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Add noise separation plots

master
Markus Kaiser 2 years ago
parent
commit
96d602819d
  1. 82
      ecml-poster/figures/noise_separation.tex
  2. 32
      ecml-poster/figures/noise_separation_attribution.tex
  3. 22
      ecml-poster/figures/noise_separation_data.tex
  4. 40
      ecml-poster/figures/noise_separation_joint.tex
  5. 7
      ecml-poster/figures/preamble/tikz_style.tex
  6. 50
      ecml-poster/poster.tex

82
ecml-poster/figures/noise_separation.tex

@ -0,0 +1,82 @@
\documentclass[beamer,tikz,crop]{standalone}
\input{preamble/tikz_standalone.tex}
\input{preamble/tikz_common.tex}
\input{preamble/tikz_style.tex}
\input{preamble/tikz_colors.tex}
\input{../abbreviations.tex}
\begin{document}
\begin{tikzpicture}
\def\datapath{\figurepath/data/choicenet_noisy_0.6}
\begin{axis}[
choicenet plot,
xlabel=,
name=p0,
]
\addplot[
data,
] table[
ignore chars={\#}, col sep=space, x=X, y=Y
] {\datapath/Xs_Ys.dat};
\end{axis}
\begin{axis}[
choicenet plot,
at={(p0.outer south east)},
anchor=north east,
xlabel=,
name=p1,
]
\addplot[
data,
mark=triangle*,
first full,
] table[
ignore chars={\#}, col sep=space, x=X, y=Y
] {\datapath/Xs_Ys_attrib_separated_0.dat};
\addplot[
data,
mark=square*,
second full,
] table[
ignore chars={\#}, col sep=space, x=X, y=Y
] {\datapath/Xs_Ys_attrib_separated_1.dat};
\end{axis}
\begin{axis}[
choicenet plot,
at={(p1.outer south east)},
anchor=north east,
name=p2,
]
\addplot[
data,
] table[
ignore chars={\#}, col sep=space, x=X, y=Y
] {\datapath/Xs_Ys.dat};
\addplot[std, second, name path=minus]
table[ignore chars={\#}, col sep=space, x=X, y expr=\thisrow{mu}-0.5 - \thisrow{2std}] {\datapath/Xt_mode_1.dat};
\addplot[std, second, name path=plus]
table[ignore chars={\#}, col sep=space, x=X, y expr=\thisrow{mu}-0.5 + \thisrow{2std}] {\datapath/Xt_mode_1.dat};
\addplot[second fill]
fill between[of=plus and minus];
\addplot[mean, second]
table[ignore chars={\#}, col sep=space, x=X, y expr=\thisrow{mu}-0.5] {\datapath/Xt_mode_1.dat};
\addplot[std, first, name path=plus]
table[ignore chars={\#}, col sep=space, x=X, y expr=\thisrow{mu} + \thisrow{2std}] {\datapath/Xt_mode_0.dat};
\addplot[std, first, name path=minus]
table[ignore chars={\#}, col sep=space, x=X, y expr=\thisrow{mu} - \thisrow{2std}] {\datapath/Xt_mode_0.dat};
\addplot[first fill]
fill between[of=plus and minus];
\addplot[mean, first]
table[ignore chars={\#}, col sep=space, x=X, y=mu] {\datapath/Xt_mode_0.dat};
\end{axis}
\end{tikzpicture}
\end{document}

32
ecml-poster/figures/noise_separation_attribution.tex

@ -0,0 +1,32 @@
\documentclass[beamer,tikz,crop]{standalone}
\input{preamble/tikz_standalone.tex}
\input{preamble/tikz_common.tex}
\input{preamble/tikz_style.tex}
\input{preamble/tikz_colors.tex}
\input{../abbreviations.tex}
\begin{document}
\begin{tikzpicture}
\def\datapath{\figurepath/data/choicenet_noisy_0.6}
\begin{axis}[
choicenet plot,
]
\addplot[
data,
mark=triangle*,
first full,
] table[
ignore chars={\#}, col sep=space, x=X, y=Y
] {\datapath/Xs_Ys_attrib_separated_0.dat};
\addplot[
data,
mark=square*,
second full,
] table[
ignore chars={\#}, col sep=space, x=X, y=Y
] {\datapath/Xs_Ys_attrib_separated_1.dat};
\end{axis}
\end{tikzpicture}
\end{document}

22
ecml-poster/figures/noise_separation_data.tex

@ -0,0 +1,22 @@
\documentclass[beamer,tikz,crop]{standalone}
\input{preamble/tikz_standalone.tex}
\input{preamble/tikz_common.tex}
\input{preamble/tikz_style.tex}
\input{preamble/tikz_colors.tex}
\input{../abbreviations.tex}
\begin{document}
\begin{tikzpicture}
\def\datapath{\figurepath/data/choicenet_noisy_0.6}
\begin{axis}[
choicenet plot,
]
\addplot[
data,
] table[
ignore chars={\#}, col sep=space, x=X, y=Y
] {\datapath/Xs_Ys.dat};
\end{axis}
\end{tikzpicture}
\end{document}

40
ecml-poster/figures/noise_separation_joint.tex

@ -0,0 +1,40 @@
\documentclass[beamer,tikz,crop]{standalone}
\input{preamble/tikz_standalone.tex}
\input{preamble/tikz_common.tex}
\input{preamble/tikz_style.tex}
\input{preamble/tikz_colors.tex}
\input{../abbreviations.tex}
\begin{document}
\begin{tikzpicture}
\def\datapath{\figurepath/data/choicenet_noisy_0.6}
\begin{axis}[
choicenet plot,
]
\addplot[
data,
] table[
ignore chars={\#}, col sep=space, x=X, y=Y
] {\datapath/Xs_Ys.dat};
\addplot[std, second, name path=minus]
table[ignore chars={\#}, col sep=space, x=X, y expr=\thisrow{mu}-0.5 - \thisrow{2std}] {\datapath/Xt_mode_1.dat};
\addplot[std, second, name path=plus]
table[ignore chars={\#}, col sep=space, x=X, y expr=\thisrow{mu}-0.5 + \thisrow{2std}] {\datapath/Xt_mode_1.dat};
\addplot[second fill]
fill between[of=plus and minus];
\addplot[mean, second]
table[ignore chars={\#}, col sep=space, x=X, y expr=\thisrow{mu}-0.5] {\datapath/Xt_mode_1.dat};
\addplot[std, first, name path=plus]
table[ignore chars={\#}, col sep=space, x=X, y expr=\thisrow{mu} + \thisrow{2std}] {\datapath/Xt_mode_0.dat};
\addplot[std, first, name path=minus]
table[ignore chars={\#}, col sep=space, x=X, y expr=\thisrow{mu} - \thisrow{2std}] {\datapath/Xt_mode_0.dat};
\addplot[first fill]
fill between[of=plus and minus];
\addplot[mean, first]
table[ignore chars={\#}, col sep=space, x=X, y=mu] {\datapath/Xt_mode_0.dat};
\end{axis}
\end{tikzpicture}
\end{document}

7
ecml-poster/figures/preamble/tikz_style.tex

@ -14,7 +14,7 @@
\pgfplotsset{data/.style = {
only marks,
mark size=1.5pt,
mark size=2pt,
% draw opacity=.75,
fill opacity=.5,
sStoneDark,
@ -39,8 +39,13 @@
\pgfplotsset{choicenet plot/.style = {
model plot,
width=.8\textwidth,
height=175pt,
clip mode=individual,
xlabel=$\rv{X}$, ylabel=$\rv{y}$,
ymin=-1.5, ymax=3.5,
xtick={-3, 0, 3},
ytick={-1, 1, 3},
}}
\pgfplotsset{multimodal plot/.style = {

50
ecml-poster/poster.tex

@ -94,39 +94,32 @@
\centering
\includestandalonewithpath{figures/semi_bimodal}
\end{figure}
\begin{itemize}
\item AMO-GP correctly recovers the latent shared function, warping and alignment
\end{itemize}
\end{block}
\begin{block}{Noise Separation}
\begin{figure}
\centering
\begin{subfigure}{.45\textwidth}
\centering
% \includestandalonewithpath{figures/toy_decomposition_shallow_gp}
\caption{
Shallow GP with RBF kernel
}
\end{subfigure}
\hspace{1ex}
\begin{subfigure}{.45\textwidth}
\begin{subfigure}{\textwidth}
\centering
% \includestandalonewithpath{figures/toy_decomposition_mo_gp}
\includestandalonewithpath{figures/noise_separation_data}
\caption{
Multi-Output GP with dependent RBF kernel
Data
}
\end{subfigure}
\\[\baselineskip]
\begin{subfigure}{.45\textwidth}
\end{subfigure}\\[2ex]
\begin{subfigure}{\textwidth}
\centering
% \includestandalonewithpath{figures/toy_decomposition_dgp}
\includestandalonewithpath{figures/noise_separation_attribution}
\caption{
Deep GP with RBF kernels
Attribution
}
\end{subfigure}
\hspace{1ex}
\begin{subfigure}{.45\textwidth}
\end{subfigure}\\[2ex]
\begin{subfigure}{\textwidth}
\centering
% \includestandalonewithpath{figures/toy_decomposition_ours}
\includestandalonewithpath{figures/noise_separation_joint}
\caption{
\structure{\textbf{AMO-GP with (dependent) RBF kernels}}
Joint
}
\end{subfigure}
\end{figure}
@ -193,18 +186,18 @@
\separatorcolumn
%
\begin{column}{\colwidth}
\begin{block}{Test}
Test
\begin{block}{Wet-Chicken RL Benchmark}
\centering
\includestandalonewithpath{figures/wetchicken}
\end{block}
% \begin{block}{Wet-Chicken RL Benchmark}
% \centering
% \includestandalonewithpath{figures/wetchicken}
% \end{block}
% \begin{block}{Interpretable Transition Model}
% \begin{figure}
% \centering
% \includestandalonewithpath{figures/dynamics_posterior}
% \end{figure}
% \begin{itemize}
% \item AMO-GP correctly recovers the latent shared function, warping and alignment
% \end{itemize}
% \end{block}
% \begin{block}{Conservative Policy}
% \begin{figure}
@ -225,6 +218,9 @@
% }
% \end{subfigure}
% \end{figure}
% \begin{itemize}
% \item AMO-GP correctly recovers the latent shared function, warping and alignment
% \end{itemize}
% \end{block}
\end{column}
%

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