{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy.linalg import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import gzip\n",
    "words = []\n",
    "vectors = []\n",
    "\n",
    "for l in gzip.open(\"../../HW2/data/glove.6B.50d.txt.gz\"):\n",
    "    t = l.strip().split()\n",
    "    words.append(t[0].decode())\n",
    "    vectors.append(list(map(float, t[1:])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wordvecs = np.array(vectors, dtype=np.double)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "word2id = {word.decode():i for i, word in enumerate(words)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3264"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word2id[\"athens\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.080518 ,  1.0627   ,  0.14356  ,  0.51284  , -0.86614  ,\n",
       "       -0.87747  , -0.081332 ,  0.62346  , -1.0791   , -0.26803  ,\n",
       "        0.042063 , -1.1343   , -0.41609  , -0.44612  ,  0.75472  ,\n",
       "        0.39791  ,  0.26783  ,  0.60382  , -1.1654   ,  0.2434   ,\n",
       "        0.65747  , -0.089722 , -1.0805   ,  0.016183 ,  0.5644   ,\n",
       "       -1.4807   ,  0.26113  , -0.0033073, -1.0285   , -0.34615  ,\n",
       "        1.4185   ,  0.80099  , -0.24248  , -0.45811  , -0.030437 ,\n",
       "        0.17815  ,  0.83084  ,  0.14041  , -0.14673  ,  0.35092  ,\n",
       "       -0.46702  , -0.22738  ,  0.71731  , -0.29418  ,  0.35399  ,\n",
       "        0.34459  , -0.27677  ,  0.56476  ,  0.75872  , -1.3752   ])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wordvecs[word2id[\"athens\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def nearest(vec, dot=False):\n",
    "    vnorm = norm(vec)\n",
    "    scores = []\n",
    "    for i in range(len(words)):\n",
    "        wvnorm = norm(wordvecs[i])\n",
    "        if not dot:\n",
    "            scores.append(np.dot(wordvecs[i], vec) / (vnorm * wvnorm))\n",
    "        else:\n",
    "            scores.append(np.dot(wordvecs[i], vec))\n",
    "    score_ids = [(s, i) for i, s in enumerate(scores)]\n",
    "    score_ids.sort()\n",
    "    score_ids.reverse()\n",
    "    return score_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "closests = nearest(wordvecs[word2id[\"athens\"]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "athens\t1.0\n",
      "thessaloniki\t0.753296458117\n",
      "budapest\t0.742295923743\n",
      "bucharest\t0.722721107685\n",
      "istanbul\t0.719985596814\n",
      "greece\t0.717178243637\n",
      "beijing\t0.709548054231\n",
      "seoul\t0.697987992237\n",
      "piraeus\t0.690471084596\n",
      "sydney\t0.688954838558\n",
      "olympics\t0.680529514873\n",
      "acropolis\t0.679410595032\n",
      "prague\t0.676343476106\n",
      "moscow\t0.673589430159\n",
      "olympic\t0.671738212512\n",
      "stockholm\t0.670935083049\n",
      "saturday\t0.665605450439\n",
      "osaka\t0.658451341105\n",
      "canberra\t0.658331596849\n",
      "tokyo\t0.657494372951\n"
     ]
    }
   ],
   "source": [
    "def print_nbest(closests):\n",
    "    for score, i in closests[:20]:\n",
    "        print(\"%s\\t%s\"%(words[i],score))\n",
    "print_nbest(closests)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def analogy_vec(a, b, c):\n",
    "    return wordvecs[word2id[b]] - wordvecs[word2id[a]] + wordvecs[word2id[c]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "woman\t0.890391411363\n",
      "girl\t0.845372614633\n",
      "man\t0.830175760255\n",
      "her\t0.784583150851\n",
      "boy\t0.776306631882\n",
      "she\t0.761976437856\n",
      "herself\t0.759762826224\n",
      "blind\t0.729675545545\n",
      "mother\t0.72303401112\n",
      "blonde\t0.71361435474\n",
      "stranger\t0.700963837999\n",
      "girls\t0.700380292013\n",
      "girlfriend\t0.698242571648\n",
      "old\t0.696897035117\n",
      "lover\t0.696171105665\n",
      "eyes\t0.693559953437\n",
      "young\t0.693181590172\n",
      "female\t0.692087983551\n",
      "teenager\t0.688937080658\n",
      "blond\t0.688488957526\n"
     ]
    }
   ],
   "source": [
    "d = analogy_vec(\"king\", \"queen\", \"man\")\n",
    "print_nbest(nearest(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "oslo\t0.800940880805\n",
      "norway\t0.724581222223\n",
      "cyprus\t0.710603502924\n",
      "accord\t0.67236605952\n",
      "iceland\t0.669985509136\n",
      "pact\t0.668983334486\n",
      "accords\t0.66773110168\n",
      "palestine\t0.666339553777\n",
      "brokered\t0.656774460813\n",
      "agreement\t0.654591264655\n",
      "norwegian\t0.65360254298\n",
      "treaty\t0.642123535162\n",
      "ankara\t0.641830299428\n",
      "copenhagen\t0.632054092737\n",
      "greece\t0.631164428988\n",
      "malta\t0.628695581307\n",
      "cypriots\t0.628685760459\n",
      "accession\t0.62584522837\n",
      "plo\t0.624709159735\n",
      "baltic\t0.61372345483\n"
     ]
    }
   ],
   "source": [
    "d = analogy_vec(\"athens\", \"greece\", \"oslo\")\n",
    "print_nbest(nearest(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "to_show = [w.lower() for w in [\"london\", \"england\",\n",
    "\"Athens\", \"Greece\",\n",
    "\"Berlin\",\"Germany\",\n",
    "\"Madrid\",\"Spain\",\n",
    "\"Rome\",\"Italy\",\n",
    "\"Paris\",\"France\",\n",
    "\"Bucharest\", \"Romania\",\n",
    "\"Budapest\", \"Hungary\",\n",
    "\"Vienna\", \"Austria\",\n",
    "\"Warsaw\", \"Poland\"]]\n",
    "\n",
    "to_show_vecs = [wordvecs[word2id[w]] for w in to_show ]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "to_show = [w.lower() for w in [\"woods\", \"golf\",\n",
    "\"agassi\", \"tennis\",\n",
    "\"bonds\",\"baseball\",\n",
    "\"manning\", \"football\",\n",
    "\"gretzky\",\"hockey\",\n",
    "\"jordan\",\"basketball\"\n",
    "]]\n",
    "\n",
    "to_show_vecs = [wordvecs[word2id[w]] for w in to_show ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = np.array(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import sklearn.decomposition\n",
    "t = sklearn.decomposition.PCA(2)\n",
    "X_pca = t.fit_transform(to_show_vecs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import offsetbox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_embedding(X, words, title=None, size=(3,3)):\n",
    "    x_min, x_max = np.min(X, 0), np.max(X, 0)\n",
    "    X = (X - x_min) / (x_max - x_min)\n",
    "\n",
    "    plt.figure(figsize=size)\n",
    "    ax = plt.subplot(111)\n",
    "    ids = {}\n",
    "    \n",
    "\n",
    "    for i in range(X.shape[0]):\n",
    "        letter = words[i]\n",
    "        ax.plot(X[i, 0], X[i, 1],\".\")\n",
    "        ax.annotate(letter, (X[i, 0], X[i, 1]), fontsize=30) \n",
    "        if i % 2 == 0:\n",
    "            ax.plot((X[i,0], X[i+1,0]), (X[i,1], X[i+1,1]))           \n",
    "\n",
    "    plt.xticks([]), plt.yticks([])\n",
    "    plt.axis('off')\n",
    "    \n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.subplots_adjust(left=-0.2, right=1.2, top=1.2, bottom=-0.2)\n",
    "    #plt.savefig(\"linear.png\", dpi=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CbCDjGl8uyWOjPBe2JEELALBlCVyAjWxGee6rk5ygPBc2N0ELALDlCVyAjW6q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y6zM6HKxQ3+WimrWe4jy8U/ARYJN0I0A0BFMStZ3VIje7fcWm1EZhXI54ntgBOBx4HDiHii2qDM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\ngEEESnqtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f06899b8978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_embedding(X_pca, to_show, size=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dog\t1.0\n",
      "cat\t0.921800527377\n",
      "dogs\t0.851315870426\n",
      "horse\t0.790758298322\n",
      "puppy\t0.775492121034\n",
      "pet\t0.772470734611\n",
      "rabbit\t0.772081457265\n",
      "pig\t0.749006160038\n",
      "snake\t0.73991884888\n",
      "baby\t0.739557028388\n",
      "bite\t0.738793700816\n",
      "boy\t0.738279946925\n",
      "cats\t0.729978577748\n",
      "animal\t0.725225523734\n",
      "monkey\t0.724720695005\n",
      "rat\t0.724584645395\n",
      "mad\t0.724435488352\n",
      "crazy\t0.71579957911\n",
      "man\t0.712192558413\n",
      "elephant\t0.711762229805\n"
     ]
    }
   ],
   "source": [
    "print_nbest(nearest(wordvecs[word2id[\"dog\"]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "schnauzer\t27.3711839224\n",
      "kongra\t20.4476052286\n",
      "danyon\t19.8920503309\n",
      "salticidae\t19.7934692784\n",
      "gracillariidae\t19.5084371554\n",
      "schnauzers\t19.4232667473\n",
      "pinscher\t19.1979447831\n",
      "powerbook\t18.9056835719\n",
      "interservices\t18.5865673921\n",
      "xiulian\t18.5051691808\n",
      "8-gigabyte\t18.4277818727\n",
      "kampi\t18.2712090148\n",
      "lzr\t18.2151542224\n",
      "poodle\t18.1942714519\n",
      "set-50\t18.1912160839\n",
      "excluder\t18.1047355801\n",
      "33-stock\t17.6907751353\n",
      "four-stroke\t17.6800107534\n",
      "powerpc\t17.591939294\n",
      "cologne-frankfurt\t17.544736007\n"
     ]
    }
   ],
   "source": [
    "print_nbest(nearest(wordvecs[word2id[\"schnauzer\"]], dot=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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