Word2Vec.ipynb

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   "source": [
    "import sys\n",
    "reload(sys)\n",
    "sys.setdefaultencoding(\"utf-8\")\n",
    "import nltk\n",
    "from nltk import tokenize\n",
    "from gensim.models import word2vec\n",
    "from sklearn.decomposition import PCA\n",
    "from bokeh.plotting import figure, output_file, show, ColumnDataSource, vplot\n",
    "from bokeh.io import output_notebook\n",
    "output_notebook()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "outputs": [],
   "source": [
    "corpus = tokenize.sent_tokenize(''.join(open('bibliaUTF8.txt').readlines()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [],
   "source": [
    "corpus = map(nltk.word_tokenize,corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "source": [
    "frequency = {}\n",
    "for s in corpus:\n",
    "    for w in s:\n",
    "        if w not in frequency:\n",
    "            frequency[w] = 0\n",
    "        else:\n",
    "            frequency[w] +=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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   "outputs": [],
   "source": [
    "model = word2vec.Word2Vec(corpus,size=120,window=5,min_count=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "[('Se\\xc3\\xb1or', 0.699536919593811),\n",
       " ('Jehov\\xc3\\xa1', 0.6935539245605469),\n",
       " ('Cristo', 0.6817972660064697),\n",
       " ('Padre', 0.6635150909423828),\n",
       " ('salvaci\\xc3\\xb3n', 0.6521729826927185),\n",
       " ('amor', 0.6480676531791687),\n",
       " ('misericordia', 0.635499894618988),\n",
       " ('Santo', 0.626288890838623),\n",
       " ('gracia', 0.6233478784561157),\n",
       " ('poder', 0.6216015815734863)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar(positive=['Dios'])"
   ]
  },
  {
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   "execution_count": 8,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "[('pecado', 0.8310112357139587),\n",
       " ('misericordia', 0.8115969300270081),\n",
       " ('Cristo', 0.8111620545387268),\n",
       " ('salvaci\\xc3\\xb3n', 0.810797393321991),\n",
       " ('Padre', 0.7946358919143677),\n",
       " ('justicia', 0.7799472212791443),\n",
       " ('fe', 0.7734013199806213),\n",
       " ('voluntad', 0.7427129149436951),\n",
       " ('nombre', 0.7271982431411743),\n",
       " ('verdad', 0.7220306992530823)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar(positive=['amor'])"
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "[('valientes', 0.8069029450416565),\n",
       " ('muertos', 0.7822678685188293),\n",
       " ('fuertes', 0.7818940281867981),\n",
       " ('guerra', 0.7598620057106018),\n",
       " ('cayeron', 0.741929829120636),\n",
       " ('varones', 0.739190936088562),\n",
       " ('unos', 0.7370514273643494),\n",
       " ('otros', 0.7295645475387573),\n",
       " ('animales', 0.725436806678772),\n",
       " ('jud\\xc3\\xados', 0.7224099636077881)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
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    }
   ],
   "source": [
    "model.most_similar(positive=['hombres'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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    {
     "data": {
      "text/plain": [
       "[('hombres', 0.7598620057106018),\n",
       " ('Samaria', 0.7002260684967041),\n",
       " ('espada', 0.6989584565162659),\n",
       " ('carros', 0.6868077516555786),\n",
       " ('mataron', 0.674161434173584),\n",
       " ('salir', 0.6686909794807434),\n",
       " ('valientes', 0.6636510491371155),\n",
       " ('batalla', 0.6617419123649597),\n",
       " ('gente', 0.6607666015625),\n",
       " ('Siquem', 0.6596595048904419)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
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   ],
   "source": [
    "model.most_similar(positive=['guerra'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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   "source": [
    "words = []\n",
    "vectors = []\n",
    "for s in corpus:\n",
    "    for w in s:\n",
    "        if w in model and w not in words and frequency[w] > 50:\n",
    "            words.append(w)\n",
    "            vectors.append(model[w])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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    {
     "data": {
      "text/plain": [
       "1280"
      ]
     },
     "execution_count": 12,
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   ],
   "source": [
    "len(words)"
   ]
  },
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   "execution_count": 13,
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   "source": [
    "pca = PCA(n_components=2)\n",
    "vectors_pca = pca.fit_transform(vectors)"
   ]
  },
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   "source": [
    "x = []\n",
    "y = []\n",
    "label = []\n",
    "for idx,v in enumerate(words):\n",
    "    label.append(v)\n",
    "    x.append(vectors_pca[idx][0])\n",
    "    y.append(vectors_pca[idx][1])"
   ]
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       "            var render_items = [{\"docid\":\"44a0fbfa-fa44-4b6f-9497-940f69e4b9b3\",\"elementid\":\"40569ec3-92ae-4ac2-9911-d426115a21ce\",\"modelid\":\"9a622960-946a-45ca-84c8-8cbc9dbdfb98\",\"notebook_comms_target\":\"ccedd965-4d17-48c9-bd89-254dc7cd2775\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[15]</strong>&gt;</code></p>"
      ],
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x7fbdde7f0b90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source = ColumnDataSource(data=dict(x=x,y=y, label=label))\n",
    "#Nodes Plotting\n",
    "o = figure(plot_height=400,plot_width=800)\n",
    "o.text('x', 'y', label, source=source)\n",
    "o.circle('x', 'y', size=10, source=source,alpha=0.5 )\n",
    "show(o)"
   ]
  }
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