From c12b11a92acb21b8538d57a38e5b4b100953d86c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Simon=20Kl=C3=BCttermann?= Date: Wed, 14 Sep 2022 16:35:37 +0200 Subject: [PATCH] initial push --- data/000.txt | 5 + data/001Anomaly Detection.txt | 17 + data/002Anomaly Detection.txt | 18 + data/003Anomaly Detection.txt | 19 + data/004Anomaly Detection.txt | 19 + data/005Anomaly Detection.txt | 17 + data/006AutoML.txt | 14 + data/007AutoAD.txt | 12 + data/008Solution 1 Metrics.txt | 14 + data/009Solution 2 OneShot Learning.txt | 9 + data/010Course.txt | 23 + data/011Questions.txt | 10 + general.txt | 12 + imgs | 1 + out/compile.bat | 3 + out/compile.sh | 3 + out/label.json | 74 + out/main.aux | 108 ++ out/main.log | 1203 +++++++++++++++++ out/main.nav | 29 + out/main.out | 0 out/main.pdf | Bin 0 -> 1067213 bytes out/main.snm | 30 + out/main.tex | 515 +++++++ out/main.toc | 0 prep/000/nonl | 0 prep/000/q | 1 + prep/01Anomaly_Detection/anomalies.pdf | Bin 0 -> 27075 bytes prep/01Anomaly_Detection/q | 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touse/TIM220328_Buterin.Cover_.FINAL2_.jpg create mode 100644 touse/TIM220328_Buterin.Cover_.FINAL2_.webp diff --git a/data/000.txt b/data/000.txt new file mode 100644 index 0000000..57c13ce --- /dev/null +++ b/data/000.txt @@ -0,0 +1,5 @@ + + + + + \ No newline at end of file diff --git a/data/001Anomaly Detection.txt b/data/001Anomaly Detection.txt new file mode 100644 index 0000000..5d7bcb3 --- /dev/null +++ b/data/001Anomaly Detection.txt @@ -0,0 +1,17 @@ + + + + + Two distributions + +One known (=normal) +One unknown (=anomalies) + +Seperate them + + + + + + + \ No newline at end of file diff --git a/data/002Anomaly Detection.txt b/data/002Anomaly Detection.txt new file mode 100644 index 0000000..f931800 --- /dev/null +++ b/data/002Anomaly Detection.txt @@ -0,0 +1,18 @@ + + + + + Two distributions + +One known (=normal) +One unknown (=anomalies) + +Seperate them +Problem: few anomalies + + + + + + + \ No newline at end of file diff --git a/data/003Anomaly Detection.txt b/data/003Anomaly Detection.txt new file mode 100644 index 0000000..e647d8d --- /dev/null +++ b/data/003Anomaly Detection.txt @@ -0,0 +1,19 @@ + + + + + Anomalies are rare, so often only a few datapoints known (e.g. Machine Failure in an Aircraft) +In practice, anomalies might appear that are not known during testing +->So train the model only on normal samples +Unsupervised Machine Learning + +What can we say without knowing anomalies? +''Understand you dataset'' + + + + + + + + \ No newline at end of file diff --git a/data/004Anomaly Detection.txt b/data/004Anomaly Detection.txt new file mode 100644 index 0000000..39b1aee --- /dev/null +++ b/data/004Anomaly Detection.txt @@ -0,0 +1,19 @@ + + + + + Anomalies are rare, so often only a few datapoints known (e.g. Machine Failure in an Aircraft) +In practice, anomalies might appear that are not known during testing +->So train the model only on normal samples +Unsupervised Machine Learning + +What can we say without knowing anomalies? +''Understand you dataset'' + + + + + + + + \ No newline at end of file diff --git a/data/005Anomaly Detection.txt b/data/005Anomaly Detection.txt new file mode 100644 index 0000000..2639985 --- /dev/null +++ b/data/005Anomaly Detection.txt @@ -0,0 +1,17 @@ + + + + + Seems easy? Now do this + +in thousands of dimensions +with complicated distributions +and overlap between anomalies and normal points + + + + + + + + \ No newline at end of file diff --git a/data/006AutoML.txt b/data/006AutoML.txt new file mode 100644 index 0000000..4dcd656 --- /dev/null +++ b/data/006AutoML.txt @@ -0,0 +1,14 @@ + + + + + Most machine learning requires Hyperparameter Optimisation +(Find model parameters that result in the best results) +->AutoML: Do this automatically as fast as possible + + + + + + + \ No newline at end of file diff --git a/data/007AutoAD.txt b/data/007AutoAD.txt new file mode 100644 index 0000000..b12a65a --- /dev/null +++ b/data/007AutoAD.txt @@ -0,0 +1,12 @@ + + + So lets combine both (Auto Anomaly Detection) +->Problem + +AutoMl requires Evaluation (loss, accuracy, AUC) to optimize +AD can only be evaluated with regards to the anomalies +->no longer unsupervised + +So most Anomaly Detection is ''unoptimized'' + + \ No newline at end of file diff --git a/data/008Solution 1 Metrics.txt b/data/008Solution 1 Metrics.txt new file mode 100644 index 0000000..ee4940a --- /dev/null +++ b/data/008Solution 1 Metrics.txt @@ -0,0 +1,14 @@ + + + + + So how to solve this? +One option: Think of some function to evaluate only the normal points +->A bit hard to do in a case study + + + + + + + \ No newline at end of file diff --git a/data/009Solution 2 OneShot Learning.txt b/data/009Solution 2 OneShot Learning.txt new file mode 100644 index 0000000..43df0b8 --- /dev/null +++ b/data/009Solution 2 OneShot Learning.txt @@ -0,0 +1,9 @@ + + + So how to solve this? +One option: ''Just find the best solution directly'' +->Zero Shot AutoML +Find best practices for hyperparameters +Requires optimisation for each model seperately -> matches the case study structure quite well! + + \ No newline at end of file diff --git a/data/010Course.txt b/data/010Course.txt new file mode 100644 index 0000000..49580fb --- /dev/null +++ b/data/010Course.txt @@ -0,0 +1,23 @@ + + + + + Basics of Scientific Computing +Basics of AD +Basics of AutoML +Build groups for each algorithm + +Choose a set of Hyperparameters +Find ''best practice`s'' for them +Maybe consider more complicated Transformations (Preprocessing, Ensemble) + +Compare between groups (best algorithm for current situation) +Evaluate on new datasets +Write a report/Present your work + + + + + + + \ No newline at end of file diff --git a/data/011Questions.txt b/data/011Questions.txt new file mode 100644 index 0000000..d2dee7d --- /dev/null +++ b/data/011Questions.txt @@ -0,0 +1,10 @@ + + + Requirements: + +MD Req 1->MD Req 8 +Basic Python/Math Knowledge +Motivation to learn something new;) + + + \ No newline at end of file diff --git a/general.txt b/general.txt new file mode 100644 index 0000000..e9f1307 --- /dev/null +++ b/general.txt @@ -0,0 +1,12 @@ + + + + +<stitle AutoML4Rad> + +<institute ls9 tu Dortmund> + +<theme CambridgeUS> +<colo dolphin> + +</plt> diff --git a/imgs b/imgs new file mode 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{fig:prep02Anomaly_Detectiondifferencepdf}{3} +\beamer@slide {Anomaly Detection<1>}{4} +\beamer@slide {Anomaly Detection}{4} +\beamer@slide {fig:prep03Anomaly_Detectionusuppdf}{4} +\beamer@slide {Anomaly Detection<1>}{5} +\beamer@slide {Anomaly Detection}{5} +\beamer@slide {fig:prep04Anomaly_Detectioncirclepdf}{5} +\beamer@slide {Anomaly Detection<1>}{6} +\beamer@slide {Anomaly Detection}{6} +\beamer@slide {fig:prep05Anomaly_Detectionanomaly_detectionpng}{6} +\beamer@slide {AutoML<1>}{7} +\beamer@slide {AutoML}{7} +\beamer@slide {fig:prep06AutoMLDownloadpng}{7} +\beamer@slide {AutoAD<1>}{8} +\beamer@slide {AutoAD}{8} +\beamer@slide {Solution 1 Metrics<1>}{9} +\beamer@slide {Solution 1 Metrics}{9} +\beamer@slide {fig:prep08Solution_1_Metricscircle2pdf}{9} +\beamer@slide {Solution 2 OneShot Learning<1>}{10} +\beamer@slide {Solution 2 OneShot Learning}{10} +\beamer@slide {Course<1>}{11} +\beamer@slide {Course}{11} +\beamer@slide {fig:prep09Coursetablepng}{11} +\beamer@slide {Questions<1>}{12} +\beamer@slide {Questions}{12} diff --git a/out/main.tex b/out/main.tex new file mode 100644 index 0000000..e639da5 --- /dev/null +++ b/out/main.tex @@ -0,0 +1,515 @@ +\UseRawInputEncoding +%\documentclass[hyperref={pdfpagelabels=false}]{beamer} +\documentclass[hyperref={pdfpagelabels=false},aspectratio=169]{beamer} +% Die Hyperref Option hyperref={pdfpagelabels=false} verhindert die Warnung: +% Package hyperref Warning: Option `pdfpagelabels' is turned off +% (hyperref) because \thepage is undefined. +% Hyperref stopped early +% + +\usepackage{lmodern} +% Das Paket lmodern erspart die folgenden Warnungen: +% LaTeX Font Warning: Font shape `OT1/cmss/m/n' in size <4> not available +% (Font) size <5> substituted on input line 22. +% LaTeX Font Warning: Size substitutions with differences +% (Font) up to 1.0pt have occurred. +% + +% Wenn \titel{\ldots} \author{\ldots} erst nach \begin{document} kommen, +% kommt folgende Warnung: +% Package hyperref Warning: Option `pdfauthor' has already been used, +% (hyperref) ... +% Daher steht es hier vor \begin{document} + +\title[AutoML4Rad]{Case Study - AutoML for Robust Anomaly Detection} +\author{Simon Kluettermann} +\date{\today} + + + \institute{ls9 tu Dortmund} + + +% Dadurch wird verhindert, dass die Navigationsleiste angezeigt wird. +\setbeamertemplate{navigation symbols}{} + +% zusaetzlich ist das usepackage{beamerthemeshadow} eingebunden +\usepackage{beamerthemeshadow} + +\hypersetup{pdfstartview={Fit}} % fits the presentation to the window when first displayed + +\usepackage{appendixnumberbeamer} +\usepackage{listings} + + +\usetheme{CambridgeUS} +\usepackage{ngerman} +\usecolortheme{dolphin} + + +% \beamersetuncovermixins{\opaqueness<1>{25}}{\opaqueness<2$\Rightarrow${15}} +% sorgt dafuer das die Elemente die erst noch (zukuenftig) kommen +% nur schwach angedeutet erscheinen +%\beamersetuncovermixins{\opaqueness<1>{25}}{\opaqueness<2$\Rightarrow${15}}%here disabled +% klappt auch bei Tabellen, wenn teTeX verwendet wird\ldots +\renewcommand{\figurename}{} + +\setbeamertemplate{footline} +{ + \leavevmode% + \hbox{% + \begin{beamercolorbox}[wd=.4\paperwidth,ht=2.25ex,dp=1ex,center]{author in head/foot}% + \usebeamerfont{author in head/foot}\insertshorttitle + \end{beamercolorbox}% + \begin{beamercolorbox}[wd=.25\paperwidth,ht=2.25ex,dp=1ex,center]{title in head/foot}% + \usebeamerfont{title in head/foot}\insertsection + \end{beamercolorbox}% + \begin{beamercolorbox}[wd=.3499\paperwidth,ht=2.25ex,dp=1ex,right]{date in head/foot}% + \usebeamerfont{date in head/foot}\insertshortdate{}\hspace*{2em} + \hyperlink{toc}{\insertframenumber{} / \inserttotalframenumber\hspace*{2ex}} + \end{beamercolorbox}}% + \vskip0pt% +} + +\usepackage[absolute,overlay]{textpos} +\usepackage{graphicx} + +\newcommand{\source}[1]{\begin{textblock*}{9cm}(0.1cm,8.9cm) + \begin{beamercolorbox}[ht=0.5cm,left]{framesource} + \usebeamerfont{framesource}\usebeamercolor[fg!66]{framesource} Source: {#1} + \end{beamercolorbox} +\end{textblock*}} + + +\begin{document} + + + +%from file ../case1/data/000.txt +\begin{frame}[label=] +\frametitle{} +\begin{titlepage} + + \centering + {\huge\bfseries \par} + \vspace{2cm} + {\LARGE\itshape Simon Kluettermann\par} + \vspace{1.5cm} + {\scshape\Large Master Thesis in Physics\par} + \vspace{0.2cm} + {\Large submitted to the \par} + \vspace{0.2cm} + {\scshape\Large Faculty of Mathematics Computer Science and Natural Sciences \par} + \vspace{0.2cm} + {\Large \par} + \vspace{0.2cm} + {\scshape\Large RWTH Aachen University} + \vspace{1cm} + + \vfill + {\scshape\Large Department of Physics\par} + \vspace{0.2cm} + {\scshape\Large Insitute for theoretical Particle Physics and Cosmology\par} + \vspace{0.2cm} + { \Large\par} + \vspace{0.2cm} + {\Large First Referee: Prof. Dr. Michael Kraemer \par} + {\Large Second Referee: Prof. Dr. Felix Kahlhoefer} + + \vfill + +% Bottom of the page + {\large November 2020 \par} +\end{titlepage} +\pagenumbering{roman} +\thispagestyle{empty} +\null +\newpage +\setcounter{page}{1} +\pagenumbering{arabic} +\end{frame} + + +%from file ../case1/data/001Anomaly Detection.txt +\begin{frame}[label=Anomaly Detection] +\frametitle{Anomaly Detection} +\begin{columns}[c] % align columns +\begin{column}{0.48\textwidth}%.48 +\begin{itemize} + + \item Two distributions + +\begin{itemize} + + \item One known (=normal) + + \item One unknown (=anomalies) + + +\end{itemize} + \item Seperate them + + +\end{itemize} +\end{column}% +\hfill% + \begin{column}{0.48\textwidth}%.48 +\begin{figure}[H] + \centering +\includegraphics[width=0.9\textwidth]{..//prep/01Anomaly_Detection/anomalies.pdf} +\label{fig:prep01Anomaly_Detectionanomaliespdf} + \end{figure} + + +\end{column}% +\hfill% +\end{columns} + +\end{frame} + + +%from file ../case1/data/002Anomaly Detection.txt +\begin{frame}[label=Anomaly Detection] +\frametitle{Anomaly Detection} +\begin{columns}[c] % align columns +\begin{column}{0.48\textwidth}%.48 +\begin{itemize} + + \item Two distributions + +\begin{itemize} + + \item One known (=normal) + + \item One unknown (=anomalies) + + +\end{itemize} + \item Seperate them + + \item Problem: few anomalies + + +\end{itemize} +\end{column}% +\hfill% + \begin{column}{0.48\textwidth}%.48 +\begin{figure}[H] + \centering +\includegraphics[width=0.9\textwidth]{..//prep/02Anomaly_Detection/difference.pdf} +\label{fig:prep02Anomaly_Detectiondifferencepdf} + \end{figure} + + +\end{column}% +\hfill% +\end{columns} + +\end{frame} + + +%from file ../case1/data/003Anomaly Detection.txt +\begin{frame}[label=Anomaly Detection] +\frametitle{Anomaly Detection} +\begin{columns}[c] % align columns +\begin{column}{0.48\textwidth}%.48 +\begin{itemize} + + \item Anomalies are rare, so often only a few datapoints known (e.g. Machine Failure in an Aircraft) + + \item In practice, anomalies might appear that are not known during testing + + \item $\Rightarrow$So train the model only on normal samples + + \item Unsupervised Machine Learning + +\begin{itemize} + + \item What can we say without knowing anomalies? + + \item ''Understand you dataset'' + + +\end{itemize} + +\end{itemize} +\end{column}% +\hfill% + \begin{column}{0.48\textwidth}%.48 +\begin{figure}[H] + \centering +\includegraphics[width=0.9\textwidth]{..//prep/03Anomaly_Detection/usup.pdf} +\label{fig:prep03Anomaly_Detectionusuppdf} + \end{figure} + + +\end{column}% +\hfill% +\end{columns} + +\end{frame} + + +%from file ../case1/data/004Anomaly Detection.txt +\begin{frame}[label=Anomaly Detection] +\frametitle{Anomaly Detection} +\begin{columns}[c] % align columns +\begin{column}{0.48\textwidth}%.48 +\begin{itemize} + + \item Anomalies are rare, so often only a few datapoints known (e.g. Machine Failure in an Aircraft) + + \item In practice, anomalies might appear that are not known during testing + + \item $\Rightarrow$So train the model only on normal samples + + \item Unsupervised Machine Learning + +\begin{itemize} + + \item What can we say without knowing anomalies? + + \item ''Understand you dataset'' + + +\end{itemize} + +\end{itemize} +\end{column}% +\hfill% + \begin{column}{0.48\textwidth}%.48 +\begin{figure}[H] + \centering +\includegraphics[width=0.9\textwidth]{..//prep/04Anomaly_Detection/circle.pdf} +\label{fig:prep04Anomaly_Detectioncirclepdf} + \end{figure} + + +\end{column}% +\hfill% +\end{columns} + +\end{frame} + + +%from file ../case1/data/005Anomaly Detection.txt +\begin{frame}[label=Anomaly Detection] +\frametitle{Anomaly Detection} +\begin{columns}[c] % align columns +\begin{column}{0.48\textwidth}%.48 +\begin{itemize} + + \item Seems easy? Now do this + +\begin{itemize} + + \item in thousands of dimensions + + \item with complicated distributions + + \item and overlap between anomalies and normal points + + +\end{itemize} + +\end{itemize} +\end{column}% +\hfill% + \begin{column}{0.48\textwidth}%.48 +\begin{figure}[H] + \centering +\includegraphics[width=0.9\textwidth]{..//prep/05Anomaly_Detection/anomaly_detection.png} +\label{fig:prep05Anomaly_Detectionanomaly_detectionpng} + \end{figure} + + +\end{column}% +\hfill% +\end{columns} + +\end{frame} + + +%from file ../case1/data/006AutoML.txt +\begin{frame}[label=AutoML] +\frametitle{AutoML} +\begin{columns}[c] % align columns +\begin{column}{0.48\textwidth}%.48 +\begin{itemize} + + \item Most machine learning requires Hyperparameter Optimisation + + \item (Find model parameters that result in the best results) + + \item $\Rightarrow$AutoML: Do this automatically as fast as possible + + +\end{itemize} +\end{column}% +\hfill% + \begin{column}{0.48\textwidth}%.48 +\begin{figure}[H] + \centering +\includegraphics[width=0.9\textwidth]{..//prep/06AutoML/Download.png} +\label{fig:prep06AutoMLDownloadpng} + \end{figure} + + +\end{column}% +\hfill% +\end{columns} + +\end{frame} + + +%from file ../case1/data/007AutoAD.txt +\begin{frame}[label=AutoAD] +\frametitle{AutoAD} +\begin{itemize} + + \item So lets combine both (Auto Anomaly Detection) + + \item $\Rightarrow$Problem + +\begin{itemize} + + \item AutoMl requires Evaluation (loss, accuracy, AUC) to optimize + + \item AD can only be evaluated with regards to the anomalies + + \item $\Rightarrow$no longer unsupervised + + +\end{itemize} + \item So most Anomaly Detection is ''unoptimized'' + + +\end{itemize} +\end{frame} + + +%from file ../case1/data/008Solution 1 Metrics.txt +\begin{frame}[label=Solution 1 Metrics] +\frametitle{Solution 1 Metrics} +\begin{columns}[c] % align columns +\begin{column}{0.48\textwidth}%.48 +\begin{itemize} + + \item So how to solve this? + + \item One option: Think of some function to evaluate only the normal points + + \item $\Rightarrow$A bit hard to do in a case study + + +\end{itemize} +\end{column}% +\hfill% + \begin{column}{0.48\textwidth}%.48 +\begin{figure}[H] + \centering +\includegraphics[width=0.9\textwidth]{..//prep/08Solution_1_Metrics/circle2.pdf} +\label{fig:prep08Solution_1_Metricscircle2pdf} + \end{figure} + + +\end{column}% +\hfill% +\end{columns} + +\end{frame} + + +%from file ../case1/data/009Solution 2 OneShot Learning.txt +\begin{frame}[label=Solution 2 OneShot Learning] +\frametitle{Solution 2 OneShot Learning} +\begin{itemize} + + \item So how to solve this? + + \item One option: ''Just find the best solution directly'' + + \item $\Rightarrow$Zero Shot AutoML + + \item Find best practices for hyperparameters + + \item Requires optimisation for each model seperately $\Rightarrow$ matches the case study structure quite well! + + +\end{itemize} +\end{frame} + + +%from file ../case1/data/010Course.txt +\begin{frame}[label=Course] +\frametitle{Course} +\begin{columns}[c] % align columns +\begin{column}{0.48\textwidth}%.48 +\begin{itemize} + + \item Basics of Scientific Computing + + \item Basics of AD + + \item Basics of AutoML + + \item Build groups for each algorithm + +\begin{itemize} + + \item Choose a set of Hyperparameters + + \item Find ''best practice`s'' for them + + \item Maybe consider more complicated Transformations (Preprocessing, Ensemble) + + +\end{itemize} + \item Compare between groups (best algorithm for current situation) + + \item Evaluate on new datasets + + \item Write a report/Present your work + + +\end{itemize} +\end{column}% +\hfill% + \begin{column}{0.48\textwidth}%.48 +\begin{figure}[H] + \centering +\includegraphics[width=0.9\textwidth]{..//prep/09Course/table.png} +\label{fig:prep09Coursetablepng} + \end{figure} + + +\end{column}% +\hfill% +\end{columns} + +\end{frame} + + +%from file ../case1/data/011Questions.txt +\begin{frame}[label=Questions] +\frametitle{Questions} +\begin{itemize} + + \item Requirements: + +\begin{itemize} + + \item MD Req 1$\Rightarrow$MD Req 8 + + \item Basic Python/Math Knowledge + + \item Motivation to learn something new;) + + +\end{itemize} + +\end{itemize} +\end{frame} + + + +\end{document} diff --git a/out/main.toc b/out/main.toc new file mode 100644 index 0000000..e69de29 diff --git a/prep/000/nonl b/prep/000/nonl new file mode 100644 index 0000000..e69de29 diff --git a/prep/000/q b/prep/000/q new file mode 100644 index 0000000..97c72ec --- /dev/null +++ b/prep/000/q @@ -0,0 +1 @@ +<titlepage> diff --git a/prep/01Anomaly_Detection/anomalies.pdf b/prep/01Anomaly_Detection/anomalies.pdf new file mode 100644 index 0000000000000000000000000000000000000000..1eab31c4db12db42ee8376d85b962610723c3982 GIT binary patch literal 27075 zcmYg$V|W};({^mz+1Sa(PGeh*ZJSLR?8fHCHX1vP)fkP@up7HcgZAt5Js;j*=geG# zd+uxg%$&oZDJ##x&B=$xP`?eR>p|lN00Hh+PH19c04_~Sds}Y+&p(YefJ-;f!xq3L zW9eh*;%*O+kU+C_v-yvq(Eo6d3-D3U@v-!=1@QgXN#5Pf2f+K^yQZa&kFA#*fd4-t 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