Paper Figures.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/entity/anaconda2/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "/home/entity/anaconda2/lib/python2.7/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_context(\"poster\")\n",
    "sns.set_style(\"ticks\")\n",
    "\n",
    "from itertools import chain\n",
    "\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.grid_search import RandomizedSearchCV\n",
    "\n",
    "import sklearn_crfsuite\n",
    "from sklearn_crfsuite import scorers\n",
    "from sklearn_crfsuite import metrics\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.grid_search import RandomizedSearchCV\n",
    "\n",
    "import regex as re\n",
    "from collections import namedtuple, defaultdict, Counter, OrderedDict\n",
    "from IPython.display import display\n",
    "from joblib import load, dump, Parallel, delayed\n",
    "\n",
    "import os, string, sys\n",
    "\n",
    "from gensim.models import word2vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class RegexFeatures(object):\n",
    "    PATTERNS = {\n",
    "        \"isInitCapitalWord\": re.compile(r'^[A-Z][a-z]+'),\n",
    "        \"isAllCapitalWord\": re.compile(r'^[A-Z][A-Z]+$'),\n",
    "        \"isAllSmallCase\": re.compile(r'^[a-z]+$'),\n",
    "        \"isWord\": re.compile(r'^[a-zA-Z][a-zA-Z]+$'),\n",
    "        \"isAlphaNumeric\": re.compile(r'^\\p{Alnum}+$'),\n",
    "        \"isSingleCapLetter\": re.compile(r'^[A-Z]$'),\n",
    "        \"containsDashes\": re.compile(r'.*--.*'),\n",
    "        \"containsDash\": re.compile(r'.*\\-.*'),\n",
    "        \"singlePunctuation\": re.compile(r'^\\p{Punct}$'),\n",
    "        \"repeatedPunctuation\": re.compile(r'^[\\.\\,!\\?\"\\':;_\\-]{2,}$'),\n",
    "        \"singleDot\": re.compile(r'[.]'),\n",
    "        \"singleComma\": re.compile(r'[,]'),\n",
    "        \"singleQuote\": re.compile(r'[\\']'),\n",
    "        \"isSpecialCharacter\": re.compile(r'^[#;:\\-/<>\\'\\\"()&]$'),\n",
    "        \"fourDigits\": re.compile(r'^\\d\\d\\d\\d$'),\n",
    "        \"isDigits\": re.compile(r'^\\d+$'),\n",
    "        \"isNumber\": re.compile(r'^((\\p{N}{,2}([,]?\\p{N}{3})+)(\\.\\p{N}+)?)$'),\n",
    "        \"containsDigit\": re.compile(r'.*\\d+.*'),\n",
    "        \"endsWithDot\": re.compile(r'\\p{Alnum}+\\.$'),\n",
    "        \"isURL\": re.compile(r'^http[s]?://'),\n",
    "        \"isMention\": re.compile(r'^(RT)?@\\p{Alnum}+$'),\n",
    "        \"isHashtag\": re.compile(r'^#\\p{Alnum}+$'),\n",
    "        \"isMoney\": re.compile(r'^\\$((\\p{N}{,2}([,]?\\p{N}{3})+)(\\.\\p{N}+)?)$'),\n",
    "    }\n",
    "    def __init__(self):\n",
    "        print \"Initialized RegexFeature\"\n",
    "    @staticmethod\n",
    "    def process(word):\n",
    "        features = dict()\n",
    "        for k, p in RegexFeatures.PATTERNS.iteritems():\n",
    "            if p.match(word):\n",
    "                features[k] = True\n",
    "        return features\n",
    "    \n",
    "    \n",
    "def classification_report_to_df(report):\n",
    "    report_list = []\n",
    "    for i, line in enumerate(report.split(\"\\n\")):\n",
    "        if i == 0:\n",
    "            report_list.append([\"class\", \"precision\", \"recall\", \"f1-score\", \"support\"])\n",
    "        else:\n",
    "            line = line.strip()\n",
    "            if line:\n",
    "                if line.startswith(\"avg\"):\n",
    "                    line = line.replace(\"avg / total\", \"avg/total\")\n",
    "                line = re.split(r'\\s+', line)\n",
    "                report_list.append(tuple(line))\n",
    "    return pd.DataFrame(report_list[1:], columns=report_list[0])\n",
    "\n",
    "\n",
    "DATA_DIR=\"data/data/\"\n",
    "CLEANED_DIR=\"data/cleaned/\"\n",
    "\n",
    "Tag = namedtuple(\"Tag\", [\"token\", \"tag\"])\n",
    "\n",
    "def load_sequences(filename, sep=\"\\t\", notypes=False, test_data=False):\n",
    "    sequences = []\n",
    "    with open(filename) as fp:\n",
    "        seq = []\n",
    "        for line in fp:\n",
    "            line = line.strip()\n",
    "            if line:\n",
    "                line = line.split(sep)\n",
    "                if test_data:\n",
    "                    assert len(line) == 1\n",
    "                    line.append(\"?\")\n",
    "                if notypes:\n",
    "                    line[1] = line[1][0]\n",
    "                seq.append(Tag(*line))\n",
    "            else:\n",
    "                sequences.append(seq)\n",
    "                seq = []\n",
    "        if seq:\n",
    "            sequences.append(seq)\n",
    "    return sequences\n",
    "\n",
    "\n",
    "def load_vocab(filename):\n",
    "    vocab = set()\n",
    "    with open(filename) as fp:\n",
    "        for line in fp:\n",
    "            line = line.strip()\n",
    "            vocab.add(line)\n",
    "    return vocab      \n",
    "\n",
    "    \n",
    "def print_transitions(trans_features):\n",
    "    for (label_from, label_to), weight in trans_features:\n",
    "        print(\"%-6s -> %-7s %0.6f\" % (label_from, label_to, weight))\n",
    "        \n",
    "def print_state_features(state_features):\n",
    "    for (attr, label), weight in state_features:\n",
    "        print(\"%0.6f %-8s %s\" % (weight, label, attr)) \n",
    "        \n",
    "        \n",
    "def plot_cm(y_test, y_pred, labels=[]):\n",
    "    labels_s = dict((k,i) for i,k in enumerate(labels))\n",
    "    cm = np.zeros((len(labels), len(labels)))\n",
    "    for i,j in zip(sum(y_test, []), sum(y_pred, [])):\n",
    "        i = labels_s[i]\n",
    "        j = labels_s[j]\n",
    "        cm[i,j] += 1\n",
    "    with plt.rc_context(rc={'xtick.labelsize': 12, 'ytick.labelsize': 12,\n",
    "                       'figure.figsize': (16,14)}):\n",
    "        sns.heatmap(cm * 100/ cm.sum(axis=1, keepdims=True),\n",
    "                    #cmap=sns.cubehelix_palette(n_colors=100, rot=-.4, as_cmap=True),\n",
    "                    cmap=\"Greys_r\",\n",
    "                    xticklabels=labels,\n",
    "                    yticklabels=labels)\n",
    "        plt.ylabel(\"True labels\")\n",
    "        plt.xlabel(\"Predicted labels\")\n",
    "    print cm.shape\n",
    "    return cm\n",
    "\n",
    "\n",
    "def print_sequences(sequences, predictions, filename, test_data=False):\n",
    "    with open(filename, \"wb+\") as fp:\n",
    "        for seq, pred in zip(sequences, predictions):\n",
    "            for t, p in zip(seq, pred):\n",
    "                token, tag = t\n",
    "                if tag[0] == \"U\":\n",
    "                    tag = \"B%s\" % tag[1:]\n",
    "                if tag[0] == \"E\":\n",
    "                    tag = \"I%s\" % tag[1:]\n",
    "                if p[0] == \"U\":\n",
    "                    p = \"B%s\" % p[1:]\n",
    "                if p[0] == \"E\":\n",
    "                    p = \"I%s\" % p[1:]\n",
    "                if test_data:\n",
    "                    line = \"\\t\".join((token, p))\n",
    "                else:\n",
    "                    line = \"\\t\".join((token, tag, p))\n",
    "                print >> fp, line\n",
    "            print >> fp, \"\"\n",
    "    print \"Done\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "WORD_SPLITTER = re.compile(r'[\\p{Punct}\\s]+')\n",
    "class DictionaryFeatures:\n",
    "    def __init__(self, dictDir):\n",
    "        self.word2dictionaries = {}\n",
    "        self.word2hashtagdictionaries = {}\n",
    "        self.dictionaries = []\n",
    "        i = 0\n",
    "        for d in os.listdir(dictDir):\n",
    "            print >> sys.stderr, \"read dict %s\"%d\n",
    "            self.dictionaries.append(d)\n",
    "            if d == '.svn':\n",
    "                continue\n",
    "            for line in open(dictDir + \"/\" + d):\n",
    "                word = line.rstrip('\\n')\n",
    "                word = word.strip(' ').lower()\n",
    "                word_hashtag = \"\".join(WORD_SPLITTER.split(word))\n",
    "                if not self.word2dictionaries.has_key(word):\n",
    "                    self.word2dictionaries[word] = str(i)\n",
    "                else:   \n",
    "                    self.word2dictionaries[word] += \"\\t%s\" % i\n",
    "                if not self.word2hashtagdictionaries.has_key(word_hashtag):\n",
    "                    self.word2hashtagdictionaries[word_hashtag] = str(i)\n",
    "                else:\n",
    "                    self.word2hashtagdictionaries[word_hashtag] += \"\\t%s\" % i\n",
    "            i += 1\n",
    "    \n",
    "    MAX_WINDOW_SIZE=6\n",
    "    def GetDictFeatures(self, words, i):\n",
    "        features = []\n",
    "        for window in range(1,self.MAX_WINDOW_SIZE):\n",
    "            start=max(i-window+1, 0)\n",
    "            end = start + window\n",
    "            phrase = ' '.join(words[start:end]).lower().strip(string.punctuation)\n",
    "            if self.word2dictionaries.has_key(phrase):\n",
    "                for j in self.word2dictionaries[phrase].split('\\t'):\n",
    "                    features.append('DICT=%s' % self.dictionaries[int(j)])\n",
    "                if window > 1:\n",
    "                    features.append('DICTWIN=%s' % window)\n",
    "        return list(set(features))\n",
    "    \n",
    "    def GetHashtagDictFeatures(self, word):\n",
    "        features = []\n",
    "        if len(word) < 2 or word[0] != \"#\":\n",
    "            return features\n",
    "        word = word[1:].lower().strip(string.punctuation)\n",
    "        if self.word2hashtagdictionaries.has_key(word):\n",
    "            for j in self.word2hashtagdictionaries[word].split('\\t'):\n",
    "                features.append('DICT_HASHTAG=%s' % self.dictionaries[int(j)])\n",
    "        return list(set(features))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8023\n"
     ]
    }
   ],
   "source": [
    "train_sequences = load_sequences(\"data/cleaned/train.BIEOU.tsv\", sep=\"\\t\", notypes=False)\n",
    "dev_sequences = (load_sequences(\"data/cleaned/dev.BIEOU.tsv\", sep=\"\\t\", notypes=False) \n",
    "                 + load_sequences(\"data/cleaned/dev_2015.BIEOU.tsv\", sep=\"\\t\", notypes=False))\n",
    "test_sequences = load_sequences(\"data/cleaned/test.BIEOU.tsv\", sep=\"\\t\", notypes=False)\n",
    "vocab = load_vocab(\"vocab.no_extras.txt\")\n",
    "print len(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'product': 5, 'facility': 4, 'movie': 8, 'company': 3, 'sportsteam': 7, 'musicartist': 6, 'person': 1, 'other': 2, 'geo-loc': 0, 'tvshow': 9}\n",
      "Counter({'geo-loc': 1323, 'person': 1180, 'other': 995, 'company': 865, 'facility': 404, 'product': 390, 'musicartist': 304, 'sportsteam': 303, 'movie': 86, 'tvshow': 75})\n"
     ]
    },
    {
     "data": {
      "image/png": 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IyEhMTU1xdnZmzJgxqvH9+/ezatUqzM3NqVixIrNmzcLKykoZv3z5Mn379uWr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5afe735c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_co_occurences = [[t[1][2:] for t in seq if t[1][0] in [\"B\", \"U\"]] \n",
    "                       for seq in (train_sequences+dev_sequences+test_sequences)]\n",
    "\n",
    "cat_types = Counter(t for seq in label_co_occurences for t in seq)\n",
    "cat_ids = {k[0]: i for i, k in enumerate(cat_types.most_common())}\n",
    "cat_names = [k[0] for k in cat_types.most_common()]\n",
    "print cat_ids\n",
    "print cat_types\n",
    "co_occur_matrix = np.zeros((len(cat_ids), len(cat_ids)))\n",
    "for seq in label_co_occurences:\n",
    "    for i, t in enumerate(seq):\n",
    "        for j, t1 in enumerate(seq):\n",
    "            if i != j:\n",
    "                co_occur_matrix[cat_ids[t], cat_ids[t1]] += 1\n",
    "for k,i in cat_ids.iteritems():\n",
    "    co_occur_matrix[cat_ids[k]] /= co_occur_matrix[cat_ids[k]].sum()\n",
    "    \n",
    "#print co_occur_matrix\n",
    "    \n",
    "with plt.rc_context(rc={'xtick.labelsize': 10, 'ytick.labelsize': 10,\n",
    "                        'font.size': 8,\n",
    "                       'figure.figsize': (8,6)}):\n",
    "    sns.heatmap(co_occur_matrix, xticklabels=cat_names, yticklabels=cat_names, annot=True, cmap=\"Greys\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num entities in Training data (N=2394, N_entities=1499, N(no-entities)=1476): min=0, max=6, median=0.0, mean(+/-std)=0.6 +/- 1.0\n",
      "Num entities in Development data (N=1420, N_entities=937, N(no-entities)=838): min=0, max=9, median=0.0, mean(+/-std)=0.7 +/- 1.0\n",
      "Num entities in Test data (N=3856, N_entities=3489, N(no-entities)=1842): min=0, max=11, median=1.0, mean(+/-std)=0.9 +/- 1.1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5afe735790>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ACtKgthugbhs9enRuu+22vPzyy1m4\ncGFatWqVffbZJz/5yU+y3nrr1XZ7dc4bb7yRM888My+++GIOPfTQ/PrXv67tluqU8vLyjBgxInff\nfXdeffXVLFq0KJtsskl22WWXDBo0KNtss01tt1inLFiwICNGjMgDDzyQt99+O/PmzctGG22Uzp07\n56ijjkqXLl1qu8U6q6ysLH379s2kSZP8XVXDRo4cmbPPPnuFc/r27Ztf/epXNdNQHffxxx/n2muv\nzSOPPJLZs2dnvfXWS6dOnfKTn/zE31E1ZM8998yMGTO+dt6tt96arl271kBHddvChQszbNiwPPzw\nw3nzzTeTJFtvvXX69OmTH/7whymVSrXb4Bru25xLLF68OEOHDs0f/vCHlJeX5+WXX67BTqufoIVa\nc+211+bKK6/MpptumsMPPzwbbLBBJk6cmBtuuCFjxozJHXfckSZNmtR2m3XGrbfemiuuuCJLlizx\nTagWlJeX56STTsqjjz6aTTbZJIcffnjWX3/9vPDCC7n33nvz8MMP55ZbbskOO+xQ263WCfPmzcvA\ngQMzadKkdOjQIX369Mlaa62Vl19+OaNGjcpDDz2Uyy67LAceeGBtt1onXXXVVZk0aZK/q2rRbrvt\nlp49e1Y61q5duxrupm5677338sMf/jAzZ87M3nvvnT59+mT69Om5//778+STT+baa69Nr169arvN\nNd4JJ5yQTz755CvHb7755nzwwQdp3rx5DXZVN82fPz/9+vXLK6+8ktatW+fII4/Meuutl4cffjgX\nXHBBnn/++Vx++eW13eYa69ucS7z00ks544wz8uabb+bzzz9fI7+fC1qoFVOnTs3VV1+dFi1aZOTI\nkdlwww2TJMcdd1yuuOKK3HjjjRk6dGh++ctf1nKndcPpp5+eBx98MPvvv3923HHHXHLJJbXdUp0z\ncuTIPProo2nXrl3uuOOOrL322hVjv/vd73Ldddflsssuy6233lqLXdYdV155ZSZPnpwjjjgiF154\n4TJjI0aMyC9/+csMGTJE0FILnn/++dx4443p2LFjJk+eXNvt1Fk77bRT/uM//qO226jTfvGLX2TG\njBkZOnRovv/971c8ftBBB2XQoEEZOXKkoKUGHHHEEV85Nnr06Lz//vsZOHBgWrduXYNd1U3XXXdd\nXnnllXTr1i033nhjGjVqlOSL84uTTz45DzzwQPbZZ5/ss88+tdzpmufbnEuMHTs2J510Ulq1apXb\nbrstffv2rcFOa449WqgVd955Z8rLy3PsscdWhCxLHX/88WncuHHuueeeLFq0qJY6rFvee++9XHrp\npbniiiss2aolf//737Puuutm0KBBy4QsSfLDH/4wSfLCCy/URmt1UqdOnTJo0KCceOKJy43tt99+\nSZJZs2bl888/r+nW6rT58+fnjDPOSPPmzfPTn/60ttuBWvPKK6/kySefTO/evZcJWZKka9eueeGF\nF/Lb3/62lroj+WJZ1/nnn58tttgiJ598cm23UyeMGjUqpVIpJ598ckXIkiQNGzbMueeemyS54447\naqu9Ndq3OZd4++23c+CBB+bee+/Nd7/73RrqsOa5ooVa8eyzzyZJpZcdr7vuuunUqVMmTpyYSZMm\nZeedd67p9uqc6667zjKtWnbBBRfkggsuqHRsnXXWSfLF8qLy8vI18vLKVc3BBx/8lWNTp05NkrRv\n3z716vl9RU266KKLMmPGjAwbNszXwSpi8eLF+fDDD7PeeustFxJTfR555JGUSqXsu+++FY/NnTs3\nDRs29AuTVcRll12WOXPm5Kabbspaa61V2+3UCTNnzkzyxZ4s/6pVq1Zp0aJFJk6cmM8//9z374J9\nm3OJww47LMccc0w1d1T7/B9GjVuyZEneeuutlEqlr7yMcquttkryfyc0VC8hy6rtscceS5Lssssu\nTi5rwSeffJLp06dnypQpufnmm3P88cenZcuWueiii2q7tTpl1KhRGTlyZI455ph069atttup86ZM\nmZL+/func+fO6dWrVzp37pwjjjgi48aNq+3W6oSXXnopyRcnlFdffXV69OiRHj16pGvXrjnwwAMz\nevToWu6wbps2bVpGjBiR733ve9ltt91qu506Y+nPs3Pnzq10fK211sqSJUvyzjvv1GRbdcK3OZeo\nK+cdghZq3CeffJKysrI0btx4mcv6vqxp06ZJko8++qgmW4NVzowZM/Kb3/wm9evXz6mnnlrb7dRJ\nd911V/baa68cfvjhGTJkSHbfffeMHDky7du3r+3W6oz3338///Vf/5U2bdrktNNOq+12yBdr7Js2\nbZoLLrggQ4YMyWGHHZYpU6bk+OOPz8iRI2u7vTXerFmzkiRDhgzJyJEj8+Mf/zhDhgzJsccemzff\nfDM/+9nPHIdaNHTo0CTJ//t//6+WO6lbunbtWnEHx381YcKEirsQOb+gJlg6RI1buHBhki/WS36V\nRo0apby8vGIu1EXTpk3LoEGDMmfOnJx77rnuOFRL9txzz2y22Wb58MMP88wzz2TUqFF54YUX8rvf\n/S6dOnWq7fbqhLPOOiuffvppLrvssq8M6KkZHTp0yCmnnJJOnTot85v6/fffPz179sypp56aiy++\nOHvttZclLNVo/vz5KS8vz4wZM3LvvfdW/IZ4//33T+fOnXPyySdn8ODB2X///S1bqWGTJk3KI488\nkr333jvbbLNNbbdTp5x44okZO3Zshg8fnvLy8uy3335p3Lhxxo8fnxtvvDFbbbVV3nrrrZSVldV2\nq9QBrmihxjVu3DhJ8tlnn33lnEWLFqVUKlXMhbpm/Pjx6du3b957772cf/75+dGPflTbLdVZW2yx\nRfbaa6/06dMnl19+eYYNG5ZZs2bl1FNPzeLFi2u7vTXeLbfckqeeeio/+9nP3DZ4FdC2bdscf/zx\nlS6H2G+//dKpU6fMnz8/TzzxRC10V3fUr18/pVIp/fv3X+4y/H322Sdbb7115s2bl4kTJ9ZSh3XX\n0j2k+vXrV9ut1Dnt27fPjTfemM022yzDhw9P3759c8ghh+SWW27JhRdemDZt2iT5Yj9IqG6uaKHG\nrbfeemnQoEEWLFiQxYsXV/rbyQ8++CBJlrsjEdQFt9xyS37zm9+kSZMmueGGG9KjR4/abokv2Xnn\nndO1a9dMmDAhEyZMqHRTb4rx2muvZciQIenSpUt+/OMfLzNWXl5eS12xIu3bt8/kyZMzffr02m5l\njbZ0ifVGG21U6XibNm3y5ptvZsaMGTXZVp03b968PPLII2nevHm6d+9e2+3USd27d8/o0aPz8ssv\n5/3330+zZs2y/fbbp1Qq5brrrkuStGzZspa7pC4QtFDj6tevn6233jrTpk3LG2+8kbZt2y435/XX\nX08SeyBQ51x//fUZOnRottxyy9xwww3Zcssta7ulOmfJkiV56KGH8uGHH+aoo46qdM7SEHjpPglU\nj1GjRmXRokV57rnn0qFDh+XGS6VSRo4cmZEjR2aXXXbJ8OHDa6FLvmzpJfnuQFS9tt122/z973+v\nuMvKv1q69NqyoZo1bty4LF68OL169artVuq89u3bL3MesXDhwkybNi2bb765ZY3UCEELtaJnz555\n7bXXMnbs2OWCljlz5mTy5MnZYIMN0rFjx1rqEGreH//4xwwdOjQdOnTIsGHDKn5jSc1q0KBB35My\nWgAAD3VJREFUBg8enDlz5mTXXXetdI390jB40003ren26pTOnTtnwIABlY7NmjUrDz74YNq0aZPd\nd9/9K+9iR7FOO+20vPXWWxk2bFjWX3/9ZcaWLFmSZ555Jkl8/65mPXv2zJ///OeMGzduua+RsrKy\nirs2VvbLLKrP+PHjUyqVsssuu9R2K3XSa6+9lgkTJqRLly7LLTV96KGHsnjx4uy111611B11jaCF\nWtG3b9/cdtttGT58eA455JA0b968Yuzyyy9PWVlZ+vXrlwYN/C9K3fDKK6/k17/+dVq2bJmbb75Z\nyFLLvv/97+ePf/xjBg8enGuuuWaZJY733ntvXn311Wy44YZ+mK5mS29ZW5kJEybkwQcfTMeOHXPG\nGWfUcGd1V6lUypQpUzJ48OBcfPHFy9xy/qqrrsq7776bdu3aZaeddqrFLtd8e+65Z7bYYouKr4P9\n99+/YuwPf/hDZs+enQ4dOghaatjS225vu+22tdxJ3TRlypRceOGF2XXXXfPf//3fqVfvi+1IZ8+e\nnSFDhqRx48Y55phjarlL6gpnsdSKLbfcMmeddVYuuuiiHHrooTnooIOy/vrr58knn8zzzz+fnXfe\nOYMGDartNuuEpb8VXmry5MlJvvitwM0331zxeK9evfzgUI2GDBmSJUuWpF27drn77ru/ct4BBxyw\nTDBJ9Tj11FPzwgsv5Mknn8wPfvCD9O7dO+uvv34mT56csWPHpkGDBjn//PNt2E2dc84552TSpEkZ\nOXJkXnrppey2225Zd911M378+Dz33HPZZJNNcsUVV9R2m2u8hg0b5rLLLsuAAQPy85//PE888US2\n2mqrvPjii3n00UfTtGnTXHLJJbXdZp3z9ttvJ0latWpVy53UTfvvv3/uvPPOPPPMMznyyCPzve99\nLx9//HHuu+++fPTRR7n00kvtz1INvum5RO/evbPNNtvkz3/+cz7++OMky+639uW5LVq0WCZAXh2V\nyu0mRy164oknMmzYsEyePDmLFi1K69atc8ABB2TAgAFu4VlDJkyYkGOOOWaZ30pW5te//nUOOeSQ\nGuqq7tlzzz2/cq39lw0fPjxdu3atgY5YvHhxbrnlljz00EN58803s3jx4my88cbp2rVrjj322Er3\nDKHmTJgwIf3798+hhx7qhLKGffTRR7npppvy2GOPZfr06SmVStlss82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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b047c6a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_lens = []\n",
    "for sequences, label in zip((train_sequences, dev_sequences, test_sequences),\n",
    "                       (\"Training\", \"Development\", \"Test\")):\n",
    "    lens = [len([t[1][2:] for t in seq if t[1][0] in [\"B\", \"U\"]])\n",
    "                       for seq in (sequences)]\n",
    "    total_lens.extend([(t, label) for t in lens])\n",
    "    print \"Num entities in {} data (N={}, N_entities={}, N(no-entities)={}): min={}, max={}, median={}, mean(+/-std)={:.1f} +/- {:.1f}\".format(\n",
    "        label, len(lens), sum(lens), len(filter(lambda x: x==0, lens)),\n",
    "        min(lens), max(lens), np.median(lens), np.mean(lens), np.std(lens))\n",
    "sns.countplot(x=\"num_entities\", hue=\"data\", data=pd.DataFrame(total_lens, columns=[\"num_entities\", \"data\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1842"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(filter(lambda x: x==0, lens))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.7.1'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entity length in Training data (N=1499, N_entities=2462): min=1, max=10, median=1.0, mean(+/-std)=1.6 +/- 1.0\n",
      "Entity length in Development data (N=937, N_entities=1567): min=1, max=14, median=1.0, mean(+/-std)=1.7 +/- 1.2\n",
      "Entity length in Test data (N=3489, N_entities=5955): min=1, max=14, median=1.0, mean(+/-std)=1.7 +/- 1.2\n"
     ]
    },
    {
     "data": {
      "image/png": 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gr7ZDjsExGRftgow2GjqLyrqPQA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5afe4af090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_lens = []\n",
    "for sequences, label in zip((train_sequences, dev_sequences, test_sequences),\n",
    "                       (\"Training\", \"Development\", \"Test\")):\n",
    "    lens = []\n",
    "    for seq in sequences:\n",
    "        e_length=0\n",
    "        e_type=None\n",
    "        for t in seq:\n",
    "            if t[1][0] == \"O\":\n",
    "                continue\n",
    "            if t[1][0] in {\"B\", \"U\"}:\n",
    "                if e_length > 0:\n",
    "                    lens.append((e_length, e_type))\n",
    "                e_length=0\n",
    "                e_type=t[1][2:]\n",
    "            e_length += 1\n",
    "        if e_length > 0:\n",
    "            lens.append((e_length, e_type))\n",
    "    total_lens.extend([t+(label,) for t in lens])\n",
    "    lens = [t[0] for t in lens]\n",
    "    print \"Entity length in {} data (N={}, N_entities={}): min={}, max={}, median={}, mean(+/-std)={:.1f} +/- {:.1f}\".format(\n",
    "        label, len(lens), sum(lens), min(lens), max(lens), np.median(lens), np.mean(lens), np.std(lens))\n",
    "\n",
    "def plot_data(x, **kwargs):\n",
    "    weights = np.ones_like(x)*1./len(x)\n",
    "    return plt.hist(x, weights=weights, **kwargs)\n",
    "g = (sns.FacetGrid(row=\"entity_type\", col=\"data\", aspect=1.5, margin_titles=True,\n",
    "                  data=pd.DataFrame(total_lens, columns=[\"entity_lengths\", \"entity_type\", \"data\"])))\n",
    "g = g.map(plot_data, \"entity_lengths\", bins=range(1,10))\n",
    "g = g.set_titles(template=\"\", col_template=\"{col_name}\")\n",
    "g.fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['person', 'geo-loc', 'other', 'company', 'facility', 'product', 'musicartist', 'sportsteam', 'movie', 'tvshow']\n",
      "Training sequences: 2394\n",
      "Development sequences: 1420\n"
     ]
    },
    {
     "data": {
      "image/png": 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jKSkpOn36dL6ueejQIdPrY5AdIQ2AVXJzc1OjRo3UuHFj03+nTJkiSdq8ebMC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\nQgghhDAgEqQJIYQQQgghhAH5H9SVA9cyJ0gGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5af6c4a5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phrase_types_training = [(t[1][2:], \"training\") for seq in (train_sequences) for t in seq if t[1][0] in [\"B\", \"U\"]]\n",
    "phrase_types_dev = [(t[1][2:], \"dev\") for seq in (dev_sequences) for t in seq if t[1][0] in [\"B\", \"U\"]]\n",
    "phrase_types_test = [(t[1][2:], \"test\") for seq in (test_sequences) for t in seq if t[1][0] in [\"B\", \"U\"]]\n",
    "df_t = pd.DataFrame(phrase_types_training+phrase_types_dev+phrase_types_test, columns=[\"phrase_type\", \"data\"])\n",
    "cat_names = [k[0] for k,v in Counter(phrase_types_training).most_common()]\n",
    "print cat_names\n",
    "#palette = [\"0.2\", \"0.5\", \"0.7\"]\n",
    "palette = [\"black\", \"yellow\", \"red\"]\n",
    "with plt.rc_context(rc={'xtick.labelsize': 10, 'ytick.labelsize': 10,\n",
    "                        'font.size': 10,\n",
    "                       'figure.figsize': (10,5)}):\n",
    "    ax = sns.countplot(x=\"phrase_type\", hue=\"data\", data=df_t,\n",
    "                       palette=palette, order=cat_names)\n",
    "    ax.set_xticklabels(ax.get_xticklabels(), rotation='vertical')\n",
    "    plt.xlabel(\"Entity Types\")\n",
    "    plt.ylabel(\"Frequency\")\n",
    "    plt.margins(0.01)\n",
    "    sns.despine(offset=2)\n",
    "    for p in ax.patches:\n",
    "        height = p.get_height()\n",
    "        ax.text(p.get_x(), height+3, '%.0f'%(height))\n",
    "        \n",
    "print \"Training sequences: {}\".format(len(train_sequences))\n",
    "print \"Development sequences: {}\".format(len(dev_sequences))\n",
    "df_t.data.value_counts()\n",
    "plt.savefig(\"Data.tiff\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6, 1, 2, 1, 1, 1, 2, 3, 1, 9]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lens[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('other', 'test'),\n",
       " ('movie', 'test'),\n",
       " ('person', 'test'),\n",
       " ('person', 'test'),\n",
       " ('geo-loc', 'test'),\n",
       " ('person', 'test'),\n",
       " ('company', 'test'),\n",
       " ('company', 'test'),\n",
       " ('geo-loc', 'test'),\n",
       " ('product', 'test')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phrase_types_test[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training lengths: 19.4 +/- 7.6\n",
      "Development lengths: 16.2 +/- 6.8\n",
      "Test lengths: 16.1 +/- 6.6\n"
     ]
    }
   ],
   "source": [
    "train_lens = [len(seq) for seq in train_sequences]\n",
    "dev_lens = [len(seq) for seq in dev_sequences]\n",
    "test_lens = [len(seq) for seq in test_sequences]\n",
    "for lens, label in zip((train_lens, dev_lens, test_lens),\n",
    "                       (\"Training\", \"Development\", \"Test\")):\n",
    "    print \"{0} lengths: {1:.1f} +/- {2:.1f}\".format(\n",
    "        label, np.mean(lens), np.std(lens))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training phrase counts: 0 - 6\n",
      "Development phrase counts: 0 - 9\n",
      "Test phrase counts: 0 - 11\n"
     ]
    }
   ],
   "source": [
    "for sequences, label in zip((train_sequences, dev_sequences, test_sequences),\n",
    "                       (\"Training\", \"Development\", \"Test\")):\n",
    "    phrase_counts = [len([t[1][2:] for t in seq if t[1][0] in [\"B\", \"U\"]])\n",
    "                       for seq in (sequences)]\n",
    "    print \"{} phrase counts: {} - {}\".format(label,\n",
    "        np.min(phrase_counts), np.max(phrase_counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = \"\"\"F\tMF\t+GZ\t+WRG\t+WCBPT\t+WCCC\t+WRFTC\t+GF\t+RSFD\tST\tBL\n",
    "10-types\t5.32\t34.8\t36.66\t41.59\t41.04\t43.3\t40.94\t\t36.22\t35.1\n",
    "company\t0\t30\t34.53\t33.33\t35.2\t33.33\t32\t\t27.72\t26.17\n",
    "facility\t0\t12.35\t9.62\t20.78\t18.56\t17.91\t14.49\t\t30.38\t19.15\n",
    "geo-loc\t5.24\t47.21\t48.06\t53.83\t54.4\t55.93\t56.73\t\t49.71\t48.36\n",
    "movie\t8\t7.41\t6.45\t8.33\t7.69\t9.52\t23.53\t\t8.33\t0\n",
    "musicartist\t0\t6.56\t8.45\t9.09\t9.52\t12.7\t6.45\t\t0\t0\n",
    "other\t5.79\t18.56\t18.69\t22.54\t20.85\t26.63\t22.11\t\t24.16\t27.74\n",
    "person\t11.37\t55.07\t58.51\t63.39\t63.81\t64.82\t64.96\t\t53.36\t50.18\n",
    "product\t2.92\t12.66\t20\t16.67\t18.18\t15.38\t10.75\t\t8.96\t11.9\n",
    "sportsteam\t0\t12.9\t27.94\t30.53\t29.01\t28.13\t27.69\t\t12.8\t13.11\n",
    "tvshow\t0\t0\t0\t16.67\t16.67\t16.67\t18.18\t\t0\t14.29\n",
    "No-types\t13.13\t48.31\t52.51\t56.73\t56.41\t57.38\t53.67\t\t50.53\t51.71\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=11, step=1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t = pd.DataFrame([[t.strip() for t in line.split(\"\\t\")] for line in data.split(\"\\n\")[1:]])\n",
    "#df_t.columns = data.split(\"\\n\")[0].split()\n",
    "df_t.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data.split(\"\\n\")[0].split(\"\\t\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 11)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
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   "outputs": [
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F</th>\n",
       "      <th>MF</th>\n",
       "      <th>+GZ</th>\n",
       "      <th>+WRG</th>\n",
       "      <th>+WCBPT</th>\n",
       "      <th>+WCCC</th>\n",
       "      <th>+WRFTC</th>\n",
       "      <th>+GF</th>\n",
       "      <th>+RSFD</th>\n",
       "      <th>ST</th>\n",
       "      <th>BL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10-types</td>\n",
       "      <td>5.32</td>\n",
       "      <td>34.8</td>\n",
       "      <td>36.66</td>\n",
       "      <td>41.59</td>\n",
       "      <td>41.04</td>\n",
       "      <td>43.3</td>\n",
       "      <td>40.94</td>\n",
       "      <td></td>\n",
       "      <td>36.22</td>\n",
       "      <td>35.1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>company</td>\n",
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       "      <td>35.2</td>\n",
       "      <td>33.33</td>\n",
       "      <td>32</td>\n",
       "      <td></td>\n",
       "      <td>27.72</td>\n",
       "      <td>26.17</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>facility</td>\n",
       "      <td>0</td>\n",
       "      <td>12.35</td>\n",
       "      <td>9.62</td>\n",
       "      <td>20.78</td>\n",
       "      <td>18.56</td>\n",
       "      <td>17.91</td>\n",
       "      <td>14.49</td>\n",
       "      <td></td>\n",
       "      <td>30.38</td>\n",
       "      <td>19.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>geo-loc</td>\n",
       "      <td>5.24</td>\n",
       "      <td>47.21</td>\n",
       "      <td>48.06</td>\n",
       "      <td>53.83</td>\n",
       "      <td>54.4</td>\n",
       "      <td>55.93</td>\n",
       "      <td>56.73</td>\n",
       "      <td></td>\n",
       "      <td>49.71</td>\n",
       "      <td>48.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>movie</td>\n",
       "      <td>8</td>\n",
       "      <td>7.41</td>\n",
       "      <td>6.45</td>\n",
       "      <td>8.33</td>\n",
       "      <td>7.69</td>\n",
       "      <td>9.52</td>\n",
       "      <td>23.53</td>\n",
       "      <td></td>\n",
       "      <td>8.33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>musicartist</td>\n",
       "      <td>0</td>\n",
       "      <td>6.56</td>\n",
       "      <td>8.45</td>\n",
       "      <td>9.09</td>\n",
       "      <td>9.52</td>\n",
       "      <td>12.7</td>\n",
       "      <td>6.45</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>other</td>\n",
       "      <td>5.79</td>\n",
       "      <td>18.56</td>\n",
       "      <td>18.69</td>\n",
       "      <td>22.54</td>\n",
       "      <td>20.85</td>\n",
       "      <td>26.63</td>\n",
       "      <td>22.11</td>\n",
       "      <td></td>\n",
       "      <td>24.16</td>\n",
       "      <td>27.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>person</td>\n",
       "      <td>11.37</td>\n",
       "      <td>55.07</td>\n",
       "      <td>58.51</td>\n",
       "      <td>63.39</td>\n",
       "      <td>63.81</td>\n",
       "      <td>64.82</td>\n",
       "      <td>64.96</td>\n",
       "      <td></td>\n",
       "      <td>53.36</td>\n",
       "      <td>50.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>product</td>\n",
       "      <td>2.92</td>\n",
       "      <td>12.66</td>\n",
       "      <td>20</td>\n",
       "      <td>16.67</td>\n",
       "      <td>18.18</td>\n",
       "      <td>15.38</td>\n",
       "      <td>10.75</td>\n",
       "      <td></td>\n",
       "      <td>8.96</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sportsteam</td>\n",
       "      <td>0</td>\n",
       "      <td>12.9</td>\n",
       "      <td>27.94</td>\n",
       "      <td>30.53</td>\n",
       "      <td>29.01</td>\n",
       "      <td>28.13</td>\n",
       "      <td>27.69</td>\n",
       "      <td></td>\n",
       "      <td>12.8</td>\n",
       "      <td>13.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>tvshow</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.67</td>\n",
       "      <td>16.67</td>\n",
       "      <td>16.67</td>\n",
       "      <td>18.18</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>14.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>No-types</td>\n",
       "      <td>13.13</td>\n",
       "      <td>48.31</td>\n",
       "      <td>52.51</td>\n",
       "      <td>56.73</td>\n",
       "      <td>56.41</td>\n",
       "      <td>57.38</td>\n",
       "      <td>53.67</td>\n",
       "      <td></td>\n",
       "      <td>50.53</td>\n",
       "      <td>51.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              F     MF    +GZ   +WRG +WCBPT  +WCCC +WRFTC    +GF +RSFD     ST  \\\n",
       "0      10-types   5.32   34.8  36.66  41.59  41.04   43.3  40.94        36.22   \n",
       "1       company      0     30  34.53  33.33   35.2  33.33     32        27.72   \n",
       "2      facility      0  12.35   9.62  20.78  18.56  17.91  14.49        30.38   \n",
       "3       geo-loc   5.24  47.21  48.06  53.83   54.4  55.93  56.73        49.71   \n",
       "4         movie      8   7.41   6.45   8.33   7.69   9.52  23.53         8.33   \n",
       "5   musicartist      0   6.56   8.45   9.09   9.52   12.7   6.45            0   \n",
       "6         other   5.79  18.56  18.69  22.54  20.85  26.63  22.11        24.16   \n",
       "7        person  11.37  55.07  58.51  63.39  63.81  64.82  64.96        53.36   \n",
       "8       product   2.92  12.66     20  16.67  18.18  15.38  10.75         8.96   \n",
       "9    sportsteam      0   12.9  27.94  30.53  29.01  28.13  27.69         12.8   \n",
       "10       tvshow      0      0      0  16.67  16.67  16.67  18.18            0   \n",
       "11     No-types  13.13  48.31  52.51  56.73  56.41  57.38  53.67        50.53   \n",
       "\n",
       "       BL  \n",
       "0    35.1  \n",
       "1   26.17  \n",
       "2   19.15  \n",
       "3   48.36  \n",
       "4       0  \n",
       "5       0  \n",
       "6   27.74  \n",
       "7   50.18  \n",
       "8    11.9  \n",
       "9   13.11  \n",
       "10  14.29  \n",
       "11  51.71  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t.columns = data.split(\"\\n\")[0].split(\"\\t\")\n",
    "df_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "      <th>F</th>\n",
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       "      <th>+WCBPT</th>\n",
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       "          F    MF   +GZ   +WRG +WCBPT  +WCCC +WRFTC    +GF +RSFD     ST    BL\n",
       "0  10-types  5.32  34.8  36.66  41.59  41.04   43.3  40.94        36.22  35.1"
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    "df_t[df_t[\"F\"] == \"10-types\"]"
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  {
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   "execution_count": 21,
   "metadata": {
    "collapsed": false
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   "outputs": [
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       "      <th>geo-loc</th>\n",
       "      <td>5.24</td>\n",
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       "      <td>55.93</td>\n",
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       "      <td>49.71</td>\n",
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       "      <th>movie</th>\n",
       "      <td>8</td>\n",
       "      <td>7.41</td>\n",
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       "      <td>8.33</td>\n",
       "      <td>7.69</td>\n",
       "      <td>9.52</td>\n",
       "      <td>23.53</td>\n",
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       "      <td>8.33</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>musicartist</th>\n",
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       "      <td>8.45</td>\n",
       "      <td>9.09</td>\n",
       "      <td>9.52</td>\n",
       "      <td>12.7</td>\n",
       "      <td>6.45</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>5.79</td>\n",
       "      <td>18.56</td>\n",
       "      <td>18.69</td>\n",
       "      <td>22.54</td>\n",
       "      <td>20.85</td>\n",
       "      <td>26.63</td>\n",
       "      <td>22.11</td>\n",
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       "      <td>24.16</td>\n",
       "      <td>27.74</td>\n",
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       "      <th>person</th>\n",
       "      <td>11.37</td>\n",
       "      <td>55.07</td>\n",
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       "      <td>63.39</td>\n",
       "      <td>63.81</td>\n",
       "      <td>64.82</td>\n",
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       "      <td>53.36</td>\n",
       "      <td>50.18</td>\n",
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       "    <tr>\n",
       "      <th>product</th>\n",
       "      <td>2.92</td>\n",
       "      <td>12.66</td>\n",
       "      <td>20</td>\n",
       "      <td>16.67</td>\n",
       "      <td>18.18</td>\n",
       "      <td>15.38</td>\n",
       "      <td>10.75</td>\n",
       "      <td></td>\n",
       "      <td>8.96</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sportsteam</th>\n",
       "      <td>0</td>\n",
       "      <td>12.9</td>\n",
       "      <td>27.94</td>\n",
       "      <td>30.53</td>\n",
       "      <td>29.01</td>\n",
       "      <td>28.13</td>\n",
       "      <td>27.69</td>\n",
       "      <td></td>\n",
       "      <td>12.8</td>\n",
       "      <td>13.11</td>\n",
       "    </tr>\n",
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       "      <th>tvshow</th>\n",
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       "    <tr>\n",
       "      <th>No-types</th>\n",
       "      <td>13.13</td>\n",
       "      <td>48.31</td>\n",
       "      <td>52.51</td>\n",
       "      <td>56.73</td>\n",
       "      <td>56.41</td>\n",
       "      <td>57.38</td>\n",
       "      <td>53.67</td>\n",
       "      <td></td>\n",
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       "      <td>51.71</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                MF    +GZ   +WRG +WCBPT  +WCCC +WRFTC    +GF +RSFD     ST  \\\n",
       "F                                                                           \n",
       "10-types      5.32   34.8  36.66  41.59  41.04   43.3  40.94        36.22   \n",
       "company          0     30  34.53  33.33   35.2  33.33     32        27.72   \n",
       "facility         0  12.35   9.62  20.78  18.56  17.91  14.49        30.38   \n",
       "geo-loc       5.24  47.21  48.06  53.83   54.4  55.93  56.73        49.71   \n",
       "movie            8   7.41   6.45   8.33   7.69   9.52  23.53         8.33   \n",
       "musicartist      0   6.56   8.45   9.09   9.52   12.7   6.45            0   \n",
       "other         5.79  18.56  18.69  22.54  20.85  26.63  22.11        24.16   \n",
       "person       11.37  55.07  58.51  63.39  63.81  64.82  64.96        53.36   \n",
       "product       2.92  12.66     20  16.67  18.18  15.38  10.75         8.96   \n",
       "sportsteam       0   12.9  27.94  30.53  29.01  28.13  27.69         12.8   \n",
       "tvshow           0      0      0  16.67  16.67  16.67  18.18            0   \n",
       "No-types     13.13  48.31  52.51  56.73  56.41  57.38  53.67        50.53   \n",
       "\n",
       "                BL  \n",
       "F                   \n",
       "10-types      35.1  \n",
       "company      26.17  \n",
       "facility     19.15  \n",
       "geo-loc      48.36  \n",
       "movie            0  \n",
       "musicartist      0  \n",
       "other        27.74  \n",
       "person       50.18  \n",
       "product       11.9  \n",
       "sportsteam   13.11  \n",
       "tvshow       14.29  \n",
       "No-types     51.71  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t=df_t.set_index(\"F\")\n",
    "df_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>+WCBPT</th>\n",
       "      <th>+WCCC</th>\n",
       "      <th>+WRFTC</th>\n",
       "      <th>+GF</th>\n",
       "      <th>ST</th>\n",
       "      <th>BL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10-types</th>\n",
       "      <td>5.32</td>\n",
       "      <td>34.8</td>\n",
       "      <td>36.66</td>\n",
       "      <td>41.59</td>\n",
       "      <td>41.04</td>\n",
       "      <td>43.3</td>\n",
       "      <td>40.94</td>\n",
       "      <td>36.22</td>\n",
       "      <td>35.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company</th>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>34.53</td>\n",
       "      <td>33.33</td>\n",
       "      <td>35.2</td>\n",
       "      <td>33.33</td>\n",
       "      <td>32</td>\n",
       "      <td>27.72</td>\n",
       "      <td>26.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>facility</th>\n",
       "      <td>0</td>\n",
       "      <td>12.35</td>\n",
       "      <td>9.62</td>\n",
       "      <td>20.78</td>\n",
       "      <td>18.56</td>\n",
       "      <td>17.91</td>\n",
       "      <td>14.49</td>\n",
       "      <td>30.38</td>\n",
       "      <td>19.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geo-loc</th>\n",
       "      <td>5.24</td>\n",
       "      <td>47.21</td>\n",
       "      <td>48.06</td>\n",
       "      <td>53.83</td>\n",
       "      <td>54.4</td>\n",
       "      <td>55.93</td>\n",
       "      <td>56.73</td>\n",
       "      <td>49.71</td>\n",
       "      <td>48.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie</th>\n",
       "      <td>8</td>\n",
       "      <td>7.41</td>\n",
       "      <td>6.45</td>\n",
       "      <td>8.33</td>\n",
       "      <td>7.69</td>\n",
       "      <td>9.52</td>\n",
       "      <td>23.53</td>\n",
       "      <td>8.33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>musicartist</th>\n",
       "      <td>0</td>\n",
       "      <td>6.56</td>\n",
       "      <td>8.45</td>\n",
       "      <td>9.09</td>\n",
       "      <td>9.52</td>\n",
       "      <td>12.7</td>\n",
       "      <td>6.45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>5.79</td>\n",
       "      <td>18.56</td>\n",
       "      <td>18.69</td>\n",
       "      <td>22.54</td>\n",
       "      <td>20.85</td>\n",
       "      <td>26.63</td>\n",
       "      <td>22.11</td>\n",
       "      <td>24.16</td>\n",
       "      <td>27.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>person</th>\n",
       "      <td>11.37</td>\n",
       "      <td>55.07</td>\n",
       "      <td>58.51</td>\n",
       "      <td>63.39</td>\n",
       "      <td>63.81</td>\n",
       "      <td>64.82</td>\n",
       "      <td>64.96</td>\n",
       "      <td>53.36</td>\n",
       "      <td>50.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product</th>\n",
       "      <td>2.92</td>\n",
       "      <td>12.66</td>\n",
       "      <td>20</td>\n",
       "      <td>16.67</td>\n",
       "      <td>18.18</td>\n",
       "      <td>15.38</td>\n",
       "      <td>10.75</td>\n",
       "      <td>8.96</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sportsteam</th>\n",
       "      <td>0</td>\n",
       "      <td>12.9</td>\n",
       "      <td>27.94</td>\n",
       "      <td>30.53</td>\n",
       "      <td>29.01</td>\n",
       "      <td>28.13</td>\n",
       "      <td>27.69</td>\n",
       "      <td>12.8</td>\n",
       "      <td>13.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tvshow</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.67</td>\n",
       "      <td>16.67</td>\n",
       "      <td>16.67</td>\n",
       "      <td>18.18</td>\n",
       "      <td>0</td>\n",
       "      <td>14.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No-types</th>\n",
       "      <td>13.13</td>\n",
       "      <td>48.31</td>\n",
       "      <td>52.51</td>\n",
       "      <td>56.73</td>\n",
       "      <td>56.41</td>\n",
       "      <td>57.38</td>\n",
       "      <td>53.67</td>\n",
       "      <td>50.53</td>\n",
       "      <td>51.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                MF    +GZ   +WRG +WCBPT  +WCCC +WRFTC    +GF     ST     BL\n",
       "F                                                                         \n",
       "10-types      5.32   34.8  36.66  41.59  41.04   43.3  40.94  36.22   35.1\n",
       "company          0     30  34.53  33.33   35.2  33.33     32  27.72  26.17\n",
       "facility         0  12.35   9.62  20.78  18.56  17.91  14.49  30.38  19.15\n",
       "geo-loc       5.24  47.21  48.06  53.83   54.4  55.93  56.73  49.71  48.36\n",
       "movie            8   7.41   6.45   8.33   7.69   9.52  23.53   8.33      0\n",
       "musicartist      0   6.56   8.45   9.09   9.52   12.7   6.45      0      0\n",
       "other         5.79  18.56  18.69  22.54  20.85  26.63  22.11  24.16  27.74\n",
       "person       11.37  55.07  58.51  63.39  63.81  64.82  64.96  53.36  50.18\n",
       "product       2.92  12.66     20  16.67  18.18  15.38  10.75   8.96   11.9\n",
       "sportsteam       0   12.9  27.94  30.53  29.01  28.13  27.69   12.8  13.11\n",
       "tvshow           0      0      0  16.67  16.67  16.67  18.18      0  14.29\n",
       "No-types     13.13  48.31  52.51  56.73  56.41  57.38  53.67  50.53  51.71"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t = df_t.drop(\"+RSFD\", axis=1)\n",
    "df_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_t = df_t.astype(\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>10-types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MF</th>\n",
       "      <td>5.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>+GZ</th>\n",
       "      <td>34.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>+WRG</th>\n",
       "      <td>36.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>+WCBPT</th>\n",
       "      <td>41.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>+WCCC</th>\n",
       "      <td>41.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>+WRFTC</th>\n",
       "      <td>43.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>+GF</th>\n",
       "      <td>40.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ST</th>\n",
       "      <td>36.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BL</th>\n",
       "      <td>35.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        10-types\n",
       "MF          5.32\n",
       "+GZ        34.80\n",
       "+WRG       36.66\n",
       "+WCBPT     41.59\n",
       "+WCCC      41.04\n",
       "+WRFTC     43.30\n",
       "+GF        40.94\n",
       "ST         36.22\n",
       "BL         35.10"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df_t.ix[\"10-types\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_tt = pd.DataFrame(df_t.ix[\"10-types\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5af7577e50>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Uvn37JkpcPYvJZMLT05OAgAAMw7DaCUbZoVf79kQGBWV3GHmak5sbi9evz+4wRETyFSVE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\n/3569uxJZGQkderUYd68eWk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AtXnptIv8904s5XwHkTX19fRUREKCIiQg8++KDOnj2rJ598Ut98841mzZpVoquqUP6K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\nzv8DgbhXhKjRzasAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5afe735710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(y=\"10-types\", x=df_tt.index, data=df_tt, color=\"red\")\n",
    "sns.despine(offset=2)\n",
    "for p in ax.patches:\n",
    "        height = p.get_height()\n",
    "        ax.text(p.get_x(), height+1, '%.2f'%(height))\n",
    "        \n",
    "ax.patches[-2].set_color(\"0.4\")\n",
    "p.set_color(\"0.2\")\n",
    "ax.set_ylabel(\"Overall FB=1 score on 10-types\")\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}