跟着深度学习好书实践tensorflow神经网络

前言

2024 年诺贝尔物理学奖授予了约翰·霍普菲尔德（John Hopfield）和图灵奖得主、AI教父杰弗里·辛顿（Geoffrey Hinton），"以表彰他们利用人工神经网络进行机器学习的奠基性发现和发明"。

辛顿在接受电话采访时表示，我没有想到（I have no idea that will happen）。

被誉为“AI教父”的杰佛瑞·埃佛勒斯·辛顿（Geoffrey Everest Hinton，1947－），在70多岁时，成为图灵奖和诺贝尔物理学奖双料得主。

远在1986年，辛顿与David Rumelhart和Ronald Williams共同发表了一篇题为“通过反向传播误差来学习”（Learning representations by back-propagating errors）的论文。

[1]Home Page of Geoffrey Hinton，https://www.cs.toronto.edu/~hinton/

[2]David E. Rumelhart, Geoffrey E. Hinton und Ronald J. Williams. Learning representations by back-propagating errors., Nature (London) 323, S. 533-536，http://www.cs.utoronto.ca/~hinton/absps/naturebp.pdf

三位科学家，并不是第一个提出这种“反向传播”方法的人。但他们将反向传播算法应用于多层神经网络并且证明了这种方法对机器学习行之有效。他们的论文也证明了，神经网络中的多个隐藏层可以学习任何函数，从而解决了闵斯基等书中提出的单层感知机存在的问题。

同一时期，辛顿与 David Ackley 和 Terry Sejnowski 共同发明了玻尔兹曼机。辛顿1986年有关反向传播算法和波尔兹曼机的两篇重要文章，研究者们兴趣盎然，他们凭借自身的信念，排除嘈杂的干扰而自得其乐，江湖貌似平静但暗流涌动，为人工智能春天之到来做好了准备。正是应了一句名言：“大隐隐于市”。

Hinton在1986年提出的通过反向传播来训练深度网络理论，标志着深度学习发展的一大转机，为近年来人工智能的发展奠定了基础。更实际点说，今天谷歌中通过语音识别进行图片检索、在手机上把语音转化为文字的技术的实现，大部分功劳要归于Hinton博士的研究。他的研究，彻底改变了人工智能，乃至整个人类发展的轨迹。

《深度学习的数学》是一本介绍深度学习数学原理的书籍。本书由日本作者涌井良幸和涌井贞美合著，由杨瑞龙翻译成中文。

作者: [日]涌井良幸 / [日]涌井贞美
出版社: 人民邮电出版社 2019
出品方: 图灵教育
原作名: ディープラーニングがわかる数学入門
译者: 杨瑞龙
出版年: 2019-4
页数: 236
定价: 69.00元
装帧: 平装
丛书: 图灵程序设计丛书·程序员的数学
ISBN/ISSN：978-7-115-50934-5
载体形态：225页 :图 ;21cm
中图分类号：TP181

内容简介

《深度学习的数学》基于丰富的图示和具体示例，通俗易懂地介绍了深度学习相关的数学知识。本书主要分为四个部分：线性代数、微积分、概率论和信息论。每一部分都详细介绍了相关的数学知识，并结合深度学习的应用进行讲解。

在线性代数部分，本书讲解了向量、矩阵、线性变换等基本概念，以及它们在深度学习中的应用，如神经网络的表示和运算。

在微积分部分，本书讲解了导数、积分、链式法则等基本概念，以及它们在深度学习中的应用，如优化算法和反向传播算法。

在概率论部分，本书讲解了概率、随机变量、期望等基本概念，以及它们在深度学习中的应用，如概率图模型和贝叶斯推断。

在信息论部分，本书讲解了信息熵、互信息、条件熵等基本概念，以及它们在深度学习中的应用，如信息论视角下的优化和学习。

第1章介绍神经网络的概况；

第2章介绍理解神经网络所需的数学基础知识；

第3章介绍神经网络的最优化；

第4章介绍神经网络和误差反向传播法；

第5章介绍深度学习和卷积神经网络。

书中使用Excel进行理论验证，帮助读者直观地体验深度学习的原理。

作者和译者简介

涌井良幸

1950年生于东京，毕业于东京教育大学（现筑波大学）数学系，现为自由职业者。

著有《用Excel学深度学习》（合著）、《统计学有什么用？》等。

涌井贞美

1952年生于东京，完成东京大学理学系研究科硕士课程，现为自由职业者。

著有《用Excel学深度学习》（合著）、《图解贝叶斯统计入门》等。

译者简介：杨瑞龙

1982年生，2008年北京大学数学科学学院硕士毕业，软件开发者，从事软件行业10年。2013年～2016年赴日工作3年，从2016年开始在哆嗒数学网公众号发表《数学上下三万年》等多篇翻译作品。

1 - 1　神经网络和深度学习　　2
1 - 2　神经元工作的数学表示　　6
1 - 3　激活函数：将神经元的工作一般化　　12
1 - 4　什么是神经网络　　18
1 - 5　用恶魔来讲解神经网络的结构　　23
1 - 6　将恶魔的工作翻译为神经网络的语言　　31
1 - 7　网络自学习的神经网络　　36

第2章　神经网络的数学基础

2 - 1　神经网络所需的函数　　40
2 - 2　有助于理解神经网络的数列和递推关系式　　46
2 - 3　神经网络中经常用到的Σ符号　　51
2 - 4　有助于理解神经网络的向量基础　　53
2 - 5　有助于理解神经网络的矩阵基础　　61
2 - 6　神经网络的导数基础　　65
2 - 7　神经网络的偏导数基础　　72
2 - 8　误差反向传播法必需的链式法则　　76
2 - 9　梯度下降法的基础：多变量函数的近似公式　　80
2 - 10　梯度下降法的含义与公式　　83
2 - 11　用Excel 体验梯度下降法　　91
2 - 12　最优化问题和回归分析　　94

第3章　神经网络的最优化

3 - 1　神经网络的参数和变量　　102
3 - 2　神经网络的变量的关系式　　111
3 - 3　学习数据和正解　　114
3 - 4　神经网络的代价函数　　119
3 - 5　用Excel体验神经网络　　127

第4章　神经网络和误差反向传播法

4 - 1　梯度下降法的回顾　　134
4 - 2　神经单元误差　　141
4 - 3　神经网络和误差反向传播法　　146
4 - 4　用Excel体验神经网络的误差反向传播法　　153

第5章　深度学习和卷积神经网络

5 - 1　小恶魔来讲解卷积神经网络的结构　　168
5 - 2　将小恶魔的工作翻译为卷积神经网络的语言　　174
5 - 3　卷积神经网络的变量关系式　　180
5 - 4　用Excel体验卷积神经网络　　193
5 - 5　卷积神经网络和误差反向传播法　　200
5 - 6　用Excel体验卷积神经网络的误差反向传播法　　212

附录

A　训练数据（1）　　222
B　训练数据（2）　　223
C　用数学式表示模式的相似度　　225

小试牛刀

使用清华镜像开源获取anaconda安装软件

https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-2024.06-1-Windows-x86_64.exe

安装成功后，打开Prompt，配置环境

因为TensorFlow可能需要使用某些特定版本的库，而这些库与您的系统上的其他应用程序可能存在冲突，使用虚拟环境可以隔离TensorFlow和其他应用程序之间的库，从而避免冲突，所以先为TensorFlow创建一个虚拟环境。

在Anaconda Prompt终端中，运行以下命令以创建名为“py39tf210_env”的虚拟环境：

conda create --name py39tf210_env

激活虚拟环境
在创建虚拟环境之后，您需要激活该虚拟环境。在Anaconda Prompt终端中，运行以下命令：

conda activate py39tf210_env

查看当前已有环境，激活的环境
conda env list

安装TensorFlow，打开anaconda prompt，然后输入在里面输入以下命令：

conda install pip

更新

python -m pip install --upgrade pip

nvidia显卡驱动下载
https://www.nvidia.cn/content/DriverDownloads/confirmation.php?url=/Windows/460.89/460.89-desktop-win10-64bit-international-dch-whql.exe&lang=cn&type=GeForce

cuda下载
https://developer.nvidia.com/cuda-toolkit
https://developer.nvidia.com/cuda-11.2.0-download-archive

cudnn下载
https://developer.nvidia.com/rdp/cudnn-archive

下载的cudnn文件夹，将bin、include、lib合并到CUDA对应版本文件夹

在文件夹NVIDIA GPU Computing Toolkit\CUDA\v11.2\extras\demo_suite CMD命令窗口，检查CUDA安装是否成功

执行bandwidthTest.exe

Microsoft Windows [版本 10.0.19045.5011]
(c) Microsoft Corporation。保留所有权利。

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\extras\demo_suite>bandwidthTest.exe
[CUDA Bandwidth Test] - Starting...
Running on...

 Device 0: NVIDIA GeForce GTX 1660 Ti
 Quick Mode

 Host to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)        Bandwidth(MB/s)
   33554432                     6315.2

 Device to Host Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)        Bandwidth(MB/s)
   33554432                     6322.3

 Device to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)        Bandwidth(MB/s)
   33554432                     249160.5

Result = PASS

NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.

执行deviceQuery.exe

Microsoft Windows [版本 10.0.19045.5011]
(c) Microsoft Corporation。保留所有权利。


C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\extras\demo_suite>deviceQuery.exe
deviceQuery.exe Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "NVIDIA GeForce GTX 1660 Ti"
  CUDA Driver Version / Runtime Version          12.0 / 11.2
  CUDA Capability Major/Minor version number:    7.5
  Total amount of global memory:                 6144 MBytes (6442123264 bytes)
  (24) Multiprocessors, ( 64) CUDA Cores/MP:     1536 CUDA Cores
  GPU Max Clock rate:                            1590 MHz (1.59 GHz)
  Memory Clock rate:                             6001 Mhz
  Memory Bus Width:                              192-bit
  L2 Cache Size:                                 1572864 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 2048 layers
  Total amount of constant memory:               zu bytes
  Total amount of shared memory per block:       zu bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  1024
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          zu bytes
  Texture alignment:                             zu bytes
  Concurrent copy and kernel execution:          Yes with 2 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Disabled
  CUDA Device Driver Mode (TCC or WDDM):         WDDM (Windows Display Driver Model)
  Device supports Unified Addressing (UVA):      Yes
  Device supports Compute Preemption:            Yes
  Supports Cooperative Kernel Launch:            Yes
  Supports MultiDevice Co-op Kernel Launch:      No
  Device PCI Domain ID / Bus ID / location ID:   0 / 1 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 12.0, CUDA Runtime Version = 11.2, NumDevs = 1, Device0 = NVIDIA GeForce GTX 1660 Ti
Result = PASS

NVIDIA的nvidia-smi（系统管理接口，监控GPU状态）提供GPU实时性能数据和管理功能

NVIDIA的nvcc-V（CUDA编译器版本信息获取工具）主要用于检查编译器兼容

在CMD窗口使用指令nvidia-smi 查看

Microsoft Windows [版本 10.0.19045.5011]
(c) Microsoft Corporation。保留所有权利。

C:\Users\admin>nvidia-smi
Fri Oct 11 15:19:13 2024
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 528.79       Driver Version: 528.79       CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name            TCC/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ... WDDM  | 00000000:01:00.0 Off |                  N/A |
| N/A   59C    P0    24W /  80W |      0MiB /  6144MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

在CMD窗口使用指令nvcc -V 查看

Microsoft Windows [版本 10.0.19045.5011]
(c) Microsoft Corporation。保留所有权利。

C:\Users\admin>nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Mon_Nov_30_19:15:10_Pacific_Standard_Time_2020
Cuda compilation tools, release 11.2, V11.2.67
Build cuda_11.2.r11.2/compiler.29373293_0

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple tensorflow==2.10.0
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple tensorflow-gpu==2.10.1

注意：本机 Windows 上的 GPU 支持仅适用于 2.10 或更早版本，从 TF 2.11 开始，Windows 不支持 CUDA 构建。要在 Windows 上使用 TensorFlow GPU，您需要在 WSL2 中构建/安装 TensorFlow 或将 tensorflow-cpu 与 TensorFlow-DirectML-Plugin 一起使用。

CPU版本和GPU版本的区别主要在于运行速度，GPU版本运行速度更快，所以如果电脑显卡支持cuda，推荐安装gpu版本的。CPU版本，无需额外准备，CPU版本一般电脑都可以安装，无需额外准备显卡的内容。GPU版本，需要提前下载 cuda 和 cuDNN。

安装前一定要查看自己电脑的环境配置，然后查询Tensorflow-gpu、Python、 cuda 、 cuDNN 版本关系，要一一对应。

tensorflow版本从2.x开始不再区分CPU版和GPU版，因此在软件配置正确的情况下，是可以找到GPU设备的。Tensorflow 2.10是最后一个在本地windows上支持GPU的版本。从2.11版本开始，需要在windows WLS2（适用于 Linux 的 Windows 子系统）上安装才能使用GPU。所以要在native-windows上使用GPU，就只能安装2.10.0版本及以下的版本，或者安装老版的tensorflow-gpu。

tensorflow gpu cuda cudnn对应表格
https://tensorflow.google.cn/install/source_windows?hl=en#gpu

tensorflow：支持 CPU 和 GPU 的最新稳定版（适用于 Ubuntu 和 Windows）
tf-nightly：预览 build（不稳定）。Ubuntu 和 Windows 均包含 GPU 支持。
旧版 TensorFlow
对于 TensorFlow 1.x，CPU 和 GPU 软件包是分开的：

tensorflow==1.15：仅支持 CPU 的版本
tensorflow-gpu==1.15：支持 GPU 的版本（适用于 Ubuntu 和 Windows）
系统要求
Python 3.6–3.9
若要支持 Python 3.9，需要使用 TensorFlow 2.5 或更高版本。
若要支持 Python 3.8，需要使用 TensorFlow 2.2 或更高版本。
pip 19.0 或更高版本（需要 manylinux2010 支持）
Ubuntu 16.04 或更高版本（64 位）
macOS 10.12.6 (Sierra) 或更高版本（64 位）（不支持 GPU）
macOS 要求使用 pip 20.3 或更高版本
Windows 7 或更高版本（64 位）
适用于 Visual Studio 2015、2017 和 2019 的 Microsoft Visual C++ 可再发行软件包
GPU 支持需要使用支持 CUDA® 的卡（适用于 Ubuntu 和 Windows）
注意：必须使用最新版本的 pip，才能安装 TensorFlow 2。
硬件要求
从 TensorFlow 1.6 开始，二进制文件使用 AVX 指令，这些指令可能无法在旧版 CPU 上运行。

安装numpy

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple numpy==1.20.3

安装pytorch

Commands for Versions >= 1.0.0
v2.4.0
Conda
OSX
# conda
conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 -c pytorch
Linux and Windows
# CUDA 11.8
conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0  pytorch-cuda=11.8 -c pytorch -c nvidia
# CUDA 12.1
conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=12.1 -c pytorch -c nvidia
# CUDA 12.4
conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=12.4 -c pytorch -c nvidia
# CPU Only
conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 cpuonly -c pytorch

(base) C:\Users\>conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=12.4 -c pytorch -c nvidia
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): done
Solving environment: -
The environment is inconsistent, please check the package plan carefully
The following packages are causing the inconsistency:

  - defaults/win-64::anaconda==2021.11=py39_0
  - defaults/win-64::astropy==4.3.1=py39hc7d831d_0
  - defaults/win-64::bkcharts==0.2=py39haa95532_0

测试配置

import tensorflow as tf
from tensorflow.keras import layers, models
import torch
import os

#将TF_ENABLE_ONEDNN_OPTS设置成1或以上。0代表显示所有信息，1表示不显示info，2表示不显示warning，3表示不显示error。
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

print('TensorFlow版本',tf.__version__)
# 查看torch当前版本号
print('torch当前版本号',torch.__version__)  
# 编译当前版本的torch使用的cuda版本号
print('torch使用的cuda版本号',torch.version.cuda)  
# 查看当前cuda是否有可用的Torch，如果输出True，则表示存在/成功安装
print('当前cuda是否有可用的Torch',torch.cuda.is_available())  

# 获取TensorFlow的构建信息
build = tf.sysconfig.get_build_info()

# 打印CUDA的版本号（如果已安装）
print(build['cuda_version'])

# 打印cuDNN的版本号（如果已安装）
print(build['cudnn_version'])

print('GPU:是否已经编译了CUDA支持', tf.test.is_built_with_cuda())
print('GPU:当前GPU设备名称', tf.test.gpu_device_name())

# 输出可用的GPU数量
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))

print("Num CPUs Available: ", len(tf.config.list_physical_devices('CPU')))

#默认情况下，TensorFlow 会映射进程可见的所有 GPU（取决于 CUDA_VISIBLE_DEVICES）的几乎全部内存。
#这是为了减少内存碎片，更有效地利用设备上相对宝贵的 GPU 内存资源
gpus = tf.config.list_physical_devices('GPU')

if gpus:
  # Restrict TensorFlow to only allocate 1GB of memory on the first GPU
  try:
    tf.config.set_logical_device_configuration(
        gpus[0],
        [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024*6)])
    logical_gpus = tf.config.list_logical_devices('GPU')
    print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
  except RuntimeError as e:
    # Virtual devices must be set before GPUs have been initialized
    print(e)

import timeit
 
#指定在cpu上运行
def cpu_run():
    with tf.device('/cpu:0'):
        cpu_a = tf.random.normal([10000, 1000])
        cpu_b = tf.random.normal([1000, 2000])
        c = tf.matmul(cpu_a, cpu_b)
    return c
 
#指定在gpu上运行 
def gpu_run():
    with tf.device('/gpu:0'):
        gpu_a = tf.random.normal([10000, 1000])
        gpu_b = tf.random.normal([1000, 2000])
        c = tf.matmul(gpu_a, gpu_b)
    return c

cpu_time = timeit.timeit(cpu_run, number=10)
gpu_time = timeit.timeit(gpu_run, number=10)
print("cpu:", cpu_time, "  gpu:", gpu_time)

主程序

## **1. 环境配置**

- pandas1.3.4
- tensorflow2.10.0
- tensorflow-gpu2.10.1
- python3.9

## **2. 运行配置**

- CPU/GPU均可
- 最小内存要求
- 特征/样本生成：3G
- 模型训练及评估：6G

- 耗时
- 测试环境：内存8G，CPU 2.3 GHz 双核Intel Core i5
- 特征/样本生成：226 s
- 模型训练及评估：740 s

## **3. 目录结构**

- comm.py: 数据集生成
- baseline.py: 模型训练，评估，提交
- evaluation.py: uauc 评估
- data/: 数据，特征，模型
- wechat_algo_data1/: 初赛数据集
- feature/: 特征
- offline_train/：离线训练数据集
- online_train/：在线训练数据集
- evaluate/：评估数据集
- submit/：在线预估结果提交
- model/: 模型文件

## **4. 运行流程**
- 新建data目录，下载比赛数据集，放在data目录下并解压，得到wechat_algo_data1目录
- 生成特征/样本：python comm.py （自动新建data目录下用于存储特征、样本和模型的各个目录）
- 训练离线模型：python baseline.py offline_train
- 评估离线模型：python baseline.py evaluate （生成data/evaluate/submit_${timestamp}.csv）
- 训练在线模型：python baseline.py online_train
- 生成提交文件：python baseline.py submit （生成data/submit/submit_${timestamp}.csv）
- 评估代码: evaluation.py

## **5. 模型及特征**
- 模型：[Wide & Deep](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454)
- 参数：
- batch_size: 128
- emded_dim: 10
- num_epochs: 1
- learning_rate: 0.1
- 特征：
- dnn 特征: userid, feedid, authorid, bgm_singer_id, bgm_song_id
- linear 特征：videoplayseconds, device，用户/feed 历史行为次数

## **6. 模型结果**

|stage |weight_uauc |read_comment|like|click_avatar|forward|
|:---- |:---- |:---- |:---- |:---- |:----|
| 离线 | 0.657003 |0.626822 |0.633864 |0.735366 |0.690416 |
| 在线 | 0.607908| 0.577496 |0.588645 |0.682383 |0.638398 |

## **7. 相关文献**
* Cheng, Heng-Tze, et al. "Wide & deep learning for recommender systems." Proceedings of the 1st workshop on deep learning for recommender systems. 2016.

# 训练集
"user_action.csv"

userid	feedid	date_	device	read_comment	comment	like	play	stay	click_avatar	forward	follow	favorite
8	71474	1	1	0	0	1	500	5366	0	0	0	0
8	73916	1	1	0	0	0	250	1533	0	0	0	0
8	50282	1	1	0	0	0	750	1302	0	0	0	0
8	11391	1	1	0	0	1	3750	5191	0	0	0	0
8	27349	1	1	0	0	0	250	800	0	0	0	0
8	30287	1	1	0	0	0	0	1496	0	0	0	0
8	1151	1	1	0	0	0	250	976	0	0	0	0
8	69745	1	1	0	0	0	250	817	0	0	0	0
8	22451	1	1	0	0	0	0	2014	0	0	0	0

"feed_info.csv"

feedid	authorid	videoplayseconds	description	ocr	asr	bgm_song_id	bgm_singer_id	manual_keyword_list	machine_keyword_list	manual_tag_list	machine_tag_list	description_char	ocr_char	asr_char
43549	6165	38	104741 122649 8109 117252 65632 23463 118668 45861 8109 142955 27736 21751 112151 116906 32715 93520 32714 80461 8109 93563 102383 10952 48706 12885 68441 93563 8097 134820 55911 80449 79213 23233 13997 53706 104690 6994	139499 59421 82007 142955 27736 83577 52394 112151 116906 93520 80461 82007 90327 28303 133091 82007 118636 99614 22694 93541 94993 82007 133091 45443 118749 82007 17273 26295 755 82007 79531 45861 60826 12240 99614 16648 117253 82007 6488 128286 16917 82007 66297 6994 146483 6994 6994 6994 92811 6478 123078 107293 82007 116352 47035 90992 111660 93115 7660 6994 91023 6994 91023 6994 117843 118696 125334 49255 140964 78844 101640 117843 82007 133091 92811 12240 123078 25794 82007 67209 140959 105807 27736 79314 68476 82007 46310 73451 133091 99617 8041 60574 150245 142561 90217 118696 125334 27736 82007 90992 99617 82007 46310 6994 73451 133091 99617 8041 23648 150245 106770 78844 133091 82007 502 94016 102311 37630 55139 93566 82007 55633 140609 27736 21763 16194 82007 3030 140609 27736 21763 16194 82007 110142 94993 136991 56344 11145 27736 83577 82007 12240 54654 25794 82007 121844 127586 12666 82007 287 826 122748 27736 140959 109451 82007 44399 136492 978 88576 5976 25794	142955 27736 83577 103956 32010 34170 89740 90327 8109 28303 31798 12214 19942 99614 22694 93541 94993 133091 84618 118749 8109 1556 26295 79531 4724 12240 99614 16648 117253 79314 93536 120507 6486 128286 122159 46310 131225 6478 123078 140147 118668 14312 8109 118668 25794 99656 125334 49255 140964 101640 134892 40003 46310 131225 12240 123078 25794 44506 116906 25794 9844 27736 112516 51703 142561 146692 125334 27736 75770 4438 133091 122013 94016 29200 55139 93566 8109 115201 93566 29288 3030 100909 27736 21763 16194 8109 86461 119958 8109 101594 10143 27736 83577 12240 31336 85335 8109 12852 26661 4438 49448 12205 27736 140959 93524 8109 134871 37630 55139 93566 27736 15998 65632 99614 77895 26564 129634 33988 27736 53964 116914 78817 4438	19356	11703	15506;7715;17582	26334;219;25209;7715;18541	81;269;159;6	269 0.8525666;81 0.8525666;8 1.1e-07;306 0.0;207 6.31e-06;10 0.27404302	26439 5247 6426 3827 1882 26018 20744 22204 30024 24307 10436 1882 2203 26439 6243 21632 3713 15640 25926 7357 20823 7356 17870 1882 20857 3653 11877 24307 21492 17178 11341 10043 20833 15357 20857 1872 12681 5043 17859 17859 17531 15208 2597 3161 22626 27055 22439 20077	25926 8491 13394 2203 26439 6243 33054 16435 11945 15640 25926 20823 17870 2874 25926 32378 20860 20268 27664 2820 13505 22179 5043 20838 21147 20268 27664 10326 31491 27464 20744 15640 29786 17597 199 20857 26054 10436 13681 2841 22179 3701 22474 1531 28575 3805 14875 32495 10517 11575 2200 20831 27464 24513 17596 25782 24307 22539 20268 24815 20752 1801 20292 20292 26208 26456 32499 20268 17549 22539 20292 17442 22626 26208 20268 27664 10517 11575 2841 27464 8481 22806 25926 4196 23482 6243 17596 15382 10517 14121 20268 27664 22181 6155 17602 13616 33234 3882 1531 20049 26456 32499 6243 20268 22181 10517 14121 20268 27664 22181 6155 17602 5344 33234 29751 29959 17442 20268 27664 4153 22241 21456 5033 22741 12837 5061 17870 20860 12604 3546 6243 22546 11662 3588 716 3546 6243 22546 11662 3588 24513 21147 32499 5043 12785 10086 22439 6243 33054 16435 2841 21468 22474 8481 26641 8481 2874 2923 66 219 32386 6243 25926 4196 24364 10043 11575 11877 29786 7305 19565 1341 8481	2203 26439 6243 33054 16435 16307 17070 24908 25920 7778 19891 2874 25926 1882 32378 20860 2227 3899 2820 13505 28798 22179 5043 20838 21147 20268 27664 18757 31491 27464 1882 17577 5966 29786 17597 20857 26054 10436 8740 2841 22179 3701 22474 17596 20833 29095 12762 1529 28575 27209 10517 11575 28798 2200 20831 27464 17531 66 24307 11388 6946 1882 24307 8481 22214 32499 20268 17549 22539 20292 22626 30042 11789 27664 10517 11575 28798 2841 27464 8481 10080 25926 8481 2206 6243 17596 10134 11796 3882 1531 14070 26456 32499 6243 10130 15911 969 20268 27664 2239 21498 21456 5033 20857 7799 5061 17870 20860 1882 7799 27464 20860 24513 1520 716 22446 6243 22546 11662 3588 1882 24513 20863 30038 15360 1882 22610 26084 26183 6243 33054 16435 2841 2066 21465 22474 5596 1882 15036 31177 21633 8512 969 30035 15921 2813 6243 25926 4196 20827 1882 30026 12837 5061 17870 20860 6243 17855 15360 20744 22179 17181 5967 24307 11574 11581 14292 6243 28598 6259 25930 17420 969
77432	9386	60	35753 27736 146603 73055 11794 101761 11794 8109 16933 52236 96307 12240 31279 137786 46760 8109 95030 7626 35990 8105 93533 8106 134820 35990 53496	35753 146603 73055 11794 101761 67496 16933 52236 12240 99614 137786 46760 22893 99893 149481 90267 32443 83722 49523 44399 30521 73055 11794 101761 11794 4430 26692 32395 4431 85868 20248 109427 77075 24343	146739 14368 79290 79213 47366 8109 33194 11989 31279 139153 7739 27736 97114 111182 8109 32443 83722 49523 44399 83265 73055 11794 11794 12240 12220 9949 45012 73055 25794 66512 8109 13547 79213 117050 121114 12240 23840 25794 79682 27736 5251 4438 32443 83722 110142 85502 8109 23509 12220 149649 133296 25794 118749 8109 134878 79213 47366 8109 73757 9844 106949 102036 113903 100901 8109 11989 104154 116819 147841 45012 22454 25794 118749 4438 71331 135636 27736 9949 147789 107673 125472 8109 92687 37527 50006 9844 102383 55805 49985 4438 86458 59637 12240 45012 32443 83722 23729 25794 79303 113003 8109 32443 83722 35339 72615 4101 27736 59394 57464 94842 112151 62197 93563 106949 25794 8109 90290 36623 93541 66151 8109 81787 12240 37630 45121 96353 27736 56338 96353 93541 66151 136194 22682 8109 121528 142314 85907 25794 45121 96353 27736 56338 8109 56338 146823 27736 107962 129369 56522 18710 8109 121274 139898 56981 27736 112151 8109 96307 96353 27736 105115 75625 12127 11989 80297 116819 44399 118814 121428 57464 141821 117050 8109 49523 104109 8109 4101 23944 92857 57464 25794 4438 75625 106972 59637 137786 18422 25794 4438 135636 27736 85592 114620 61803 8109 134878 79213 47366 110142 128739 42526 129746 57464 8109 96150 22454 19019 93541 93536 44352 8109 9844 77895 9844 121428 22414 25794 110142 149596 100491 8109 93324 56522 27013 118480 22682 145811 78276 32440 85907 25794 118668 86339 90810 2517 2517 47082 107673 45061 72615 108277 8109 40621 106949 144795 8109 134449 25794 8109 62436 117780 8109 121577 137786 24885 85056 146492 61803 4438 96439 116906 75625 12240 107973 25794 94171 8109 107673 77905 9973 109024 25794 110142 135558 63991 56280 8109 6510 6510 93541 56280 12240 63991 25794 118668 107962 4438 73757 9844 135558 107962 145339 38594 27736 31877 8109 106949 30426 72615 105251 75625 12127 56344 45012 85051 25794 38594 8109 26597 93541 66151 137550 107973 71826 35380 135636 27736 85592 107673 132715 58694 27736 53807 146823 8109 121274 79213 117050 121114 8109 29802 26564 44398 7409 77601 75625 12127 122650 25794 8109 38594 96099 44137 8109 150623 147516 62339 23632 7307 57464 56344 47366 25794 110142 93536 46071 27736 45829 8109 34517 15099 25794 6851 40277 142518 27736 8109 77895 62690 27736 56976 37527 93563 45412 100901 52611 10818 4438 64771 4438 147565 142518 9844 125334 93563 148254 52611 25794 128896 100901 79290 66989 4438			8199;18322;4243	24078;19924	194;267;159;6	267 0.99293476;194 0.99293476	31010 32495 6243 13923 15360 30483 2709 26084 2709 1882 6976 1119 32191 66 21460 2841 30031 2220 11445 20077 30055 21492 1882 10436 1216 9019 20016 106 12187 1878 20831 1879 12681 106 12187 28535 11383	31010 32495 13923 15360 30483 2709 26084 15160 6976 1119 32191 66 2841 22179 11445 20077 30055 21492 5104 5329 28798 33075 20070 7261 24307 5318 24513 10083 10043 32066 17278 30483 2709 26084 2709 961 1018 28161 7235 962 8890 27393 9781 14241 5513	7259 20851 5061 26207 17573 17531 15117 20072 1882 12584 15360 9119 29499 30031 2220 1487 1487 20857 28704 6243 12787 23108 5061 25630 1882 7261 24307 5318 24513 10083 10043 7261 19631 30483 2709 2709 2841 22717 16567 19020 10226 30483 8481 29242 23786 1882 12762 29499 17531 10045 24513 12371 28798 2841 26084 14040 8481 20857 2818 6243 32280 969 7261 24307 5318 24513 3907 32376 1882 6267 23705 22717 27061 17571 33330 8481 31491 27464 1882 30031 17531 15117 20072 1882 16435 2206 23786 2682 32028 15366 32280 22439 1882 9119 29499 7305 13842 25897 23492 27788 10226 4967 8481 31491 27464 969 23804 27464 19800 66 6243 16567 19020 7685 30038 23963 24513 6491 1882 11670 6439 8512 11383 2206 3653 11877 12669 11364 969 24513 20859 15360 2841 10226 7261 24307 5318 20078 8481 17585 25121 1882 7261 24307 5318 3779 19565 16174 30749 9202 6243 15268 10134 28798 31238 11371 15640 14040 20857 23786 8481 1882 20085 11598 11354 20838 11792 5329 1882 28161 12678 2841 12837 17770 9119 21492 6243 25926 12860 21492 20838 11792 5329 14040 13660 5033 1882 9416 16174 12836 18765 19777 20072 8481 17770 9119 21492 6243 25926 12860 1882 25926 12860 32562 6243 5270 25926 10434 18895 12836 14049 11296 1882 25123 20851 31075 12960 6243 15640 1882 21460 21492 6243 20824 8512 8486 25298 15360 9119 29499 21456 26322 25897 10043 11792 27050 28798 21800 10045 1882 24513 10083 13394 8512 1882 30749 9202 5416 20860 31376 28798 8481 969 8486 23801 15360 11445 20077 30055 32376 8481 969 19800 66 6243 2820 10424 19791 29781 4947 13394 1882 30031 17531 15117 20072 24513 28680 24307 7261 19631 2223 28798 1882 20857 1541 4967 20857 2239 20838 20833 32505 8512 1882 2206 17181 2206 27050 4944 8481 24513 16133 22351 1882 9416 20087 12836 5414 6269 6426 23786 5033 32333 17264 7259 19777 20072 8481 24307 13016 22351 30043 95 25926 28798 25926 28798 24513 7305 23963 3826 2585 16174 23786 10045 1882 5009 11383 23786 32066 1882 29922 8481 1882 16323 25616 70 1882 32505 28798 11445 20077 7771 21474 18881 32500 4947 13394 969 16435 18605 25926 8486 2841 27464 20072 8481 8486 32562 26446 1882 23963 17190 14223 19804 13858 14040 8481 24513 4956 30328 26543 12771 1882 1541 1541 20838 12771 2841 30328 26543 8481 24307 5270 25926 969 16435 2206 4956 5270 25926 26493 6426 27464 6243 22439 3879 1882 23786 10038 18876 16174 23343 8486 25298 15360 12785 10226 18876 8481 6426 27464 1882 5990 20838 11792 5329 7799 32336 27464 20072 17076 32562 28703 25306 25299 19800 66 6243 2820 10424 23963 9201 10222 12955 24307 6243 25274 15360 32562 1882 25123 20851 17531 10045 24513 12371 28798 1882 19800 2220 5967 10042 22629 32221 16174 8486 25298 15360 27343 8481 1882 6426 27464 32028 32376 26444 12650 1882 5990 15640 20825 8481 22179 26467 30055 12653 67 2233 28798 12785 15117 20072 8481 24513 20833 10230 5311 6243 17173 20090 1882 2206 2223 16265 14743 8481 1202 27976 9187 31605 6243 1882 17181 16650 6243 24442 8512 20857 11680 17278 22439 1211 14040 27061 4948 969 30031 15640 969 1804 18895 31605 2206 32499 20857 21494 24937 1211 14040 8481 28161 25920 22439 17573 4913 29499 969
12921	2996	15	114413 107973 117252 27736 41035 32715 125374 121275 32714 134820 130882 134820 121275			10288	11354		11696;6926	202;23;160;6	23 0.64771646;202 0.64771646	27077 10050 27464 20072 26018 6243 21492 14750 7357 31937 5036 11720 12949 7356 12681 19901 11379 12681 11720 12949
34538	12577	18	60473 9864 8109 135558 108803 89307 37630 6481 40061 93566 13997 49752 88822 35990	39654 95201 22675 49521 135558 108803 89307 37630 6481 40061 93566	7446 47082 37630 99660 9844 113002 9844 113002 9844 27 39909 78557 30819 95343 17745 54593 879 84541 33616 56196 93542 95463 8109 26564 99569 4438	24435	13645	5454;1197;3727		328;13;159;6	267 0.21298289;191 0.21298289;8 0.34298885;306 0.0;207 0.03609685;10 0.26915717	21489 24672 2223 1882 4956 1545 10424 19791 12837 1525 9116 20860 3161 11278 19642 106 12187	9015 21209 5026 11192 4956 1545 10424 19791 12837 1525 9116 20860	20825 13395 14765 24513 7305 12837 10337 8885 2206 21457 2206 21457 2206 19634 5318 19634 19634 5318 5318 26439 21465 31376 30749 27298 12358 255 2841 15640 17766 20833 12743 20839 21292 1882 5967 20857 17446 12836 969
75414	7185	16	105860 4691 134820 55911 80449 79213 23233 13997 53706 104690	73055 66447 88970	104002 4438 104002 4438	23428	10730	17083;4123	8058;219;21639;15621;25209	81;269;159;6	269 0.86163938;81 0.86163938	25782 19583 7768 3907 14068 12681 5043 17859 17859 17531 15208 2597 3161 22626 27055 22439 20077	30483 23721 1518 22741 25118	23112 969 23112 969
34539	2595	43	123046 116187 8092 22091 77895 11717 93522 100860 146036 56882 134820 125215 14332 1482 6994	61498 70163 1428 123046 116187 44971 3063 32144 54606 113005 36385 86055 32144 39671 127146 113003 116906 28486 123046 116187 96158 6994 150134 59080 11109 139400 35425 49880 109355 125215 14332 1482 13818 40059 14332 50550 28467 145820 79577 101112 61794 57498 14332 51973 13818 40059 28467 145820 79577	147566 45843 10952 25290 79030 11717 34198 99319 25146 4438 107652 11717 3871 8097 111993 27736 4438 58982 27736 8109 104424 84541 123078 22662 8109 29504 85051 25146 4438 34517 3871 8097 29640 45312 93522 100860 146036 69024 33602 2078 23233 4438 143358 90843 5070 36854 105393 104154 146036 27736 28379 8109 90270 51131 70959 118278 8109 92220 106952 4438 107673 5070 101258 110142 93536 73300 27736 69024 87887 8109 62339 104154 146036 8109 58123 53102 33602 131560 27736 69024 33847 4438 22091 34198 77895 107652 11717 82242 4438 121388 4438	16541	17296	529;2666;26954	529;9298	45;239;159;169	239 0.68520623;45 0.68520623;8 0.0;306 0.0;207 0.07740904;10 0.27942485	27442 1543 31605 1868 20857 22179 17181 7261 8512 20825 10434 12836 4947 10090 15208 8311 12681 32987 1528 6439 20838 12371	13842 6439 32379 25274 8847 1251 27442 1543 31605 10198 14040 18883 17188 7264 12371 25123 6426 22827 20831 24364 17188 7264 7585 31372 32556 25121 25926 6439 27442 1543 31605 22959 1528 6439 20838 12371 17585 20861 9114 1528 6439 8512 24307 6428 32336 12371 17187 20831 6439 13922 7305 19912 1528 6439 16269 17591 17585 20861 9114 6428 32336 12371 17187	15371 6498 31376 30024 24307 21492 10083 20072 22639 26464 7261 8512 7799 31491 23654 13505 15360 969 10043 8493 7261 8512 11582 1872 21477 26656 6243 969 1514 7261 6243 1882 26080 30743 2841 15640 27464 30420 1882 15360 32505 18876 13505 15360 969 2206 2223 11582 1872 10222 18895 66 8512 20825 10434 12836 4947 10090 11704 13811 7613 27949 11677 15208 2597 969 2203 25121 26018 2223 1119 19785 24307 27314 10086 7305 13842 4947 10090 6243 31936 14292 1882 20072 24940 25121 25121 31239 26322 1882 5111 20833 23787 969 23963 1119 6332 21474 24513 20833 21474 23472 6243 11704 13811 95 12750 1882 22179 26467 7305 13842 4947 10090 1882 11715 32183 31376 11352 7613 12837 7802 6243 11704 13811 7613 26054 969 20857 22179 7799 17181 10043 8493 7261 8512 20825 3146 969 22392 24930 969
101310	8115	15	106872 36623 26564 8109 110838 148383 26564 33895 110690 133005 134820 118599 134820 76826	9837 110838 148383 26564 33895 110690 133005	117252 44854 26564 110142 93536 91254 8109 9837 28467 96353 12240 31279 148383 26564 33895 110690 110838 93566 287 50242 16892 110838 93566 287 114678 8109 24802 40682 134913 111660 8109 33895 102364 148254 128784 33895 19695 55795 8109 110838 93566 132192 53514 88911 16650 3871 8097 141540 40075 8109 9844 50006 26564 33895 110690 8097	19367	10532		18351;13433;25057;11002;14687;10664;19485;12865	9;6	237 0.90296459;246 0.90296459	20831 11704 11598 11354 5967 1882 1217 23805 10083 14040 5967 12194 13395 14765 6153 1514 12681 4948 2875 12681 27061 1514	2200 1217 23805 10083 14040 5967 12194 13395 14765 6153 1514	26018 10155 5967 24513 20833 5311 32214 1882 2200 6428 21492 2841 30031 2220 10083 14040 5967 12194 13395 14765 1217 23805 20860 66 11443 3792 1217 23805 20860 66 24815 23472 1882 22351 16075 15506 17070 30055 24815 1882 12194 22779 21494 24937 28704 12194 3588 24815 12661 1882 1217 23805 20860 19074 3882 19944 32392 11463 3703 11582 1872 25118 9419 9126 1882 2206 11383 5967 12194 13395 14765 1872
13769	11696	25	144240 54435 37630 117252 27736 40670 134820 144240 65651 6510 134820 130882	63733 106137 31785 73044 25794 65020 90843 107673 287 25341 25794 27438 12237 76067 51259 46674 26892 115928 1181 143307 82007 287 62219 93536 16407 80067 82007 287 62219 93536 74110 82007 3881 144240 99614 54748 82007 114039 90223 6476 26564 74075 93541 72364 82007 144240 127395 37630 26564 117252 40670	123075 11342 79301 23908 110100 112151 110677 107673 112164 32444 140205 106949 4438 78106 4438 135187 24334 84163 46310 37527 11819 8109 5070 79301 33988 79682 110202 4438				8800;11061;15752	9;6	17 0.74826479;37 0.74826479	12783 22473 5277 10040 12837 26018 6243 8311 32562 12681 12783 22473 21456 1541 12681 19901 11379	20857 15640 7261 23039 30060 32499 5009 8481 7259 15640 26018 2223 23963 66 10156 8484 8481 26084 8488 2839 11342 17196 1553 15631 9419 22744 13842 11357 5009 31075 26464 8134 20753 66 14057 20833 3653 17791 66 14057 20833 3653 9201 26464 2818 12783 22473 22179 19074 15366 2203 22430 20085 12712 1520 5967 12787 12787 20838 7578 19804 12783 22473 18895 12837 5967 26018 8311 32562	30191 2606 17583 5391 20861 15640 12836 4895 15360 23963 24930 7262 31134 23786 969 3797 1119 969 5505 1525 5505 18657 10517 8512 2726 1882 1119 17583 11581 14292 20857 2818 32394 22179 969
6311	9533	60	102036 39661 66989 110142 118715 93522 24335 8109 16650 33804 25794 90843 27736 5251 8097 79072 27736 108358 22882 26564 89316 102383 27736 10751 78511 4438 43127 80607 45012 26564 110142 93536 111883 27736 141185 43768 49085 134820 127696 27736 106096 80607 134820 116237 125923 134820 43127 80607 134820 72032 107016 134820	122344 140404 55785 26564 93563 102383 27736 79072 102291 90843 31279 117347 116237 287 49575 56976 82007 123078 97133 44399 9870 97713 82007 110690 56196 116237 27736 49575 56976 122222 82007 117252 45012 26564 109888 110142 93536 59964 27736 30904 82007 287 110142 93536 37630 55019 45443 121478 27736 12885 50664 37527 82007 9844 147516 59287 110142 93536 83089 9844 12240 106949 82007 45443 25794 86458 82007 883 145095 27736 148386 18836 25794 57356 82007 12885 83089 27736 37527 82007 9844 51772 82007 9844 59287 25794 12885 121478 82007 146739 83099 25794 110142 121697 5251 82007 64771 90270 25794 85868 75625 113003 82007 34517 27736 121478 12240 77084 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"feed_embeddings.csv"

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-0.00355704 0.10707334 -0.00053576 0.02729356 0.00084593 0.07320160 0.14263286 -0.01009675 0.03055519 -0.00371649 -0.04643294 -0.01979426 -0.07637622 -0.09936619 0.06036915 0.00276245 0.06294693 -0.07097097 -0.05578409 -0.04505128 -0.03035136 0.04228539 -0.00141903 0.12060711 -0.00117660 -0.01382081 0.03997000 -0.03499091 -0.04934507 -0.01265479 0.04935357 0.05638725 0.05751546 0.00188572 0.02878160 -0.01070151 -0.07113525 0.01362490 0.01492100 0.01968378 -0.06246135 0.02276803 0.07418473 -0.03766726 0.05716049 0.00894559 -0.08444819 0.03740668 -0.02071392 -0.01066765 0.02912099 -0.07753864 -0.02807910 -0.00059477 0.04379735 -0.01833879 -0.05781115 -0.02306593 -0.12833962 -0.01132700 0.00043332 0.00257051 0.03555497 -0.04141807 -0.01489299 0.04796924 -0.01890572 0.01598846 0.04329584 0.01310057 0.10577817 -0.01981361 -0.02564441 0.03488779 -0.05417980 0.05422264 -0.07412330 0.04594867 -0.00021197 0.05756741 -0.02416278 0.07442671 -0.07296880 0.00578909 0.03120154 -0.02525996 -0.10727894 -0.00922724 -0.03089080 -0.01099734 0.00411152 0.01555896 -0.04203807 0.10058553 -0.04675984 -0.10714402 0.05093826 0.02926494 -0.02081728 -0.00729950 0.02160104 0.02766119 0.04699481 -0.07011296 -0.00523427 -0.04952104 0.03863125 -0.03070737 0.01310245 -0.03063107 -0.06485168 0.00553488 -0.00803247 -0.01921815 0.02247080 -0.00982030 -0.10513162 0.01794683 0.06531870 -0.02805996 0.01312310 0.02652609 0.06482637 0.05824970 -0.03478849 -0.07496799 0.04272895 -0.02852358 0.05656609 0.00454572 0.04394037 -0.04370771 -0.02451568 0.03000730 -0.03822605 0.02595372 -0.01245839 0.04209964 0.00992974 0.02053569 -0.03851036 0.02718321 -0.01058342 -0.02044339 -0.02939100 -0.01937112 0.05849671 -0.05092414 0.03183075 -0.00557423 0.00362723 0.08008144 0.00160273 0.03769433 -0.05383030 -0.03931151 -0.01455422 0.07187381 0.00338787 -0.06378862 0.02159593 0.05940752 -0.05459800 0.03299179 -0.01048890 -0.03459304 -0.05829837 -0.06199726 0.04610071 0.04733562 0.05601728 0.05026469 0.04634680 -0.03348678 -0.00660666 -0.01435589 0.02473231 0.05401890 -0.03287175 0.00733055 0.03569730 0.05239975 -0.03714010 0.00983087 0.00963711 -0.07282051 0.00712978 0.01197440 -0.01428354 -0.00000964 -0.04204410 -0.05032920 0.01509099 -0.04764790 0.00645663 0.04084183 0.00231274 0.03463424 -0.06453673 -0.07960699 0.06469877 0.02176066 -0.09742584 -0.02178211 0.00928854 -0.12112571 -0.02051966 -0.03950721 0.02019501 0.00753530 -0.05773720 -0.00585445 -0.00409730 -0.03304575 0.00041452 -0.01587901 -0.00391938 -0.02983808 -0.14276297 0.03593273 -0.05425276 0.06543014 -0.01580369 -0.03310246 -0.01813433 -0.01330116 0.02138102 0.05228459 0.02069326 -0.02252935 -0.01197431 0.04346422 -0.01321351 -0.04852602 0.04417197 0.02779470 0.01449795 0.01670827 -0.07340848 0.03534355 -0.04339332 -0.08006717 0.08272849 -0.00104691 -0.07832366 0.01218867 0.00667058 -0.08910172 -0.02372465 -0.02286686 -0.04001194 0.00500422 0.01011096

# 测试集
"test_a.csv"

userid	feedid	device
14298	67227	1
68356	91864	2
49925	104657	2
60529	23738	2
131482	69038	1
52981	33636	1
55058	22853	2
78168	100222	1
135784	78982	2

# coding: utf-8 comm.py 
import os
import time
import logging 
LOG_FORMAT = "%(asctime)s - %(levelname)s - %(message)s" 
logging.basicConfig(level=logging.INFO, format=LOG_FORMAT) 
logger = logging.getLogger(__file__)
import numpy as np
import pandas as pd

# 存储数据的根目录
ROOT_PATH = "./data"
# 比赛数据集路径
DATASET_PATH = os.path.join(ROOT_PATH, "wechat_algo_data1")
# 训练集
USER_ACTION = os.path.join(DATASET_PATH, "user_action.csv")
FEED_INFO = os.path.join(DATASET_PATH, "feed_info.csv")
FEED_EMBEDDINGS = os.path.join(DATASET_PATH, "feed_embeddings.csv")
# 测试集
TEST_FILE = os.path.join(DATASET_PATH, "test_a.csv")
END_DAY = 15
SEED = 2021

# 初赛待预测行为列表
ACTION_LIST = ["read_comment", "like", "click_avatar",  "forward"]
# 复赛待预测行为列表
# ACTION_LIST = ["read_comment", "like", "click_avatar",  "forward", "comment", "follow", "favorite"]
# 用于构造特征的字段列表
FEA_COLUMN_LIST = ["read_comment", "like", "click_avatar",  "forward", "comment", "follow", "favorite"]
# 每个行为的负样本下采样比例(下采样后负样本数/原负样本数)
ACTION_SAMPLE_RATE = {"read_comment": 0.2, "like": 0.2, "click_avatar": 0.2, "forward": 0.1, "comment": 0.1, "follow": 0.1, "favorite": 0.1}

# 各个阶段数据集的设置的最后一天
STAGE_END_DAY = {"online_train": 14, "offline_train": 12, "evaluate": 13, "submit": 15}
# 各个行为构造训练数据的天数
ACTION_DAY_NUM = {"read_comment": 5, "like": 5, "click_avatar": 5, "forward": 5, "comment": 5, "follow": 5, "favorite": 5}


def create_dir():
    """
    创建所需要的目录
    """
    # 创建data目录
    if not os.path.exists(ROOT_PATH):
        print('Create dir: %s'%ROOT_PATH)
        os.mkdir(ROOT_PATH)
    # data目录下需要创建的子目录
    need_dirs = ["offline_train", "online_train", "evaluate", "submit",
                 "feature", "model", "model/online_train", "model/offline_train"]
    for need_dir in need_dirs:
        need_dir = os.path.join(ROOT_PATH, need_dir)
        if not os.path.exists(need_dir):
            print('Create dir: %s'%need_dir)
            os.mkdir(need_dir)


def check_file():
    '''
    检查数据文件是否存在
    '''
    paths = [USER_ACTION, FEED_INFO, TEST_FILE]
    flag = True
    not_exist_file = []
    for f in paths:
        if not os.path.exists(f):
            not_exist_file.append(f)
            flag = False
    return flag, not_exist_file


def statis_data():
    """
    统计特征最大，最小，均值
    """
    paths = [USER_ACTION, FEED_INFO, TEST_FILE]
    pd.set_option('display.max_columns', None)
    for path in paths:
        df = pd.read_csv(path)
        print(path + " statis: ")
        print(df.describe())
        print('Distinct count:')
        print(df.nunique())


def statis_feature(start_day=1, before_day=7, agg='sum'):
    """
    统计用户/feed 过去n天各类行为的次数
    :param start_day: Int. 起始日期
    :param before_day: Int. 时间范围（天数）
    :param agg: String. 统计方法
    """
    history_data = pd.read_csv(USER_ACTION)[["userid", "date_", "feedid"] + FEA_COLUMN_LIST]
    feature_dir = os.path.join(ROOT_PATH, "feature")
    for dim in ["userid", "feedid"]:
        print(dim)
        user_data = history_data[[dim, "date_"] + FEA_COLUMN_LIST]
        res_arr = []
        for start in range(start_day, END_DAY-before_day+1):
            temp = user_data[((user_data["date_"]) >= start) & (user_data["date_"] < (start + before_day))]
            temp = temp.drop(columns=['date_'])
            temp = temp.groupby([dim]).agg([agg]).reset_index()
            temp.columns = list(map(''.join, temp.columns.values))
            temp["date_"] = start + before_day
            res_arr.append(temp)
        dim_feature = pd.concat(res_arr)
        feature_path = os.path.join(feature_dir, dim+"_feature.csv")
        print('Save to: %s'%feature_path)
        dim_feature.to_csv(feature_path, index=False)


def generate_sample(stage="offline_train"):
    """
    对负样本进行下采样，生成各个阶段所需样本
    :param stage: String. Including "online_train"/"offline_train"/"evaluate"/"submit"
    :return: List of sample df
    """
    day = STAGE_END_DAY[stage]
    if stage == "submit":
        sample_path = TEST_FILE
    else:
        sample_path = USER_ACTION
    stage_dir = os.path.join(ROOT_PATH, stage)
    df = pd.read_csv(sample_path)
    df_arr = []
    if stage == "evaluate":
        # 线下评估
        col = ["userid", "feedid", "date_", "device"] + ACTION_LIST
        df = df[df["date_"] == day][col]
        file_name = os.path.join(stage_dir, stage + "_" + "all" + "_" + str(day) + "_generate_sample.csv")
        print('Save to: %s'%file_name)
        df.to_csv(file_name, index=False)
        df_arr.append(df)
    elif stage == "submit":
        # 线上提交
        file_name = os.path.join(stage_dir, stage + "_" + "all" + "_" + str(day) + "_generate_sample.csv")
        df["date_"] = 15
        print('Save to: %s'%file_name)
        df.to_csv(file_name, index=False)
        df_arr.append(df)
    else:
        # 线下/线上训练
        # 同行为取按时间最近的样本
        for action in ACTION_LIST:
            df = df.drop_duplicates(subset=['userid', 'feedid', action], keep='last')
        # 负样本下采样
        for action in ACTION_LIST:
            action_df = df[(df["date_"] <= day) & (df["date_"] >= day - ACTION_DAY_NUM[action] + 1)]
            df_neg = action_df[action_df[action] == 0]
            df_pos = action_df[action_df[action] == 1]
            df_neg = df_neg.sample(frac=ACTION_SAMPLE_RATE[action], random_state=SEED, replace=False)
            df_all = pd.concat([df_neg, df_pos])
            col = ["userid", "feedid", "date_", "device"] + [action]
            file_name = os.path.join(stage_dir, stage + "_" + action + "_" + str(day) + "_generate_sample.csv")
            print('Save to: %s'%file_name)
            df_all[col].to_csv(file_name, index=False)
            df_arr.append(df_all[col])
    return df_arr


def concat_sample(sample_arr, stage="offline_train"):
    """
    基于样本数据和特征，生成特征数据
    :param sample_arr: List of sample df
    :param stage: String. Including "online_train"/"offline_train"/"evaluate"/"submit"
    """
    day = STAGE_END_DAY[stage]
    # feed信息表
    feed_info = pd.read_csv(FEED_INFO)
    feed_info = feed_info.set_index('feedid')
    # 基于userid统计的历史行为的次数
    user_date_feature_path = os.path.join(ROOT_PATH, "feature", "userid_feature.csv")
    user_date_feature = pd.read_csv(user_date_feature_path)
    user_date_feature = user_date_feature.set_index(["userid", "date_"])
    # 基于feedid统计的历史行为的次数
    feed_date_feature_path = os.path.join(ROOT_PATH, "feature", "feedid_feature.csv")
    feed_date_feature = pd.read_csv(feed_date_feature_path)
    feed_date_feature = feed_date_feature.set_index(["feedid", "date_"])

    for index, sample in enumerate(sample_arr):
        features = ["userid", "feedid", "device", "authorid", "bgm_song_id", "bgm_singer_id",
                    "videoplayseconds"]
        if stage == "evaluate":
            action = "all"
            features += ACTION_LIST
        elif stage == "submit":
            action = "all"
        else:
            action = ACTION_LIST[index]
            features += [action]
        print("action: ", action)
        sample = sample.join(feed_info, on="feedid", how="left", rsuffix="_feed")
        sample = sample.join(feed_date_feature, on=["feedid", "date_"], how="left", rsuffix="_feed")
        sample = sample.join(user_date_feature, on=["userid", "date_"], how="left", rsuffix="_user")
        feed_feature_col = [b+"sum" for b in FEA_COLUMN_LIST]
        user_feature_col = [b+"sum_user" for b in FEA_COLUMN_LIST]
        sample[feed_feature_col] = sample[feed_feature_col].fillna(0.0)
        sample[user_feature_col] = sample[user_feature_col].fillna(0.0)
        sample[feed_feature_col] = np.log(sample[feed_feature_col] + 1.0)
        sample[user_feature_col] = np.log(sample[user_feature_col] + 1.0)
        features += feed_feature_col
        features += user_feature_col

        sample[["authorid", "bgm_song_id", "bgm_singer_id"]] += 1  # 0 用于填未知
        sample[["authorid", "bgm_song_id", "bgm_singer_id", "videoplayseconds"]] = \
            sample[["authorid", "bgm_song_id", "bgm_singer_id", "videoplayseconds"]].fillna(0)
        sample["videoplayseconds"] = np.log(sample["videoplayseconds"] + 1.0)

        sample[["authorid", "bgm_song_id", "bgm_singer_id"]] = \
            sample[["authorid", "bgm_song_id", "bgm_singer_id"]].astype(int)
        file_name = os.path.join(ROOT_PATH, stage, stage + "_" + action + "_" + str(day) + "_concate_sample.csv")
        print('Save to: %s'%file_name)
        sample[features].to_csv(file_name, index=False)


def main():
    t = time.time()
    statis_data()
    logger.info('Create dir and check file')
    create_dir()
    flag, not_exists_file = check_file()
    if not flag:
        print("请检查目录中是否存在下列文件: ", ",".join(not_exists_file))
        return
    logger.info('Generate statistic feature')
    statis_feature()
    for stage in STAGE_END_DAY:
        logger.info("Stage: %s"%stage)
        logger.info('Generate sample')
        sample_arr = generate_sample(stage)
        logger.info('Concat sample with feature')
        concat_sample(sample_arr, stage)
    print('Time cost: %.2f s'%(time.time()-t))


if __name__ == "__main__":
    main()

程序运行结果

./data\wechat_algo_data1\user_action.csv statis: 
             userid        feedid         date_        device  read_comment  \
count  7.317882e+06  7.317882e+06  7.317882e+06  7.317882e+06  7.317882e+06   
mean   1.249679e+05  5.669863e+04  7.801455e+00  1.765396e+00  3.501587e-02   
std    7.239444e+04  3.278194e+04  4.063833e+00  4.237514e-01  1.838199e-01   
min    8.000000e+00  0.000000e+00  1.000000e+00  1.000000e+00  0.000000e+00   
25%    6.133000e+04  2.814900e+04  4.000000e+00  2.000000e+00  0.000000e+00   
50%    1.256370e+05  5.682500e+04  8.000000e+00  2.000000e+00  0.000000e+00   
75%    1.878630e+05  8.522900e+04  1.100000e+01  2.000000e+00  0.000000e+00   
max    2.502360e+05  1.128710e+05  1.400000e+01  2.000000e+00  1.000000e+00   

            comment          like          play          stay  click_avatar  \
count  7.317882e+06  7.317882e+06  7.317882e+06  7.317882e+06  7.317882e+06   
mean   4.046253e-04  2.580487e-02  2.631760e+04  3.101158e+04  7.533327e-03   
std    2.011123e-02  1.585528e-01  6.477679e+04  1.013239e+05  8.646720e-02   
min    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   
25%    0.000000e+00  0.000000e+00  2.017000e+03  5.189000e+03  0.000000e+00   
50%    0.000000e+00  0.000000e+00  1.328900e+04  1.782900e+04  0.000000e+00   
75%    0.000000e+00  0.000000e+00  3.600000e+04  4.133900e+04  0.000000e+00   
max    1.000000e+00  1.000000e+00  3.855337e+07  8.262444e+07  1.000000e+00   

            forward        follow      favorite  
count  7.317882e+06  7.317882e+06  7.317882e+06  
mean   3.821188e-03  7.211103e-04  1.342465e-03  
std    6.169754e-02  2.684381e-02  3.661506e-02  
min    0.000000e+00  0.000000e+00  0.000000e+00  
25%    0.000000e+00  0.000000e+00  0.000000e+00  
50%    0.000000e+00  0.000000e+00  0.000000e+00  
75%    0.000000e+00  0.000000e+00  0.000000e+00  
max    1.000000e+00  1.000000e+00  1.000000e+00  
Distinct count:
userid           20000
feedid           96564
date_               14
device               2
read_comment         2
comment              2
like                 2
play            201721
stay            220343
click_avatar         2
forward              2
follow               2
favorite             2
dtype: int64
./data\wechat_algo_data1\feed_info.csv statis: 
              feedid       authorid  videoplayseconds   bgm_song_id  \
count  106444.000000  106444.000000     106444.000000  53462.000000   
mean    56443.543704    9488.196639         34.446545  12556.594946   
std     32582.304899    5384.927792        277.086122   7319.430812   
min         0.000000       0.000000          2.000000      0.000000   
25%     28243.750000    4844.750000         14.000000   6137.000000   
50%     56419.500000    9584.000000         26.000000  12518.500000   
75%     84659.250000   14122.000000         54.000000  18965.750000   
max    112871.000000   18788.000000      59960.000000  25158.000000   

       bgm_singer_id  
count   53462.000000  
mean     8795.041338  
std      5023.494371  
min         0.000000  
25%      4589.250000  
50%      8614.000000  
75%     13218.000000  
max     17499.000000  
Distinct count:
feedid                  106444
authorid                 18789
videoplayseconds            76
description              99526
ocr                      76148
asr                      70969
bgm_song_id              25159
bgm_singer_id            17500
manual_keyword_list      49460
machine_keyword_list     54800
manual_tag_list           3314
machine_tag_list        105819
description_char         99416
ocr_char                 75760
asr_char                 70969
dtype: int64
./data\wechat_algo_data1\test_a.csv statis: 
2024-10-10 17:39:46,152 - INFO - Create dir and check file
2024-10-10 17:39:46,168 - INFO - Generate statistic feature
              userid         feedid         device
count  421985.000000  421985.000000  421985.000000
mean   124695.072128   57071.963089       1.752171
std     72321.066783   32436.065649       0.431752
min        25.000000       0.000000       1.000000
25%     61122.000000   29674.000000       2.000000
50%    126473.000000   57114.000000       2.000000
75%    188144.000000   84952.000000       2.000000
max    250224.000000  112871.000000       2.000000
Distinct count:
userid     9757
feedid    35157
device        2
dtype: int64
Create dir: ./data\offline_train
Create dir: ./data\online_train
Create dir: ./data\evaluate
Create dir: ./data\submit
Create dir: ./data\feature
Create dir: ./data\model
Create dir: ./data\model/online_train
Create dir: ./data\model/offline_train
userid
Save to: ./data\feature\userid_feature.csv
feedid
Save to: ./data\feature\feedid_feature.csv
2024-10-10 17:40:06,186 - INFO - Stage: online_train
2024-10-10 17:40:06,186 - INFO - Generate sample
Save to: ./data\online_train\online_train_read_comment_14_generate_sample.csv
Save to: ./data\online_train\online_train_like_14_generate_sample.csv
Save to: ./data\online_train\online_train_click_avatar_14_generate_sample.csv
Save to: ./data\online_train\online_train_forward_14_generate_sample.csv
2024-10-10 17:40:25,009 - INFO - Concat sample with feature
action:  read_comment
Save to: ./data\online_train\online_train_read_comment_14_concate_sample.csv
action:  like
Save to: ./data\online_train\online_train_like_14_concate_sample.csv
action:  click_avatar
Save to: ./data\online_train\online_train_click_avatar_14_concate_sample.csv
action:  forward
Save to: ./data\online_train\online_train_forward_14_concate_sample.csv
2024-10-10 17:41:04,241 - INFO - Stage: offline_train
2024-10-10 17:41:04,241 - INFO - Generate sample
Save to: ./data\offline_train\offline_train_read_comment_12_generate_sample.csv
Save to: ./data\offline_train\offline_train_like_12_generate_sample.csv
Save to: ./data\offline_train\offline_train_click_avatar_12_generate_sample.csv
Save to: ./data\offline_train\offline_train_forward_12_generate_sample.csv
2024-10-10 17:41:22,671 - INFO - Concat sample with feature
action:  read_comment
Save to: ./data\offline_train\offline_train_read_comment_12_concate_sample.csv
action:  like
Save to: ./data\offline_train\offline_train_like_12_concate_sample.csv
action:  click_avatar
Save to: ./data\offline_train\offline_train_click_avatar_12_concate_sample.csv
action:  forward
Save to: ./data\offline_train\offline_train_forward_12_concate_sample.csv
2024-10-10 17:41:57,547 - INFO - Stage: evaluate
2024-10-10 17:41:57,547 - INFO - Generate sample
Save to: ./data\evaluate\evaluate_all_13_generate_sample.csv
2024-10-10 17:42:03,660 - INFO - Concat sample with feature
action:  all
Save to: ./data\evaluate\evaluate_all_13_concate_sample.csv
2024-10-10 17:42:16,306 - INFO - Stage: submit
2024-10-10 17:42:16,306 - INFO - Generate sample
Save to: ./data\submit\submit_all_15_generate_sample.csv
2024-10-10 17:42:17,025 - INFO - Concat sample with feature
action:  all
Save to: ./data\submit\submit_all_15_concate_sample.csv
Time cost: 172.95 s

参见：

press-physicsprize2024.pdf (nobelprize.org)

Home Page of Geoffrey Hinton

http://www.cs.utoronto.ca/~hinton/absps/naturebp.pdf

https://dl.acm.org/doi/pdf/10.1145/2988450.2988454

https://www.annualreviews.org/content/journals/10.1146/annurev-conmatphys-031113-133924

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