\documentclass[]{beamer}
%uncomment for notes
%\documentclass[handout]{beamer}
%\setbeameroption{show notes}
%\usepackage{pgfpages}
%\pgfpagesuselayout{4 on 1}[a4paper,border shrink=5mm,landscape]

\graphicspath{{output/}{resources/}}
\DeclareGraphicsExtensions{.pdf,.jpeg,.png,.jpg}
\usepackage[utf8]{inputenc}
\usepackage{epstopdf}
\usepackage{subcaption}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage[round]{natbib}
\usepackage{tikz}
\usetikzlibrary{fit}
\usetikzlibrary{arrows}
\usetikzlibrary{positioning}
\usepackage{hyperref}
\hypersetup{
    colorlinks=true,
    linkcolor=blue,
    filecolor=magenta,
    urlcolor=cyan,
}

\title{QSAR biodegradation dataset classification using neural networks}
\author{<NAME>, <NAME>}
\institute{MFF, Charles University in Prague}
\date{December 2019}

\usepackage{natbib}
\usepackage{graphicx}
\setbeamertemplate{footline}[frame number]
\setbeamercolor{footline}{fg=blue}
\setbeamerfont{footline}{series=\bfseries}

\begin{document}
\frame[noframenumbering,plain]{
  \titlepage
}
\frame{
  \frametitle{QSAR biodegradation dataset \cite{dataset}}
  \begin{itemize}
    \item Dataset from OpenML: \href{https://www.openml.org/d/1494}{qsar-biodeg}
    \item Classification dataset where the task is to predict whether a protein is bio-degradable based on its chemical properties
    \item Published by Milano Chemometrics and QSAR Research Group
    \item 41 attributes in total, some real valued, other integer valued
    \item Number of proteins in the dataset is 1055
    \item Class 0 is bio-degradable
  \end{itemize}
}

\frame{
  \frametitle{Method}
  \begin{itemize}
    \item Multi Layered Perceptron (2 layers)
    \item 40 neurons in the hidden layer
    \item ReLU activation function
    \item trained as logistic regression with NLL loss and sigmoid activation function
    \item optimized using Adam \cite{Adam}
    \item experimented with dropout (0.5) \cite{dropout}
    \item weight decay (0.0045) for regularization 
    \item trained for 120 epochs
    \item evaluated using cross validation (10 splits)
  \end{itemize}
}

\frame{
  \frametitle{Single layer network training}
  \includegraphics[width=\linewidth]{logreg}
}

\frame{
  \frametitle{Two layers network training}
  \includegraphics[width=\linewidth]{mlp}
}

\frame{
  \frametitle{Confusion matrix}
  \includegraphics[width=\linewidth]{conf}
}

\frame{
  \frametitle{Precision recall}
  \includegraphics[width=\linewidth]{pr}
}
\frame{
  \frametitle{Our results and comparisons}
  \begin{table}[]
    \begin{tabular}{|l|l|}
      \hline
      \textbf{Method}                                             & \textbf{Predictive accuracy} \\ \hline
      \texttt{svm.classes.SVC}                   & 89.19\%                      \\ \hline
      \textbf{Ours - 2 layers} & \textbf{87.76\%}             \\ \hline
      \texttt{xgboost}                           & 87.68\%                      \\ \hline
      \texttt{weka.RandomForest}                 & 86.67\%                      \\ \hline
      \textbf{Ours - 1 layer}  & \textbf{85.12\%}             \\ \hline
      \texttt{weka.VotedPerceptron}              & 79.43\%                      \\ \hline
      \texttt{weka.NaiveBayes}                   & 75.64\%                      \\ \hline
      \texttt{neural\_network.MLPClassifier}     & 66.26\%                      \\ \hline
    \end{tabular}
  \end{table}
}

\frame{
  \cite{*}
  \bibliographystyle{plainnat}
  \bibliography{references}
}

\end{document}
