.data,lb.target,test_size=0.25)
print(y_train,x_test)
# 进行标准化处理
# 特征值和目标值都必须进行标准化处理(实例化2个标准化API)
# 特征值标准化处理
std_x = StandardScaler()
x_train = std_x.fit_transform(x_train)
x_test = std_x.transform(x_test)
# 目标值标准化处理
std_y = StandardScaler()
y_train = std_y.fit_transform(y_train.reshape(-1,1)) # 要求数据必须是二维我们需要使用reshape(-1,1)进行转换
y_test = std_y.transform(y_test.reshape(-1,1))
# 调用保存好的模型进行预测
model = joblib.load("test.pkl")
y_predict = std_y.inverse_transform(model.predict(x_test))
print("调用模型预测的结果:",y_predict)
return None
if __name__=="__main__":
mylinear()