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notebook.tex
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% Default to the notebook output style
% Inherit from the specified cell style.
\documentclass[11pt]{article}
\usepackage[T1]{fontenc}
% Nicer default font (+ math font) than Computer Modern for most use cases
\usepackage{mathpazo}
% Basic figure setup, for now with no caption control since it's done
% automatically by Pandoc (which extracts  syntax from Markdown).
\usepackage{graphicx}
% We will generate all images so they have a width \maxwidth. This means
% that they will get their normal width if they fit onto the page, but
% are scaled down if they would overflow the margins.
\makeatletter
\def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth
\else\Gin@nat@width\fi}
\makeatother
\let\Oldincludegraphics\includegraphics
% Set max figure width to be 80% of text width, for now hardcoded.
\renewcommand{\includegraphics}[1]{\Oldincludegraphics[width=.8\maxwidth]{#1}}
% Ensure that by default, figures have no caption (until we provide a
% proper Figure object with a Caption API and a way to capture that
% in the conversion process - todo).
\usepackage{caption}
\DeclareCaptionLabelFormat{nolabel}{}
\captionsetup{labelformat=nolabel}
\usepackage{adjustbox} % Used to constrain images to a maximum size
\usepackage{xcolor} % Allow colors to be defined
\usepackage{enumerate} % Needed for markdown enumerations to work
\usepackage{geometry} % Used to adjust the document margins
\usepackage{amsmath} % Equations
\usepackage{amssymb} % Equations
\usepackage{textcomp} % defines textquotesingle
% Hack from http://tex.stackexchange.com/a/47451/13684:
\AtBeginDocument{%
\def\PYZsq{\textquotesingle}% Upright quotes in Pygmentized code
}
\usepackage{upquote} % Upright quotes for verbatim code
\usepackage{eurosym} % defines \euro
\usepackage[mathletters]{ucs} % Extended unicode (utf-8) support
\usepackage[utf8x]{inputenc} % Allow utf-8 characters in the tex document
\usepackage{fancyvrb} % verbatim replacement that allows latex
\usepackage{grffile} % extends the file name processing of package graphics
% to support a larger range
% The hyperref package gives us a pdf with properly built
% internal navigation ('pdf bookmarks' for the table of contents,
% internal cross-reference links, web links for URLs, etc.)
\usepackage{hyperref}
\usepackage{longtable} % longtable support required by pandoc >1.10
\usepackage{booktabs} % table support for pandoc > 1.12.2
\usepackage[inline]{enumitem} % IRkernel/repr support (it uses the enumerate* environment)
\usepackage[normalem]{ulem} % ulem is needed to support strikethroughs (\sout)
% normalem makes italics be italics, not underlines
% Colors for the hyperref package
\definecolor{urlcolor}{rgb}{0,.145,.698}
\definecolor{linkcolor}{rgb}{.71,0.21,0.01}
\definecolor{citecolor}{rgb}{.12,.54,.11}
% ANSI colors
\definecolor{ansi-black}{HTML}{3E424D}
\definecolor{ansi-black-intense}{HTML}{282C36}
\definecolor{ansi-red}{HTML}{E75C58}
\definecolor{ansi-red-intense}{HTML}{B22B31}
\definecolor{ansi-green}{HTML}{00A250}
\definecolor{ansi-green-intense}{HTML}{007427}
\definecolor{ansi-yellow}{HTML}{DDB62B}
\definecolor{ansi-yellow-intense}{HTML}{B27D12}
\definecolor{ansi-blue}{HTML}{208FFB}
\definecolor{ansi-blue-intense}{HTML}{0065CA}
\definecolor{ansi-magenta}{HTML}{D160C4}
\definecolor{ansi-magenta-intense}{HTML}{A03196}
\definecolor{ansi-cyan}{HTML}{60C6C8}
\definecolor{ansi-cyan-intense}{HTML}{258F8F}
\definecolor{ansi-white}{HTML}{C5C1B4}
\definecolor{ansi-white-intense}{HTML}{A1A6B2}
% commands and environments needed by pandoc snippets
% extracted from the output of `pandoc -s`
\providecommand{\tightlist}{%
\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}
\DefineVerbatimEnvironment{Highlighting}{Verbatim}{commandchars=\\\{\}}
% Add ',fontsize=\small' for more characters per line
\newenvironment{Shaded}{}{}
\newcommand{\KeywordTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
\newcommand{\DataTypeTok}[1]{\textcolor[rgb]{0.56,0.13,0.00}{{#1}}}
\newcommand{\DecValTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
\newcommand{\BaseNTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
\newcommand{\FloatTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
\newcommand{\CharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\StringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\CommentTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textit{{#1}}}}
\newcommand{\OtherTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{{#1}}}
\newcommand{\AlertTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
\newcommand{\FunctionTok}[1]{\textcolor[rgb]{0.02,0.16,0.49}{{#1}}}
\newcommand{\RegionMarkerTok}[1]{{#1}}
\newcommand{\ErrorTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
\newcommand{\NormalTok}[1]{{#1}}
% Additional commands for more recent versions of Pandoc
\newcommand{\ConstantTok}[1]{\textcolor[rgb]{0.53,0.00,0.00}{{#1}}}
\newcommand{\SpecialCharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\VerbatimStringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\SpecialStringTok}[1]{\textcolor[rgb]{0.73,0.40,0.53}{{#1}}}
\newcommand{\ImportTok}[1]{{#1}}
\newcommand{\DocumentationTok}[1]{\textcolor[rgb]{0.73,0.13,0.13}{\textit{{#1}}}}
\newcommand{\AnnotationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
\newcommand{\CommentVarTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
\newcommand{\VariableTok}[1]{\textcolor[rgb]{0.10,0.09,0.49}{{#1}}}
\newcommand{\ControlFlowTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
\newcommand{\OperatorTok}[1]{\textcolor[rgb]{0.40,0.40,0.40}{{#1}}}
\newcommand{\BuiltInTok}[1]{{#1}}
\newcommand{\ExtensionTok}[1]{{#1}}
\newcommand{\PreprocessorTok}[1]{\textcolor[rgb]{0.74,0.48,0.00}{{#1}}}
\newcommand{\AttributeTok}[1]{\textcolor[rgb]{0.49,0.56,0.16}{{#1}}}
\newcommand{\InformationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
\newcommand{\WarningTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
% Define a nice break command that doesn't care if a line doesn't already
% exist.
\def\br{\hspace*{\fill} \\* }
% Math Jax compatability definitions
\def\gt{>}
\def\lt{<}
% Document parameters
\title{Quora\_Duplicates}
% Pygments definitions
\makeatletter
\def\PY@reset{\let\PY@it=\relax \let\PY@bf=\relax%
\let\PY@ul=\relax \let\PY@tc=\relax%
\let\PY@bc=\relax \let\PY@ff=\relax}
\def\PY@tok#1{\csname PY@tok@#1\endcsname}
\def\PY@toks#1+{\ifx\relax#1\empty\else%
\PY@tok{#1}\expandafter\PY@toks\fi}
\def\PY@do#1{\PY@bc{\PY@tc{\PY@ul{%
\PY@it{\PY@bf{\PY@ff{#1}}}}}}}
\def\PY#1#2{\PY@reset\PY@toks#1+\relax+\PY@do{#2}}
\expandafter\def\csname PY@tok@vc\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
\expandafter\def\csname PY@tok@nb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
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\expandafter\def\csname PY@tok@nd\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.67,0.13,1.00}{##1}}}
\expandafter\def\csname PY@tok@gh\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,0.50}{##1}}}
\expandafter\def\csname PY@tok@mf\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
\expandafter\def\csname PY@tok@cm\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
\expandafter\def\csname PY@tok@c1\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
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\expandafter\def\csname PY@tok@cs\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
\expandafter\def\csname PY@tok@err\endcsname{\def\PY@bc##1{\setlength{\fboxsep}{0pt}\fcolorbox[rgb]{1.00,0.00,0.00}{1,1,1}{\strut ##1}}}
\expandafter\def\csname PY@tok@s\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\expandafter\def\csname PY@tok@fm\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,1.00}{##1}}}
\expandafter\def\csname PY@tok@mb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
\expandafter\def\csname PY@tok@k\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
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\expandafter\def\csname PY@tok@ge\endcsname{\let\PY@it=\textit}
\expandafter\def\csname PY@tok@sr\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.40,0.53}{##1}}}
\expandafter\def\csname PY@tok@sd\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
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\expandafter\def\csname PY@tok@nt\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
\expandafter\def\csname PY@tok@cpf\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
\expandafter\def\csname PY@tok@ni\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.60,0.60,0.60}{##1}}}
\expandafter\def\csname PY@tok@kp\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
\expandafter\def\csname PY@tok@si\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.73,0.40,0.53}{##1}}}
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\expandafter\def\csname PY@tok@nv\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
\expandafter\def\csname PY@tok@gs\endcsname{\let\PY@bf=\textbf}
\expandafter\def\csname PY@tok@ss\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
\expandafter\def\csname PY@tok@sx\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
\expandafter\def\csname PY@tok@kc\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
\def\PYZbs{\char`\\}
\def\PYZus{\char`\_}
\def\PYZob{\char`\{}
\def\PYZcb{\char`\}}
\def\PYZca{\char`\^}
\def\PYZam{\char`\&}
\def\PYZlt{\char`\<}
\def\PYZgt{\char`\>}
\def\PYZsh{\char`\#}
\def\PYZpc{\char`\%}
\def\PYZdl{\char`\$}
\def\PYZhy{\char`\-}
\def\PYZsq{\char`\'}
\def\PYZdq{\char`\"}
\def\PYZti{\char`\~}
% for compatibility with earlier versions
\def\PYZat{@}
\def\PYZlb{[}
\def\PYZrb{]}
\makeatother
% Exact colors from NB
\definecolor{incolor}{rgb}{0.0, 0.0, 0.5}
\definecolor{outcolor}{rgb}{0.545, 0.0, 0.0}
% Prevent overflowing lines due to hard-to-break entities
\sloppy
% Setup hyperref package
\hypersetup{
breaklinks=true, % so long urls are correctly broken across lines
colorlinks=true,
urlcolor=urlcolor,
linkcolor=linkcolor,
citecolor=citecolor,
}
% Slightly bigger margins than the latex defaults
\geometry{verbose,tmargin=1in,bmargin=1in,lmargin=1in,rmargin=1in}
\begin{document}
\maketitle
\section{Quora Duplicate Questions Checker
(NLP)}\label{quora-duplicate-questions-checker-nlp}
This is the submission for a task on flagging duplicate questions on the
Quora dataset from Kaggle.
\subsection{The Approach}\label{the-approach}
The questions will be converted to respective feature vectors using
\textbf{\emph{doc2vec}} (gensim's implementation). Then a
\textbf{\emph{Siamese network}} will be trained to predict the duplicacy
of questions. The code with all the explanation is given below.
\subsubsection{Importing the libraries}\label{importing-the-libraries}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}1}]:} \PY{k+kn}{import} \PY{n+nn}{sys}
\PY{k+kn}{import} \PY{n+nn}{os}
\PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}
\PY{k+kn}{import} \PY{n+nn}{pandas} \PY{k}{as} \PY{n+nn}{pd}
\PY{k+kn}{import} \PY{n+nn}{gensim}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{cross\PYZus{}validation} \PY{k}{import} \PY{n}{train\PYZus{}test\PYZus{}split}
\PY{k+kn}{from} \PY{n+nn}{keras}\PY{n+nn}{.}\PY{n+nn}{models} \PY{k}{import} \PY{n}{Sequential}\PY{p}{,} \PY{n}{Model}
\PY{k+kn}{from} \PY{n+nn}{keras}\PY{n+nn}{.}\PY{n+nn}{layers} \PY{k}{import} \PY{n}{Dense}\PY{p}{,} \PY{n}{BatchNormalization}\PY{p}{,} \PY{n}{Activation}\PY{p}{,} \PY{n}{Input}\PY{p}{,} \PY{n}{Add}\PY{p}{,} \PY{n}{Concatenate}\PY{p}{,} \PY{n}{Lambda}
\PY{k+kn}{from} \PY{n+nn}{keras}\PY{n+nn}{.}\PY{n+nn}{optimizers} \PY{k}{import} \PY{n}{RMSprop}\PY{p}{,} \PY{n}{SGD}\PY{p}{,} \PY{n}{Adam}
\PY{k+kn}{from} \PY{n+nn}{keras} \PY{k}{import} \PY{n}{backend} \PY{k}{as} \PY{n}{K}
\end{Verbatim}
\subsubsection{Reading the CSV file into Pandas
Dataframe}\label{reading-the-csv-file-into-pandas-dataframe}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}2}]:} \PY{n}{df} \PY{o}{=} \PY{n}{pd}\PY{o}{.}\PY{n}{read\PYZus{}csv}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{train.csv}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}3}]:} \PY{n}{df}\PY{o}{.}\PY{n}{head}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}3}]:} id qid1 qid2 question1 \textbackslash{}
0 0 1 2 What is the step by step guide to invest in sh{\ldots}
1 1 3 4 What is the story of Kohinoor (Koh-i-Noor) Dia{\ldots}
2 2 5 6 How can I increase the speed of my internet co{\ldots}
3 3 7 8 Why am I mentally very lonely? How can I solve{\ldots}
4 4 9 10 Which one dissolve in water quikly sugar, salt{\ldots}
question2 is\_duplicate
0 What is the step by step guide to invest in sh{\ldots} 0
1 What would happen if the Indian government sto{\ldots} 0
2 How can Internet speed be increased by hacking{\ldots} 0
3 Find the remainder when [math]23\^{}\{24\}[/math] i{\ldots} 0
4 Which fish would survive in salt water? 0
\end{Verbatim}
\subsubsection{Preparing data for Doc2Vec training (unsupervised
learning)}\label{preparing-data-for-doc2vec-training-unsupervised-learning}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}4}]:} \PY{c+c1}{\PYZsh{}List of sentences and labels to make labelled sentences}
\PY{n}{questions} \PY{o}{=} \PY{n+nb}{list}\PY{p}{(}\PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{question1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)} \PY{o}{+} \PY{n+nb}{list}\PY{p}{(}\PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{question2}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}
\PY{n}{label\PYZus{}list} \PY{o}{=} \PY{n+nb}{list}\PY{p}{(}\PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{qid1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)} \PY{o}{+} \PY{n+nb}{list}\PY{p}{(}\PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{qid2}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}
\PY{c+c1}{\PYZsh{}Encoding to unicode}
\PY{k}{for} \PY{n}{i}\PY{p}{,}\PY{n}{question} \PY{o+ow}{in} \PY{n+nb}{enumerate}\PY{p}{(}\PY{n}{questions}\PY{p}{)}\PY{p}{:}
\PY{n}{questions}\PY{p}{[}\PY{n}{i}\PY{p}{]} \PY{o}{=} \PY{n+nb}{str}\PY{p}{(}\PY{n}{question}\PY{p}{)}\PY{o}{.}\PY{n}{encode}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{utf\PYZhy{}8}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{c+c1}{\PYZsh{}Tokenizing the sentences}
\PY{n}{questions} \PY{o}{=} \PY{p}{[}\PY{n+nb}{list}\PY{p}{(}\PY{n}{gensim}\PY{o}{.}\PY{n}{utils}\PY{o}{.}\PY{n}{tokenize}\PY{p}{(}\PY{n}{question}\PY{p}{,} \PY{n}{deacc}\PY{o}{=}\PY{k+kc}{True}\PY{p}{,} \PY{n}{lower}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}\PY{p}{)} \PY{k}{for} \PY{n}{question} \PY{o+ow}{in} \PY{n}{questions}\PY{p}{]}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}5}]:} \PY{c+c1}{\PYZsh{}Creating LabeledLineSentence iterator to feed into Doc2Vec input}
\PY{k}{class} \PY{n+nc}{LabeledLineSentence}\PY{p}{(}\PY{n+nb}{object}\PY{p}{)}\PY{p}{:}
\PY{k}{def} \PY{n+nf}{\PYZus{}\PYZus{}init\PYZus{}\PYZus{}}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{,} \PY{n}{label\PYZus{}list}\PY{p}{,} \PY{n}{questions}\PY{p}{)}\PY{p}{:}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{questions} \PY{o}{=} \PY{n}{questions}
\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{label\PYZus{}list} \PY{o}{=} \PY{n}{label\PYZus{}list}
\PY{k}{def} \PY{n+nf}{\PYZus{}\PYZus{}iter\PYZus{}\PYZus{}}\PY{p}{(}\PY{n+nb+bp}{self}\PY{p}{)}\PY{p}{:}
\PY{k}{for} \PY{n}{label}\PY{p}{,} \PY{n}{ques} \PY{o+ow}{in} \PY{n+nb}{zip}\PY{p}{(}\PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{label\PYZus{}list}\PY{p}{,} \PY{n+nb+bp}{self}\PY{o}{.}\PY{n}{questions}\PY{p}{)}\PY{p}{:}
\PY{k}{yield} \PY{n}{gensim}\PY{o}{.}\PY{n}{models}\PY{o}{.}\PY{n}{doc2vec}\PY{o}{.}\PY{n}{TaggedDocument}\PY{p}{(}\PY{n}{words}\PY{o}{=}\PY{n}{ques}\PY{p}{,} \PY{n}{tags}\PY{o}{=}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{QUES\PYZus{}}\PY{l+s+si}{\PYZpc{}s}\PY{l+s+s1}{\PYZsq{}} \PY{o}{\PYZpc{}} \PY{n}{label}\PY{p}{]}\PY{p}{)}
\PY{n}{it} \PY{o}{=} \PY{n}{LabeledLineSentence}\PY{p}{(}\PY{n}{label\PYZus{}list}\PY{p}{,} \PY{n}{questions}\PY{p}{)}
\end{Verbatim}
\subsubsection{Training the Doc2Vec model for generating feature vectors
of
sentence.}\label{training-the-doc2vec-model-for-generating-feature-vectors-of-sentence.}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}6}]:} \PY{c+c1}{\PYZsh{}Initialize}
\PY{n}{size\PYZus{}of\PYZus{}vector} \PY{o}{=} \PY{l+m+mi}{300}
\PY{n}{model} \PY{o}{=} \PY{n}{gensim}\PY{o}{.}\PY{n}{models}\PY{o}{.}\PY{n}{Doc2Vec}\PY{p}{(}\PY{n}{vector\PYZus{}size}\PY{o}{=}\PY{n}{size\PYZus{}of\PYZus{}vector}\PY{p}{,} \PY{n}{window}\PY{o}{=}\PY{l+m+mi}{8}\PY{p}{,} \PY{n}{workers}\PY{o}{=}\PY{l+m+mi}{16}\PY{p}{,} \PY{n}{negative}\PY{o}{=}\PY{l+m+mi}{20}\PY{p}{,} \PY{n}{epochs}\PY{o}{=}\PY{l+m+mi}{10}\PY{p}{,} \PY{n}{alpha}\PY{o}{=}\PY{l+m+mf}{0.025}\PY{p}{,} \PY{n}{min\PYZus{}alpha}\PY{o}{=}\PY{l+m+mf}{0.005}\PY{p}{)}
\PY{c+c1}{\PYZsh{}Keeping only l2 normalized vectors, trick to save memory}
\PY{c+c1}{\PYZsh{}model.init\PYZus{}sims(replace=True)}
\PY{c+c1}{\PYZsh{}Build the vocabulary to train}
\PY{n}{model}\PY{o}{.}\PY{n}{build\PYZus{}vocab}\PY{p}{(}\PY{n}{it}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Vocab built!}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
Vocab built!
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}7}]:} \PY{c+c1}{\PYZsh{}Train the model}
\PY{n}{model}\PY{o}{.}\PY{n}{train}\PY{p}{(}\PY{n}{it}\PY{p}{,} \PY{n}{start\PYZus{}alpha}\PY{o}{=}\PY{n}{model}\PY{o}{.}\PY{n}{alpha}\PY{p}{,} \PY{n}{end\PYZus{}alpha}\PY{o}{=}\PY{n}{model}\PY{o}{.}\PY{n}{min\PYZus{}alpha}\PY{p}{,} \PY{n}{total\PYZus{}examples} \PY{o}{=} \PY{n}{model}\PY{o}{.}\PY{n}{corpus\PYZus{}count}\PY{p}{,} \PY{n}{epochs} \PY{o}{=} \PY{n}{model}\PY{o}{.}\PY{n}{epochs}\PY{p}{)}
\PY{c+c1}{\PYZsh{}Saving the model to disk}
\PY{n}{model}\PY{o}{.}\PY{n}{save}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{TrainedModel.doc2vec}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\end{Verbatim}
\paragraph{Printing a feature vector}\label{printing-a-feature-vector}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}8}]:} \PY{n}{model}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{QUES\PYZus{}1}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}8}]:} array([ 0.1010121 , -0.13212034, 0.20697571, -0.07772458, -0.2904885 ,
0.09290215, -0.35331514, -0.356143 , -0.04176326, -0.04576672,
-0.13709651, -0.0357114 , 0.01211934, 0.00779985, -0.24946555,
-0.073835 , -0.11245155, 0.32899585, -0.00636845, -0.07514311,
-0.01152763, 0.1030973 , -0.17321198, 0.35756987, 0.05059336,
-0.19144808, 0.0167082 , 0.47161594, 0.2958863 , 0.0713123 ,
0.14996015, 0.1820064 , -0.02615429, -0.02944973, -0.22602351,
-0.04852638, 0.01828839, 0.08953261, -0.09423032, -0.2090554 ,
0.21356587, -0.04206707, 0.02655583, 0.08692597, 0.02247708,
-0.09069867, -0.02576054, -0.1784472 , -0.11130156, 0.12017581,
-0.02301975, 0.26107648, 0.02411825, -0.00085904, -0.04000963,
-0.060376 , 0.150168 , 0.12186993, -0.02538476, 0.13142252,
0.10832738, 0.24926871, -0.17978851, -0.12546104, -0.1591322 ,
-0.10587286, 0.13702202, -0.01735692, -0.19662704, 0.02405479,
-0.12616156, 0.39206648, 0.06786001, 0.30039877, -0.28961372,
-0.19145077, 0.13219096, 0.40669823, 0.01285363, -0.03149781,
0.10214559, -0.12658314, 0.23072517, 0.35446617, 0.08455846,
-0.03517002, 0.02154905, -0.14335732, 0.00624254, -0.37736687,
0.11878463, -0.15520589, 0.34558412, -0.06958005, -0.0330475 ,
-0.15189403, 0.12950218, 0.16860373, -0.18401319, -0.06694235,
0.17546912, -0.17970309, -0.04844933, 0.07793888, -0.0875636 ,
0.02255841, 0.261998 , -0.03245204, 0.05971956, -0.16846095,
0.12183729, 0.0427808 , -0.29858944, 0.09705504, -0.08442573,
0.06107755, 0.0724733 , 0.09792531, -0.15622084, -0.10198328,
-0.06692696, 0.21271828, -0.01105559, -0.27630642, 0.12670761,
0.26679012, 0.07788135, 0.02026083, 0.01551994, 0.13290422,
0.18124965, 0.11931239, 0.13974665, -0.34092465, 0.10707347,
0.10834585, -0.09710421, -0.17700018, 0.01055264, -0.00971784,
-0.20750703, 0.18812527, 0.01341095, 0.01392172, -0.15868117,
0.04152286, -0.12371553, 0.14770119, 0.24267566, 0.11264772,
0.15946844, 0.06907013, -0.3151104 , -0.09306239, 0.13305897,
0.28941658, -0.22603902, 0.09415465, 0.4037891 , -0.03411846,
0.33212474, 0.24666229, 0.07147261, -0.1648877 , 0.01279794,
0.24543793, -0.02995815, 0.09809332, 0.00916121, -0.17340578,
-0.09141479, 0.09721921, -0.02650241, -0.09873933, -0.44106048,
-0.14436454, 0.16782294, -0.0209225 , -0.21465234, 0.36337948,
-0.03800158, 0.1484501 , -0.12360807, 0.21617503, -0.00947627,
-0.29139492, 0.11330323, -0.1477199 , -0.29244423, -0.13078557,
0.10263634, -0.1351873 , -0.11927554, 0.20717072, 0.03187283,
0.14184962, -0.10231453, -0.1492476 , -0.2550247 , -0.05389496,
0.19661802, -0.37907076, 0.05750739, 0.1983454 , 0.01011348,
-0.10737077, -0.0777571 , -0.03389528, 0.00447315, -0.04775464,
0.1275274 , 0.1122389 , -0.14150189, 0.15856227, -0.26712427,
0.00923262, 0.08039069, 0.03669498, 0.24909459, -0.18924199,
0.21262857, 0.43104798, -0.15186763, 0.04053361, 0.0376123 ,
-0.15917836, -0.05491212, 0.08423794, 0.1388688 , 0.22103867,
0.01562748, 0.12144377, 0.23632106, 0.05226205, -0.1915901 ,
0.24155405, 0.153927 , 0.47088996, -0.1258004 , -0.1460464 ,
-0.01991732, 0.07155553, 0.06668622, 0.1679224 , 0.14619705,
0.11921348, -0.03682666, -0.27336243, 0.04093792, 0.02942304,
0.04224849, 0.09238243, 0.02632977, 0.10930859, -0.07894878,
-0.09116194, -0.00960128, 0.05176696, 0.22052719, -0.25249916,
0.1410123 , -0.1589397 , -0.01649868, -0.21937363, 0.26863217,
0.07434249, -0.03674535, -0.03793586, 0.3251129 , 0.02966833,
0.28218228, -0.28578466, -0.17783535, -0.17231773, -0.25786635,
-0.01312948, -0.06459756, -0.26336363, 0.0135734 , -0.29203704,
-0.06049855, -0.06102484, 0.05645002, 0.08622446, 0.05945703,
-0.08082741, 0.35183737, 0.31119382, -0.22611924, -0.01557379,
0.02036766, -0.01220612, -0.27822742, -0.08799634, 0.11194889,
0.3291304 , -0.2504264 , -0.06701718, 0.01165668, 0.05978274],
dtype=float32)
\end{Verbatim}
\subsubsection{Create training data
variables}\label{create-training-data-variables}
Features
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}9}]:} \PY{c+c1}{\PYZsh{}Load the model and the dataframe}
\PY{n}{model} \PY{o}{=} \PY{n}{gensim}\PY{o}{.}\PY{n}{models}\PY{o}{.}\PY{n}{Doc2Vec}\PY{o}{.}\PY{n}{load}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{TrainedModel.doc2vec}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{df} \PY{o}{=} \PY{n}{pd}\PY{o}{.}\PY{n}{read\PYZus{}csv}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{train.csv}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{c+c1}{\PYZsh{}Getting final array of training data}
\PY{n}{size\PYZus{}of\PYZus{}vector} \PY{o}{=} \PY{l+m+mi}{300}
\PY{n}{train\PYZus{}count} \PY{o}{=} \PY{n+nb}{len}\PY{p}{(}\PY{n}{df}\PY{o}{.}\PY{n}{index}\PY{p}{)}
\PY{n}{train\PYZus{}data} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{zeros}\PY{p}{(}\PY{p}{(}\PY{n}{train\PYZus{}count}\PY{p}{,} \PY{l+m+mi}{2}\PY{p}{,} \PY{n}{size\PYZus{}of\PYZus{}vector}\PY{p}{)}\PY{p}{)}
\PY{k}{for} \PY{n}{idx}\PY{p}{,} \PY{n}{row} \PY{o+ow}{in} \PY{n}{df}\PY{o}{.}\PY{n}{iterrows}\PY{p}{(}\PY{p}{)}\PY{p}{:}
\PY{n}{q1\PYZus{}label} \PY{o}{=} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{QUES\PYZus{}}\PY{l+s+s2}{\PYZdq{}} \PY{o}{+} \PY{n+nb}{str}\PY{p}{(}\PY{n}{row}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{qid1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}
\PY{n}{q2\PYZus{}label} \PY{o}{=} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{QUES\PYZus{}}\PY{l+s+s2}{\PYZdq{}} \PY{o}{+} \PY{n+nb}{str}\PY{p}{(}\PY{n}{row}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{qid2}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}
\PY{n}{train\PYZus{}data}\PY{p}{[}\PY{n}{idx}\PY{p}{,}\PY{l+m+mi}{0}\PY{p}{]} \PY{o}{=} \PY{n}{model}\PY{p}{[}\PY{n}{q1\PYZus{}label}\PY{p}{]}
\PY{n}{train\PYZus{}data}\PY{p}{[}\PY{n}{idx}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{]} \PY{o}{=} \PY{n}{model}\PY{p}{[}\PY{n}{q2\PYZus{}label}\PY{p}{]}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}10}]:} \PY{n}{train\PYZus{}data}\PY{o}{.}\PY{n}{shape}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}10}]:} (404290, 2, 300)
\end{Verbatim}
Labels
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}11}]:} \PY{n}{train\PYZus{}labels} \PY{o}{=} \PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{is\PYZus{}duplicate}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}12}]:} \PY{n}{train\PYZus{}labels}\PY{o}{.}\PY{n}{shape}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}12}]:} (404290,)
\end{Verbatim}
Split data for validation
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}13}]:} \PY{n}{X\PYZus{}train}\PY{p}{,} \PY{n}{X\PYZus{}test}\PY{p}{,} \PY{n}{Y\PYZus{}train}\PY{p}{,} \PY{n}{Y\PYZus{}test} \PY{o}{=} \PY{n}{train\PYZus{}test\PYZus{}split}\PY{p}{(}\PY{n}{train\PYZus{}data}\PY{p}{,} \PY{n}{train\PYZus{}labels}\PY{p}{,} \PY{n}{test\PYZus{}size}\PY{o}{=}\PY{l+m+mf}{0.2}\PY{p}{,} \PY{n}{random\PYZus{}state}\PY{o}{=}\PY{l+m+mi}{42}\PY{p}{)}
\end{Verbatim}
\subsubsection{We have completed the preprocessing of data. Now comes
the real
part.}\label{we-have-completed-the-preprocessing-of-data.-now-comes-the-real-part.}
\subsection{The Siamese Network}\label{the-siamese-network}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}14}]:} \PY{k}{def} \PY{n+nf}{euclidean\PYZus{}distance}\PY{p}{(}\PY{n}{vects}\PY{p}{)}\PY{p}{:}
\PY{n}{x}\PY{p}{,} \PY{n}{y} \PY{o}{=} \PY{n}{vects}
\PY{k}{return} \PY{n}{K}\PY{o}{.}\PY{n}{sqrt}\PY{p}{(}\PY{n}{K}\PY{o}{.}\PY{n}{sum}\PY{p}{(}\PY{n}{K}\PY{o}{.}\PY{n}{square}\PY{p}{(}\PY{n}{x} \PY{o}{\PYZhy{}} \PY{n}{y}\PY{p}{)}\PY{p}{,} \PY{n}{axis}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,} \PY{n}{keepdims}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{eucl\PYZus{}dist\PYZus{}output\PYZus{}shape}\PY{p}{(}\PY{n}{shapes}\PY{p}{)}\PY{p}{:}
\PY{n}{shape1}\PY{p}{,} \PY{n}{shape2} \PY{o}{=} \PY{n}{shapes}
\PY{k}{return} \PY{p}{(}\PY{n}{shape1}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{,} \PY{l+m+mi}{1}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{cosine\PYZus{}distance}\PY{p}{(}\PY{n}{vests}\PY{p}{)}\PY{p}{:}
\PY{n}{x}\PY{p}{,} \PY{n}{y} \PY{o}{=} \PY{n}{vests}
\PY{n}{x} \PY{o}{=} \PY{n}{K}\PY{o}{.}\PY{n}{l2\PYZus{}normalize}\PY{p}{(}\PY{n}{x}\PY{p}{,} \PY{n}{axis}\PY{o}{=}\PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{)}
\PY{n}{y} \PY{o}{=} \PY{n}{K}\PY{o}{.}\PY{n}{l2\PYZus{}normalize}\PY{p}{(}\PY{n}{y}\PY{p}{,} \PY{n}{axis}\PY{o}{=}\PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{)}
\PY{k}{return} \PY{o}{\PYZhy{}}\PY{n}{K}\PY{o}{.}\PY{n}{mean}\PY{p}{(}\PY{n}{x} \PY{o}{*} \PY{n}{y}\PY{p}{,} \PY{n}{axis}\PY{o}{=}\PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{,} \PY{n}{keepdims}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{cos\PYZus{}dist\PYZus{}output\PYZus{}shape}\PY{p}{(}\PY{n}{shapes}\PY{p}{)}\PY{p}{:}
\PY{n}{shape1}\PY{p}{,} \PY{n}{shape2} \PY{o}{=} \PY{n}{shapes}
\PY{k}{return} \PY{p}{(}\PY{n}{shape1}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{contrastive\PYZus{}loss}\PY{p}{(}\PY{n}{y\PYZus{}true}\PY{p}{,} \PY{n}{y\PYZus{}pred}\PY{p}{)}\PY{p}{:}
\PY{n}{margin} \PY{o}{=} \PY{l+m+mi}{1}
\PY{k}{return} \PY{n}{K}\PY{o}{.}\PY{n}{mean}\PY{p}{(}\PY{n}{y\PYZus{}true} \PY{o}{*} \PY{n}{K}\PY{o}{.}\PY{n}{square}\PY{p}{(}\PY{n}{y\PYZus{}pred}\PY{p}{)} \PY{o}{+} \PY{p}{(}\PY{l+m+mi}{1} \PY{o}{\PYZhy{}} \PY{n}{y\PYZus{}true}\PY{p}{)} \PY{o}{*} \PY{n}{K}\PY{o}{.}\PY{n}{square}\PY{p}{(}\PY{n}{K}\PY{o}{.}\PY{n}{maximum}\PY{p}{(}\PY{n}{margin} \PY{o}{\PYZhy{}} \PY{n}{y\PYZus{}pred}\PY{p}{,} \PY{l+m+mi}{0}\PY{p}{)}\PY{p}{)}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{create\PYZus{}base\PYZus{}network}\PY{p}{(}\PY{n}{input\PYZus{}dim}\PY{p}{)}\PY{p}{:}
\PY{l+s+sd}{\PYZsq{}\PYZsq{}\PYZsq{}}
\PY{l+s+sd}{ Base network for feature extraction.}
\PY{l+s+sd}{ \PYZsq{}\PYZsq{}\PYZsq{}}
\PY{n+nb}{input} \PY{o}{=} \PY{n}{Input}\PY{p}{(}\PY{n}{shape}\PY{o}{=}\PY{p}{(}\PY{n}{input\PYZus{}dim}\PY{p}{,} \PY{p}{)}\PY{p}{)}
\PY{n}{dense1} \PY{o}{=} \PY{n}{Dense}\PY{p}{(}\PY{l+m+mi}{128}\PY{p}{)}\PY{p}{(}\PY{n+nb}{input}\PY{p}{)}
\PY{n}{bn1} \PY{o}{=} \PY{n}{BatchNormalization}\PY{p}{(}\PY{p}{)}\PY{p}{(}\PY{n}{dense1}\PY{p}{)}
\PY{n}{relu1} \PY{o}{=} \PY{n}{Activation}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{relu}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{(}\PY{n}{bn1}\PY{p}{)}
\PY{n}{dense2} \PY{o}{=} \PY{n}{Dense}\PY{p}{(}\PY{l+m+mi}{128}\PY{p}{)}\PY{p}{(}\PY{n}{relu1}\PY{p}{)}
\PY{n}{bn2} \PY{o}{=} \PY{n}{BatchNormalization}\PY{p}{(}\PY{p}{)}\PY{p}{(}\PY{n}{dense2}\PY{p}{)}
\PY{n}{res2} \PY{o}{=} \PY{n}{Add}\PY{p}{(}\PY{p}{)}\PY{p}{(}\PY{p}{[}\PY{n}{relu1}\PY{p}{,} \PY{n}{bn2}\PY{p}{]}\PY{p}{)}
\PY{n}{relu2} \PY{o}{=} \PY{n}{Activation}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{relu}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{(}\PY{n}{res2}\PY{p}{)}
\PY{n}{dense3} \PY{o}{=} \PY{n}{Dense}\PY{p}{(}\PY{l+m+mi}{128}\PY{p}{)}\PY{p}{(}\PY{n}{relu2}\PY{p}{)}
\PY{n}{bn3} \PY{o}{=} \PY{n}{BatchNormalization}\PY{p}{(}\PY{p}{)}\PY{p}{(}\PY{n}{dense3}\PY{p}{)}
\PY{n}{res3} \PY{o}{=} \PY{n}{Add}\PY{p}{(}\PY{p}{)}\PY{p}{(}\PY{p}{[}\PY{n}{relu2}\PY{p}{,} \PY{n}{bn3}\PY{p}{]}\PY{p}{)}
\PY{n}{relu3} \PY{o}{=} \PY{n}{Activation}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{relu}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{(}\PY{n}{res3}\PY{p}{)}
\PY{n}{feats} \PY{o}{=} \PY{n}{Concatenate}\PY{p}{(}\PY{p}{)}\PY{p}{(}\PY{p}{[}\PY{n}{relu3}\PY{p}{,} \PY{n}{relu2}\PY{p}{,} \PY{n}{relu1}\PY{p}{]}\PY{p}{)}
\PY{n}{bn4} \PY{o}{=} \PY{n}{BatchNormalization}\PY{p}{(}\PY{p}{)}\PY{p}{(}\PY{n}{feats}\PY{p}{)}
\PY{n}{model} \PY{o}{=} \PY{n}{Model}\PY{p}{(}\PY{n+nb}{input}\PY{o}{=}\PY{n+nb}{input}\PY{p}{,} \PY{n}{output}\PY{o}{=}\PY{n}{bn4}\PY{p}{)}
\PY{k}{return} \PY{n}{model}
\PY{k}{def} \PY{n+nf}{compute\PYZus{}accuracy}\PY{p}{(}\PY{n}{predictions}\PY{p}{,} \PY{n}{labels}\PY{p}{)}\PY{p}{:}
\PY{l+s+sd}{\PYZsq{}\PYZsq{}\PYZsq{}}
\PY{l+s+sd}{ Compute classification accuracy with a fixed threshold on distances.}
\PY{l+s+sd}{ \PYZsq{}\PYZsq{}\PYZsq{}}
\PY{k}{return} \PY{n}{np}\PY{o}{.}\PY{n}{mean}\PY{p}{(}\PY{n}{np}\PY{o}{.}\PY{n}{equal}\PY{p}{(}\PY{n}{predictions}\PY{o}{.}\PY{n}{ravel}\PY{p}{(}\PY{p}{)} \PY{o}{\PYZlt{}} \PY{l+m+mf}{0.5}\PY{p}{,} \PY{n}{labels}\PY{p}{)}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{create\PYZus{}network}\PY{p}{(}\PY{n}{input\PYZus{}dim}\PY{p}{)}\PY{p}{:}
\PY{c+c1}{\PYZsh{} network definition}
\PY{n}{base\PYZus{}network} \PY{o}{=} \PY{n}{create\PYZus{}base\PYZus{}network}\PY{p}{(}\PY{n}{input\PYZus{}dim}\PY{p}{)}
\PY{n}{input\PYZus{}a} \PY{o}{=} \PY{n}{Input}\PY{p}{(}\PY{n}{shape}\PY{o}{=}\PY{p}{(}\PY{n}{input\PYZus{}dim}\PY{p}{,}\PY{p}{)}\PY{p}{)}
\PY{n}{input\PYZus{}b} \PY{o}{=} \PY{n}{Input}\PY{p}{(}\PY{n}{shape}\PY{o}{=}\PY{p}{(}\PY{n}{input\PYZus{}dim}\PY{p}{,}\PY{p}{)}\PY{p}{)}
\PY{c+c1}{\PYZsh{} because we re\PYZhy{}use the same instance `base\PYZus{}network`,}
\PY{c+c1}{\PYZsh{} the weights of the network}
\PY{c+c1}{\PYZsh{} will be shared across the two branches}
\PY{n}{processed\PYZus{}a} \PY{o}{=} \PY{n}{base\PYZus{}network}\PY{p}{(}\PY{n}{input\PYZus{}a}\PY{p}{)}
\PY{n}{processed\PYZus{}b} \PY{o}{=} \PY{n}{base\PYZus{}network}\PY{p}{(}\PY{n}{input\PYZus{}b}\PY{p}{)}
\PY{n}{distance} \PY{o}{=} \PY{n}{Lambda}\PY{p}{(}\PY{n}{euclidean\PYZus{}distance}\PY{p}{,} \PY{n}{output\PYZus{}shape}\PY{o}{=}\PY{n}{eucl\PYZus{}dist\PYZus{}output\PYZus{}shape}\PY{p}{)}\PY{p}{(}\PY{p}{[}\PY{n}{processed\PYZus{}a}\PY{p}{,} \PY{n}{processed\PYZus{}b}\PY{p}{]}\PY{p}{)}
\PY{n}{model} \PY{o}{=} \PY{n}{Model}\PY{p}{(}\PY{n+nb}{input}\PY{o}{=}\PY{p}{[}\PY{n}{input\PYZus{}a}\PY{p}{,} \PY{n}{input\PYZus{}b}\PY{p}{]}\PY{p}{,} \PY{n}{output}\PY{o}{=}\PY{n}{distance}\PY{p}{)}
\PY{k}{return} \PY{n}{model}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}15}]:} \PY{n}{net} \PY{o}{=} \PY{n}{create\PYZus{}network}\PY{p}{(}\PY{l+m+mi}{300}\PY{p}{)}
\PY{c+c1}{\PYZsh{}Using Adam Optimizer}
\PY{n}{net}\PY{o}{.}\PY{n}{compile}\PY{p}{(}\PY{n}{loss}\PY{o}{=}\PY{n}{contrastive\PYZus{}loss}\PY{p}{,} \PY{n}{optimizer} \PY{o}{=} \PY{n}{Adam}\PY{p}{(}\PY{n}{lr}\PY{o}{=}\PY{l+m+mf}{0.001}\PY{p}{)}\PY{p}{)}
\end{Verbatim}
\subsection{Running the final
training}\label{running-the-final-training}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}16}]:} \PY{k}{for} \PY{n}{epoch} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{l+m+mi}{50}\PY{p}{)}\PY{p}{:}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Real Epoch }\PY{l+s+si}{\PYZpc{}d}\PY{l+s+s1}{/50}\PY{l+s+s1}{\PYZsq{}}\PY{o}{\PYZpc{}}\PY{p}{(}\PY{n}{epoch}\PY{o}{+}\PY{l+m+mi}{1}\PY{p}{)}\PY{p}{)}
\PY{n}{net}\PY{o}{.}\PY{n}{fit}\PY{p}{(}\PY{p}{[}\PY{n}{X\PYZus{}train}\PY{p}{[}\PY{p}{:}\PY{p}{,}\PY{l+m+mi}{0}\PY{p}{,}\PY{p}{:}\PY{p}{]}\PY{p}{,} \PY{n}{X\PYZus{}train}\PY{p}{[}\PY{p}{:}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{,}\PY{p}{:}\PY{p}{]}\PY{p}{]}\PY{p}{,} \PY{n}{Y\PYZus{}train}\PY{p}{,}
\PY{n}{validation\PYZus{}data}\PY{o}{=}\PY{p}{(}\PY{p}{[}\PY{n}{X\PYZus{}test}\PY{p}{[}\PY{p}{:}\PY{p}{,}\PY{l+m+mi}{0}\PY{p}{,}\PY{p}{:}\PY{p}{]}\PY{p}{,} \PY{n}{X\PYZus{}test}\PY{p}{[}\PY{p}{:}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{,}\PY{p}{:}\PY{p}{]}\PY{p}{]}\PY{p}{,} \PY{n}{Y\PYZus{}test}\PY{p}{)}\PY{p}{,}
\PY{n}{batch\PYZus{}size}\PY{o}{=}\PY{l+m+mi}{128}\PY{p}{,} \PY{n}{nb\PYZus{}epoch}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,} \PY{n}{shuffle}\PY{o}{=}\PY{k+kc}{True}\PY{p}{,} \PY{p}{)}
\PY{c+c1}{\PYZsh{} Compute final accuracy on training and test sets}
\PY{n}{pred} \PY{o}{=} \PY{n}{net}\PY{o}{.}\PY{n}{predict}\PY{p}{(}\PY{p}{[}\PY{n}{X\PYZus{}test}\PY{p}{[}\PY{p}{:}\PY{p}{,}\PY{l+m+mi}{0}\PY{p}{,}\PY{p}{:}\PY{p}{]}\PY{p}{,} \PY{n}{X\PYZus{}test}\PY{p}{[}\PY{p}{:}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{,}\PY{p}{:}\PY{p}{]}\PY{p}{]}\PY{p}{,} \PY{n}{batch\PYZus{}size}\PY{o}{=}\PY{l+m+mi}{128}\PY{p}{)}
\PY{n}{te\PYZus{}acc} \PY{o}{=} \PY{n}{compute\PYZus{}accuracy}\PY{p}{(}\PY{n}{pred}\PY{p}{,} \PY{n}{Y\PYZus{}test}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{* Accuracy on test set: }\PY{l+s+si}{\PYZpc{}0.2f}\PY{l+s+si}{\PYZpc{}\PYZpc{}}\PY{l+s+s1}{\PYZsq{}} \PY{o}{\PYZpc{}} \PY{p}{(}\PY{l+m+mi}{100} \PY{o}{*} \PY{n}{te\PYZus{}acc}\PY{p}{)}\PY{p}{)}
\end{Verbatim}
\paragraph{Saving the trained model}\label{saving-the-trained-model}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}17}]:} \PY{n}{model\PYZus{}json} \PY{o}{=} \PY{n}{net}\PY{o}{.}\PY{n}{to\PYZus{}json}\PY{p}{(}\PY{p}{)}
\PY{k}{with} \PY{n+nb}{open}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{model.json}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{w}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)} \PY{k}{as} \PY{n}{json\PYZus{}file}\PY{p}{:}
\PY{n}{json\PYZus{}file}\PY{o}{.}\PY{n}{write}\PY{p}{(}\PY{n}{model\PYZus{}json}\PY{p}{)}
\PY{c+c1}{\PYZsh{} serialize weights to HDF5 (saving the weights)}
\PY{n}{net}\PY{o}{.}\PY{n}{save\PYZus{}weights}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{model.h5}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Saved model to disk}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\end{Verbatim}
\subsubsection{\texorpdfstring{Till now, we have trained successfully on
the training set and acheived \emph{81.78\% accuracy} on the validation
set.}{Till now, we have trained successfully on the training set and acheived 81.78\% accuracy on the validation set.}}\label{till-now-we-have-trained-successfully-on-the-training-set-and-acheived-81.78-accuracy-on-the-validation-set.}
\section{Now, We will predict the values for test
set}\label{now-we-will-predict-the-values-for-test-set}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}18}]:} \PY{n}{test\PYZus{}df} \PY{o}{=} \PY{n}{pd}\PY{o}{.}\PY{n}{read\PYZus{}csv}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{test.csv}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}19}]:} \PY{n}{test\PYZus{}df}\PY{o}{.}\PY{n}{head}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}19}]:} test\_id question1 \textbackslash{}
0 0 How does the Surface Pro himself 4 compare wit{\ldots}
1 1 Should I have a hair transplant at age 24? How{\ldots}
2 2 What but is the best way to send money from Ch{\ldots}
3 3 Which food not emulsifiers?
4 4 How "aberystwyth" start reading?
question2
0 Why did Microsoft choose core m3 and not core {\ldots}
1 How much cost does hair transplant require?
2 What you send money to China?
3 What foods fibre?
4 How their can I start reading?
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}20}]:} \PY{n}{q1} \PY{o}{=} \PY{n+nb}{list}\PY{p}{(}\PY{n}{test\PYZus{}df}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{question1}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{p}{)}
\PY{n}{q2} \PY{o}{=} \PY{n+nb}{list}\PY{p}{(}\PY{n}{test\PYZus{}df}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{question2}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}21}]:} \PY{c+c1}{\PYZsh{}Encode to unicode}
\PY{k}{for} \PY{n}{i}\PY{p}{,}\PY{n}{question} \PY{o+ow}{in} \PY{n+nb}{enumerate}\PY{p}{(}\PY{n}{q1}\PY{p}{)}\PY{p}{:}
\PY{n}{q1}\PY{p}{[}\PY{n}{i}\PY{p}{]} \PY{o}{=} \PY{n+nb}{str}\PY{p}{(}\PY{n}{question}\PY{p}{)}\PY{o}{.}\PY{n}{encode}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{utf\PYZhy{}8}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{k}{for} \PY{n}{i}\PY{p}{,}\PY{n}{question} \PY{o+ow}{in} \PY{n+nb}{enumerate}\PY{p}{(}\PY{n}{q2}\PY{p}{)}\PY{p}{:}
\PY{n}{q2}\PY{p}{[}\PY{n}{i}\PY{p}{]} \PY{o}{=} \PY{n+nb}{str}\PY{p}{(}\PY{n}{question}\PY{p}{)}\PY{o}{.}\PY{n}{encode}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{utf\PYZhy{}8}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}22}]:} \PY{c+c1}{\PYZsh{}Tokenizing the sentences}
\PY{n}{q1} \PY{o}{=} \PY{p}{[}\PY{n+nb}{list}\PY{p}{(}\PY{n}{gensim}\PY{o}{.}\PY{n}{utils}\PY{o}{.}\PY{n}{tokenize}\PY{p}{(}\PY{n}{question}\PY{p}{,} \PY{n}{deacc}\PY{o}{=}\PY{k+kc}{True}\PY{p}{,} \PY{n}{lower}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}\PY{p}{)} \PY{k}{for} \PY{n}{question} \PY{o+ow}{in} \PY{n}{q1}\PY{p}{]}
\PY{n}{q2} \PY{o}{=} \PY{p}{[}\PY{n+nb}{list}\PY{p}{(}\PY{n}{gensim}\PY{o}{.}\PY{n}{utils}\PY{o}{.}\PY{n}{tokenize}\PY{p}{(}\PY{n}{question}\PY{p}{,} \PY{n}{deacc}\PY{o}{=}\PY{k+kc}{True}\PY{p}{,} \PY{n}{lower}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}\PY{p}{)} \PY{k}{for} \PY{n}{question} \PY{o+ow}{in} \PY{n}{q2}\PY{p}{]}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}23}]:} \PY{c+c1}{\PYZsh{}Load Doc2Vec model to use infer\PYZus{}vector()}
\PY{c+c1}{\PYZsh{}model = gensim.models.Doc2Vec.load(\PYZdq{}TrainedModel.doc2vec\PYZdq{})}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}24}]:} \PY{n}{size\PYZus{}of\PYZus{}vector} \PY{o}{=} \PY{l+m+mi}{300} \PY{c+c1}{\PYZsh{}Redundant}
\PY{n}{test\PYZus{}count} \PY{o}{=} \PY{n+nb}{len}\PY{p}{(}\PY{n}{test\PYZus{}df}\PY{o}{.}\PY{n}{index}\PY{p}{)}
\PY{n}{test\PYZus{}data\PYZus{}vector} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{zeros}\PY{p}{(}\PY{p}{(}\PY{n}{test\PYZus{}count}\PY{p}{,} \PY{l+m+mi}{2}\PY{p}{,} \PY{n}{size\PYZus{}of\PYZus{}vector}\PY{p}{)}\PY{p}{)}
\PY{k}{for} \PY{n}{idx}\PY{p}{,} \PY{n}{q} \PY{o+ow}{in} \PY{n+nb}{enumerate}\PY{p}{(}\PY{n}{q1}\PY{p}{)}\PY{p}{:}
\PY{n}{test\PYZus{}data\PYZus{}vector}\PY{p}{[}\PY{n}{idx}\PY{p}{,}\PY{l+m+mi}{0}\PY{p}{]} \PY{o}{=} \PY{n}{model}\PY{o}{.}\PY{n}{infer\PYZus{}vector}\PY{p}{(}\PY{n}{q}\PY{p}{)}
\PY{k}{for} \PY{n}{idx}\PY{p}{,} \PY{n}{q} \PY{o+ow}{in} \PY{n+nb}{enumerate}\PY{p}{(}\PY{n}{q2}\PY{p}{)}\PY{p}{:}
\PY{n}{test\PYZus{}data\PYZus{}vector}\PY{p}{[}\PY{n}{idx}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{]} \PY{o}{=} \PY{n}{model}\PY{o}{.}\PY{n}{infer\PYZus{}vector}\PY{p}{(}\PY{n}{q}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}25}]:} \PY{c+c1}{\PYZsh{}Run The Siamese Netwrok cell from above}
\PY{c+c1}{\PYZsh{}then directly load model weights by uncommenting these lines}
\PY{c+c1}{\PYZsh{}net = create\PYZus{}network(300) \PYZsh{}redundant}
\PY{c+c1}{\PYZsh{}net.compile(loss=contrastive\PYZus{}loss, optimizer = Adam(lr=0.001))}
\PY{c+c1}{\PYZsh{}net.load\PYZus{}weights(\PYZdq{}model.h5\PYZdq{}) \PYZsh{}redundant}
\PY{c+c1}{\PYZsh{}print(\PYZdq{}Loaded model from disk\PYZdq{})}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}26}]:} \PY{c+c1}{\PYZsh{}Predict on the test data}
\PY{n}{is\PYZus{}duplicate} \PY{o}{=} \PY{n}{net}\PY{o}{.}\PY{n}{predict}\PY{p}{(}\PY{p}{[}\PY{n}{test\PYZus{}data\PYZus{}vector}\PY{p}{[}\PY{p}{:}\PY{p}{,}\PY{l+m+mi}{0}\PY{p}{,}\PY{p}{:}\PY{p}{]}\PY{p}{,} \PY{n}{test\PYZus{}data\PYZus{}vector}\PY{p}{[}\PY{p}{:}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{,}\PY{p}{:}\PY{p}{]}\PY{p}{]}\PY{p}{,} \PY{n}{batch\PYZus{}size}\PY{o}{=}\PY{l+m+mi}{128}\PY{p}{)}
\end{Verbatim}
The above prediciton gives the distance between 2 vectors. Lesser the
distance, higher the chance for them being duplicate.
\subsubsection{Preparing the final
DataFrame}\label{preparing-the-final-dataframe}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}27}]:} \PY{n}{final\PYZus{}data} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{zeros}\PY{p}{(}\PY{p}{(}\PY{n}{test\PYZus{}count}\PY{p}{,}\PY{l+m+mi}{2}\PY{p}{)}\PY{p}{)}
\PY{n}{final\PYZus{}data}\PY{p}{[}\PY{p}{:}\PY{p}{,}\PY{l+m+mi}{0}\PY{p}{]} \PY{o}{=} \PY{n+nb}{list}\PY{p}{(}\PY{n}{test\PYZus{}df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{test\PYZus{}id}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}
\PY{n}{final\PYZus{}data}\PY{p}{[}\PY{p}{:}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{]} \PY{o}{=} \PY{n+nb}{list}\PY{p}{(}\PY{n}{is\PYZus{}duplicate}\PY{p}{)}
\PY{c+c1}{\PYZsh{}Calculating the probability of being duplicate}
\PY{n}{final\PYZus{}data}\PY{p}{[}\PY{p}{:}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{]} \PY{o}{=} \PY{l+m+mi}{1} \PY{o}{\PYZhy{}} \PY{n}{final\PYZus{}data}\PY{p}{[}\PY{p}{:}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{]}
\PY{c+c1}{\PYZsh{}Setting negative values to 0}
\PY{n}{final\PYZus{}data}\PY{p}{[}\PY{p}{:}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{]}\PY{p}{[}\PY{n}{final\PYZus{}data}\PY{p}{[}\PY{p}{:}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{]} \PY{o}{\PYZlt{}} \PY{l+m+mi}{0}\PY{p}{]} \PY{o}{=} \PY{l+m+mi}{0}
\PY{n}{final\PYZus{}df} \PY{o}{=} \PY{n}{pd}\PY{o}{.}\PY{n}{DataFrame}\PY{p}{(}\PY{n}{data} \PY{o}{=} \PY{n}{final\PYZus{}data}\PY{p}{,} \PY{n}{columns}\PY{o}{=}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{test\PYZus{}id}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{is\PYZus{}duplicate}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{p}{)}
\PY{n}{final\PYZus{}df}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{test\PYZus{}id}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]} \PY{o}{=} \PY{n}{final\PYZus{}df}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{test\PYZus{}id}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{.}\PY{n}{astype}\PY{p}{(}\PY{n+nb}{int}\PY{p}{)}
\PY{n}{final\PYZus{}df}\PY{o}{.}\PY{n}{head}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}27}]:} test\_id is\_duplicate
0 0 0.118746
1 1 0.120376
2 2 0.062846
3 3 0.449672
4 4 0.484841
\end{Verbatim}
\subsection{Saving the output to
Submission.CSV}\label{saving-the-output-to-submission.csv}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}28}]:} \PY{n}{final\PYZus{}df}\PY{o}{.}\PY{n}{to\PYZus{}csv}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{submission.csv}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{encoding}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{utf\PYZhy{}8}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{index} \PY{o}{=} \PY{k+kc}{False}\PY{p}{)}
\end{Verbatim}
\subsubsection{Finally, we have calculated our predicitions and saved
them to the CSV file in the prescribed
format.}\label{finally-we-have-calculated-our-predicitions-and-saved-them-to-the-csv-file-in-the-prescribed-format.}
% Add a bibliography block to the postdoc
\end{document}