\subsection{Evaluation}

\begin{frame}{Evaluation}
    \begin{itemize}
        \item 46920 different simulations
        
        \item All results are available at \url{https://github.com/GordonLesti/SlidingWindowFilter-evaluator/releases/download/v1.0.1/swf-result.csv.zip}
    \end{itemize}
\end{frame}

\begin{frame}{Evaluation}{Top $F_{1}score_{\mu}$ Results}
    \begin{center}
        \tiny
        \begin{tabular}{llllllllll}
            \textbf{distance} & \textbf{scb} & \textbf{filter} & \textbf{window} & \textbf{threshold} & \textbf{precision\_$\mu$} & \textbf{recall\_$\mu$} & \textbf{f1score\_$\mu$} & \textbf{\#(nnc)}\\
            \hline
            $\eta$DTW & 36 & LNCE(300) & Mid  & HAveD & 0.8414 & 0.6579 & 0.7384 & 4214\\
            $\eta$DTW & 36 & NoFilter & Mid  & HAveD & 0.8414 & 0.6579 & 0.7384 & 4893\\
            $\eta$DTW & 28 & LNCE(300) & Mid  & HAveD & 0.812 & 0.6725 & 0.7359 & 4170\\
            $\eta$DTW & 28 & NoFilter & Mid  & HAveD & 0.8118 & 0.6719 & 0.7353 & 4841\\
            $\eta$DTW & 36 & VAR(240) & Mid  & HAveD & 0.8399 & 0.6485 & 0.7319 & 2975\\
            $\eta$DTW & 36 & VAR(260) & Mid & HAveD & 0.8399 & 0.6485 & 0.7319 & 3126\\
            $\eta$DTW & 36 & VAR(280) & Mid & HAveD & 0.8399 & 0.6485 & 0.7319 & 3330\\
            $\eta$DTW & 36 & VAR(300) & Mid & HAveD & 0.8399 & 0.6485 & 0.7319 & 3574\\
            $\eta$DTW & 36 & LNCE(220) & Mid & HAveD & 0.8399 & 0.6485 & 0.7319 & 3806\\
            $\eta$DTW & 36 & LNCE(240) & Mid & HAveD & 0.8399 & 0.6485 & 0.7319 & 3929
        \end{tabular}
    \end{center}
\end{frame}

\begin{frame}{Evaluation}{Domination Configuration}
    \begin{center}
        \tiny
        \begin{tabular}{llllllllll}
            \textbf{distance} & \textbf{scb} & \textbf{filter} & \textbf{window} & \textbf{threshold} & \textbf{precision\_$\mu$} & \textbf{recall\_$\mu$} & \textbf{f1score\_$\mu$} & \textbf{\#(nnc)}\\
            \hline
            \textcolor{red}{$\eta$DTW} & \textcolor{red}{36} & LNCE(300) & \textcolor{red}{Mid}  & \textcolor{red}{HAveD} & 0.8414 & 0.6579 & 0.7384 & 4214\\
            \textcolor{red}{$\eta$DTW} & \textcolor{red}{36} & NoFilter & \textcolor{red}{Mid}  & \textcolor{red}{HAveD} & 0.8414 & 0.6579 & 0.7384 & 4893\\
            $\eta$DTW & 28 & LNCE(300) & Mid  & HAveD & 0.812 & 0.6725 & 0.7359 & 4170\\
            $\eta$DTW & 28 & NoFilter & Mid  & HAveD & 0.8118 & 0.6719 & 0.7353 & 4841\\
            \textcolor{red}{$\eta$DTW} & \textcolor{red}{36} & VAR(240) & \textcolor{red}{Mid}  & \textcolor{red}{HAveD} & 0.8399 & 0.6485 & 0.7319 & 2975\\
            \textcolor{red}{$\eta$DTW} & \textcolor{red}{36} & VAR(260) & \textcolor{red}{Mid}  & \textcolor{red}{HAveD} & 0.8399 & 0.6485 & 0.7319 & 3126\\
            \textcolor{red}{$\eta$DTW} & \textcolor{red}{36} & VAR(280) & \textcolor{red}{Mid}  & \textcolor{red}{HAveD} & 0.8399 & 0.6485 & 0.7319 & 3330\\
            \textcolor{red}{$\eta$DTW} & \textcolor{red}{36} & VAR(300) & \textcolor{red}{Mid}  & \textcolor{red}{HAveD} & 0.8399 & 0.6485 & 0.7319 & 3574\\
            \textcolor{red}{$\eta$DTW} & \textcolor{red}{36} & LNCE(220) & \textcolor{red}{Mid}  & \textcolor{red}{HAveD} & 0.8399 & 0.6485 & 0.7319 & 3806\\
            \textcolor{red}{$\eta$DTW} & \textcolor{red}{36} & LNCE(240) & \textcolor{red}{Mid}  & \textcolor{red}{HAveD} & 0.8399 & 0.6485 & 0.7319 & 3929
        \end{tabular}
    \end{center}
\end{frame}

\begin{frame}{Evaluation}{Simulations ranked by $F_{\frac{1}{2}}score_{\mu}$}
    \begin{center}
        \resizebox {0.5\textwidth} {!} {
            \begin{tikzpicture}[spy using outlines={circle, magnification=6, connect spies}]
                \begin{axis}[
                    xmin=0,
                    xmax=1,
                    ymin=0,
                    ymax=1,
                    width=\axisdefaultwidth,
                    height=\axisdefaultwidth,
                    xlabel=$Precision_{\mu}$,
                    ylabel=$Recall_{\mu}$,
                    samples=100,
                    colorbar horizontal,
                    colormap/viridis high res,
                    title=$F_{\frac{1}{2}}score_{\mu}$]
                    \addplot[only marks, scatter, scatter src=explicit, mark size=1] table[x=x,y=y,meta=fscore] {../data/fig/result/f0.5score.dat};
                    \addplot[gray, domain=0.081:1] {(0.25 * 0.1 * x) / (1.25 * x - 0.1)};
                    \addplot[gray, domain=0.161:1] {(0.25 * 0.2 * x) / (1.25 * x - 0.2)};
                    \addplot[gray, domain=0.25:1] {(0.25 * 0.3 * x) / (1.25 * x - 0.3)};
                    \addplot[gray, domain=0.33:1] {(0.25 * 0.4 * x) / (1.25 * x - 0.4)};
                    \addplot[gray, domain=0.41:1] {(0.25 * 0.5 * x) / (1.25 * x - 0.5)};
                    \addplot[gray, domain=0.49:1] {(0.25 * 0.6 * x) / (1.25 * x - 0.6)};
                    \addplot[gray, domain=0.57:1] {(0.25 * 0.7 * x) / (1.25 * x - 0.7)};
                    \addplot[gray, domain=0.65:1] {(0.25 * 0.8 * x) / (1.25 * x - 0.8)};
                    \addplot[gray, domain=0.73:1] {(0.25 * 0.9 * x) / (1.25 * x - 0.9)};
                    \coordinate (spypoint) at (axis cs:0.8767619815,0.6115168539);
                    \coordinate (magnifyglass) at (axis cs:0.2,0.8);
                \end{axis}
                \spy [size=2cm] on (spypoint)
                    in node[fill=white] at (magnifyglass);
            \end{tikzpicture}
        }
    \end{center}
\end{frame}

\begin{frame}{Evaluation}{Simulations ranked by $F_{1}score_{\mu}$}
    \begin{center}
        \resizebox {0.5\textwidth} {!} {
            \begin{tikzpicture}[spy using outlines={circle, magnification=6, connect spies}]
                \begin{axis}[
                    xmin=0,
                    xmax=1,
                    ymin=0,
                    ymax=1,
                    width=\axisdefaultwidth,
                    height=\axisdefaultwidth,
                    xlabel=$Precision_{\mu}$,
                    ylabel=$Recall_{\mu}$,
                    samples=100,
                    colorbar horizontal,
                    colormap/viridis high res,
                    title=$F_{1}score_{\mu}$]
                    \addplot[only marks, scatter, scatter src=explicit, mark size=1] table[x=x,y=y,meta=fscore] {../data/fig/result/f1score.dat};
                    \addplot[gray, domain=0.051:1] {(0.1 * x) / (2 * x - 0.1)};
                    \addplot[gray, domain=0.11:1] {(0.2 * x) / (2 * x - 0.2)};
                    \addplot[gray, domain=0.16:1] {(0.3 * x) / (2 * x - 0.3)};
                    \addplot[gray, domain=0.21:1] {(0.4 * x) / (2 * x - 0.4)};
                    \addplot[gray, domain=0.26:1] {(0.5 * x) / (2 * x - 0.5)};
                    \addplot[gray, domain=0.31:1] {(0.6 * x) / (2 * x - 0.6)};
                    \addplot[gray, domain=0.36:1] {(0.7 * x) / (2 * x - 0.7)};
                    \addplot[gray, domain=0.41:1] {(0.8 * x) / (2 * x - 0.8)};
                    \addplot[gray, domain=0.46:1] {(0.9 * x) / (2 * x - 0.9)};
                    \coordinate (spypoint) at (axis cs:0.8413867433,0.6578651685);
                    \coordinate (magnifyglass) at (axis cs:0.2,0.8);
                \end{axis}
                \spy [size=2cm] on (spypoint)
                    in node[fill=white] at (magnifyglass);
            \end{tikzpicture}
        }
    \end{center}
\end{frame}

\begin{frame}{Evaluation}{Simulations ranked by $F_{2}score_{\mu}$}
    \begin{center}
        \resizebox {0.5\textwidth} {!} {
            \begin{tikzpicture}[spy using outlines={circle, magnification=6, connect spies}]
                \begin{axis}[
                    xmin=0,
                    xmax=1,
                    ymin=0,
                    ymax=1,
                    width=\axisdefaultwidth,
                    height=\axisdefaultwidth,
                    xlabel=$Precision_{\mu}$,
                    ylabel=$Recall_{\mu}$,
                    samples=100,
                    colorbar horizontal,
                    colormap/viridis high res,
                    title=$F_{2}score_{\mu}$]
                    \addplot[only marks, scatter, scatter src=explicit, mark size=1] table[x=x,y=y,meta=fscore] {../data/fig/result/f2score.dat};
                    \addplot[gray, domain=0.021:1] {(4 * 0.1 * x) / (5 * x - 0.1)};
                    \addplot[gray, domain=0.041:1] {(4 * 0.2 * x) / (5 * x - 0.2)};
                    \addplot[gray, domain=0.061:1] {(4 * 0.3 * x) / (5 * x - 0.3)};
                    \addplot[gray, domain=0.081:1] {(4 * 0.4 * x) / (5 * x - 0.4)};
                    \addplot[gray, domain=0.101:1] {(4 * 0.5 * x) / (5 * x - 0.5)};
                    \addplot[gray, domain=0.121:1] {(4 * 0.6 * x) / (5 * x - 0.6)};
                    \addplot[gray, domain=0.141:1] {(4 * 0.7 * x) / (5 * x - 0.7)};
                    \addplot[gray, domain=0.17:1] {(4 * 0.8 * x) / (5 * x - 0.8)};
                    \addplot[gray, domain=0.19:1] {(4 * 0.9 * x) / (5 * x - 0.9)};
                    \coordinate (spypoint) at (axis cs:0.8124893942,0.6724719101);
                    \coordinate (magnifyglass) at (axis cs:0.2,0.8);
                \end{axis}
                \spy [size=2cm] on (spypoint)
                    in node[fill=white] at (magnifyglass);
            \end{tikzpicture}
        }
    \end{center}
\end{frame}

\input{experiment/evaluation/influence_of_time_series_distance_measure.tex}
\input{experiment/evaluation/influence_of_window_size_determination.tex}
\input{experiment/evaluation/influence_of_threshold_determination.tex}
\input{experiment/evaluation/influence_of_sliding_window_filter.tex}
\input{experiment/evaluation/differentiation_according_to_gestures.tex}
\input{experiment/evaluation/differentiation_according_to_experimentees.tex}
