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move effect of stored finished configurations into data generation

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hannes.kuchelmeister
2020-04-06 11:24:36 +02:00
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30_Thesis/sections/60_evaluation.tex
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@@ -26,13 +26,6 @@ Und, dass zB die Frage bzgl Anzahl auf die technischen Eigenschaften des Recomme
\item How does the amount of stored finished configurations relate to satisfaction with a recommendation? \item How does the amount of stored finished configurations relate to satisfaction with a recommendation?
\end{itemize} \end{itemize}
\section{Effect of Stored Finished Configurations}
\label{sec:Evaluation:EffectFinishedConfiguration}
\todo[inline]{potentially move to data generation}
When evaluating a subset of stored finished configurations it is important to avoid outliers. This is the reason why a process inspired by \emph{cross validation} \todo{referenz hinzufügen} is used. The configuration database is randomly ordered and sliced into sub databases of the needed size. As an example, if the evaluated stored data size is 20, a configuration database containing 100 configurations is split into five sub databases of size 20. Now the evaluation is done on each of the sub databases and as a result the average is taken. This avoid that randomly a subset can be picked which either performs much better than most other possible combinations of databases or which performs much worse. This way the data is more aligned to the \emph{expected value} \todo{referenz}.
\section{Use Case} \section{Use Case}
\label{sec:Evaluation:UseCase} \label{sec:Evaluation:UseCase}
@@ -80,7 +73,7 @@ The stakeholders in this use case are: a forest owner, an athlete, an environmen
\todo[inline]{Kapitel nochmal abschließen mit: hier liegen als sich widersprechende Präferenzen vor. Und: was sollen die Stakeholder jetzt entscheiden? in welcher Situation befinden sie sich? Wie setzt sich eine Gruppe zusammen? Aus 4 Personen von je einem Typ?} \todo[inline]{Kapitel nochmal abschließen mit: hier liegen als sich widersprechende Präferenzen vor. Und: was sollen die Stakeholder jetzt entscheiden? in welcher Situation befinden sie sich? Wie setzt sich eine Gruppe zusammen? Aus 4 Personen von je einem Typ?}
\section{Generating Data} \section{Data Generation}
\label{sec:Evaluation:GeneratingGroups} \label{sec:Evaluation:GeneratingGroups}
\todo[inline]{Dieses Kapitel ist für mich noch nicht konsistent. Auf der Abbildung fehlen Elemente (Präferenzen \& Gruppen generieren), im Text ist das Paaren von Präferenzen(?) und Konfigurationen nicht beschrieben. Und: was wird da wirklich gepaart: präferenzen oder Gruppen?} \todo[inline]{Dieses Kapitel ist für mich noch nicht konsistent. Auf der Abbildung fehlen Elemente (Präferenzen \& Gruppen generieren), im Text ist das Paaren von Präferenzen(?) und Konfigurationen nicht beschrieben. Und: was wird da wirklich gepaart: präferenzen oder Gruppen?}
@@ -94,16 +87,18 @@ The whole process explained in \todo[inline]{hier einen besseren Übergang schaf
\label{fig:Evaluation:GeneratingDataProcess} \label{fig:Evaluation:GeneratingDataProcess}
\end{figure} \end{figure}
\subsection{Generating Unfinished Configurations} \subsection{Unfinished Configurations Generation}
Unfinished configurations are generated using all finished configurations and taking a subset of the contained characteristics. This way all generated configurations will be valid and lead to valid solutions. For the results that are presented in this chapter around $\frac{1}{7} \approx 15\%$ \todo{why this number} of characteristics is kept. Unfinished configurations are generated using all finished configurations and taking a subset of the contained characteristics. This way all generated configurations will be valid and lead to valid solutions. For the results that are presented in this chapter around $\frac{1}{7} \approx 15\%$ \todo{why this number} of characteristics is kept.
\subsection{Preference Generation}
\subsection{Generating Preferences}
For the forest use case, the idea is that there are multiple types of user profiles. Each group profile is represented by a neutral, negative or positive attitude towards a characteristic. During data generation the attitude is converted to a preference \todo{hier evlt nochmal nennen, dass du Präferenzen zwischen 0 und 1 verwendest, steht aktuell nur in der Grafik} using a normal distribution. \autoref{fig:Evaluation:DataGeneration} shows how the user profile can be converted to preferences. The actual group member profiles are shown in \autoref{tab:Evaluation:GroupMemberMappings}. For the forest use case, the idea is that there are multiple types of user profiles. Each group profile is represented by a neutral, negative or positive attitude towards a characteristic. During data generation the attitude is converted to a preference \todo{hier evlt nochmal nennen, dass du Präferenzen zwischen 0 und 1 verwendest, steht aktuell nur in der Grafik} using a normal distribution. \autoref{fig:Evaluation:DataGeneration} shows how the user profile can be converted to preferences. The actual group member profiles are shown in \autoref{tab:Evaluation:GroupMemberMappings}.
\subsection{The Effect of Stored Finished Configurations}
When evaluating a subset of stored finished configurations it is important to avoid outliers. This is the reason why a process inspired by \emph{cross validation} \todo{referenz hinzufügen} is used. The configuration database is randomly ordered and sliced into sub databases of the needed size. As an example, if the evaluated stored data size is 20, a configuration database containing 100 configurations is split into five sub databases of size 20. Now the evaluation is done on each of the sub databases and as a result the average is taken. This avoid that randomly a subset can be picked which either performs much better than most other possible combinations of databases or which performs much worse. This way the data is more aligned to the \emph{expected value} \todo{referenz}.
\pgfplotsset{height=5cm,width=\textwidth,compat=1.8} \pgfplotsset{height=5cm,width=\textwidth,compat=1.8}
\pgfmathdeclarefunction{gauss}{2}{% \pgfmathdeclarefunction{gauss}{2}{%
\pgfmathparse{1/(#2*sqrt(2*pi))*exp(-((x-#1)^2)/(2*#2^2))}% \pgfmathparse{1/(#2*sqrt(2*pi))*exp(-((x-#1)^2)/(2*#2^2))}%