#!/usr/bin/python
# -*- coding: utf-8 -*
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
from linear_regression_model import linearRegressionModel as lrm
if __name__ == '__main__':
x, y = make_regression(7000)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.5)
y_lrm_train = y_train.reshape(-1, 1)
y_lrm_test = y_test.reshape(-1, 1)
linear = lrm(x.shape[1])
linear.train(x_train, y_lrm_train, x_test, y_lrm_test)
y_predict = linear.predict(x_test)
print("Tensorflow R2: ", r2_score(y_predict.ravel(), y_lrm_test.ravel()))
lr = LinearRegression()
y_predict = lr.fit(x_train, y_train).predict(x_test)
print("Sklearn R2: ", r2_score(y_predict, y_test)) #采用r2_score评分函数
#!/usr/bin/python
# -*- coding: utf-8 -*
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
from linear_regression_model import linearRegressionModel as lrm
if __name__ == '__main__':
x, y = make_regression(7000)
x_train,x_test,y_train, y_test = train_test_split(x, y, test_size=0.5)
y_lrm_train = y_train.reshape(-1, 1)
y_lrm_test = y_test.reshape(-1, 1)
linear = lrm(x.shape[1])
linear.train(x_train, y_lrm_train,x_test,y_lrm_test)
y_predict = linear.predict(x_test)
print("Tensorflow R2: ", r2_score(y_predict.ravel(), y_lrm_test.ravel()))
lr = LinearRegression()
y_predict = lr.fit(x_train, y_train).predict(x_test)
print("Sklearn R2: ", r2_score(y_predict, y_test)) #采用r2_score评分函数
#!/usr/bin/python
# -*- coding: utf-8 -*
"""
训练模型并和 sklearn 库线性回归模型对比
"""
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
from linear_regression_model import linearRegressionModel as lrm
if __name__ == '__main__':
x, y = make_regression(7000)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.5)
print(x_train.shape) #(3500, 100)
print(y_train.shape) #(3500,)
y_lrm_train = y_train.reshape(-1, 1)
y_lrm_test = y_test.reshape(-1, 1)
print(y_lrm_train.shape) #(3500, 1)
linear = lrm(x.shape[1]) #参数是输入的特征数目
linear.train(x_train, y_lrm_train, x_test, y_lrm_test)
y_predict = linear.predict(x_test)
print("Tensorflow R2: ", r2_score(y_predict.ravel(), y_lrm_test.ravel()))
print(y_lrm_test.ravel().shape) #(3500,)
lr = LinearRegression()
y_predict = lr.fit(x_train, y_train).predict(x_test)
print("Sklearn R2: ", r2_score(y_predict, y_test)) #采用r2_score评分函数
# !/usr/bin/python
# -*- coding: utf-8 -*-
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from linear_regression_model import linearRegressionModel as lrm
from sklearn.linear_model import LinearRegression as LRM
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
if __name__ == '__main__':
# 生成线性回归数据集
x, y = make_regression(7000)
# 数据集分割成训练集和测试集
x_train,x_test,y_train, y_test = train_test_split(x, y, test_size=0.5)
y_train = y_train.reshape(-1, 1)
y_test = y_test.reshape(-1, 1)
# 自己实现的线性回归模型
LRM1 = lrm(x.shape[1])
LRM1.train(x_train, y_train, x_test, y_test)
y_predict = LRM1.predict(x_test)
print("Tensorflow R2: ", r2_score(y_test.ravel(), y_predict.ravel()))
print("Tensorflow MSE: ", mean_squared_error(y_test.ravel(), y_predict.ravel()))
# sklearn里面的线性回归模型
LRM2 = LRM()
y_predict = LRM2.fit(x_train, y_train).predict(x_test)
print("Sklearn R2: ", r2_score(y_test.ravel(), y_predict.ravel()))
print("Sklearn MSE: ", mean_squared_error(y_test.ravel(), y_predict.ravel()))