机器学习-逻辑回归算法推理+代码复现

ゝ一世哀愁。 2023-02-15 05:24 42阅读 0赞

# 1、算法推理： #

这里算法推理和代码复现原型参考：[https://blog.csdn.net/zouxy09/article/details/20319673][https_blog.csdn.net_zouxy09_article_details_20319673]

# 2、算法应用背景-课题设计： #

企业财务风险预警是企业风险预警系统的一个重要组成部分，它能有效的预知部分财务风险。本课题将风险公司记为ST，非风险公司记为非ST，ST判断标准如下：

1.）连续两年年报显示净利润为负值

2）净资产收益率、总资产净利润率为负值。

其影响特征变量如下：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0xpbnV4X2Jpbg_size_16_color_FFFFFF_t_70][]

注: 现金比率= 货币资金÷ 流动负债;

盈利现金比率= 经营活动的现金净流量÷ 净利润;

主营业务鲜明程度= 主营业务利润÷ |净利润|;

请利用2018年的财务指标相关数据，构建基于BP神经网络的财务风险预警模型。

# 3、代码复现 #

## 3.1  train  & predict ##

from numpy import *
    import matplotlib.pyplot as plt
    import time
    import numpy as np
    from data import Dataset
    import pickle
    # calculate the sigmoid function
    #算法推理来自于：https://blog.csdn.net/zouxy09/article/details/20319673
    #commit code to https://github.com/Linby1103/Pre-warning.git
    
    class LR(object):
        def __init__(self,train_data,label_):
    
            self.traindata=train_data
            self.label=label_
    
        def sigmoid(self,z):
            return 1.0 / (1 + exp(-z))
    
        def train(self,opts,weight_path="weights.pkl"):
            # train a logistic regression model using some optional optimize algorithm
            # traindata is a mat datatype, each row stands for one sample
            #label is mat datatype too, each row is the corresponding label
            #opts is optimize option include step and maximum number of iterations
            # calculate training time
            startTime = time.time()
    
            numSamples, numFeatures = shape(self.traindata)
            alpha = opts['alpha'];
            maxIter = opts['maxIter']
            weights = ones((numFeatures, 1))
    
    
            # optimize through gradient descent algorilthm
            for k in range(maxIter):
                if opts['optimizeType'] == 'gradDescent':  # gradient descent algorilthm
                    output = self.sigmoid(self.traindata * weights)
                    error = self.label - output  
                    weights = weights + alpha * self.traindata.transpose() * error  # (y-err)*x  BP
                elif opts['optimizeType'] == 'stocGradDescent':  # stochastic gradient descent
                    for i in range(numSamples):
                        output = self.sigmoid(self.traindata[i, :] * weights)#注意这traindata是转化为mat类型的数据，所以这里用* 和用dot做矩阵的乘法是一样的
                        error = self.label[i, 0] - output
                        weights = weights + alpha * self.traindata[i, :].transpose() * error
                elif opts['optimizeType'] == 'smoothStocGradDescent':  # smooth stochastic gradient descent
                    # randomly select samples to optimize for reducing cycle fluctuations
                    dataIndex = range(numSamples)
                    for i in range(numSamples):
                        alpha = 4.0 / (1.0 + k + i) + 0.01
                        randIndex = int(random.uniform(0, len(dataIndex)))
                        output = self.sigmoid(self.traindata[randIndex, :] * weights)
                        error = self.label[randIndex, 0] - output
                        weights = weights + alpha * self.traindata[randIndex, :].transpose() * error
                        del (dataIndex[randIndex])  # during one interation, delete the optimized sample
                else:
                    raise NameError('Not support optimize method type!')
    
            print('training finished!  %fs!' % (time.time() - startTime))
    
            try:
                pickf = open(weight_path, 'wb')
                print("Save weight to {}".format(weight_path))
                data = {"weight": weights}
                pickle.dump(data, pickf)
            except Exception as e:
                print("Write weight to {} fail! {}".format(pickf,e))
                pickf.close()
            finally:
                pickf.close()
    
        # test  trained Logistic Regression model given test set
        def predict(self,weights, test_x, test_y):
            numSamples, numFeatures = shape(test_x)
            matchCount = 0
            result=[]
            for i in range(numSamples):
                prob = 1 if self.sigmoid(test_x[i, :] * weights)[0, 0] > 0.5 else 0
                result.append(prob)
                if prob == bool(test_y[i, 0]):
                    matchCount += 1
            accuracy = float(matchCount) / numSamples
            print("accuracy={}".format(accuracy))
            return result
        def load_weight(self,path):
            weight={}
            try:
                 weight = pickle.load(open(path,'rb'))["weight"]
                 print(weight.shape)
            except Exception as e:
                print("Load weight faild! {}".format(e))
            finally:
                return weight
    
    
    
    def loadData():
        train_x = []
        train_y = []
        fileIn = open('./data/testdata.txt')
        for line in fileIn.readlines():
            lineArr = line.strip().split()
            train_x.append([1.0, float(lineArr[0]), float(lineArr[1])])
            train_y.append(float(lineArr[2]))
        return mat(train_x), mat(train_y).transpose()
    
    if __name__=="__main__":
        STATUS = ['非ST','ST']
        save_model="weights.pkl"
    
        train, label = Dataset.GetDatafromDict()
        trainarrayx=mat(train)
        labelarray=mat(label).transpose()
        logistreg = LR(trainarrayx,labelarray)
        # print(trainarrayx.shape,labelarray.shape)
        opts={"alpha":0.001,"maxIter":200,"optimizeType":"gradDescent"}
        logistreg.train(opts,save_model)
        weights=logistreg.load_weight(save_model)
        res=logistreg.predict(weights,trainarrayx,labelarray)
    
        for i in range(len(res)):
            print("预测结果 :{}---原始数据:{}".format(STATUS[res[i]], STATUS[label[i]]))

## 3.2 Create dataset-数据来源于excel ##

import xlrd
    import os
    import numpy as np
    
    
    class ExcelReade(object):
        def __init__(self, excel_name, sheet_name):
            """
            # 我把excel放在工程包的当前文件夹中：
            # 1.需要先获取到工程文件的地址
            # 2.再找到excel的文件地址（比写死的绝对路径灵活）
    
                os.path.relpath(__file__)
                1.根据系统获取绝对路径
                2.会根据电脑系统自动匹配路径：mac路径用/,windows路径用\
                3.直接使用__file__方法是不会自动适配环境的
            """
    
            self.excel_path = excel_name
            # 打开指定的excel文件
            self.date = xlrd.open_workbook(self.excel_path)
            # 找到指定的sheet页
            self.table = self.date.sheet_by_name(sheet_name)
            self.rows = self.table.nrows  # 获取总行数
            self.cols = self.table.ncols  # 获取总列数
    
        def data_dict(self):
            if self.rows <= 1:
                print("总行数小于等于1，路径：", end='')
                print(self.excel_path)
                return False
            else:
                # 将列表的第一行设置为字典的key值
                keys = self.table.row_values(0)
                # 定义一个数组
                data = []
                # 从第二行开始读取数据，循环rows（总行数）-1次
                for i in range(1, self.rows):
                    # 循环内定义一个字典，每次循环都会清空
                    dict = {}
                    # 从第一列开始读取数据，循环cols（总列数）次
                    for j in range(0, self.cols):
                        # 将value值关联同一列的key值
                        dict[keys[j]] = self.table.row_values(i)[j] if self.table.row_values(i)[j] != "NULL" else "0"
                    # 将关联后的字典放到数组里
                    data.append(dict)
    
    
                return data
    #Stkcd	净利润2017	净利润2018	净资产收益率	总资产净利润率	x1	x2	x3	x4	x5	x6	x7	x8	x9	x10	x11	x12	x13	x14
    
    def GetDatafromDict():
        start = ExcelReade('./data/dataset.xlsx', 'Sheet1')
        data = start.data_dict()
    
        train=[]
        label=[]
    
        for i in range(len(data)):
            array=[]
            array.append(float(data[i]['净利润2017']))
            array.append(float(data[i]['净利润2018']))
            array.append(float(data[i]['净资产收益率']))
            array.append(float(data[i]['总资产净利润率']))
            array.append(float(data[i]['x1']))
            array.append(float(data[i]['x2']))
            array.append(float(data[i]['x3']))
            array.append(float(data[i]['x4']))
            array.append(float(data[i]['x5']))
            array.append(float(data[i]['x6']))
            array.append(float(data[i]['x7']))
            array.append(float(data[i]['x8']))
            array.append(float(data[i]['x9']))
            array.append(float(data[i]['x10']))
            array.append(float(data[i]['x11']))
            array.append(float(data[i]['x12']))
            array.append(float(data[i]['x13']))
            array.append(float(data[i]['x14']))
            train.append(array)
            if float(data[i]['净利润2017'])*float(data[i]['净利润2018'])<0:
                label.append(0)
            else:
                label.append(1)
        return np.array(train) ,np.array(label)
    
    
    
    if __name__ == '__main__':
        start = ExcelReade('C:/Users/91324/Desktop/datasset.xlsx','Sheet1')
        data = start.data_dict()
        GetDatafromDict(data)

数据集，代码上传至：[https://github.com/Linby1103/Pre-warning.git][https_github.com_Linby1103_Pre-warning.git]

[https_blog.csdn.net_zouxy09_article_details_20319673]: https://blog.csdn.net/zouxy09/article/details/20319673
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0xpbnV4X2Jpbg_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20200604170633765.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0xpbnV4X2Jpbg==,size_16,color_FFFFFF,t_70
[https_github.com_Linby1103_Pre-warning.git]: https://github.com/Linby1103/Pre-warning.git

机器学习-逻辑回归算法推理+代码复现

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