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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json\n",
      "import gensim\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "from pattern.en import parse\n",
      "from pattern.en import tag as ptag\n",
      "import string\n",
      "import tsne  # local copy in the repo"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "The stuff for processing all the book files"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sample_parsed = parse(\"He is a text sample showing you what happens when you use this function for her.\", \n",
      "   tokenize = True,         # Split punctuation marks from words?\n",
      "       tags = True,         # Parse part-of-speech tags? (NN, JJ, ...)\n",
      "     chunks = False,        # Parse chunks? (NP, VP, PNP, ...)\n",
      "  relations = False,         # Parse chunk relations? (-SBJ, -OBJ, ...)\n",
      "    lemmata = False,        # Parse lemmata? (ate => eat)\n",
      "   encoding = 'utf-8',      # Input string encoding.\n",
      "     tagset = None)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# just showing you what it looks like\n",
      "sample_parsed"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "u'He/PRP is/VBZ a/DT text/NN sample/NN showing/VBG you/PRP what/WP happens/VBZ when/WRB you/PRP use/VB this/DT function/NN for/IN her/PRP ./.'"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\" A bunch of functions that do text cleanup and create the strings of nouns for the model training.\"\"\"\n",
      "\n",
      "def tag_by_line(line, tags):\n",
      "    \"\"\" Look for only the words with those tags \"\"\"\n",
      "    words = []\n",
      "    #print ptag(line)\n",
      "    for word, foundtag in ptag(line):\n",
      "        if foundtag in tags:\n",
      "            words.append(word)\n",
      "    return words\n",
      "\n",
      "def cleanup(text):\n",
      "    \"\"\" This function cleans up the text a bit, and removes newlines. \"\"\"\n",
      "    data = text.decode('utf8')\n",
      "    data = data.replace('\\r', '')  # windows newline in some files\n",
      "    data = data.replace('\\n', ' ')  # turn newline into space\n",
      "    data = data.replace('_', '')\n",
      "    data = data.replace('--', ' -- ')\n",
      "    data = data.replace(',', ' ')\n",
      "    data = data.replace('.', ' ')\n",
      "    data = data.strip()  # strip remaining white space chars from edges\n",
      "    return data\n",
      "\n",
      "\n",
      "def cleanup_and_pos(text, pos=['NN']):\n",
      "    \"\"\" \n",
      "    Run the cleanup function, convert to a list of tag types, clean out misc stuff left.\n",
      "    Also converts back to a space sep string of the text we want in the model/output only\n",
      "    \"\"\"\n",
      "    # put your own list of strings here, based on reading outputs:\n",
      "    other_things_to_strip = ['s', '\\'s', 'n\\'t', '\\'ll', 'chapter', 'CHAPTER']\n",
      "    \n",
      "    data = cleanup(text)\n",
      "    words = tag_by_line(data, pos)   # returns a list of words of that POS (part of speech)\n",
      "    cleantext = [word.lower() for word in words if word not in string.punctuation]\n",
      "    cleantext = [word for word in cleantext if word not in other_things_to_strip]\n",
      "    newdata = ' '.join(cleantext)  # make it a single string, the model likes that\n",
      "    return newdata"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Model text generator - based on Radim's sample code for gensim.\n",
      "This produces a line at a time of cleaned, tag-only text for the model to train from.\n",
      "\"\"\"\n",
      " \n",
      "import os\n",
      "import re\n",
      "\n",
      "class MySentences(object):\n",
      "    def __init__(self, dirname, tags=['NN']):\n",
      "        self.dirname = dirname\n",
      "        self.tags = tags\n",
      "    def __iter__(self):\n",
      "        for fname in os.listdir(self.dirname):\n",
      "            for line in open(os.path.join(self.dirname, fname)):\n",
      "                yield (cleanup_and_pos(line, pos=self.tags)).split()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\" An alternate from gensim tutorials - just use all words in the model. \"\"\"\n",
      "\n",
      "class DirOfPlainTextCorpus(object):\n",
      "    \"\"\"Iterate over sentences of all plaintext files in a directory \"\"\"\n",
      "    SPLIT_SENTENCES = re.compile(u\"[.!?:]\\s+\")  # split sentences on these characters\n",
      "\n",
      "    def __init__(self, dirname):\n",
      "        self.dirname = dirname\n",
      "\n",
      "    def __iter__(self):\n",
      "        for fn in os.listdir(self.dirname):\n",
      "            text = open(os.path.join(self.dirname, fn)).read()\n",
      "            for sentence in self.SPLIT_SENTENCES.split(text):\n",
      "                yield gensim.utils.simple_preprocess(sentence, deacc=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 228
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### updates datadict, uses model, tags, writes labeled txt file out\n",
      "\n",
      "def clean_dict_keys(datadict):\n",
      "    \"\"\" Removes words that make bad keys/nouns for the word swaps. \"\"\"\n",
      "    for key in datadict.keys():\n",
      "        if key == 'xi':\n",
      "            datadict.pop(key, None)\n",
      "        if ',' in key:\n",
      "            datadict.pop(key, None)\n",
      "            continue\n",
      "        if '--' in key:\n",
      "            datadict.pop(key, None)\n",
      "            continue\n",
      "        if 'xx' in key:\n",
      "            datadict.pop(key, None)\n",
      "            continue\n",
      "        if 'xv' in key:\n",
      "            datadict.pop(key, None)\n",
      "            continue\n",
      "        if '\\n' in key:\n",
      "            datadict.pop(key, None)\n",
      "            continue\n",
      "        if '.' in key:\n",
      "            datadict.pop(key, None)\n",
      "            continue\n",
      "    return datadict\n",
      "\n",
      "def cap_me(word, capflag):\n",
      "    \"\"\" Capitalizes the word if it was in the original text \"\"\"\n",
      "    if capflag:\n",
      "        return word.capitalize()\n",
      "    else:\n",
      "        return word\n",
      "\n",
      "def build_dict_write_file(model, outputtextfile, outputdatafile, tags=['NN']):\n",
      "    \"\"\" Writes out the labeled text with word swaps, returns datadict \"\"\"\n",
      "    \n",
      "    screen_outs = ['CHAPTER', 'chapter', 'Chapter', '', ',', 'xi']  # too common and not useful\n",
      "    datadict = {}\n",
      "    \n",
      "    print tags\n",
      "    \n",
      "    with open('books/austen_pride.txt', 'r') as handle:\n",
      "        lines = handle.read()\n",
      "        lines = lines.split('\\n\\r')\n",
      "        full_text = ''\n",
      "        for sample in lines:\n",
      "            new = sample.replace('\\n\\r',' ')\n",
      "            new = new.replace('_', '')\n",
      "            new = new.replace(',', ', ')\n",
      "            new = new.replace('--', ' -- ')\n",
      "            line = parse(new, \n",
      "               tokenize = True,     # Split punctuation marks from words?\n",
      "               tags = True,         # Parse part-of-speech tags? (NN, JJ, ...)\n",
      "             chunks = False,        # Parse chunks? (NP, VP, PNP, ...)\n",
      "          relations = False,        # Parse chunk relations? (-SBJ, -OBJ, ...)\n",
      "            lemmata = False,        # Parse lemmata? (ate => eat)\n",
      "           encoding = 'utf-8',      # Input string encoding.\n",
      "             tagset = None)\n",
      "            line = line.replace('\\n', ' ')\n",
      "            sample_spaced = line.split(' ')\n",
      "            line_text = '<p>'\n",
      "            for word in sample_spaced:\n",
      "                capFlag = False\n",
      "                newWord = word\n",
      "                word = word.replace('\\n', ' ')\n",
      "                if word.strip() == '':\n",
      "                    continue\n",
      "                bits = word.split('/')\n",
      "                if len(bits) == 2:\n",
      "                    if bits[1] in tags and bits[0] not in screen_outs:\n",
      "                        if bits[0][0] in \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\":  # check for first cap\n",
      "                            capFlag = True\n",
      "                        \n",
      "                        try:\n",
      "                            # best match for original word\n",
      "                            best = model.most_similar(positive=[bits[0].lower()], negative=[], topn=1)   # (word, score)\n",
      "                            if best[0][0].strip() in screen_outs:\n",
      "                                raise 'WordError'\n",
      "                            # add original word to datadict, with best match as word for it\n",
      "                            datadict[bits[0].lower().encode('utf8')] = {\"word\": best[0][0].lower(), \n",
      "                                                                        \"score\": \"%.3f\" % best[0][1]}\n",
      "                            # add the reverse word to the same dict, for graphing in front end\n",
      "                            best_reverse = model.most_similar(positive=[best[0][0]], negative=[], topn=1)\n",
      "                            if best_reverse[0][0] in screen_outs:\n",
      "                                raise 'WordError'\n",
      "                            datadict[best[0][0].lower().encode('utf8')] = {\"word\": best_reverse[0][0].lower(), \n",
      "                                                                           \"score\": \"%.3f\" % best_reverse[0][1]}\n",
      "                            newWord = '<span class=\"color\" title=\"Was: <span class=\\'purple\\'>'\\\n",
      "                                    + bits[0] + '</span> / Word2Vec similarity: ' + str(round(best[0][1],3))\\\n",
      "                                    + '\" formerWord=\"' + bits[0].lower() + '\" newWord=\"' + best[0][0].lower() + '\" score=\"'\\\n",
      "                                    + str(round(best[0][1],3)) + '\">' + cap_me(best[0][0],capFlag) + '</span>'\n",
      "                            line_text += newWord\n",
      "                        except Exception, e:  # on model errors etc, just continue past it\n",
      "                            print e\n",
      "                            line_text += word.replace('/NN', '')\n",
      "                            pass\n",
      "                    \n",
      "                    else:\n",
      "                        if bits[0] in [',',':','.','\"',';','?', '!', '\\'s', '\\'ll', 'n\\'t']:\n",
      "                            line_text = line_text.strip() # remove spaces before these\n",
      "                        line_text += bits[0]\n",
      "                    if line_text[-1] != '\"':  # no space after quotes; hacky.\n",
      "                        line_text += ' '\n",
      "                else:\n",
      "                    if word.strip() != '':\n",
      "                        line_text += word.strip()\n",
      "            full_text += line_text + '\\n'\n",
      "\n",
      "    print \"vocab before clean\", datadict.keys()\n",
      "    datadict = clean_dict_keys(datadict)\n",
      "    print \"vocab after clean\", datadict.keys()\n",
      "    \n",
      "    with open(outputtextfile, 'w') as handle:\n",
      "        handle.write(full_text.encode('utf8'))\n",
      "    jsonfile = json.dumps(datadict)\n",
      "    with open(outputdatafile, 'w') as handle:\n",
      "        handle.write(jsonfile)\n",
      "\n",
      "    print \"Wrote out to data directory:\", outputtextfile, outputdatafile\n",
      "    return datadict"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 241
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# The wrapper function that runs everything, writes files, and returns useful things.\n",
      "def run_all(tags=['NN'], filepref='data/pride_NN'):\n",
      "    \n",
      "    # notice here i require at least 2 uses of each word in the corpus - reduces noise\n",
      "    \n",
      "    # uncomment the version of the model you want to try: tag limited, or all words.\n",
      "    #model = gensim.models.Word2Vec(MySentences('books', tags=tags), min_count=2, size=200, workers=2)\n",
      "    model = gensim.models.Word2Vec(DirOfPlainTextCorpus('books'), min_count=3, workers=2)\n",
      "    model.save(filepref + '_model_austen_allwordsmin3')\n",
      "    \n",
      "    # if you want to save time and use a saved file, comment out the above and uncomment this with right path\n",
      "    #model = gensim.models.Word2Vec.load('data/pride_NNPRP_model_austen_all')\n",
      "    \n",
      "    # does the text tagging and word replacement\n",
      "    print tags\n",
      "    datadict = build_dict_write_file(model, filepref + '_labeled.txt', filepref + '_data.json', tags=tags)\n",
      "    \n",
      "    # tsne input files part\n",
      "    make_score_files(model, datadict, filepref)\n",
      "    \n",
      "    # the actual tsne graph bit\n",
      "    X = np.loadtxt(filepref + \"_scores.csv\")\n",
      "    labels = np.genfromtxt(filepref + \"_words.csv\", dtype=str)\n",
      "    Y = tsne.tsne(X, 2, 50, 20.0)   # see tsne.py in repo\n",
      "    do_tsne_files(filepref + '_coords.tsv', Y, labels, datadict, axis_off=True)\n",
      "    \n",
      "    return Y, labels, datadict, model"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 242
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "# run it all together and get the useful outputs - this takes a while and prints a lot of stuff.\n",
      "Y, labels, datadict, model = run_all(tags=['NN'], filepref='data/pride_NNAll3')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Do the file splitting part for the UI - Make sure to rename the label file for the output"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!split -l 200 data/pride_NNAll3_labeled.txt part"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 234
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash\n",
      "mv partaa data/parts/part1.txt\n",
      "mv partab data/parts/part2.txt\n",
      "mv partac data/parts/part3.txt\n",
      "mv partad data/parts/part4.txt\n",
      "mv partae data/parts/part5.txt\n",
      "mv partaf data/parts/part6.txt\n",
      "mv partag data/parts/part7.txt\n",
      "mv partah data/parts/part8.txt\n",
      "mv partai data/parts/part9.txt\n",
      "mv partaj data/parts/part10.txt\n",
      "mv partak data/parts/part11.txt\n",
      "mv partal data/parts/part12.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 235
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls data/parts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "part1.txt   part11.txt  part2.txt   part4.txt   part6.txt   part8.txt\r\n",
        "part10.txt  part12.txt  part3.txt   part5.txt   part7.txt   part9.txt\r\n"
       ]
      }
     ],
     "prompt_number": 236
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Play with your model if you want... "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.most_similar(['husband'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 232,
       "text": [
        "[(u'nerves', 0.8918779492378235),\n",
        " (u'lifting', 0.7963227033615112),\n",
        " (u'wishes', 0.7679949998855591),\n",
        " (u'nephew', 0.7674976587295532),\n",
        " (u'senses', 0.7639766931533813),\n",
        " (u'daughter', 0.7601332664489746),\n",
        " (u'ladyship', 0.7527087330818176),\n",
        " (u'daughters', 0.7525165677070618),\n",
        " (u'thoughts', 0.7426179647445679),\n",
        " (u'mother', 0.7310776710510254)]"
       ]
      }
     ],
     "prompt_number": 232
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.most_similar(['wife'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 231,
       "text": [
        "[(u'son', 0.7893723249435425),\n",
        " (u'reviving', 0.7113327980041504),\n",
        " (u'daughter', 0.7054953575134277),\n",
        " (u'admittance', 0.6823280453681946),\n",
        " (u'attentions', 0.658092737197876),\n",
        " (u'warmed', 0.6542254090309143),\n",
        " (u'niece', 0.6514275074005127),\n",
        " (u'addresses', 0.6490938663482666),\n",
        " (u'proposals', 0.647223174571991),\n",
        " (u'behaviour', 0.6413060426712036)]"
       ]
      }
     ],
     "prompt_number": 231
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "TSNE Plotting Stuff"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# for tsne - write out the scores and labels as 2 row syncd files - can be used in R now.\n",
      "\n",
      "def make_score_files(model, datadict, filelabel):\n",
      "    with open(filelabel + '_scores.csv', 'w') as scorefile:\n",
      "        with open(filelabel + '_words.csv', 'w') as wordfile:\n",
      "            for word in datadict.keys():\n",
      "                try:\n",
      "                    score = model[word.lower()]\n",
      "                    scores = [str(x) for x in score]\n",
      "                    scorefile.write('\\t'.join(scores) + '\\n')\n",
      "                    wordfile.write(word.lower() + '\\n')\n",
      "                except:\n",
      "                    print \"Not found:\", word, datadict[word]\n",
      "                    continue\n",
      "            scorefile.write('\\n')\n",
      "            wordfile.write('\\n')\n",
      "    print 'Wrote ', scorefile, wordfile"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!paste data/pride_NN_words.csv data/pride_NN_scores.csv > data_for_r.tsv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 52
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "The scores and words csv files you just wrote out can be used in the R code in the repo, if you want to try that for the plot instead."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# this is a function you can call independently with output from your run-all run, to jigger with details if you want.\n",
      "\n",
      "def do_tsne_files(coordsfilename, Y, labels, datadict, axis_off=True):\n",
      "    \"\"\" Makes the tsne grah file and writes out the coords for the UI. \"\"\"\n",
      "    plt.figure(figsize=(15, 15))\n",
      "    if axis_off:\n",
      "        plt.axis('off')   # only turn off after checking the axis min/max\n",
      "    plt.scatter(Y[:,0], Y[:,1], s=10, color='gray', alpha=0.2)\n",
      "    print \"X and Y dims \", plt.axis()\n",
      "    with open(coordsfilename, 'w') as handle1:\n",
      "        handle1.write('word\\tx\\ty\\tnearest\\n')\n",
      "        for label, x, y in zip(labels, Y[:, 0], Y[:, 1]):\n",
      "            score = datadict[label]['score']\n",
      "            handle1.write('\\t'.join([label, str(x), str(y), str(score)]) + '\\n')\n",
      "            #plt.text(x, y, label, size=8, alpha=0.5)\n",
      "    print \"wrote out\", coordsfilename"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 204
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "do_tsne_files('misc.tsv', Y, labels, datadict, axis_off=False)   # run this again with axis on, to check axes limits for the graph..."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "X and Y dims  (-150.0, 150.0, -150.0, 150.0)\n",
        "wrote out misc.tsv\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RPAGUU2pZLpf18PCgfD4vY4yy2ay63a7794sEZ4PloOl0eu7HWqaNjQ2dn58/Kdd0xkEs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r7tNprWHpOkRZ/9O2uHNQSxq5AzP/GmSaOgsAwiAnoS96Iqxs3vfvziaoJibtz6G6fi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\n3Tg/1nu8v783FD7L5TLy+fzc8avqtWVOVTdHOlWrAyC4E74oHT5JDi4hQ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HUOzd/xdsIz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\nkQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11002b990>"
       ]
      }
     ],
     "prompt_number": 246
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}