目录
- 1线性回归
- 1.1简单线性回归
- 1.2 多元线性回归的正规方程解
- 1.3 使用梯度下降求解多元线性回归
- 1.4 sklearn中的线性回归模型
1线性回归
1.1简单线性回归
在简单线性回归中，输入x只有一个特征，通过调整a和b的参数值，来拟合从x到y的线性关系。下图为进行拟合所需要优化的目标，也即是MES（Mean Squared Error)，只不过省略了平均的部分（除以m）。
对于简单线性回归，只有两个参数a和b，通过对MSE优化目标求导后取极值点极值（最小二乘法），即可求得最优a和b如下，所以在训练简单线性回归模型时，也只需要根据数据求解这两个参数值即可。
下面使用波士顿房价数据集中，索引为5（第6个特征）的特征RM (average number of rooms per dwelling：每个住宅的平均房间数)来进行简单线性回归。其中使用的评价指标为：
以及R方指标。这里R方指标一般情况下可以将对模型的评价压缩到[0,1]之间（也可能是负数），值越接近1效果越好。在式子分子分母中的两项都可以认作在算均方误差，只不过分子使用我们训练的线性回归模型的输出，分母则使用真实标签数据的平均值来做均方误差，表示对每个样本，都是一个固定值瞎猜结果，其值等价于实际标签值的方差。通过模型的均方误差和瞎猜产生的误差做对比，就可以将评价指标限定在[0,1]（如果模型比瞎猜还差就会成为负数）。
# 以sklearn的形式对simple linear regression 算法进行封装
import numpy as np
import sklearn.datasets as datasets
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error,mean_absolute_error
np.random.seed(123)class SimpleLinearRegression():def __init__(self):"""initialize model parameters"""self.a_=Noneself.b_=Nonedef fit(self,x_train,y_train):"""training model parametersParameters----------x_train:train x ,shape:data [N,]y_train:train y ,shape:data [N,]"""assert (x_train.ndim==1 and y_train.ndim==1),\"""Simple Linear Regression model can only solve single feature training data"""assert len(x_train)==len(y_train),\"""the size of x_train must be equal to y_train"""x_mean=np.mean(x_train)y_mean=np.mean(y_train)self.a_=np.vdot((x_train-x_mean),(y_train-y_mean))/np.vdot((x_train-x_mean),(x_train-x_mean))self.b_=y_mean-self.a_*x_meandef predict(self,input_x):"""make predictions based on a batch of dataParameters----------input_x:shape->[N,]"""assert input_x.ndim==1 ,\"""Simple Linear Regression model can only solve single feature data"""return np.array([self.pred_(x) for x in input_x])def pred_(self,x):"""give a prediction based on single input x"""return self.a_*x+self.b_def __repr__(self):return "SimpleLinearRegressionModel"if __name__ == '__main__':boston_data = datasets.load_boston()print(boston_data['DESCR'])x = boston_data['data'][:, 5] # total x data (506,)y = boston_data['target'] # total y data (506,)# keep data with target value less than 50.x = x[y < 50] # total x data (490,)y = y[y < 50] # total x data (490,)plt.scatter(x, y)plt.show()# train size:(343,) test size:(147,)x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)regs = SimpleLinearRegression()regs.fit(x_train, y_train)y_hat = regs.predict(x_test)rmse = np.sqrt(np.sum((y_hat - y_test) ** 2) / len(x_test))mse = mean_squared_error(y_test, y_hat)mae = mean_absolute_error(y_test, y_hat)# noticeR_squared_Error = 1 - mse / np.var(y_test)print('mean squared error:%.2f' % (mse))print('root mean squared error:%.2f' % (rmse))print('mean absolute error:%.2f' % (mae))print('R squared Error:%.2f' % (R_squared_Error))a = regs.a_b = regs.b_x_plot = np.linspace(4, 8, 50)y_plot = x_plot * a + bplt.scatter(x, y)plt.plot(x_plot, y_plot, color='red')plt.show()
输出结果：
mean squared error:26.74
root mean squared error:5.17
mean absolute error:3.85
R squared Error:0.50
数据的可视化：
1.2 多元线性回归的正规方程解
多元线性回归中，单个x的样本拥有了多个特征，也就是上图中带下标的x。
其结构可以用向量乘法表示出来：
为了便于计算，一般会将x增加一个为1的特征，方便与截距bias计算。
而多元线性回归的优化目标与简单线性回归一致。
通过矩阵求导计算，可以得到方程解，但求解的时间复杂度很高。
下面使用正规方程解的形式，来对波士顿房价的所有特征做多元线性回归。
from math import sqrt
import numpy as np
#from PlayML.metrics import r2_score
from sklearn.model_selection import train_test_split
import sklearn.datasets as datasets
#from PlayML.metrics import root_mean_squared_error
np.random.seed(123)def mean_squared_error(y_true, y_predict):"""计算y_true和y_predict之间的MSE"""assert len(y_true) == len(y_predict), \"the size of y_true must be equal to the size of y_predict"return np.sum((y_true - y_predict)**2) / len(y_true)
def r2_score(y_true, y_predict):"""计算y_true和y_predict之间的R Square"""return 1 - mean_squared_error(y_true, y_predict)/np.var(y_true)def root_mean_squared_error(y_true, y_predict):"""计算y_true和y_predict之间的RMSE"""return sqrt(mean_squared_error(y_true, y_predict))class LinearRegression():def __init__(self):self.coef_=None # coeffientself.intercept_=None # interceptionself.theta_=Nonedef fit_normal(self, x_train, y_train):"""use normal equation solution for multiple linear regresion as model parametersParameters----------theta=(X^T * X)^-1 * X^T * y"""assert x_train.shape[0] == y_train.shape[0],\"""size of the x_train must be equal to y_train """X_b=np.hstack([np.ones((len(x_train), 1)), x_train])self.theta_=np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y_train) # (featere,1)self.coef_=self.theta_[1:]self.intercept_=self.theta_[0]def predict(self,x_pred):"""给定待预测数据集X_predict，返回表示X_predict的结果向量"""assert self.intercept_ is not None and self.coef_ is not None, \"must fit before predict!"assert x_pred.shape[1] == len(self.coef_), \"the feature number of X_predict must be equal to X_train"X_b=np.hstack([np.ones((len(x_pred),1)),x_pred])return X_b.dot(self.theta_)def score(self,x_test,y_test):"""Calculate evaluating indicator socreParameters---------x_test:x test datay_test:true label y for x test data"""y_pred=self.predict(x_test)return r2_score(y_test,y_pred)def __repr__(self):return "LinearRegression"if __name__ == '__main__':# use boston house price dataset for testboston_data = datasets.load_boston()x = boston_data['data'] # total x data (506,)y = boston_data['target'] # total y data (506,)# keep data with target value less than 50.x = x[y < 50] # total x data (490,)y = y[y < 50] # total x data (490,)# train size:(343,) test size:(147,)x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3,random_state=123)regs = LinearRegression()regs.fit_normal(x_train, y_train)# calc errorscore=regs.score(x_test,y_test)rmse=root_mean_squared_error(y_test,regs.predict(x_test))print('R squared error:%.2f' % (score))print('Root mean squared error:%.2f' % (rmse))输出结果：
R squared error:0.79
Root mean squared error:3.36
1.3 使用梯度下降求解多元线性回归
使用梯度下降的方法，在数据比较多的情况下会比方程解快很多。代码也不难，只是关于参数梯度的向量化推导有一点复杂，还有就是训练前要先进行数据标准化。
损失函数与之前相同，还是使用MSE损失。
为了让梯度为列向量，再做依次转置，得到最终的结果。
import numpy as np
from math import sqrt
import sklearn.datasets as datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScalerdef accuracy_score(y_true, y_predict):"""计算y_true和y_predict之间的准确率"""assert len(y_true) == len(y_predict), \"the size of y_true must be equal to the size of y_predict"return np.sum(y_true == y_predict) / len(y_true)def mean_squared_error(y_true, y_predict):"""计算y_true和y_predict之间的MSE"""assert len(y_true) == len(y_predict), \"the size of y_true must be equal to the size of y_predict"return np.sum((y_true - y_predict)**2) / len(y_true)def root_mean_squared_error(y_true, y_predict):"""计算y_true和y_predict之间的RMSE"""return sqrt(mean_squared_error(y_true, y_predict))def mean_absolute_error(y_true, y_predict):"""计算y_true和y_predict之间的MAE"""assert len(y_true) == len(y_predict), \"the size of y_true must be equal to the size of y_predict"return np.sum(np.absolute(y_true - y_predict)) / len(y_true)def r2_score(y_true, y_predict):"""计算y_true和y_predict之间的R Square"""return 1 - mean_squared_error(y_true, y_predict)/np.var(y_true)class LinearRegression:def __init__(self):"""初始化Linear Regression模型"""self.coef_ = Noneself.intercept_ = Noneself._theta = Nonedef fit_normal(self, X_train, y_train):"""根据训练数据集X_train, y_train训练Linear Regression模型"""assert X_train.shape[0] == y_train.shape[0], \"the size of X_train must be equal to the size of y_train"X_b = np.hstack([np.ones((len(X_train), 1)), X_train])self._theta = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y_train)self.intercept_ = self._theta[0]self.coef_ = self._theta[1:]return selfdef fit_gd(self, X_train, y_train, eta=0.01, n_iters=1e4):"""根据训练数据集X_train, y_train, 使用梯度下降法训练Linear Regression模型"""assert X_train.shape[0] == y_train.shape[0], \"the size of X_train must be equal to the size of y_train"def J(theta, X_b, y):try:return np.sum((y - X_b.dot(theta)) ** 2) / len(y)except:return float('inf')def dJ(theta, X_b, y):return X_b.T.dot(X_b.dot(theta) - y) * 2. / len(y)def gradient_descent(X_b, y, initial_theta, eta, n_iters=1e4, epsilon=1e-8):theta = initial_thetacur_iter = 0while cur_iter < n_iters:gradient = dJ(theta, X_b, y)last_theta = thetatheta = theta - eta * gradientif (abs(J(theta, X_b, y) - J(last_theta, X_b, y)) < epsilon):breakcur_iter += 1return thetaX_b = np.hstack([np.ones((len(X_train), 1)), X_train])initial_theta = np.zeros(X_b.shape[1])self._theta = gradient_descent(X_b, y_train, initial_theta, eta, n_iters)self.intercept_ = self._theta[0]self.coef_ = self._theta[1:]return selfdef predict(self, X_predict):"""给定待预测数据集X_predict，返回表示X_predict的结果向量"""assert self.intercept_ is not None and self.coef_ is not None, \"must fit before predict!"assert X_predict.shape[1] == len(self.coef_), \"the feature number of X_predict must be equal to X_train"X_b = np.hstack([np.ones((len(X_predict), 1)), X_predict])return X_b.dot(self._theta)def score(self, X_test, y_test):"""根据测试数据集 X_test 和 y_test 确定当前模型的准确度"""y_predict = self.predict(X_test)return r2_score(y_test, y_predict)def __repr__(self):return "LinearRegression()"if __name__ == '__main__':# use boston house price datasetboston_data = datasets.load_boston()x = boston_data['data'] # total x size (506,)y = boston_data['target'] # total y size (506,)# keep data with target value less than 50.x = x[y < 50] # total x size (490,)y = y[y < 50] # total x size (490,)# notice! need data normalizescaler=StandardScaler().fit(x)x=scaler.transform(x)# train size:(343,) test size:(147,)x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=123)regs = LinearRegression()regs.fit_gd(x_train, y_train)# calc errorscore = regs.score(x_test, y_test)rmse = root_mean_squared_error(y_test, regs.predict(x_test))print('R squared error:%.2f' % (score))print('Root mean squared error:%.2f' % (rmse))print('coeffient:', regs.coef_.shape)print('interception:', regs.intercept_.shape)
1.4 sklearn中的线性回归模型
import sklearn.datasets as datasets
from sklearn.linear_model import LinearRegression
import numpy as np
from sklearn.model_selection import train_test_split
from math import sqrt
np.random.seed(123)def mean_squared_error(y_true, y_predict):"""计算y_true和y_predict之间的MSE"""assert len(y_true) == len(y_predict), \"the size of y_true must be equal to the size of y_predict"return np.sum((y_true - y_predict)**2) / len(y_true)def root_mean_squared_error(y_true, y_predict):"""计算y_true和y_predict之间的RMSE"""return sqrt(mean_squared_error(y_true, y_predict))if __name__ == '__main__':# use boston house price datasetboston_data = datasets.load_boston()x = boston_data['data'] # total x size (506,)y = boston_data['target'] # total y size (506,)# keep data with target value less than 50.x = x[y < 50] # total x size (490,)y = y[y < 50] # total x size (490,)# train size:(343,) test size:(147,)x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=123)regs = LinearRegression()regs.fit(x_train, y_train)# calc errorscore = regs.score(x_test, y_test)rmse = root_mean_squared_error(y_test, regs.predict(x_test))print('R squared error:%.2f' % (score))print('Root mean squared error:%.2f' % (rmse))print('coeffient:',regs.coef_.shape)print('interception:',regs.intercept_.shape)
R squared error:0.79
Root mean squared error:3.36
coeffient: (13,)
interception: ()
