diff --git a/docs/report/bib-rapport.bib b/docs/report/bib-rapport.bib
index 8d87334..4f22d1f 100644
--- a/docs/report/bib-rapport.bib
+++ b/docs/report/bib-rapport.bib
@@ -1,256 +1,279 @@
@misc{Aylien16,
Author = {Aylien},
Date-Added = {2018-02-17 20:56:11 +0000},
Date-Modified = {2018-02-17 21:00:33 +0000},
Howpublished = {\url{http://blog.aylien.com/source-code-classification-using-deep-learning/}},
Keywords = {data science, research},
Month = {August},
Title = {Source Code Classification Using Deep Learning [blog post]},
Year = {2016}}
@misc{universal-ctags,
Author = {Universal Ctags Team},
Date-Added = {2018-02-17 20:53:07 +0000},
Date-Modified = {2018-02-17 20:54:44 +0000},
Howpublished = {\url{http://ctags.io/}},
Title = {Universal Ctags},
Year = {2001--2018}}
@misc{sloccount,
Author = {David A. Wheeler},
Date-Added = {2018-02-17 20:47:15 +0000},
Date-Modified = {2018-02-17 20:51:51 +0000},
Howpublished = {\url{https://www.dwheeler.com/sloccount/}},
Title = {SLOCCount},
Year = {2004--2018}}
@misc{cloc,
Author = {Al Danial},
Date-Added = {2018-02-17 20:46:02 +0000},
Date-Modified = {2018-02-17 20:46:38 +0000},
Howpublished = {\url{https://github.com/AlDanial/cloc}},
Title = {cloc},
Year = {2006--2018}}
@misc{guesslang,
Author = {Y. Somda},
Date-Added = {2018-02-17 20:27:54 +0000},
Date-Modified = {2018-02-17 20:43:42 +0000},
Howpublished = {\url{http://guesslang.readthedocs.io/}},
Title = {Guesslang},
Year = {2017--2018}}
@misc{linguist,
Author = {Github},
Date-Added = {2018-02-17 20:21:27 +0000},
Date-Modified = {2018-02-17 20:26:46 +0000},
Howpublished = {\url{https://github.com/github/linguist}},
Title = {Linguist},
Year = {2011--2018}}
@misc{ohcount,
Author = {Black Duck Software},
Date-Added = {2018-02-17 20:11:31 +0000},
Date-Modified = {2018-02-17 21:03:52 +0000},
Title = {Ohcount},
Howpublished = {\url{https://github.com/blackducksoftware/ohcount}},
Year = {2008--2018}}
@inproceedings{vanDam16,
Author = {J. K. v. Dam and V. Zaytsev},
Booktitle = {2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER)},
Doi = {10.1109/SANER.2016.92},
Keywords = {meta data;natural language processing;pattern classification;program diagnostics;software maintenance;text analysis;embedded code fragments;file extensions;grammar-based text analysis;keyword search;legacy code analysis;multinominal naïve Bayes;n-grams;natural language classifiers;natural language processing field;normalised compression distance;skip-grams;software artefact metadata;software language identification;statistical language models;universal IDE support;Cascading style sheets;HTML;Java;Natural languages;Software;Training;Training data;language identification;natural language processing;software language engineering},
Month = {March},
Pages = {624-628},
Title = {Software Language Identification with Natural Language Classifiers},
Volume = {1},
Year = {2016},
Bdsk-Url-1 = {http://dx.doi.org/10.1109/SANER.2016.92}}
@article{Klein11,
Archiveprefix = {arXiv},
Author = {David Klein and Kyle Murray and Simon Weber},
Bibsource = {dblp computer science bibliography, http://dblp.org},
Biburl = {http://dblp.org/rec/bib/journals/corr/abs-1106-4064},
Eprint = {1106.4064},
Journal = {CoRR},
Timestamp = {Wed, 07 Jun 2017 14:41:07 +0200},
Title = {Algorithmic Programming Language Identification},
Url = {http://arxiv.org/abs/1106.4064},
Volume = {abs/1106.4064},
Year = {2011},
Bdsk-Url-1 = {http://arxiv.org/abs/1106.4064}}
@inproceedings{Gilda17,
Author = {S. Gilda},
Booktitle = {2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE)},
Doi = {10.1109/JCSSE.2017.8025917},
Keywords = {feature extraction;learning (artificial intelligence);neural nets;pattern classification;programming languages;software engineering;source code (software);artificial neural network;convolutional neural network;file extension;intelligent feature extraction;multilayer neural network;neural networks;programming languages;software development industry;source code classification;supervised learning;word embedding layers;Feature extraction;HTML;Syntactics;Training;Artificial neural network;Feature extraction;Multi-layer neural network;Supervised learning},
Month = {July},
Pages = {1-6},
Title = {Source code classification using Neural Networks},
Year = {2017},
Bdsk-Url-1 = {http://dx.doi.org/10.1109/JCSSE.2017.8025917}}
@article{Zevin17,
Archiveprefix = {arXiv},
Author = {Shaul Zevin and Catherine Holzem},
Bibsource = {dblp computer science bibliography, http://dblp.org},
Biburl = {http://dblp.org/rec/bib/journals/corr/ZevinH17},
Eprint = {1703.07638},
Journal = {CoRR},
Timestamp = {Wed, 07 Jun 2017 14:41:28 +0200},
Title = {Machine Learning Based Source Code Classification Using Syntax Oriented Features},
Url = {http://arxiv.org/abs/1703.07638},
Volume = {abs/1703.07638},
Year = {2017},
Bdsk-Url-1 = {http://arxiv.org/abs/1703.07638}}
@inproceedings{Ugurel02,
Acmid = {775141},
Address = {New York, NY, USA},
Author = {Ugurel, Secil and Krovetz, Robert and Giles, C. Lee},
Booktitle = {Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
Doi = {10.1145/775047.775141},
Isbn = {1-58113-567-X},
Location = {Edmonton, Alberta, Canada},
Numpages = {7},
Pages = {632--638},
Publisher = {ACM},
Series = {KDD '02},
Title = {What's the Code?: Automatic Classification of Source Code Archives},
Url = {http://doi.acm.org/10.1145/775047.775141},
Year = {2002},
Bdsk-Url-1 = {http://doi.acm.org/10.1145/775047.775141},
Bdsk-Url-2 = {http://dx.doi.org/10.1145/775047.775141}}
@inproceedings{Wang15,
author = {Peng Wang and
Jiaming Xu and
Bo Xu and
Cheng{-}Lin Liu and
Heng Zhang and
Fangyuan Wang and
Hongwei Hao},
title = {Semantic Clustering and Convolutional Neural Network for Short Text
Categorization},
booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational
Linguistics and the 7th International Joint Conference on Natural
Language Processing of the Asian Federation of Natural Language Processing,
{ACL} 2015, July 26-31, 2015, Beijing, China, Volume 2: Short Papers},
pages = {352--357},
year = {2015},
url = {http://aclweb.org/anthology/P/P15/P15-2058.pdf},
timestamp = {Mon, 03 Aug 2015 08:13:34 +0200},
biburl = {http://dblp.org/rec/bib/conf/acl/WangXXLZWH15},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
@inproceedings{Khasnabish14,
author = {Jyotiska Nath Khasnabish and
Mitali Sodhi and
Jayati Deshmukh and
G. Srinivasaraghavan},
title = {Detecting Programming Language from Source Code Using Bayesian Learning
Techniques},
booktitle = {Machine Learning and Data Mining in Pattern Recognition - 10th International
Conference, {MLDM} 2014, St. Petersburg, Russia, July 21-24, 2014.
Proceedings},
pages = {513--522},
year = {2014},
url = {https://doi.org/10.1007/978-3-319-08979-9_39},
doi = {10.1007/978-3-319-08979-9_39},
timestamp = {Wed, 17 May 2017 14:25:11 +0200},
biburl = {http://dblp.org/rec/bib/conf/mldm/KhasnabishSDS14},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
@misc{Heres16,
Author = {Daniël Heres},
Howpublished = {\url{http://blog.aylien.com/source-code-classification-using-deep-learning/}},
Month = {July},
Title = {Detecting the Programming Language of Source Code Snippets using Machine Learning and Neural Networks [blog post]},
Year = {2016}}
@Inbook{Aggarwal12,
author={Aggarwal, Charu C.
and Zhai, ChengXiang},
editor={Aggarwal, Charu C.
and Zhai, ChengXiang},
title={A Survey of Text Classification Algorithms},
bookTitle={Mining Text Data},
year={2012},
publisher={Springer US},
address={Boston, MA},
pages={163--222},
abstract={The problem of classification has been widely studied in the data mining, machine learning, database, and information retrieval communities with applications in a number of diverse domains, such as target marketing, medical diagnosis, news group filtering, and document organization. In this paper we will provide a survey of a wide variety of text classification algorithms.},
isbn={978-1-4614-3223-4},
doi={10.1007/978-1-4614-3223-4_6},
url={https://doi.org/10.1007/978-1-4614-3223-4_6}
}
@article{Chen09,
title = {Feature selection for text classification with Naïve Bayes},
journal = {Expert Systems with Applications},
volume = {36},
number = {3, Part 1},
pages = {5432 - 5435},
year = {2009},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2008.06.054},
url = {http://www.sciencedirect.com/science/article/pii/S0957417408003564},
author = {Jingnian Chen and Houkuan Huang and Shengfeng Tian and Youli Qu},
keywords = {Text classification, Feature selection, Text preprocessing, Naïve Bayes}
}
@misc{MLatB16,
Author = {Machine Learning at Berkeley},
Howpublished = {\url{https://ml.berkeley.edu/blog/2016/12/03/github/}},
Keywords = {data science, research},
Month = {December},
Title = {Github Programming Language Classification [blog post]},
Year = {2016}
}
@article{Cavnar94,
title={N-gram-based text categorization},
author={Cavnar, William B and Trenkle, John M and others},
journal={Ann arbor mi},
volume={48113},
number={2},
pages={161--175},
year={1994},
publisher={Citeseer}
}
@article{Kim15,
author = {Yoon Kim and
Yacine Jernite and
David Sontag and
Alexander M. Rush},
title = {Character-Aware Neural Language Models},
journal = {CoRR},
volume = {abs/1508.06615},
year = {2015},
url = {http://arxiv.org/abs/1508.06615},
archivePrefix = {arXiv},
eprint = {1508.06615},
timestamp = {Wed, 07 Jun 2017 14:41:17 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/KimJSR15},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{Kim14,
author = {Yoon Kim},
title = {Convolutional Neural Networks for Sentence Classification},
journal = {CoRR},
volume = {abs/1408.5882},
year = {2014},
url = {http://arxiv.org/abs/1408.5882},
archivePrefix = {arXiv},
eprint = {1408.5882},
timestamp = {Wed, 07 Jun 2017 14:40:07 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/Kim14f},
bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+
+@inproceedings{kenlm,
+ author = {Kenneth Heafield},
+ title = {{KenLM:} Faster and Smaller Language Model Queries},
+ year = {2011},
+ month = {July},
+ booktitle = {Proceedings of the {EMNLP} 2011 Sixth Workshop on Statistical Machine Translation},
+ address = {Edinburgh, Scotland, United Kingdom},
+ pages = {187--197},
+ url = {https://kheafield.com/papers/avenue/kenlm.pdf},
+}
+
+@article{scikit-learn,
+ title={Scikit-learn: Machine Learning in {P}ython},
+ author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
+ and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
+ and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
+ Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
+ journal={Journal of Machine Learning Research},
+ volume={12},
+ pages={2825--2830},
+ year={2011}
}
\ No newline at end of file
diff --git a/docs/report/report-en.pdf b/docs/report/report-en.pdf
index c8a7df3..8fe4843 100644
Binary files a/docs/report/report-en.pdf and b/docs/report/report-en.pdf differ
diff --git a/docs/report/report-en.tex b/docs/report/report-en.tex
index 8a819f7..c65e740 100644
--- a/docs/report/report-en.tex
+++ b/docs/report/report-en.tex
@@ -1,322 +1,461 @@
\documentclass[a4paper,12pt]{article}
\usepackage[english]{babel}
\usepackage[parfill]{parskip}
\usepackage{graphicx}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{amsthm}
\usepackage{xunicode}
\usepackage[charter]{mathdesign}
\usepackage{url}
\usepackage{hyperref}
\usepackage{multirow}
+\usepackage[toc,page]{appendix}
+\usepackage{tabularx}
+\usepackage{longtable}
\DeclareTextCommand{\nobreakspace}{T1}{\leavevmode\nobreak\ }
\title{Large-scale Programming Language Detection}
\author{Yuan YIN}
\date{}
\begin{document}
\maketitle
+
\begin{abstract}
(to be completed)
\end{abstract}
+
+ \tableofcontents
\section{Introduction}
Programming Language Detection is a problem of identifying which programming language is a piece of source code written in. We here define the piece of source code as a textual sequential representation of an artefact, which is normally in the form of character sequence or, more generally, byte sequence. More precisely, the objective is to build a model which could predict the language of a given byte sequence.
-The formal definition of the problem as follows: on the input, given an byte sequence $d$ and the number of languages $n$,
+The formal definition of the problem as follows: on the input, given a byte sequence $d$ and the number of a languages $n$,
\[l_d = \underset{l_i\in \{l_1, ..., l_n\}}{\arg \max}\ m(d, l_i),\]
-where $l_d$ is the projected language, model $m$ calculates a value indicating the likelihood of a document written in language $l_i$ and the most likely one is chosen as the language of the document.
+where $l_d$ is the projected language, model $m$ calculates a value indicating the likelihood of a document written in language $l_i$ and the most likely one is chosen as the recognised language of the document.
In general, Programming Language Detection could be utilised in different situations, here are several example applications: language composition of software project in version control systems. For example, GitHub team is developing the project Linguist to return which languages are the project written in; code searching in plain text, in order to track the popularity of a language; language detection helps also IDEs to choose the language whose support functionalities, like syntax highlighting, are implemented.
We dive into this problem in the context of \emph{Software Heritage}. \emph{Software Heritage}, initiated by Inria, is an archive in which 4 billions source code files from 80 millions projects are stored.
The reason why the language detection is requested by \emph{Software Heritage} is that the language of a file could not be found in its filename extension. In \emph{Software Heritage}, every source code file is a blob which contains raw content of the file, that means a sequence of bytes without any extra information, such as filename (including filename extension), metadata, \emph{etc}. Since each blob could be represented by an intrinsic identifier generated from the blob itself, the duplication of files is avoided. For this reason, all existing tools depending on filenames fail in our context, and the methods for recognising the language from a sequence of bytes is strongly demanded.
+(To be fixed after the redaction)
+
In this report, we introduce briefly the state-of-the-art methods in Section 2. In Section 3, the procedure of making a feasible dataset is related. In Section 4, we explain the methods that we took in account for the evaluation.
+We provide the implemented methods and more detailed results on Forge of \emph{Software Heritage} \footnote{\url{http://}}.
-\section{State of the Art}
+\section{Related Works}
-The existing approaches could be divided into two groups: practical methods and machine learning methods.
+The existing approaches could be divided into two categories: practical methods and machine learning methods.
Practical methods are mostly based on several empirical or external information, basic ideas are presented as follows:
\begin{itemize}
\item Judging from filename extension. Ohcount\cite{ohcount} and Linguist\cite{linguist} practice the detection by hashing filename extension. The problem from this straightforward method is that some extensions are related to different languages, \emph{e.g.} \texttt{*.m} refers to a file written in Objective-C or MATLAB, \texttt{*.pl} points to Python or Prolog.
\item Grammar-based approaches. The principal is to parse through all languages, which is complex in modelling and demand an heavy consumption of calculation time.
\item Heuristics approaches. Most of them, such as SLOCCount\cite{sloccount}, use predefined regular expressions to capture empirically discovered features, \emph{e.g.} a file start with ``\texttt{\#include}'' is probably written in C. Some other looks for hints in the file, such as shebang lines, Vim modelines, Emacs modelines, \emph{etc}.
\end{itemize}
In Machine learning, the problem is regarded as a sub-problem of \emph{text categorisation} or \emph{text classification}, which means that given a piece of text, we find a function that predicts which category the text belongs to. The state-of-the-art methods build such function based on example input-output pairs, which are categorised as \emph{supervised learning}.
Ugurel \emph{et al.} \cite{Ugurel02} selects firstly the features by Expected Entropy Loss for each language, then vectorise the tested document into a vector representing the presence of a selected feature. Since Support Vector Machine (SVM) is binary classifier, the $n$-class classification is resolved by training $n \choose 2$ SVMs in the form of decision tree. Van Dam and Zaytsev \cite{vanDam16} test several popular and performant methods in Natural Language Processing. Multi-nominal Naïve Bayes (MNB), one of the variants of Naïve Bayes Classifiers, utilises unified frequency of a word or a sequence of words in a byte-sequence to decide the most possibly corresponding programming language. $N$-gram model and skip-gram model calculate for each gram the possibility of its appearance after $N$ grams. Normalised Compression Distance compares a piece of compressed code to the examples in the training set, then chooses the nearest language on as projection. MNB and $N$-gram model outperform others according to the experimental results. Gilda\cite{Gilda17} adopts a general setup of Convolutional Neurone Network (ConvNet) in NLP and proofs its performance.
-In this report, we will test several NLP methods on a larger dataset.
-
\section{Dataset}
Supervised learning methods require a dataset containing labeled inputs to train and to evaluate the model. Nowadays, since Programming Language Detection is not seriously considered as an important subject in machine learning, for the reason that it could be resolved by adopting existing classifiers of ML, the articles are rarely accompanied by a publicly available dataset. Therefore, we natively build a novel dataset for our experiments.
-GitHub is one of the most popular web-based hosting service for Git version control system, reporting having more than 57 million repositories. We decide to build the dataset using GitHub.
+GitHub\footnote{\url{https://www.github.com/}} is one of the most popular web-based hosting service for Git version control system, reporting having more than 57 million repositories. We decide to build the dataset using GitHub.
\paragraph{Ground Truth Supposition}
In the context of \emph{Software Heritage}, our aim is to cover as many languages as possible for classification, thus the dataset we build possesses inevitably a large amount of files, which is unaffordable to be labeled manually. We thus seek help from automatic labelling tools.
-Linguist\cite{linguist} is the tool of language detection developed by the GitHub team for unveiling the language composition in git repository, service provided on GitHub through API. There exists a command line version Linguist producing list of files by language for repository. Given that filename extensions are visible for Linguist and such features boost enormously on accuracy of classification (we will show this claim in later experiment), we suppose that the language recognised by Linguist is the ground truth language attributed to it.
+Linguist \cite{linguist} is the tool of language detection developed by the GitHub team for unveiling the language composition in git repository, service provided on GitHub through API. There exists a command line version Linguist producing list of files by language for repository. Given that filename extensions are visible for Linguist and such features boost enormously on accuracy of classification (we will show this claim in later experiment), we suppose that the language recognised by Linguist is the ground truth language attributed to it.
-\paragraph{Repository Fetching}
+\paragraph{Source Code Recuperation and Languages Included}
-For each language, we fetch the first 75 repositories which top on the list ordered by number of stars, which shows the popularity of the repository. To avoid huge repositories, we ignore all repositories whose size is superior to 150 MiB. All lists
+The dataset is built in the context of \emph{Software Heritage}. Therefore, the list of languages we consider integrating in the system covers as many languages as possible.
-We initially took the entire language list of Linguist (version 6.0.1) into account for repository fetching, then we eliminated some languages, \emph{i.e.} data description languages, which we could not fetch any repository.
+We initially took the entire language list of Linguist (version 6.0.1) into account for repository fetching. For each language, we fetch the first 75 repositories which top on the list ordered by number of stars, manifesting the popularity of the repository. To avoid huge repositories, we ignore all repositories whose size is superior to 150 MiB.
-\paragraph{Imbalance Among Classes}
+We then eliminate some languages, \emph{i.e.} data description languages, which we could not fetch any repository from GitHub. We successfully fetched 323 valid languages, showed in Table~\ref{tab:lan}.
-Despite of our efforts on balancing the number of repositories for each class, a significant imbalance is eventually observed among language classes. For example, C++ has 180,093 files, but Omgrofl has only 16 files.
-
-(Graph needed)
\section{Methods for Evaluation}
In this section, we describe several NLP methods here tested on our dataset:
\begin{itemize}
\item $n$-gram-based frequency distance model,
\item $n$-gram model,
\item Multinominal Naïve Bayes (MNB), and
\item Convolutional Neurone Networks (ConvNet).
\end{itemize}
The first approach is regarded as a baseline method for the evaluation of the accuracy and the efficiency of the model.
\subsection{Baseline: $n$-gram-based frequency distance}
\paragraph{$n$-gram}
An $n$-gram is a slice of a larger sequence with $n$ units. In NLP, the sequence is naturally the string. Depending on different problems, an unit represents a character or a word.
For example, the string ``print(n)'' with 8 characters could generate following character based $n$-grams:
\begin{itemize}
- \item unigrams: \texttt{, p, r, ..., ), }
- \item bigrams: \texttt{ p, p r, r i, ..., n ), ) }
- \item trigrams: \texttt{ p r, p r i, r i t, ..., ( n ), n ) }
+ \item unigrams: \texttt{p, r, ..., )}
+ \item bigrams: \texttt{\textvisiblespace p, pr, ri, ..., n), )\textvisiblespace}
+ \item trigrams: \texttt{\textvisiblespace\textvisiblespace p, \textvisiblespace pr, pri, rit, ..., n)\textvisiblespace, )\textvisiblespace\textvisiblespace}
\item ...
\end{itemize}
or word-based $n$-grams:
\begin{itemize}
\item unigrams: \texttt{, print, (, n, ), }
\item bigrams: \texttt{ print, print (, ( n, n ), ) }
\item trigrams: \texttt{ print (, print ( n, ( n ), n ) }
\item ...
\end{itemize}
-Strings are often padded with \texttt{} and \texttt{} around to mark its start and end. In general, a $k$-unity sequence generates exactly $k-(n-1)$ n-grams.
+Strings are often padded with start mark \texttt{} and end mark \texttt{}. In general, a $k$-unity sequence generates exactly $k-(n-1)$ n-grams.
Cavnar and Trenkle \cite{Cavnar94} introduce an early NLP method using the distance between two $n$-gram frequency profiles.
According to Zipf's law, an empirical observation expressing that the $n$-th most common word in a human language occurs with a frequency inversely proportional to $n$. By retaining the most common words, it is possible to obtain a list describing the characteristics of the language.
Given a training set, at the training phase, a bag of $n$-grams is generated for each document in the training set. By gathering all bags of a language and counting the occurrences of each $n$-gram, a list of $n$-grams ordered by number of occurrences is created as the \emph{category profile} of the class. Only the most frequent 300 $n$-grams are kept, since they are highly correlated to the language.
The \emph{distance} between category profile and document profile is defined as follows:
Given trained category profiles $p_{l_1}, ..., p_{l_k}$ for $k$ languages, and document profile $p_{d}$ of test document $d$,
\[
distance(p_{l_i}, p_{d}) = \sum_{w\in p_{d}} | rankdist(w, p_d, p_{l_i})|
\]
\[
rankdist(w, p_d, p_{l_i})=
\begin{cases}
|rank(w, p_d) - rank(w, p_{l_i})| & \text{if }rank(w, p_{l_i}) \text{ exists,} \\
|p_d| & \text{else}
\end{cases}
\]
where $p$ containing an ordered list of word, $rank(w, p)$ returns the rank of $w$ in list $p$. $rankdist(w, p_d, p_{l_i})$ returns the out-of-place distance between two profiles if $w$ appears in $p_{l_i}$. If $w$ is an out-of-vocabulary word, the distance is the length of document profile $p_d$.
We then categorise the document as language with minimum distance.
\subsection{Multinominal Naïve Bayes}
This approach is introduced by van Dam and Zaytsev \cite{vanDam16}.
We assume in Naïve Bayes model that each word of the document is independent from each other. According to Bayes' Theorem,
\begin{eqnarray*}
P(l|w_1w_2...w_n) & = & \frac{P(w_1w_2...w_n|l)P(l)}{P(w_1w_2...w_n)} \\
& = & c\cdot P(l) P(w_1w_2...w_n|l)\\
& = & c\cdot P(l) \prod_{i = 1}^n P(w_i|l)
\end{eqnarray*}
Probability of $w_i$ in language $l$ is estimated by its occurrences in language with bag-of-word assumption:
\[
P(w_i|l) = \frac{C_l(w_i) + 1}{\sum_{w\in V}C_l(w) + |V|}
\]
where $C_l$ gives frequency of a word, $V$ is the vocabulary all languages.
-Assumption of independence of words is quite limited for classification because it ignores the context of words, in practice we actually use unigrams to 5-grams as (words) in the original method.
+Assumption of independence of words is quite limited for classification, in practice we actually use unigrams to 5-grams to replace words in the original method for taking the context of words into account.
\subsection{$n$-gram model}
The approach is introduced by van Dam and Zaytsev\cite{vanDam16}. As the precedent method, $n$-gram model utilises also statistical properties of $n$-grams but in another way.
Originally, $n$-gram model aims at predicting the possibility of an unit after knowing $n-1$ units occurred before. Given an unit $w_i$, the probability of its occurrence in a sequence is defined as:
\[
P(w_i | w_1...w_{i-1})
\]
According to Markov assumption, we omit older context in the sequence,
\[
P(w_i | w_1...w_{i-1}) \approx P(w_i | w_{i-(n-1)}...w_{i-1})
\]
In reality, the probability could be estimated by maximum likelihood estimation (MLE):
\[
P(w_i | w_{i-(n-1)}...w_{i-1}) = \frac{C(w_{i-(n-1)}...w_{i-1}w_{i})}{C(w_{i-(n-1)}...w_{i-1})}
\]
where $C$ gives the count of given $n$-gram.
By chain rule of probability and precedent estimation,
\[
P(w_1w_2...w_n)\approx \prod_{i = 1}^n P(w_i|w_{i-(n-1)}...w_{i-1})
\]
Now we transform such model into a classifier. Given a sequence $w_1w_2...w_n$, we assume that each language $l$ appears with the same probability and the probability of a given sequence is fixed.
According to Bayes' Theorem,
\begin{eqnarray*}
P(l|w_1w_2...w_n) & = & \frac{P(w_1w_2...w_n|l)P(l)}{P(w_1w_2...w_n)} \\
& = & c\cdot P(w_1w_2...w_n|l)\\
& = & c\cdot \prod_{i = 1}^n P(w_i|w_{i-(n-1)}...w_{i-1}, l)
\end{eqnarray*}
Rather than counting $n$-grams in the document, the probability of $n$-gram is estimated from the $n$-gram frequency of language, obtained from training set.
\[
P(w_i | w_{i-(n-1)}...w_{i-1}, l) = \frac{C_l(w_{i-(n-1)}...w_{i-1}w_{i})}{C_l(w_{i-(n-1)}...w_{i-1})}
\]
where $l$ is $C_l$ gives the count of language $l$ in training set.
While estimating the probability of $n$-grams, the smoothing techniques are required because of possible occurrence of \emph{out-of-vocabulary (OOV)} $n$-gram. In our case, Modified Kneser-Ney is applied since it is one of the methods that gives better experimental results in \cite{vanDam16}.
-\subsection{Convolutional Neurone Network (ConvNet)}
+\subsection{Convolutional Neural Network (ConvNet)}
-Convolutional Neurone Network is one of the most popular machine learning branch usually used for image classification. It is a class of deep feed-forward artificial neural networks.
+Convolutional Neural Network is one of the most popular machine learning branch usually used for image classification. It is a class of deep feed-forward artificial neural networks.
The following two architectures are tested in Section 4.
-\subsubsection{Word-level Approach}
+\subsubsection{Word-level Approach (Ongoing)}
Although Gilda \cite{Gilda17} shows the performance of his own architecture, we are not able to rebuild the same network due to the lack of network architecture details and hyper-parameter configuration. We move our vision to other architectures.
Kim \cite{Kim14} introduces a ConvNet for natural language sentence classification.
In this architecture, word is the unit of the input. The $i$-th word $w_i$ is transformed into a vector $\mathbf{x}_i \in \mathbb{R}^k$ by word embedding level using \texttt{word2vec}. Word vectors are then concatenated to form the representation of the document, an $n\times k$ matrix.
The number of words $n$ of the document is fixed by the model. Therefore, a document longer than $n$ words needs to be pruned and the shorter one needs padding, by concatenating zero-vectors at the beginning or the end of the matrix.
-In the convolutional levels, by using a \emph{filter} $\mathbf{w} \in R^{hk}$, a \emph{feature} $c_i$ is then generated,
-\[c_i = f(\mathbf{w}\cdot(\mathbf{x}_i\ ||\ \mathbf{x}_{i+1}\ ||\ ...\ ||\ \mathbf{x}_{i+h-1}) + b)\]
+In the convolutional levels, by using a \emph{filter} $\mathbf{w_h} \in R^{hk}$, a \emph{feature} $c_i$ is then generated,
+\[c_i = f(\mathbf{w_h}\cdot(\mathbf{x}_i\ ||\ \mathbf{x}_{i+1}\ ||\ ...\ ||\ \mathbf{x}_{i+h-1}) + b)\]
+where $||$ is vector concatenate operator, $b\in \mathbb{R}$ is a bias term, $f$ is an \emph{activation function} outputting a feature from a set of inputs.
-where $||$ is vector concatenate operator, $b\in \mathbb{R}$ is a bias term, $f$ is an \emph{activation function} .
+This procedure utilises the similar principle of $n$-gram model, but rather than extracting features from original words, ConvNet works on their vector representation.
-Each level produces a \emph{feature map}
+Each filter produces a \emph{feature map}, a vector $\mathbf{c}^h\in \mathbb{R}^{n - h+1}$. A max-over-time-pooling is then applied on the feature map $\mathbf{c}^h$, aiming at choosing the most important features with the highest values and avoiding overfitting at training stage. We then obtain the final feature map of this $h\times k$ filter.
-\subsubsection{Byte-level Approach}
+Several filters are often applied to obtain the corresponding feature maps. Each filter represents a \emph{} They are then concatenated vertically into a final feature map $\mathbf{c}$.
+\subsubsection{Byte-level Approach (Ongoing)}
\section{Experimental Results}
In this section, we present several questions that we are willing to answer by experiments on our customised dataset.
-\subsection{Implementation}
+\subsection{Implementation and System Setup (Ongoing)}
+
+We implement the methods described in Section 4 in Python 3, in order of finally integrating one of them in \emph{Software Heritage}.
-For finally integrating one of the methods described in Section 4 in \emph{Software Heritage}, we implemented them in Python 3.
+Baseline method is implemented natively in Python. We implement MNB using Scikit-learn. $n$-gram model is implemented with KenLM \cite{kenlm}. The last two ConvNets are both implemented with Keras using Tensorflow as backend.
+
+We execute principally the training and test phase on a portable computer with 2.7 GHz Intel Core i5 processor running macOS 10.3. The training phase of two ConvNet methods are executed in an instance with one Intel Sandy Bridge virtual CPU, equipped with one NVIDIA Tesla K80 GPU on Google Cloud Platform. The instance is configured for making use of Tensorflow backend with GPU acceleration using CUDA Deep Neural Network Library (cuDNN).
+
+\subsection{Training Set And Test Set (Ongoing)}
+
+Files of the training set are randomly picked at first, then the test set is built from the remaining samples. To avoid the imbalance of the training set that impacts the performance of several methods in Section 4, for each language, we restrain the maximum number of training files to 500 and the maximum number of test files to 1000.
\subsection{Model Quality Metrics}
-Given class $c$, test results of documents could be regrouped into 4 categories:
+Given a class $c$, test results of documents could be regrouped into 4 categories:
\begin{itemize}
\item True Positive (TP): document in $c$ predicted as member of $c$
\item False Positive (FP): document not in $c$ predicted as member of $c$
\item True Negative (TN): document not in $c$ predicted as member of a class other than $c$
\item False Negative (FN): document in $c$ predicted as member of a class other than $c$
\end{itemize}
In the context of classification, the quality of methods is measured by Precision, Recall and $F_1$ score.
Recall is also called True Positive Rate (TPR). It is the fraction of correctly classified samples over all samples should be predicted as in $c$:
\[\text{recall} = \frac{\text{\#TP}}{\text{\#TP}+\text{\#FN}}\]
Precision is also called Positive Predictive Value (PPV). It is the fraction of correctly classified samples over all samples predicted as in $c$:
\[\text{precision} = \frac{\text{\#TP}}{\text{\#TP}+\text{\#FP}}\]
The harmonic mean of precision and recall is called $F_1$ score, introduced for balancing two metrics:
\[
F_1 = \left(\frac{\text{precision}^{-1} + \text{recall}^{-1}}{2}\right)^{-1} = 2\cdot\frac{\text{precision}\cdot\text{recall}}{\text{precision}+\text{recall}}
\]
-In following subsections, we use $F_1$ as the measurement of the model validity. (?)
-
-\subsection{Training Set And Test Set}
-
-Files of the training set are randomly picked at first, then the test set is built from the remaining samples. To avoid the imbalance of the training set that impacts the performance of several methods in Section 4, for each language, we restrain the maximum number of training files to 500 and the maximum number of test files to 1000.
-
-\subsection{Generality of Dataset}
+In following subsections, we use $F_1$ as the measurement of the model validity by language.
\subsection{Evaluation of Models}
-\subsubsection{Impact from Number of Classes}
-
-\subsubsection{Confusion d'inter-classes}
-
\subsection{Benchmark}
\subsection{Filename Extension Is Important}
We know empirically that filename extension is a critical feature of classification. However, we hope to figure out how important it is. Knowing that ConvNet is good at choosing features distinguishing inputs, we test the performance using Byte-level ConvNet. In order to make use of the extension as feature, we add the corresponding extension at the end of the test file.
-For convenience, we test only for 20 languages in the list.
+For convenience, we test only for 20 languages in the list. Table~\ref{tab:ext} shows that by adding the extension into the code the detection accuracy could be dramatically improved.
\begin{table}[h]
\centering
\begin{tabular}{|c|c|c|}
\hline
- & With Extension & Without Extension \\
+ & Without Extension & With Extension \\
\hline
Overall Accuracy / \% & 91.27 & 97.39 \\
\hline
\end{tabular}
- \caption{\label{dsa} Comparison of accuracy with extension and accuracy without extension with Byte-level ConvNet Classification on 20 classes.}
+ \caption{\label{tab:ext} Comparison of accuracy with extension and accuracy without extension with Byte-level ConvNet Classification on 20 classes.}
\end{table}
-\section{Challenge for Large-scale Deployment}
+\subsubsection{Impact of Number of Languages (Ongoing)}
+
+\subsubsection{Interclass Confusion (Ongoing)}
+
+
+\section{Application in \emph{Software Heritage} (Ongoing)}
+
+\section{Challenge for Large-scale Deployment (Ongoing)}
+
+\subsection{Imbalance Among Classes}
+
+Despite of our efforts on balancing the number of repositories for each class, a significant imbalance is eventually observed among language classes. For example, C++ has 180,093 files, but Omgrofl has only 16 files.
+
+(Graph needed)
\subsection{Deploying in Distributed Systems}
\subsection{Discovering New Languages}
-\subsubsection{Regroupement hiérarchique agglomératif}
+\subsubsection{Agglomerative Hierarchical Clustering}
\subsection{Integrating New Languages}
\subsubsection{Retraining}
\subsubsection{Incremental Learning}
-\section{Conclusion}
+\section{Conclusion (Ongoing)}
+
+\clearpage
+
+\begin{appendices}
+
+\section{Language List}
+\begin{table*}[h!]
+\centering
+\tiny
+\begin{tabularx}{\textwidth}{|X|X|X|X|X|X|}
+\hline
+1C Enterprise & ABAP & ActionScript & Ada & Agda & AGS Script \\
+\hline
+Alloy & AMPL & AngelScript & ANTLR & Apex & API Blueprint \\
+\hline
+APL & AppleScript & Arc & ASP & AspectJ & Assembly \\
+\hline
+ATS & Augeas & AutoHotkey & AutoIt & Awk & Ballerina \\
+\hline
+Batchfile & Befunge & BitBake & BlitzBasic & BlitzMax & Bluespec \\
+\hline
+Boo & Brainfuck & Brightscript & Bro & C & C\# \\
+\hline
+C++ & Cap'n Proto & CartoCSS & Ceylon & Chapel & Charity \\
+\hline
+ChucK & Cirru & Clarion & Clean & Click & CLIPS \\
+\hline
+Clojure & CMake & COBOL & CoffeeScript & ColdFusion & Common Lisp \\
+\hline
+Common Workflow Language & Component Pascal & Cool & Coq & Crystal & Csound \\
+\hline
+Csound Document & Csound Score & CSS & Cuda & CWeb & Cycript \\
+\hline
+D & Dart & DataWeave & DIGITAL Command Language & DM & Dogescript \\
+\hline
+DTrace & Dylan & E & eC & ECL & Eiffel \\
+\hline
+Elixir & Elm & Emacs Lisp & EmberScript & EQ & Erlang \\
+\hline
+F\# & Factor & Fancy & Fantom & Filebench WML & FLUX \\
+\hline
+Forth & Fortran & FreeMarker & Frege & Game Maker Language & GAMS \\
+\hline
+GAP & GDB & GDScript & Genie & Genshi & Gherkin \\
+\hline
+GLSL & Glyph & Gnuplot & Go & Golo & Gosu \\
+\hline
+Grace & Grammatical Framework & Groovy & Hack & Harbour & Haskell \\
+\hline
+Haxe & HCL & HLSL & HTML & Hy & HyPhy \\
+\hline
+IDL & Idris & IGOR Pro & Inform 7 & Inno Setup & Io \\
+\hline
+Ioke & Isabelle & J & Jasmin & Java & JavaScript \\
+\hline
+Jolie & JSONiq & Julia & Jupyter Notebook & Kit & Kotlin \\
+\hline
+KRL & LabVIEW & Lasso & Lean & Lex & LFE \\
+\hline
+LilyPond & Limbo & Liquid & LiveScript & LLVM & Logos \\
+\hline
+Logtalk & LOLCODE & LookML & LoomScript & LSL & Lua \\
+\hline
+M & M4 & Makefile & Mako & Markdown & Mask \\
+\hline
+Mathematica & Matlab & Max & MAXScript & Mercury & Meson \\
+\hline
+Metal & Mirah & Modelica & Modula-2 & Module Management System & Monkey \\
+\hline
+Moocode & MoonScript & MQL4 & MQL5 & MTML & mupad \\
+\hline
+NCL & Nearley & Nemerle & nesC & NetLinx & NetLinx+ERB \\
+\hline
+NetLogo & NewLisp & Nextflow & Nim & Nit & Nix \\
+\hline
+NSIS & Nu & Objective-C & Objective-C++ & Objective-J & OCaml \\
+\hline
+Omgrofl & ooc & Opa & Opal & OpenEdge ABL & OpenSCAD \\
+\hline
+Ox & Oxygene & Oz & P4 & Pan & Papyrus \\
+\hline
+Parrot & Pascal & PAWN & Pep8 & Perl & Perl 6 \\
+\hline
+PHP & PicoLisp & PigLatin & Pike & PLpgSQL & PLSQL \\
+\hline
+PogoScript & Pony & PostScript & POV-Ray SDL & PowerBuilder & PowerShell \\
+\hline
+Processing & Prolog & Propeller Spin & Puppet & PureBasic & PureScript \\
+\hline
+Python & QMake & QML & R & Racket & Ragel \\
+\hline
+RAML & Rascal & REALbasic & Rebol & Red & Redcode \\
+\hline
+Ren'Py & RenderScript & reStructuredText & REXX & Ring & RMarkdown \\
+\hline
+RobotFramework & Roff & Rouge & RPC & Ruby & Rust \\
+\hline
+SaltStack & SAS & Scala & Scheme & Scilab & Self \\
+\hline
+ShaderLab & Shell & ShellSession & Shen & Slash & Smali \\
+\hline
+Smalltalk & Smarty & SMT & Solidity & SourcePawn & SQF \\
+\hline
+SQLPL & Squirrel & SRecode Template & Stan & Standard ML & Stata \\
+\hline
+SuperCollider & Swift & SystemVerilog & Tcl & Tea & Terra \\
+\hline
+TeX & Thrift & TI Program & TLA & Turing & TXL \\
+\hline
+TypeScript & Uno & UnrealScript & UrWeb & Vala & VCL \\
+\hline
+Verilog & VHDL & Vim script & Visual Basic & Volt & Vue \\
+\hline
+wdl & WebAssembly & WebIDL & wisp & X10 & xBase \\
+\hline
+XC & Xojo & XProc & XQuery & XS & XSLT \\
+\hline
+Xtend & Yacc & YAML & Zephir & Zimpl \\
+\cline{1-5}
+\end{tabularx}
+\caption{\label{tab:lan} Language List of the dataset, 323 languages engaged.}
+\end{table*}
+
+\end{appendices}
\bibliography{bib-rapport}
\bibliographystyle{unsrt}
%Rapport
%
%Il doit faire de 15 à 30 pages et, dans la mesure du possible, doit être en grande part lisible par des non-spécialistes. Son plan peut être par exemple :
%présentation du domaine de recherche (le jury n'est pas constitué seulement de spécialistes du domaine, tenez-en compte !) ;
%énoncé et motivation du sujet ;
%résultats existants s'y rapportant (état de l'art, commentaire d'article, ...) ;
%vos résultats personnels (clairement identifiés comme tels).
%Le rapport devra être assorti d'un résumé d'une page compréhensible par quiconque.
-
-\tableofcontents
\end{document}
\ No newline at end of file
diff --git a/scripts/comparison.pdf b/scripts/comparison_extension.pdf
similarity index 100%
rename from scripts/comparison.pdf
rename to scripts/comparison_extension.pdf
diff --git a/scripts/draw_accuracy.py b/scripts/draw_accuracy.py
index dae0c64..f7a4a18 100644
--- a/scripts/draw_accuracy.py
+++ b/scripts/draw_accuracy.py
@@ -1,191 +1,202 @@
#!/bin/bash/python3
import sys, os
from pickle import load
from collections import namedtuple, Counter
try:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
except ImportError:
raise ImportError('Please run `pip3 install matplotlib\' in command line.')
def heatmap(path):
with open(path, 'rb') as f:
data = load(f)
mat = process(data)
labels = sorted(data)
fig, ax = plt.subplots()
fig.set_size_inches(100,100)
heatmap = ax.matshow(mat, cmap='Blues')
fig = plt.gcf()
ax.set_frame_on(False)
ax.set_yticks(np.arange(len(labels)), minor=False)
ax.set_xticks(np.arange(len(labels)), minor=False)
ax.set_xlabel('Classification of test files')
ax.set_ylabel('Ground truth class of test files')
ax.set_xticklabels(labels, minor=False)
ax.set_yticklabels(labels, minor=False)
ax.xaxis.tick_top()
ax.xaxis.set_label_position('top')
plt.xticks(rotation=90)
ax.grid(False)
'''
for i in np.arange(len(mat)):
for j in np.arange(len(mat[i])):
ax.text(i, j, "%.1f" % (mat[i][j] * 100), color='white')
'''
ax = plt.gca()
for t in ax.xaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
for t in ax.yaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
fig.savefig("results.pdf", bbox_inches='tight')
def process(data):
'''
'''
ldata = sorted(data)
length = len(ldata)
out = [[0 for x in range(length)] for y in range(length)]
for lang in ldata:
index_lan = ldata.index(lang)
ok = data[lang][0]
if data[lang][1] > 1000 :
test_size = 1000
else:
test_size = data[lang][1]
result = [x[1] for x in data[lang][3]]
counter = dict(Counter(result))
for res_lan in counter.keys():
index_res = ldata.index(res_lan)
out[index_lan][index_res] = counter.get(res_lan, 0) / test_size
return out
def get_recall(data):
ldata = sorted(data)
out = {}
true_pos = 0
false_neg = 0
for lang in ldata:
ok = data[lang][0]
if data[lang][1] > 1000:
test_size = 1000
else:
test_size = data[lang][1]
result = [x[1] for x in data[lang][3]]
counter = dict(Counter(result))
out[lang] = counter.get(lang, 0) / test_size
true_pos += counter.get(lang,0)
false_neg += test_size - counter.get(lang,0)
print(true_pos)
print(false_neg)
print(true_pos / (true_pos + false_neg))
return out
def get_precision(data):
ldata = sorted(data)
out = {}
true_pos = 0
false_pos = 0
a = []
for lang in ldata:
a += [x[1] for x in data[lang][3]]
counter_all = dict(Counter(a))
for lang in ldata:
ok = data[lang][0]
if data[lang][1] > 1000:
test_size = 1000
else:
test_size = data[lang][1]
result = [x[1] for x in data[lang][3]]
counter = dict(Counter(result))
- out[lang] = counter.get(lang, 0) / counter_all.get(lang,0)
+ try:
+ out[lang] = counter.get(lang, 0) / counter_all.get(lang, 0)
+ except ZeroDivisionError:
+ out[lang] = 0
true_pos += counter.get(lang,0)
false_pos += counter_all.get(lang,0) - counter.get(lang,0)
return out
def get_f1(recalls, precisions):
f1 = {}
for k in list(recalls.keys()):
recall = recalls[k]
precision = precisions[k]
- f1[k] = 2 / (1 / recall + 1 / precision)
+ f1[k] = harmonic_mean([recall, precision])
return f1
+
+def harmonic_mean(l):
+ l_pos = [x for x in l if x > 0]
+ H_T = len(l)
+ H_0 = H_T - len(l_pos)
+ if H_T == H_0:
+ return 0
+ else:
+ return ((H_T - H_0) / sum([1 / x for x in l_pos])) * ((H_T - H_0) / H_T)
-
-def compare(results):
+def compare(results, suffix):
datas = []
for result in results:
with open(result, 'rb') as f:
datas.append(load(f))
dicts = []
for data in datas:
recalls = get_recall(data)
precisions = get_precision(data)
dicts.append(get_f1(recalls, precisions))
all_lang = sorted(list(set().union(dicts[0].keys(),dicts[1].keys())))[::-1]
n = len(all_lang)
accs = []
for d in dicts:
accs.append([d.get(lang, 0) for lang in all_lang])
fig, ax = plt.subplots()
fig.set_size_inches(10, 75 * len(results))
ind = np.arange(n)
width = 0.75 / len(results)
opacity = 0.4
rectss = []
colors = ['b', 'r', 'c', 'm', 'y', 'g']
for idx, result in enumerate(results):
rectss.append(ax.barh(ind - (idx - len(results) / 2) * width, accs[idx], width, alpha=opacity, color=colors[idx % len(colors)], label=os.path.basename(result)))
ax.set_xlabel('Accuracy / %')
ax.set_yticks(ind + width / 2)
ax.set_yticklabels(all_lang)
vals = ax.get_xticks()
ax.set_xticklabels(['{:3.0f}%'.format(x * 100) for x in vals])
ax.xaxis.tick_top()
ax.legend()
def autolabel(rects):
for rect in rects:
width = rect.get_width()
ax.text(width + 0.01, rect.get_y() + rect.get_height() / 2., '{0:.1f}%'.format(width * 100), ha='left', va='center')
for rects in rectss:
autolabel(rects)
plt.ylim([-1,n+1])
fig.tight_layout()
- fig.savefig("comparison.pdf", bbox_inches='tight')
+ fig.savefig("comparison_{}.pdf".format(suffix), bbox_inches='tight')
if __name__ == '__main__':
if len(sys.argv) == 2:
heatmap(sys.argv[1])
elif len(sys.argv) > 2:
- compare(sys.argv[1:])
+ compare(sys.argv[1:-1], sys.argv[-1])
else:
print('Please check arguments.')