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python实现线性回归之简单回归

2020-04-28 16热度

python实现线性回归之简单回归

代码来源:https://github.com/eriklindernoren/ML-From-Scratch

首先定义一个基本的回归类,作为各种回归方法的基类:

class Regression(object):

""" Base regression model. Models the relationship between a scalar dependent variable y and the independent variables X. Parameters: ----------- n_iterations: float The number of training iterations the algorithm will tune the weights for. learning_rate: float The step length that will be used when updating the weights. """ def __init__(self, n_iterations, learning_rate): self.n_iterations = n_iterations self.learning_rate = learning_rate def initialize_wights(self, n_features): """ Initialize weights randomly [-1/N, 1/N] """ limit = 1 / math.sqrt(n_features) self.w = np.random.uniform(-limit, limit, (n_features, )) def fit(self, X, y): # Insert constant ones for bias weights X = np.insert(X, 0, 1, axis=1) self.training_errors = [] self.initialize_weights(n_features=X.shape[1]) # Do gradient descent for n_iterations for i in range(self.n_iterations): y_pred = X.dot(self.w) # Calculate l2 loss mse = np.mean(0.5 * (y - y_pred)**2 + self.regularization(self.w)) self.training_errors.append(mse) # Gradient of l2 loss w.r.t w grad_w = -(y - y_pred).dot(X) + self.regularization.grad(self.w) # Update the weights self.w -= self.learning_rate * grad_w def predict(self, X): # Insert constant ones for bias weights X = np.insert(X, 0, 1, axis=1) y_pred = X.dot(self.w) return y_pred 

说明:初始化时传入两个参数,一个是迭代次数,另一个是学习率。initialize_weights()用于初始化权重。fit()用于训练。需要注意的是,对于原始的输入X,需要将其最前面添加一项为偏置项。predict()用于输出预测值。

接下来是简单线性回归,继承上面的基类:

class LinearRegression(Regression):

"""Linear model. Parameters: ----------- n_iterations: float The number of training iterations the algorithm will tune the weights for. learning_rate: float The step length that will be used when updating the weights. gradient_descent: boolean True or false depending if gradient descent should be used when training. If false then we use batch optimization by least squares. """ def __init__(self, n_iterations=100, learning_rate=0.001, gradient_descent=True): self.gradient_descent = gradient_descent # No regularization self.regularization = lambda x: 0 self.regularization.grad = lambda x: 0 super(LinearRegression, self).__init__(n_iterations=n_iterations, learning_rate=learning_rate) def fit(self, X, y): # If not gradient descent => Least squares approximation of w if not self.gradient_descent: # Insert constant ones for bias weights X = np.insert(X, 0, 1, axis=1) # Calculate weights by least squares (using Moore-Penrose pseudoinverse) U, S, V = np.linalg.svd(X.T.dot(X)) S = np.diag(S) X_sq_reg_inv = V.dot(np.linalg.pinv(S)).dot(U.T) self.w = X_sq_reg_inv.dot(X.T).dot(y) else: super(LinearRegression, self).fit(X, y) 

这里使用两种方式进行计算。如果规定gradient_descent=True,那么使用随机梯度下降算法进行训练,否则使用标准方程法进行训练。

最后是使用:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import make_regression
import sys
sys.path.append("/content/drive/My Drive/learn/ML-From-Scratch/")

from mlfromscratch.utils import train_test_split, polynomial_features
from mlfromscratch.utils import mean_squared_error, Plot
from mlfromscratch.supervised_learning import LinearRegression

def main():

X, y = make_regression(n_samples=100, n_features=1, noise=20) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4) n_samples, n_features = np.shape(X) model = LinearRegression(n_iterations=100) model.fit(X_train, y_train) # Training error plot n = len(model.training_errors) training, = plt.plot(range(n), model.training_errors, label="Training Error") plt.legend(handles=[training]) plt.title("Error Plot") plt.ylabel('Mean Squared Error') plt.xlabel('Iterations') plt.savefig("test1.png") plt.show() y_pred = model.predict(X_test) mse = mean_squared_error(y_test, y_pred) print ("Mean squared error: %s" % (mse)) y_pred_line = model.predict(X) # Color map cmap = plt.get_cmap('viridis') # Plot the results m1 = plt.scatter(366 * X_train, y_train, color=cmap(0.9), s=10) m2 = plt.scatter(366 * X_test, y_test, color=cmap(0.5), s=10) plt.plot(366 * X, y_pred_line, color='black', linewidth=2, label="Prediction") plt.suptitle("Linear Regression") plt.title("MSE: %.2f" % mse, fontsize=10) plt.xlabel('Day') plt.ylabel('Temperature in Celcius') plt.legend((m1, m2), ("Training data", "Test data"), loc='lower right') plt.savefig("test2.png") plt.show() 

if name == "__main__":

main() 

利用sklearn库生成线性回归数据,然后将其拆分为训练集和测试集。

utils下的mean_squared_error():

def mean_squared_error(y_true, y_pred):

""" Returns the mean squared error between y_true and y_pred """ mse = np.mean(np.power(y_true - y_pred, 2)) return mse

结果:

Mean squared error: 532.3321383700828

原文地址https://www.cnblogs.com/xiximayou/p/12802118.html

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