context_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import collections\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from context_predictor.predictor import *\n",
    "%aimport context_predictor.predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = read_data('../data/abs1.txt')\n",
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11.5 s, sys: 908 ms, total: 12.4 s\n",
      "Wall time: 12.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "vocabulary_size = 5000\n",
    "\n",
    "stop_words = stopwords.words('english')\n",
    "\n",
    "vocab, reversed_vocab = build_vocabulary(data, vocabulary_size, stop_words)\n",
    "\n",
    "len(reversed_vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 45.4 s, sys: 704 ms, total: 46.1 s\n",
      "Wall time: 46.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "qq = clean_data(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test = qq[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = []\n",
    "for sentence in test:\n",
    "    dataset.extend(build_dataset(sentence, vocab))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('background', ['currently']),\n",
       " ('currently', ['background', 'used', 'prevent']),\n",
       " ('used', ['currently', 'prevent', 'stroke']),\n",
       " ('prevent', ['currently', 'used', 'stroke', 'patients']),\n",
       " ('stroke', ['used', 'prevent', 'patients']),\n",
       " ('patients', ['prevent', 'stroke', 'atrial', 'fibrillation']),\n",
       " ('atrial', ['patients', 'fibrillation']),\n",
       " ('fibrillation', ['patients', 'atrial']),\n",
       " ('measuring', ['coagulation', 'function', 'may']),\n",
       " ('coagulation', ['measuring', 'function', 'may', 'help']),\n",
       " ('function', ['measuring', 'coagulation', 'may', 'help', 'clinicians']),\n",
       " ('may', ['measuring', 'coagulation', 'function', 'help', 'clinicians']),\n",
       " ('help', ['coagulation', 'function', 'may', 'clinicians', 'understand']),\n",
       " ('clinicians', ['function', 'may', 'help', 'understand']),\n",
       " ('understand', ['help', 'clinicians', 'effects']),\n",
       " ('effects', ['understand', 'drug']),\n",
       " ('drug', ['effects', 'associated']),\n",
       " ('associated', ['drug', 'risk', 'bleeding']),\n",
       " ('risk', ['associated', 'bleeding']),\n",
       " ('bleeding', ['associated', 'risk']),\n",
       " ('methods', ['results']),\n",
       " ('results', ['methods', 'given']),\n",
       " ('given', ['results', 'patients']),\n",
       " ('patients', ['given', 'atrial']),\n",
       " ('atrial', ['patients', 'fibrillation']),\n",
       " ('fibrillation', ['atrial']),\n",
       " ('mean', ['age']),\n",
       " ('age', ['mean', 'years']),\n",
       " ('years', ['age', 'men']),\n",
       " ('men', ['years', 'mean']),\n",
       " ('mean', ['men', 'score']),\n",
       " ('score', ['mean']),\n",
       " ('times', ['plasma']),\n",
       " ('plasma', ['times', 'soluble', 'fibrin']),\n",
       " ('soluble', ['plasma', 'fibrin', 'levels']),\n",
       " ('fibrin', ['plasma', 'soluble', 'levels']),\n",
       " ('levels', ['soluble', 'fibrin', 'examined']),\n",
       " ('examined', ['levels']),\n",
       " ('patients', ['baseline']),\n",
       " ('baseline', ['patients', 'least']),\n",
       " ('least', ['baseline', '2', 'weeks', 'thereafter']),\n",
       " ('2', ['least', 'weeks', 'thereafter']),\n",
       " ('weeks', ['least', '2', 'thereafter']),\n",
       " ('thereafter', ['least', '2', 'weeks']),\n",
       " ('48', ['patients']),\n",
       " ('patients', ['48', 'able']),\n",
       " ('able', ['patients', 'blood']),\n",
       " ('blood', ['able', 'exact', 'times']),\n",
       " ('exact', ['blood', 'times', 'namely']),\n",
       " ('times', ['blood', 'exact', 'namely']),\n",
       " ('namely', ['exact', 'times']),\n",
       " ('administration', ['corresponding']),\n",
       " ('corresponding', ['administration']),\n",
       " ('peak', ['concentrations']),\n",
       " ('concentrations', ['peak']),\n",
       " ('mean', ['peak']),\n",
       " ('peak', ['mean', '48']),\n",
       " ('48', ['peak', 'patients']),\n",
       " ('patients', ['48']),\n",
       " ('median', ['peak', 'level']),\n",
       " ('peak', ['median', 'level']),\n",
       " ('level', ['median', 'peak']),\n",
       " ('multiple', ['regression', 'analysis', 'showed']),\n",
       " ('regression', ['multiple', 'analysis', 'showed']),\n",
       " ('analysis', ['multiple', 'regression', 'showed', 'female']),\n",
       " ('showed', ['multiple', 'regression', 'analysis', 'female', 'sex']),\n",
       " ('female', ['analysis', 'showed', 'sex', 'high', 'brain']),\n",
       " ('sex', ['showed', 'female', 'high', 'brain']),\n",
       " ('high', ['female', 'sex', 'brain', 'peptide']),\n",
       " ('brain', ['female', 'sex', 'high', 'peptide']),\n",
       " ('peptide', ['high', 'brain', 'high', 'dose']),\n",
       " ('high', ['peptide', 'dose', 'independent']),\n",
       " ('dose', ['peptide', 'high', 'independent', 'factors']),\n",
       " ('independent', ['high', 'dose', 'factors']),\n",
       " ('factors', ['dose', 'independent', 'peak']),\n",
       " ('peak', ['factors']),\n",
       " ('patients', ['peak']),\n",
       " ('peak', ['patients', 'experienced']),\n",
       " ('experienced', ['peak', 'significantly', 'bleeding']),\n",
       " ('significantly', ['experienced', 'bleeding', 'events']),\n",
       " ('bleeding', ['experienced', 'significantly', 'events']),\n",
       " ('events', ['significantly', 'bleeding']),\n",
       " ('among', ['29', '46']),\n",
       " ('29', ['among', '46', 'patients']),\n",
       " ('46', ['among', '29', 'patients', 'newly', 'treated']),\n",
       " ('patients', ['29', '46', 'newly', 'treated']),\n",
       " ('newly', ['46', 'patients', 'treated']),\n",
       " ('treated', ['46', 'patients', 'newly', 'without']),\n",
       " ('without', ['treated', 'previous']),\n",
       " ('previous', ['without', 'examined']),\n",
       " ('examined', ['previous', 'coagulation', 'function']),\n",
       " ('coagulation', ['examined', 'function']),\n",
       " ('function', ['examined', 'coagulation', 'exact']),\n",
       " ('exact', ['function', 'peak']),\n",
       " ('peak', ['exact', 'times']),\n",
       " ('times', ['peak']),\n",
       " ('29', ['patients', 'peak']),\n",
       " ('patients', ['29', 'peak']),\n",
       " ('peak', ['29', 'patients', 'significantly']),\n",
       " ('significantly', ['peak', 'prolonged']),\n",
       " ('prolonged', ['significantly', 'baseline']),\n",
       " ('baseline', ['prolonged']),\n",
       " ('comparable', ['baseline']),\n",
       " ('baseline', ['comparable']),\n",
       " ('peak', ['levels']),\n",
       " ('levels', ['peak', 'newly']),\n",
       " ('newly', ['levels', 'treated', 'patients']),\n",
       " ('treated', ['newly', 'patients', 'significantly']),\n",
       " ('patients', ['newly', 'treated', 'significantly', 'reduced']),\n",
       " ('significantly', ['treated', 'patients', 'reduced']),\n",
       " ('reduced', ['patients', 'significantly', 'baseline']),\n",
       " ('baseline', ['reduced']),\n",
       " ('conclusions', ['japanese', 'patients']),\n",
       " ('japanese', ['conclusions', 'patients']),\n",
       " ('patients', ['conclusions', 'japanese', 'atrial']),\n",
       " ('atrial', ['patients', 'fibrillation', 'receiving']),\n",
       " ('fibrillation', ['atrial', 'receiving']),\n",
       " ('receiving', ['atrial', 'fibrillation', 'prolonged']),\n",
       " ('prolonged', ['receiving', 'peak']),\n",
       " ('peak', ['prolonged', 'could']),\n",
       " ('could', ['peak', 'indicate', 'increased', 'risk']),\n",
       " ('indicate', ['could', 'increased', 'risk']),\n",
       " ('increased', ['could', 'indicate', 'risk', 'bleeding']),\n",
       " ('risk', ['could', 'indicate', 'increased', 'bleeding']),\n",
       " ('bleeding', ['increased', 'risk']),\n",
       " ('peak', ['levels']),\n",
       " ('levels', ['peak', 'reduced', 'relative']),\n",
       " ('reduced', ['levels', 'relative', 'baseline']),\n",
       " ('relative', ['levels', 'reduced', 'baseline']),\n",
       " ('baseline', ['reduced', 'relative']),\n",
       " ('valuable', ['measures', 'coagulation']),\n",
       " ('measures', ['valuable', 'coagulation', 'status']),\n",
       " ('coagulation', ['valuable', 'measures', 'status', 'patients']),\n",
       " ('status', ['measures', 'coagulation', 'patients', 'receiving']),\n",
       " ('patients', ['coagulation', 'status', 'receiving', 'regardless']),\n",
       " ('receiving', ['status', 'patients', 'regardless']),\n",
       " ('regardless', ['patients', 'receiving', 'prior']),\n",
       " ('prior', ['regardless', 'history']),\n",
       " ('history', ['prior'])]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dataset_ohe = []\n",
    "for elem in dataset:\n",
    "    dataset_ohe.append((get_ohe([elem[0]], vocab), normalize_ohe(get_ohe(elem[1], vocab))))\n",
    "dataset_ohe = np.array(dataset_ohe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 139)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_ohe[:,0].T.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(139, 5000)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_ohe[:,1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(get_ohe(dataset[0][1], vocab)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[['gastrin',\n",
       "   'is',\n",
       "   'a',\n",
       "   'hormone',\n",
       "   'produced',\n",
       "   'by',\n",
       "   'g-cells',\n",
       "   'in',\n",
       "   'the',\n",
       "   'normal',\n",
       "   'gastric',\n",
       "   'antrum'],\n",
       "  ['however',\n",
       "   'colorectal',\n",
       "   'carcinoma',\n",
       "   'cells',\n",
       "   'may',\n",
       "   'aberrantly',\n",
       "   'produce',\n",
       "   'gastrin',\n",
       "   'and',\n",
       "   'exhibit',\n",
       "   'increased',\n",
       "   'expression',\n",
       "   'of',\n",
       "   'cholecystokinin',\n",
       "   'b',\n",
       "   'cck-b)/gastrin',\n",
       "   'receptors'],\n",
       "  ['gastrin',\n",
       "   'is',\n",
       "   'trophic',\n",
       "   'for',\n",
       "   'the',\n",
       "   'normal',\n",
       "   'gastric',\n",
       "   'oxyntic',\n",
       "   'mucosa',\n",
       "   'and',\n",
       "   'exerts',\n",
       "   'a',\n",
       "   'growth-promoting',\n",
       "   'action',\n",
       "   'on',\n",
       "   'gastrointestinal',\n",
       "   'malignancy'],\n",
       "  ['thus',\n",
       "   'gastrin',\n",
       "   'may',\n",
       "   'act',\n",
       "   'as',\n",
       "   'an',\n",
       "   'autocrine/paracrine',\n",
       "   'or',\n",
       "   'endocrine',\n",
       "   'factor',\n",
       "   'in',\n",
       "   'the',\n",
       "   'initiation',\n",
       "   'and',\n",
       "   'progression',\n",
       "   'of',\n",
       "   'colorectal',\n",
       "   'carcinoma'],\n",
       "  ['the',\n",
       "   'molecular',\n",
       "   'mechanisms',\n",
       "   'involved',\n",
       "   'have',\n",
       "   'not',\n",
       "   'been',\n",
       "   'elucidated'],\n",
       "  ['hypergastrinemia',\n",
       "   'induced',\n",
       "   'by',\n",
       "   'helicobacter',\n",
       "   'pylori',\n",
       "   'infection',\n",
       "   'is',\n",
       "   'associated',\n",
       "   'with',\n",
       "   'increased',\n",
       "   'cyclooxygenase-2',\n",
       "   'cox-2',\n",
       "   'expression',\n",
       "   'in',\n",
       "   'gastric',\n",
       "   'and',\n",
       "   'colorectal',\n",
       "   'tissues',\n",
       "   'suggesting',\n",
       "   'the',\n",
       "   'possibility',\n",
       "   'that',\n",
       "   'gastrin',\n",
       "   'up-regulates',\n",
       "   'cox-2',\n",
       "   'expression',\n",
       "   'in',\n",
       "   'these',\n",
       "   'tissues',\n",
       "   'this',\n",
       "   'has',\n",
       "   'not',\n",
       "   'been',\n",
       "   'confirmed'],\n",
       "  ['we',\n",
       "   'report',\n",
       "   'here',\n",
       "   'that',\n",
       "   'gastrin',\n",
       "   'significantly',\n",
       "   'increases',\n",
       "   'the',\n",
       "   'expression',\n",
       "   'of',\n",
       "   'cox-2',\n",
       "   'mrna',\n",
       "   'and',\n",
       "   'protein',\n",
       "   'the',\n",
       "   'activity',\n",
       "   'of',\n",
       "   'the',\n",
       "   'cox-2',\n",
       "   'promoter',\n",
       "   'and',\n",
       "   'the',\n",
       "   'release',\n",
       "   'of',\n",
       "   'prostaglandin',\n",
       "   'e(2',\n",
       "   'from',\n",
       "   'a',\n",
       "   'rat',\n",
       "   'intestinal',\n",
       "   'epithelial',\n",
       "   'cell',\n",
       "   'line',\n",
       "   'transfected',\n",
       "   'with',\n",
       "   'the',\n",
       "   'cck-b',\n",
       "   'receptor'],\n",
       "  ['these',\n",
       "   'actions',\n",
       "   'were',\n",
       "   'dependent',\n",
       "   'upon',\n",
       "   'the',\n",
       "   'activation',\n",
       "   'of',\n",
       "   'multiple',\n",
       "   'mapk',\n",
       "   'signal',\n",
       "   'pathways',\n",
       "   'including',\n",
       "   'erk5',\n",
       "   'kinase',\n",
       "   'transactivation',\n",
       "   'of',\n",
       "   'the',\n",
       "   'epidermal',\n",
       "   'growth',\n",
       "   'factor',\n",
       "   'receptor',\n",
       "   'and',\n",
       "   'the',\n",
       "   'increased',\n",
       "   'expression',\n",
       "   'and',\n",
       "   'activities',\n",
       "   'of',\n",
       "   'transcription',\n",
       "   'factors',\n",
       "   'elk-1',\n",
       "   'activating',\n",
       "   'transcription',\n",
       "   'factor-2',\n",
       "   'c-fos',\n",
       "   'c-jun',\n",
       "   'activator',\n",
       "   'protein-1',\n",
       "   'and',\n",
       "   'myocyte',\n",
       "   'enhancer',\n",
       "   'factor-2'],\n",
       "  ['thus',\n",
       "   'our',\n",
       "   'findings',\n",
       "   'identify',\n",
       "   'the',\n",
       "   'signaling',\n",
       "   'pathways',\n",
       "   'coupling',\n",
       "   'the',\n",
       "   'cck-b',\n",
       "   'receptor',\n",
       "   'with',\n",
       "   'up-regulation',\n",
       "   'of',\n",
       "   'cox-2',\n",
       "   'expression'],\n",
       "  ['this',\n",
       "   'effect',\n",
       "   'may',\n",
       "   'contribute',\n",
       "   'to',\n",
       "   'this',\n",
       "   'hormone-dependent',\n",
       "   'gastrointestinal',\n",
       "   'carcinogenesis',\n",
       "   'especially',\n",
       "   'in',\n",
       "   'the',\n",
       "   'colon']],\n",
       " [['objective',\n",
       "   'to',\n",
       "   'study',\n",
       "   'the',\n",
       "   'anti-platelet',\n",
       "   'effect',\n",
       "   'and',\n",
       "   'influence',\n",
       "   'of',\n",
       "   'radix',\n",
       "   'paeoniae',\n",
       "   'rubra',\n",
       "   'and',\n",
       "   'radix',\n",
       "   'paeoniae',\n",
       "   'alba',\n",
       "   'on',\n",
       "   \"rat's\",\n",
       "   'endogenous',\n",
       "   'metabolites',\n",
       "   'by',\n",
       "   'animal',\n",
       "   'experiment',\n",
       "   'and',\n",
       "   'uplc-ms',\n",
       "   'based',\n",
       "   'metabolomic',\n",
       "   'method'],\n",
       "  ['method',\n",
       "   'after',\n",
       "   'administration',\n",
       "   'of',\n",
       "   '80%',\n",
       "   'ethanol',\n",
       "   'extracts',\n",
       "   'of',\n",
       "   'radix',\n",
       "   'paeoniae',\n",
       "   'rubra',\n",
       "   'and',\n",
       "   'radix',\n",
       "   'paeoniae',\n",
       "   'alba',\n",
       "   'for',\n",
       "   '6',\n",
       "   'd',\n",
       "   'the',\n",
       "   'serum',\n",
       "   'samples',\n",
       "   'were',\n",
       "   'analyzed',\n",
       "   'by',\n",
       "   'ultra-performance',\n",
       "   'liquid',\n",
       "   'chromatography-',\n",
       "   'mass',\n",
       "   'spectrometry',\n",
       "   'uplc-ms',\n",
       "   'and',\n",
       "   'partial',\n",
       "   'least',\n",
       "   'squares-discriminant',\n",
       "   'analysis',\n",
       "   'pls-da',\n",
       "   'to',\n",
       "   'find',\n",
       "   'out',\n",
       "   'the',\n",
       "   'potential',\n",
       "   'biomarker'],\n",
       "  ['result',\n",
       "   'both',\n",
       "   'of',\n",
       "   'the',\n",
       "   'extracts',\n",
       "   'of',\n",
       "   'radix',\n",
       "   'paeoniae',\n",
       "   'rubra',\n",
       "   'and',\n",
       "   'radix',\n",
       "   'paeoniae',\n",
       "   'alba',\n",
       "   'have',\n",
       "   'good',\n",
       "   'effects',\n",
       "   'of',\n",
       "   'inhibition',\n",
       "   'on',\n",
       "   'platelet',\n",
       "   'coacervation',\n",
       "   'and',\n",
       "   'the',\n",
       "   'effect',\n",
       "   'of',\n",
       "   'radix',\n",
       "   'paeoniae',\n",
       "   'alba',\n",
       "   'is',\n",
       "   'better',\n",
       "   'than',\n",
       "   'that',\n",
       "   'of',\n",
       "   'radix',\n",
       "   'paeoniae',\n",
       "   'rubra'],\n",
       "  ['malic',\n",
       "   'acid',\n",
       "   'alpha-acetone',\n",
       "   'dicarboxylic',\n",
       "   'acid',\n",
       "   'leukotrienea4',\n",
       "   'lta4',\n",
       "   'prostaglandine2',\n",
       "   'pge2',\n",
       "   'and',\n",
       "   'prostaglandin',\n",
       "   'f2alpha',\n",
       "   'pgf2alpha',\n",
       "   'are',\n",
       "   'proved',\n",
       "   'to',\n",
       "   'be',\n",
       "   'significant',\n",
       "   'expressed',\n",
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       "   'v3',\n",
       "   'and',\n",
       "   'v5',\n",
       "   'regions',\n",
       "   'of',\n",
       "   'the',\n",
       "   'two',\n",
       "   'parental',\n",
       "   'clades'],\n",
       "  ['sequencing',\n",
       "   'of',\n",
       "   'nt',\n",
       "   'samples',\n",
       "   'showed',\n",
       "   'numerous',\n",
       "   'differences',\n",
       "   'between',\n",
       "   'sample',\n",
       "   'and',\n",
       "   'probe',\n",
       "   'sequences',\n",
       "   'resulting',\n",
       "   'in',\n",
       "   'a',\n",
       "   'lack',\n",
       "   'of',\n",
       "   'hybridization',\n",
       "   'and',\n",
       "   'revealed',\n",
       "   'the',\n",
       "   'limitations',\n",
       "   'of',\n",
       "   'the',\n",
       "   'selected',\n",
       "   'probes',\n",
       "   'in',\n",
       "   'terms',\n",
       "   'of',\n",
       "   'specificity',\n",
       "   'and',\n",
       "   'sensitivity'],\n",
       "  ['we',\n",
       "   'demonstrated',\n",
       "   'the',\n",
       "   'feasibility',\n",
       "   'of',\n",
       "   'deia',\n",
       "   'genotyping',\n",
       "   'six',\n",
       "   'subtypes',\n",
       "   'plus',\n",
       "   'the',\n",
       "   'two',\n",
       "   'most',\n",
       "   'prevalent',\n",
       "   'circulating',\n",
       "   'recombinant',\n",
       "   'forms',\n",
       "   'were',\n",
       "   'discriminated',\n",
       "   'by',\n",
       "   'using',\n",
       "   'the',\n",
       "   \"5'\",\n",
       "   'end',\n",
       "   'of',\n",
       "   'the',\n",
       "   'env',\n",
       "   'gene'],\n",
       "  ['this',\n",
       "   'method',\n",
       "   'can',\n",
       "   'be',\n",
       "   'adapted',\n",
       "   'to',\n",
       "   'the',\n",
       "   'local',\n",
       "   'situation',\n",
       "   'by',\n",
       "   'including',\n",
       "   'only',\n",
       "   'probes',\n",
       "   'that',\n",
       "   'correspond',\n",
       "   'to',\n",
       "   'the',\n",
       "   'prevalent',\n",
       "   'strains']],\n",
       " [['a',\n",
       "   'review',\n",
       "   'of',\n",
       "   'the',\n",
       "   'standards',\n",
       "   'needs',\n",
       "   'of',\n",
       "   'the',\n",
       "   'mitochondrial',\n",
       "   'proteomics',\n",
       "   'communities',\n",
       "   'is',\n",
       "   'presented',\n",
       "   'based',\n",
       "   'on',\n",
       "   'the',\n",
       "   'presentations',\n",
       "   'and',\n",
       "   'discussions',\n",
       "   'at',\n",
       "   'national',\n",
       "   'institute',\n",
       "   'of',\n",
       "   'standards',\n",
       "   'and',\n",
       "   'technology',\n",
       "   'nist',\n",
       "   'workshop',\n",
       "   'systems',\n",
       "   'biology',\n",
       "   'approaches',\n",
       "   'to',\n",
       "   'health',\n",
       "   'care',\n",
       "   'mitochondrial',\n",
       "   'proteomics',\n",
       "   'held',\n",
       "   'on',\n",
       "   'september',\n",
       "   '17-18',\n",
       "   '2002'],\n",
       "  ['the',\n",
       "   'mitochondrial',\n",
       "   'proteomics',\n",
       "   'areas',\n",
       "   'addressed',\n",
       "   'for',\n",
       "   'standards',\n",
       "   'needs',\n",
       "   'are',\n",
       "   'model',\n",
       "   'systems',\n",
       "   'methods',\n",
       "   'and',\n",
       "   'data'],\n",
       "  ['this',\n",
       "   'review',\n",
       "   'outlines',\n",
       "   'the',\n",
       "   'challenges',\n",
       "   'in',\n",
       "   'the',\n",
       "   'field',\n",
       "   'proposes',\n",
       "   'standards',\n",
       "   'efforts',\n",
       "   'that',\n",
       "   'the',\n",
       "   'community',\n",
       "   'would',\n",
       "   'like',\n",
       "   'to',\n",
       "   'see',\n",
       "   'pursued',\n",
       "   'to',\n",
       "   'meet',\n",
       "   'those',\n",
       "   'challenges',\n",
       "   'and',\n",
       "   'is',\n",
       "   'followed',\n",
       "   'by',\n",
       "   'a',\n",
       "   'summary',\n",
       "   'and',\n",
       "   \"nist's\",\n",
       "   'planned',\n",
       "   'efforts',\n",
       "   'to',\n",
       "   'address',\n",
       "   'these',\n",
       "   'standards',\n",
       "   'requirements']]]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qq[40:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building the NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%autoreload 2\n",
    "from context_predictor.predictor import *\n",
    "%aimport context_predictor.predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pr = Predictor(vocabulary_size, 1)\n",
    "pr.init_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter -> 0, c is 8.517193794250488\n",
      "iter -> 1, c is 8.517192840576172\n",
      "iter -> 2, c is 8.517192840576172\n",
      "iter -> 3, c is 8.517193794250488\n",
      "iter -> 4, c is 8.517193794250488\n",
      "iter -> 5, c is 8.517194747924805\n",
      "iter -> 6, c is 8.517192840576172\n",
      "iter -> 7, c is 8.517193794250488\n",
      "iter -> 8, c is 8.517193794250488\n",
      "iter -> 9, c is 8.517192840576172\n",
      "...\n"
     ]
    }
   ],
   "source": [
    "for n, text in enumerate(qq[:10000]):\n",
    "    dataset = []\n",
    "    for sentence in text:\n",
    "        dataset.extend(build_dataset(sentence, vocab))\n",
    "    \n",
    "    dataset_ohe = []\n",
    "    for elem in dataset:\n",
    "        dataset_ohe.append((get_ohe([elem[0]], vocab),\n",
    "                            normalize_ohe(get_ohe(elem[1], vocab))))\n",
    "    dataset_ohe = np.array(dataset_ohe)\n",
    "    \n",
    "    c = pr.train(dataset_ohe[:, 0], dataset_ohe[:, 1])\n",
    "    \n",
    "    print(\"iter -> {}, c is {}\".format(n, c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def prediction(q, topn_input=10):\n",
    "    preds = pr.predict(get_ohe([q], vocab).reshape(1, vocabulary_size))\n",
    "    preds = preds[0].reshape(vocabulary_size)\n",
    "\n",
    "    print(q, '->', reversed_vocab[np.argmax(preds)])\n",
    "\n",
    "    plt.figure(figsize=(5,3))\n",
    "    plt.plot(preds)\n",
    "    plt.show()\n",
    "\n",
    "    topn = topn_input\n",
    "\n",
    "    print('TOP-{}:'.format(topn))\n",
    "    print('-------')\n",
    "\n",
    "    for id in reversed(np.argsort(preds)[-topn:]):\n",
    "        print(reversed_vocab[id], ' - ', id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amino -> acid\n"
     ]
    },
    {
     "data": {
      "image/png": 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cng3NpJHvnNNcy6DWeD76CA46qH47suDzz/P3CsXEuYVZuDDd8JJmzjSX7/TH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\nfbK2wDAMozmYOBuGYRhGzjB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cuEehwKZAj8Lny6raoD2X2i/Dhw/P2oSW5L77hte1J3h7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\nE2fDMAzDyBn/D2CyQTnq85YbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3341007198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TOP-10:\n",
      "-------\n",
      "acid  -  66\n",
      "acids  -  435\n",
      "analysis  -  17\n",
      "showed  -  29\n",
      "sequence  -  128\n",
      "sequences  -  282\n",
      "residues  -  417\n",
      "protein  -  7\n",
      "deduced  -  4079\n",
      "substitutions  -  2389\n"
     ]
    }
   ],
   "source": [
    "prediction('amino')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acid -> method\n"
     ]
    },
    {
     "data": {
      "image/png": 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g6NgGIjJLRN7PVYPvGHRhM+Gf67mYpGa1CktY3VFfpA8+gHXXbX18ypRoepKk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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32f8ece438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TOP-10:\n",
      "-------\n",
      "method  -  99\n",
      "nephropathy  -  3942\n",
      "levels  -  22\n",
      "otherwise  -  3864\n",
      "antioxidant  -  1071\n",
      "treated  -  199\n",
      "amino  -  286\n",
      "mucosa  -  1904\n",
      "acid  -  66\n",
      "used  -  18\n"
     ]
    }
   ],
   "source": [
    "prediction('acid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "membrane -> surfaces\n"
     ]
    },
    {
     "data": {
      "image/png": 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d+w1NmuiWz3FI7jzGmJlY5+sOcyNsX/IEVxjtRKS7K4zeWKc+2XXfgcATwAXG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\nY6hzVhRFUZSMoc5ZURRFUTLG/wckjF1AaE4y7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32f8ee8828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TOP-10:\n",
      "-------\n",
      "surfaces  -  1668\n",
      "high-resolution  -  3903\n",
      "associated  -  24\n",
      "cytoplasm  -  1760\n",
      "cell  -  5\n",
      "plasma  -  211\n",
      "fusion  -  859\n",
      "bound  -  1034\n",
      "encoding  -  795\n",
      "appeared  -  1222\n"
     ]
    }
   ],
   "source": [
    "prediction('membrane')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wistar -> rats\n"
     ]
    },
    {
     "data": {
      "image/png": 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Jk0PYNttknxNXll97rXFZmpl9//uNy+Tii8O7Y+7cUKZJcvVety6EP/hgtj4x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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32f53e10f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TOP-10:\n",
      "-------\n",
      "rats  -  149\n",
      "male  -  450\n",
      "divided  -  1276\n",
      "female  -  645\n",
      "neonatal  -  1645\n",
      "maintained  -  1467\n",
      "3  -  76\n",
      "slices  -  4092\n",
      "skeletal  -  1363\n",
      "mobile  -  4269\n"
     ]
    }
   ],
   "source": [
    "prediction('wistar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rats -> factor\n"
     ]
    },
    {
     "data": {
      "image/png": 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JE+cGpt7vnB94oH5pRfHQQ3DNNcVtatUcOmuW+166tHZzawf5y19qG39cdtgB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\nnA3DMAwjY5g4G4ZhGEbGMHE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BlcDfQ9LYD1gT+KwGOhfxvag4G+EMGTIkbRcakmC+lWpF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\nPAeFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32f53b09b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TOP-10:\n",
      "-------\n",
      "factor  -  92\n",
      "studied  -  185\n",
      "exposed  -  692\n",
      "increased  -  21\n",
      "control  -  47\n",
      "wistar  -  3269\n",
      "diabetic  -  1004\n",
      "male  -  450\n",
      "female  -  645\n",
      "comparison  -  540\n"
     ]
    }
   ],
   "source": [
    "prediction('rats')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alkaline -> using\n"
     ]
    },
    {
     "data": {
      "image/png": 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YJek8oB1wG1BvZg1RvYcCj0S+HpCUaq2vNrOlZrYaeCW+IZI+DO5tbhn7oVlp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\nOQshhBCZoeQshBBCZIaSsxBCCJEZ/wM6J7W0v5566wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32f537b240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TOP-10:\n",
      "-------\n",
      "using  -  8\n",
      "phosphatase  -  1686\n",
      "ph  -  419\n",
      "activity  -  12\n",
      "assay  -  225\n",
      "protease  -  1446\n",
      "test  -  173\n",
      "gene  -  10\n",
      "-  -  975\n",
      "studied  -  185\n"
     ]
    }
   ],
   "source": [
    "prediction('alkaline')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dna -> lesions\n"
     ]
    },
    {
     "data": {
      "image/png": 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rvLQbhaw36Ljggnhx5Mm7XD1J693O+5K6vGDG2YhEki9ClLi8heHMme77yy+T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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32ac7873c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TOP-10:\n",
      "-------\n",
      "lesions  -  495\n",
      "detected  -  127\n",
      "methylated  -  4216\n",
      "coated  -  4488\n",
      "system  -  67\n",
      "sequence  -  128\n",
      "interfere  -  4231\n",
      "replication  -  603\n",
      "well  -  58\n",
      "segregation  -  4386\n"
     ]
    }
   ],
   "source": [
    "prediction('dna')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction('lesions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0077514499425888062"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JSD('dna', 'system')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0067892745137214661"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JSD('dna', 'lesions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mast -> cells\n"
     ]
    },
    {
     "data": {
      "image/png": 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nn5+TO/nkEPbcc+H4vPPC8eOPh+PFi/PtXb8+yCxaVDqfr7sud1zonOPn5ZRT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\nZ0mSCmNxliSpMBZnSZIKY3GWJKkwvwE3Z5GatjkACQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32f531add8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TOP-10:\n",
      "-------\n",
      "cells  -  1\n",
      "mucosal  -  1932\n",
      "cell  -  5\n",
      "identity  -  1870\n",
      "pneumoniae  -  1744\n",
      "within  -  75\n",
      "growth  -  57\n",
      "beta  -  565\n",
      "negative  -  329\n",
      "accumulation  -  584\n"
     ]
    }
   ],
   "source": [
    "prediction('mast')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cells -> number\n"
     ]
    },
    {
     "data": {
      "image/png": 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CcjJG2ybrP3Wp9Z/VxpMmcePOOk/rzQ9+AC+9VFkO8tEyK/59qtXpvvtcHhi1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\nw4yzYRiGYeQMM86GYRiGkTP+P31tEAkasFXcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32f529da90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TOP-10:\n",
      "-------\n",
      "number  -  96\n",
      "increases  -  536\n",
      "stimulated  -  1135\n",
      "germ  -  2685\n",
      "ability  -  295\n",
      "poorly  -  1230\n",
      "animals  -  308\n",
      "active  -  247\n",
      "largely  -  1185\n",
      "cells  -  1\n"
     ]
    }
   ],
   "source": [
    "prediction('cells')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Want to calculate divergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def D(q1, q2):\n",
    "    p = pr.predict(get_ohe([q1], vocab).reshape(1, vocabulary_size))\n",
    "    p = p[0].reshape(vocabulary_size)\n",
    "    \n",
    "    q = pr.predict(get_ohe([q2], vocab).reshape(1, vocabulary_size))\n",
    "    q = q[0].reshape(vocabulary_size)\n",
    "    \n",
    "    return np.sum(p * np.log(p / q))\n",
    "\n",
    "def Divergence(p, q):\n",
    "    return np.sum(p * np.log(p / q))\n",
    "\n",
    "def H(q1, q2):\n",
    "    p = pr.predict(get_ohe([q1], vocab).reshape(1, vocabulary_size))\n",
    "    p = p[0].reshape(vocabulary_size)\n",
    "    \n",
    "    q = pr.predict(get_ohe([q2], vocab).reshape(1, vocabulary_size))\n",
    "    q = q[0].reshape(vocabulary_size)\n",
    "    return -1 * np.sum(p * np.log(q))\n",
    "\n",
    "def JSD(q1, q2):\n",
    "    p = pr.predict(get_ohe([q1], vocab).reshape(1, vocabulary_size))\n",
    "    p = p[0].reshape(vocabulary_size)\n",
    "    \n",
    "    q = pr.predict(get_ohe([q2], vocab).reshape(1, vocabulary_size))\n",
    "    q = q[0].reshape(vocabulary_size)\n",
    "    \n",
    "    m = (p + q) / 2\n",
    "    \n",
    "    return (Divergence(p, m) + Divergence(q, m)) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.014500629"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D('wistar', 'rats')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.021840919"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D('wistar', 'acid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.021629822"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D('amino', 'acid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.017146185"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D('amino', 'rats')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.5257854461669922"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H('wistar', 'rats')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.5331249237060547"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H('wistar', 'acid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0036293854936957359"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JSD('wistar', 'rats')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0054964134469628334"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JSD('wistar', 'acid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0054355449974536896"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JSD('amino', 'acid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0042744032107293606"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JSD('amino', 'rats')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}