IR Project-d4-affinity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.corpus import wordnet\n",
    "import os\n",
    "import re\n",
    "import math\n",
    "from scipy import spatial\n",
    "import numpy as np\n",
    "import pprint\n",
    "import sklearn.cluster\n",
    "from sklearn.cluster import spectral_clustering\n",
    "import pickle\n",
    "\n",
    "\n",
    "def SpecialCharacters(word):\n",
    "\tif len(word)<3:\n",
    "\t\treturn 1\n",
    "\treturn 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'about', 'above', 'across', 'after', 'again', 'against', 'all', 'almost', 'alone', 'along', 'already', 'also', 'although', 'always', 'among', 'an', 'and', 'another', 'any', 'anybody', 'anyone', 'anything', 'anywhere', 'are', 'area', 'areas', 'around', 'as', 'ask', 'asked', 'asking', 'asks', 'at', 'away', 'b', 'back', 'backed', 'backing', 'backs', 'be', 'became', 'because', 'become', 'becomes', 'been', 'before', 'began', 'behind', 'being', 'beings', 'best', 'better', 'between', 'big', 'both', 'but', 'by', 'c', 'came', 'can', 'cannot', 'case', 'cases', 'certain', 'certainly', 'clear', 'clearly', 'come', 'could', 'd', 'did', 'differ', 'different', 'differently', 'do', 'does', 'done', 'down', 'down', 'downed', 'downing', 'downs', 'during', 'e', 'each', 'early', 'either', 'end', 'ended', 'ending', 'ends', 'enough', 'even', 'evenly', 'ever', 'every', 'everybody', 'everyone', 'everything', 'everywhere', 'f', 'face', 'faces', 'fact', 'facts', 'far', 'felt', 'few', 'find', 'finds', 'first', 'for', 'four', 'from', 'full', 'fully', 'further', 'furthered', 'furthering', 'furthers', 'g', 'gave', 'general', 'generally', 'get', 'gets', 'give', 'given', 'gives', 'go', 'going', 'good', 'goods', 'got', 'great', 'greater', 'greatest', 'group', 'grouped', 'grouping', 'groups', 'h', 'had', 'has', 'have', 'having', 'he', 'her', 'here', 'herself', 'high', 'high', 'high', 'higher', 'highest', 'him', 'himself', 'his', 'how', 'however', 'i', 'if', 'important', 'in', 'interest', 'interested', 'interesting', 'interests', 'into', 'is', 'it', 'its', 'itself', 'j', 'just', 'k', 'keep', 'keeps', 'kind', 'knew', 'know', 'known', 'knows', 'l', 'large', 'largely', 'last', 'later', 'latest', 'least', 'less', 'let', 'lets', 'like', 'likely', 'long', 'longer', 'longest', 'm', 'made', 'make', 'making', 'man', 'many', 'may', 'me', 'member', 'members', 'men', 'might', 'more', 'most', 'mostly', 'mr', 'mrs', 'much', 'must', 'my', 'myself', 'n', 'necessary', 'need', 'needed', 'needing', 'needs', 'never', 'new', 'new', 'newer', 'newest', 'next', 'no', 'nobody', 'non', 'noone', 'not', 'nothing', 'now', 'nowhere', 'number', 'numbers', 'o', 'of', 'off', 'often', 'old', 'older', 'oldest', 'on', 'once', 'one', 'only', 'open', 'opened', 'opening', 'opens', 'or', 'order', 'ordered', 'ordering', 'orders', 'other', 'others', 'our', 'out', 'over', 'p', 'part', 'parted', 'parting', 'parts', 'per', 'perhaps', 'place', 'places', 'point', 'pointed', 'pointing', 'points', 'possible', 'present', 'presented', 'presenting', 'presents', 'problem', 'problems', 'put', 'puts', 'q', 'quite', 'r', 'rather', 'really', 'right', 'right', 'room', 'rooms', 's', 'said', 'same', 'saw', 'say', 'says', 'second', 'seconds', 'see', 'seem', 'seemed', 'seeming', 'seems', 'sees', 'several', 'shall', 'she', 'should', 'show', 'showed', 'showing', 'shows', 'side', 'sides', 'since', 'small', 'smaller', 'smallest', 'so', 'some', 'somebody', 'someone', 'something', 'somewhere', 'state', 'states', 'still', 'still', 'such', 'sure', 't', 'take', 'taken', 'than', 'that', 'the', 'their', 'them', 'then', 'there', 'therefore', 'these', 'they', 'thing', 'things', 'think', 'thinks', 'this', 'those', 'though', 'thought', 'thoughts', 'three', 'through', 'thus', 'to', 'today', 'together', 'too', 'took', 'toward', 'turn', 'turned', 'turning', 'turns', 'two', 'u', 'under', 'until', 'up', 'upon', 'us', 'use', 'used', 'uses', 'v', 'very', 'w', 'want', 'wanted', 'wanting', 'wants', 'was', 'way', 'ways', 'we', 'well', 'wells', 'went', 'were', 'what', 'when', 'where', 'whether', 'which', 'while', 'who', 'whole', 'whose', 'why', 'will', 'with', 'within', 'without', 'work', 'worked', 'working', 'works', 'would', 'x', 'y', 'year', 'years', 'yet', 'you', 'young', 'younger', 'youngest', 'your', 'yours', 'z', ';', ',', \"'\", '-', '(', ')', ':', '/', '.', '?']\n"
     ]
    }
   ],
   "source": [
    "inp_stop=open('data/stopwords.txt','r+') #read stopword from file\n",
    "stopwords=eval(inp_stop.read())\n",
    "print stopwords\n",
    "id=-1\n",
    "i=0\n",
    "directory=\"d\"\n",
    "II={}\n",
    "Document_TF_Count={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "book pencil bus table\n",
      "signal road pen traffic\n",
      "fish boat river sail\n",
      "pencil pen \n"
     ]
    }
   ],
   "source": [
    "Documents=os.listdir(directory)\n",
    "for Document in Documents:\n",
    "# \tif i<4:\n",
    "\ti=i+1\n",
    "# \telse:\n",
    "# \t\tbreak\n",
    "\tf=open(directory+\"/\"+Document,'r');\n",
    "\tc=f.read()\n",
    "# \tc=f.read().split('\\n')\n",
    "# \tprint c\n",
    "\tf.close();\n",
    "\t# id=c[4].replace('DOC ID:','')\n",
    "\tid=id+1\n",
    "\tcontent=c\n",
    "# \tcontent=c[5].replace('CONTENT:','')\n",
    "\tprint content\n",
    "\twords=word_tokenize(str(content))\n",
    "\tlower_words=[x.lower() for x in words if not SpecialCharacters(x) and x not in stopwords]\n",
    "\tupper_words=[x for x in words if x.upper()==x and not SpecialCharacters(x)]\n",
    "\twords=[]\n",
    "\twords=lower_words+upper_words\n",
    "\tWordCount1={}\n",
    "\t#\t\n",
    "\tfor w in words:\n",
    "\t\t# if word in stopwords:\n",
    "\t\t#Term Frequency \n",
    "\t\tif w in WordCount1:\n",
    "\t\t\tWordCount1[w]=WordCount1[w]+1\n",
    "\t\telse:\n",
    "\t\t\tWordCount1[w]=1\n",
    "\t\t#Inverted Index\t\n",
    "\t\tif w in II:\n",
    "\t\t\tif id not in II[w]:\n",
    "\t\t\t\tII[w].append(id)\n",
    "\t\telse:\n",
    "\t\t\tII[w]=[]\n",
    "\t\t\tII[w].append(id)\n",
    "\tDocument_TF_Count[id]=WordCount1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: {'pencil': 1, 'bus': 1, 'signal': 0, 'sail': 0, 'pen': 0, 'book': 1, 'traffic': 0, 'road': 0, 'table': 1, 'river': 0, 'fish': 0, 'boat': 0}, 1: {'pencil': 0, 'bus': 0, 'signal': 1, 'sail': 0, 'pen': 1, 'book': 0, 'traffic': 1, 'boat': 0, 'table': 0, 'river': 0, 'fish': 0, 'road': 1}, 2: {'pencil': 0, 'bus': 0, 'fish': 1, 'sail': 1, 'pen': 0, 'book': 0, 'traffic': 0, 'road': 0, 'table': 0, 'river': 1, 'signal': 0, 'boat': 1}, 3: {'pencil': 1, 'bus': 0, 'signal': 0, 'sail': 0, 'pen': 1, 'book': 0, 'traffic': 0, 'road': 0, 'table': 0, 'river': 0, 'fish': 0, 'boat': 0}}\n"
     ]
    }
   ],
   "source": [
    "#Assign Term Frequency as Zero\n",
    "vocab=II.keys()\n",
    "for v in vocab:\n",
    "\tfor key in Document_TF_Count:\n",
    "\t\tif v not in Document_TF_Count[key]:\n",
    "\t\t\tDocument_TF_Count[key][v]=0\n",
    "print Document_TF_Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: {'pencil': 1, 'bus': 1, 'signal': 0, 'sail': 0, 'pen': 0, 'book': 1, 'traffic': 0, 'road': 0, 'table': 1, 'river': 0, 'fish': 0, 'boat': 0}, 1: {'pencil': 0, 'bus': 0, 'signal': 1, 'sail': 0, 'pen': 1, 'book': 0, 'traffic': 1, 'boat': 0, 'table': 0, 'river': 0, 'fish': 0, 'road': 1}, 2: {'pencil': 0, 'bus': 0, 'fish': 1, 'sail': 1, 'pen': 0, 'book': 0, 'traffic': 0, 'road': 0, 'table': 0, 'river': 1, 'signal': 0, 'boat': 1}, 3: {'pencil': 1, 'bus': 0, 'signal': 0, 'sail': 0, 'pen': 1, 'book': 0, 'traffic': 0, 'road': 0, 'table': 0, 'river': 0, 'fish': 0, 'boat': 0}}\n",
      "[0, 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "##Stemming#\n",
    "stemmer=PorterStemmer()\n",
    "l=len(vocab)\n",
    "for key in Document_TF_Count:\n",
    "\tfor t1 in range(l):\n",
    "\t\tfor t2 in range(t1+1,l):\n",
    "\t\t\tv1=vocab[t1]\n",
    "\t\t\tv2=vocab[t2]\n",
    "\t\t\tstem1=stemmer.stem(v1)\n",
    "\t\t\tstem2=stemmer.stem(v2)\n",
    "\n",
    "\t\t\ttc1=Document_TF_Count[key][v1]\n",
    "\t\t\ttc2=Document_TF_Count[key][v2]\t\n",
    "\t\t\tif tc1>0 and tc2>0 and v1!=v2 and stem1==stem2 and v1.upper()!=v2 and v2.upper()!=v1:\n",
    "\t\t\t#terms should exists in the same document not be equal, \n",
    "\t\t\t#even upper or lower case should not be equal but stem should be\t\t\t\n",
    "\t\t\t\t# print stem1\n",
    "\t\t\t\t# print stem2\n",
    "\t\t\t\tDocument_TF_Count[key][v1]=tc1+1\n",
    "\t\t\t\tDocument_TF_Count[key][v2]=tc1+1\n",
    "print Document_TF_Count\n",
    "#Calculate TF-IDF\n",
    "print Document_TF_Count.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Document_TF_Count', 'wb') as fp:\n",
    "    pickle.dump(Document_TF_Count, fp)\n",
    "\n",
    "with open ('Document_TF_Count', 'rb') as fp:\n",
    "    Document_TF_Count = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Term Vector without sumation similarity\n",
      "{'boat': [0.0, 0.0, 0.6020599913279623, 0.0],\n",
      " 'book': [0.6020599913279623, 0.0, 0.0, 0.0],\n",
      " 'bus': [0.6020599913279623, 0.0, 0.0, 0.0],\n",
      " 'fish': [0.0, 0.0, 0.6020599913279623, 0.0],\n",
      " 'pen': [0.0, 0.30102999566398114, 0.0, 0.30102999566398114],\n",
      " 'pencil': [0.30102999566398114, 0.0, 0.0, 0.30102999566398114],\n",
      " 'river': [0.0, 0.0, 0.6020599913279623, 0.0],\n",
      " 'road': [0.0, 0.6020599913279623, 0.0, 0.0],\n",
      " 'sail': [0.0, 0.0, 0.6020599913279623, 0.0],\n",
      " 'signal': [0.0, 0.6020599913279623, 0.0, 0.0],\n",
      " 'table': [0.6020599913279623, 0.0, 0.0, 0.0],\n",
      " 'traffic': [0.0, 0.6020599913279623, 0.0, 0.0]}\n"
     ]
    }
   ],
   "source": [
    "term_vector={}\n",
    "for v in vocab:\n",
    "\tidf=math.log((float(id+1)/len(II[v])),10)\n",
    "\tarray=[]\n",
    "\tfor key in Document_TF_Count:\n",
    "\t\tk=int(key)\n",
    "\t\tarray+=[Document_TF_Count[key][v]*idf]\n",
    "\tterm_vector[v]=array\n",
    "print \"Term Vector without sumation similarity\"\n",
    "pprint.pprint(term_vector)\n",
    "#~ print \"++++++++++++++++++\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('termvector', 'wb') as fp:\n",
    "    pickle.dump(term_vector, fp)\n",
    "\n",
    "with open ('termvector', 'rb') as fp:\n",
    "    term_vector = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update TF-IDF using TMI\n",
    "def sim_f(word1, word2):\n",
    "\tif word1==word2:\n",
    "\t\treturn 0\n",
    "\tmax_value=0.0\n",
    "# \twordFromList1 = wordnet.synsets(word1)\n",
    "# \twordFromList2 = wordnet.synsets(word2)\n",
    "# \tfor wl1 in wordFromList1:\n",
    "# \t\tif wl1 in wordFromList2:\n",
    "# \t\t\treturn 1\n",
    "\treturn 0\t\n",
    "# \t\tfor wl2 in wordFromList2:\n",
    "# \t\t\tp=wl1.wup_similarity(wl2)\n",
    "# \t\t\tif max_value < p: \n",
    "# \t\t\t\tmax_value = p\n",
    "# \treturn max_value\n",
    "\n",
    "def sim_f1(word1, word2):\n",
    "\tif word1==word2:\n",
    "\t\treturn 0\n",
    "\tmax_value=0.0\n",
    "\twordFromList1 = wordnet.synsets(word1)\n",
    "\twordFromList2 = wordnet.synsets(word2)\n",
    "\tfor wl1 in wordFromList1:\n",
    "\t\tif wl1 in wordFromList2:\n",
    "\t\t\treturn 1\n",
    "\treturn 0\t\n",
    "# \t\tfor wl2 in wordFromList2:\n",
    "# \t\t\tp=wl1.wup_similarity(wl2)\n",
    "# \t\t\tif max_value < p: \n",
    "# \t\t\t\tmax_value = p\n",
    "# \treturn max_value\n",
    "\n",
    "def sim_f2(word1, word2):\n",
    "\tif word1==word2:\n",
    "\t\treturn 0\n",
    "\tmax_value=0.0\n",
    "\twordFromList1 = wordnet.synsets(word1)\n",
    "\twordFromList2 = wordnet.synsets(word2)\n",
    "\tfor wl1 in wordFromList1:\n",
    "# \t\tif wl1 in wordFromList2:\n",
    "# \t\t\treturn 1\n",
    "# \treturn 0\t\n",
    "\t\tfor wl2 in wordFromList2:\n",
    "\t\t\tp=wl1.wup_similarity(wl2)\n",
    "\t\t\tif max_value < p: \n",
    "\t\t\t\tmax_value = p\n",
    "\treturn max_value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Similirity {'pencil': {'pencil': 0, 'bus': 0.6666666666666666, 'signal': 0.4, 'sail': 0.625, 'pen': 0.8888888888888888, 'book': 0.5882352941176471, 'traffic': 0.36363636363636365, 'boat': 0.6666666666666666, 'table': 0.6666666666666666, 'river': 0.2857142857142857, 'fish': 0.47058823529411764, 'road': 0.625}, 'bus': {'pencil': 0.6666666666666666, 'bus': 0, 'signal': 0.5, 'sail': 0.6666666666666666, 'pen': 0.6666666666666666, 'book': 0.5882352941176471, 'traffic': 0.35294117647058826, 'boat': 0.75, 'table': 0.6666666666666666, 'river': 0.26666666666666666, 'fish': 0.47058823529411764, 'road': 0.625}, 'signal': {'pencil': 0.4, 'bus': 0.5, 'signal': 0, 'sail': 0.3157894736842105, 'pen': 0.2222222222222222, 'book': 0.6, 'traffic': 0.4444444444444444, 'boat': 0.2222222222222222, 'table': 0.4, 'river': 0.26666666666666666, 'fish': 0.2857142857142857, 'road': 0.4}, 'sail': {'pencil': 0.625, 'bus': 0.6666666666666666, 'signal': 0.4, 'sail': 0, 'pen': 0.75, 'book': 0.6666666666666666, 'traffic': 0.5714285714285714, 'boat': 0.8888888888888888, 'table': 0.625, 'river': 0.3076923076923077, 'fish': 0.5333333333333333, 'road': 0.7142857142857143}, 'pen': {'pencil': 0.8888888888888888, 'bus': 0.6666666666666666, 'signal': 0.3333333333333333, 'sail': 0.75, 'pen': 0, 'book': 0.5882352941176471, 'traffic': 0.2857142857142857, 'boat': 0.6666666666666666, 'table': 0.6666666666666666, 'river': 0.26666666666666666, 'fish': 0.75, 'road': 0.625}, 'book': {'pencil': 0.5882352941176471, 'bus': 0.5882352941176471, 'signal': 0.6, 'sail': 0.6666666666666666, 'pen': 0.5882352941176471, 'book': 0, 'traffic': 0.8, 'boat': 0.5882352941176471, 'table': 0.5882352941176471, 'river': 0.2857142857142857, 'fish': 0.5, 'road': 0.6666666666666666}, 'traffic': {'pencil': 0.36363636363636365, 'bus': 0.35294117647058826, 'signal': 0.4444444444444444, 'sail': 0.5714285714285714, 'pen': 0.2857142857142857, 'book': 0.8, 'traffic': 0, 'boat': 0.25, 'table': 0.5454545454545454, 'river': 0.2, 'fish': 0.3333333333333333, 'road': 0.6666666666666666}, 'boat': {'pencil': 0.6666666666666666, 'bus': 0.75, 'signal': 0.2857142857142857, 'sail': 0.8888888888888888, 'pen': 0.6666666666666666, 'book': 0.5882352941176471, 'traffic': 0.25, 'boat': 0, 'table': 0.6666666666666666, 'river': 0.26666666666666666, 'fish': 0.47058823529411764, 'road': 0.625}, 'table': {'pencil': 0.6666666666666666, 'bus': 0.6666666666666666, 'signal': 0.4, 'sail': 0.625, 'pen': 0.6666666666666666, 'book': 0.5882352941176471, 'traffic': 0.5454545454545454, 'boat': 0.6666666666666666, 'table': 0, 'river': 0.3076923076923077, 'fish': 0.47058823529411764, 'road': 0.625}, 'river': {'pencil': 0.2857142857142857, 'bus': 0.26666666666666666, 'signal': 0.26666666666666666, 'sail': 0.3076923076923077, 'pen': 0.26666666666666666, 'book': 0.2857142857142857, 'traffic': 0.18181818181818182, 'boat': 0.26666666666666666, 'table': 0.3076923076923077, 'river': 0, 'fish': 0.36363636363636365, 'road': 0.3076923076923077}, 'fish': {'pencil': 0.47058823529411764, 'bus': 0.47058823529411764, 'signal': 0.4, 'sail': 0.5333333333333333, 'pen': 0.75, 'book': 0.5, 'traffic': 0.3333333333333333, 'boat': 0.47058823529411764, 'table': 0.47058823529411764, 'river': 0.36363636363636365, 'fish': 0, 'road': 0.5333333333333333}, 'road': {'pencil': 0.625, 'bus': 0.625, 'signal': 0.4, 'sail': 0.7142857142857143, 'pen': 0.625, 'book': 0.6666666666666666, 'traffic': 0.6666666666666666, 'boat': 0.625, 'table': 0.625, 'river': 0.3076923076923077, 'fish': 0.5333333333333333, 'road': 0}}\n"
     ]
    }
   ],
   "source": [
    "sim={}\n",
    "for term1 in term_vector:\n",
    "\tsim[term1]={}\n",
    "\tfor term2 in term_vector:\n",
    "\t\tsim[term1][term2]=sim_f2(term1,term2)\n",
    "\n",
    "with open('sim', 'wb') as fp:\n",
    "    pickle.dump(sim, fp)\n",
    "\n",
    "with open ('sim', 'rb') as fp:\n",
    "    sim = pickle.load(fp)\n",
    "print \"Similirity\",sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Term Vector sumation similarity\n",
      "{'boat': [23.64902218741366,\n",
      "          21.565057521001062,\n",
      "          24.699713954069907,\n",
      "          6.578635640156678],\n",
      " 'book': [11.80809161241831,\n",
      "          10.757553501459538,\n",
      "          11.84753632921607,\n",
      "          3.1732560337709357],\n",
      " 'bus': [2.3295393128506774,\n",
      "         1.8262456592761653,\n",
      "         2.1187902085100117,\n",
      "         0.5797614731306303],\n",
      " 'fish': [64.97861267436362,\n",
      "          59.91019887371499,\n",
      "          67.42494576839523,\n",
      "          18.12288805364567],\n",
      " 'pen': [7.524243921822745,\n",
      "         6.831723960940127,\n",
      "         7.791120084259965,\n",
      "         2.288724553298774],\n",
      " 'pencil': [1.457929586843203,\n",
      "            1.1036246154165854,\n",
      "            1.2330003113715868,\n",
      "            0.5686122140319644],\n",
      " 'river': [31.06815277323863,\n",
      "           28.654002708510465,\n",
      "           32.60442347877912,\n",
      "           8.636492975915129],\n",
      " 'road': [116.20750077971213,\n",
      "          107.43497287000125,\n",
      "          119.68994081108065,\n",
      "          32.32977708761651],\n",
      " 'sail': [4.181893615472304,\n",
      "          3.9198858939243792,\n",
      "          4.710355125357251,\n",
      "          1.2013512499797558],\n",
      " 'signal': [2.350001482490582,\n",
      "            2.531934436623843,\n",
      "            2.2090770171756176,\n",
      "            0.5842211767700967],\n",
      " 'table': [43.523981800212894,\n",
      "           39.76367614662147,\n",
      "           44.457828720090475,\n",
      "           12.010158948015206],\n",
      " 'traffic': [16.711098783498734,\n",
      "             15.972511575806907,\n",
      "             17.045292599348294,\n",
      "             4.550056187090233]}\n"
     ]
    }
   ],
   "source": [
    "for term in term_vector:\n",
    " \tl=len(term_vector[term])\n",
    " \t#~ print term\n",
    " \tfor i in range(l):\n",
    "\t\tsumation=0\n",
    "\t\tfor v in vocab:\n",
    "\t\t\tsumation+=term_vector[v][i]*sim[term][v]\t\n",
    "\t\tterm_vector[term][i]+=sumation\t\t\n",
    "print \"Term Vector sumation similarity\"\n",
    "pprint.pprint(term_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance Matrix\n",
      "array({'pencil': {'pencil': 0.99999999999999978, 'bus': 0.99508199040277434, 'signal': 0.98339219025809876, 'sail': 0.98813508774975123, 'pen': 0.99289738536211469, 'book': 0.99198169697357963, 'traffic': 0.99041880925817793, 'road': 0.99142639504022345, 'table': 0.99182494182455561, 'river': 0.99088015220835424, 'fish': 0.99130270452450175, 'boat': 0.99128637925633301}, 'bus': {'pencil': 0.99508199040277434, 'bus': 0.99999999999999989, 'signal': 0.99021031652196401, 'sail': 0.99545267930515213, 'pen': 0.99765823200013659, 'book': 0.99811735993441864, 'traffic': 0.9968441837592058, 'road': 0.9975687432031024, 'table': 0.99789460031340149, 'river': 0.99735771827717512, 'fish': 0.99752833082228887, 'boat': 0.99762319097987484}, 'signal': {'pencil': 0.98339219025809876, 'bus': 0.99021031652196401, 'signal': 0.99999999999999978, 'sail': 0.99137611442633133, 'pen': 0.99347160176719085, 'book': 0.99519998454698844, 'traffic': 0.99653615479191371, 'road': 0.99486857552968588, 'table': 0.99466816436577798, 'river': 0.99406451297088516, 'fish': 0.99447535589976266, 'boat': 0.99373533358838717}, 'sail': {'pencil': 0.98813508774975123, 'bus': 0.99545267930515213, 'signal': 0.99137611442633133, 'sail': 1.0000000000000002, 'pen': 0.99923549326982353, 'book': 0.99873160402164507, 'traffic': 0.99862073926616446, 'road': 0.99919122877065114, 'table': 0.99910135737844619, 'river': 0.99951821134758534, 'fish': 0.99934100114714364, 'boat': 0.99948693395454558}, 'pen': {'pencil': 0.99289738536211469, 'bus': 0.99765823200013659, 'signal': 0.99347160176719085, 'sail': 0.99923549326982353, 'pen': 0.99999999999999978, 'book': 0.99970286712052303, 'traffic': 0.99943167627845608, 'road': 0.99983660125045726, 'table': 0.99984471771085515, 'river': 0.99984160832400126, 'fish': 0.99986308296973159, 'boat': 0.99987271093320806}, 'book': {'pencil': 0.99198169697357963, 'bus': 0.99811735993441864, 'signal': 0.99519998454698844, 'sail': 0.99873160402164507, 'pen': 0.99970286712052303, 'book': 1.0, 'traffic': 0.9998150749746807, 'road': 0.99993532071876434, 'table': 0.99996388391545787, 'river': 0.99981159684606924, 'fish': 0.99989405871202275, 'boat': 0.99981725272299171}, 'traffic': {'pencil': 0.99041880925817793, 'bus': 0.9968441837592058, 'signal': 0.99653615479191371, 'sail': 0.99862073926616446, 'pen': 0.99943167627845608, 'book': 0.9998150749746807, 'traffic': 1.0000000000000002, 'road': 0.9998320453588363, 'table': 0.99977601836396224, 'river': 0.99966610153687585, 'fish': 0.99975925002098498, 'boat': 0.99958621201862985}, 'road': {'pencil': 0.99142639504022345, 'bus': 0.9975687432031024, 'signal': 0.99486857552968588, 'sail': 0.99919122877065114, 'pen': 0.99983660125045726, 'book': 0.99993532071876434, 'traffic': 0.9998320453588363, 'road': 1.0, 'table': 0.99998819719314513, 'river': 0.99995432030378317, 'fish': 0.99999091194165513, 'boat': 0.99993912470332391}, 'table': {'pencil': 0.99182494182455561, 'bus': 0.99789460031340149, 'signal': 0.99466816436577798, 'sail': 0.99910135737844619, 'pen': 0.99984471771085515, 'book': 0.99996388391545787, 'traffic': 0.99977601836396224, 'road': 0.99998819719314513, 'table': 1.0000000000000002, 'river': 0.99993439626539982, 'fish': 0.99997846208494234, 'boat': 0.99993928639637286}, 'river': {'pencil': 0.99088015220835424, 'bus': 0.99735771827717512, 'signal': 0.99406451297088516, 'sail': 0.99951821134758534, 'pen': 0.99984160832400126, 'book': 0.99981159684606924, 'traffic': 0.99966610153687585, 'road': 0.99995432030378317, 'table': 0.99993439626539982, 'river': 0.99999999999999989, 'fish': 0.99998512104812687, 'boat': 0.99998979766965967}, 'fish': {'pencil': 0.99130270452450175, 'bus': 0.99752833082228887, 'signal': 0.99447535589976266, 'sail': 0.99934100114714364, 'pen': 0.99986308296973159, 'book': 0.99989405871202275, 'traffic': 0.99975925002098498, 'road': 0.99999091194165513, 'table': 0.99997846208494234, 'river': 0.99998512104812687, 'fish': 0.99999999999999989, 'boat': 0.99997552665804179}, 'boat': {'pencil': 0.99128637925633301, 'bus': 0.99762319097987484, 'signal': 0.99373533358838717, 'sail': 0.99948693395454558, 'pen': 0.99987271093320806, 'book': 0.99981725272299171, 'traffic': 0.99958621201862985, 'road': 0.99993912470332391, 'table': 0.99993928639637286, 'river': 0.99998979766965967, 'fish': 0.99997552665804179, 'boat': 0.99999999999999989}}, dtype=object)\n"
     ]
    }
   ],
   "source": [
    "Matrix={}\n",
    "for tv1 in term_vector:\n",
    "\tMatrix[tv1]={}\n",
    "\tfor tv2 in term_vector:\n",
    "\t\tMatrix[tv1][tv2] =1-spatial.distance.cosine(term_vector[tv1],term_vector[tv2])\n",
    "        \n",
    "Distance_Matrix=np.array(Matrix)\n",
    "print \"Distance Matrix\"\n",
    "pprint.pprint(Distance_Matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('matrix', 'wb') as fp:\n",
    "    pickle.dump(Matrix, fp)\n",
    "\n",
    "with open ('matrix', 'rb') as fp:\n",
    "    Matrix = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.99999999999999978, 0.99508199040277434, 0.98339219025809876, 0.98813508774975123, 0.99289738536211469, 0.99198169697357963, 0.99041880925817793, 0.99128637925633301, 0.99182494182455561, 0.99088015220835424, 0.99130270452450175, 0.99142639504022345], [0.99508199040277434, 0.99999999999999989, 0.99021031652196401, 0.99545267930515213, 0.99765823200013659, 0.99811735993441864, 0.9968441837592058, 0.99762319097987484, 0.99789460031340149, 0.99735771827717512, 0.99752833082228887, 0.9975687432031024], [0.98339219025809876, 0.99021031652196401, 0.99999999999999978, 0.99137611442633133, 0.99347160176719085, 0.99519998454698844, 0.99653615479191371, 0.99373533358838717, 0.99466816436577798, 0.99406451297088516, 0.99447535589976266, 0.99486857552968588], [0.98813508774975123, 0.99545267930515213, 0.99137611442633133, 1.0000000000000002, 0.99923549326982353, 0.99873160402164507, 0.99862073926616446, 0.99948693395454558, 0.99910135737844619, 0.99951821134758534, 0.99934100114714364, 0.99919122877065114], [0.99289738536211469, 0.99765823200013659, 0.99347160176719085, 0.99923549326982353, 0.99999999999999978, 0.99970286712052303, 0.99943167627845608, 0.99987271093320806, 0.99984471771085515, 0.99984160832400126, 0.99986308296973159, 0.99983660125045726], [0.99198169697357963, 0.99811735993441864, 0.99519998454698844, 0.99873160402164507, 0.99970286712052303, 1.0, 0.9998150749746807, 0.99981725272299171, 0.99996388391545787, 0.99981159684606924, 0.99989405871202275, 0.99993532071876434], [0.99041880925817793, 0.9968441837592058, 0.99653615479191371, 0.99862073926616446, 0.99943167627845608, 0.9998150749746807, 1.0000000000000002, 0.99958621201862985, 0.99977601836396224, 0.99966610153687585, 0.99975925002098498, 0.9998320453588363], [0.99128637925633301, 0.99762319097987484, 0.99373533358838717, 0.99948693395454558, 0.99987271093320806, 0.99981725272299171, 0.99958621201862985, 0.99999999999999989, 0.99993928639637286, 0.99998979766965967, 0.99997552665804179, 0.99993912470332391], [0.99182494182455561, 0.99789460031340149, 0.99466816436577798, 0.99910135737844619, 0.99984471771085515, 0.99996388391545787, 0.99977601836396224, 0.99993928639637286, 1.0000000000000002, 0.99993439626539982, 0.99997846208494234, 0.99998819719314513], [0.99088015220835424, 0.99735771827717512, 0.99406451297088516, 0.99951821134758534, 0.99984160832400126, 0.99981159684606924, 0.99966610153687585, 0.99998979766965967, 0.99993439626539982, 0.99999999999999989, 0.99998512104812687, 0.99995432030378317], [0.99130270452450175, 0.99752833082228887, 0.99447535589976266, 0.99934100114714364, 0.99986308296973159, 0.99989405871202275, 0.99975925002098498, 0.99997552665804179, 0.99997846208494234, 0.99998512104812687, 0.99999999999999989, 0.99999091194165513], [0.99142639504022345, 0.9975687432031024, 0.99486857552968588, 0.99919122877065114, 0.99983660125045726, 0.99993532071876434, 0.9998320453588363, 0.99993912470332391, 0.99998819719314513, 0.99995432030378317, 0.99999091194165513, 1.0]]\n",
      "Vocab ['pencil' 'bus' 'signal' 'sail' 'pen' 'book' 'traffic' 'boat' 'table'\n",
      " 'river' 'fish' 'road']\n",
      "Labels {0: 'pencil', 1: 'bus', 2: 'signal', 3: 'sail', 4: 'pen', 5: 'book', 6: 'traffic', 7: 'boat', 8: 'table', 9: 'river', 10: 'fish', 11: 'road'}\n"
     ]
    }
   ],
   "source": [
    "i=0\n",
    "l=len(vocab)\n",
    "w, h = 8, 5;\n",
    "DM = [[0 for x in range(l)] for y in range(l)]\n",
    "labels={}\n",
    "\n",
    "i=0\n",
    "for l1 in Matrix:\n",
    "\tlabels[i]=l1\n",
    "\tj=0\n",
    "\tfor l2 in Matrix[l1]:\n",
    "\t\tif Matrix[l1][l2]<0.5:\n",
    "\t\t\tDM[i][j]=0            \n",
    "\t\telse:\n",
    "\t\t\tDM[i][j]=Matrix[l1][l2]\n",
    "\t\tj=j+1\n",
    "\ti=i+1\t\n",
    "vocab=np.asarray(vocab)\t\n",
    "\n",
    "print DM\n",
    "print \"Vocab\",vocab\n",
    "print \"Labels\",labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MCL Start\t\n",
    "import mcl_c\n",
    "import networkx as nx\n",
    "DM=np.array(DM)\n",
    "G = nx.from_numpy_matrix(np.matrix(DM))\n",
    "#~ print \"============\"\n",
    "#~ print DM\n",
    "#~ print \"============\"\n",
    "#~ print G.nodes()\n",
    "#~ print \"============\"\n",
    "#~ print labels\n",
    "#~ #inflate > more finer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clusters {0: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 1: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 2: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 3: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 4: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 5: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 6: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 7: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 8: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 9: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 10: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 11: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]}\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f9b3acbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jupFp0QfJq3MhOf8/3BC9t8OC/8v9I7glNJuvjvU8r+AHON74f9RYFbww8T1GxWTQKNJM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\nQOYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f9b6cfe90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "M, clusters = mcl_c.mcl(DM, expand_factor = 4, inflate_factor = 2, max_loop = 10, mult_factor = 4)\n",
    "#5,2,10,5\n",
    "print \"Clusters\",clusters\n",
    "pos=mcl_c.draw(G,M,clusters,labels)\n",
    "#MCL END\n",
    "\n",
    "final={}\n",
    "no=0\n",
    "for k in clusters:\n",
    "    t=[]\n",
    "    for v in clusters[k]:\n",
    "        t+=[labels[v]]\n",
    "#     if len(t)==len(clusters[k]):\n",
    "#         for i in t:\n",
    "    final[no]=t\n",
    "    no=no+1\n",
    "# print clusters  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pencil', 'bus', 'signal', 'sail', 'pen', 'book', 'traffic', 'boat', 'table', 'river', 'fish', 'road'] Sport\n"
     ]
    }
   ],
   "source": [
    "keywords=['History','Technology','Sport']\n",
    "unique=[]\n",
    "for l in final:\n",
    "    keypoint=[]\n",
    "    for k in keywords:\n",
    "        point=0\n",
    "        wordFromList1 = wordnet.synsets(k)\n",
    "        for i in final[l]:\n",
    "            wordFromList2 = wordnet.synsets(i)\n",
    "            p=0\n",
    "            for wl1 in wordFromList1:\n",
    "                for wl2 in wordFromList2:\n",
    "                    sim_p=wl1.wup_similarity(wl2)\n",
    "                    if sim_p>p:\n",
    "                        p=sim_p\n",
    "            if len(wordFromList1)>0 or len(wordFromList1)>0: \n",
    "                point=point+p\n",
    "        keypoint.append(point)\n",
    "    s=0\n",
    "    for kp in keypoint:\n",
    "        s=s+kp\n",
    "    if s not in unique:    \n",
    "        max_value = max(keypoint)\n",
    "        max_index = keypoint.index(max_value)\n",
    "        print final[l],keywords[max_index]\n",
    "        unique+=[s]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pencil\n",
      "bus\n",
      "signal\n",
      "sail\n",
      "boat, book, fish, pen, river, road, table, traffic\n",
      "5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.59327577479981186"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "affprop = sklearn.cluster.AffinityPropagation(affinity=\"precomputed\", damping=0.6)\n",
    "# affprop = sklearn.cluster.AffinityPropagation(preference=-565,affinity=\"euclidean\",convergence_iter=5, damping=0.5)\n",
    "affprop.fit(DM)\n",
    "count=0\n",
    "for cluster_id in np.unique(affprop.labels_):\n",
    "    exemplar = vocab[affprop.cluster_centers_indices_[cluster_id]]\n",
    "    cluster = np.unique(vocab[np.nonzero(affprop.labels_==cluster_id)])\n",
    "    cluster_str = \", \".join(cluster)\n",
    "    print(cluster_str)\n",
    "    count=count+1\n",
    "print count\n",
    "metrics.silhouette_score(DM,affprop.labels_,metric='sqeuclidean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 1 0 4 3 1 2 0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-0.5081473581768865"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "agg_cluster = AgglomerativeClustering(n_clusters=5, affinity='precomputed',linkage='complete')\n",
    "clust=agg_cluster.fit_predict(DM)\n",
    "print clust\n",
    "# print agg.children_\n",
    "metrics.silhouette_score(DM,agg_cluster.labels_,metric='sqeuclidean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = []\n",
    "# should really call this cluster dict\n",
    "node_dict = {}\n",
    "n_samples = len(DM)\n",
    "def get_all_children(k, verbose=False):\n",
    "    i,j = agg_cluster.children_[k]\n",
    "\n",
    "    if k in node_dict:\n",
    "        return node_dict[k]['children']\n",
    "\n",
    "    if i < leaf_count:\n",
    "        left = [i]\n",
    "    else:\n",
    "        # read the AgglomerativeClustering doc. to see why I select i-n_samples\n",
    "        left = get_all_children(i-n_samples)\n",
    "\n",
    "    if j < leaf_count:\n",
    "        right = [j]\n",
    "    else:\n",
    "        right = get_all_children(j-n_samples)\n",
    "\n",
    "    if verbose:\n",
    "        print k,i,j,left, right\n",
    "    left_pos = np.mean(map(lambda ii: X[ii], left),axis=0)\n",
    "    right_pos = np.mean(map(lambda ii: X[ii], right),axis=0)\n",
    "\n",
    "    # this assumes that agg_cluster used euclidean distances\n",
    "    dist = metrics.pairwise_distances([left_pos,right_pos],metric='euclidean')[0,1]\n",
    "\n",
    "    all_children = [x for y in [left,right] for x in y]\n",
    "    pos = np.mean(map(lambda ii: X[ii], all_children),axis=0)\n",
    "\n",
    "    # store the results to speed up any additional or recursive evaluations\n",
    "    node_dict[k] = {'top_child':[i,j],'children':all_children, 'pos':pos,'dist':dist, 'node_i':k + n_samples}\n",
    "    return all_children\n",
    "    #return node_di|ct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "global name 'leaf_count' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-de85e5d4a5fb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0magg_cluster\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mget_all_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Every row in the linkage matrix has the format [idx1, idx2, distance, sample_count].\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mZ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'top_child'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'top_child'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dist'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'children'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miteritems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-20-145ed9c782b2>\u001b[0m in \u001b[0;36mget_all_children\u001b[0;34m(k, verbose)\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnode_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'children'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mleaf_count\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m         \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: global name 'leaf_count' is not defined"
     ]
    }
   ],
   "source": [
    "for k,x in enumerate(agg_cluster.children_):   \n",
    "    get_all_children(k,verbose=False)\n",
    "\n",
    "# Every row in the linkage matrix has the format [idx1, idx2, distance, sample_count].\n",
    "Z = [[v['top_child'][0],v['top_child'][1],v['dist'],len(v['children'])] for k,v in node_dict.iteritems()]\n",
    "# create a version with log scaled distances for easier visualization\n",
    "Z_log =[[v['top_child'][0],v['top_child'][1],np.log(1.0+v['dist']),len(v['children'])] for k,v in node_dict.iteritems()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.cluster import hierarchy\n",
    "plt.figure()\n",
    "dn = hierarchy.dendrogram(Z_log,p=4,truncate_mode='level')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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