Python3入门机器学习经典算法与应用——逻辑回归实现

古城微笑少年丶 2022-12-22 04:47 138阅读 0赞

# 逻辑回归实现 #

逻辑回归实现

import numpy as np
    from .metrics import accuracy_score
    
    class LogisticRegression:
    
        def __init__(self):
            """初始化Logistic Regression模型"""
            self.coef_ = None
            self.intercept_ = None
            self._theta = None
    
        def _sigmoid(self, t):
            return 1. / (1. + np.exp(-t))
    
        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Logistic 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):
                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):
                return X_b.T.dot(self._sigmoid(X_b.dot(theta)) - y) / len(y)
    
            def gradient_descent(X_b, y, initial_theta, eta, n_iters=1e4, epsilon=1e-8):
    
                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 accuracy_score(y_test, y_predict)
    
        def __repr__(self):
            return "LogisticRegression()"

# metrics #

import numpy as np
    from math import sqrt
    
    
    def 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)