doc2vec_main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Atlas Obscura Doc2Vec model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim\n",
    "from gensim.models.doc2vec import Doc2Vec, TaggedDocument\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "from nltk.corpus import stopwords\n",
    "from collections import namedtuple, Counter\n",
    "from random import shuffle\n",
    "from scipy.stats import percentileofscore\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import multiprocessing\n",
    "import time\n",
    "import numpy as np\n",
    "import re\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "numcores = multiprocessing.cpu_count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def standardtext(df, textfield):\n",
    "    newcolname = 'norm'+textfield\n",
    "    df[newcolname] = df[textfield].str.replace(u'\\xa0', u' ')\n",
    "    df[newcolname] = df[newcolname].str.replace(u'\\xa0', u' ')\n",
    "    df[newcolname] = df[newcolname].str.replace(u'/', u' ')\n",
    "    df[newcolname] = df[newcolname].str.replace(u'\\\\', u' ')\n",
    "    df[newcolname] = df[newcolname].str.replace(u'\\u201C', '\"')\n",
    "    df[newcolname] = df[newcolname].str.replace(u'\\u201D', '\"')\n",
    "    df[newcolname] = df[newcolname].str.replace(u'\\u2018', '\\'')\n",
    "    df[newcolname] = df[newcolname].str.replace(u'\\u2019', '\\'')\n",
    "    df[newcolname] = df[newcolname].str.replace('…', '\\'')\n",
    "    df[newcolname] = df[newcolname].str.replace('\\t', ' ')\n",
    "    df[newcolname] = df[newcolname].str.replace(r\"[^A-Za-z0-9(),!?@\\'\\`\\\"\\_\\n]\", \" \")\n",
    "    df[newcolname] = df[newcolname].str.replace(r\"[\\'\\`]s\", \"\")\n",
    "    df[newcolname] = df[newcolname].str.lower()\n",
    "    \n",
    "    # Pad punctuation with spaces on both sides\n",
    "    for char in ['.', '\"', ',', '(', ')', '!', '?', ';', ':']:\n",
    "        df[newcolname] = df[newcolname].str.replace(char, ' ' + char + ' ')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14115\n"
     ]
    }
   ],
   "source": [
    "# Load AO data\n",
    "\n",
    "aotable = pickle.load(open('data/AOmasterdata-nodup-week3', 'rb'))\n",
    "\n",
    "# remove quotes and double quotes from names of locations\n",
    "aotable['title'] = [re.sub('\"', '', re.sub(\"(^ )|(')\",\"\", x)) for x in list(aotable['title'])]\n",
    "\n",
    "# combine subtitle with description\n",
    "aotable['subdesc'] = aotable['subtitle'] + aotable['description']\n",
    "\n",
    "print(len(aotable))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.88 s, sys: 124 ms, total: 3 s\n",
      "Wall time: 3.05 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# create tokens\n",
    "\n",
    "aotable = standardtext(aotable, 'subdesc')\n",
    "\n",
    "tokenizer = RegexpTokenizer(r'\\w+')\n",
    "\n",
    "aotable[\"tokens\"] = aotable[\"normsubdesc\"].apply(tokenizer.tokenize)\n",
    "\n",
    "#stop_words = stopwords.words('english')\n",
    "    \n",
    "#aotable[\"tokens\"] = aotable[\"tokens\"].apply(lambda tokens: [w for w in tokens if not w in stop_words] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>subtitle</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>location</th>\n",
       "      <th>url</th>\n",
       "      <th>physical_status</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>keywords</th>\n",
       "      <th>description</th>\n",
       "      <th>imgurl</th>\n",
       "      <th>subdesc</th>\n",
       "      <th>normsubdesc</th>\n",
       "      <th>tokens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5561</td>\n",
       "      <td>Lake Hillier</td>\n",
       "      <td>An Australian lake whose pink hue defies scien...</td>\n",
       "      <td></td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>https://www.atlasobscura.com/places/lake-hillier</td>\n",
       "      <td></td>\n",
       "      <td>-34.094179</td>\n",
       "      <td>123.203276</td>\n",
       "      <td>wonders of salt,natural wonders,watery wonders</td>\n",
       "      <td>From a distance, Lake Hillier of Australia’s R...</td>\n",
       "      <td>https://assets.atlasobscura.com/media/W1siZiIs...</td>\n",
       "      <td>An Australian lake whose pink hue defies scien...</td>\n",
       "      <td>an australian lake whose pink hue defies scien...</td>\n",
       "      <td>[an, australian, lake, whose, pink, hue, defie...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4290</td>\n",
       "      <td>Gippsland Lakes Bioluminescence</td>\n",
       "      <td>Australian Lake set aglow by Noctiluca Scintil...</td>\n",
       "      <td>Raymond Island</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Raymond Island, Australia</td>\n",
       "      <td>https://www.atlasobscura.com/places/lake-gipps...</td>\n",
       "      <td></td>\n",
       "      <td>-37.922431</td>\n",
       "      <td>147.791342</td>\n",
       "      <td>watery wonders,bioluminescence</td>\n",
       "      <td>The conditions were rare, and they were perfec...</td>\n",
       "      <td>https://assets.atlasobscura.com/media/W1siZiIs...</td>\n",
       "      <td>Australian Lake set aglow by Noctiluca Scintil...</td>\n",
       "      <td>australian lake set aglow by noctiluca scintil...</td>\n",
       "      <td>[australian, lake, set, aglow, by, noctiluca, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id                            title  \\\n",
       "0  5561                     Lake Hillier   \n",
       "1  4290  Gippsland Lakes Bioluminescence   \n",
       "\n",
       "                                            subtitle            city  \\\n",
       "0  An Australian lake whose pink hue defies scien...                   \n",
       "1  Australian Lake set aglow by Noctiluca Scintil...  Raymond Island   \n",
       "\n",
       "     country                   location  \\\n",
       "0  Australia                  Australia   \n",
       "1  Australia  Raymond Island, Australia   \n",
       "\n",
       "                                                 url physical_status  \\\n",
       "0   https://www.atlasobscura.com/places/lake-hillier                   \n",
       "1  https://www.atlasobscura.com/places/lake-gipps...                   \n",
       "\n",
       "         lat         lng                                        keywords  \\\n",
       "0 -34.094179  123.203276  wonders of salt,natural wonders,watery wonders   \n",
       "1 -37.922431  147.791342                  watery wonders,bioluminescence   \n",
       "\n",
       "                                         description  \\\n",
       "0  From a distance, Lake Hillier of Australia’s R...   \n",
       "1  The conditions were rare, and they were perfec...   \n",
       "\n",
       "                                              imgurl  \\\n",
       "0  https://assets.atlasobscura.com/media/W1siZiIs...   \n",
       "1  https://assets.atlasobscura.com/media/W1siZiIs...   \n",
       "\n",
       "                                             subdesc  \\\n",
       "0  An Australian lake whose pink hue defies scien...   \n",
       "1  Australian Lake set aglow by Noctiluca Scintil...   \n",
       "\n",
       "                                         normsubdesc  \\\n",
       "0  an australian lake whose pink hue defies scien...   \n",
       "1  australian lake set aglow by noctiluca scintil...   \n",
       "\n",
       "                                              tokens  \n",
       "0  [an, australian, lake, whose, pink, hue, defie...  \n",
       "1  [australian, lake, set, aglow, by, noctiluca, ...  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aotable.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lentok = aotable['tokens'].apply(len)\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.hist(lentok, 50, color='#f26c4fff')\n",
    "plt.xlabel('Number of words per document', fontsize=20)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.ylabel('Frequency', fontsize=20)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.axvline(50, linestyle='--', linewidth=3, color='#1b325fff')\n",
    "plt.savefig('fig/SUP_doclength.jpg', format='jpg', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove documents with less than 50 words in their corpora\n",
    "\n",
    "aotable = aotable[aotable['tokens'].apply(len) > 50].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1 s, sys: 14.9 ms, total: 1.02 s\n",
      "Wall time: 1.02 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Convert to named tuples\n",
    "\n",
    "aodocs = namedtuple('aodocs', 'words tags')\n",
    "\n",
    "allao = []\n",
    "\n",
    "for index, row in aotable.iterrows():    \n",
    "    '''\n",
    "    Tags must be specified as lists (length 1)\n",
    "    otherwise the model assumes each *character* is a separate tag\n",
    "    '''\n",
    "    plname = [str(row['id'])]     \n",
    "    word_tokens = row['tokens']\n",
    "\n",
    "    allao.append(TaggedDocument(words = word_tokens, tags = plname))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14090 documents\n",
      "TaggedDocument(['an', 'australian', 'lake', 'whose', 'pink', 'hue', 'defies', 'scientific', 'explanation', 'from', 'a', 'distance', 'lake', 'hillier', 'of', 'australia', 'recherche', 'archipelago', 'looks', 'like', 'a', 'swath', 'of', 'solid', 'bubble', 'gum', 'pink', 'draw', 'closer', 'and', 'the', 'color', 'takes', 'on', 'a', 'more', 'watery', 'translucent', 'quality', 'but', 'remains', 'unmistakably', 'pink', 'whereas', 'the', 'causes', 'behind', 'the', 'unusual', 'coloring', 'of', 'other', 'pink', 'lakes', 'such', 'as', 'the', 'nearby', 'pink', 'lake', 'and', 'senegal', 'lake', 'retba', 'have', 'been', 'definitively', 'confirmed', 'the', 'reason', 'for', 'lake', 'hillier', 'color', 'remains', 'a', 'mystery', 'theories', 'abound', 'of', 'course', 'some', 'speculate', 'that', 'lake', 'hillier', 'color', 'like', 'that', 'of', 'the', 'other', 'lakes', 'is', 'the', 'result', 'of', 'high', 'salinity', 'combined', 'with', 'the', 'presence', 'of', 'a', 'salt', 'loving', 'algae', 'species', 'known', 'as', 'dunaliella', 'salina', 'and', 'pink', 'bacteria', 'known', 'as', 'halobacteria', 'unlike', 'other', 'pink', 'lakes', 'however', 'which', 'regularly', 'change', 'colors', 'in', 'accordance', 'with', 'temperature', 'fluctuations', 'lake', 'hillier', 'maintains', 'its', 'pink', 'shade', 'year', 'round', 'the', 'water', 'retains', 'its', 'pink', 'hue', 'when', 'bottled', 'whatever', 'the', 'cause', 'the', 'water', 'does', 'not', 'appear', 'to', 'pose', 'any', 'danger', 'to', 'humans', 'swimming', 'in', 'it', 'is', 'not', 'possible', 'anyway', 'since', 'the', 'island', 'is', 'used', 'only', 'for', 'research', 'purposes', 'and', 'tourists', 'can', 'admire', 'it', 'only', 'from', 'above', 'on', 'helicopter', 'rides'], ['5561'])\n"
     ]
    }
   ],
   "source": [
    "# Examine input data\n",
    "print(str(len(allao))+' documents')\n",
    "print(allao[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build and train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doc2Vec(dbow+w,d150,n5,w8,mc2,s0.001,t4) : building vocabulary and initiating model.\n",
      "CPU times: user 1.89 s, sys: 18.2 ms, total: 1.91 s\n",
      "Wall time: 1.92 s\n"
     ]
    }
   ],
   "source": [
    "# Model parameters\n",
    "initalpha = 0.05\n",
    "genstorun = 50\n",
    "alphastep = initalpha*0.9/genstorun\n",
    "\n",
    "# Build the model\n",
    "model = Doc2Vec(dm=0, dbow_words=1,\n",
    "                vector_size=150, window=8, min_count=2, \n",
    "                epochs=50, alpha=initalpha, min_alpha=initalpha, workers=numcores)\n",
    "\n",
    "print(\"%s : building vocabulary and initiating model.\" % model)\n",
    "%time model.build_vocab(allao)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Description embeddings: 14090\n",
      "Word embeddings: 52210\n",
      "docvecs count: 14090\n",
      "14090\n",
      "14090\n"
     ]
    }
   ],
   "source": [
    "# check that model has all the data and that documents have unique tags\n",
    "print(\"Description embeddings: %s\" % len(model.docvecs))\n",
    "print(\"Word embeddings: %s\" % len(model.wv.vocab))\n",
    "print(\"docvecs count: %s\" % model.docvecs.count)\n",
    "print(len(model.docvecs.vectors_docs))\n",
    "print(model.corpus_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 45min 30s, sys: 7.01 s, total: 45min 38s\n",
      "Wall time: 12min 12s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Train the model \n",
    "\n",
    "aoshuf = allao[:]  \n",
    "\n",
    "'''\n",
    "To ensure each document is trained an approximately equal number of times,\n",
    "I will shuffle the documents going into training on each iteration.\n",
    "'''\n",
    "for epoch in range(genstorun):   \n",
    "    shuffle(aoshuf)\n",
    "    model.train(aoshuf, total_examples=model.corpus_count, epochs=1)\n",
    "    model.alpha -= alphastep\n",
    "    model.min_alpha = model.alpha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore and engineer features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "compute \"weirdness\" score: mean distance from document to all other documents\n",
    "the higher the value, the more different that entry is\n",
    "'''\n",
    "\n",
    "weirdness = []\n",
    "for i in aotable['id']:\n",
    "    mostsimj = model.docvecs.most_similar(str(i), topn=model.docvecs.count)\n",
    "    cosj =[]\n",
    "    for j in  mostsimj:\n",
    "        cosj.append(j[1])\n",
    "    weirdness.append((i, np.mean(cosj)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weird = pd.DataFrame(weirdness)\n",
    "weird.columns = [\"id\", \"weirdness\"]\n",
    "\n",
    "plt.hist(weird['weirdness'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pre-sort array\n",
    "weirdsort =  sorted(weird['weirdness'])\n",
    "\n",
    "'''\n",
    "calculate percentiles\n",
    "since greater mean cosine similarity means greater similarity to all documents,\n",
    "weirdness should be the inverse of that, so higher values = more isolated\n",
    "'''\n",
    "\n",
    "weird['weirdpct'] = weird['weirdness'].apply(lambda x: 100 - round(percentileofscore(weirdsort, x),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>subtitle</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>location</th>\n",
       "      <th>url</th>\n",
       "      <th>physical_status</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>keywords</th>\n",
       "      <th>description</th>\n",
       "      <th>imgurl</th>\n",
       "      <th>subdesc</th>\n",
       "      <th>normsubdesc</th>\n",
       "      <th>tokens</th>\n",
       "      <th>weirdness</th>\n",
       "      <th>weirdpct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5561</td>\n",
       "      <td>Lake Hillier</td>\n",
       "      <td>An Australian lake whose pink hue defies scien...</td>\n",
       "      <td></td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>https://www.atlasobscura.com/places/lake-hillier</td>\n",
       "      <td></td>\n",
       "      <td>-34.094179</td>\n",
       "      <td>123.203276</td>\n",
       "      <td>wonders of salt,natural wonders,watery wonders</td>\n",
       "      <td>From a distance, Lake Hillier of Australia’s R...</td>\n",
       "      <td>https://assets.atlasobscura.com/media/W1siZiIs...</td>\n",
       "      <td>An Australian lake whose pink hue defies scien...</td>\n",
       "      <td>an australian lake whose pink hue defies scien...</td>\n",
       "      <td>[an, australian, lake, whose, pink, hue, defie...</td>\n",
       "      <td>0.132377</td>\n",
       "      <td>26.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4290</td>\n",
       "      <td>Gippsland Lakes Bioluminescence</td>\n",
       "      <td>Australian Lake set aglow by Noctiluca Scintil...</td>\n",
       "      <td>Raymond Island</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Raymond Island, Australia</td>\n",
       "      <td>https://www.atlasobscura.com/places/lake-gipps...</td>\n",
       "      <td></td>\n",
       "      <td>-37.922431</td>\n",
       "      <td>147.791342</td>\n",
       "      <td>watery wonders,bioluminescence</td>\n",
       "      <td>The conditions were rare, and they were perfec...</td>\n",
       "      <td>https://assets.atlasobscura.com/media/W1siZiIs...</td>\n",
       "      <td>Australian Lake set aglow by Noctiluca Scintil...</td>\n",
       "      <td>australian lake set aglow by noctiluca scintil...</td>\n",
       "      <td>[australian, lake, set, aglow, by, noctiluca, ...</td>\n",
       "      <td>0.142318</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>596</td>\n",
       "      <td>The Haunted Bookshop</td>\n",
       "      <td>Occult books, Tarot, Oddities.</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Melbourne, Australia</td>\n",
       "      <td>https://www.atlasobscura.com/places/haunted-bo...</td>\n",
       "      <td></td>\n",
       "      <td>-37.815472</td>\n",
       "      <td>144.961689</td>\n",
       "      <td>repositories of knowledge,occult,bookstores</td>\n",
       "      <td>The creepy Haunted Bookshop in Melbourne is lo...</td>\n",
       "      <td>https://assets.atlasobscura.com/media/W1siZiIs...</td>\n",
       "      <td>Occult books, Tarot, Oddities.The creepy Haunt...</td>\n",
       "      <td>occult books ,  tarot ,  oddities the creepy h...</td>\n",
       "      <td>[occult, books, tarot, oddities, the, creepy, ...</td>\n",
       "      <td>0.122837</td>\n",
       "      <td>70.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id                            title  \\\n",
       "0  5561                     Lake Hillier   \n",
       "1  4290  Gippsland Lakes Bioluminescence   \n",
       "2   596             The Haunted Bookshop   \n",
       "\n",
       "                                            subtitle            city  \\\n",
       "0  An Australian lake whose pink hue defies scien...                   \n",
       "1  Australian Lake set aglow by Noctiluca Scintil...  Raymond Island   \n",
       "2                     Occult books, Tarot, Oddities.       Melbourne   \n",
       "\n",
       "     country                   location  \\\n",
       "0  Australia                  Australia   \n",
       "1  Australia  Raymond Island, Australia   \n",
       "2  Australia       Melbourne, Australia   \n",
       "\n",
       "                                                 url physical_status  \\\n",
       "0   https://www.atlasobscura.com/places/lake-hillier                   \n",
       "1  https://www.atlasobscura.com/places/lake-gipps...                   \n",
       "2  https://www.atlasobscura.com/places/haunted-bo...                   \n",
       "\n",
       "         lat         lng                                        keywords  \\\n",
       "0 -34.094179  123.203276  wonders of salt,natural wonders,watery wonders   \n",
       "1 -37.922431  147.791342                  watery wonders,bioluminescence   \n",
       "2 -37.815472  144.961689     repositories of knowledge,occult,bookstores   \n",
       "\n",
       "                                         description  \\\n",
       "0  From a distance, Lake Hillier of Australia’s R...   \n",
       "1  The conditions were rare, and they were perfec...   \n",
       "2  The creepy Haunted Bookshop in Melbourne is lo...   \n",
       "\n",
       "                                              imgurl  \\\n",
       "0  https://assets.atlasobscura.com/media/W1siZiIs...   \n",
       "1  https://assets.atlasobscura.com/media/W1siZiIs...   \n",
       "2  https://assets.atlasobscura.com/media/W1siZiIs...   \n",
       "\n",
       "                                             subdesc  \\\n",
       "0  An Australian lake whose pink hue defies scien...   \n",
       "1  Australian Lake set aglow by Noctiluca Scintil...   \n",
       "2  Occult books, Tarot, Oddities.The creepy Haunt...   \n",
       "\n",
       "                                         normsubdesc  \\\n",
       "0  an australian lake whose pink hue defies scien...   \n",
       "1  australian lake set aglow by noctiluca scintil...   \n",
       "2  occult books ,  tarot ,  oddities the creepy h...   \n",
       "\n",
       "                                              tokens  weirdness  weirdpct  \n",
       "0  [an, australian, lake, whose, pink, hue, defie...   0.132377     26.78  \n",
       "1  [australian, lake, set, aglow, by, noctiluca, ...   0.142318      4.00  \n",
       "2  [occult, books, tarot, oddities, the, creepy, ...   0.122837     70.29  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge weirdness score with main table\n",
    "aotable = pd.merge(aotable, weird, on='id', how='left')\n",
    "\n",
    "aotable.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Is weirdness just capturing length of text? (see figures made from each value)\n",
    "doclengths = aotable['tokens'].apply(len)\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.scatter(aotable['weirdness'], doclengths, color='#f26c4fff', alpha=0.2, s=30)\n",
    "plt.yscale('log')\n",
    "plt.ylabel('Document length', fontsize=16)\n",
    "plt.xlabel('Weirdness score', fontsize=16)\n",
    "# plt.axhline(50, linestyle='--', linewidth=3, color='#1b325fff')\n",
    "plt.tight_layout()\n",
    "plt.savefig('fig/SUP_weirddist_150b_50gen.jpg', format='jpg', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do close documents seem similar?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27700 Roman Wharf Timber\n",
      "keywords: ruins,bridges,roman,plaques,ruins (internal),bridges (internal),roman (internal),plaques (internal)\n",
      "\n",
      "\n",
      "most similar:\n",
      "27278 Uppland Runic Inscription 53\n",
      "keywords: archaeology,ancient,history,pictograms,vikings,language\n",
      "\n",
      "\n",
      "Least similar:\n",
      "2268 Graceland Cemetery\n",
      "keywords: tombs,graveyards,cemeteries,catacombs, crypts, & cemeteries\n"
     ]
    }
   ],
   "source": [
    "doc_id = str(random.choice(aotable['id']))\n",
    "similars = model.docvecs.most_similar(doc_id, topn = model.docvecs.count)\n",
    "\n",
    "print(doc_id + \" \" + aotable[aotable['id'] == int(doc_id)]['title'].values[0])\n",
    "print('keywords: ' + aotable[aotable['id'] == int(doc_id)]['keywords'].values[0])\n",
    "#print('description: ' + aotable[aotable['id'] == int(doc_id)]['description'].values[0])\n",
    "\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"most similar:\") \n",
    "print(similars[0][0] + \" \" + aotable[aotable['id'] == int(similars[0][0])]['title'].values[0])\n",
    "print('keywords: ' + aotable[aotable['id'] == int(similars[0][0])]['keywords'].values[0])\n",
    "#print('description: ' + aotable[aotable['id'] == int(similars[0][0])]['description'].values[0])\n",
    "\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Least similar:\") \n",
    "print(similars[-1][0] + \" \" + aotable[aotable['id'] == int(similars[-1][0])]['title'].values[0])\n",
    "print('keywords: ' + aotable[aotable['id'] == int(similars[-1][0])]['keywords'].values[0])\n",
    "#print('description: ' + aotable[aotable['id'] == int(similars[-1][0])]['description'].values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('toys', 0.556920051574707),\n",
       " ('todmorden', 0.5308510065078735),\n",
       " ('juguete', 0.48672518134117126),\n",
       " ('vintage', 0.47662168741226196),\n",
       " ('holsters', 0.47208383679389954),\n",
       " ('squarepants', 0.4654431641101837),\n",
       " ('dyersville', 0.4517592489719391),\n",
       " ('rideable', 0.4504704177379608),\n",
       " ('antique', 0.4406795799732208),\n",
       " ('collectibles', 0.43548497557640076)]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar('toy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tm_infervec = model.infer_vector(['outdoor', 'sculpture'], \n",
    "                                 steps=200, alpha=0.025, min_alpha=0.001)\n",
    "\n",
    "tm_mostsim = model.docvecs.most_similar(positive=[tm_infervec], topn=8)\n",
    "\n",
    "tm_samp = [x[0] for x in tm_mostsim]\n",
    "tm_vecas = [x[1] for x in tm_mostsim]\n",
    "\n",
    "print(tm_vecas)\n",
    "aotable[aotable['id'].isin(tm_samp)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.4868084192276001, 0.4709842801094055, 0.4664589464664459, 0.46481946110725403, 0.4559105634689331, 0.4539436101913452, 0.4539351165294647, 0.45389100909233093]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>subtitle</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>location</th>\n",
       "      <th>url</th>\n",
       "      <th>physical_status</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>keywords</th>\n",
       "      <th>description</th>\n",
       "      <th>imgurl</th>\n",
       "      <th>subdesc</th>\n",
       "      <th>normsubdesc</th>\n",
       "      <th>tokens</th>\n",
       "      <th>weirdness</th>\n",
       "      <th>weirdpct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1442</th>\n",
       "      <td>15851</td>\n",
       "      <td>Nuremberg Toy Museum</td>\n",
       "      <td>This historic toy museum brings a little whims...</td>\n",
       "      <td>Nuremberg</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Nuremberg, Germany</td>\n",
       "      <td>https://www.atlasobscura.com/places/nuremberg-...</td>\n",
       "      <td></td>\n",
       "      <td>49.454627</td>\n",
       "      <td>11.074437</td>\n",
       "      <td>toy museums,toys</td>\n",
       "      <td>While it is probably true that Nuremberg is mo...</td>\n",
       "      <td>https://assets.atlasobscura.com/media/W1siZiIs...</td>\n",
       "      <td>This historic toy museum brings a little whims...</td>\n",
       "      <td>this historic toy museum brings a little whims...</td>\n",
       "      <td>[this, historic, toy, museum, brings, a, littl...</td>\n",
       "      <td>0.129963</td>\n",
       "      <td>36.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>9157</td>\n",
       "      <td>Ettore Guatelli Museum</td>\n",
       "      <td>A rambling museum which honors the beauty in e...</td>\n",
       "      <td>Gaiano</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Gaiano, Italy</td>\n",
       "      <td>https://www.atlasobscura.com/places/ettore-gua...</td>\n",
       "      <td></td>\n",
       "      <td>44.724528</td>\n",
       "      <td>10.174832</td>\n",
       "      <td>museums,collections</td>\n",
       "      <td>There is no tool too simple nor artifact too m...</td>\n",
       "      <td>https://assets.atlasobscura.com/media/W1siZiIs...</td>\n",
       "      <td>A rambling museum which honors the beauty in e...</td>\n",
       "      <td>a rambling museum which honors the beauty in e...</td>\n",
       "      <td>[a, rambling, museum, which, honors, the, beau...</td>\n",
       "      <td>0.124056</td>\n",
       "      <td>64.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2308</th>\n",
       "      <td>19145</td>\n",
       "      <td>Tin Toy Museum Kitahara Collection</td>\n",
       "      <td>More tin toys than you can shake a clapping mo...</td>\n",
       "      <td>Yokohama</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Yokohama, Japan</td>\n",
       "      <td>https://www.atlasobscura.com/places/toys-club-...</td>\n",
       "      <td></td>\n",
       "      <td>35.437665</td>\n",
       "      <td>139.653121</td>\n",
       "      <td>toys,toy museums,museums and collections,museu...</td>\n",
       "      <td>A random walk through the narrow residential s...</td>\n",
       "      <td>https://assets.atlasobscura.com/media/W1siZiIs...</td>\n",
       "      <td>More tin toys than you can shake a clapping mo...</td>\n",
       "      <td>more tin toys than you can shake a clapping mo...</td>\n",
       "      <td>[more, tin, toys, than, you, can, shake, a, cl...</td>\n",
       "      <td>0.130038</td>\n",
       "      <td>36.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6482</th>\n",
       "      <td>8366</td>\n",
       "      <td>Muzeum Zabawek (Toy Museum)</td>\n",
       "      <td>The largest toy museum in Poland showcases Eur...</td>\n",
       "      <td>Karpacz</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Karpacz, Poland</td>\n",
       "      <td>https://www.atlasobscura.com/places/muzeum-zab...</td>\n",
       "      <td></td>\n",
       "      <td>50.782978</td>\n",
       "      <td>15.761395</td>\n",
       "      <td>museums,toys,unique collections</td>\n",
       "      <td>This two-story toy museum on the Czech-Polish ...</td>\n",
       "      <td>https://assets.atlasobscura.com/media/W1siZiIs...</td>\n",
       "      <td>The largest toy museum in Poland showcases Eur...</td>\n",
       "      <td>the largest toy museum in poland showcases eur...</td>\n",
       "      <td>[the, largest, toy, museum, in, poland, showca...</td>\n",
       "      <td>0.137858</td>\n",
       "      <td>10.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7223</th>\n",
       "      <td>2658</td>\n",
       "      <td>Pegasus Small World Toy Museum</td>\n",
       "      <td>Museum featuring strange and enchanting toys o...</td>\n",
       "      <td>Aeugst am Albis</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Aeugst am Albis, Switzerland</td>\n",
       "      <td>https://www.atlasobscura.com/places/pegasus-sm...</td>\n",
       "      <td></td>\n",
       "      <td>47.284205</td>\n",
       "      <td>8.487077</td>\n",
       "      <td>museums and collections,toys,dolls,unique coll...</td>\n",
       "      <td>Billed as an “enchanted world of miniatures,” ...</td>\n",
       "      <td>https://assets.atlasobscura.com/media/W1siZiIs...</td>\n",
       "      <td>Museum featuring strange and enchanting toys o...</td>\n",
       "      <td>museum featuring strange and enchanting toys o...</td>\n",
       "      <td>[museum, featuring, strange, and, enchanting, ...</td>\n",
       "      <td>0.134844</td>\n",
       "      <td>18.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8859</th>\n",
       "      <td>2768</td>\n",
       "      <td>Flutter</td>\n",
       "      <td>Delightful disarray of found objects and clutter.</td>\n",
       "      <td>Portland</td>\n",
       "      <td>United States</td>\n",
       "      <td>Portland, Oregon</td>\n",
       "      <td>https://www.atlasobscura.com/places/flutter</td>\n",
       "      <td></td>\n",
       "      <td>45.551589</td>\n",
       "      <td>-122.675509</td>\n",
       "      <td>unique collections,commercial curiosities,purv...</td>\n",
       "      <td>Nobody, it seems, can resist the charms of thi...</td>\n",
       "      <td>https://assets.atlasobscura.com/media/W1siZiIs...</td>\n",
       "      <td>Delightful disarray of found objects and clutt...</td>\n",
       "      <td>delightful disarray of found objects and clutt...</td>\n",
       "      <td>[delightful, disarray, of, found, objects, and...</td>\n",
       "      <td>0.108634</td>\n",
       "      <td>98.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11991</th>\n",
       "      <td>21417</td>\n",
       "      <td>Little Nadine Earles Doll House Grave</td>\n",
       "      <td>Inside this small brick bungalow is a collecti...</td>\n",
       "      <td>Lanett</td>\n",
       "      <td>United States</td>\n",
       "      <td>Lanett, Alabama</td>\n",
       "      <td>https://www.atlasobscura.com/places/little-nad...</td>\n",
       "      <td></td>\n",
       "      <td>32.870040</td>\n",
       "      <td>-85.202659</td>\n",
       "      <td>gravestones,cemeteries,architectural oddities,...</td>\n",
       "      <td>Nadine Earles was not yet four years old when ...</td>\n",
       "      <td>https://assets.atlasobscura.com/media/W1siZiIs...</td>\n",
       "      <td>Inside this small brick bungalow is a collecti...</td>\n",
       "      <td>inside this small brick bungalow is a collecti...</td>\n",
       "      <td>[inside, this, small, brick, bungalow, is, a, ...</td>\n",
       "      <td>0.139065</td>\n",
       "      <td>8.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12603</th>\n",
       "      <td>20869</td>\n",
       "      <td>National Farm Toy Museum</td>\n",
       "      <td>The farm toy capital of the world hosts a muse...</td>\n",
       "      <td>Dyersville</td>\n",
       "      <td>United States</td>\n",
       "      <td>Dyersville, Iowa</td>\n",
       "      <td>https://www.atlasobscura.com/places/national-f...</td>\n",
       "      <td></td>\n",
       "      <td>42.470973</td>\n",
       "      <td>-91.111035</td>\n",
       "      <td>collections,farms,toys,museums and collections</td>\n",
       "      <td>A toy museum may not seem like the most obviou...</td>\n",
       "      <td>https://assets.atlasobscura.com/media/W1siZiIs...</td>\n",
       "      <td>The farm toy capital of the world hosts a muse...</td>\n",
       "      <td>the farm toy capital of the world hosts a muse...</td>\n",
       "      <td>[the, farm, toy, capital, of, the, world, host...</td>\n",
       "      <td>0.136553</td>\n",
       "      <td>13.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id                                  title  \\\n",
       "1442   15851                   Nuremberg Toy Museum   \n",
       "1999    9157                 Ettore Guatelli Museum   \n",
       "2308   19145     Tin Toy Museum Kitahara Collection   \n",
       "6482    8366            Muzeum Zabawek (Toy Museum)   \n",
       "7223    2658         Pegasus Small World Toy Museum   \n",
       "8859    2768                               Flutter    \n",
       "11991  21417  Little Nadine Earles Doll House Grave   \n",
       "12603  20869               National Farm Toy Museum   \n",
       "\n",
       "                                                subtitle             city  \\\n",
       "1442   This historic toy museum brings a little whims...        Nuremberg   \n",
       "1999   A rambling museum which honors the beauty in e...           Gaiano   \n",
       "2308   More tin toys than you can shake a clapping mo...         Yokohama   \n",
       "6482   The largest toy museum in Poland showcases Eur...          Karpacz   \n",
       "7223   Museum featuring strange and enchanting toys o...  Aeugst am Albis   \n",
       "8859   Delightful disarray of found objects and clutter.         Portland   \n",
       "11991  Inside this small brick bungalow is a collecti...           Lanett   \n",
       "12603  The farm toy capital of the world hosts a muse...       Dyersville   \n",
       "\n",
       "             country                      location  \\\n",
       "1442         Germany            Nuremberg, Germany   \n",
       "1999           Italy                 Gaiano, Italy   \n",
       "2308           Japan               Yokohama, Japan   \n",
       "6482          Poland               Karpacz, Poland   \n",
       "7223     Switzerland  Aeugst am Albis, Switzerland   \n",
       "8859   United States              Portland, Oregon   \n",
       "11991  United States               Lanett, Alabama   \n",
       "12603  United States              Dyersville, Iowa   \n",
       "\n",
       "                                                     url physical_status  \\\n",
       "1442   https://www.atlasobscura.com/places/nuremberg-...                   \n",
       "1999   https://www.atlasobscura.com/places/ettore-gua...                   \n",
       "2308   https://www.atlasobscura.com/places/toys-club-...                   \n",
       "6482   https://www.atlasobscura.com/places/muzeum-zab...                   \n",
       "7223   https://www.atlasobscura.com/places/pegasus-sm...                   \n",
       "8859         https://www.atlasobscura.com/places/flutter                   \n",
       "11991  https://www.atlasobscura.com/places/little-nad...                   \n",
       "12603  https://www.atlasobscura.com/places/national-f...                   \n",
       "\n",
       "             lat         lng  \\\n",
       "1442   49.454627   11.074437   \n",
       "1999   44.724528   10.174832   \n",
       "2308   35.437665  139.653121   \n",
       "6482   50.782978   15.761395   \n",
       "7223   47.284205    8.487077   \n",
       "8859   45.551589 -122.675509   \n",
       "11991  32.870040  -85.202659   \n",
       "12603  42.470973  -91.111035   \n",
       "\n",
       "                                                keywords  \\\n",
       "1442                                    toy museums,toys   \n",
       "1999                                 museums,collections   \n",
       "2308   toys,toy museums,museums and collections,museu...   \n",
       "6482                     museums,toys,unique collections   \n",
       "7223   museums and collections,toys,dolls,unique coll...   \n",
       "8859   unique collections,commercial curiosities,purv...   \n",
       "11991  gravestones,cemeteries,architectural oddities,...   \n",
       "12603     collections,farms,toys,museums and collections   \n",
       "\n",
       "                                             description  \\\n",
       "1442   While it is probably true that Nuremberg is mo...   \n",
       "1999   There is no tool too simple nor artifact too m...   \n",
       "2308   A random walk through the narrow residential s...   \n",
       "6482   This two-story toy museum on the Czech-Polish ...   \n",
       "7223   Billed as an “enchanted world of miniatures,” ...   \n",
       "8859   Nobody, it seems, can resist the charms of thi...   \n",
       "11991  Nadine Earles was not yet four years old when ...   \n",
       "12603  A toy museum may not seem like the most obviou...   \n",
       "\n",
       "                                                  imgurl  \\\n",
       "1442   https://assets.atlasobscura.com/media/W1siZiIs...   \n",
       "1999   https://assets.atlasobscura.com/media/W1siZiIs...   \n",
       "2308   https://assets.atlasobscura.com/media/W1siZiIs...   \n",
       "6482   https://assets.atlasobscura.com/media/W1siZiIs...   \n",
       "7223   https://assets.atlasobscura.com/media/W1siZiIs...   \n",
       "8859   https://assets.atlasobscura.com/media/W1siZiIs...   \n",
       "11991  https://assets.atlasobscura.com/media/W1siZiIs...   \n",
       "12603  https://assets.atlasobscura.com/media/W1siZiIs...   \n",
       "\n",
       "                                                 subdesc  \\\n",
       "1442   This historic toy museum brings a little whims...   \n",
       "1999   A rambling museum which honors the beauty in e...   \n",
       "2308   More tin toys than you can shake a clapping mo...   \n",
       "6482   The largest toy museum in Poland showcases Eur...   \n",
       "7223   Museum featuring strange and enchanting toys o...   \n",
       "8859   Delightful disarray of found objects and clutt...   \n",
       "11991  Inside this small brick bungalow is a collecti...   \n",
       "12603  The farm toy capital of the world hosts a muse...   \n",
       "\n",
       "                                             normsubdesc  \\\n",
       "1442   this historic toy museum brings a little whims...   \n",
       "1999   a rambling museum which honors the beauty in e...   \n",
       "2308   more tin toys than you can shake a clapping mo...   \n",
       "6482   the largest toy museum in poland showcases eur...   \n",
       "7223   museum featuring strange and enchanting toys o...   \n",
       "8859   delightful disarray of found objects and clutt...   \n",
       "11991  inside this small brick bungalow is a collecti...   \n",
       "12603  the farm toy capital of the world hosts a muse...   \n",
       "\n",
       "                                                  tokens  weirdness  weirdpct  \n",
       "1442   [this, historic, toy, museum, brings, a, littl...   0.129963     36.98  \n",
       "1999   [a, rambling, museum, which, honors, the, beau...   0.124056     64.84  \n",
       "2308   [more, tin, toys, than, you, can, shake, a, cl...   0.130038     36.67  \n",
       "6482   [the, largest, toy, museum, in, poland, showca...   0.137858     10.51  \n",
       "7223   [museum, featuring, strange, and, enchanting, ...   0.134844     18.53  \n",
       "8859   [delightful, disarray, of, found, objects, and...   0.108634     98.73  \n",
       "11991  [inside, this, small, brick, bungalow, is, a, ...   0.139065      8.13  \n",
       "12603  [the, farm, toy, capital, of, the, world, host...   0.136553     13.76  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tm_infervec = model.infer_vector(['toys'], \n",
    "                                 steps=200, alpha=0.025, min_alpha=0.001)\n",
    "\n",
    "mostsimtest = model.docvecs.most_similar(positive=[tm_infervec], topn=8)\n",
    "\n",
    "samp = [x[0] for x in mostsimtest]\n",
    "vecas = [x[1] for x in mostsimtest]\n",
    "\n",
    "print(vecas)\n",
    "aotable[aotable['id'].isin(samp)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supplementary material"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>keyword</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>museums</td>\n",
       "      <td>1383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>museums and collections</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>art</td>\n",
       "      <td>914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>history</td>\n",
       "      <td>912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>architecture</td>\n",
       "      <td>799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   keyword  count\n",
       "1                  museums   1383\n",
       "2  museums and collections   1200\n",
       "3                      art    914\n",
       "4                  history    912\n",
       "5             architecture    799"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "megastring = ','.join(list(aotable['keywords']))\n",
    "\n",
    "keys = megastring.split(\",\")\n",
    "\n",
    "keystable = pd.DataFrame(Counter(keys), index=['count']).T.reset_index().rename(columns={'index':'keyword'})\n",
    "\n",
    "keystable = keystable.sort_values(by='count', ascending=False).reset_index(drop=True).drop(0)\n",
    "\n",
    "keystable.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.hist(keystable['count'], bins=50, color='#f26c4fff')\n",
    "plt.xlabel('Keyword occurences', fontsize=20)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.ylabel('Frequency', fontsize=20)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.tight_layout()\n",
    "plt.savefig('fig/SUP_keywordfreq.jpg', format='jpg', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.barh(range(20), keystable['count'].head(20), height=0.9, color='#f26c4fff')\n",
    "plt.xlabel('Frequency', fontsize=20)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.ylabel('Keywords', fontsize=20)\n",
    "plt.yticks(range(20), keystable['keyword'].head(20), fontsize=14)\n",
    "plt.gca().invert_yaxis()\n",
    "plt.tight_layout()\n",
    "plt.savefig('fig/SUP_keywordsTop20.jpg', format='jpg', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the model: cosine similarity distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for each article, find the cosine similarity to each other article\n",
    "cossim = []\n",
    "for i in range(model.corpus_count):\n",
    "    for j in range(i+1, model.corpus_count):\n",
    "        cossim.append(model.docvecs.similarity(i,j))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.hist(cossim, bins=100, color='#f26c4fff')\n",
    "plt.xlabel('Cosine similarity', fontsize=20)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.ylabel('Frequency', fontsize=20)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.tight_layout()\n",
    "plt.savefig('fig/SUP_CosSimDist.jpg', format='jpg', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the model: tag clustering"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "d2vdf = pd.DataFrame(model.docvecs.vectors_docs)\n",
    "\n",
    "d2vdf['ismuseum'] = [\"mus\" if 'museum' in x else \"notmus\" for x in aotable['keywords']]\n",
    "\n",
    "colmap ={\"mus\":'red', \"notmus\":'blue'}\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.scatter(d2vdf[0], d2vdf[1], c=d2vdf['ismuseum'].apply(lambda x: colmap[x]), alpha=0.4, s=70)\n",
    "plt.xlabel('Vector 0', fontsize=20)\n",
    "plt.ylabel('Vector 1', fontsize=20)\n",
    "plt.title(\"'Museum' tag\", fontsize=30)\n",
    "#plt.legend(labels=('museum', 'not museum'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## saving results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SAVING OUTPUT\n",
    "with open('data/AOmasterdata-nodup-week4-clean', 'wb') as outfile:\n",
    "     pickle.dump(aotable, outfile)\n",
    "\n",
    "with open('data/taggeddocs-week4-clean', 'wb') as outfile:\n",
    "     pickle.dump(allao, outfile)\n",
    "\n",
    "model.save('data/model-week4-clean')"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}