TypoAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jellyfish\n",
    "import urllib.request\n",
    "import jsonlines\n",
    "import gzip\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools as it\n",
    "\n",
    "GH_CORPUS_URL = \"https://github-typo-corpus.s3.amazonaws.com/data/github-typo-corpus.v1.0.0.jsonl.gz\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_github_typo_data(url, languages):\n",
    "    def extract_typos(commit):\n",
    "        for edit in commit[\"edits\"]:\n",
    "            if edit[\"src\"][\"lang\"] not in languages: continue\n",
    "            if edit[\"tgt\"][\"lang\"] not in languages: continue\n",
    "            if \"is_typo\" not in edit: continue\n",
    "            if not edit[\"is_typo\"]: continue\n",
    "\n",
    "            source = edit[\"src\"][\"text\"]\n",
    "            target = edit[\"tgt\"][\"text\"]\n",
    "            distance = jellyfish.damerau_levenshtein_distance(source, target)\n",
    "            yield {\"source\": source, \"target\": target, \"distance\": distance}\n",
    "\n",
    "    response = urllib.request.urlopen(url)\n",
    "    with gzip.GzipFile(fileobj=response) as f:\n",
    "        commits = jsonlines.Reader(f)\n",
    "        typos = it.chain.from_iterable(extract_typos(commit) for commit in commits)\n",
    "        df = pd.DataFrame(typos)\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min, sys: 3.49 s, total: 1min 4s\n",
      "Wall time: 1min 13s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = get_github_typo_data(GH_CORPUS_URL, languages={\"eng\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Damerau-Levenshtein Distance')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"Frequency of Damerau-Levenshtein Distances of\\nTypos in GitHub Commits\"\n",
    "df.distance.plot(kind=\"hist\", bins=range(1, 17), density=True, title=title, xticks=range(1, 16), align = \"left\", rwidth=0.8)\n",
    "plt.xlabel(\"Damerau-Levenshtein Distance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}