README.md

language-detection.el

Emacs Lisp library that automatically detects the programming language in a buffer or string. Implemented as a random forest classifier, trained in scikit-learn and deployed to Emacs Lisp.

Usage

• M-x language-detection-buffer

• Interactive function, outputs the buffer's language to *Messages*
• When called non-interactively, returns the language

• (language-detection-string STRING)

• Non-interactive function, returns the language of STRING

Model performance

Language classification accuracy of this model compared to a few existing systems on three different datasets:

	Stack Overflow	Rosetta Code	Github Linguist
language-detection.el	73%	84%	87%
Highlight.js	36%	54%	56%
Pygments	10%	25%	37%
SourceClassifier	51%	48%	65%

Computational performance

The actual random forest lookup is fast, the bottleneck is regular expression-based tokenization.

Function Name                         Call Count  Elapsed time  Average Time
language-detection-buffer             441         8.0538804580  0.0182627674
language-detection-string             441         8.0525858239  0.0182598318
language-detection-tokenize-string    441         6.2478335339  0.0141674229
language-detection-token-frequencies  441         1.6420298549  0.0037234237
language-detection-forest-lookup      441         0.0973103440  0.0002206583
language-detection-tree-lookup        4851        0.0885046049  1.824...e-05

Supported languages

• ada
• awk
• c
• clojure
• cpp
• csharp
• css
• dart
• delphi
• emacslisp
• erlang
• fortran
• fsharp
• go
• groovy
• haskell
• html
• java
• javascript
• json
• latex
• lisp
• lua
• matlab
• objc
• perl
• php
• prolog
• python
• r
• ruby
• rust
• scala
• shell
• smalltalk
• sql
• swift
• visualbasic
• xml

EWW syntax highlighting

Write this in your .emacs:

(require 'cl-lib)

(defun eww-tag-pre (dom)
  (let ((shr-folding-mode 'none)
        (shr-current-font 'default))
    (shr-ensure-newline)
    (insert (eww-fontify-pre dom))
    (shr-ensure-newline)))

(defun eww-fontify-pre (dom)
  (with-temp-buffer
    (shr-generic dom)
    (let ((mode (eww-buffer-auto-detect-mode)))
      (when mode
        (eww-fontify-buffer mode)))
    (buffer-string)))

(defun eww-fontify-buffer (mode)
  (delay-mode-hooks (funcall mode))
  (font-lock-default-function mode)
  (font-lock-default-fontify-region (point-min)
                                    (point-max)
                                    nil))

(defun eww-buffer-auto-detect-mode ()
  (let* ((map '((ada ada-mode)
                (awk awk-mode)
                (c c-mode)
                (cpp c++-mode)
                (clojure clojure-mode lisp-mode)
                (csharp csharp-mode java-mode)
                (css css-mode)
                (dart dart-mode)
                (delphi delphi-mode)
                (emacslisp emacs-lisp-mode)
                (erlang erlang-mode)
                (fortran fortran-mode)
                (fsharp fsharp-mode)
                (go go-mode)
                (groovy groovy-mode)
                (haskell haskell-mode)
                (html html-mode)
                (java java-mode)
                (javascript javascript-mode)
                (json json-mode javascript-mode)
                (latex latex-mode)
                (lisp lisp-mode)
                (lua lua-mode)
                (matlab matlab-mode octave-mode)
                (objc objc-mode c-mode)
                (perl perl-mode)
                (php php-mode)
                (prolog prolog-mode)
                (python python-mode)
                (r r-mode)
                (ruby ruby-mode)
                (rust rust-mode)
                (scala scala-mode)
                (shell shell-script-mode)
                (smalltalk smalltalk-mode)
                (sql sql-mode)
                (swift swift-mode)
                (visualbasic visual-basic-mode)
                (xml sgml-mode)))
         (language (language-detection-string
                    (buffer-substring-no-properties (point-min) (point-max))))
         (modes (cdr (assoc language map)))
         (mode (cl-loop for mode in modes
                        when (fboundp mode)
                        return mode)))
    (message (format "%s" language))
    (when (fboundp mode)
      mode)))

(setq shr-external-rendering-functions
      '((pre . eww-tag-pre)))

Classifier training procedure

The model consists of a Random Forest ensemble of 11 Decision Tree classifiers.

It was trained on code snippets from the Stack Overflow data dump, extracted from <pre> tags. The number of snippets per language was capped at 10,000. The actual training snippets can be unpickled from snippets.cpkl.

The snippets were tokenized simple regex: ([a-zA-Z0-9_]+|[^ a-zA-Z0-9_\n\t]+). The set of single tokens and pairs of consecutive tokens are pruned to the top 500 token/token pairs per language.

Finally, the tokens are counted for frequency and placed in a 7772-dimensional bag-of-words vector. (7772 is the total number of unique tokens/pairs.) For example, this is a bag corresponding to a short Haskell snippet:

"         : 0.057971
(         : 0.0144928
)         : 0.0144928
->        : 0.0289855
-> IO     : 0.0144928
.         : 0.0289855
. h       : 0.0289855
::        : 0.0289855
IO        : 0.0144928
h         : 0.0289855
import    : 0.0289855

For classification I use a Random Forest with 11 decision trees. The hyperparameters were found using random grid search. Here's a confusion matrix:

The decision trees are written out as Emacs Lisp arrays and traversed recursively until leaves are found in each tree.