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@@ -2,6 +2,7 @@ FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime
|
||||
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||||
RUN apt-get update
|
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RUN apt-get install -y git
|
||||
RUN apt-get install texlive-latex-base texlive-fonts-recommended texlive-fonts-extra texlive-bibtex-extra
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||||
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||||
COPY requirements.txt /tmp/requirements.txt
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||||
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||||
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||||
BIN
Reports/February/MP2324_verslag2_Mylle_Victor.pdf
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Reports/February/MP2324_verslag2_Mylle_Victor.pdf
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Reports/February/ea-en.pdf
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Reports/February/ea-en.pdf
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Reports/February/ea-nl.pdf
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Reports/February/ea-nl.pdf
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Reports/February/eb-en.pdf
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Reports/February/eb-en.pdf
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Reports/February/ugent-doc.cls
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Reports/February/ugent-doc.cls
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@@ -0,0 +1,185 @@
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%
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||||
% Ghent University document class
|
||||
% Created by DF Benoit, December 15, 2022
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%
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||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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||||
|
||||
\NeedsTeXFormat{LaTeX2e}
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||||
\ProvidesClass{ugent-doc}
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||||
|
||||
% Required packages
|
||||
\RequirePackage{kvoptions}
|
||||
\RequirePackage{geometry}
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||||
\RequirePackage{calc}
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||||
\RequirePackage{graphicx}
|
||||
\RequirePackage{xcolor}
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||||
|
||||
% ugent-doc specific options (kvoptions)
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||||
\SetupKeyvalOptions{family=ugd,prefix=ugd@} %UGentArticle
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|
||||
% Declare the class specific options
|
||||
\DeclareStringOption[eb]{faculty}[eb]
|
||||
\DeclareStringOption[en]{language}[en]
|
||||
\DeclareStringOption[article]{doctype}[article]
|
||||
\DeclareBoolOption[true]{sftitles} % Default: true
|
||||
\ProcessKeyvalOptions*
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||||
|
||||
% Pass options not specified above to the parent class
|
||||
% \@unusedoptionlist is a macro in kvoptions
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||||
\LoadClass[\@unusedoptionlist]{\ugd@doctype}
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||||
|
||||
% All sections, subsections and subsubsections in sans serif
|
||||
\ifugd@sftitles
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||||
\RequirePackage[sf]{titlesec}
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||||
\fi
|
||||
|
||||
% Define UGent colors
|
||||
%= = = = = = = = = = =
|
||||
% Base colors
|
||||
% UGent blue
|
||||
\definecolor{ugentblue}{RGB}{30,100,200}
|
||||
% UGent yellow
|
||||
\definecolor{ugentyellow}{RGB}{255,210,0}
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||||
% UGent white
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||||
\definecolor{ugentwhite}{RGB}{255,255,255}
|
||||
% UGent black
|
||||
\definecolor{ugentblack}{RGB}{0,0,0}
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||||
|
||||
% Faculty specific colors
|
||||
% Faculty of Literature & Philosophy
|
||||
\definecolor{ugent-lw}{RGB}{241,164,43}
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||||
% Faculty of Law
|
||||
\definecolor{ugent-re}{RGB}{220,78,40}
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||||
% Faculty of Science
|
||||
\definecolor{ugent-we}{RGB}{45,140,168}
|
||||
% Faculty of Medicine and Health Sciences
|
||||
\definecolor{ugent-ge}{RGB}{232,94,113}
|
||||
% Faculty of Engineering and Architecture
|
||||
\definecolor{ugent-ea}{RGB}{139,190,232}
|
||||
% Faculty of Economics and Business Administration
|
||||
\definecolor{ugent-eb}{RGB}{174,176,80}
|
||||
% Faculty of Veterinary Medicine
|
||||
\definecolor{ugent-di}{RGB}{130,84,145}
|
||||
% Faculty of Psychology and Educational Sciences
|
||||
\definecolor{ugent-pp}{RGB}{251,126,58}
|
||||
% Faculty of Bioscience Engineering
|
||||
\definecolor{ugent-bw}{RGB}{39,171,173}
|
||||
% Faculty of Pharmaceutical Sciences
|
||||
\definecolor{ugent-fw}{RGB}{190,81,144}
|
||||
% Faculty of Political and Social Sciences
|
||||
\definecolor{ugent-ps}{RGB}{113,168,96}
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||||
|
||||
% Define new commands
|
||||
\def\thetitle#1{\def\@thetitle{#1}}
|
||||
\def\thesubtitle#1{\def\@thesubtitle{#1}}
|
||||
\def\infoboxa#1{\def\@infoboxa{#1}}
|
||||
\def\infoboxb#1{\def\@infoboxb{#1}}
|
||||
\def\infoboxc#1{\def\@infoboxc{#1}}
|
||||
\def\infoboxd#1{\def\@infoboxd{#1}}
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||||
|
||||
% Initialize new commands as 'empty'
|
||||
\def\@thetitle{}
|
||||
\def\@thesubtitle{}
|
||||
\def\@infoboxa{}
|
||||
\def\@infoboxb{}
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||||
\def\@infoboxc{}
|
||||
\def\@infoboxd{}
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||||
|
||||
% Define lengths based on UGent document grid
|
||||
% See: https://styleguide.ugent.be/basic-principles/grid-and-layout.html
|
||||
\newlength{\longedge}
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||||
\setlength{\longedge}{\maxof{\paperheight}{\paperwidth}}
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||||
\newlength{\gridunit}
|
||||
\setlength{\gridunit}{\longedge/28} %Divide long edge by 7 and next by 4
|
||||
\newlength{\subpaperheight}
|
||||
\setlength{\subpaperheight}{\paperheight-7\gridunit} %Type area: 3 units for faculty logo, 4 units for UGent logo
|
||||
\newlength{\subpaperwidth}
|
||||
\setlength{\subpaperwidth}{\paperwidth-\gridunit} %Left margin of 1 gridunit
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||||
|
||||
% Define strut based on \gridunit
|
||||
\newcommand{\mystrut}[1][-.5]{\rule[#1\gridunit]{0pt}{0pt}}
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||||
|
||||
% Set default page layout
|
||||
% Can be overwritten in preamble of document
|
||||
\renewcommand{\baselinestretch}{1.15} % line spacing
|
||||
\geometry{bottom=2.5cm,top=2.5cm,left=3cm,right=2cm} % margins
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||||
|
||||
% Redefine the titlepage in accordance with UGent styleguide
|
||||
\renewcommand\maketitle{\begin{titlepage}%
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||||
\thispagestyle{empty} % by default, the pagestyle of title page is plain
|
||||
\newgeometry{top=0cm, bottom=0cm, left=0cm, right=0cm} % set special margins
|
||||
\setlength{\parindent}{0cm} % necessary to put minipages/boxes at extreme left of page
|
||||
\setlength{\parsep}{0cm} % necessary to stack minipages/boxes without space
|
||||
\setlength{\fboxsep}{0cm} % no border around minipages/boxes
|
||||
\setlength{\parskip}{0cm}
|
||||
\setlength{\lineskip}{0cm}
|
||||
|
||||
\ifugd@sftitles
|
||||
\sffamily % Titlepage in sans serif font
|
||||
\fi
|
||||
|
||||
\includegraphics[height=3\gridunit]{\ugd@faculty-\ugd@language.pdf}%
|
||||
|
||||
\makebox[\gridunit]{}% Left margin of 1 gridunit
|
||||
\colorbox{ugent-\ugd@faculty!30}{%
|
||||
%\colorbox{ugentwhite}{%
|
||||
\begin{minipage}[c][\subpaperheight][t]{\subpaperwidth}%
|
||||
\vskip 5\gridunit % top margin within minipage
|
||||
\hskip \gridunit % left margin of 1 within the colorbox
|
||||
%\fbox{%
|
||||
\begin{minipage}{\subpaperwidth-2\gridunit} % tile minipage, right margin of 1
|
||||
\raggedright\bfseries\huge
|
||||
\textcolor{ugentblue}{\mystrut\@thetitle}\newline
|
||||
\Large\textcolor{ugentblue}{\@thesubtitle}
|
||||
\mystrut[1]
|
||||
\end{minipage}%}
|
||||
|
||||
\vskip\fill % Push down to bottom of minipage
|
||||
|
||||
\ifx\@infoboxa\empty\else % ony put box if not empty
|
||||
\hskip\gridunit % left margin of infobox
|
||||
%\fbox{%
|
||||
\begin{minipage}[b]{\subpaperwidth-3\gridunit} % right margin of 1
|
||||
\@infoboxa
|
||||
\end{minipage}%}
|
||||
|
||||
\baselineskip0pt\mystrut
|
||||
\fi
|
||||
|
||||
\ifx\@infoboxb\empty\else % ony put box if not empty
|
||||
\hskip\gridunit % left margin of infobox
|
||||
%\fbox{%
|
||||
\begin{minipage}[b]{\subpaperwidth-3\gridunit} % right margin of 1
|
||||
\@infoboxb
|
||||
\end{minipage}%}
|
||||
|
||||
\baselineskip0pt\mystrut
|
||||
\fi
|
||||
|
||||
\ifx\@infoboxc\empty\else % ony put box if not empty
|
||||
\hskip\gridunit % left margin of infobox
|
||||
%\fbox{%
|
||||
\begin{minipage}[b]{\subpaperwidth-3\gridunit} % right margin of 1
|
||||
\@infoboxc
|
||||
\end{minipage}%}
|
||||
|
||||
\baselineskip0pt\mystrut
|
||||
\fi
|
||||
|
||||
\ifx\@infoboxd\empty\else % ony put box if not empty
|
||||
\hskip\gridunit % left margin of infobox
|
||||
%\fbox{%
|
||||
\begin{minipage}[b]{\subpaperwidth-3\gridunit} % right margin of 1
|
||||
\@infoboxd
|
||||
\end{minipage}%}
|
||||
\fi
|
||||
|
||||
\baselineskip0pt\mystrut[-1]
|
||||
\end{minipage}
|
||||
}%
|
||||
|
||||
\includegraphics[height=4\gridunit]{ugent-\ugd@language.pdf}%
|
||||
\end{titlepage}
|
||||
\restoregeometry
|
||||
}
|
||||
BIN
Reports/February/ugent-en.pdf
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BIN
Reports/February/ugent-en.pdf
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Reports/February/verslag.aux
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Reports/February/verslag.aux
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@@ -0,0 +1,34 @@
|
||||
\relax
|
||||
\providecommand\hyper@newdestlabel[2]{}
|
||||
\@nameuse{bbl@beforestart}
|
||||
\abx@aux@refcontext{nyt/global//global/global}
|
||||
\providecommand\HyperFirstAtBeginDocument{\AtBeginDocument}
|
||||
\HyperFirstAtBeginDocument{\ifx\hyper@anchor\@undefined
|
||||
\global\let\oldcontentsline\contentsline
|
||||
\gdef\contentsline#1#2#3#4{\oldcontentsline{#1}{#2}{#3}}
|
||||
\global\let\oldnewlabel\newlabel
|
||||
\gdef\newlabel#1#2{\newlabelxx{#1}#2}
|
||||
\gdef\newlabelxx#1#2#3#4#5#6{\oldnewlabel{#1}{{#2}{#3}}}
|
||||
\AtEndDocument{\ifx\hyper@anchor\@undefined
|
||||
\let\contentsline\oldcontentsline
|
||||
\let\newlabel\oldnewlabel
|
||||
\fi}
|
||||
\fi}
|
||||
\global\let\hyper@last\relax
|
||||
\gdef\HyperFirstAtBeginDocument#1{#1}
|
||||
\providecommand\HyField@AuxAddToFields[1]{}
|
||||
\providecommand\HyField@AuxAddToCoFields[2]{}
|
||||
\@writefile{toc}{\boolfalse {citerequest}\boolfalse {citetracker}\boolfalse {pagetracker}\boolfalse {backtracker}\relax }
|
||||
\@writefile{lof}{\boolfalse {citerequest}\boolfalse {citetracker}\boolfalse {pagetracker}\boolfalse {backtracker}\relax }
|
||||
\@writefile{lot}{\boolfalse {citerequest}\boolfalse {citetracker}\boolfalse {pagetracker}\boolfalse {backtracker}\relax }
|
||||
\babel@aux{english}{}
|
||||
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {1}Intermediate Results}{1}{section.1}\protected@file@percent }
|
||||
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {1.1}Net Regulation Volume Modeling}{1}{subsection.1.1}\protected@file@percent }
|
||||
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsubsection}{\numberline {1.1.1}Input Features}{1}{subsubsection.1.1.1}\protected@file@percent }
|
||||
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsubsection}{\numberline {1.1.2}Models}{1}{subsubsection.1.1.2}\protected@file@percent }
|
||||
\@writefile{lot}{\defcounter {refsection}{0}\relax }\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces Performance of Autoregressive Models}}{1}{table.1}\protected@file@percent }
|
||||
\newlabel{tab:general_models}{{1}{1}{Performance of Autoregressive Models}{table.1}{}}
|
||||
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsubsection}{\numberline {1.1.3}Charging Policy}{2}{subsubsection.1.1.3}\protected@file@percent }
|
||||
\@writefile{lot}{\defcounter {refsection}{0}\relax }\@writefile{lot}{\contentsline {table}{\numberline {2}{\ignorespaces Comparison of Energy Storage Policies Using Predicted NRV. Battery of 2MWh with 1MW charge/discharge power. Evaluated on data from 01-01-2023 until 08-10-2023.}}{2}{table.2}\protected@file@percent }
|
||||
\newlabel{table:energy_storage_policies}{{2}{2}{Comparison of Energy Storage Policies Using Predicted NRV. Battery of 2MWh with 1MW charge/discharge power. Evaluated on data from 01-01-2023 until 08-10-2023}{table.2}{}}
|
||||
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {2}Schedule next months}{3}{section.2}\protected@file@percent }
|
||||
2592
Reports/February/verslag.bcf
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2592
Reports/February/verslag.bcf
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Load Diff
1000
Reports/February/verslag.log
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1000
Reports/February/verslag.log
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Load Diff
6
Reports/February/verslag.out
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6
Reports/February/verslag.out
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@@ -0,0 +1,6 @@
|
||||
\BOOKMARK [1][-]{section.1}{Intermediate Results}{}% 1
|
||||
\BOOKMARK [2][-]{subsection.1.1}{Net Regulation Volume Modeling}{section.1}% 2
|
||||
\BOOKMARK [3][-]{subsubsection.1.1.1}{Input Features}{subsection.1.1}% 3
|
||||
\BOOKMARK [3][-]{subsubsection.1.1.2}{Models}{subsection.1.1}% 4
|
||||
\BOOKMARK [3][-]{subsubsection.1.1.3}{Charging Policy}{subsection.1.1}% 5
|
||||
\BOOKMARK [1][-]{section.2}{Schedule next months}{}% 6
|
||||
BIN
Reports/February/verslag.pdf
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BIN
Reports/February/verslag.pdf
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Reports/February/verslag.run.xml
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Reports/February/verslag.run.xml
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|
||||
<?xml version="1.0" standalone="yes"?>
|
||||
<!-- logreq request file -->
|
||||
<!-- logreq version 1.0 / dtd version 1.0 -->
|
||||
<!-- Do not edit this file! -->
|
||||
<!DOCTYPE requests [
|
||||
<!ELEMENT requests (internal | external)*>
|
||||
<!ELEMENT internal (generic, (provides | requires)*)>
|
||||
<!ELEMENT external (generic, cmdline?, input?, output?, (provides | requires)*)>
|
||||
<!ELEMENT cmdline (binary, (option | infile | outfile)*)>
|
||||
<!ELEMENT input (file)+>
|
||||
<!ELEMENT output (file)+>
|
||||
<!ELEMENT provides (file)+>
|
||||
<!ELEMENT requires (file)+>
|
||||
<!ELEMENT generic (#PCDATA)>
|
||||
<!ELEMENT binary (#PCDATA)>
|
||||
<!ELEMENT option (#PCDATA)>
|
||||
<!ELEMENT infile (#PCDATA)>
|
||||
<!ELEMENT outfile (#PCDATA)>
|
||||
<!ELEMENT file (#PCDATA)>
|
||||
<!ATTLIST requests
|
||||
version CDATA #REQUIRED
|
||||
>
|
||||
<!ATTLIST internal
|
||||
package CDATA #REQUIRED
|
||||
priority (9) #REQUIRED
|
||||
active (0 | 1) #REQUIRED
|
||||
>
|
||||
<!ATTLIST external
|
||||
package CDATA #REQUIRED
|
||||
priority (1 | 2 | 3 | 4 | 5 | 6 | 7 | 8) #REQUIRED
|
||||
active (0 | 1) #REQUIRED
|
||||
>
|
||||
<!ATTLIST provides
|
||||
type (static | dynamic | editable) #REQUIRED
|
||||
>
|
||||
<!ATTLIST requires
|
||||
type (static | dynamic | editable) #REQUIRED
|
||||
>
|
||||
<!ATTLIST file
|
||||
type CDATA #IMPLIED
|
||||
>
|
||||
]>
|
||||
<requests version="1.0">
|
||||
<internal package="biblatex" priority="9" active="0">
|
||||
<generic>latex</generic>
|
||||
<provides type="dynamic">
|
||||
<file>verslag.bcf</file>
|
||||
</provides>
|
||||
<requires type="dynamic">
|
||||
<file>verslag.bbl</file>
|
||||
</requires>
|
||||
<requires type="static">
|
||||
<file>blx-dm.def</file>
|
||||
<file>apa.dbx</file>
|
||||
<file>blx-compat.def</file>
|
||||
<file>biblatex.def</file>
|
||||
<file>standard.bbx</file>
|
||||
<file>apa.bbx</file>
|
||||
<file>apa.cbx</file>
|
||||
<file>biblatex.cfg</file>
|
||||
<file>english.lbx</file>
|
||||
<file>american.lbx</file>
|
||||
<file>american-apa.lbx</file>
|
||||
<file>english-apa.lbx</file>
|
||||
</requires>
|
||||
</internal>
|
||||
<external package="biblatex" priority="5" active="0">
|
||||
<generic>biber</generic>
|
||||
<cmdline>
|
||||
<binary>biber</binary>
|
||||
<infile>verslag</infile>
|
||||
</cmdline>
|
||||
<input>
|
||||
<file>verslag.bcf</file>
|
||||
</input>
|
||||
<output>
|
||||
<file>verslag.bbl</file>
|
||||
</output>
|
||||
<provides type="dynamic">
|
||||
<file>verslag.bbl</file>
|
||||
</provides>
|
||||
<requires type="dynamic">
|
||||
<file>verslag.bcf</file>
|
||||
</requires>
|
||||
<requires type="editable">
|
||||
<file>./references.bib</file>
|
||||
</requires>
|
||||
</external>
|
||||
</requests>
|
||||
BIN
Reports/February/verslag.synctex.gz
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BIN
Reports/February/verslag.synctex.gz
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Reports/February/verslag.tex
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Reports/February/verslag.tex
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|
||||
\documentclass[12pt,a4paper,faculty=ea,language=en,doctype=article]{ugent-doc}
|
||||
|
||||
% Optional: margins and spacing
|
||||
%-------------------------------
|
||||
% Uncomment and adjust to change the default values set by the template
|
||||
% Note: the defaults are suggested values by Ghent University
|
||||
%\geometry{bottom=2.5cm,top=2.5cm,left=3cm,right=2cm}
|
||||
%\renewcommand{\baselinestretch}{1.15} % line spacing
|
||||
|
||||
% Font
|
||||
%------
|
||||
\usepackage[T1]{fontenc}
|
||||
\usepackage[utf8]{inputenc} % allows non-ascii input characters
|
||||
% Comment or remove the two lines below to use the default Computer Modern font
|
||||
\usepackage{libertine}
|
||||
\usepackage{libertinust1math}
|
||||
\usepackage{enumitem}
|
||||
% NOTE: because the UGent font Panno is proprietary, it is not possible to use it
|
||||
% in Overleaf. But UGent does not suggest to use Panno for documents (or maybe only for
|
||||
% the titlepage). For the body, the UGent suggestion is to use a good serif font (for
|
||||
% LaTeX this could be libertine or Computer Modern).
|
||||
|
||||
% Proper word splitting
|
||||
%-----------------------
|
||||
\usepackage[english]{babel}
|
||||
|
||||
% Mathematics
|
||||
%-------------
|
||||
\usepackage{amsmath}
|
||||
|
||||
% Figures
|
||||
%---------
|
||||
\usepackage{graphicx} % optional: the package is already loaded by the template
|
||||
\graphicspath{{./figures/}}
|
||||
|
||||
% Bibliography settings
|
||||
%-----------------------
|
||||
\usepackage[backend=biber, style=apa, sorting=nyt, hyperref=true]{biblatex}
|
||||
\addbibresource{./references.bib}
|
||||
\usepackage{csquotes} % Suggested when using babel+biblatex
|
||||
|
||||
% Hyperreferences
|
||||
%-----------------
|
||||
\usepackage[colorlinks=true, allcolors=ugentblue]{hyperref}
|
||||
|
||||
% Whitespace between paragraphs and no indentation
|
||||
%--------------------------------------------------
|
||||
\usepackage[parfill]{parskip}
|
||||
|
||||
% Input for title page
|
||||
%----------------------
|
||||
|
||||
% The title
|
||||
\thesubtitle{February Intermediate Report}
|
||||
|
||||
%% Note: a stricter UGent style could be achieved with, e.g.:
|
||||
\usepackage{ulem} % for colored underline
|
||||
\renewcommand{\ULthickness}{2pt} % adjust thickness of underline
|
||||
\thetitle{Forecasting and generative modeling of the Belgian electricity market}
|
||||
% Note: do not forget to reset the \ULthickness to 1pt after invoking \maketitle
|
||||
% (otherwise all underlines in the rest of your document will be too thick):
|
||||
%\renewcommand{\ULthickness}{1pt}
|
||||
|
||||
% The first (top) infobox at bottom of titlepage
|
||||
\infoboxa{\bfseries\large Master Thesis}
|
||||
|
||||
% The second infobox at bottom of titlepage
|
||||
\infoboxb{Name:
|
||||
\begin{tabular}[t]{l}
|
||||
Victor Mylle
|
||||
\end{tabular}
|
||||
}
|
||||
|
||||
% The third infobox at bottom of titlepage
|
||||
\infoboxc{
|
||||
Promotors:
|
||||
\begin{tabular}[t]{l}
|
||||
prof. dr. ir. Chris Develder \\
|
||||
prof. Bert Claessens
|
||||
\end{tabular}
|
||||
\\\\
|
||||
Supervisor:
|
||||
\begin{tabular}[t]{l}
|
||||
Jonas Van Gompel
|
||||
\end{tabular}
|
||||
}
|
||||
|
||||
% The last (bottom) infobox at bottom of titlepage
|
||||
\infoboxd{Academic year: 2023--2024} % note dash, not hyphen
|
||||
|
||||
|
||||
\begin{document}
|
||||
|
||||
% =====================================================================
|
||||
% Cover
|
||||
% =====================================================================
|
||||
|
||||
% ------------ TITLE PAGE ---------
|
||||
\maketitle
|
||||
\renewcommand{\ULthickness}{1pt}
|
||||
|
||||
% =====================================================================
|
||||
% Front matter
|
||||
% =====================================================================
|
||||
|
||||
% ------------ TABLE OF CONTENTS ---------
|
||||
% {\hypersetup{hidelinks}\tableofcontents} % hide link color in toc
|
||||
% \newpage
|
||||
% \begin{titlepage}
|
||||
|
||||
% \centering % Centers everything on the page
|
||||
|
||||
% % Logo or Image (Optional)
|
||||
% % \includegraphics[width=0.5\textwidth]{path_to_logo.jpg}
|
||||
|
||||
% \vspace*{2cm} % Add vertical space
|
||||
|
||||
% {\large Title: Forecasting and generative modeling of the Belgian electricity market\par}
|
||||
|
||||
% \vspace{2cm}
|
||||
% {\Large Victor Mylle\par}
|
||||
|
||||
% \vspace{1cm}
|
||||
% {\large Period of Internship: 3 July 2023 - 31 August 2023\par}
|
||||
|
||||
% \vspace{1cm}
|
||||
% {\large Mentor: dr. ir. Femke De Backere\par}
|
||||
% {\large TechWolf supervisor: ir. Jens-Joris Decorte}
|
||||
|
||||
% \end{titlepage}
|
||||
|
||||
\newpage
|
||||
|
||||
\section{Intermediate Results}
|
||||
|
||||
\subsection{Net Regulation Volume Modeling}
|
||||
Using a generative model, we try to predict the NRV for the next day. The model is trained on historical data and uses multiple input features to model the NRV. The data for the input features can all be downloaded from \href{https://www.elia.be/en/grid-data/open-data}{Elia Open Data}.
|
||||
|
||||
\subsubsection{Input Features}
|
||||
The generative model uses multiple input features to predict the NRV.
|
||||
|
||||
\begin{itemize}[noitemsep]
|
||||
\item NRV History (NRV of yesterday)
|
||||
\item Load Forecast (Forecasted load of tomorrow)
|
||||
\item Load History (Load of yesterday)
|
||||
\item Wind Forecast (Forecasted wind of tomorrow)
|
||||
\item Wind History (Wind of yesterday)
|
||||
\item Implicit net position (Nominal net position of tomorrow)
|
||||
\item Time features (Day of the week + quarter of the day)
|
||||
\item Photovoltaic Forecast\textsuperscript{*}
|
||||
\item Photovoltaic History\textsuperscript{*}
|
||||
\end{itemize}
|
||||
\textsuperscript{*} These features are not used currently, the data was not available. These features can easily be added without changing any code.
|
||||
|
||||
\subsubsection{Models}
|
||||
In the intermediate report of November, baselines were discussed. Now, other more advanced models are used. Samples must be generated using the model, this means the model can't just output one value but a distribution is needed. Quantile Regression can be used for this task. The model then outputs the values of multiple quantiles. For example, the model outputs the value for which 10\% of the data is lower, the value for which 50\% of the data is lower, etc. This way, the model outputs a distribution which can be used to sample from. The NRV predicitons are done in a quarter-hourly resolution. To predict the NRV for the next day, 96 values need to be sampled. This can be done in an autoregressive manner. The model outputs the quantiles for the first quarter-hour, a sample is drawn from this distribution and this sample is used as input for the next quarter-hour. This process is repeated 96 times.
|
||||
|
||||
\begin{table}[h]
|
||||
\centering
|
||||
\begin{tabular}{lcc}
|
||||
\hline
|
||||
\textbf{Model} & \textbf{test\_L1Loss} & \textbf{test\_CRPSLoss} \\
|
||||
\hline
|
||||
Linear Model & 101.639 & 68.485 \\
|
||||
Non Linear Model & 102.031 & 68.968 \\
|
||||
LSTM/GRU Model & 104.261 & 66.052 \\
|
||||
\hline
|
||||
\end{tabular}
|
||||
\caption{Performance of Autoregressive Models}
|
||||
\label{tab:general_models}
|
||||
\end{table}
|
||||
|
||||
At the moment, I am experimenting with a diffusion model to generatively model the NRV but more research and expermimenting needs to be done.
|
||||
|
||||
\subsubsection{Charging Policy}
|
||||
Using the predicted NRV, a policy can be implemented to charge and discharge a battery. The goal of the policy is to maximize the profit made by selling the stored electricity. A simple policy is implemented to charge and discharge the battery based on 2 thresholds determined by the predicted NRV. The policy is evaluated on historical data and the profit is calculated. To determine the charge and discharge threshold, 1000 full NRV predictions are done for the next day and for each of these predicitions, the thresholds are determined. Next, the mean of these thresholds is used as the final threshold.
|
||||
|
||||
\begin{table}[h]
|
||||
\centering
|
||||
\begin{tabular}{lccc}
|
||||
\hline
|
||||
\textbf{Policy} & \textbf{Total Profit (€)} & \textbf{Charge Cycles} \\
|
||||
\hline
|
||||
Baseline (charge: €150, discharge: €175) & 251,202.59 & 725 \\
|
||||
Baseline (yesterday imbalance price) & 342,980.09 & 903 \\
|
||||
GRU Predicted NRV (mean thresholds) & 339,846.91 & 842 \\
|
||||
Diffusion Predicted NRV (mean thresholds) & 338,168.03 & 886 \\
|
||||
\hline
|
||||
\end{tabular}
|
||||
\caption{Comparison of Energy Storage Policies Using Predicted NRV. Battery of 2MWh with 1MW charge/discharge power. Evaluated on data from 01-01-2023 until 08-10-2023.}
|
||||
\label{table:energy_storage_policies}
|
||||
\end{table}
|
||||
|
||||
The recommended charge cycles for a battery is <400 cycles per year. The policy also needs to take this into account. A penalty parameter can be introduced and determined so that the policy is penalized for every charge cycle above 400. The policy can then be optimized using this penalty parameter. I am currenlty experimenting with this.
|
||||
|
||||
|
||||
\newpage
|
||||
\section{Schedule next months}
|
||||
\begin{itemize}
|
||||
\item Baselines with penalties for charge cycles above 400
|
||||
\item Better visualizations of the policy profit results.
|
||||
\item Case studies of days with extreme thresholds
|
||||
|
||||
\item Finetuning of models and hyperparametres based on model errors and profits of the policy
|
||||
\item Ablation study of input features
|
||||
|
||||
\item Experiment further with diffusion models
|
||||
|
||||
\item During the experimenting, I will write my thesis and update the results and conclusions chapters.
|
||||
\end{itemize}
|
||||
|
||||
\end{document}
|
||||
|
||||
@@ -144,5 +144,61 @@ Test data: 01-01-2023 until 08-10–2023
|
||||
- [x] Profit penalty parameter als over charge cycles voor een dag -> parameter bepalen op training data (convex probleem) (< 400 charge cycles per jaar) (over een dag kijken hoeveel charge cycles -> profit - penalty * charge cycles erover, (misschien belonen als eronder charge cycles))
|
||||
|
||||
- [ ] Meer verschil bekijken tussen GRU en diffusion
|
||||
- [ ] Andere lagen voor diffusion model (GRU, kijken naar TSDiff)
|
||||
- [x] Policies met andere modellen (Linear, Non Linear)
|
||||
- [ ] (In Progress) Andere lagen voor diffusion model (GRU, kijken naar TSDiff)
|
||||
- [x] Policies met andere modellen (Linear, Non Linear)
|
||||
|
||||
- [ ] Visualize the policies over the whole testr set -> thresholds plotten voor elke dag (elke policy) -> mss distribution om overzichtelijk te houden (mean and std)
|
||||
- [ ] Probleem met diffusion model (activation function? waarom direct grote waardes?)
|
||||
|
||||
- [ ] Autoregressive confidence problem -> Quantiles zelf uit elkaar halen (helpt dit?)
|
||||
|
||||
- [ ] time steps reducing for diffusion model (UNet activation functions?)
|
||||
|
||||
- [ ] (State space model? S4)
|
||||
|
||||
|
||||
|
||||
TODO:
|
||||
- [x] diffusion model oefening generative models vragen -> geen lab hierop
|
||||
- [ ] Non autoregressive models policy testen (Non Linear eerst) -> als dit al slect, niet verder kijken, wel vermelden
|
||||
- [ ] Policy in test set -> over charge cycles (stop trading electricity)
|
||||
|
||||
- [ ] penalty bepalen op training data
|
||||
|
||||
- [ ] cycles en profit herschalen naar per jaar
|
||||
|
||||
|
||||
baseline -> NRV van gisteren gebruiken om thresholds te bepalen voor vandaag
|
||||
andere policies -> NRV van vandaag voorspellen met model en thresholds bepalen voor vandaag
|
||||
|
||||
|
||||
Eerste baseline -> thresholds bepalen op training data maar ook stoppen als 400 cycles (herschalen) per jaar bereikt zijn -> thresholds zouden anders moeten zijn (Ook met penalty parameter)
|
||||
-> deze toepassen op test set (ook stoppen als 400/jaar bereikt zijn)
|
||||
|
||||
|
||||
Visualizatie van thresholds over test set voor baselines en complexere modellen -> zonder penalties tonen
|
||||
|
||||
|
||||
|
||||
|
||||
1 a 2 Case studies (extreme gevallen, thresholds 150, -5, normale mss)
|
||||
|
||||
- Generatie van NRV (echte NRV)
|
||||
- Thresholds die eruit komen
|
||||
- Profit en charge cycles
|
||||
|
||||
|
||||
|
||||
Policy volledig fixen en later training script met policy direct erachter (tijdens schrijven door laten runnen)
|
||||
|
||||
1) Policy
|
||||
2) Finetuning van modellen (+ vergelijken met elkaar opbv profit en error)
|
||||
3) Ablation Study (input features weghalen en kijken wat er gebeurt)
|
||||
( 4) Diffusion tussendoor )
|
||||
|
||||
|
||||
Inleiding +
|
||||
Literatuurstudie +
|
||||
Tabellen die we gaan bespreken -> updaten met nieuwe data dan
|
||||
|
||||
Nog eens 3e meeting opbrengen voor 2e deel maart.
|
||||
@@ -25,12 +25,19 @@ class NrvDataset(Dataset):
|
||||
self.sequence_length = sequence_length
|
||||
self.predict_sequence_length = predict_sequence_length
|
||||
|
||||
self.samples_to_skip = self.skip_samples(dataframe=dataframe)
|
||||
self.samples_to_skip = self.skip_samples(dataframe=dataframe, full_day_skip=self.full_day_skip)
|
||||
total_indices = set(
|
||||
range(len(dataframe) - self.sequence_length - self.predict_sequence_length)
|
||||
)
|
||||
self.valid_indices = sorted(list(total_indices - set(self.samples_to_skip)))
|
||||
|
||||
# full day indices
|
||||
full_day_skipped_samples = self.skip_samples(dataframe=dataframe, full_day_skip=True)
|
||||
full_day_total_indices = set(
|
||||
range(len(dataframe) - self.sequence_length - self.predict_sequence_length)
|
||||
)
|
||||
self.full_day_valid_indices = sorted(list(full_day_total_indices - set(full_day_skipped_samples)))
|
||||
|
||||
self.history_features = []
|
||||
if self.data_config.LOAD_HISTORY:
|
||||
self.history_features.append("total_load")
|
||||
@@ -73,7 +80,7 @@ class NrvDataset(Dataset):
|
||||
|
||||
self.history_features, self.forecast_features = self.preprocess_data(dataframe)
|
||||
|
||||
def skip_samples(self, dataframe):
|
||||
def skip_samples(self, dataframe, full_day_skip):
|
||||
nan_rows = dataframe[dataframe.isnull().any(axis=1)]
|
||||
nan_indices = nan_rows.index
|
||||
skip_indices = [
|
||||
@@ -91,7 +98,7 @@ class NrvDataset(Dataset):
|
||||
|
||||
# add indices that are not the start of a day (00:15) to the skip indices (use datetime column)
|
||||
# get indices of all 00:15 timestamps
|
||||
if self.full_day_skip:
|
||||
if full_day_skip:
|
||||
start_of_day_indices = dataframe[
|
||||
dataframe["datetime"].dt.time != pd.Timestamp("00:00:00").time()
|
||||
].index
|
||||
|
||||
@@ -45,3 +45,53 @@ class SimpleDiffusionModel(DiffusionModel):
|
||||
self.layers.append(nn.ReLU())
|
||||
|
||||
self.layers.append(nn.Linear(hidden_sizes[-1] + time_dim + other_inputs_dim, input_size))
|
||||
|
||||
class GRUDiffusionModel(DiffusionModel):
|
||||
def __init__(self, input_size: int, hidden_sizes: list, other_inputs_dim: int, gru_hidden_size: int, time_dim: int = 64):
|
||||
super(GRUDiffusionModel, self).__init__(time_dim)
|
||||
|
||||
self.other_inputs_dim = other_inputs_dim
|
||||
self.gru_hidden_size = gru_hidden_size
|
||||
|
||||
# GRU layer
|
||||
self.gru = nn.GRU(input_size=input_size + time_dim + other_inputs_dim,
|
||||
hidden_size=gru_hidden_size,
|
||||
num_layers=3,
|
||||
batch_first=True)
|
||||
|
||||
# Fully connected layers after GRU
|
||||
self.fc_layers = nn.ModuleList()
|
||||
prev_size = gru_hidden_size
|
||||
for hidden_size in hidden_sizes:
|
||||
self.fc_layers.append(nn.Linear(prev_size, hidden_size))
|
||||
self.fc_layers.append(nn.ReLU())
|
||||
prev_size = hidden_size
|
||||
|
||||
# Final output layer
|
||||
self.fc_layers.append(nn.Linear(prev_size, input_size))
|
||||
|
||||
def forward(self, x, t, inputs):
|
||||
batch_size, seq_len = x.shape
|
||||
x = x.unsqueeze(-1).repeat(1, 1, seq_len)
|
||||
|
||||
# Positional encoding for each time step
|
||||
t = t.unsqueeze(-1).type(torch.float)
|
||||
t = self.pos_encoding(t, self.time_dim) # Shape: [batch_size, seq_len, time_dim]
|
||||
|
||||
# repeat time encoding for each time step t is shape [batch_size, time_dim], i want [batch_size, seq_len, time_dim]
|
||||
t = t.unsqueeze(1).repeat(1, seq_len, 1)
|
||||
|
||||
# Concatenate x, t, and inputs along the feature dimension
|
||||
x = torch.cat((x, t, inputs), dim=-1) # Shape: [batch_size, seq_len, input_size + time_dim + other_inputs_dim]
|
||||
|
||||
# Pass through GRU
|
||||
output, hidden = self.gru(x) # Hidden Shape: [batch_size, seq_len, 1]
|
||||
|
||||
# Get last hidden state
|
||||
x = hidden[-1]
|
||||
|
||||
# Process each time step's output with fully connected layers
|
||||
for layer in self.fc_layers:
|
||||
x = layer(x)
|
||||
|
||||
return x
|
||||
@@ -2,7 +2,7 @@
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -31,7 +31,7 @@
|
||||
"from datetime import datetime\n",
|
||||
"import torch.nn as nn\n",
|
||||
"from src.models.time_embedding_layer import TimeEmbedding\n",
|
||||
"from src.models.diffusion_model import SimpleDiffusionModel\n",
|
||||
"from src.models.diffusion_model import SimpleDiffusionModel, GRUDiffusionModel\n",
|
||||
"from src.trainers.diffusion_trainer import DiffusionTrainer\n",
|
||||
"from torchinfo import summary\n",
|
||||
"\n",
|
||||
@@ -62,30 +62,99 @@
|
||||
"\n",
|
||||
"data_config.NOMINAL_NET_POSITION = True\n",
|
||||
"\n",
|
||||
"data_processor = DataProcessor(data_config, path=\"../../\")\n",
|
||||
"data_processor = DataProcessor(data_config, path=\"../../\", lstm=True)\n",
|
||||
"data_processor.set_batch_size(1024)\n",
|
||||
"data_processor.set_full_day_skip(True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"ClearML Task: created new task id=b71216825809432682ea3c7841c07612\n",
|
||||
"ClearML results page: http://192.168.1.182:8080/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/b71216825809432682ea3c7841c07612/output/log\n"
|
||||
"torch.Size([1024, 96, 96])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"500 model found when searching for `file:///workspaces/Thesis/src/notebooks/checkpoint.pt`\n",
|
||||
"Selected model `Autoregressive Non Linear Quantile Regression + Quarter + DoW + Net` (id=bc0cb0d7fc614e2e8b0edf5b85348646)\n"
|
||||
"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py:536: UserWarning: Using a target size (torch.Size([1024, 96])) that is different to the input size (torch.Size([2, 1024, 96])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
|
||||
" return F.mse_loss(input, target, reduction=self.reduction)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"torch.Size([556, 96, 96])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py:536: UserWarning: Using a target size (torch.Size([556, 96])) that is different to the input size (torch.Size([2, 556, 96])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
|
||||
" return F.mse_loss(input, target, reduction=self.reduction)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n",
|
||||
"torch.Size([1024, 96, 96])\n",
|
||||
"torch.Size([556, 96, 96])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"KeyboardInterrupt\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -95,14 +164,15 @@
|
||||
"epochs=150\n",
|
||||
"\n",
|
||||
"#### Model ####\n",
|
||||
"model = SimpleDiffusionModel(96, [512, 512, 512], other_inputs_dim=inputDim[1], time_dim=64)\n",
|
||||
"# model = SimpleDiffusionModel(96, [512, 512, 512], other_inputs_dim=inputDim[1], time_dim=64)\n",
|
||||
"model = GRUDiffusionModel(96, [256, 256], other_inputs_dim=inputDim[2], time_dim=64, gru_hidden_size=128)\n",
|
||||
"\n",
|
||||
"#### ClearML ####\n",
|
||||
"task = clearml_helper.get_task(task_name=\"Diffusion Model\")\n",
|
||||
"# task = clearml_helper.get_task(task_name=\"Diffusion Model\")\n",
|
||||
"\n",
|
||||
"#### Trainer ####\n",
|
||||
"trainer = DiffusionTrainer(model, data_processor, \"cuda\")\n",
|
||||
"trainer.train(epochs, learningRate, task)"
|
||||
"trainer.train(epochs, learningRate, None)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -246,7 +316,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.11"
|
||||
"version": "3.10.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
253
src/policies/PolicyEvaluator.py
Normal file
253
src/policies/PolicyEvaluator.py
Normal file
@@ -0,0 +1,253 @@
|
||||
from clearml import Task
|
||||
from tqdm import tqdm
|
||||
from src.policies.simple_baseline import BaselinePolicy
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import torch
|
||||
import plotly.express as px
|
||||
|
||||
from src.utils.imbalance_price_calculator import ImbalancePriceCalculator
|
||||
|
||||
|
||||
class PolicyEvaluator:
|
||||
def __init__(self, baseline_policy: BaselinePolicy, task: Task = None):
|
||||
self.baseline_policy = baseline_policy
|
||||
|
||||
self.ipc = ImbalancePriceCalculator(data_path="")
|
||||
self.dates = baseline_policy.test_data["DateTime"].dt.date.unique()
|
||||
self.dates = pd.to_datetime(self.dates)
|
||||
|
||||
### Load Imbalance Prices ###
|
||||
imbalance_prices = pd.read_csv("data/imbalance_prices.csv", sep=";")
|
||||
imbalance_prices["DateTime"] = pd.to_datetime(
|
||||
imbalance_prices["DateTime"], utc=True
|
||||
)
|
||||
self.imbalance_prices = imbalance_prices.sort_values(by=["DateTime"])
|
||||
|
||||
self.penalties = [0, 100, 300, 500, 800, 1000, 1500]
|
||||
self.profits = []
|
||||
|
||||
self.task = task
|
||||
|
||||
def get_imbanlance_prices_for_date(self, date):
|
||||
imbalance_prices_day = self.imbalance_prices[
|
||||
self.imbalance_prices["DateTime"].dt.date == date
|
||||
]
|
||||
return imbalance_prices_day["Positive imbalance price"].values
|
||||
|
||||
def evaluate_for_date(
|
||||
self,
|
||||
date,
|
||||
idx_samples,
|
||||
test_loader,
|
||||
charge_thresholds=np.arange(-100, 250, 25),
|
||||
discharge_thresholds=np.arange(-100, 250, 25),
|
||||
):
|
||||
idx = test_loader.dataset.get_idx_for_date(date.date())
|
||||
|
||||
print("Evaluated for idx: ", idx)
|
||||
(initial, samples) = idx_samples[idx]
|
||||
|
||||
if len(initial.shape) == 2:
|
||||
initial = initial.cpu().numpy()[0][-1]
|
||||
else:
|
||||
initial = initial.cpu().numpy()[-1]
|
||||
samples = samples.cpu().numpy()
|
||||
|
||||
initial = np.repeat(initial, samples.shape[0])
|
||||
combined = np.concatenate((initial.reshape(-1, 1), samples), axis=1)
|
||||
|
||||
reconstructed_imbalance_prices = (
|
||||
self.ipc.get_imbalance_prices_2023_for_date_vectorized(date, combined)
|
||||
)
|
||||
reconstructed_imbalance_prices = torch.tensor(
|
||||
reconstructed_imbalance_prices, device="cuda"
|
||||
)
|
||||
|
||||
real_imbalance_prices = self.get_imbanlance_prices_for_date(date.date())
|
||||
|
||||
for penalty in self.penalties:
|
||||
found_charge_thresholds, found_discharge_thresholds = (
|
||||
self.baseline_policy.get_optimal_thresholds(
|
||||
reconstructed_imbalance_prices,
|
||||
charge_thresholds,
|
||||
discharge_thresholds,
|
||||
penalty,
|
||||
)
|
||||
)
|
||||
|
||||
predicted_charge_threshold = found_charge_thresholds.mean(axis=0)
|
||||
predicted_discharge_threshold = found_discharge_thresholds.mean(axis=0)
|
||||
|
||||
### Determine Profits and Charge Cycles ###
|
||||
simulated_profit, simulated_charge_cycles = self.baseline_policy.simulate(
|
||||
torch.tensor([[real_imbalance_prices]]),
|
||||
torch.tensor([predicted_charge_threshold]),
|
||||
torch.tensor([predicted_discharge_threshold]),
|
||||
)
|
||||
self.profits.append(
|
||||
[
|
||||
date,
|
||||
penalty,
|
||||
simulated_profit[0][0].item(),
|
||||
simulated_charge_cycles[0][0].item(),
|
||||
predicted_charge_threshold.item(),
|
||||
predicted_discharge_threshold.item(),
|
||||
]
|
||||
)
|
||||
|
||||
def evaluate_test_set(self, idx_samples, test_loader):
|
||||
self.profits = []
|
||||
try:
|
||||
for date in tqdm(self.dates):
|
||||
self.evaluate_for_date(date, idx_samples, test_loader)
|
||||
except KeyboardInterrupt:
|
||||
print("Interrupted")
|
||||
raise KeyboardInterrupt
|
||||
|
||||
except Exception as e:
|
||||
print(e)
|
||||
pass
|
||||
|
||||
self.profits = pd.DataFrame(
|
||||
self.profits,
|
||||
columns=[
|
||||
"Date",
|
||||
"Penalty",
|
||||
"Profit",
|
||||
"Charge Cycles",
|
||||
"Charge Threshold",
|
||||
"Discharge Threshold",
|
||||
],
|
||||
)
|
||||
|
||||
def plot_profits_table(self):
|
||||
# Check if task or penalties are not set
|
||||
if (
|
||||
self.task is None
|
||||
or not hasattr(self, "penalties")
|
||||
or not hasattr(self, "profits")
|
||||
):
|
||||
print("Task, penalties, or profits not defined.")
|
||||
return
|
||||
|
||||
if self.profits.empty:
|
||||
print("Profits DataFrame is empty.")
|
||||
return
|
||||
|
||||
# Aggregate profits and charge cycles by penalty, calculating totals and per-year values
|
||||
aggregated = self.profits.groupby("Penalty").agg(
|
||||
Total_Profit=("Profit", "sum"),
|
||||
Total_Charge_Cycles=("Charge Cycles", "sum"),
|
||||
Num_Days=("Date", "nunique"),
|
||||
)
|
||||
aggregated["Profit_Per_Year"] = (
|
||||
aggregated["Total_Profit"] / aggregated["Num_Days"] * 365
|
||||
)
|
||||
aggregated["Charge_Cycles_Per_Year"] = (
|
||||
aggregated["Total_Charge_Cycles"] / aggregated["Num_Days"] * 365
|
||||
)
|
||||
|
||||
# Reset index to make 'Penalty' a column again and drop unnecessary columns
|
||||
final_df = aggregated.reset_index().drop(
|
||||
columns=["Total_Profit", "Total_Charge_Cycles", "Num_Days"]
|
||||
)
|
||||
|
||||
# Rename columns to match expected output
|
||||
final_df.columns = [
|
||||
"Penalty",
|
||||
"Total Profit (per year)",
|
||||
"Total Charge Cycles (per year)",
|
||||
]
|
||||
|
||||
# Profits till 400
|
||||
profits_till_400 = self.get_profits_till_400()
|
||||
|
||||
# aggregate the final_df and profits_till_400 with columns: Penalty, total profit, total charge cycles, profit till 400, total charge cycles
|
||||
final_df = final_df.merge(profits_till_400, on="Penalty")
|
||||
|
||||
# Log the final results table
|
||||
self.task.get_logger().report_table(
|
||||
"Test Set Results", "Profits per Penalty", iteration=0, table_plot=final_df
|
||||
)
|
||||
|
||||
def plot_thresholds_per_day(self):
|
||||
if self.task is None:
|
||||
return
|
||||
|
||||
fig = px.line(
|
||||
self.profits[self.profits["Penalty"] == 0],
|
||||
x="Date",
|
||||
y=["Charge Threshold", "Discharge Threshold"],
|
||||
title="Charge and Discharge Thresholds per Day",
|
||||
)
|
||||
|
||||
fig.update_layout(
|
||||
width=1000,
|
||||
height=600,
|
||||
title_x=0.5,
|
||||
)
|
||||
|
||||
self.task.get_logger().report_plotly(
|
||||
"Thresholds per Day", "Thresholds per Day", iteration=0, figure=fig
|
||||
)
|
||||
|
||||
def get_profits_as_scalars(self):
|
||||
aggregated = self.profits.groupby("Penalty").agg(
|
||||
Total_Profit=("Profit", "sum"),
|
||||
Total_Charge_Cycles=("Charge Cycles", "sum"),
|
||||
Num_Days=("Date", "nunique"),
|
||||
)
|
||||
aggregated["Profit_Per_Year"] = (
|
||||
aggregated["Total_Profit"] / aggregated["Num_Days"] * 365
|
||||
)
|
||||
aggregated["Charge_Cycles_Per_Year"] = (
|
||||
aggregated["Total_Charge_Cycles"] / aggregated["Num_Days"] * 365
|
||||
)
|
||||
|
||||
# Reset index to make 'Penalty' a column again and drop unnecessary columns
|
||||
final_df = aggregated.reset_index().drop(
|
||||
columns=["Total_Profit", "Total_Charge_Cycles", "Num_Days"]
|
||||
)
|
||||
|
||||
# Rename columns to match expected output
|
||||
final_df.columns = ["Penalty", "Total Profit", "Total Charge Cycles"]
|
||||
return final_df
|
||||
|
||||
def get_profits_till_400(self, profits: pd.DataFrame = None):
|
||||
if profits is None:
|
||||
profits = self.profits
|
||||
|
||||
# calculates profits until 400 charge cycles per year are reached
|
||||
number_of_days = len(profits["Date"].unique())
|
||||
usable_charge_cycles = (400 / 365) * number_of_days
|
||||
|
||||
# now sum the profit until the usable charge cycles are reached
|
||||
penalty_profits = {}
|
||||
penalty_charge_cycles = {}
|
||||
|
||||
for index, row in profits.iterrows():
|
||||
penalty = row["Penalty"]
|
||||
profit = row["Profit"]
|
||||
charge_cycles = row["Charge Cycles"]
|
||||
|
||||
if penalty not in penalty_profits:
|
||||
penalty_profits[penalty] = 0
|
||||
penalty_charge_cycles[penalty] = 0
|
||||
|
||||
if penalty_charge_cycles[penalty] < usable_charge_cycles:
|
||||
penalty_profits[penalty] += profit
|
||||
penalty_charge_cycles[penalty] += charge_cycles
|
||||
|
||||
df = pd.DataFrame(
|
||||
list(
|
||||
zip(
|
||||
penalty_profits.keys(),
|
||||
penalty_profits.values(),
|
||||
penalty_charge_cycles.values(),
|
||||
)
|
||||
),
|
||||
columns=["Penalty", "Profit_till_400", "Cycles_till_400"],
|
||||
)
|
||||
|
||||
return df
|
||||
200
src/policies/baselines/BaselinePolicyEvaluator.py
Normal file
200
src/policies/baselines/BaselinePolicyEvaluator.py
Normal file
@@ -0,0 +1,200 @@
|
||||
from clearml import Task
|
||||
from src.policies.simple_baseline import BaselinePolicy
|
||||
from src.policies.PolicyEvaluator import PolicyEvaluator
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from tqdm import tqdm
|
||||
import torch
|
||||
|
||||
|
||||
class BaselinePolicyEvaluator(PolicyEvaluator):
|
||||
def __init__(self, baseline_policy: BaselinePolicy, task: Task = None):
|
||||
super(BaselinePolicyEvaluator, self).__init__(baseline_policy, task)
|
||||
|
||||
self.train_profits = []
|
||||
|
||||
def determine_thresholds_for_date(self, date):
|
||||
charge_thresholds = np.arange(-100, 250, 25)
|
||||
discharge_thresholds = np.arange(-100, 250, 25)
|
||||
|
||||
real_imbalance_prices = self.get_imbanlance_prices_for_date(date.date())
|
||||
|
||||
for penalty in self.penalties:
|
||||
found_charge_thresholds, found_discharge_thresholds = (
|
||||
self.baseline_policy.get_optimal_thresholds(
|
||||
torch.tensor([real_imbalance_prices]),
|
||||
charge_thresholds,
|
||||
discharge_thresholds,
|
||||
penalty,
|
||||
)
|
||||
)
|
||||
|
||||
best_charge_threshold = found_charge_thresholds
|
||||
best_discharge_threshold = found_discharge_thresholds
|
||||
|
||||
simulated_profit, simulated_charge_cycles = self.baseline_policy.simulate(
|
||||
torch.tensor([[real_imbalance_prices]]),
|
||||
torch.tensor([best_charge_threshold]),
|
||||
torch.tensor([best_discharge_threshold]),
|
||||
)
|
||||
|
||||
self.train_profits.append(
|
||||
[
|
||||
date,
|
||||
penalty,
|
||||
simulated_profit[0][0].item(),
|
||||
simulated_charge_cycles[0][0].item(),
|
||||
best_charge_threshold.item(),
|
||||
best_discharge_threshold.item(),
|
||||
]
|
||||
)
|
||||
|
||||
def determine_best_thresholds(self):
|
||||
self.train_profits = []
|
||||
dates = self.baseline_policy.train_data["DateTime"].dt.date.unique()
|
||||
dates = pd.to_datetime(dates)
|
||||
try:
|
||||
for date in tqdm(dates):
|
||||
self.determine_thresholds_for_date(date)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
pass
|
||||
|
||||
self.train_profits = pd.DataFrame(
|
||||
self.train_profits,
|
||||
columns=[
|
||||
"Date",
|
||||
"Penalty",
|
||||
"Profit",
|
||||
"Charge Cycles",
|
||||
"Charge Threshold",
|
||||
"Discharge Threshold",
|
||||
],
|
||||
)
|
||||
|
||||
number_of_days = len(self.train_profits["Date"].unique())
|
||||
usable_charge_cycles = (400 / 365) * number_of_days
|
||||
|
||||
intermediate_values = {penalty: {} for penalty in self.penalties}
|
||||
|
||||
# find the best threshold combination for each penalty based on the total profit on the data
|
||||
for penalty in self.penalties:
|
||||
profits_for_penalty = self.train_profits[
|
||||
self.train_profits["Penalty"] == penalty
|
||||
]
|
||||
|
||||
for index, row in profits_for_penalty.iterrows():
|
||||
charge_threshold = row["Charge Threshold"]
|
||||
discharge_threshold = row["Discharge Threshold"]
|
||||
|
||||
if (charge_threshold, discharge_threshold) not in intermediate_values[
|
||||
penalty
|
||||
]:
|
||||
intermediate_values[penalty][
|
||||
(charge_threshold, discharge_threshold)
|
||||
] = (0, 0)
|
||||
|
||||
new_charge_cycles = (
|
||||
intermediate_values[penalty][
|
||||
(charge_threshold, discharge_threshold)
|
||||
][1]
|
||||
+ row["Charge Cycles"]
|
||||
)
|
||||
new_profit = (
|
||||
intermediate_values[penalty][
|
||||
(charge_threshold, discharge_threshold)
|
||||
][0]
|
||||
+ row["Profit"]
|
||||
)
|
||||
|
||||
if new_charge_cycles <= usable_charge_cycles:
|
||||
intermediate_values[penalty][
|
||||
(charge_threshold, discharge_threshold)
|
||||
] = (new_profit, new_charge_cycles)
|
||||
|
||||
best_thresholds = {penalty: [0, 0, 0, 0] for penalty in self.penalties}
|
||||
|
||||
for penalty in self.penalties:
|
||||
best_profit = 0
|
||||
for threshold, values in intermediate_values[penalty].items():
|
||||
if values[0] > best_profit:
|
||||
best_profit = values[0]
|
||||
best_thresholds[penalty][0] = threshold[0]
|
||||
best_thresholds[penalty][1] = threshold[1]
|
||||
best_thresholds[penalty][2] = best_profit
|
||||
best_thresholds[penalty][3] = values[1]
|
||||
|
||||
# create dataframe from best_thresholds with columns, Penalty, Charge Threshold, Discharge Threshold, Profit
|
||||
data = [
|
||||
(penalty, values[0], values[1], values[2], values[3])
|
||||
for penalty, values in best_thresholds.items()
|
||||
]
|
||||
|
||||
best_thresholds_df = pd.DataFrame(
|
||||
data,
|
||||
columns=[
|
||||
"Penalty",
|
||||
"Charge Threshold",
|
||||
"Discharge Threshold",
|
||||
"Profit (training data)",
|
||||
f"Charge Cycles (training data: max {usable_charge_cycles})",
|
||||
],
|
||||
)
|
||||
|
||||
if self.task:
|
||||
self.task.get_logger().report_table(
|
||||
"Baseline Train Data",
|
||||
"Best Thresholds for each Penalty on Training Data (up to 400 cycles / year)",
|
||||
iteration=0,
|
||||
table_plot=best_thresholds_df,
|
||||
)
|
||||
|
||||
return best_thresholds
|
||||
|
||||
def evaluate_test_set(self, thresholds: dict):
|
||||
"""Evaluate the test set using the given thresholds (multiple penalties)
|
||||
|
||||
Args:
|
||||
thresholds (dict): Dictionary with penalties as keys and the corresponding thresholds tuple as values
|
||||
"""
|
||||
self.profits = []
|
||||
try:
|
||||
for date in tqdm(self.dates):
|
||||
real_imbalance_prices = self.get_imbanlance_prices_for_date(date.date())
|
||||
for penalty in thresholds.keys():
|
||||
charge_threshold = thresholds[penalty][0]
|
||||
discharge_threshold = thresholds[penalty][1]
|
||||
|
||||
simulated_profit, simulated_charge_cycles = (
|
||||
self.baseline_policy.simulate(
|
||||
torch.tensor([[real_imbalance_prices]]),
|
||||
torch.tensor([charge_threshold]),
|
||||
torch.tensor([discharge_threshold]),
|
||||
)
|
||||
)
|
||||
|
||||
self.profits.append(
|
||||
[
|
||||
date,
|
||||
penalty,
|
||||
simulated_profit[0][0].item(),
|
||||
simulated_charge_cycles[0][0].item(),
|
||||
charge_threshold,
|
||||
discharge_threshold,
|
||||
]
|
||||
)
|
||||
|
||||
self.profits = pd.DataFrame(
|
||||
self.profits,
|
||||
columns=[
|
||||
"Date",
|
||||
"Penalty",
|
||||
"Profit",
|
||||
"Charge Cycles",
|
||||
"Charge Threshold",
|
||||
"Discharge Threshold",
|
||||
],
|
||||
)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
pass
|
||||
76
src/policies/baselines/YesterdayBaselinePolicyExecutor.py
Normal file
76
src/policies/baselines/YesterdayBaselinePolicyExecutor.py
Normal file
@@ -0,0 +1,76 @@
|
||||
from datetime import timedelta
|
||||
from clearml import Task
|
||||
from src.policies.simple_baseline import BaselinePolicy
|
||||
from src.policies.PolicyEvaluator import PolicyEvaluator
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from tqdm import tqdm
|
||||
import torch
|
||||
|
||||
|
||||
class YesterdayBaselinePolicyEvaluator(PolicyEvaluator):
|
||||
def __init__(self, baseline_policy: BaselinePolicy, task: Task = None):
|
||||
super(YesterdayBaselinePolicyEvaluator, self).__init__(baseline_policy, task)
|
||||
|
||||
def evaluate_for_date(
|
||||
self,
|
||||
date,
|
||||
charge_thresholds=np.arange(-100, 250, 25),
|
||||
discharge_thresholds=np.arange(-100, 250, 25),
|
||||
):
|
||||
|
||||
real_imbalance_prices = self.get_imbanlance_prices_for_date(date.date())
|
||||
yesterday_imbalance_prices = self.get_imbanlance_prices_for_date(
|
||||
date.date() - timedelta(days=1)
|
||||
)
|
||||
yesterday_imbalance_prices = torch.tensor(
|
||||
np.array([yesterday_imbalance_prices]), device="cpu"
|
||||
)
|
||||
|
||||
for penalty in self.penalties:
|
||||
yesterday_charge_thresholds, yesterday_discharge_thresholds = (
|
||||
self.baseline_policy.get_optimal_thresholds(
|
||||
yesterday_imbalance_prices,
|
||||
charge_thresholds,
|
||||
discharge_thresholds,
|
||||
penalty,
|
||||
)
|
||||
)
|
||||
|
||||
yesterday_profit, yesterday_charge_cycles = self.baseline_policy.simulate(
|
||||
torch.tensor([[real_imbalance_prices]]),
|
||||
torch.tensor([yesterday_charge_thresholds.mean(axis=0)]),
|
||||
torch.tensor([yesterday_discharge_thresholds.mean(axis=0)]),
|
||||
)
|
||||
|
||||
self.profits.append(
|
||||
[
|
||||
date,
|
||||
penalty,
|
||||
yesterday_profit[0][0].item(),
|
||||
yesterday_charge_cycles[0][0].item(),
|
||||
yesterday_charge_thresholds.mean(axis=0).item(),
|
||||
yesterday_discharge_thresholds.mean(axis=0).item(),
|
||||
]
|
||||
)
|
||||
|
||||
def evaluate_test_set(self):
|
||||
self.profits = []
|
||||
try:
|
||||
for date in tqdm(self.dates):
|
||||
self.evaluate_for_date(date)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
pass
|
||||
|
||||
self.profits = pd.DataFrame(
|
||||
self.profits,
|
||||
columns=[
|
||||
"Date",
|
||||
"Penalty",
|
||||
"Profit",
|
||||
"Charge Cycles",
|
||||
"Charge Threshold",
|
||||
"Discharge Threshold",
|
||||
],
|
||||
)
|
||||
21
src/policies/baselines/global_threshold_baseline.py
Normal file
21
src/policies/baselines/global_threshold_baseline.py
Normal file
@@ -0,0 +1,21 @@
|
||||
from src.utils.clearml import ClearMLHelper
|
||||
|
||||
#### ClearML ####
|
||||
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
|
||||
task = clearml_helper.get_task(task_name="Global Thresholds Baseline")
|
||||
task.execute_remotely(queue_name="default", exit_process=True)
|
||||
|
||||
from src.policies.baselines.BaselinePolicyEvaluator import BaselinePolicyEvaluator
|
||||
from src.policies.simple_baseline import BaselinePolicy, Battery
|
||||
|
||||
### Policy Evaluator ###
|
||||
battery = Battery(2, 1)
|
||||
baseline_policy = BaselinePolicy(battery, data_path="")
|
||||
policy_evaluator = BaselinePolicyEvaluator(baseline_policy, task)
|
||||
|
||||
thresholds = policy_evaluator.determine_best_thresholds()
|
||||
policy_evaluator.evaluate_test_set(thresholds)
|
||||
|
||||
policy_evaluator.plot_profits_table()
|
||||
|
||||
task.close()
|
||||
22
src/policies/baselines/yesterday_baseline.py
Normal file
22
src/policies/baselines/yesterday_baseline.py
Normal file
@@ -0,0 +1,22 @@
|
||||
from src.utils.clearml import ClearMLHelper
|
||||
|
||||
#### ClearML ####
|
||||
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
|
||||
task = clearml_helper.get_task(task_name="Global Thresholds Baseline")
|
||||
task.execute_remotely(queue_name="default", exit_process=True)
|
||||
|
||||
from src.policies.baselines.BaselinePolicyEvaluator import BaselinePolicyEvaluator
|
||||
from src.policies.simple_baseline import BaselinePolicy, Battery
|
||||
from src.policies.baselines.YesterdayBaselinePolicyExecutor import (
|
||||
YesterdayBaselinePolicyEvaluator,
|
||||
)
|
||||
|
||||
### Policy Evaluator ###
|
||||
battery = Battery(2, 1)
|
||||
baseline_policy = BaselinePolicy(battery, data_path="")
|
||||
policy_evaluator = YesterdayBaselinePolicyEvaluator(baseline_policy, task)
|
||||
|
||||
policy_evaluator.evaluate_test_set()
|
||||
policy_evaluator.plot_profits_table()
|
||||
|
||||
task.close()
|
||||
@@ -1197,7 +1197,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.11"
|
||||
"version": "3.10.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@@ -8,7 +8,8 @@ import pandas as pd
|
||||
import datetime
|
||||
from tqdm import tqdm
|
||||
from src.utils.imbalance_price_calculator import ImbalancePriceCalculator
|
||||
import time
|
||||
import seaborn as sns
|
||||
import matplotlib.pyplot as plt
|
||||
import plotly.express as px
|
||||
|
||||
### import functions ###
|
||||
@@ -16,7 +17,7 @@ from src.trainers.quantile_trainer import auto_regressive as quantile_auto_regre
|
||||
from src.trainers.diffusion_trainer import sample_diffusion
|
||||
from src.utils.clearml import ClearMLHelper
|
||||
|
||||
# argparse to parse task id and model type
|
||||
### Arguments ###
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--task_id', type=str, default=None)
|
||||
parser.add_argument('--model_type', type=str, default=None)
|
||||
@@ -27,6 +28,7 @@ assert args.task_id is not None, "Please specify task id"
|
||||
assert args.model_type is not None, "Please specify model type"
|
||||
assert args.model_name is not None, "Please specify model name"
|
||||
|
||||
### Baseline Policy ###
|
||||
battery = Battery(2, 1)
|
||||
baseline_policy = BaselinePolicy(battery, data_path="")
|
||||
|
||||
@@ -124,6 +126,9 @@ def get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc,
|
||||
predicted_nrv_profits_cycles = {i: [0, 0] for i in penalties}
|
||||
baseline_profits_cycles = {i: [0, 0] for i in penalties}
|
||||
|
||||
_charge_thresholds = {}
|
||||
_discharge_thresholds = {}
|
||||
|
||||
initial, nrvs, target = predict_NRV(model, date, data_processor, test_loader)
|
||||
|
||||
initial = np.repeat(initial, nrvs.shape[0])
|
||||
@@ -139,6 +144,10 @@ def get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc,
|
||||
|
||||
for penalty in penalties:
|
||||
found_charge_thresholds, found_discharge_thresholds = baseline_policy.get_optimal_thresholds(reconstructed_imbalance_prices, charge_thresholds, discharge_thresholds, penalty)
|
||||
|
||||
_charge_thresholds[penalty] = found_charge_thresholds
|
||||
_discharge_thresholds[penalty] = found_discharge_thresholds
|
||||
|
||||
next_day_charge_threshold = found_charge_thresholds.mean(axis=0)
|
||||
next_day_discharge_threshold = found_discharge_thresholds.mean(axis=0)
|
||||
yesterday_charge_thresholds, yesterday_discharge_thresholds = baseline_policy.get_optimal_thresholds(yesterday_imbalance_prices, charge_thresholds, discharge_thresholds, penalty)
|
||||
@@ -153,23 +162,26 @@ def get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc,
|
||||
baseline_profits_cycles[penalty][0] += yesterday_profit.item()
|
||||
baseline_profits_cycles[penalty][1] += yesterday_charge_cycles.item()
|
||||
|
||||
return predicted_nrv_profits_cycles, baseline_profits_cycles
|
||||
return predicted_nrv_profits_cycles, baseline_profits_cycles, _charge_thresholds, _discharge_thresholds
|
||||
|
||||
def next_day_test_set(model, data_processor, test_loader, ipc, predict_NRV: callable):
|
||||
penalties = [0, 10, 50, 150, 300, 500, 600, 800, 1000, 1500, 2000, 2500]
|
||||
penalties = [0, 50, 250, 500, 1000, 1500]
|
||||
predicted_nrv_profits_cycles = {i: [0, 0] for i in penalties}
|
||||
baseline_profits_cycles = {i: [0, 0] for i in penalties}
|
||||
|
||||
# get all dates in test set
|
||||
dates = baseline_policy.test_data["DateTime"].dt.date.unique()
|
||||
charge_thresholds = {}
|
||||
discharge_thresholds = {}
|
||||
|
||||
# dates back to datetime
|
||||
dates = baseline_policy.test_data["DateTime"].dt.date.unique()
|
||||
dates = pd.to_datetime(dates)
|
||||
|
||||
for date in tqdm(dates):
|
||||
try:
|
||||
new_predicted_nrv_profits_cycles, new_baseline_profits_cycles = get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc, predict_NRV, penalties)
|
||||
new_predicted_nrv_profits_cycles, new_baseline_profits_cycles, new_charge_thresholds, new_discharge_thresholds = get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc, predict_NRV, penalties)
|
||||
|
||||
charge_thresholds[date] = new_charge_thresholds
|
||||
discharge_thresholds[date] = new_discharge_thresholds
|
||||
|
||||
for penalty in penalties:
|
||||
predicted_nrv_profits_cycles[penalty][0] += new_predicted_nrv_profits_cycles[penalty][0]
|
||||
predicted_nrv_profits_cycles[penalty][1] += new_predicted_nrv_profits_cycles[penalty][1]
|
||||
@@ -178,16 +190,15 @@ def next_day_test_set(model, data_processor, test_loader, ipc, predict_NRV: call
|
||||
baseline_profits_cycles[penalty][1] += new_baseline_profits_cycles[penalty][1]
|
||||
|
||||
except Exception as e:
|
||||
# print(f"Error for date {date}")
|
||||
continue
|
||||
print(f"Error for date {date}")
|
||||
|
||||
return predicted_nrv_profits_cycles, baseline_profits_cycles
|
||||
return predicted_nrv_profits_cycles, baseline_profits_cycles, charge_thresholds, discharge_thresholds
|
||||
|
||||
def main():
|
||||
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
|
||||
task = clearml_helper.get_task(task_name="Policy Test")
|
||||
|
||||
task.execute_remotely(queue_name="default", exit_process=True)
|
||||
# task.execute_remotely(queue_name="default", exit_process=True)
|
||||
|
||||
configuration, model, data_processor, test_loader = load_model(args.task_id)
|
||||
|
||||
@@ -205,7 +216,92 @@ def main():
|
||||
|
||||
ipc = ImbalancePriceCalculator(data_path="")
|
||||
|
||||
predicted_nrv_profits_cycles, baseline_profits_cycles = next_day_test_set(model, data_processor, test_loader, ipc, predict_NRV)
|
||||
predicted_nrv_profits_cycles, baseline_profits_cycles, charge_thresholds, discharge_thresholds = next_day_test_set(model, data_processor, test_loader, ipc, predict_NRV)
|
||||
# the charge_thresholds is a dictionary with date as key. The values of the dictionary is another dictionary with keys as penalties and values as the charge thresholds
|
||||
# create density plot that shows a density plot of the charge thresholds for each penalty (use seaborn displot) (One plot with a different color for each penalty)
|
||||
|
||||
charge_thresholds_for_penalty = {}
|
||||
for d in charge_thresholds.values():
|
||||
for penalty, thresholds in d.items():
|
||||
if penalty not in charge_thresholds_for_penalty:
|
||||
charge_thresholds_for_penalty[penalty] = []
|
||||
charge_thresholds_for_penalty[penalty].extend(thresholds)
|
||||
|
||||
discharge_thresholds_for_penalty = {}
|
||||
for d in discharge_thresholds.values():
|
||||
for penalty, thresholds in d.items():
|
||||
if penalty not in discharge_thresholds_for_penalty:
|
||||
discharge_thresholds_for_penalty[penalty] = []
|
||||
discharge_thresholds_for_penalty[penalty].extend(thresholds)
|
||||
|
||||
def plot_threshold_distribution(thresholds: dict, title: str):
|
||||
data_to_plot = []
|
||||
for penalty, values in thresholds.items():
|
||||
for value in values:
|
||||
data_to_plot.append({'Penalty': penalty, 'Value': value.item()})
|
||||
df = pd.DataFrame(data_to_plot)
|
||||
palette = sns.color_palette("bright", len(thresholds.keys()))
|
||||
fig = sns.displot(data=df, x="Value", hue="Penalty", kind="kde", palette=palette)
|
||||
plt.title('Density of Charge Thresholds by Penalty')
|
||||
plt.xlabel('Charge Threshold')
|
||||
plt.ylabel('Density')
|
||||
plt.legend(title='Penalty')
|
||||
task.get_logger().report_matplotlib_figure(
|
||||
"Policy Results",
|
||||
title,
|
||||
iteration=0,
|
||||
figure=fig
|
||||
)
|
||||
plt.close()
|
||||
|
||||
### Plot charge thresholds distribution ###
|
||||
plot_threshold_distribution(charge_thresholds_for_penalty, "Charge Thresholds")
|
||||
|
||||
### Plot discharge thresholds distribution ###
|
||||
plot_threshold_distribution(discharge_thresholds_for_penalty, "Discharge Thresholds")
|
||||
|
||||
def plot_thresholds_per_day(thresholds: dict, title: str):
|
||||
# plot mean charge threshold per day (per penalty (other color))
|
||||
data_to_plot = []
|
||||
for date, values in thresholds.items():
|
||||
for penalty, value in values.items():
|
||||
mean_val = value.mean().item()
|
||||
std_val = value.std().item() # Calculate standard deviation
|
||||
data_to_plot.append({'Date': date, 'Penalty': penalty, 'Mean': mean_val, 'StdDev': std_val})
|
||||
print(f"Date: {date}, Penalty: {penalty}, Mean: {mean_val}, StdDev: {std_val}")
|
||||
df = pd.DataFrame(data_to_plot)
|
||||
df["Date"] = pd.to_datetime(df["Date"])
|
||||
|
||||
fig = px.line(
|
||||
df,
|
||||
x="Date",
|
||||
y="Mean",
|
||||
color="Penalty",
|
||||
title=title,
|
||||
labels={"Mean": "Threshold", "Date": "Date"},
|
||||
markers=True, # Adds markers to the lines
|
||||
hover_data=["Penalty"], # Adds additional hover information
|
||||
)
|
||||
|
||||
fig.update_layout(
|
||||
width=1000, # Set the width of the figure
|
||||
height=600, # Set the height of the figure
|
||||
title_x=0.5, # Center the title horizontally
|
||||
)
|
||||
|
||||
task.get_logger().report_plotly(
|
||||
"Thresholds per Day",
|
||||
title,
|
||||
iteration=0,
|
||||
figure=fig
|
||||
)
|
||||
|
||||
### Plot mean charge thresholds per day ###
|
||||
plot_thresholds_per_day(charge_thresholds, "Mean Charge Thresholds per Day")
|
||||
|
||||
### Plot mean discharge thresholds per day ###
|
||||
plot_thresholds_per_day(discharge_thresholds, "Mean Discharge Thresholds per Day")
|
||||
|
||||
|
||||
# create dataframe with columns "name", "penalty", "profit", "cycles"
|
||||
df = pd.DataFrame(columns=["name", "penalty", "profit", "cycles"])
|
||||
|
||||
@@ -33,67 +33,29 @@ class AutoRegressiveTrainer(Trainer):
|
||||
self.model.output_size = 1
|
||||
|
||||
def debug_plots(self, task, train: bool, data_loader, sample_indices, epoch):
|
||||
num_samples = len(sample_indices)
|
||||
rows = num_samples # One row per sample since we only want one column
|
||||
|
||||
# check if self has get_plot_error
|
||||
if hasattr(self, "get_plot_error"):
|
||||
cols = 2
|
||||
print("Using get_plot_error")
|
||||
else:
|
||||
cols = 1
|
||||
print("Using get_plot")
|
||||
|
||||
fig = make_subplots(
|
||||
rows=rows,
|
||||
cols=cols,
|
||||
subplot_titles=[f"Sample {i+1}" for i in range(num_samples)],
|
||||
)
|
||||
|
||||
for i, idx in enumerate(sample_indices):
|
||||
auto_regressive_output = self.auto_regressive(data_loader.dataset, [idx])
|
||||
for actual_idx, idx in sample_indices.items():
|
||||
auto_regressive_output = self.auto_regressive(data_loader.dataset, [idx]*1000)
|
||||
if len(auto_regressive_output) == 3:
|
||||
initial, predictions, target = auto_regressive_output
|
||||
else:
|
||||
initial, predictions, _, target = auto_regressive_output
|
||||
initial, _, predictions, target = auto_regressive_output
|
||||
|
||||
initial = initial.squeeze(0)
|
||||
predictions = predictions.squeeze(0)
|
||||
target = target.squeeze(0)
|
||||
|
||||
# keep one initial
|
||||
initial = initial[0]
|
||||
target = target[0]
|
||||
|
||||
sub_fig = self.get_plot(initial, target, predictions, show_legend=(i == 0))
|
||||
predictions = predictions
|
||||
|
||||
row = i + 1
|
||||
col = 1
|
||||
fig = self.get_plot(initial, target, predictions, show_legend=(0 == 0))
|
||||
|
||||
for trace in sub_fig.data:
|
||||
fig.add_trace(trace, row=row, col=col)
|
||||
|
||||
if cols == 2:
|
||||
error_sub_fig = self.get_plot_error(
|
||||
target, predictions
|
||||
)
|
||||
for trace in error_sub_fig.data:
|
||||
fig.add_trace(trace, row=row, col=col + 1)
|
||||
|
||||
loss = self.criterion(
|
||||
predictions.to(self.device), target.to(self.device)
|
||||
).item()
|
||||
|
||||
fig["layout"]["annotations"][i].update(
|
||||
text=f"{self.criterion.__class__.__name__}: {loss:.6f}"
|
||||
task.get_logger().report_matplotlib_figure(
|
||||
title="Training" if train else "Testing",
|
||||
series=f'Sample {actual_idx}',
|
||||
iteration=epoch,
|
||||
figure=fig,
|
||||
)
|
||||
|
||||
# y axis same for all plots
|
||||
# fig.update_yaxes(range=[-1, 1], col=1)
|
||||
|
||||
fig.update_layout(height=1000 * rows)
|
||||
task.get_logger().report_plotly(
|
||||
title=f"{'Training' if train else 'Test'} Samples",
|
||||
series="full_day",
|
||||
iteration=epoch,
|
||||
figure=fig,
|
||||
)
|
||||
|
||||
def auto_regressive(self, data_loader, idx, sequence_length: int = 96):
|
||||
self.model.eval()
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
from clearml import Task
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from src.policies.PolicyEvaluator import PolicyEvaluator
|
||||
from torchinfo import summary
|
||||
from src.losses.crps_metric import crps_from_samples
|
||||
from src.data.preprocessing import DataProcessor
|
||||
@@ -13,13 +14,25 @@ import seaborn as sns
|
||||
import matplotlib.patches as mpatches
|
||||
|
||||
|
||||
def sample_diffusion(model: DiffusionModel, n: int, inputs: torch.tensor, noise_steps=1000, beta_start=1e-4, beta_end=0.02, ts_length=96):
|
||||
def sample_diffusion(
|
||||
model: DiffusionModel,
|
||||
n: int,
|
||||
inputs: torch.tensor,
|
||||
noise_steps=1000,
|
||||
beta_start=1e-4,
|
||||
beta_end=0.02,
|
||||
ts_length=96,
|
||||
):
|
||||
device = next(model.parameters()).device
|
||||
beta = torch.linspace(beta_start, beta_end, noise_steps).to(device)
|
||||
alpha = 1. - beta
|
||||
alpha = 1.0 - beta
|
||||
alpha_hat = torch.cumprod(alpha, dim=0)
|
||||
|
||||
inputs = inputs.repeat(n, 1).to(device)
|
||||
if len(inputs.shape) == 2:
|
||||
inputs = inputs.repeat(n, 1)
|
||||
elif len(inputs.shape) == 3:
|
||||
inputs = inputs.repeat(n, 1, 1)
|
||||
|
||||
model.eval()
|
||||
with torch.no_grad():
|
||||
x = torch.randn(inputs.shape[0], ts_length).to(device)
|
||||
@@ -35,53 +48,74 @@ def sample_diffusion(model: DiffusionModel, n: int, inputs: torch.tensor, noise_
|
||||
else:
|
||||
noise = torch.zeros_like(x)
|
||||
|
||||
x = 1/torch.sqrt(_alpha) * (x-((1-_alpha) / (torch.sqrt(1 - _alpha_hat))) * predicted_noise) + torch.sqrt(_beta) * noise
|
||||
x = (
|
||||
1
|
||||
/ torch.sqrt(_alpha)
|
||||
* (x - ((1 - _alpha) / (torch.sqrt(1 - _alpha_hat))) * predicted_noise)
|
||||
+ torch.sqrt(_beta) * noise
|
||||
)
|
||||
x = torch.clamp(x, -1.0, 1.0)
|
||||
return x
|
||||
|
||||
|
||||
|
||||
class DiffusionTrainer:
|
||||
def __init__(self, model: nn.Module, data_processor: DataProcessor, device: torch.device):
|
||||
def __init__(
|
||||
self,
|
||||
model: nn.Module,
|
||||
data_processor: DataProcessor,
|
||||
device: torch.device,
|
||||
policy_evaluator: PolicyEvaluator = None,
|
||||
):
|
||||
self.model = model
|
||||
self.device = device
|
||||
|
||||
self.noise_steps = 1000
|
||||
self.beta_start = 1e-4
|
||||
self.noise_steps = 30
|
||||
self.beta_start = 0.0001
|
||||
self.beta_end = 0.02
|
||||
self.ts_length = 96
|
||||
|
||||
|
||||
self.data_processor = data_processor
|
||||
|
||||
self.beta = torch.linspace(self.beta_start, self.beta_end, self.noise_steps).to(self.device)
|
||||
self.alpha = 1. - self.beta
|
||||
self.beta = torch.linspace(self.beta_start, self.beta_end, self.noise_steps).to(
|
||||
self.device
|
||||
)
|
||||
self.alpha = 1.0 - self.beta
|
||||
self.alpha_hat = torch.cumprod(self.alpha, dim=0)
|
||||
|
||||
self.best_score = None
|
||||
self.policy_evaluator = policy_evaluator
|
||||
|
||||
def noise_time_series(self, x: torch.tensor, t: int):
|
||||
""" Add noise to time series
|
||||
"""Add noise to time series
|
||||
Args:
|
||||
x (torch.tensor): shape (batch_size, time_steps)
|
||||
t (int): index of time step
|
||||
"""
|
||||
sqrt_alpha_hat = torch.sqrt(self.alpha_hat[t])[:, None]
|
||||
sqrt_one_minus_alpha_hat = torch.sqrt(1. - self.alpha_hat[t])[:, None]
|
||||
sqrt_one_minus_alpha_hat = torch.sqrt(1.0 - self.alpha_hat[t])[:, None]
|
||||
noise = torch.randn_like(x)
|
||||
return sqrt_alpha_hat * x + sqrt_one_minus_alpha_hat * noise, noise
|
||||
|
||||
|
||||
def sample_timesteps(self, n: int):
|
||||
""" Sample timesteps for noise
|
||||
"""Sample timesteps for noise
|
||||
Args:
|
||||
n (int): number of samples
|
||||
"""
|
||||
return torch.randint(low=1, high=self.noise_steps, size=(n,))
|
||||
|
||||
|
||||
def sample(self, model: DiffusionModel, n: int, inputs: torch.tensor):
|
||||
x = sample_diffusion(model, n, inputs, self.noise_steps, self.beta_start, self.beta_end, self.ts_length)
|
||||
x = sample_diffusion(
|
||||
model,
|
||||
n,
|
||||
inputs,
|
||||
self.noise_steps,
|
||||
self.beta_start,
|
||||
self.beta_end,
|
||||
self.ts_length,
|
||||
)
|
||||
model.train()
|
||||
return x
|
||||
|
||||
|
||||
def random_samples(self, train: bool = True, num_samples: int = 10):
|
||||
train_loader, test_loader = self.data_processor.get_dataloaders(
|
||||
predict_sequence_length=96
|
||||
@@ -92,21 +126,47 @@ class DiffusionTrainer:
|
||||
else:
|
||||
loader = test_loader
|
||||
|
||||
indices = np.random.randint(0, len(loader.dataset) - 1, size=num_samples)
|
||||
# set seed
|
||||
np.random.seed(42)
|
||||
|
||||
actual_indices = np.random.choice(
|
||||
loader.dataset.full_day_valid_indices, num_samples, replace=False
|
||||
)
|
||||
indices = {}
|
||||
for i in actual_indices:
|
||||
indices[i] = loader.dataset.valid_indices.index(i)
|
||||
|
||||
print(actual_indices)
|
||||
|
||||
return indices
|
||||
|
||||
|
||||
def init_clearml_task(self, task):
|
||||
task.add_tags(self.model.__class__.__name__)
|
||||
task.add_tags(self.__class__.__name__)
|
||||
|
||||
input_data = torch.randn(1024, 96).to(self.device)
|
||||
time_steps = torch.randn(1024).long().to(self.device)
|
||||
other_input_data = torch.randn(1024, self.model.other_inputs_dim).to(self.device)
|
||||
|
||||
task.set_configuration_object("model", str(summary(self.model, input_data=[input_data, time_steps, other_input_data])))
|
||||
if self.data_processor.lstm:
|
||||
inputDim = self.data_processor.get_input_size()
|
||||
other_input_data = torch.randn(
|
||||
1024, inputDim[1], self.model.other_inputs_dim
|
||||
).to(self.device)
|
||||
else:
|
||||
other_input_data = torch.randn(1024, self.model.other_inputs_dim).to(
|
||||
self.device
|
||||
)
|
||||
task.set_configuration_object(
|
||||
"model",
|
||||
str(
|
||||
summary(
|
||||
self.model, input_data=[input_data, time_steps, other_input_data]
|
||||
)
|
||||
),
|
||||
)
|
||||
|
||||
self.data_processor = task.connect(self.data_processor, name="data_processor")
|
||||
|
||||
|
||||
def train(self, epochs: int, learning_rate: float, task: Task = None):
|
||||
self.best_score = None
|
||||
optimizer = torch.optim.Adam(self.model.parameters(), lr=learning_rate)
|
||||
@@ -120,8 +180,8 @@ class DiffusionTrainer:
|
||||
predict_sequence_length=self.ts_length
|
||||
)
|
||||
|
||||
train_sample_indices = self.random_samples(train=True, num_samples=10)
|
||||
test_sample_indices = self.random_samples(train=False, num_samples=10)
|
||||
train_sample_indices = self.random_samples(train=True, num_samples=5)
|
||||
test_sample_indices = self.random_samples(train=False, num_samples=5)
|
||||
|
||||
for epoch in range(epochs):
|
||||
running_loss = 0.0
|
||||
@@ -140,92 +200,150 @@ class DiffusionTrainer:
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
|
||||
running_loss /= len(train_loader.dataset)
|
||||
|
||||
if epoch % 20 == 0 and epoch != 0:
|
||||
if epoch % 40 == 0 and epoch != 0:
|
||||
self.test(test_loader, epoch, task)
|
||||
|
||||
if task:
|
||||
task.get_logger().report_scalar(
|
||||
title=criterion.__class__.__name__,
|
||||
series='train',
|
||||
series="train",
|
||||
iteration=epoch,
|
||||
value=loss.item(),
|
||||
)
|
||||
|
||||
if epoch % 100 == 0 and epoch != 0:
|
||||
self.debug_plots(task, True, train_loader, train_sample_indices, epoch)
|
||||
self.debug_plots(task, False, test_loader, test_sample_indices, epoch)
|
||||
if epoch % 150 == 0 and epoch != 0:
|
||||
self.debug_plots(
|
||||
task, True, train_loader, train_sample_indices, epoch
|
||||
)
|
||||
self.debug_plots(
|
||||
task, False, test_loader, test_sample_indices, epoch
|
||||
)
|
||||
|
||||
if task:
|
||||
task.close()
|
||||
|
||||
|
||||
def debug_plots(self, task, training: bool, data_loader, sample_indices, epoch):
|
||||
for i, idx in enumerate(sample_indices):
|
||||
for actual_idx, idx in sample_indices.items():
|
||||
features, target, _ = data_loader.dataset[idx]
|
||||
|
||||
features = features.to(self.device)
|
||||
features = features.unsqueeze(0)
|
||||
|
||||
self.model.eval()
|
||||
with torch.no_grad():
|
||||
samples = self.sample(self.model, 100, features).cpu().numpy()
|
||||
|
||||
ci_99_upper = np.quantile(samples, 0.99, axis=0)
|
||||
ci_99_lower = np.quantile(samples, 0.01, axis=0)
|
||||
samples = self.data_processor.inverse_transform(samples)
|
||||
target = self.data_processor.inverse_transform(target)
|
||||
|
||||
ci_95_upper = np.quantile(samples, 0.95, axis=0)
|
||||
ci_95_lower = np.quantile(samples, 0.05, axis=0)
|
||||
ci_99_upper = np.quantile(samples, 0.995, axis=0)
|
||||
ci_99_lower = np.quantile(samples, 0.005, axis=0)
|
||||
|
||||
ci_90_upper = np.quantile(samples, 0.9, axis=0)
|
||||
ci_90_lower = np.quantile(samples, 0.1, axis=0)
|
||||
ci_95_upper = np.quantile(samples, 0.975, axis=0)
|
||||
ci_95_lower = np.quantile(samples, 0.025, axis=0)
|
||||
|
||||
ci_50_upper = np.quantile(samples, 0.5, axis=0)
|
||||
ci_50_lower = np.quantile(samples, 0.5, axis=0)
|
||||
ci_90_upper = np.quantile(samples, 0.95, axis=0)
|
||||
ci_90_lower = np.quantile(samples, 0.05, axis=0)
|
||||
|
||||
ci_50_lower = np.quantile(samples, 0.25, axis=0)
|
||||
ci_50_upper = np.quantile(samples, 0.75, axis=0)
|
||||
|
||||
sns.set_theme()
|
||||
time_steps = np.arange(0, 96)
|
||||
|
||||
fig, ax = plt.subplots(figsize=(20, 10))
|
||||
ax.plot(time_steps, samples.mean(axis=0), label="Mean of NRV samples", linewidth=3)
|
||||
ax.plot(
|
||||
time_steps,
|
||||
samples.mean(axis=0),
|
||||
label="Mean of NRV samples",
|
||||
linewidth=3,
|
||||
)
|
||||
# ax.fill_between(time_steps, ci_lower, ci_upper, color='b', alpha=0.2, label='Full Interval')
|
||||
|
||||
ax.fill_between(time_steps, ci_99_lower, ci_99_upper, color='b', alpha=0.2, label='99% Interval')
|
||||
ax.fill_between(time_steps, ci_95_lower, ci_95_upper, color='b', alpha=0.2, label='95% Interval')
|
||||
ax.fill_between(time_steps, ci_90_lower, ci_90_upper, color='b', alpha=0.2, label='90% Interval')
|
||||
ax.fill_between(time_steps, ci_50_lower, ci_50_upper, color='b', alpha=0.2, label='50% Interval')
|
||||
ax.fill_between(
|
||||
time_steps,
|
||||
ci_99_lower,
|
||||
ci_99_upper,
|
||||
color="b",
|
||||
alpha=0.2,
|
||||
label="99% Interval",
|
||||
)
|
||||
ax.fill_between(
|
||||
time_steps,
|
||||
ci_95_lower,
|
||||
ci_95_upper,
|
||||
color="b",
|
||||
alpha=0.2,
|
||||
label="95% Interval",
|
||||
)
|
||||
ax.fill_between(
|
||||
time_steps,
|
||||
ci_90_lower,
|
||||
ci_90_upper,
|
||||
color="b",
|
||||
alpha=0.2,
|
||||
label="90% Interval",
|
||||
)
|
||||
ax.fill_between(
|
||||
time_steps,
|
||||
ci_50_lower,
|
||||
ci_50_upper,
|
||||
color="b",
|
||||
alpha=0.2,
|
||||
label="50% Interval",
|
||||
)
|
||||
|
||||
ax.plot(target, label="Real NRV", linewidth=3)
|
||||
# full_interval_patch = mpatches.Patch(color='b', alpha=0.2, label='Full Interval')
|
||||
ci_99_patch = mpatches.Patch(color='b', alpha=0.3, label='99% Interval')
|
||||
ci_95_patch = mpatches.Patch(color='b', alpha=0.4, label='95% Interval')
|
||||
ci_90_patch = mpatches.Patch(color='b', alpha=0.5, label='90% Interval')
|
||||
ci_50_patch = mpatches.Patch(color='b', alpha=0.6, label='50% Interval')
|
||||
ci_99_patch = mpatches.Patch(color="b", alpha=0.3, label="99% Interval")
|
||||
ci_95_patch = mpatches.Patch(color="b", alpha=0.4, label="95% Interval")
|
||||
ci_90_patch = mpatches.Patch(color="b", alpha=0.5, label="90% Interval")
|
||||
ci_50_patch = mpatches.Patch(color="b", alpha=0.6, label="50% Interval")
|
||||
|
||||
|
||||
ax.legend(handles=[ci_99_patch, ci_95_patch, ci_90_patch, ci_50_patch, ax.lines[0], ax.lines[1]])
|
||||
ax.legend(
|
||||
handles=[
|
||||
ci_99_patch,
|
||||
ci_95_patch,
|
||||
ci_90_patch,
|
||||
ci_50_patch,
|
||||
ax.lines[0],
|
||||
ax.lines[1],
|
||||
]
|
||||
)
|
||||
|
||||
task.get_logger().report_matplotlib_figure(
|
||||
title="Training" if training else "Testing",
|
||||
series=f'Sample {i}',
|
||||
series=f"Sample {actual_idx}",
|
||||
iteration=epoch,
|
||||
figure=fig,
|
||||
)
|
||||
|
||||
plt.close()
|
||||
|
||||
def test(self, data_loader: torch.utils.data.DataLoader, epoch: int, task: Task = None):
|
||||
def test(
|
||||
self, data_loader: torch.utils.data.DataLoader, epoch: int, task: Task = None
|
||||
):
|
||||
all_crps = []
|
||||
for inputs, targets, _ in data_loader:
|
||||
generated_samples = {}
|
||||
for inputs, targets, idx_batch in data_loader:
|
||||
inputs, targets = inputs.to(self.device), targets.to(self.device)
|
||||
|
||||
print(inputs.shape, targets.shape)
|
||||
|
||||
number_of_samples = 100
|
||||
sample = self.sample(self.model, number_of_samples, inputs)
|
||||
|
||||
|
||||
# reduce samples from (batch_size*number_of_samples, time_steps) to (batch_size, number_of_samples, time_steps)
|
||||
samples_batched = sample.reshape(inputs.shape[0], number_of_samples, 96)
|
||||
|
||||
# add samples to generated_samples generated_samples[idx.item()] = (initial, samples)
|
||||
for i, (idx, samples) in enumerate(zip(idx_batch, samples_batched)):
|
||||
generated_samples[idx.item()] = (
|
||||
self.data_processor.inverse_transform(inputs[i][:96]),
|
||||
self.data_processor.inverse_transform(samples),
|
||||
)
|
||||
|
||||
# calculate crps
|
||||
crps = crps_from_samples(samples_batched, targets)
|
||||
crps_mean = crps.mean(axis=1)
|
||||
@@ -241,16 +359,38 @@ class DiffusionTrainer:
|
||||
|
||||
if task:
|
||||
task.get_logger().report_scalar(
|
||||
title="CRPS",
|
||||
series='test',
|
||||
value=mean_crps,
|
||||
iteration=epoch
|
||||
title="CRPS", series="test", value=mean_crps, iteration=epoch
|
||||
)
|
||||
|
||||
if self.policy_evaluator:
|
||||
_, test_loader = self.data_processor.get_dataloaders(
|
||||
predict_sequence_length=self.ts_length, full_day_skip=True
|
||||
)
|
||||
|
||||
self.policy_evaluator.evaluate_test_set(generated_samples, test_loader)
|
||||
|
||||
df = self.policy_evaluator.get_profits_as_scalars()
|
||||
|
||||
for idx, row in df.iterrows():
|
||||
task.get_logger().report_scalar(
|
||||
title="Profit",
|
||||
series=f"penalty_{row['Penalty']}",
|
||||
value=row["Total Profit"],
|
||||
iteration=epoch,
|
||||
)
|
||||
|
||||
df = self.policy_evaluator.get_profits_till_400()
|
||||
for idx, row in df.iterrows():
|
||||
task.get_logger().report_scalar(
|
||||
title="Profit_till_400",
|
||||
series=f"penalty_{row['Penalty']}",
|
||||
value=row["Profit_till_400"],
|
||||
iteration=epoch,
|
||||
)
|
||||
|
||||
def save_checkpoint(self, val_loss, task, iteration: int):
|
||||
torch.save(self.model, "checkpoint.pt")
|
||||
task.update_output_model(
|
||||
model_path="checkpoint.pt", iteration=iteration, auto_delete_file=False
|
||||
)
|
||||
self.best_score = val_loss
|
||||
|
||||
|
||||
@@ -1,5 +1,6 @@
|
||||
import torch
|
||||
from tqdm import tqdm
|
||||
from src.policies.PolicyEvaluator import PolicyEvaluator
|
||||
from src.losses.crps_metric import crps_from_samples
|
||||
from src.trainers.trainer import Trainer
|
||||
from src.trainers.autoregressive_trainer import AutoRegressiveTrainer
|
||||
@@ -10,6 +11,9 @@ import plotly.graph_objects as go
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from scipy.interpolate import CubicSpline
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
import matplotlib.patches as mpatches
|
||||
|
||||
|
||||
def sample_from_dist(quantiles, preds):
|
||||
@@ -28,10 +32,11 @@ def sample_from_dist(quantiles, preds):
|
||||
|
||||
# random probabilities of (1000, 1)
|
||||
import random
|
||||
|
||||
probs = np.array([random.random() for _ in range(1000)])
|
||||
|
||||
spline = CubicSpline(quantiles, preds, axis=1)
|
||||
|
||||
|
||||
samples = spline(probs)
|
||||
|
||||
# get the diagonal
|
||||
@@ -39,6 +44,7 @@ def sample_from_dist(quantiles, preds):
|
||||
|
||||
return samples
|
||||
|
||||
|
||||
def auto_regressive(dataset, model, quantiles, idx_batch, sequence_length: int = 96):
|
||||
device = next(model.parameters()).device
|
||||
prev_features, targets = dataset.get_batch(idx_batch)
|
||||
@@ -62,7 +68,7 @@ def auto_regressive(dataset, model, quantiles, idx_batch, sequence_length: int =
|
||||
predictions_full = new_predictions_full.unsqueeze(1)
|
||||
|
||||
for i in range(sequence_length - 1):
|
||||
if len(list(prev_features.shape)) == 2:
|
||||
if len(list(prev_features.shape)) == 2:
|
||||
new_features = torch.cat(
|
||||
(prev_features[:, 1:96], samples), dim=1
|
||||
) # (batch_size, 96)
|
||||
@@ -99,9 +105,7 @@ def auto_regressive(dataset, model, quantiles, idx_batch, sequence_length: int =
|
||||
) # (batch_size, sequence_length)
|
||||
|
||||
with torch.no_grad():
|
||||
new_predictions_full = model(
|
||||
prev_features
|
||||
) # (batch_size, quantiles)
|
||||
new_predictions_full = model(prev_features) # (batch_size, quantiles)
|
||||
predictions_full = torch.cat(
|
||||
(predictions_full, new_predictions_full.unsqueeze(1)), dim=1
|
||||
) # (batch_size, sequence_length, quantiles)
|
||||
@@ -120,6 +124,7 @@ def auto_regressive(dataset, model, quantiles, idx_batch, sequence_length: int =
|
||||
target_full.unsqueeze(-1),
|
||||
)
|
||||
|
||||
|
||||
class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
|
||||
def __init__(
|
||||
self,
|
||||
@@ -129,10 +134,13 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
|
||||
data_processor: DataProcessor,
|
||||
quantiles: list,
|
||||
device: torch.device,
|
||||
policy_evaluator: PolicyEvaluator = None,
|
||||
debug: bool = True,
|
||||
):
|
||||
|
||||
self.quantiles = quantiles
|
||||
self.test_set_samples = {}
|
||||
self.policy_evaluator = policy_evaluator
|
||||
|
||||
criterion = PinballLoss(quantiles=quantiles)
|
||||
super().__init__(
|
||||
@@ -147,6 +155,7 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
|
||||
|
||||
def calculate_crps_from_samples(self, task, dataloader, epoch: int):
|
||||
crps_from_samples_metric = []
|
||||
generated_samples = {}
|
||||
|
||||
with torch.no_grad():
|
||||
total_samples = len(dataloader.dataset) - 96
|
||||
@@ -155,24 +164,59 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
|
||||
|
||||
if len(idx_batch) == 0:
|
||||
continue
|
||||
|
||||
|
||||
for idx in tqdm(idx_batch):
|
||||
computed_idx_batch = [idx] * 100
|
||||
_, _, samples, targets = self.auto_regressive(
|
||||
initial, _, samples, targets = self.auto_regressive(
|
||||
dataloader.dataset, idx_batch=computed_idx_batch
|
||||
)
|
||||
|
||||
generated_samples[idx.item()] = (
|
||||
self.data_processor.inverse_transform(initial),
|
||||
self.data_processor.inverse_transform(samples),
|
||||
)
|
||||
|
||||
samples = samples.unsqueeze(0)
|
||||
targets = targets.squeeze(-1)
|
||||
targets = targets[0].unsqueeze(0)
|
||||
|
||||
|
||||
crps = crps_from_samples(samples, targets)
|
||||
|
||||
crps_from_samples_metric.append(crps[0].mean().item())
|
||||
|
||||
task.get_logger().report_scalar(
|
||||
title="CRPS_from_samples", series="test", value=np.mean(crps_from_samples_metric), iteration=epoch
|
||||
title="CRPS_from_samples",
|
||||
series="test",
|
||||
value=np.mean(crps_from_samples_metric),
|
||||
iteration=epoch,
|
||||
)
|
||||
|
||||
# using the policy evaluator, evaluate the policy with the generated samples
|
||||
if self.policy_evaluator is not None:
|
||||
_, test_loader = self.data_processor.get_dataloaders(
|
||||
predict_sequence_length=self.model.output_size, full_day_skip=True
|
||||
)
|
||||
self.policy_evaluator.evaluate_test_set(generated_samples, test_loader)
|
||||
df = self.policy_evaluator.get_profits_as_scalars()
|
||||
|
||||
# for each row, report the profits
|
||||
for idx, row in df.iterrows():
|
||||
task.get_logger().report_scalar(
|
||||
title="Profit",
|
||||
series=f"penalty_{row['Penalty']}",
|
||||
value=row["Total Profit"],
|
||||
iteration=epoch,
|
||||
)
|
||||
|
||||
df = self.policy_evaluator.get_profits_till_400()
|
||||
for idx, row in df.iterrows():
|
||||
task.get_logger().report_scalar(
|
||||
title="Profit_till_400",
|
||||
series=f"penalty_{row['Penalty']}",
|
||||
value=row["Profit_till_400"],
|
||||
iteration=epoch,
|
||||
)
|
||||
|
||||
def log_final_metrics(self, task, dataloader, train: bool = True):
|
||||
metrics = {metric.__class__.__name__: 0.0 for metric in self.metrics_to_track}
|
||||
transformed_metrics = {
|
||||
@@ -192,19 +236,25 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
|
||||
|
||||
if train == False:
|
||||
for idx in tqdm(idx_batch):
|
||||
computed_idx_batch = [idx] * 100
|
||||
_, outputs, samples, targets = self.auto_regressive(
|
||||
computed_idx_batch = [idx] * 250
|
||||
initial, outputs, samples, targets = self.auto_regressive(
|
||||
dataloader.dataset, idx_batch=computed_idx_batch
|
||||
)
|
||||
|
||||
# save the samples for the idx, these will be used for evaluating the policy
|
||||
self.test_set_samples[idx.item()] = (
|
||||
self.data_processor.inverse_transform(initial),
|
||||
self.data_processor.inverse_transform(samples),
|
||||
)
|
||||
|
||||
samples = samples.unsqueeze(0)
|
||||
targets = targets.squeeze(-1)
|
||||
targets = targets[0].unsqueeze(0)
|
||||
|
||||
|
||||
crps = crps_from_samples(samples, targets)
|
||||
|
||||
crps_from_samples_metric.append(crps[0].mean().item())
|
||||
|
||||
|
||||
_, outputs, samples, targets = self.auto_regressive(
|
||||
dataloader.dataset, idx_batch=idx_batch
|
||||
)
|
||||
@@ -258,39 +308,39 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
|
||||
|
||||
if train == False:
|
||||
task.get_logger().report_single_value(
|
||||
name="test_CRPS_from_samples_transformed", value=np.mean(crps_from_samples_metric)
|
||||
name="test_CRPS_from_samples_transformed",
|
||||
value=np.mean(crps_from_samples_metric),
|
||||
)
|
||||
|
||||
def get_plot_error(
|
||||
self,
|
||||
next_day,
|
||||
predictions,
|
||||
):
|
||||
metric = PinballLoss(quantiles=self.quantiles)
|
||||
fig = go.Figure()
|
||||
# def get_plot_error(
|
||||
# self,
|
||||
# next_day,
|
||||
# predictions,
|
||||
# ):
|
||||
# metric = PinballLoss(quantiles=self.quantiles)
|
||||
# fig = go.Figure()
|
||||
|
||||
next_day_np = next_day.view(-1).cpu().numpy()
|
||||
predictions_np = predictions.cpu().numpy()
|
||||
# next_day_np = next_day.view(-1).cpu().numpy()
|
||||
# predictions_np = predictions.cpu().numpy()
|
||||
|
||||
if True:
|
||||
next_day_np = self.data_processor.inverse_transform(next_day_np)
|
||||
predictions_np = self.data_processor.inverse_transform(predictions_np)
|
||||
# if True:
|
||||
# next_day_np = self.data_processor.inverse_transform(next_day_np)
|
||||
# predictions_np = self.data_processor.inverse_transform(predictions_np)
|
||||
|
||||
# for each time step, calculate the error using the metric
|
||||
errors = []
|
||||
for i in range(96):
|
||||
# # for each time step, calculate the error using the metric
|
||||
# errors = []
|
||||
# for i in range(96):
|
||||
|
||||
target_tensor = torch.tensor(next_day_np[i]).unsqueeze(0)
|
||||
prediction_tensor = torch.tensor(predictions_np[i]).unsqueeze(0)
|
||||
# target_tensor = torch.tensor(next_day_np[i]).unsqueeze(0)
|
||||
# prediction_tensor = torch.tensor(predictions_np[i]).unsqueeze(0)
|
||||
|
||||
errors.append(metric(prediction_tensor, target_tensor))
|
||||
# errors.append(metric(prediction_tensor, target_tensor))
|
||||
|
||||
# plot the error
|
||||
fig.add_trace(go.Scatter(x=np.arange(96), y=errors, name=metric.__class__.__name__))
|
||||
fig.update_layout(title=f"Error of {metric.__class__.__name__} for each time step")
|
||||
|
||||
return fig
|
||||
# # plot the error
|
||||
# fig.add_trace(go.Scatter(x=np.arange(96), y=errors, name=metric.__class__.__name__))
|
||||
# fig.update_layout(title=f"Error of {metric.__class__.__name__} for each time step")
|
||||
|
||||
# return fig
|
||||
|
||||
def get_plot(
|
||||
self,
|
||||
@@ -312,33 +362,114 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
|
||||
next_day_np = self.data_processor.inverse_transform(next_day_np)
|
||||
predictions_np = self.data_processor.inverse_transform(predictions_np)
|
||||
|
||||
ci_99_upper = np.quantile(predictions_np, 0.995, axis=0)
|
||||
ci_99_lower = np.quantile(predictions_np, 0.005, axis=0)
|
||||
|
||||
ci_95_upper = np.quantile(predictions_np, 0.975, axis=0)
|
||||
ci_95_lower = np.quantile(predictions_np, 0.025, axis=0)
|
||||
|
||||
ci_90_upper = np.quantile(predictions_np, 0.95, axis=0)
|
||||
ci_90_lower = np.quantile(predictions_np, 0.05, axis=0)
|
||||
|
||||
ci_50_lower = np.quantile(predictions_np, 0.25, axis=0)
|
||||
ci_50_upper = np.quantile(predictions_np, 0.75, axis=0)
|
||||
|
||||
# Add traces for current and next day
|
||||
fig.add_trace(go.Scatter(x=np.arange(96), y=current_day_np, name="Current Day"))
|
||||
fig.add_trace(go.Scatter(x=96 + np.arange(96), y=next_day_np, name="Next Day"))
|
||||
# fig.add_trace(go.Scatter(x=np.arange(96), y=current_day_np, name="Current Day"))
|
||||
# fig.add_trace(go.Scatter(x=96 + np.arange(96), y=next_day_np, name="Next Day"))
|
||||
|
||||
for i, q in enumerate(self.quantiles):
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=96 + np.arange(96),
|
||||
y=predictions_np[:, i],
|
||||
name=f"Prediction (Q={q})",
|
||||
line=dict(dash="dash"),
|
||||
)
|
||||
)
|
||||
# for i, q in enumerate(self.quantiles):
|
||||
# fig.add_trace(
|
||||
# go.Scatter(
|
||||
# x=96 + np.arange(96),
|
||||
# y=predictions_np[:, i],
|
||||
# name=f"Prediction (Q={q})",
|
||||
# line=dict(dash="dash"),
|
||||
# )
|
||||
# )
|
||||
|
||||
# Update the layout
|
||||
fig.update_layout(
|
||||
title="Predictions and Quantiles of the Linear Model",
|
||||
showlegend=show_legend,
|
||||
# # Update the layout
|
||||
# fig.update_layout(
|
||||
# title="Predictions and Quantiles of the Linear Model",
|
||||
# showlegend=show_legend,
|
||||
# )
|
||||
|
||||
sns.set_theme()
|
||||
time_steps = np.arange(0, 96)
|
||||
|
||||
fig, ax = plt.subplots(figsize=(20, 10))
|
||||
ax.plot(
|
||||
time_steps,
|
||||
predictions_np.mean(axis=0),
|
||||
label="Mean of NRV samples",
|
||||
linewidth=3,
|
||||
)
|
||||
# ax.fill_between(time_steps, ci_lower, ci_upper, color='b', alpha=0.2, label='Full Interval')
|
||||
|
||||
ax.fill_between(
|
||||
time_steps,
|
||||
ci_99_lower,
|
||||
ci_99_upper,
|
||||
color="b",
|
||||
alpha=0.2,
|
||||
label="99% Interval",
|
||||
)
|
||||
ax.fill_between(
|
||||
time_steps,
|
||||
ci_95_lower,
|
||||
ci_95_upper,
|
||||
color="b",
|
||||
alpha=0.2,
|
||||
label="95% Interval",
|
||||
)
|
||||
ax.fill_between(
|
||||
time_steps,
|
||||
ci_90_lower,
|
||||
ci_90_upper,
|
||||
color="b",
|
||||
alpha=0.2,
|
||||
label="90% Interval",
|
||||
)
|
||||
ax.fill_between(
|
||||
time_steps,
|
||||
ci_50_lower,
|
||||
ci_50_upper,
|
||||
color="b",
|
||||
alpha=0.2,
|
||||
label="50% Interval",
|
||||
)
|
||||
|
||||
ax.plot(next_day_np, label="Real NRV", linewidth=3)
|
||||
# full_interval_patch = mpatches.Patch(color='b', alpha=0.2, label='Full Interval')
|
||||
ci_99_patch = mpatches.Patch(color="b", alpha=0.3, label="99% Interval")
|
||||
ci_95_patch = mpatches.Patch(color="b", alpha=0.4, label="95% Interval")
|
||||
ci_90_patch = mpatches.Patch(color="b", alpha=0.5, label="90% Interval")
|
||||
ci_50_patch = mpatches.Patch(color="b", alpha=0.6, label="50% Interval")
|
||||
|
||||
ax.legend(
|
||||
handles=[
|
||||
ci_99_patch,
|
||||
ci_95_patch,
|
||||
ci_90_patch,
|
||||
ci_50_patch,
|
||||
ax.lines[0],
|
||||
ax.lines[1],
|
||||
]
|
||||
)
|
||||
return fig
|
||||
|
||||
def auto_regressive(self, dataset, idx_batch, sequence_length: int = 96):
|
||||
return auto_regressive(dataset, self.model, self.quantiles, idx_batch, sequence_length)
|
||||
return auto_regressive(
|
||||
dataset, self.model, self.quantiles, idx_batch, sequence_length
|
||||
)
|
||||
|
||||
def plot_quantile_percentages(
|
||||
self, task, data_loader, train: bool = True, iteration: int = None, full_day: bool = False
|
||||
self,
|
||||
task,
|
||||
data_loader,
|
||||
train: bool = True,
|
||||
iteration: int = None,
|
||||
full_day: bool = False,
|
||||
):
|
||||
quantiles = self.quantiles
|
||||
total = 0
|
||||
@@ -368,20 +499,18 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
|
||||
|
||||
else:
|
||||
inputs = inputs.to(self.device)
|
||||
outputs = self.model(inputs).cpu().numpy() # (batch_size, num_quantiles)
|
||||
targets = targets.squeeze(-1).cpu().numpy() # (batch_size, 1)
|
||||
outputs = (
|
||||
self.model(inputs).cpu().numpy()
|
||||
) # (batch_size, num_quantiles)
|
||||
targets = targets.squeeze(-1).cpu().numpy() # (batch_size, 1)
|
||||
|
||||
for i, q in enumerate(quantiles):
|
||||
quantile_counter[q] += np.sum(
|
||||
targets < outputs[:, i]
|
||||
)
|
||||
quantile_counter[q] += np.sum(targets < outputs[:, i])
|
||||
|
||||
total += len(targets)
|
||||
|
||||
# to numpy array of length len(quantiles)
|
||||
percentages = np.array(
|
||||
[quantile_counter[q] / total for q in quantiles]
|
||||
)
|
||||
percentages = np.array([quantile_counter[q] / total for q in quantiles])
|
||||
|
||||
bar_width = 0.35
|
||||
index = np.arange(len(quantiles))
|
||||
@@ -389,9 +518,7 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
|
||||
# Plotting the bars
|
||||
fig, ax = plt.subplots(figsize=(15, 10))
|
||||
|
||||
bar1 = ax.bar(
|
||||
index, quantiles, bar_width, label="Ideal", color="brown"
|
||||
)
|
||||
bar1 = ax.bar(index, quantiles, bar_width, label="Ideal", color="brown")
|
||||
bar2 = ax.bar(
|
||||
index + bar_width, percentages, bar_width, label="NN model", color="blue"
|
||||
)
|
||||
@@ -441,7 +568,6 @@ class NonAutoRegressiveQuantileRegression(Trainer):
|
||||
):
|
||||
self.quantiles = quantiles
|
||||
|
||||
|
||||
criterion = NonAutoRegressivePinballLoss(quantiles=quantiles)
|
||||
super().__init__(
|
||||
model=model,
|
||||
|
||||
@@ -86,7 +86,7 @@ class Trainer:
|
||||
|
||||
def random_samples(self, train: bool = True, num_samples: int = 10):
|
||||
train_loader, test_loader = self.data_processor.get_dataloaders(
|
||||
predict_sequence_length=self.model.output_size
|
||||
predict_sequence_length=96
|
||||
)
|
||||
|
||||
if train:
|
||||
@@ -94,7 +94,14 @@ class Trainer:
|
||||
else:
|
||||
loader = test_loader
|
||||
|
||||
indices = np.random.randint(0, len(loader.dataset) - 1, size=num_samples)
|
||||
np.random.seed(42)
|
||||
actual_indices = np.random.choice(loader.dataset.full_day_valid_indices, num_samples, replace=False)
|
||||
indices = {}
|
||||
for i in actual_indices:
|
||||
indices[i] = loader.dataset.valid_indices.index(i)
|
||||
|
||||
print(actual_indices)
|
||||
|
||||
return indices
|
||||
|
||||
def train(self, epochs: int, remotely: bool = False, task: Task = None):
|
||||
@@ -107,8 +114,8 @@ class Trainer:
|
||||
predict_sequence_length=self.model.output_size
|
||||
)
|
||||
|
||||
train_samples = self.random_samples(train=True)
|
||||
test_samples = self.random_samples(train=False)
|
||||
train_samples = self.random_samples(train=True, num_samples=5)
|
||||
test_samples = self.random_samples(train=False, num_samples=5)
|
||||
|
||||
self.init_clearml_task(task)
|
||||
|
||||
@@ -189,7 +196,7 @@ class Trainer:
|
||||
|
||||
if task:
|
||||
self.finish_training(task=task)
|
||||
task.close()
|
||||
# task.close()
|
||||
except Exception:
|
||||
if task:
|
||||
task.close()
|
||||
|
||||
@@ -1,3 +1,14 @@
|
||||
from src.utils.clearml import ClearMLHelper
|
||||
|
||||
#### ClearML ####
|
||||
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
|
||||
task = clearml_helper.get_task(
|
||||
task_name="Autoregressive Quantile Regression: Non Linear"
|
||||
)
|
||||
task.execute_remotely(queue_name="default", exit_process=True)
|
||||
|
||||
from src.policies.PolicyEvaluator import PolicyEvaluator
|
||||
from src.policies.simple_baseline import BaselinePolicy, Battery
|
||||
from src.models.lstm_model import GRUModel
|
||||
from src.data import DataProcessor, DataConfig
|
||||
from src.trainers.quantile_trainer import AutoRegressiveQuantileTrainer
|
||||
@@ -11,11 +22,6 @@ import torch.nn as nn
|
||||
from src.models.time_embedding_layer import TimeEmbedding
|
||||
|
||||
|
||||
#### ClearML ####
|
||||
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
|
||||
task = clearml_helper.get_task(task_name="Autoregressive Quantile Regression: Linear + Quarter + DoW + Load + Wind + Net")
|
||||
|
||||
|
||||
#### Data Processor ####
|
||||
data_config = DataConfig()
|
||||
|
||||
@@ -32,7 +38,6 @@ data_config.DAY_OF_WEEK = True
|
||||
data_config.NOMINAL_NET_POSITION = True
|
||||
|
||||
|
||||
|
||||
data_config = task.connect(data_config, name="data_features")
|
||||
|
||||
data_processor = DataProcessor(data_config, path="", lstm=False)
|
||||
@@ -58,22 +63,35 @@ else:
|
||||
|
||||
model_parameters = {
|
||||
"learning_rate": 0.0001,
|
||||
"hidden_size": 512,
|
||||
"num_layers": 2,
|
||||
"hidden_size": 256,
|
||||
"num_layers": 4,
|
||||
"dropout": 0.2,
|
||||
"time_feature_embedding": 4,
|
||||
"time_feature_embedding": 16,
|
||||
}
|
||||
|
||||
model_parameters = task.connect(model_parameters, name="model_parameters")
|
||||
|
||||
time_embedding = TimeEmbedding(data_processor.get_time_feature_size(), model_parameters["time_feature_embedding"])
|
||||
time_embedding = TimeEmbedding(
|
||||
data_processor.get_time_feature_size(), model_parameters["time_feature_embedding"]
|
||||
)
|
||||
# lstm_model = GRUModel(time_embedding.output_dim(inputDim), len(quantiles), hidden_size=model_parameters["hidden_size"], num_layers=model_parameters["num_layers"], dropout=model_parameters["dropout"])
|
||||
# non_linear_model = NonLinearRegression(time_embedding.output_dim(inputDim), len(quantiles), hiddenSize=model_parameters["hidden_size"], numLayers=model_parameters["num_layers"], dropout=model_parameters["dropout"])
|
||||
linear_model = LinearRegression(time_embedding.output_dim(inputDim), len(quantiles))
|
||||
non_linear_model = NonLinearRegression(
|
||||
time_embedding.output_dim(inputDim),
|
||||
len(quantiles),
|
||||
hiddenSize=model_parameters["hidden_size"],
|
||||
numLayers=model_parameters["num_layers"],
|
||||
dropout=model_parameters["dropout"],
|
||||
)
|
||||
# linear_model = LinearRegression(time_embedding.output_dim(inputDim), len(quantiles))
|
||||
|
||||
model = nn.Sequential(time_embedding, linear_model)
|
||||
model = nn.Sequential(time_embedding, non_linear_model)
|
||||
optimizer = torch.optim.Adam(model.parameters(), lr=model_parameters["learning_rate"])
|
||||
|
||||
### Policy Evaluator ###
|
||||
battery = Battery(2, 1)
|
||||
baseline_policy = BaselinePolicy(battery, data_path="")
|
||||
policy_evaluator = PolicyEvaluator(baseline_policy, task)
|
||||
|
||||
#### Trainer ####
|
||||
trainer = AutoRegressiveQuantileTrainer(
|
||||
model,
|
||||
@@ -82,6 +100,7 @@ trainer = AutoRegressiveQuantileTrainer(
|
||||
data_processor,
|
||||
quantiles,
|
||||
"cuda",
|
||||
policy_evaluator=policy_evaluator,
|
||||
debug=False,
|
||||
)
|
||||
|
||||
@@ -91,3 +110,15 @@ trainer.add_metrics_to_track(
|
||||
trainer.early_stopping(patience=30)
|
||||
trainer.plot_every(5)
|
||||
trainer.train(task=task, epochs=epochs, remotely=True)
|
||||
|
||||
### Policy Evaluation ###
|
||||
idx_samples = trainer.test_set_samples
|
||||
_, test_loader = trainer.data_processor.get_dataloaders(
|
||||
predict_sequence_length=trainer.model.output_size, full_day_skip=True
|
||||
)
|
||||
|
||||
policy_evaluator.evaluate_test_set(idx_samples, test_loader)
|
||||
policy_evaluator.plot_profits_table()
|
||||
policy_evaluator.plot_thresholds_per_day()
|
||||
|
||||
task.close()
|
||||
|
||||
@@ -1,25 +1,17 @@
|
||||
from clearml import Task
|
||||
from src.data import DataProcessor, DataConfig
|
||||
from src.trainers.trainer import Trainer
|
||||
from src.utils.clearml import ClearMLHelper
|
||||
from src.models import *
|
||||
from src.losses import *
|
||||
import torch
|
||||
import numpy as np
|
||||
from torch.nn import MSELoss, L1Loss
|
||||
from datetime import datetime
|
||||
import torch.nn as nn
|
||||
from src.models.time_embedding_layer import TimeEmbedding
|
||||
from src.models.diffusion_model import SimpleDiffusionModel
|
||||
from src.trainers.diffusion_trainer import DiffusionTrainer
|
||||
|
||||
|
||||
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
|
||||
task = clearml_helper.get_task(task_name="Diffusion Training")
|
||||
|
||||
# execute remotely
|
||||
task.execute_remotely(queue_name="default", exit_process=True)
|
||||
print("Running remotely")
|
||||
|
||||
from src.models import *
|
||||
from src.losses import *
|
||||
from src.models.time_embedding_layer import TimeEmbedding
|
||||
from src.models.diffusion_model import GRUDiffusionModel, SimpleDiffusionModel
|
||||
from src.trainers.diffusion_trainer import DiffusionTrainer
|
||||
from src.data import DataProcessor, DataConfig
|
||||
from src.policies.simple_baseline import BaselinePolicy, Battery
|
||||
from src.policies.PolicyEvaluator import PolicyEvaluator
|
||||
|
||||
#### Data Processor ####
|
||||
data_config = DataConfig()
|
||||
@@ -38,25 +30,37 @@ data_config.NOMINAL_NET_POSITION = True
|
||||
data_config = task.connect(data_config, name="data_features")
|
||||
|
||||
data_processor = DataProcessor(data_config, path="", lstm=False)
|
||||
data_processor.set_batch_size(8192)
|
||||
data_processor.set_batch_size(64)
|
||||
data_processor.set_full_day_skip(True)
|
||||
|
||||
inputDim = data_processor.get_input_size()
|
||||
print("Input dim: ", inputDim)
|
||||
|
||||
model_parameters = {
|
||||
"epochs": 5000,
|
||||
"learning_rate": 0.0001,
|
||||
"hidden_sizes": [512, 512, 512],
|
||||
"time_dim": 64,
|
||||
"hidden_sizes": [128, 128],
|
||||
"time_dim": 8,
|
||||
}
|
||||
|
||||
model_parameters = task.connect(model_parameters, name="model_parameters")
|
||||
|
||||
#### Model ####
|
||||
model = SimpleDiffusionModel(96, model_parameters["hidden_sizes"], other_inputs_dim=inputDim[1], time_dim=model_parameters["time_dim"])
|
||||
model = SimpleDiffusionModel(
|
||||
96,
|
||||
model_parameters["hidden_sizes"],
|
||||
other_inputs_dim=inputDim[1],
|
||||
time_dim=model_parameters["time_dim"],
|
||||
)
|
||||
# model = GRUDiffusionModel(96, model_parameters["hidden_sizes"], other_inputs_dim=inputDim[2], time_dim=model_parameters["time_dim"], gru_hidden_size=128)
|
||||
|
||||
print("Starting training ...")
|
||||
### Policy Evaluator ###
|
||||
battery = Battery(2, 1)
|
||||
baseline_policy = BaselinePolicy(battery, data_path="")
|
||||
policy_evaluator = PolicyEvaluator(baseline_policy, task)
|
||||
|
||||
#### Trainer ####
|
||||
trainer = DiffusionTrainer(model, data_processor, "cuda")
|
||||
trainer.train(model_parameters["epochs"], model_parameters["learning_rate"], task)
|
||||
trainer = DiffusionTrainer(
|
||||
model, data_processor, "cuda", policy_evaluator=policy_evaluator
|
||||
)
|
||||
trainer.train(model_parameters["epochs"], model_parameters["learning_rate"], task)
|
||||
|
||||
@@ -10,6 +10,6 @@ class ClearMLHelper:
|
||||
Task.ignore_requirements("torchvision")
|
||||
Task.ignore_requirements("tensorboard")
|
||||
task = Task.init(project_name=self.project_name, task_name=task_name, continue_last_task=False)
|
||||
task.set_base_docker(f"docker.io/clearml/pytorch-cuda-gcc:2.0.0-cuda11.7-cudnn8-runtime --env GIT_SSL_NO_VERIFY=true --env CLEARML_AGENT_GIT_USER=VictorMylle --env CLEARML_AGENT_GIT_PASS=Voetballer1" )
|
||||
task.set_base_docker(f"docker.io/clearml/pytorch-cuda-gcc:2.0.0-cuda11.7-cudnn8-runtime")
|
||||
task.set_packages("requirements.txt")
|
||||
return task
|
||||
Reference in New Issue
Block a user