统计学习方法逻辑斯蒂回归

古城微笑少年丶 2022-04-10 01:45 264阅读 0赞

逻辑斯谛回归（logistic regression） 是统计学习中的**经典分类**方法。 最大熵是**概率模型学习的一个准则**， 将其**推广到分类**问题得到**最大熵模型（maximum entropy model）** 。逻辑斯谛回归模型与最大熵模型都属于**对数线性模型**。本文只介绍逻辑斯谛回归。  
设X是连续随机变量， X服从Logistic distribution，  
分布函数：![20181219161435194.png][]  
密度函数：![20181219161448971.png][]  
μ为位置参数， γ大于0为形状参数， (μ,1/2)中心对称  
![20181219161510229.png][]

![2018121916152782.png][]

Sigmoid：![20181219161559611.png][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 1][]  
双曲正切函数（tanh）：![20181219161710665.png][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 2][]

**二项逻辑斯蒂回归**  
Binomial logistic regression model  
由条件概率P(Y|X)表示的分类模型  
形式化为logistic distribution  
X取实数， Y取值1,0  
![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 3][]

事件的几率odds： 事件发生与事件不发生的概率之比为![20181219162044209.png][]称为事件的发生比(the odds of experiencing an event),  
对数几率：![20181219162105123.png][]  
对逻辑斯蒂回归：![20181219162118535.png][]

**似然函数**  
logistic分类器是**由一组权值系数组成**的， 最关键的问题就是如何获取这组权值， 通过极大似然函数估计获得， 并且  
Y~f(x;w)  
似然函数是统计模型中参数的函数。 给定输出x时， 关于参数θ的似然函数L(θ|x)（在数值上） 等于给定参数θ后变量X的概率： **L(θ|x)=P(X=x|θ)**  
似然函数的重要性不是它的取值， 而是**当参数变化时概率密度函数到底是变大还是变小**。  
极大似然函数： **似然函数取得最大值表示相应的参数能够使得统计模型最为合理**。  
那么对于上述m个观测事件， 设![20181219162326168.png][]  
其联合概率密度函数， 即似然函数为：![20181219162342825.png][]  
目标： 求出使这一似然函数的值最大的参数估， w1,w2,…,wn，使得L(w)取得 最大值。  
对L(w)取对数。

对数似然函数![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 4][]  
对L(w)求极大值， 得到w的估计值。  
通常采用梯度下降法及拟牛顿法， 学到的模型：  
![20181219162455425.png][]

直接上代码吧，w的极大值采用梯度下降法，用的iris的数据集：

import numpy as np
    import matplotlib.pyplot as plt
    from sklearn.datasets import load_iris
    
    class LogisticsRegression:
    
    	def __init__(self):
    		"""初始化Logistics Regression模型"""
    		self.coef_ = None
    		self.intercept_ = None
    		self._theta = None
    
    	def _sigmoid(self, t):
    		return 1. / (1. + np.exp(-t))
    
    	def accuracy_score(self, 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 fit(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Logistics 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):
    			'''
    			损失函数
    			'''
    			y_hat = self._sigmoid(X_b.dot(theta))
    			try:
    				return np.sum(y*np.log(y_hat) + (1-y)*np.log(1 - y_hat)) / len(y)
    			except:
    				return float('inf')
    
    		def dJ(theta, X_b, y):
    			'''
    			求梯度
    			'''
    			return X_b.T.dot(self._sigmoid(X_b.dot(theta))  - y) / len(X_b)
    
    		def gradient_descent(X_b, y, initial_theta, eta, n_iters=1e4, epsilon=1e-8):
    			'''
    			梯度下降
    			'''
    			theta = initial_theta
    			cur_iter = 0
    			while cur_iter < n_iters:
    				gradient = dJ(theta, X_b, y)
    				last_theta = theta
    				theta = theta - eta * gradient
    				if (abs(J(theta, X_b, y) - J(last_theta, X_b, y)) < epsilon):
    					break
    				cur_iter += 1
    			return theta
    
    		X_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 self
    
    	def predict_proba(self, X_predict):
    		"""给定待预测数据集X_predict，返回表示X_predict的结果概率向量"""
    		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 self._sigmoid(X_b.dot(self._theta))
    	
    	def 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"
    		proba = self.predict_proba(X_predict)
    		return np.array(proba>=0.5, dtype = 'int')
    
    	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 self.accuracy_score(y_test, y_predict)
    
    	def __repr__(self):
    		return "LogisticsRegression()"
    
    iris = load_iris()
    
    X = iris.data
    y = iris.target
    
    # 二项LogisticsRegression只适用二分类
    X = X[y<2, :2]
    y = y[y<2]
    
    # # 画出数据
    # plt.scatter(X[y == 0, 0], X[y == 0, 1], color="red")
    # plt.scatter(X[y == 1, 0], X[y == 1, 1], color="blue")
    # plt.show()
    
    def train_test_split(X, y, test_ratio=0.2, seed=None):
    	"""将数据 X 和 y 按照test_ratio分割成X_train, X_test, y_train, y_test"""
    	assert X.shape[0] == y.shape[0], \
    		"the size of X must be equal to the size of y"
    	assert 0.0 <= test_ratio <= 1.0, \
    		"test_ration must be valid"
    
    	if seed:
    		np.random.seed(seed)
    
    	shuffled_indexes = np.random.permutation(len(X))
    
    	test_size = int(len(X) * test_ratio)
    	test_indexes = shuffled_indexes[:test_size]
    	train_indexes = shuffled_indexes[test_size:]
    
    	X_train = X[train_indexes]
    	y_train = y[train_indexes]
    
    	X_test = X[test_indexes]
    	y_test = y[test_indexes]
    
    	return X_train, X_test, y_train, y_test
    
    X_train, X_test, y_train, y_test = train_test_split(X, y, seed = 888)
    log_reg = LogisticsRegression()
    log_reg.fit(X_train, y_train)
    print('测试集预测准确率：'+ str(log_reg.score(X_test, y_test)))
    print('测试集合预测概率：'+ str(log_reg.predict_proba(X_test)))
    print('测试集合标签：'+ str(y_test))
    print('测试集合预测标签：' + str(log_reg.predict(X_test)))

结果：

![20181220114454903.png][]  
**多项logistic回归**  
设Y的取值集合为![20181219162525476.png][]  
多项logistic回归模型  
![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 5][]

[20181219161435194.png]: /images/20220404/dabbd451e1424238aa663c07322bd5fb.png
[20181219161448971.png]: /images/20220404/9f34fa236aa94f1ba922edf0d474a818.png
[20181219161510229.png]: /images/20220404/74ce5e450c924bf09db3aad6ece30c26.png
[2018121916152782.png]: /images/20220404/1f7d74d539c245f7a81a850f5818800e.png
[20181219161559611.png]: https://img-blog.csdnimg.cn/20181219161559611.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70]: /images/20220404/bf5afb0b88f249d1ac570259e8dd3875.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 1]: /images/20220404/010df4011a32439999c4f85c234d0f29.png
[20181219161710665.png]: https://img-blog.csdnimg.cn/20181219161710665.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 2]: /images/20220404/fc4df8130216404f8b46e23dc27c360d.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 3]: /images/20220404/4e0ba339cec14bc48106f3490794a01a.png
[20181219162044209.png]: /images/20220404/ee480e6e043b49d9aba6262e9dae78b4.png
[20181219162105123.png]: /images/20220404/779086719a034a96bc634a6872101082.png
[20181219162118535.png]: /images/20220404/d83f8ceea0604f1fbc75148562337d17.png
[20181219162326168.png]: /images/20220404/be0540367b174452afb8102233f09b30.png
[20181219162342825.png]: /images/20220404/79bc5e5430ee42a9a22aa31d1d74a3b6.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 4]: /images/20220404/6884ccc8f68e4ffcbf1ff75535ab2f7e.png
[20181219162455425.png]: /images/20220404/c1f685c80a7a42948d5364054a398e8d.png
[20181220114454903.png]: /images/20220404/4ad959753c554adf9f9ace507778b673.png
[20181219162525476.png]: /images/20220404/70386b9d1fbb455b927e1355a50ff73d.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 5]: /images/20220404/75de8c2b355b4ca3a32185ee8c182f6d.png