childes-english-analysis.ipynb

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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import nltk\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# from pretty import pprint\n",
    "from bokeh.models import ColumnDataSource, LabelSet\n",
    "from bokeh.plotting import figure, show, output_file\n",
    "from bokeh.io import output_notebook\n",
    "output_notebook()\n",
    "\n",
    "from gensim.models import Word2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16087"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Word2Vec.load(\"w2v/w2v_eng_uk_na_corpora\")\n",
    "len(model.wv.vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"eng_na_uk.vocab\",\"w\") as op:\n",
    "    op.write(\"\\n\".join(model.wv.vocab.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "top_2000_words =[word for word,_ in Counter(model.wv.vocab).most_common(2000)]\n",
    "top_2000_vecs = model[top_2000_words]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "tsne = TSNE(n_components=2, random_state=0)\n",
    "top_2000_tsne = tsne.fit_transform(top_2000_vecs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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\",\"dtype\":\"float64\",\"shape\":[2000]},\"x2\":{\"__ndarray__\":\"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T-SNE for most common words\"},\"id\":\"0ebd7edb-988d-428d-b80b-a54949ed31da\",\"type\":\"Title\"}],\"root_ids\":[\"5a98cfc5-cb5c-4583-8a0b-85d9a52682a3\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.10\"}};\n",
       "    var render_items = [{\"docid\":\"9f4cc036-bd4e-43cc-a878-a412bccc90b7\",\"elementid\":\"1114ffd8-1fe3-4876-86fe-82a4cbe2b33d\",\"modelid\":\"5a98cfc5-cb5c-4583-8a0b-85d9a52682a3\"}];\n",
       "\n",
       "    root.Bokeh.embed.embed_items(docs_json, render_items);\n",
       "  }\n",
       "\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to embed document because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "5a98cfc5-cb5c-4583-8a0b-85d9a52682a3"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = figure(tools=\"pan,wheel_zoom,reset,save\",\n",
    "           toolbar_location=\"above\",\n",
    "           title=\"word2vec T-SNE for most common words\")\n",
    "\n",
    "source = ColumnDataSource(data=dict(x1=top_2000_tsne[:,0],\n",
    "                                    x2=top_2000_tsne[:,1],\n",
    "                                    names=top_2000_words))\n",
    "\n",
    "p.scatter(x=\"x1\", y=\"x2\", size=8, source=source)\n",
    "\n",
    "labels = LabelSet(x=\"x1\", y=\"x2\", text=\"names\", y_offset=6,\n",
    "                  text_font_size=\"8pt\", text_color=\"#555555\",\n",
    "                  source=source, text_align='center')\n",
    "p.add_layout(labels)\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('saying', 0.720932126045227), ('doing', 0.5543146133422852), ('smashing', 0.5484839677810669), ('doin', 0.5476279854774475), ('whistling', 0.5422560572624207), ('singin', 0.5416629314422607), ('barmy', 0.539007842540741), ('meaning', 0.5353029370307922), ('cheating', 0.5318008661270142), ('singing', 0.5277507305145264)]\n",
      "\n",
      "\n",
      "[('saying', 1.0818170309066772), ('smashing', 0.9988341331481934), ('whistling', 0.9760260581970215), ('barmy', 0.9696086645126343), ('interfering', 0.956294596195221), ('doing', 0.943742573261261), ('cheating', 0.939643383026123), ('singin', 0.9315813779830933), ('doin', 0.9268364310264587), ('wobbling', 0.9244562387466431)]\n"
     ]
    }
   ],
   "source": [
    "x=\"say\"\n",
    "y=\"sayin\"\n",
    "xx=\"ask\"\n",
    "# yy=\"hearing\"\n",
    "print model.wv.most_similar(positive=[x, y], negative=[xx])\n",
    "print \"\\n\"\n",
    "print model.wv.most_similar_cosmul(positive=[x, y], negative=[xx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('lettuce', 0.6647210121154785), ('salmon', 0.6597726345062256), ('crackers', 0.6532133221626282), ('rice', 0.6488854885101318), ('raisins', 0.6331318616867065), ('cheerios', 0.63209468126297), ('tomatoes', 0.630882203578949), ('cherries', 0.6294560432434082), ('steak', 0.627549409866333), ('mushrooms', 0.6264097690582275)]\n",
      "\n",
      "\n",
      "[('salmon', 0.9554268717765808), ('lettuce', 0.9443752765655518), ('crackers', 0.9424830079078674), ('rice', 0.9385457038879395), ('tomatoes', 0.9235421419143677), ('burgers', 0.9235407710075378), ('steak', 0.9233205914497375), ('mustard', 0.923240065574646), ('syrup', 0.9218165874481201), ('cheerios', 0.9213539361953735)]\n"
     ]
    }
   ],
   "source": [
    "x=\"peach\"\n",
    "y=\"peaches\"\n",
    "xx=\"apple\"\n",
    "# yy=\"hearing\"\n",
    "print model.wv.most_similar(positive=[x, y], negative=[xx])\n",
    "print \"\\n\"\n",
    "print model.wv.most_similar_cosmul(positive=[x, y], negative=[xx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
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}