5 Word Embeddings.ipynb

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    "# SISU Digital Humanities: Textual and Language Analysis on Social Media<br />\n",
    "### Session 5: Word Embeddings\n",
    "Created by Tom van Nuenen (tom.van_nuenen@kcl.ac.uk) <br />\n"
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    "# Word Embeddings\n",
    "\n",
    "Today, we'll have a look at word embeddings using Gensim's `word2vec` and `doc2vec` methods. \n",
    "\n",
    "The goal of word vector embedding models is to learn dense, numerical vector representations for each term in a corpus vocabulary. If successful, the vectors for each term encode information about the meaning or concept the term represents, as well as the relationship between it and other terms in the vocabulary. Word vector models are  fully unsupervised: they learn all of these meanings and relationships without any advance knowledge.\n",
    "\n",
    "After working through today's notebook, you'll be able to:\n",
    "\n",
    "1. Use Gensim's word2vec method to create word vectors for a corpus;\n",
    "2. Use these word vectors to reflect on implicit binaries and normativities in your data;\n",
    "3. Visualize topic models using K-means clustering and t-SNE."
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     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/tomvannuenen/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     /Users/tomvannuenen/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     /Users/tomvannuenen/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
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   "source": [
    "# General\n",
    "from pprint import pprint\n",
    "from collections import Counter\n",
    "import os\n",
    "import re\n",
    "import logging\n",
    "import string\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import smart_open\n",
    "import multiprocessing \n",
    "from time import time  # To time our operations\n",
    "from collections import defaultdict  # For word frequency\n",
    "\n",
    "# Gensim\n",
    "import gensim\n",
    "from gensim.models import Word2Vec\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.models.phrases import Phrases, Phraser\n",
    "\n",
    "# NLTK\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "nltk.download('stopwords')\n",
    "nltk.download('punkt')\n",
    "nltk.download('wordnet')\n",
    "from nltk import sent_tokenize, word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer \n",
    "wordnet_lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "# Spacy\n",
    "import spacy \n",
    "\n",
    "# Plotting\n",
    "from bokeh.plotting import figure, show, output_notebook\n",
    "from bokeh.models import HoverTool, ColumnDataSource, value\n",
    "\n",
    "# Clustering \n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.neighbors import KDTree\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "# Suppressing warnings\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\", DeprecationWarning)"
   ]
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   "source": [
    "## Preprocessing\n",
    "\n",
    "We'll start by cleaning up our data a bit. Let's load it up."
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   "source": [
    "# load into df\n",
    "df_com = pd.read_csv(\"data/icc3-comments.csv\", lineterminator='\\n')"
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       "                                                    text  \\\n",
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       "3      Hello everyone. Good morning.This is the first...   \n",
       "4      Well I'm a Chinese so I presume the most impor...   \n",
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   "source": [
    "df_com"
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   "source": [
    "# Get rid of empty values and reset index\n",
    "df_com = df_com[~df_com['text'].isin(['[removed]', '[deleted]' ])].dropna(subset=['text']).reset_index(drop=True)"
   ]
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   "source": [
    "Let's create a small function that cleans up our text by removing all escape-tabs and escape-newlines, as well as all non symbol characters (except for the dot). It also normalizes spaces to a single character and removes leading and trailing spaces."
   ]
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  {
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    "id": "JEdJGFrnY_nG"
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   "outputs": [],
   "source": [
    "def clean_text(text):\n",
    "    # Normalize tabs and remove newlines\n",
    "    no_tabs = text.replace('\\t', ' ').replace('\\n', '');\n",
    "    # Remove all characters except A-Z and a dot.\n",
    "    alphas_only = re.sub(\"[^a-zA-Z\\.]\", \" \", no_tabs);\n",
    "    # Normalize spaces to 1\n",
    "    multi_spaces = re.sub(\" +\", \" \", alphas_only);\n",
    "    # Strip trailing and leading spaces\n",
    "    no_spaces = multi_spaces.strip();\n",
    "    return no_spaces"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "We can now use the Pandas `.apply` method, allowing us to apply a function along an axis of the DataFrame. We'll use a lambda function: a small stand-in function that can take arguments, but only one expression. This is what a lambda looks like. Do you see how it works?"
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     "user_tz": -180
    },
    "id": "UmgmLpMd9fF_"
   },
   "outputs": [],
   "source": [
    "df_com[\"text_clean\"] = df_com[\"text\"].apply(lambda x: clean_text(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9Ok7Ham8-wa9"
   },
   "source": [
    "We now have an additional column in our DataFrame with cleaned up text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 510
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1008,
     "status": "ok",
     "timestamp": 1596036455593,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "7u2Z_-yO-GHJ",
    "outputId": "c0badac3-ed07-48e7-9aa9-7ae690170cc3"
   },
   "outputs": [
    {
     "data": {
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       "      <th>likes</th>\n",
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       "      <td>Welcome to this run of our course Here at the ...</td>\n",
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       "      <td>Hello everyone. Good morning.This is the first...</td>\n",
       "      <td>2016-10-10 01:01:07 UTC</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Hello everyone. Good morning.This is the first...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16363370</td>\n",
       "      <td>19cb3801-93be-4268-9b62-b012517b85ba</td>\n",
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       "      <td>1.5</td>\n",
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       "      <td>5</td>\n",
       "      <td>Well I'm a Chinese so I presume the most impor...</td>\n",
       "      <td>2016-10-10 01:01:19 UTC</td>\n",
       "      <td>4</td>\n",
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       "      <td>Well I m a Chinese so I presume the most impor...</td>\n",
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      "text/plain": [
       "         id                             author_id  parent_id  step  \\\n",
       "0  16363017  2b065715-ca93-45a1-b129-fb015822bdde        NaN   1.1   \n",
       "1  16363067  2b065715-ca93-45a1-b129-fb015822bdde        NaN   1.2   \n",
       "2  16363229  d0adfa81-69cd-4a49-92f3-4a8092983376        NaN   1.1   \n",
       "3  16363369  d0adfa81-69cd-4a49-92f3-4a8092983376        NaN   1.1   \n",
       "4  16363370  19cb3801-93be-4268-9b62-b012517b85ba        NaN   1.5   \n",
       "\n",
       "   week_number  step_number  \\\n",
       "0            1            1   \n",
       "1            1            2   \n",
       "2            1            1   \n",
       "3            1            1   \n",
       "4            1            5   \n",
       "\n",
       "                                                text                timestamp  \\\n",
       "0  Welcome to this run of our course! Here at the...  2016-10-10 00:21:15 UTC   \n",
       "1  Our Educator and Mentor team is eager to know ...  2016-10-10 00:26:08 UTC   \n",
       "2  Hello, Professor Kulich. Happy and excited to ...  2016-10-10 00:46:01 UTC   \n",
       "3  Hello everyone. Good morning.This is the first...  2016-10-10 01:01:07 UTC   \n",
       "4  Well I'm a Chinese so I presume the most impor...  2016-10-10 01:01:19 UTC   \n",
       "\n",
       "   likes first_reported_at first_reported_reason moderation_state moderated  \\\n",
       "0     15               NaN                   NaN              NaN       NaN   \n",
       "1      1               NaN                   NaN              NaN       NaN   \n",
       "2      1               NaN                   NaN              NaN       NaN   \n",
       "3      0               NaN                   NaN              NaN       NaN   \n",
       "4      4               NaN                   NaN              NaN       NaN   \n",
       "\n",
       "                                          text_clean  \n",
       "0  Welcome to this run of our course Here at the ...  \n",
       "1  Our Educator and Mentor team is eager to know ...  \n",
       "2  Hello Professor Kulich. Happy and excited to j...  \n",
       "3  Hello everyone. Good morning.This is the first...  \n",
       "4  Well I m a Chinese so I presume the most impor...  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_com.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KntxWVim-1K_"
   },
   "source": [
    "Let's turn it into a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1308,
     "status": "ok",
     "timestamp": 1596036458863,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "FLjNJaCd3D17"
   },
   "outputs": [],
   "source": [
    "text_li = df_com['text_clean'].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fYwCcPjN-7id"
   },
   "source": [
    "Next, we'll create a function that uses NLTK's `sent_tokenize()` method. This tokenizer splits our texts into sentences, which in turn are split into tokens. We'll also remove stopwords."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1332,
     "status": "ok",
     "timestamp": 1596036461949,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "xEOgByps3aVx"
   },
   "outputs": [],
   "source": [
    "def sentence_tokenize(text):\n",
    "    sentence_doc = sent_tokenize(text)\n",
    "    sentences = [gensim.utils.simple_preprocess(str(doc), deacc=True) for doc in sentence_doc]  # deacc=True removes punctuations\n",
    "    stop = set(stopwords.words('english') + ['’', '“', '”', 'nbsp', 'http'])\n",
    "    no_stop = [[word for word in sentence if word not in stop] for sentence in sentences]\n",
    "    return no_stop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 7818,
     "status": "ok",
     "timestamp": 1596036478305,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "k-8QFdsy3bPO"
   },
   "outputs": [],
   "source": [
    "com_sent_li = [sentence_tokenize(text) for text in text_li]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G0svSRqk7v7Z"
   },
   "source": [
    "Note that we now have a list (of comments) of lists (sentences) of lists (tokens). Let's index the first token of the first sentence of the first comment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1411,
     "status": "ok",
     "timestamp": 1596036481829,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "EgdIVhrt4VxE",
    "outputId": "dca9339e-1e89-46a7-f03c-c5de96a8d178"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'welcome'"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "com_sent_li[0][0][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wmiEo5_66t16"
   },
   "source": [
    "We actually don't need the comment-level demarcation for the rest of our analysis. We can *flatten* our `com_sent_li` object to do so – this way, we create a list (of sentences) of lists (tokens)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1542,
     "status": "ok",
     "timestamp": 1596036484338,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "gVb8-KIU7_l-"
   },
   "outputs": [],
   "source": [
    "sent_li = []\n",
    "for sentence in com_sent_li:\n",
    "    for tokens in sentence:\n",
    "        sent_li.append(tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5q9nQ2K08QGX"
   },
   "source": [
    "Writing the same in a list comprehension looks like this, by the way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1097,
     "status": "ok",
     "timestamp": 1596036487737,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "aPfdxQNr7PMi"
   },
   "outputs": [],
   "source": [
    "sent_li = [tokens for sentence in com_sent_li for tokens in sentence]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "p33QbO4G8kGs"
   },
   "source": [
    "Next, let's create a trigrams model using Gensim's `Phrases` and `Phraser` classes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 12502,
     "status": "ok",
     "timestamp": 1596036500687,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "QBZvKWy93ZWm",
    "outputId": "ed9ba9dc-9f11-4ca9-f9ab-9dc533991a13"
   },
   "outputs": [],
   "source": [
    "bigram = Phrases(sent_li, min_count=5, threshold=80)\n",
    "trigram = Phrases(bigram[sent_li], threshold=80)  \n",
    "bigram_mod = Phraser(bigram)\n",
    "trigram_mod = Phraser(trigram)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J65kFo458nJH"
   },
   "source": [
    "And let's run that model over our list of lists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4199,
     "status": "ok",
     "timestamp": 1596036506357,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "gdZ5MqsG8mfF"
   },
   "outputs": [],
   "source": [
    "trigrams = [trigram_mod[bigram_mod[sentence]] for sentence in sent_li]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Wk2mxX1HZOpk"
   },
   "source": [
    "## Word2Vec\n",
    "\n",
    "Let's create our word embeddings model. Its input is a text corpus (split up in sentences) and its output is a set of \"vectors\" in N dimensions. It allows us to group the vectors of similar words together in vectorspace. We can then reduce the dimensionality to visualize the results in a way humans can understand (such as in a 2-dimensional space), or to perform linear algebra in order to find how words are related.\n",
    "\n",
    "Word2vec is one example of a word embeddings model. It learns by taking words and their contexts (e.g. sentences) into account, and can then try to predict other words. Given enough data, usage and contexts, word2vec can make accurate guesses about a word’s meaning based on its appearances. Those guesses can be used to establish a word’s association with other words (e.g. “man” is to “boy” what “woman” is to “girl”), or cluster documents and classify them by topic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ykxhE2K3Bqfj"
   },
   "source": [
    "### How many cores?\n",
    "Word2Vec can work using independent threads doing simultaneous training. In general, you'll never want to use more workers than the number of CPU cores you have in your machine. So let's check out how many you have.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1587,
     "status": "ok",
     "timestamp": 1596036532568,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "LN1nsJ7cO_oT",
    "outputId": "a3cca3ef-0756-4da7-d33d-6fa8e7bd28bb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cores = multiprocessing.cpu_count() # Count the number of cores in a computer\n",
    "cores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hIt-VOPQCQiP"
   },
   "source": [
    "We now instantiate and train our Word2Vec model, using the parameters below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 39180,
     "status": "ok",
     "timestamp": 1596037818439,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "sQq117-pZOpl"
   },
   "outputs": [],
   "source": [
    "num_features = 300        # Word vector dimensionality (how many features each word will be given)\n",
    "min_word_count = 2        # Minimum word count to be taken into account\n",
    "num_workers = cores       # Number of threads to run in parallel (equal to your amount of cores)\n",
    "context = 6               # Context window size\n",
    "downsampling = 0 #1e-2    # Downsample setting for frequent words\n",
    "seed_n = 1                # Seed for the random number generator (to create reproducible results) \n",
    "sg_n = 1                  # Skip-gram = 1, CBOW = 0\n",
    "\n",
    "model = Word2Vec(trigrams, workers=num_workers, \\\n",
    "            size=num_features, min_count = min_word_count, \\\n",
    "            window = context, sample = downsampling, seed=seed_n, sg=sg_n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xrNV3eYeZOpo"
   },
   "source": [
    "That was it! We can save this model in a Gensim object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 39251,
     "status": "ok",
     "timestamp": 1596035659089,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
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    },
    "id": "uOzf_HW4ZOpo",
    "outputId": "878c1a0a-1750-4e61-b57f-eb78e67ea9a1"
   },
   "outputs": [],
   "source": [
    "model.save(\"word2vec.vec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 39645,
     "status": "ok",
     "timestamp": 1596035659717,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "4l3NDavhZOpr",
    "outputId": "cfae7151-f7bd-4df9-fc86-b41067992e84"
   },
   "outputs": [],
   "source": [
    "model = Word2Vec.load(\"word2vec.vec\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wWQC4S9IZOpu"
   },
   "source": [
    "How many terms are in our vocabulary?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1197,
     "status": "ok",
     "timestamp": 1596037853832,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "SegRmcybZOpw",
    "outputId": "c2622fb6-d414-49d5-c072-9e0c2719f40b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13,779 terms in the vocabulary.\n"
     ]
    }
   ],
   "source": [
    "# Note that Gensim v3 uses model.wv.vocab, while v4 uses model.vocab - please change accordingly if necessary\n",
    "print('{:,} terms in the vocabulary.'.format(len(model.wv.vocab)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xw4th__0ZOp0"
   },
   "source": [
    "### Getting related terms\n",
    "\n",
    "With the information in our word embeddings model, we can try to find similarities between words that interest us (i.e. words that have a similar vector). Let's create a function that retrieves related terms to some input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1492,
     "status": "ok",
     "timestamp": 1596037858997,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "v1HVouFqZOp0"
   },
   "outputs": [],
   "source": [
    "def get_related_terms(token, topn=20):\n",
    "    \"\"\"\n",
    "    look up the topn most similar terms to token and print them as a formatted list\n",
    "    \"\"\"\n",
    "\n",
    "    for word, similarity in model.most_similar(positive=[token], topn=topn):\n",
    "        print(word, round(similarity, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1356,
     "status": "ok",
     "timestamp": 1596037862238,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "CDyV-ntGZOp3",
    "outputId": "b27dcc9f-ebc7-43cc-f301-f5120a0f7554"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "african 0.882\n",
      "eastern 0.874\n",
      "asian 0.863\n",
      "immigrants 0.861\n",
      "islamic 0.859\n",
      "north_south 0.858\n",
      "latin_american 0.853\n",
      "africans 0.845\n",
      "dialects 0.842\n",
      "western 0.842\n",
      "indigenous 0.842\n",
      "predominantly 0.842\n",
      "irish 0.842\n",
      "europeans 0.841\n",
      "colonization 0.841\n",
      "origins 0.839\n",
      "population 0.838\n",
      "middle_eastern 0.838\n",
      "ethnically 0.836\n",
      "australian 0.835\n"
     ]
    }
   ],
   "source": [
    "get_related_terms(u'european')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LAF3aRsHZOp6"
   },
   "source": [
    "### Word algebra\n",
    "\n",
    "Word algebra, also known as analogy completion, means doing math with words (like the famous example \"king - man + woman = queen\". The core idea is that once words are represented as numerical vectors, you can do math with them. The mathematical procedure works as follows:\n",
    "\n",
    "1. Provide a set of words or phrases you want to add or subtract.\n",
    "2. Look up the vectors that represent those terms in the word vector model.\n",
    "3. Add and subtract those vectors to produce a new, combined vector.\n",
    "4. Look up the most similar vector(s) to this new, combined vector via cosine similarity.\n",
    "5. Return the word(s) associated with the similar vector(s).\n",
    "\n",
    "Let's try it out. We'll create a function that does this for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1516,
     "status": "ok",
     "timestamp": 1596037867247,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "X8RsoWBAZOp7"
   },
   "outputs": [],
   "source": [
    "def word_algebra(add=[], subtract=[], topn=10):\n",
    "    \"\"\"\n",
    "    combine the vectors associated with the words provided\n",
    "    in add= and subtract=, look up the topn most similar\n",
    "    terms to the combined vector, and print the result(s)\n",
    "    \"\"\"\n",
    "    answers = model.most_similar(positive=add, negative=subtract, topn=topn)\n",
    "    \n",
    "    for term, similarity in answers:\n",
    "        print(term)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1733,
     "status": "ok",
     "timestamp": 1596037867714,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "hXhZB8SOZOp9",
    "outputId": "c49eb164-300c-4878-e587-ae94d1d3029c",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "issue\n",
      "obvious\n",
      "causes\n",
      "sides\n",
      "root\n",
      "forth\n",
      "lies\n",
      "function\n",
      "emphasise\n",
      "exhibit\n"
     ]
    }
   ],
   "source": [
    "word_algebra(add=['cultural','problem'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1479,
     "status": "ok",
     "timestamp": 1596037867715,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "ho-F51R3ZOqB",
    "outputId": "d129dd45-c615-4551-9779-3c6b7af9ff05"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variations\n",
      "styles\n",
      "hc_lc\n",
      "interactions\n",
      "able_recognize\n",
      "adjustment\n",
      "processes\n",
      "categories\n",
      "encounters\n",
      "communicative\n"
     ]
    }
   ],
   "source": [
    "word_algebra(add=['cultural','problem'], subtract=['man'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5p4obNSvZOqG"
   },
   "source": [
    "## Word Vector Visualization with t-SNE\n",
    "\n",
    "t-Distributed Stochastic Neighbor Embedding, or t-SNE, is a dimensionality reduction technique to assist with visualizing high-dimensional datasets. It attempts to map high-dimensional data onto a low two- or three-dimensional representation. It tries to keep the relative distances between points as closely as possible in both high-dimensional and low-dimensional space.\n",
    "\n",
    "Scikit-learn provides a convenient implementation of the t-SNE algorithm with its `TSNE` class.\n",
    "\n",
    "Our input for t-SNE will be the DataFrame of word vectors we created before. Let's first drop some stopwords, and\n",
    "take only the 5,000 most frequent terms in the vocabulary for time's sake.\n",
    "\n",
    "First, we need to create a DataFrame with the terms as the row labels, and the 100 dimensions of the word vector model as the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 253
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1844,
     "status": "ok",
     "timestamp": 1596037883761,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "-Tusa5xMZOqH",
    "outputId": "6b25fae7-af0e-41ad-fa77-a29866d70709"
   },
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>different</th>\n",
       "      <td>0.020864</td>\n",
       "      <td>-0.036423</td>\n",
       "      <td>-0.117106</td>\n",
       "      <td>-0.138592</td>\n",
       "      <td>-0.013320</td>\n",
       "      <td>0.051274</td>\n",
       "      <td>0.061817</td>\n",
       "      <td>0.086441</td>\n",
       "      <td>-0.045858</td>\n",
       "      <td>-0.044461</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007419</td>\n",
       "      <td>-0.110917</td>\n",
       "      <td>-0.056807</td>\n",
       "      <td>0.093585</td>\n",
       "      <td>-0.042660</td>\n",
       "      <td>0.021983</td>\n",
       "      <td>-0.012540</td>\n",
       "      <td>0.049146</td>\n",
       "      <td>0.032113</td>\n",
       "      <td>0.038967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>culture</th>\n",
       "      <td>-0.089575</td>\n",
       "      <td>0.009948</td>\n",
       "      <td>-0.089878</td>\n",
       "      <td>-0.064490</td>\n",
       "      <td>-0.028623</td>\n",
       "      <td>0.001827</td>\n",
       "      <td>0.059268</td>\n",
       "      <td>0.014839</td>\n",
       "      <td>-0.035972</td>\n",
       "      <td>-0.011894</td>\n",
       "      <td>...</td>\n",
       "      <td>0.109846</td>\n",
       "      <td>-0.048446</td>\n",
       "      <td>-0.005470</td>\n",
       "      <td>-0.016890</td>\n",
       "      <td>-0.042515</td>\n",
       "      <td>-0.064860</td>\n",
       "      <td>0.020312</td>\n",
       "      <td>-0.102158</td>\n",
       "      <td>-0.001769</td>\n",
       "      <td>0.014197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>people</th>\n",
       "      <td>0.034825</td>\n",
       "      <td>0.007348</td>\n",
       "      <td>-0.028750</td>\n",
       "      <td>-0.014251</td>\n",
       "      <td>0.037092</td>\n",
       "      <td>0.087869</td>\n",
       "      <td>0.029141</td>\n",
       "      <td>0.054536</td>\n",
       "      <td>0.034948</td>\n",
       "      <td>-0.018707</td>\n",
       "      <td>...</td>\n",
       "      <td>0.092785</td>\n",
       "      <td>-0.001300</td>\n",
       "      <td>-0.016272</td>\n",
       "      <td>0.071611</td>\n",
       "      <td>-0.048502</td>\n",
       "      <td>-0.025568</td>\n",
       "      <td>0.006646</td>\n",
       "      <td>0.002297</td>\n",
       "      <td>0.033431</td>\n",
       "      <td>0.023648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13779 rows × 300 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0         1         2         3         4         5    \\\n",
       "pens         -0.071777 -0.012775 -0.065915 -0.115861  0.069841  0.050224   \n",
       "snake        -0.050524 -0.007629 -0.036474 -0.089833  0.085683  0.053400   \n",
       "hindsight    -0.072400 -0.013743 -0.081995 -0.127475  0.019953  0.021737   \n",
       "competently  -0.044828 -0.003042 -0.077056 -0.132246  0.028013  0.052331   \n",
       "safecreative -0.059065 -0.012718 -0.058940 -0.118076  0.064464  0.017560   \n",
       "...                ...       ...       ...       ...       ...       ...   \n",
       "think        -0.113001  0.004582 -0.038043 -0.096367  0.013499  0.113994   \n",
       "cultural     -0.042113  0.050266 -0.034401 -0.047939  0.023789  0.018629   \n",
       "different     0.020864 -0.036423 -0.117106 -0.138592 -0.013320  0.051274   \n",
       "culture      -0.089575  0.009948 -0.089878 -0.064490 -0.028623  0.001827   \n",
       "people        0.034825  0.007348 -0.028750 -0.014251  0.037092  0.087869   \n",
       "\n",
       "                   6         7         8         9    ...       290       291  \\\n",
       "pens          0.084207  0.090258 -0.044392  0.041476  ...  0.040967 -0.060461   \n",
       "snake         0.082193  0.068238 -0.060112  0.036512  ...  0.042715 -0.061790   \n",
       "hindsight     0.065020  0.069140 -0.036209  0.013391  ...  0.052135 -0.060243   \n",
       "competently   0.071501  0.066206 -0.037323  0.023777  ...  0.041571 -0.044194   \n",
       "safecreative  0.077404  0.072513 -0.034379  0.024794  ...  0.056786 -0.058259   \n",
       "...                ...       ...       ...       ...  ...       ...       ...   \n",
       "think         0.137279  0.080626  0.000652 -0.049199  ...  0.055704  0.057805   \n",
       "cultural     -0.051416 -0.065410 -0.018043 -0.013228  ...  0.044619 -0.026098   \n",
       "different     0.061817  0.086441 -0.045858 -0.044461  ...  0.007419 -0.110917   \n",
       "culture       0.059268  0.014839 -0.035972 -0.011894  ...  0.109846 -0.048446   \n",
       "people        0.029141  0.054536  0.034948 -0.018707  ...  0.092785 -0.001300   \n",
       "\n",
       "                   292       293       294       295       296       297  \\\n",
       "pens         -0.040834  0.024077 -0.039086  0.004867  0.014293 -0.045933   \n",
       "snake        -0.020632  0.034443 -0.042395  0.003541 -0.000640 -0.044510   \n",
       "hindsight    -0.060657  0.060296 -0.080406  0.004948  0.021079 -0.043984   \n",
       "competently  -0.056700  0.053672 -0.051024  0.006473  0.025016 -0.028347   \n",
       "safecreative -0.036119  0.036368 -0.056881  0.008136  0.008696 -0.051099   \n",
       "...                ...       ...       ...       ...       ...       ...   \n",
       "think        -0.018887  0.070893 -0.121991  0.002611  0.027904 -0.061192   \n",
       "cultural      0.051893  0.074644 -0.050740  0.044840 -0.009082 -0.008333   \n",
       "different    -0.056807  0.093585 -0.042660  0.021983 -0.012540  0.049146   \n",
       "culture      -0.005470 -0.016890 -0.042515 -0.064860  0.020312 -0.102158   \n",
       "people       -0.016272  0.071611 -0.048502 -0.025568  0.006646  0.002297   \n",
       "\n",
       "                   298       299  \n",
       "pens         -0.016928 -0.034440  \n",
       "snake        -0.017364 -0.037284  \n",
       "hindsight     0.002858 -0.054514  \n",
       "competently   0.002246 -0.041878  \n",
       "safecreative -0.004120 -0.067410  \n",
       "...                ...       ...  \n",
       "think        -0.032370 -0.095199  \n",
       "cultural      0.075245  0.026154  \n",
       "different     0.032113  0.038967  \n",
       "culture      -0.001769  0.014197  \n",
       "people        0.033431  0.023648  \n",
       "\n",
       "[13779 rows x 300 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build a list of the terms, integer indices, and term counts from the word2vec model vocabulary\n",
    "ordered_vocab = [(term, voc.index, voc.count) for term, voc in model.wv.vocab.items()]\n",
    "\n",
    "# sort by the term counts, so the most common terms appear first\n",
    "ordered_vocab.sort(key = lambda x: x[2])  \n",
    "\n",
    "# unzip the terms, integer indices, and counts into separate lists\n",
    "ordered_terms, term_indices, term_counts = zip(*ordered_vocab)\n",
    "\n",
    "# create a DataFrame with the vectors as data, and the terms as row labels\n",
    "word_vectors = pd.DataFrame(model.wv.syn0norm[term_indices, :], index=ordered_terms)\n",
    "\n",
    "word_vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mSeCHT9HZOqK"
   },
   "outputs": [],
   "source": [
    "tsne = TSNE()\n",
    "tsne_vectors = tsne.fit_transform(word_vectors.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kZTyvI2gZOqO"
   },
   "outputs": [],
   "source": [
    "tsne_vectors = pd.DataFrame(tsne_vectors,\n",
    "                            index=pd.Index(word_vectors.index),\n",
    "                            columns=['x_coord', 'y_coord'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gO4nrS52ZOqQ"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x_coord</th>\n",
       "      <th>y_coord</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pens</th>\n",
       "      <td>-16.694654</td>\n",
       "      <td>-32.048573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>snake</th>\n",
       "      <td>-37.612736</td>\n",
       "      <td>-40.024883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hindsight</th>\n",
       "      <td>17.202772</td>\n",
       "      <td>-3.773781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>competently</th>\n",
       "      <td>32.182758</td>\n",
       "      <td>18.246811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>safecreative</th>\n",
       "      <td>12.749897</td>\n",
       "      <td>-35.108528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                x_coord    y_coord\n",
       "pens         -16.694654 -32.048573\n",
       "snake        -37.612736 -40.024883\n",
       "hindsight     17.202772  -3.773781\n",
       "competently   32.182758  18.246811\n",
       "safecreative  12.749897 -35.108528"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsne_vectors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IyDMY7rsZOqT"
   },
   "outputs": [],
   "source": [
    "tsne_vectors['word'] = tsne_vectors.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VgpLvkq9ZOqV"
   },
   "outputs": [
    {
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       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
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       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
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       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
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       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
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       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
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       "\n",
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       "\n",
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       "        metadata,\n",
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       "    events.on('delete.Cell', handleClearOutput);\n",
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       "      safe: true,\n",
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       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
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       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1001\");\n",
       "    if (el != null) {\n",
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       "\n",
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       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
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       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\": \"T2yuo9Oe71Cz/I4X9Ac5+gpEa5a8PpJCDlqKYO0CfAuEszu1JrXLl8YugMqYe3sM\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\": \"98GDGJ0kOMCUMUePhksaQ/GYgB3+NH9h996V88sh3aOiUNX3N+fLXAtry6xctSZ6\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js\": \"89bArO+nlbP3sgakeHjCo1JYxYR5wufVgA3IbUvDY+K7w4zyxJqssu7wVnfeKCq8\"};\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
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       "      element.async = false;\n",
       "      element.src = url;\n",
       "      if (url in hashes) {\n",
       "        element.crossOrigin = \"anonymous\";\n",
       "        element.integrity = \"sha384-\" + hashes[url];\n",
       "      }\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\": \"T2yuo9Oe71Cz/I4X9Ac5+gpEa5a8PpJCDlqKYO0CfAuEszu1JrXLl8YugMqYe3sM\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\": \"98GDGJ0kOMCUMUePhksaQ/GYgB3+NH9h996V88sh3aOiUNX3N+fLXAtry6xctSZ6\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js\": \"89bArO+nlbP3sgakeHjCo1JYxYR5wufVgA3IbUvDY+K7w4zyxJqssu7wVnfeKCq8\"};\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Dl8fu0UNZOqY"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tomvannuenen/opt/anaconda3/lib/python3.8/site-packages/bokeh/plotting/_tools.py:132: UserWarning: ResetTool(id='1027', ...) are being repeated\n",
      "  warnings.warn(\"%s are being repeated\" % \",\".join(repeated_tools))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
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       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"5cafb588-30fd-4684-b4ff-a842ebc6179d\" data-root-id=\"1003\"></div>\n"
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     "metadata": {},
     "output_type": "display_data"
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oreans\",\"siddiqah\",\"saving\",\"first_foremost\",\"undoubtedly\",\"complaining\",\"ingredients\",\"taken_account\",\"thesis\",\"pragmatic\",\"solid\",\"trough\",\"philosopher\",\"peculiarities\",\"transfer\",\"stars\",\"welfare\",\"revealing\",\"commonality\",\"coping\",\"vous\",\"respective\",\"politicians\",\"bothered\",\"satisfy\",\"combined\",\"intellectual\",\"officers\",\"consequently\",\"accomplishment\",\"underneath\",\"adichie\",\"relying\",\"cafe\",\"attribute\",\"simpler\",\"fifth\",\"strikes\",\"culinary\",\"costumes\",\"avid\",\"deserve\",\"konw\",\"soccer\",\"attentive\",\"kazakh\",\"outdoor\",\"tiring\",\"humorous\",\"tolerate\",\"alive\",\"continues\",\"attempts\",\"process_sorting_identities\",\"recommendation\",\"kinda\",\"worship\",\"save_face\",\"technological\",\"czech\",\"rate\",\"wore\",\"aiste\",\"embedded\",\"representing\",\"row\",\"obtain\",\"stone\",\"gym\",\"narrative\",\"horizontal\",\"replace\",\"hierarchical\",\"awkwardness\",\"jesus\",\"designed\",\"deliberately\",\"conversely\",\"severe\",\"troubles\",\"stem\",\"rock\",\"fails\",\"joan\",\"interlocutors\",\"nurture\",\"performing\",\"win\",\"constructed\",\"filters\",\"employment\",\"slavery\",\"purely\",\"quit\",\"self_esteem\",\"respectively\",\"stops\",\"peel_onion\",\"membership\",\"bound\",\"apple\",\"representation\",\"obliged\",\"stare\",\"affective_approach\",\"emotional_intelligence\",\"sequential\",\"masculinity\",\"appropriate_style_standards\",\"adjust_develop_use\",\"contracts\",\"exemple\",\"smartphones\",\"polychromic\",\"mr_johnson\",\"presume\",\"tutor\",\"short_term\",\"closely_related\",\"principal\",\"hundreds\",\"seaside\",\"display\",\"geographic\",\"saved\",\"pool\",\"county\",\"receiver\",\"moocs\",\"texts\",\"geographically\",\"realities\",\"latvia\",\"supported\",\"leisure\",\"pronunciation\",\"specialist\",\"unpack\",\"mannerisms\",\"nationals\",\"translated\",\"farming\",\"th_century\",\"confirmed\",\"subordinate\",\"tremendous\",\"continents\",\"malaysian\",\"masters\",\"treatment\",\"spectrum\",\"jamaica\",\"old_fashioned\",\"completion\",\"uganda\",\"migrated\",\"luxembourg\",\"ningbo\",\"learnings\",\"taking_account\",\"professionals\",\"loose\",\"delhi\",\"confess\",\"suprised\",\"milk\",\"starbucks\",\"van\",\"patients\",\"paragraph\",\"expresses\",\"insider\",\"indicate\",\"treating\",\"bags\",\"million\",\"desperate\",\"bush\",\"invaded\",\"businesses\",\"carried\",\"sphere\",\"collaborate\",\"grandma\",\"thrown\",\"roommates\",\"motto\",\"remove\",\"thick\",\"mis\",\"beings\",\"rubbish\",\"hundred\",\"refreshing\",\"gate\",\"communicates\",\"conflicting\",\"months_ago\",\"confuse\",\"stems\",\"public_spaces\",\"insecure\",\"practically\",\"hanging\",\"repeated\",\"bathroom\",\"dilemma\",\"belonged\",\"died\",\"oriental\",\"introductions\",\"pray\",\"conclude\",\"friendliness\",\"passage\",\"peter\",\"twenty_years\",\"behaves\",\"break_ice\",\"sense_humour\",\"practitioner\",\"modes\",\"tight\",\"fathers\",\"damascus\",\"resident\",\"intentionally\",\"board\",\"sits\",\"sought\",\"musical\",\"visa\",\"varieties\",\"northeast\",\"commenting\",\"sense_humor\",\"acted\",\"obstacle\",\"harsh\",\"unconscious\",\"expat\",\"facets\",\"lovers\",\"partially\",\"serves\",\"smoothly\",\"silly\",\"praise\",\"deliver\",\"forcing\",\"wished\",\"nonetheless\",\"siblings\",\"remaining\",\"warning\",\"exams\",\"nuanced\",\"conform\",\"season\",\"fitting\",\"guides\",\"contain\",\"urge\",\"dig\",\"take_advantage\",\"unconfortable\",\"didnt\",\"silently\",\"unit\",\"richness\",\"ect\",\"mirror\",\"que\",\"stomach\",\"ethnography\",\"inputs\",\"digging\",\"familiarity\",\"spelling\",\"accurately\",\"analysing\",\"broadly\",\"horizons\",\"clever\",\"supportive\",\"evaluate\",\"classify\",\"handed\",\"images\",\"hobby\",\"endless\",\"unavoidable\",\"interconnected\",\"adds\",\"fb\",\"makus\",\"lyrics\",\"zhejiang_province\",\"uncommon\",\"enabled\",\"shyness\",\"technical\",\"liberty\",\"antwerp\",\"violent\",\"magic\",\"rosa\",\"racist\",\"arrangements\",\"concentration\",\"alienation\",\"categorizations\",\"last_minute\",\"servant\",\"spatial\",\"agnostic\",\"recommended\",\"agendas\",\"redacted_com\",\"clarification\",\"ritual\",\"useless\",\"sexual\",\"stable\",\"colours\",\"selves\",\"criticize\",\"shorter\",\"document\",\"record\",\"perception_reality\",\"handy\",\"arguing\",\"defend\",\"poem_perception\",\"stereotypes_prejudice\",\"peeled\",\"newly\",\"youngsters\",\"extension\",\"rank\",\"nationalistic\",\"filial\",\"alisa\",\"categorization\",\"indirectly\",\"reviewer\",\"exclusive\",\"olivia\",\"protection\",\"kim\",\"consistent\",\"hygiene\",\"appropriate_styles\",\"poly_chronic\",\"mono_chronic\",\"paper_diary\",\"use_calendar_app\",\"outlook_calendar\",\"stresses\",\"cinema\",\"evolve\",\"suspect\",\"solve_problems\",\"chi\",\"delighted\",\"natives\",\"distinguishing\",\"foot\",\"forum\",\"musician\",\"apt\",\"secure\",\"implied\",\"travellers\",\"covering\",\"accomplish\",\"satisfying\",\"scho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\",\"dtype\":\"float32\",\"order\":\"little\",\"shape\":[13779]},\"y_coord\":{\"__ndarray__\":\"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\",\"dtype\":\"float32\",\"order\":\"little\",\"shape\":[13779]}},\"selected\":{\"id\":\"1050\"},\"selection_policy\":{\"id\":\"1051\"}},\"id\":\"1002\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"overlay\":{\"id\":\"1029\"}},\"id\":\"1024\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"blue\"},\"line_alpha\":{\"value\":0.2},\"line_color\":{\"value\":\"blue\"},\"size\":{\"units\":\"screen\",\"value\":10},\"x\":{\"field\":\"x_coord\"},\"y\":{\"field\":\"y_coord\"}},\"id\":\"1040\",\"type\":\"Circle\"},{\"attributes\":{\"formatter\":{\"id\":\"1047\"},\"ticker\":{\"id\":\"1019\"},\"visible\":false},\"id\":\"1018\",\"type\":\"LinearAxis\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1029\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"overlay\":{\"id\":\"1028\"}},\"id\":\"1025\",\"type\":\"BoxSelectTool\"},{\"attributes\":{},\"id\":\"1027\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"1023\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"axis\":{\"id\":\"1018\"},\"dimension\":1,\"grid_line_color\":null,\"ticker\":null},\"id\":\"1021\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1010\",\"type\":\"LinearScale\"},{\"attributes\":{\"text\":\"t-SNE Word Embeddings\",\"text_font_size\":{\"value\":\"14pt\"}},\"id\":\"1004\",\"type\":\"Title\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"blue\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"blue\"},\"size\":{\"units\":\"screen\",\"value\":10},\"x\":{\"field\":\"x_coord\"},\"y\":{\"field\":\"y_coord\"}},\"id\":\"1041\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"1022\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"1012\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1019\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1015\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1050\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1051\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"1026\",\"type\":\"ResetTool\"},{\"attributes\":{\"formatter\":{\"id\":\"1049\"},\"ticker\":{\"id\":\"1015\"},\"visible\":false},\"id\":\"1014\",\"type\":\"LinearAxis\"},{\"attributes\":{\"axis\":{\"id\":\"1014\"},\"grid_line_color\":null,\"ticker\":null},\"id\":\"1017\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1047\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":{\"id\":\"1023\"},\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1022\"},{\"id\":\"1023\"},{\"id\":\"1024\"},{\"id\":\"1025\"},{\"id\":\"1026\"},{\"id\":\"1027\"},{\"id\":\"1037\"}]},\"id\":\"1030\",\"type\":\"Toolbar\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"blue\"},\"line_alpha\":{\"value\":0.2},\"size\":{\"units\":\"screen\",\"value\":10},\"x\":{\"field\":\"x_coord\"},\"y\":{\"field\":\"y_coord\"}},\"id\":\"1042\",\"type\":\"Circle\"},{\"attributes\":{\"data_source\":{\"id\":\"1002\"},\"glyph\":{\"id\":\"1040\"},\"hover_glyph\":{\"id\":\"1042\"},\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1041\"},\"selection_glyph\":null,\"view\":{\"id\":\"1044\"}},\"id\":\"1043\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"source\":{\"id\":\"1002\"}},\"id\":\"1044\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1008\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1049\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"callback\":null,\"tooltips\":\"@word\"},\"id\":\"1037\",\"type\":\"HoverTool\"},{\"attributes\":{},\"id\":\"1006\",\"type\":\"DataRange1d\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1028\",\"type\":\"BoxAnnotation\"}],\"root_ids\":[\"1003\"]},\"title\":\"Bokeh Application\",\"version\":\"2.2.3\"}};\n",
       "  var render_items = [{\"docid\":\"4e7c60d5-8b14-42c5-b83f-ab600526922f\",\"root_ids\":[\"1003\"],\"roots\":{\"1003\":\"5cafb588-30fd-4684-b4ff-a842ebc6179d\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(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",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1003"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add our DataFrame as a ColumnDataSource for Bokeh\n",
    "plot_data = ColumnDataSource(tsne_vectors)\n",
    "\n",
    "# create the plot and configure the\n",
    "# title, dimensions, and tools\n",
    "tsne_plot = figure(title='t-SNE Word Embeddings',\n",
    "                   plot_width = 800,\n",
    "                   plot_height = 800,\n",
    "                   tools= ('pan, wheel_zoom, box_zoom, box_select, reset, reset'),\n",
    "                   active_scroll='wheel_zoom')\n",
    "\n",
    "# add a hover tool to display words on roll-over\n",
    "tsne_plot.add_tools(HoverTool(tooltips = '@word'))\n",
    "\n",
    "# draw the words as circles on the plot\n",
    "tsne_plot.circle('x_coord', 'y_coord', source=plot_data,\n",
    "                 color='blue', line_alpha=0.2, fill_alpha=0.1,\n",
    "                 size=10, hover_line_color='black')\n",
    "\n",
    "# configure visual elements of the plot\n",
    "tsne_plot.title.text_font_size = value('14pt')\n",
    "tsne_plot.xaxis.visible = False\n",
    "tsne_plot.yaxis.visible = False\n",
    "tsne_plot.grid.grid_line_color = None\n",
    "tsne_plot.outline_line_color = None\n",
    "\n",
    "# show the plot\n",
    "show(tsne_plot);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L6Y4eXciZOqb"
   },
   "source": [
    "## K-means clustering\n",
    "Another thing we can do is cluster the words using K-Means clustering. \n",
    "\n",
    "K-Means clustering aims to partition N observations into K clusters in which each observation belongs to the cluster with the nearest mean (called the \"cluster centre\"), which serves as a prototype of the cluster.\n",
    "\n",
    "Since our words are all represented as vectors, applying K-Means is easy to do since the clustering algorithm will simply look at differences between vectors (and centers)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1284,
     "status": "ok",
     "timestamp": 1596036750897,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "rJodQkitZOqb"
   },
   "outputs": [],
   "source": [
    "def clustering_on_wordvecs(word_vectors, num_clusters):\n",
    "    # Initalize a k-means object and use it to extract centroids\n",
    "    kmeans_clustering = KMeans(n_clusters = num_clusters, init='k-means++');\n",
    "    idx = kmeans_clustering.fit_predict(word_vectors);\n",
    "    \n",
    "    return kmeans_clustering.cluster_centers_, idx;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1301,
     "status": "ok",
     "timestamp": 1596036752843,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "qPRtxyZfZOqd"
   },
   "outputs": [],
   "source": [
    "Z = model.wv.syn0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 21475,
     "status": "ok",
     "timestamp": 1596036775406,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "Em2CvpgRZOqf"
   },
   "outputs": [],
   "source": [
    "centers, clusters = clustering_on_wordvecs(Z, 10);\n",
    "centroid_map = dict(zip(model.wv.index2word, clusters));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "15_523YKZOqj"
   },
   "source": [
    "Next, we get words in each cluster that are closest to the cluster center. To do this, we initialize a KDTree on the word vectors, and query it for the Top K words on each cluster center. Using the Index 2 word dictionary, we than correspond each word vector back to it’s original word representation and add them to a dataframe for easier printing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1144,
     "status": "ok",
     "timestamp": 1596036783240,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "O9aD76xcZOqk"
   },
   "outputs": [],
   "source": [
    "def get_top_words(index2word, k, centers, wordvecs):\n",
    "    tree = KDTree(wordvecs);\n",
    "    # Use closest points for each cluster center to query closest 20 points to it\n",
    "    closest_points = [tree.query(np.reshape(x, (1, -1)), k=k) for x in centers];\n",
    "    closest_words_idxs = [x[1] for x in closest_points];\n",
    "    # Query Word Index  for each position in the above array, and added to a Dictionary\n",
    "    closest_words = {};\n",
    "    for i in range(0, len(closest_words_idxs)):\n",
    "        closest_words['Cluster #' + str(i)] = [index2word[j] for j in closest_words_idxs[i][0]]\n",
    "    # Create DataFrame from dictionary\n",
    "    df = pd.DataFrame(closest_words);\n",
    "    df.index = df.index+1\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OtBFZL_HZOqm"
   },
   "source": [
    "Let’s get the top words and print the first 20 in each cluster:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2179,
     "status": "ok",
     "timestamp": 1596036787730,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "SwzjSQJlZOqn"
   },
   "outputs": [],
   "source": [
    "top_words = get_top_words(model.wv.index2word, 5000, centers, Z);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1942,
     "status": "ok",
     "timestamp": 1596036787731,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "tI19jNzsZOqq",
    "outputId": "0525155d-011f-4001-f7cd-e87eb67a8948",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>Cluster #0</th>\n",
       "      <th>Cluster #1</th>\n",
       "      <th>Cluster #2</th>\n",
       "      <th>Cluster #3</th>\n",
       "      <th>Cluster #4</th>\n",
       "      <th>Cluster #5</th>\n",
       "      <th>Cluster #6</th>\n",
       "      <th>Cluster #7</th>\n",
       "      <th>Cluster #8</th>\n",
       "      <th>Cluster #9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hardware</td>\n",
       "      <td>globalised_world</td>\n",
       "      <td>anticipate</td>\n",
       "      <td>kansas</td>\n",
       "      <td>tendencies</td>\n",
       "      <td>use_google_calendar</td>\n",
       "      <td>professor_kulich</td>\n",
       "      <td>buying</td>\n",
       "      <td>florida</td>\n",
       "      <td>negatives</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ableist</td>\n",
       "      <td>creating_shared_meanings</td>\n",
       "      <td>break_ice</td>\n",
       "      <td>starter</td>\n",
       "      <td>universally</td>\n",
       "      <td>monthly</td>\n",
       "      <td>comments_replies</td>\n",
       "      <td>cashier</td>\n",
       "      <td>quebec</td>\n",
       "      <td>dialogic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fleeing</td>\n",
       "      <td>advantage</td>\n",
       "      <td>disturbing</td>\n",
       "      <td>centres</td>\n",
       "      <td>derived</td>\n",
       "      <td>scheduling</td>\n",
       "      <td>worthwhile</td>\n",
       "      <td>hurry</td>\n",
       "      <td>decided_move</td>\n",
       "      <td>overtime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bemused</td>\n",
       "      <td>behavioural</td>\n",
       "      <td>intentionally</td>\n",
       "      <td>donald</td>\n",
       "      <td>socioeconomic</td>\n",
       "      <td>paper_diary</td>\n",
       "      <td>exercises</td>\n",
       "      <td>bumping</td>\n",
       "      <td>zimbabwe</td>\n",
       "      <td>preferably</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>abound</td>\n",
       "      <td>hinder</td>\n",
       "      <td>withdrawn</td>\n",
       "      <td>examination</td>\n",
       "      <td>caste</td>\n",
       "      <td>mobile_app</td>\n",
       "      <td>informative</td>\n",
       "      <td>cash</td>\n",
       "      <td>oversea</td>\n",
       "      <td>depict</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>insults</td>\n",
       "      <td>motivations</td>\n",
       "      <td>sth</td>\n",
       "      <td>lima</td>\n",
       "      <td>transmitted</td>\n",
       "      <td>alert</td>\n",
       "      <td>thankyou</td>\n",
       "      <td>sights</td>\n",
       "      <td>zambia</td>\n",
       "      <td>generated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>engages</td>\n",
       "      <td>generalize</td>\n",
       "      <td>choosing</td>\n",
       "      <td>brahmins</td>\n",
       "      <td>familial</td>\n",
       "      <td>organised</td>\n",
       "      <td>ernesto</td>\n",
       "      <td>cup_tea</td>\n",
       "      <td>bangladesh</td>\n",
       "      <td>threats</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>definetely</td>\n",
       "      <td>acknowledging</td>\n",
       "      <td>agreeing</td>\n",
       "      <td>fermented</td>\n",
       "      <td>inequality</td>\n",
       "      <td>electronic_calendar</td>\n",
       "      <td>alot</td>\n",
       "      <td>broke</td>\n",
       "      <td>uruguay</td>\n",
       "      <td>discusses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>rap</td>\n",
       "      <td>seeks</td>\n",
       "      <td>ambiguous</td>\n",
       "      <td>grandad</td>\n",
       "      <td>beings</td>\n",
       "      <td>diaries</td>\n",
       "      <td>proceed</td>\n",
       "      <td>promised</td>\n",
       "      <td>kerala</td>\n",
       "      <td>presumptions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>discriminatory</td>\n",
       "      <td>shared_meanings</td>\n",
       "      <td>investigate</td>\n",
       "      <td>sky</td>\n",
       "      <td>attribute</td>\n",
       "      <td>scheduled</td>\n",
       "      <td>survey</td>\n",
       "      <td>laughing</td>\n",
       "      <td>philippines</td>\n",
       "      <td>transit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Cluster #0                Cluster #1     Cluster #2   Cluster #3  \\\n",
       "1         hardware          globalised_world     anticipate       kansas   \n",
       "2          ableist  creating_shared_meanings      break_ice      starter   \n",
       "3          fleeing                 advantage     disturbing      centres   \n",
       "4          bemused               behavioural  intentionally       donald   \n",
       "5           abound                    hinder      withdrawn  examination   \n",
       "6          insults               motivations            sth         lima   \n",
       "7          engages                generalize       choosing     brahmins   \n",
       "8       definetely             acknowledging       agreeing    fermented   \n",
       "9              rap                     seeks      ambiguous      grandad   \n",
       "10  discriminatory           shared_meanings    investigate          sky   \n",
       "\n",
       "       Cluster #4           Cluster #5        Cluster #6 Cluster #7  \\\n",
       "1      tendencies  use_google_calendar  professor_kulich     buying   \n",
       "2     universally              monthly  comments_replies    cashier   \n",
       "3         derived           scheduling        worthwhile      hurry   \n",
       "4   socioeconomic          paper_diary         exercises    bumping   \n",
       "5           caste           mobile_app       informative       cash   \n",
       "6     transmitted                alert          thankyou     sights   \n",
       "7        familial            organised           ernesto    cup_tea   \n",
       "8      inequality  electronic_calendar              alot      broke   \n",
       "9          beings              diaries           proceed   promised   \n",
       "10      attribute            scheduled            survey   laughing   \n",
       "\n",
       "      Cluster #8    Cluster #9  \n",
       "1        florida     negatives  \n",
       "2         quebec      dialogic  \n",
       "3   decided_move      overtime  \n",
       "4       zimbabwe    preferably  \n",
       "5        oversea        depict  \n",
       "6         zambia     generated  \n",
       "7     bangladesh       threats  \n",
       "8        uruguay     discusses  \n",
       "9         kerala  presumptions  \n",
       "10   philippines       transit  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_words[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iwP_v_AGX88e"
   },
   "source": [
    "## **Doc2Vec**\n",
    "\n",
    "You might be interested in the relationship between comments or posts, instead of single words. You can do this using tf-idf, as you saw in week 2. But word embeddings offer another unsupervised approach to find similarities.\n",
    "\n",
    "Let's read our data again. We’ll need to associate a tag/number with each document of the training corpus. In our case, the tag is simply a zero-based line number.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2043,
     "status": "ok",
     "timestamp": 1596037257163,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "zF3SWaiOX7DV"
   },
   "outputs": [],
   "source": [
    "def read_text(inp):\n",
    "    for i, line in enumerate(inp):\n",
    "        tokens = gensim.utils.simple_preprocess(line)\n",
    "        yield gensim.models.doc2vec.TaggedDocument(tokens, [i])\n",
    "\n",
    "comments = list(read_text(df_com['text_clean']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nWb9ViBzbxMP"
   },
   "source": [
    "Instantiate a Doc2Vec model with a vector size with 50 dimensions and iterating over the training corpus 40 times. We set the minimum word count to 2 in order to discard words with very few occurrences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1475,
     "status": "ok",
     "timestamp": 1596037380724,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "rXJFIbGIbkCq"
   },
   "outputs": [],
   "source": [
    "model = gensim.models.doc2vec.Doc2Vec(vector_size=50, min_count=2, epochs=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jSYSv_yibogQ"
   },
   "source": [
    "Build the vocabulary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 7183,
     "status": "ok",
     "timestamp": 1596037386864,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "pp0ISnCrbn5R"
   },
   "outputs": [],
   "source": [
    "model.build_vocab(comments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xspWe0XIcHbp"
   },
   "source": [
    "Train the model on the corpus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 99532,
     "status": "ok",
     "timestamp": 1596037504178,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "72vHdeQmcINQ"
   },
   "outputs": [],
   "source": [
    "model.train(comments, total_examples=model.corpus_count, epochs=model.epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q7iAdN6pcvds"
   },
   "source": [
    "To assess our new model, we’ll first infer new vectors for each document of the training corpus, compare the inferred vectors with the training corpus, and then returning the rank of the document based on self-similarity. Basically, we’re pretending as if the training corpus is some new unseen data and then seeing how they compare with the trained model. The expectation is that we’ve likely overfit our model (i.e., all of the ranks will be less than 2) and so we should be able to find similar documents very easily. Additionally, we’ll keep track of the second ranks for a comparison of less similar documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 429
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 350784,
     "status": "error",
     "timestamp": 1596037764255,
     "user": {
      "displayName": "Tom van Nuenen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhtEyfMMXnweSdKHvOqheyZd6KLkXGOtz3AYYAIBA=s64",
      "userId": "10012302451096885058"
     },
     "user_tz": -180
    },
    "id": "UUwjxPHWcsPz",
    "outputId": "0762e492-fea3-4220-ca48-19c5eace95b8"
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-59-b15e619c37cc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdoc_id\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomments\u001b[0m\u001b[0;34m)\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[1;32m      4\u001b[0m     \u001b[0minferred_vector\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfer_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomments\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdoc_id\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0msims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdocvecs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmost_similar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minferred_vector\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtopn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdocvecs\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[0m\u001b[1;32m      6\u001b[0m     \u001b[0mrank\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdocid\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdocid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msim\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msims\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdoc_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mranks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrank\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/gensim/models/keyedvectors.py\u001b[0m in \u001b[0;36mmost_similar\u001b[0;34m(self, positive, negative, topn, clip_start, clip_end, indexer)\u001b[0m\n\u001b[1;32m   1738\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmost_similar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtopn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1739\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1740\u001b[0;31m         \u001b[0mdists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvectors_docs_norm\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mclip_start\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mclip_end\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmean\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1741\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtopn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1742\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mdists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mdot\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "ranks = []\n",
    "second_ranks = []\n",
    "for doc_id in range(len(comments)):\n",
    "    inferred_vector = model.infer_vector(comments[doc_id].words)\n",
    "    sims = model.docvecs.most_similar([inferred_vector], topn=len(model.docvecs))\n",
    "    rank = [docid for docid, sim in sims].index(doc_id)\n",
    "    ranks.append(rank)\n",
    "\n",
    "    second_ranks.append(sims[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9242eLY5dATL"
   },
   "source": [
    "Let's find some similar comments!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cRi45HBOc2q6"
   },
   "outputs": [],
   "source": [
    "print('Comment ({}): «{}»\\n'.format(doc_id, ' '.join(comments[doc_id].words)))\n",
    "print(u'SIMILAR/DISSIMILAR COMMENTS PER MODEL %s:\\n' % model)\n",
    "for label, index in [('MOST', 0), ('SECOND-MOST', 1), ('MEDIAN', len(sims)//2), ('LEAST', len(sims) - 1)]:\n",
    "    print(u'%s %s: «%s»\\n' % (label, sims[index], ' '.join(comments[sims[index][0]].words)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ip0ee9CydIHA"
   },
   "source": [
    "And let's automate that a bit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CTVjJZPndEBl"
   },
   "outputs": [],
   "source": [
    "# Pick a random document from the corpus and infer a vector from the model\n",
    "import random\n",
    "doc_id = random.randint(0, len(comments) - 1)\n",
    "\n",
    "# Compare and print the second-most-similar document\n",
    "print('Train Document ({}): «{}»\\n'.format(doc_id, ' '.join(comments[doc_id].words)))\n",
    "sim_id = second_ranks[doc_id]\n",
    "print('Similar Document {}: «{}»\\n'.format(sim_id, ' '.join(comments[sim_id[0]].words)))\n"
   ]
  }
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