283 lines
7.8 KiB
Plaintext
283 lines
7.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'1.0.0'"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import torch\n",
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"import torch.nn as nn\n",
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"import numpy as np\n",
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"torch.__version__"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 3.1 logistic回归实战\n",
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"在这一章里面，我们将处理一下结构化数据，并使用logistic回归对结构化数据进行简单的分类。\n",
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"## 3.1.1 logistic回归介绍\n",
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"logistic回归是一种广义线性回归（generalized linear model），与多重线性回归分析有很多相同之处。它们的模型形式基本上相同，都具有 wx + b，其中w和b是待求参数，其区别在于他们的因变量不同，多重线性回归直接将wx+b作为因变量，即y =wx+b,而logistic回归则通过函数L将wx+b对应一个隐状态p，p =L(wx+b),然后根据p 与1-p的大小决定因变量的值。如果L是logistic函数，就是logistic回归，如果L是多项式函数就是多项式回归。\n",
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"\n",
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"说的更通俗一点，就是logistic回归会在线性回归后再加一层logistic函数的调用。\n",
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"\n",
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"logistic回归主要是进行二分类预测，我们在激活函数时候讲到过 Sigmod函数，Sigmod函数是最常见的logistic函数，因为Sigmod函数的输出的是是对于0~1之间的概率值，当概率大于0.5预测为1，小于0.5预测为0。\n",
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"\n",
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"下面我们就来使用公开的数据来进行介绍"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3.1.2 UCI German Credit 数据集\n",
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"\n",
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"UCI German Credit是UCI的德国信用数据集，里面有原数据和数值化后的数据。\n",
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"\n",
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"German Credit数据是根据个人的银行贷款信息和申请客户贷款逾期发生情况来预测贷款违约倾向的数据集，数据集包含24个维度的，1000条数据，\n",
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"\n",
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"在这里我们直接使用处理好的数值化的数据，作为展示。\n",
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"\n",
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"[地址](https://archive.ics.uci.edu/ml/machine-learning-databases/statlog/german/)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3.2 代码实战\n",
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"我们这里使用的 german.data-numeric是numpy处理好数值化数据，我们直接使用numpy的load方法读取即可"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"data=np.loadtxt(\"german.data-numeric\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"数据读取完成后我们要对数据做一下归一化的处理"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"n,l=data.shape\n",
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"for j in range(l-1):\n",
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" meanVal=np.mean(data[:,j])\n",
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" stdVal=np.std(data[:,j])\n",
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" data[:,j]=(data[:,j]-meanVal)/stdVal"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"打乱数据"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"np.random.shuffle(data)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"区分训练集和测试集，由于这里没有验证集，所以我们直接使用测试集的准确度作为评判好坏的标准\n",
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"\n",
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"区分规则：900条用于训练，100条作为测试\n",
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"\n",
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"german.data-numeric的格式为，前24列为24个维度，最后一个为要打的标签（0，1），所以我们将数据和标签一起区分出来"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_data=data[:900,:l-1]\n",
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"train_lab=data[:900,l-1]-1\n",
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"test_data=data[900:,:l-1]\n",
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"test_lab=data[900:,l-1]-1"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"下面我们定义模型，模型很简单"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"class LR(nn.Module):\n",
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" def __init__(self):\n",
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" super(LR,self).__init__()\n",
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" self.fc=nn.Linear(24,2) # 由于24个维度已经固定了，所以这里写24\n",
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" def forward(self,x):\n",
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" out=self.fc(x)\n",
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" out=torch.sigmoid(out)\n",
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" return out\n",
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" "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"测试集上的准确率"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"def test(pred,lab):\n",
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" t=pred.max(-1)[1]==lab\n",
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" return torch.mean(t.float())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"下面就是对一些设置"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"net=LR() \n",
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"criterion=nn.CrossEntropyLoss() # 使用CrossEntropyLoss损失\n",
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"optm=torch.optim.Adam(net.parameters()) # Adam优化\n",
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"epochs=1000 # 训练1000次\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"下面开始训练了"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch:100,Loss:0.6313,Accuracy：0.76\n",
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"Epoch:200,Loss:0.6065,Accuracy：0.79\n",
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"Epoch:300,Loss:0.5909,Accuracy：0.80\n",
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"Epoch:400,Loss:0.5801,Accuracy：0.81\n",
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"Epoch:500,Loss:0.5720,Accuracy：0.82\n",
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"Epoch:600,Loss:0.5657,Accuracy：0.81\n",
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"Epoch:700,Loss:0.5606,Accuracy：0.81\n",
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"Epoch:800,Loss:0.5563,Accuracy：0.81\n",
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"Epoch:900,Loss:0.5527,Accuracy：0.81\n",
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"Epoch:1000,Loss:0.5496,Accuracy：0.80\n"
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]
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}
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],
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"source": [
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"for i in range(epochs):\n",
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" # 指定模型为训练模式，计算梯度\n",
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" net.train()\n",
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" # 输入值都需要转化成torch的Tensor\n",
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" x=torch.from_numpy(train_data).float()\n",
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" y=torch.from_numpy(train_lab).long()\n",
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" y_hat=net(x)\n",
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" loss=criterion(y_hat,y) # 计算损失\n",
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" optm.zero_grad() # 前一步的损失清零\n",
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" loss.backward() # 反向传播\n",
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" optm.step() # 优化\n",
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" if (i+1)%100 ==0 : # 这里我们每100次输出相关的信息\n",
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" # 指定模型为计算模式\n",
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" net.eval()\n",
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" test_in=torch.from_numpy(test_data).float()\n",
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" test_l=torch.from_numpy(test_lab).long()\n",
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" test_out=net(test_in)\n",
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" # 使用我们的测试函数计算准确率\n",
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" accu=test(test_out,test_l)\n",
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" print(\"Epoch:{},Loss:{:.4f},Accuracy：{:.2f}\".format(i+1,loss.item(),accu))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"训练完成了，我们的准确度达到了80%"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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