{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# 1A.2 - Deviner la langue d'un texte (correction)\n", "\n", "Calcul d'un score pour d\u00e9tecter la langue d'un texte. Ce notebook aborde les dictionnaires, les fichiers et les graphiques (correction)."]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "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", "    return a;\n", "}\n", "var update_menu_string = function(begin, lfirst, llast, sformat, send, keep_item, begin_format, end_format) {\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", "    var format_open = 0;\n", "    for (i = 0; i <= llast; i++)\n", "        tags.push(\"h\" + i);\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 \u00e0 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('\u00b6')) {\n", "            title = title.substring(0,title.length-1).replace(\"<\", \"&lt;\")\n", "         .replace(\">\", \"&gt;\").replace(\"&\", \"&amp;\");\n", "        }\n", "        if (title.length == 0) {\n", "            continue;\n", "        }\n", "\n", "        while (level < memo_level) {\n", "            text_menu += end_format + \"</ul>\\n\";\n", "            format_open -= 1;\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);\n", "        text_menu += begin_format + sformat.replace(\"__HREF__\", href).replace(\"__TITLE__\", title);\n", "        format_open += 1;\n", "    }\n", "    while (1 < memo_level) {\n", "        text_menu += end_format + \"</ul>\\n\";\n", "        memo_level -= 1;\n", "        format_open -= 1;\n", "    }\n", "    text_menu += send;\n", "    //text_menu += \"\\n\" + text_memo;\n", "\n", "    while (format_open > 0) {\n", "        text_menu += end_format;\n", "        format_open -= 1;\n", "    }\n", "    return text_menu;\n", "};\n", "var update_menu = function() {\n", "    var sbegin = \"\";\n", "    var sformat = '<a href=\"#__HREF__\">__TITLE__</a>';\n", "    var send = \"\";\n", "    var begin_format = '<li>';\n", "    var end_format = '</li>';\n", "    var keep_item = -1;\n", "    var text_menu = update_menu_string(sbegin, 2, 4, sformat, send, keep_item,\n", "       begin_format, end_format);\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": ["from jyquickhelper import add_notebook_menu\n", "add_notebook_menu()"]}, {"cell_type": "code", "execution_count": 2, "metadata": {"collapsed": true}, "outputs": [], "source": ["%matplotlib inline"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Q1 : lire un fichier"]}, {"cell_type": "code", "execution_count": 3, "metadata": {"collapsed": true}, "outputs": [], "source": ["def read_file(filename):\n", "    with open(filename, \"r\", encoding=\"utf-8\") as f:\n", "        return f.read()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Les probl\u00e8mes d'[encoding](https://fr.wikipedia.org/wiki/Codage_des_caract%C3%A8res) sont toujours d\u00e9licats. Un encoding d\u00e9finit la fa\u00e7on dont une s\u00e9quence d'octets repr\u00e9sente une cha\u00eene de caract\u00e8res. Il y a 256 valeurs possible d'octets et la langue chinoise contient beaucoup plus de caract\u00e8res. Il faut donc utiliser plusieurs octets pour repr\u00e9senter un caract\u00e8re un peu comme le Morse qui n'utilise que deux symboles pour repr\u00e9senter 26 lettres. Quand on ne conna\u00eet pas l'encoding, on utilise un module [chardet](http://chardet.readthedocs.io/en/latest/index.html) et la fonction [detect](http://chardet.readthedocs.io/en/latest/usage.html)."]}, {"cell_type": "code", "execution_count": 4, "metadata": {"collapsed": true}, "outputs": [], "source": ["import chardet\n", "\n", "def read_file_enc(filename):\n", "    with open(filename, \"rb\") as f:\n", "        b = f.read()\n", "    res = chardet.detect(b)\n", "    enc = res[\"encoding\"]\n", "    return res, b.decode(enc)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["On teste la fonction avec un petit fichier qu'on cr\u00e9e pour l'occasion."]}, {"cell_type": "code", "execution_count": 5, "metadata": {"collapsed": true}, "outputs": [], "source": ["with open(\"texte.txt\", \"w\", encoding=\"utf-8\") as f:\n", "    f.write(\"un corbeau sur un arbre perch\u00e9 tenait en son bec un fromage\")"]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [{"data": {"text/plain": ["'un corbeau sur un arbre perch\u00e9 tenait en son bec un fromage'"]}, "execution_count": 7, "metadata": {}, "output_type": "execute_result"}], "source": ["lu = read_file(\"texte.txt\")\n", "lu"]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [{"data": {"text/plain": ["'un corbeau sur un arbre perch\u00c3\u00a9 tenait en son bec un fromage'"]}, "execution_count": 8, "metadata": {}, "output_type": "execute_result"}], "source": ["enc, lu = read_file_enc('texte.txt')\n", "lu"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Visiblement, ce n'est pas toujours \u00e9vident mais suffisant pour ce qu'on veut en faire \u00e0 savoir des statistiques. Les probl\u00e8mes avec la langue latine devraient \u00eatre statistiquement n\u00e9gligeables pour ce que nous souhaitons en faire."]}, {"cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [{"data": {"text/plain": ["{'confidence': 0.6213765491709115,\n", " 'encoding': 'ISO-8859-9',\n", " 'language': 'Turkish'}"]}, "execution_count": 9, "metadata": {}, "output_type": "execute_result"}], "source": ["enc"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Q2 : histogramme"]}, {"cell_type": "code", "execution_count": 9, "metadata": {"collapsed": true}, "outputs": [], "source": ["def histogram(texte):\n", "    d = {}\n", "    for c in texte:\n", "        d[c] = d.get(c, 0) + 1\n", "    return d"]}, {"cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [{"data": {"text/plain": ["{' ': 11,\n", " 'a': 4,\n", " 'b': 3,\n", " 'c': 3,\n", " 'e': 7,\n", " 'f': 1,\n", " 'g': 1,\n", " 'h': 1,\n", " 'i': 1,\n", " 'm': 1,\n", " 'n': 6,\n", " 'o': 3,\n", " 'p': 1,\n", " 'r': 6,\n", " 's': 2,\n", " 't': 2,\n", " 'u': 5,\n", " '\u00a9': 1,\n", " '\u00c3': 1}"]}, "execution_count": 11, "metadata": {}, "output_type": "execute_result"}], "source": ["histogram(lu)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Le module [collections](https://docs.python.org/3/library/collections.html) propose un objet [Counter](https://docs.python.org/3/library/collections.html#collections.Counter) qui impl\u00e9mente ce calcul."]}, {"cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [{"data": {"text/plain": ["Counter({' ': 11,\n", "         'a': 4,\n", "         'b': 3,\n", "         'c': 3,\n", "         'e': 7,\n", "         'f': 1,\n", "         'g': 1,\n", "         'h': 1,\n", "         'i': 1,\n", "         'm': 1,\n", "         'n': 6,\n", "         'o': 3,\n", "         'p': 1,\n", "         'r': 6,\n", "         's': 2,\n", "         't': 2,\n", "         'u': 5,\n", "         '\u00a9': 1,\n", "         '\u00c3': 1})"]}, "execution_count": 12, "metadata": {}, "output_type": "execute_result"}], "source": ["from collections import Counter\n", "\n", "def histogram2(texte):\n", "    return Counter(texte)\n", "\n", "histogram2(lu)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Comme pour beaucoup de fonctions faisant partie des extensions du langage Python, elle est plus rapide que la version que nous pourrions impl\u00e9menter."]}, {"cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["13.8 \u00b5s \u00b1 1.17 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 100000 loops each)\n"]}], "source": ["%timeit histogram(lu)"]}, {"cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["7.56 \u00b5s \u00b1 546 ns per loop (mean \u00b1 std. dev. of 7 runs, 100000 loops each)\n"]}], "source": ["%timeit histogram2(lu)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Q3 : normalisation"]}, {"cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [{"data": {"text/plain": ["{' ': 0.18333333333333332,\n", " 'a': 0.06666666666666667,\n", " 'b': 0.05,\n", " 'c': 0.05,\n", " 'e': 0.11666666666666667,\n", " 'f': 0.016666666666666666,\n", " 'g': 0.016666666666666666,\n", " 'h': 0.016666666666666666,\n", " 'i': 0.016666666666666666,\n", " 'm': 0.016666666666666666,\n", " 'n': 0.1,\n", " 'o': 0.05,\n", " 'p': 0.016666666666666666,\n", " 'r': 0.1,\n", " 's': 0.03333333333333333,\n", " 't': 0.03333333333333333,\n", " 'u': 0.08333333333333333,\n", " '\u00a9': 0.016666666666666666,\n", " '\u00c3': 0.016666666666666666}"]}, "execution_count": 15, "metadata": {}, "output_type": "execute_result"}], "source": ["def normalize(hist):\n", "    s = sum(hist.values())\n", "    if s == 0:\n", "        return {}\n", "    else:\n", "        return {k: v/s for k,v in hist.items()}\n", "    \n", "normalize(histogram2(lu))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Q4 : calcul\n", "\n", "On essaye avec la fr\u00e9quence de la lettre ``H``. (donn\u00e9es : [articles.zip](http://http://www.xavierdupre.fr/enseignement/complements/articles.zip))"]}, {"cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [{"data": {"text/plain": ["{'afp1.txt': 0.0067247820672478205,\n", " 'afp2.txt': 0.007575757575757576,\n", " 'arthur_charpentier1.txt': 0.007012142979305627,\n", " 'arthur_charpentier2.txt': 0.02588801926550271,\n", " 'arthur_charpentier3.txt': 0.004853022739877981,\n", " 'blog1.txt': 0.010752688172043012,\n", " 'blog2.txt': 0.007556675062972292,\n", " 'blog3.txt': 0.010554089709762533,\n", " 'blogny1.txt': 0.029830508474576273,\n", " 'elpais1.txt': 0.01349112426035503,\n", " 'elpais2.txt': 0.005625270445694505,\n", " 'elpais3.txt': 0.005441436539246361,\n", " 'elpais4.txt': 0.00224408769204212,\n", " 'elpais5.txt': 0.009715025906735751,\n", " 'elpais6.txt': 0.0051919661155895615,\n", " 'elpais7.txt': 0.005625270445694505,\n", " 'inconnu1.txt': 0,\n", " 'inconnu2.txt': 0.00016849199663016007,\n", " 'lemonde1.txt': 0.010804020100502512,\n", " 'lemonde10.txt': 0.007139797229758675,\n", " 'lemonde11.txt': 0.0021551724137931034,\n", " 'lemonde2.txt': 0.0055272108843537416,\n", " 'lemonde3.txt': 0.0014691478942213516,\n", " 'lemonde4.txt': 0.004875076173065204,\n", " 'lemonde5.txt': 0.0044822949350067235,\n", " 'lemonde6.txt': 0.007034547444114429,\n", " 'lemonde7.txt': 0.0020463847203274215,\n", " 'lemonde8.txt': 0.0034299968818210166,\n", " 'lemonde9.txt': 0.008162299639202697,\n", " 'lequipe1.txt': 0.00572041473006793,\n", " 'lequipe2.txt': 0.005029013539651838,\n", " 'nytimes1.txt': 0.030130034887408817,\n", " 'nytimes2.txt': 0.031933508311461065,\n", " 'nytimes3.txt': 0.02547634339541854,\n", " 'nytimes4.txt': 0.03934426229508197,\n", " 'nytimes5.txt': 0.035542582417582416,\n", " 'nytimes6.txt': 0.030610255410411385,\n", " 'nytimes7.txt': 0.04194094414143314,\n", " 'nytimes8.txt': 0.03151779230210603,\n", " 'nytimes9.txt': 0.03840526700804682}"]}, "execution_count": 16, "metadata": {}, "output_type": "execute_result"}], "source": ["from pyensae.datasource import download_data\n", "texts = download_data(\"articles.zip\")\n", "\n", "h = {text: normalize(histogram2(read_file_enc(text)[1])).get('h', 0) for text in texts}\n", "h"]}, {"cell_type": "markdown", "metadata": {}, "source": ["On regarde les valeurs obtenus pour les articles du monde et du new-york time."]}, {"cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [{"data": {"text/plain": ["<matplotlib.legend.Legend at 0x153646d5f28>"]}, "execution_count": 17, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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VSJ+jhCCJq6k+GG5iyYPBcBNXfh8mz9I3jEUOQwlBEk+kDVb8Av40F/bUwjmfgku/DVlD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\nAP8f3xwICSsd3f4AAAAASUVORK5CYII=\n", "text/plain": ["<matplotlib.figure.Figure at 0x153643030b8>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["import matplotlib.pyplot as plt\n", "lemonde = list(sorted(v for k,v in h.items() if \"lemonde\" in k))\n", "ny = list(sorted(v for k,v in h.items() if \"times\" in k))\n", "plt.plot(lemonde, label=\"lemonde\")\n", "plt.plot(ny, label=\"times\")\n", "plt.legend()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Ca marche plut\u00f4t bien."]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Q5 : score\n", "\n", "On recommence avec deux lettres et on trace un nuage de points pour les articles des m\u00eames journaux."]}, {"cell_type": "code", "execution_count": 17, "metadata": {"collapsed": true}, "outputs": [], "source": ["scores = {}\n", "for text in texts:\n", "    norm = normalize(histogram2(read_file_enc(text)[1]))\n", "    h, w = norm.get('h', 0), norm.get('w', 0)\n", "    scores[text] = (h, w)\n", "\n", "lem = [v for k,v in scores.items() if \"lemonde\" in k]\n", "ny = [v for k,v in scores.items() if \"times\" in k]"]}, {"cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [{"data": {"text/plain": ["<matplotlib.legend.Legend at 0x15364cadb70>"]}, "execution_count": 19, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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uc9GbgiblQp49SWf6sKdO9j/eNmBIJ4/tibrT52pVrj5/BvhNRLyfqJ3l1q1+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\nqf8P6IUduiEaHO8AAAAASUVORK5CYII=\n", "text/plain": ["<matplotlib.figure.Figure at 0x15364bc5748>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["plt.scatter(x=[_[0] for _ in lem], y=[_[1] for _ in lem], label=\"lemonde\")\n", "plt.scatter(x=[_[0] for _ in ny], y=[_[1] for _ in ny], label=\"times\")\n", "plt.xlabel(\"h\")\n", "plt.ylabel(\"w\")\n", "plt.legend()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Les textes anglais et fran\u00e7ais apparaissent bien s\u00e9par\u00e9s. On place les autres."]}, {"cell_type": "code", "execution_count": 19, "metadata": {"collapsed": true}, "outputs": [], "source": ["other = [v for k,v in scores.items() if \"times\" not in k and \"monde\" not in k]"]}, {"cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [{"data": {"text/plain": ["<matplotlib.legend.Legend at 0x15364ed8c50>"]}, "execution_count": 21, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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oFDQiIhIqBY2IiIRKQSMiIqFS0IiISKgUNCIiEioFjYiIhEpBIyIioVLQiIhI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\nEZFQKWhERCRUChoREQmVgkZEREL1/wFPi/jgLczthAAAAABJRU5ErkJggg==\n", "text/plain": ["<matplotlib.figure.Figure at 0x15364c2cac8>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["plt.scatter(x=[_[0] for _ in lem], y=[_[1] for _ in lem], label=\"lemonde\")\n", "plt.scatter(x=[_[0] for _ in ny], y=[_[1] for _ in ny], label=\"times\")\n", "plt.scatter(x=[_[0] for _ in other], y=[_[1] for _ in other], label=\"autres\", s=15)\n", "plt.xlabel(\"h\")\n", "plt.ylabel(\"w\")\n", "plt.legend()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["On ajoute le nom du texte sur le graphique."]}, {"cell_type": "code", "execution_count": 21, "metadata": {"collapsed": true}, "outputs": [], "source": ["text_others = [k for k,v in scores.items() if \"times\" not in k and \"monde\" not in k]"]}, {"cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [{"data": {"text/plain": ["<matplotlib.legend.Legend at 0x15366bb7d30>"]}, "execution_count": 23, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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mLBBhWdYqYB6wyBhzEDhDVhAEwBhzGCgFFDPGdAPaAz8Bay4HPDdgPfDP23hbIiIiIgWC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\nQ54kSVKGDHmSJEkZ+n8V5ksUlllORAAAAABJRU5ErkJggg==\n", "text/plain": ["<matplotlib.figure.Figure at 0x153667dd668>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots(1, 1, figsize=(10,10))\n", "ax.scatter(x=[_[0] for _ in lem], y=[_[1] for _ in lem], label=\"lemonde\")\n", "ax.scatter(x=[_[0] for _ in ny], y=[_[1] for _ in ny], label=\"times\")\n", "ax.scatter(x=[_[0] for _ in other], y=[_[1] for _ in other], label=\"autres\", s=15)\n", "for (x,y), t in zip(other, text_others):\n", "    ax.text(x, y, t, ha='right', rotation=-30, wrap=True)\n", "ax.set_xlabel(\"h\")\n", "ax.set_ylabel(\"w\")\n", "ax.legend()"]}, {"cell_type": "code", "execution_count": 23, "metadata": {"collapsed": true}, "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": "3.6.4"}}, "nbformat": 4, "nbformat_minor": 2}