<reponame>bufanx/ML-Learning<filename>PlayML/LogisticRegression.py
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.interception_ = None
        self._theta = None

    def _sigmoid(self, t):
        return 1./(1.+np.exp(-t))

    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.interception_ 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 predict_proba(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.interception_ 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 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(X_b)
            except:
                return float('inf')

        def dJ(theta, X_b, y):
            # res = np.empty(len(theta))
            # res[0] = np.sum(X_b.dot(theta) - y)
            # for i in range(1, len(theta)):
            #     res[i] = (X_b.dot(theta) - y).dot(X_b[:, i])
            #
            # return res * 2 / len(X_b)
            return X_b.T.dot(self._sigmoid(X_b.dot(theta)) - y) / len(X_b)

        def dJ_debug(theta, X_b, y, epsilon=0.01):
            res = np.empty(len(theta))
            for i in range(len(theta)):
                theta_1 = theta.copy()
                theta_1[i] += epsilon
                theta_2 = theta.copy()
                theta_2[i] -= epsilon
                res[i] = (J(theta_1, X_b, y) - J(theta_2, X_b, y)) / (2 * epsilon)
            return res

        def gradient_descent(X_b, y, initial_theta, eta, n_iters=1e4, epsilon=1e-8):

            theta = initial_theta
            i_iter = 0

            while i_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

                i_iter += 1

            return theta

        X_b = np.hstack([np.ones((len(X_train), 1)), X_train])
        inital_theta = np.zeros(X_b.shape[1])
        self._theta = gradient_descent(X_b, y_train, inital_theta, eta, n_iters)

        self.interception_ = self._theta[0]
        self.coef_ = self._theta[1:]

        return self

    def score(self, X_test, y_test):
        """根据测试数据集,X_test和y_test确定当前模型准确度"""
        y_predict = self.predict(X_test)
        return accuracy_score(y_test, y_predict)

    def __repr__(self):
        return "LogisticRegression()"

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.interception_ = None
        self._theta = None

    def _sigmoid(self, t):
        return 1./(1.+np.exp(-t))

    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.interception_ 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 predict_proba(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.interception_ 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 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(X_b)
            except:
                return float('inf')

        def dJ(theta, X_b, y):
            # res = np.empty(len(theta))
            # res[0] = np.sum(X_b.dot(theta) - y)
            # for i in range(1, len(theta)):
            #     res[i] = (X_b.dot(theta) - y).dot(X_b[:, i])
            #
            # return res * 2 / len(X_b)
            return X_b.T.dot(self._sigmoid(X_b.dot(theta)) - y) / len(X_b)

        def dJ_debug(theta, X_b, y, epsilon=0.01):
            res = np.empty(len(theta))
            for i in range(len(theta)):
                theta_1 = theta.copy()
                theta_1[i] += epsilon
                theta_2 = theta.copy()
                theta_2[i] -= epsilon
                res[i] = (J(theta_1, X_b, y) - J(theta_2, X_b, y)) / (2 * epsilon)
            return res

        def gradient_descent(X_b, y, initial_theta, eta, n_iters=1e4, epsilon=1e-8):

            theta = initial_theta
            i_iter = 0

            while i_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

                i_iter += 1

            return theta

        X_b = np.hstack([np.ones((len(X_train), 1)), X_train])
        inital_theta = np.zeros(X_b.shape[1])
        self._theta = gradient_descent(X_b, y_train, inital_theta, eta, n_iters)

        self.interception_ = self._theta[0]
        self.coef_ = self._theta[1:]

        return self

    def score(self, X_test, y_test):
        """根据测试数据集,X_test和y_test确定当前模型准确度"""
        y_predict = self.predict(X_test)
        return accuracy_score(y_test, y_predict)

    def __repr__(self):
        return "LogisticRegression()"
初始化Logistic Regression模型 给定待预测数据集X_predict,返回表示X_predict的结果向量 给定待预测数据集X_predict,返回表示X_predict的结果向量 根据训练数据集X_train,y_train,使用梯度下降法训练Logistic Regression模型 # res = np.empty(len(theta)) # res[0] = np.sum(X_b.dot(theta) - y) # for i in range(1, len(theta)): #     res[i] = (X_b.dot(theta) - y).dot(X_b[:, i]) # # return res * 2 / len(X_b) 根据测试数据集,X_test和y_test确定当前模型准确度