【第三课】kaggle案例分析三

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比赛题目介绍

• TalkingData是中国最大的第三方移动数据平台，移动设备用户日常的选择和行为用户画像。目前，TalkingData正在寻求每天在中国活跃的5亿移动设备70%以上的行为数据，帮助客户更好的了解与其用户互动。
• TalkingData提供了大约20万用户的数据(全部真实且经过脱敏处理)，按照年龄和性别分成12个组，比如男性22到25岁，女性30到35岁，同时提供了用户行为属性，比如在什么样的时间点出现什么样的地理位置等等，选手通过这些信息去推测用户是分在哪一个性别年龄里面。
• 什么是用户画像
• 用户画像可以理解为就是为用户画标签，标签是不固定的，依照企业业务场景动态添加、删除等，有的标签是永久的，有的标签是可以动态变更的
• 用户画像可以看做是综合性的标签系统，在自身拥有(或从第三方获得)的原始数据中，从多个维度对用户进行产品进行刻画，提取出商业价值潜力的语义信息，是常见的企业级大数据应用
• 对于人常见标签与维度，地域、年龄、性别、文化、职业、收入、生活习惯、消费习惯等
• 对于产品常见刻画维度，产品类别、活跃频率、产品喜好、产品驱动、使用习惯、产品消费等
• 技术栈：无监督学习、半监督学习(技巧性很强)，大多数是有监督学习
• 用户画像的作用
• 精准营销，分析产品潜在客户，针对特定群体利用短信邮件等方式进行营销
• 用户统计，比如中国大学购买书籍人数TOP10，全国分城市指数等
• 数据挖掘，构建智能推荐系统，利用关联规则计算，喜欢红酒的通常喜欢什么运动品牌，利用聚类算法分析，喜欢红酒的人的年龄段分布情况
• 进行效果评估，完善产品运用，提升服务质量，其实这也就是相当于市场调研、用户调研，迅速下定位服务群体，提供高水平的服务
• 对服务或产品进行私人定制，即个性化的服务某类群体甚至每一位用户(这是未来的消费趋势)比如，某公司退出一款5-10岁儿童的玩具，通过用户画像分析，发现某些特征的比重最大，就给产品提供了非常可观的决策依据
• 业务经营分析以及竞争分析，影响企业发展战略

人工神经网络原理

• 知识地图
• 从单层感知器到多层感知器
• 从多层感知器到自编码器
• 从多层感知器到卷积神经网络，再到深度残差网络
• 从多层感知器到递归神经网络，再到LSTM
• 从单层感知器到Hopfield神经网络，再到Bolazmann机和RBM
• 用RBM堆叠成DBN，DBN与多层自编码器结合成DBN-DNN
• 神经网络要素
• 网路结构(全连接、分层、有时滞回路、权值共享、激活函数)
• 运行机制(异步更新、同步更新、前馈)
• 训练算法，训练中使用的trick(mini batch BN drop out等)，损失函数的定义
• 训练数据(数据的预处理，输入和输出的构成等)
• 单层感知器
• 输入节点
• 输出节点
• 权向量
• 偏置因子
• 激活函数
• 学习率
• 单层感知器学习算法
• 单层感知器的局限
  激活函数

sng(w1​x1​+w2​x2​+w3x3​)=0

单层感知器类比于线性分类器

感知器学习的规则

1958年，首先定义了一个具有单层计算单元的神经网络结构，称为感知器
感知器的学习规则规定，学习信号等于神经元期望输出(教师信号)与实际输出之差

r=dj​−oj​

式中，dj​为期望的输出，oj​=f(WjT​X)感知器采用了与阈值转移函数类似的符号转移函数，其表达式为

f(WjT​X)=sgn(WjT​X)={1,−1,​(WjT​≥0)(WjT​<0)​

因此，权值调整公式为：

ΔWj​=±2ηX

感知器学习规则只适用于二进制神经元，初始值可取任意值。
感知器学习规则代表一种有导师学习，由于感知器理论是研究其他神经网络的基础，该规则对于神经网络的有导师学习具有极为重要的意义。

多层前馈神经网络（BP网络）

• 隐藏层与隐藏节点
• 前馈-----每一层的节点仅和下一层节点相连

BP学习算法本质就是梯度下降法

神经网络的两个过程：1.训练过程 2.推断过程

小结：数学背景：梯度下降法(使用误差平方和构造误差函数)
梯度下降法给出权值，通过反向传播误差逐层训练，推断时误差往后走，构造完后给出权值，误差信号通过公式计算梯度，得出梯度对权值进行迭代改变，知道权值不再变化或者次数足够收敛，就结束整个训练过程

在python深度学习中，可以使用keras进行简单的神经网络等设计
keras框架是简单方便的，模块可以作为数据或序列的运算图等，keras中的各个设计环节都是单独的一个模块，所以需要根据自己的需求来设计
keras中文文档
keras默认使用tensorflow，如果使用theano需要修改文件中的路径内的json文件

keras的基本使用

• Sequential 模型构建
• 序贯模型是多个网络层的线性堆叠
• 可以通过向Sequential模型传递一个layer的list来构造该模型

from keras.models import Sequential
from keras.layers import Dense,Activation
model = Sequential([    
Dense(32,units=784),    
Activation('relu'),    
Dense(10),    
Activation('softmax'),])

• 也可以通过.add()方法一个个的将layer加入模型中

model = Sequential()
model.add(Dense(32,input_shape=(784,))
)model.add(Activation('relu'))

• 指定输入数据的shape
• input shape（元祖型数据）
• input dim

model = Sequential()
model.add(Dense(32,input_dim=784))

• input length

model = Sequentail()
model.add(Dense(32,input_shape=784))

• batch_size

评分标准

• 选手算出用户在不同分组上的概率，现实中一个用户只能在一个分组。理想状态下如果能算出这个概率是1，其他是0的话，这个答案就是没有任何损失的。一般来说，提交的答案中，某个用户会有或大或小的概率属于多个组别，这个时候就有概率上的损失，这个损失的高低代表答案的水平。我们优化函数或者说是评估指标就是下面的损失函数

logloss=−N1​i=1∑N​j=1∑M​yij​log(pij​)

max(min(p,1−10−15),10−15)

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