{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Completion Trie and metrics\n",
    "\n",
    "Evaluation of a completion system on wikpedia pages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id=\"my_id_menu_nb\">run previous cell, wait for 2 seconds</div>\n",
       "<script>\n",
       "function repeat_indent_string(n){\n",
       "    var a = \"\" ;\n",
       "    for ( ; n > 0 ; --n) {\n",
       "        a += \"    \";\n",
       "    }\n",
       "    return a;\n",
       "}\n",
       "var update_menu_string = function(begin, lfirst, llast, sformat, send, keep_item) {\n",
       "    var anchors = document.getElementsByClassName(\"section\");\n",
       "    if (anchors.length == 0) {\n",
       "        anchors = document.getElementsByClassName(\"text_cell_render rendered_html\");\n",
       "    }\n",
       "    var i,t;\n",
       "    var text_menu = begin;\n",
       "    var text_memo = \"<pre>\\nlength:\" + anchors.length + \"\\n\";\n",
       "    var ind = \"\";\n",
       "    var memo_level = 1;\n",
       "    var href;\n",
       "    var tags = [];\n",
       "    var main_item = 0;\n",
       "    for (i = 0; i <= llast; i++) {\n",
       "        tags.push(\"h\" + i);\n",
       "    }\n",
       "\n",
       "    for (i = 0; i < anchors.length; i++) {\n",
       "        text_memo += \"**\" + anchors[i].id + \"--\\n\";\n",
       "\n",
       "        var child = null;\n",
       "        for(t = 0; t < tags.length; t++) {\n",
       "            var r = anchors[i].getElementsByTagName(tags[t]);\n",
       "            if (r.length > 0) {\n",
       "child = r[0];\n",
       "break;\n",
       "            }\n",
       "        }\n",
       "        if (child == null){\n",
       "            text_memo += \"null\\n\";\n",
       "            continue;\n",
       "        }\n",
       "        if (anchors[i].hasAttribute(\"id\")) {\n",
       "            // when converted in RST\n",
       "            href = anchors[i].id;\n",
       "            text_memo += \"#1-\" + href;\n",
       "            // passer à child suivant (le chercher)\n",
       "        }\n",
       "        else if (child.hasAttribute(\"id\")) {\n",
       "            // in a notebook\n",
       "            href = child.id;\n",
       "            text_memo += \"#2-\" + href;\n",
       "        }\n",
       "        else {\n",
       "            text_memo += \"#3-\" + \"*\" + \"\\n\";\n",
       "            continue;\n",
       "        }\n",
       "        var title = child.textContent;\n",
       "        var level = parseInt(child.tagName.substring(1,2));\n",
       "\n",
       "        text_memo += \"--\" + level + \"?\" + lfirst + \"--\" + title + \"\\n\";\n",
       "\n",
       "        if ((level < lfirst) || (level > llast)) {\n",
       "            continue ;\n",
       "        }\n",
       "        if (title.endsWith('¶')) {\n",
       "            title = title.substring(0,title.length-1).replace(\"<\", \"&lt;\").replace(\">\", \"&gt;\").replace(\"&\", \"&amp;\")\n",
       "        }\n",
       "\n",
       "        if (title.length == 0) {\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        while (level < memo_level) {\n",
       "            text_menu += \"</ul>\\n\";\n",
       "            memo_level -= 1;\n",
       "        }\n",
       "        if (level == lfirst) {\n",
       "            main_item += 1;\n",
       "        }\n",
       "        if (keep_item != -1 && main_item != keep_item + 1) {\n",
       "            // alert(main_item + \" - \" + level + \" - \" + keep_item);\n",
       "            continue;\n",
       "        }\n",
       "        while (level > memo_level) {\n",
       "            text_menu += \"<ul>\\n\";\n",
       "            memo_level += 1;\n",
       "        }\n",
       "        text_menu += repeat_indent_string(level-2) + sformat.replace(\"__HREF__\", href).replace(\"__TITLE__\", title);\n",
       "    }\n",
       "    while (1 < memo_level) {\n",
       "        text_menu += \"</ul>\\n\";\n",
       "        memo_level -= 1;\n",
       "    }\n",
       "    text_menu += send;\n",
       "    //text_menu += \"\\n\" + text_memo;\n",
       "    return text_menu;\n",
       "};\n",
       "var update_menu = function() {\n",
       "    var sbegin = \"\";\n",
       "    var sformat = '<li><a href=\"#__HREF__\">__TITLE__</a></li>';\n",
       "    var send = \"\";\n",
       "    var keep_item = -1;\n",
       "    var text_menu = update_menu_string(sbegin, 2, 4, sformat, send, keep_item);\n",
       "    var menu = document.getElementById(\"my_id_menu_nb\");\n",
       "    menu.innerHTML=text_menu;\n",
       "};\n",
       "window.setTimeout(update_menu,2000);\n",
       "            </script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wikipedia titles, uniform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from mlstatpy.data.wikipedia import download_titles\n",
    "\n",
    "file_titles = download_titles(country=\"fr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from mlstatpy.data.wikipedia import enumerate_titles\n",
    "\n",
    "list_titles = list(\n",
    "    sorted(set(_ for _ in enumerate_titles(file_titles) if \"A\" <= _[0] <= \"Z\"))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3108490,\n",
       " ['A',\n",
       "  'A & A',\n",
       "  'A (Airport Express)',\n",
       "  'A (Ayumi Hamasaki)',\n",
       "  \"A (Disque d'Ayumi Hamasaki)\"],\n",
       " ['Fantasy in the sky',\n",
       "  'Fantasy mythique',\n",
       "  'Fantasy of manners',\n",
       "  'Fantasy tennis',\n",
       "  'Fantasy urbaine'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list_titles), list_titles[:5], list_titles[1000000:1000005]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from mlstatpy.nlp.completion import CompletionTrieNode\n",
    "\n",
    "\n",
    "def gain_dynamique_moyen_par_mot(queries, weights):\n",
    "    per = list(zip(weights, queries))\n",
    "    total = sum(w * len(q) for q, w in zip(queries, weights))\n",
    "    trie = CompletionTrieNode.build([(None, q) for _, q in per])\n",
    "    trie.precompute_stat()\n",
    "    trie.update_stat_dynamic()\n",
    "    wks = [(w, p, len(w) - trie.min_keystroke0(w)[0]) for p, w in per]\n",
    "    wks_dyn = [(w, p, len(w) - trie.min_dynamic_keystroke(w)[0]) for p, w in per]\n",
    "    wks_dyn2 = [(w, p, len(w) - trie.min_dynamic_keystroke2(w)[0]) for p, w in per]\n",
    "    gain = sum(g * p / total for w, p, g in wks)\n",
    "    gain_dyn = sum(g * p / total for w, p, g in wks_dyn)\n",
    "    gain_dyn2 = sum(g * p / total for w, p, g in wks_dyn2)\n",
    "    ave_length = sum(len(w) * p / total for p, w in per)\n",
    "    return gain, gain_dyn, gain_dyn2, ave_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time 0\n",
      "time: 0.21504800644533353s - nb=200 gain (0.820872274143302, 0.820872274143302, 0.820872274143302, 1.0)\n",
      "time: 0.6058446756721159s - nb=500 gain (0.7976588628762532, 0.7976588628762532, 0.7976588628762532, 1.0)\n",
      "time: 1.009366944402156s - nb=800 gain (0.779308535065277, 0.779308535065277, 0.779308535065277, 1.0)\n",
      "time: 1.2731077523609795s - nb=1000 gain (0.7819106501794998, 0.7819106501794998, 0.7819106501794998, 1.0)\n",
      "time: 3.0382918326608044s - nb=2000 gain (0.7491075326810025, 0.7491075326810025, 0.7491075326810025, 1.0)\n",
      "time: 6.941259884811901s - nb=5000 gain (0.7193327903836085, 0.7193534087277493, 0.7193534087277493, 1.0)\n",
      "time: 12.096078319013222s - nb=8000 gain (0.6971821041145199, 0.6971821041145199, 0.6971821041145199, 1.0)\n",
      "time: 17.030497306746902s - nb=10000 gain (0.6881011563817098, 0.6881371807341721, 0.6881371807341721, 1.0)\n",
      "time: 30.55692095058407s - nb=20000 gain (0.6579791591697565, 0.6582343738435791, 0.6582343738435791, 1.0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>time</th>\n",
       "      <th>mks</th>\n",
       "      <th>mks'</th>\n",
       "      <th>mks\"</th>\n",
       "      <th>ave_len</th>\n",
       "      <th>%mks</th>\n",
       "      <th>mks/mks</th>\n",
       "      <th>%mks'</th>\n",
       "      <th>mks'/mks</th>\n",
       "      <th>%mks\"</th>\n",
       "      <th>mks\"/mks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10000</td>\n",
       "      <td>17.030497</td>\n",
       "      <td>0.688101</td>\n",
       "      <td>0.688137</td>\n",
       "      <td>0.688137</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.688101</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.688137</td>\n",
       "      <td>1.000052</td>\n",
       "      <td>0.688137</td>\n",
       "      <td>1.000052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20000</td>\n",
       "      <td>30.556921</td>\n",
       "      <td>0.657979</td>\n",
       "      <td>0.658234</td>\n",
       "      <td>0.658234</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.657979</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.658234</td>\n",
       "      <td>1.000388</td>\n",
       "      <td>0.658234</td>\n",
       "      <td>1.000388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    size       time       mks      mks'      mks\"  ave_len      %mks  mks/mks  \\\n",
       "7  10000  17.030497  0.688101  0.688137  0.688137      1.0  0.688101      1.0   \n",
       "8  20000  30.556921  0.657979  0.658234  0.658234      1.0  0.657979      1.0   \n",
       "\n",
       "      %mks'  mks'/mks     %mks\"  mks\"/mks  \n",
       "7  0.688137  1.000052  0.688137  1.000052  \n",
       "8  0.658234  1.000388  0.658234  1.000388  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import time, random, pandas\n",
    "\n",
    "\n",
    "def benchmark(sizes):\n",
    "    print(\"time\", 0)\n",
    "    allres = []\n",
    "    for size in sizes:\n",
    "        begin = time.perf_counter()\n",
    "        if size is None:\n",
    "            size = len(list_titles)\n",
    "            spl = list_titles\n",
    "        else:\n",
    "            spl = random.sample(list_titles, size)\n",
    "        spl.sort()\n",
    "        res = gain_dynamique_moyen_par_mot(spl, [1.0] * len(spl))\n",
    "        dt = time.perf_counter() - begin\n",
    "        print(\n",
    "            \"time: {0}s - nb={1}\".format(dt, len(spl)),\n",
    "            \"gain\",\n",
    "            tuple(_ / res[-1] for _ in res),\n",
    "        )\n",
    "        allres.append((size, dt) + res)\n",
    "        # with open(\"sample%d.txt\" % len(spl), \"w\", encoding=\"utf-8\") as f:\n",
    "        #    f.write(\"\\n\".join(spl))\n",
    "    df = pandas.DataFrame(allres, columns=\"size time mks mks' mks\\\" ave_len\".split())\n",
    "    for c in \"mks mks' mks\\\"\".split():\n",
    "        df[\"%\" + c] = df[c] / df[\"ave_len\"]\n",
    "        df[c + \"/mks\"] = df[c] / df[\"mks\"]\n",
    "    return df\n",
    "\n",
    "\n",
    "df = benchmark([200, 500, 800, 1000, 2000, 5000, 8000, 10000, 20000])\n",
    "df.tail(n=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x15a756202b0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15a66484898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "f, ax = plt.subplots(2, 2, figsize=(14, 10))\n",
    "df.plot(x=\"size\", y=\"time\", ax=ax[1, 0])\n",
    "df.plot(x=\"size\", y=[\"mks\", \"mks'\", 'mks\"', \"ave_len\"], ax=ax[0, 0])\n",
    "df.plot(x=\"size\", y=[\"%mks\", \"%mks'\", '%mks\"'], ax=ax[0, 1])\n",
    "df.plot(x=\"size\", y=[\"mks'/mks\", 'mks\"/mks'], ax=ax[1, 1])\n",
    "ax[0, 0].legend()\n",
    "ax[0, 1].legend()\n",
    "ax[1, 0].legend()\n",
    "ax[1, 1].legend()\n",
    "ax[1, 1].set_ylim([0.9, 1.1])\n",
    "ax[0, 0].set_title(\"Raw Gain\")\n",
    "ax[0, 1].set_title(\"Relative Gain\")\n",
    "ax[1, 0].set_title(\"Time\")\n",
    "ax[1, 1].set_title(\"Comparison between MKS\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reduce the alphabet size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from mlstatpy.data.wikipedia import enumerate_titles\n",
    "\n",
    "list_titles = list(\n",
    "    sorted(set(_ for _ in enumerate_titles(file_titles) if \"A\" <= _[0] <= \"Z\"))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time 0\n",
      "time: 7.59344921135289s - nb=5000 gain (0.716585290640898, 0.716585290640898, 0.716585290640898, 1.0)\n",
      "time: 3.8923985946166795s - nb=4581 gain (0.41594360086768417, 0.4448874994683378, 0.4448874994683378, 1.0)\n",
      "time: 5.085379287694195s - nb=4942 gain (0.5571683533987387, 0.5620376961406324, 0.5620376961406324, 1.0)\n",
      "time: 5.121866923020207s - nb=4974 gain (0.5983975448244626, 0.6052151883090817, 0.6052151883090817, 1.0)\n",
      "time: 5.501076360438674s - nb=4991 gain (0.6380275314306908, 0.6382847383691052, 0.6382847383691052, 1.0)\n",
      "time: 5.524899975880544s - nb=4988 gain (0.6475382003395598, 0.6479497864896859, 0.6479497864896859, 1.0)\n",
      "time: 6.245833967660474s - nb=4997 gain (0.6639308855291576, 0.6639308855291576, 0.6639308855291576, 1.0)\n",
      "time: 6.012760238038936s - nb=4997 gain (0.6712028636672216, 0.6712028636672216, 0.6712028636672216, 1.0)\n",
      "time: 6.076252674864918s - nb=4997 gain (0.6838256469329845, 0.6839490681696653, 0.6839490681696653, 1.0)\n",
      "time: 6.111897439143831s - nb=4999 gain (0.6822851853756178, 0.6823160384634976, 0.6823160384634976, 1.0)\n",
      "time: 5.873518026578495s - nb=4997 gain (0.6900718921309502, 0.6900718921309502, 0.6900718921309502, 1.0)\n",
      "time: 6.684070891827105s - nb=4999 gain (0.6925798323648767, 0.6925798323648767, 0.6925798323648767, 1.0)\n",
      "time: 6.735858496876062s - nb=4997 gain (0.6969017445687994, 0.6969017445687994, 0.6969017445687994, 1.0)\n",
      "time: 6.131690155300021s - nb=4999 gain (0.6960868000205542, 0.6960868000205542, 0.6960868000205542, 1.0)\n",
      "time: 6.2186773552921295s - nb=4999 gain (0.7022574175965309, 0.7022574175965309, 0.7022574175965309, 1.0)\n",
      "time: 5.907541621836572s - nb=4998 gain (0.6991010265166325, 0.6991010265166325, 0.6991010265166325, 1.0)\n",
      "time: 6.31432889332882s - nb=4999 gain (0.7022368488712789, 0.7022471332339055, 0.7022471332339055, 1.0)\n",
      "time: 5.892940837380593s - nb=4998 gain (0.7070717459272685, 0.7070717459272685, 0.7070717459272685, 1.0)\n",
      "time: 6.061792582734597s - nb=4999 gain (0.7097547179513399, 0.7097547179513399, 0.7097547179513399, 1.0)\n",
      "time: 6.094942944771901s - nb=4999 gain (0.7079858075795616, 0.7080166606674415, 0.7080166606674415, 1.0)\n",
      "time: 6.141645954818159s - nb=4999 gain (0.7118732966524257, 0.7118732966524257, 0.7118732966524257, 1.0)\n",
      "time: 5.9873731844709255s - nb=4999 gain (0.7094359027099135, 0.7094359027099135, 0.7094359027099135, 1.0)\n",
      "time: 6.0718454556808865s - nb=4999 gain (0.7120892682675833, 0.7120892682675833, 0.7120892682675833, 1.0)\n",
      "time: 6.133951068150054s - nb=4999 gain (0.7124903584100222, 0.7124903584100222, 0.7124903584100222, 1.0)\n",
      "time: 6.292655432947868s - nb=4999 gain (0.713611353936324, 0.713611353936324, 0.713611353936324, 1.0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>time</th>\n",
       "      <th>mks</th>\n",
       "      <th>mks'</th>\n",
       "      <th>mks\"</th>\n",
       "      <th>ave_len</th>\n",
       "      <th>%mks</th>\n",
       "      <th>mks/mks</th>\n",
       "      <th>%mks'</th>\n",
       "      <th>mks'/mks</th>\n",
       "      <th>%mks\"</th>\n",
       "      <th>mks\"/mks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>6.133951</td>\n",
       "      <td>0.712490</td>\n",
       "      <td>0.712490</td>\n",
       "      <td>0.712490</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.712490</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.712490</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.712490</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>6.292655</td>\n",
       "      <td>0.713611</td>\n",
       "      <td>0.713611</td>\n",
       "      <td>0.713611</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.713611</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.713611</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.713611</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    size      time       mks      mks'      mks\"  ave_len      %mks  mks/mks  \\\n",
       "23    24  6.133951  0.712490  0.712490  0.712490      1.0  0.712490      1.0   \n",
       "24    25  6.292655  0.713611  0.713611  0.713611      1.0  0.713611      1.0   \n",
       "\n",
       "       %mks'  mks'/mks     %mks\"  mks\"/mks  \n",
       "23  0.712490       1.0  0.712490       1.0  \n",
       "24  0.713611       1.0  0.713611       1.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import time, random, pandas\n",
    "\n",
    "\n",
    "def char_modulo(c, size):\n",
    "    if len(c) != 1:\n",
    "        raise Exception(\"unexpected size '%s'\" % c)\n",
    "    # if len(c) != len(c.lower()):\n",
    "    #    raise Exception(\"unexpected lower size '%s' != '%s' (%d != %d)\" % (c, c.lower(), len(c), len(c.lower())))\n",
    "    if size is None:\n",
    "        return c\n",
    "    else:\n",
    "        cl = c.lower()\n",
    "        if len(cl) > len(c):\n",
    "            cl = c\n",
    "        o = ord(cl)\n",
    "        a = 97\n",
    "        d = (o - a) + size * 10\n",
    "        return chr(97 + (d % size))\n",
    "\n",
    "\n",
    "def reduce_alphabet(sample, size):\n",
    "    return [\"\".join(char_modulo(c, size) for c in word) for word in sample]\n",
    "\n",
    "\n",
    "def benchmark_size(size, alphabet_sizes):\n",
    "    if size is None:\n",
    "        size = len(list_titles)\n",
    "        sample = list_titles\n",
    "    else:\n",
    "        sample = random.sample(list_titles, size)\n",
    "    print(\"time\", 0)\n",
    "    allres = []\n",
    "    for size in alphabet_sizes:\n",
    "        begin = time.perf_counter()\n",
    "        spl = reduce_alphabet(sample, size)\n",
    "        spl = list(sorted(set(spl)))\n",
    "        res = gain_dynamique_moyen_par_mot(spl, [1.0] * len(spl))\n",
    "        dt = time.perf_counter() - begin\n",
    "        print(\n",
    "            \"time: {0}s - nb={1}\".format(dt, len(spl)),\n",
    "            \"gain\",\n",
    "            tuple(_ / res[-1] for _ in res),\n",
    "        )\n",
    "        if size is None:\n",
    "            size = max(_ for _ in alphabet_sizes if _ is not None) + 5\n",
    "        allres.append((size, dt) + res)\n",
    "        # with open(\"sample%d.txt\" % len(spl), \"w\", encoding=\"utf-8\") as f:\n",
    "        #    f.write(\"\\n\".join(spl))\n",
    "    df = pandas.DataFrame(allres, columns=\"size time mks mks' mks\\\" ave_len\".split())\n",
    "    for c in \"mks mks' mks\\\"\".split():\n",
    "        df[\"%\" + c] = df[c] / df[\"ave_len\"]\n",
    "        df[c + \"/mks\"] = df[c] / df[\"mks\"]\n",
    "    return df\n",
    "\n",
    "\n",
    "df = benchmark_size(5000, [None] + list(range(2, 26)))\n",
    "df.tail(n=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.sort_values(\"size\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x15a74bf40b8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15a13497630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "f, ax = plt.subplots(2, 2, figsize=(14, 10))\n",
    "df.plot(x=\"size\", y=\"time\", ax=ax[1, 0])\n",
    "df.plot(x=\"size\", y=[\"mks\", \"mks'\", 'mks\"', \"ave_len\"], ax=ax[0, 0])\n",
    "df.plot(x=\"size\", y=[\"%mks\", \"%mks'\", '%mks\"'], ax=ax[0, 1])\n",
    "df.plot(x=\"size\", y=[\"mks'/mks\", 'mks\"/mks'], ax=ax[1, 1])\n",
    "ax[0, 0].legend()\n",
    "ax[0, 1].legend()\n",
    "ax[1, 0].legend()\n",
    "ax[1, 1].legend()\n",
    "# ax[1,1].set_ylim([0.9, 1.1])\n",
    "ax[0, 0].set_title(\"Raw Gain\")\n",
    "ax[0, 1].set_title(\"Relative Gain\")\n",
    "ax[1, 0].set_title(\"Time\")\n",
    "ax[1, 1].set_title(\"Comparison between MKS\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wikipedia titles, uniform, longer test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time 0\n",
      "time: 52.057980205573585s - nb=50000 gain (0.6162242515637921, 0.616305075104518, 0.616305075104518, 1.0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>time</th>\n",
       "      <th>mks</th>\n",
       "      <th>mks'</th>\n",
       "      <th>mks\"</th>\n",
       "      <th>ave_len</th>\n",
       "      <th>%mks</th>\n",
       "      <th>mks/mks</th>\n",
       "      <th>%mks'</th>\n",
       "      <th>mks'/mks</th>\n",
       "      <th>%mks\"</th>\n",
       "      <th>mks\"/mks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50000</td>\n",
       "      <td>52.05798</td>\n",
       "      <td>0.616224</td>\n",
       "      <td>0.616305</td>\n",
       "      <td>0.616305</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.616224</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.616305</td>\n",
       "      <td>1.000131</td>\n",
       "      <td>0.616305</td>\n",
       "      <td>1.000131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    size      time       mks      mks'      mks\"  ave_len      %mks  mks/mks  \\\n",
       "0  50000  52.05798  0.616224  0.616305  0.616305      1.0  0.616224      1.0   \n",
       "\n",
       "      %mks'  mks'/mks     %mks\"  mks\"/mks  \n",
       "0  0.616305  1.000131  0.616305  1.000131  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = benchmark([50000])\n",
    "df2.tail(n=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time 0\n",
      "time: 52.51158252780897s - nb=50000 gain (0.615225173328998, 0.6153599275825006, 0.6153599275825006, 1.0)\n",
      "time: 105.0721302614229s - nb=100000 gain (0.5836043296652512, 0.5841384772496148, 0.5841384772496148, 1.0)\n",
      "time: 187.86111486480695s - nb=200000 gain (0.5507786166438062, 0.5518801462043321, 0.5518801462043321, 1.0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>time</th>\n",
       "      <th>mks</th>\n",
       "      <th>mks'</th>\n",
       "      <th>mks\"</th>\n",
       "      <th>ave_len</th>\n",
       "      <th>%mks</th>\n",
       "      <th>mks/mks</th>\n",
       "      <th>%mks'</th>\n",
       "      <th>mks'/mks</th>\n",
       "      <th>%mks\"</th>\n",
       "      <th>mks\"/mks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100000</td>\n",
       "      <td>105.072130</td>\n",
       "      <td>0.583604</td>\n",
       "      <td>0.584138</td>\n",
       "      <td>0.584138</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.583604</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.584138</td>\n",
       "      <td>1.000915</td>\n",
       "      <td>0.584138</td>\n",
       "      <td>1.000915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200000</td>\n",
       "      <td>187.861115</td>\n",
       "      <td>0.550779</td>\n",
       "      <td>0.551880</td>\n",
       "      <td>0.551880</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.550779</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.551880</td>\n",
       "      <td>1.002000</td>\n",
       "      <td>0.551880</td>\n",
       "      <td>1.002000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     size        time       mks      mks'      mks\"  ave_len      %mks  \\\n",
       "1  100000  105.072130  0.583604  0.584138  0.584138      1.0  0.583604   \n",
       "2  200000  187.861115  0.550779  0.551880  0.551880      1.0  0.550779   \n",
       "\n",
       "   mks/mks     %mks'  mks'/mks     %mks\"  mks\"/mks  \n",
       "1      1.0  0.584138  1.000915  0.584138  1.000915  \n",
       "2      1.0  0.551880  1.002000  0.551880  1.002000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = benchmark(\n",
    "    [50000, 100000, 200000]\n",
    ")  # , 500000, 500000, 1000000, 2000000, None]) too long in python\n",
    "df2.tail(n=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x15a132f8be0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15a63353d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfall = pandas.concat([df, df2])\n",
    "f, ax = plt.subplots(2, 2, figsize=(14, 10))\n",
    "dfall.plot(x=\"size\", y=\"time\", ax=ax[1, 0])\n",
    "dfall.plot(x=\"size\", y=[\"mks\", \"mks'\", 'mks\"', \"ave_len\"], ax=ax[0, 0])\n",
    "dfall.plot(x=\"size\", y=[\"%mks\", \"%mks'\", '%mks\"'], ax=ax[0, 1])\n",
    "dfall.plot(x=\"size\", y=[\"mks'/mks\", 'mks\"/mks'], ax=ax[1, 1])\n",
    "ax[0, 0].legend()\n",
    "ax[0, 1].legend()\n",
    "ax[1, 0].legend()\n",
    "ax[1, 1].legend()\n",
    "ax[1, 1].set_ylim([0.9, 1.1])\n",
    "ax[0, 0].set_title(\"Raw Gain\")\n",
    "ax[0, 1].set_title(\"Relative Gain\")\n",
    "ax[1, 0].set_title(\"Time\")\n",
    "ax[1, 1].set_title(\"Comparison between MKS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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