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hhl_15_python机器学习迭代.py
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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
from sklearn.metrics import mean_squared_error
from sklearn.externals import joblib
def linear1():
"""
用线性方程对波士顿房价进行预测
"""
# 1 获取数据集
boston = load_boston()
# 2 对数据进行划分
x_train, x_test,y_train, y_test = train_test_split(boston.data,boston.target,random_state=22)
# 3 标准化
# 实例化一个转换器、
transfer = StandardScaler()
x_train = transfer.fit_transform(x_train)
x_test = transfer.fit_transform(x_test)
# 4 预估器
estimator = LinearRegression()
estimator.fit(x_train,y_train)
# 5 得出模型
print("线性方程的权重系数",estimator.coef_)
print("线性方程偏置为",estimator.intercept_)
# 6 模型评估
y_predict = estimator.predict(x_test)
print("预测的房价", y_predict)
error = mean_squared_error(y_test,y_predict)
print("线性方程的均方差",error)
return None
def linear2():
"""
# 梯度下降的方法对波士顿房价进行预
"""
# 1 获取数据
boston = load_boston()
# 2 数据进行划分
x_train,x_test,y_train,y_test = train_test_split(boston.data,boston.target,random_state=22)
# 3 标准化 ,实例化一个转化器类
transfer = StandardScaler()
x_train = transfer.fit_transform(x_train)
x_test = transfer.transform(x_test)
# 4 模型预估器
estimator = SGDRegressor(learning_rate="constant", eta0=0.01, max_iter=10000, penalty="l1")
estimator.fit(x_train,y_train)
# 5 得出模型
print("梯度下降权重",estimator.coef_)
print("梯度下降偏置度",estimator.intercept_)
# 6 模型评估
# 方法一直接对比真实值和预估值
y_predict = estimator.predict(x_test)
print("预测房价",y_predict)
error = mean_squared_error(y_test,y_predict)
print("梯度下降均方误差",error)
return None
def linear3():
"""
用岭回归对波士顿房价进行预测
"""
# 1 获取数据
boston = load_boston()
# 2 对数据集 进行划分
x_train,x_test,y_train,y_test = train_test_split(boston)
# 3 标准化
transfer = StandardScaler()
x_train = transfer.fit_transform(x_train)
x_test = transfer.transform(x_test)
# 4 模型预估器
estimator = Ridge(alpha=0.5,max_iter=10000)
estimator.fit(x_train,y_train)
# 保存模型
# joblib.dump(estimator, "my_ridge.pkl")
# 加载模型
estimator = joblib.load("my_ridge.pkl")
# 5）得出模型
print("岭回归-权重系数为：\n", estimator.coef_)
print("岭回归-偏置为：\n", estimator.intercept_)
# 6）模型评估
y_predict = estimator.predict(x_test)
print("预测房价：\n", y_predict)
error = mean_squared_error(y_test, y_predict)
print("岭回归-均方误差为：\n", error)
return None
if __name__=="__main__":
# 用线性方程对波士顿房价进行预测
# linear1()
# 用梯度下降对波士顿房价进行预测
# linear2()
# 用岭回归对波士顿房价进行预测
linear3()