expand_dictionary.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <p style=\"text-align: center;\"> Creating and Refining Dictionary with Word Embedding Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: left;\">Charter School Identities Project<br/><br/>Creator: Jaren Haber, PhD Candidate<br/>Institution: Department of Sociology, University of California, Berkeley<br/>Date created: July 20, 2018<br/>Date last modified: October 29, 2019\n",
    "    \n",
    "Description: This notebook views the word embedding model generated from charter school websites and uses it to expand and refine a dictionary for the educational ideology of inquiry-based learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import key packages\n",
    "import gensim # for word embedding models\n",
    "import _pickle as cPickle # Optimized version of pickle\n",
    "import gc # For managing garbage collector\n",
    "from collections import Counter # For counting terms across the corpus\n",
    "\n",
    "# Import functions from other scripts\n",
    "import sys; sys.path.insert(0, \"../data_management/tools/\") # To load functions from files in data_management/tools\n",
    "from textlist_file import write_list, load_list # For saving and loading text lists to/from file\n",
    "from df_tools import check_df, convert_df, load_filtered_df, replace_df_nulls # For displaying basic DF info, storing DFs for memory efficiency, and loading a filtered DF\n",
    "from quickpickle import quickpickle_dump, quickpickle_load # For quickly loading & saving pickle files in Python\n",
    "#import count_dict # For counting word frequencies in corpus (to assess candidate words)\n",
    "#from count_dict import load_dict, Page, dict_precalc, dict_count, create_cols, count_words, collect_counts, count_master"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FOR CLEANING, TOKENIZING, AND STEMMING THE TEXT\n",
    "from nltk import word_tokenize, sent_tokenize # widely used text tokenizer\n",
    "from nltk.stem.porter import PorterStemmer # approximate but effective (and common) method of normalizing words: stems words by implementing a hierarchy of linguistic rules that transform or cut off word endings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# FOR VISUALIZATIONS\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE # For visualizing word embeddings\n",
    "from scipy.spatial import distance # To use cosine distances for tSNE metric\n",
    "\n",
    "# Visualization parameters\n",
    "%pylab inline \n",
    "%matplotlib inline\n",
    "#matplotlib.style.use('white')\n",
    "\n",
    "import seaborn as sns # To make matplotlib prettier\n",
    "sns.set(style='white')\n",
    "#sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define file paths\n",
    "home = '/home/jovyan/work/'\n",
    "wem_path = home + 'wem4themes/data/wem_model_300dims.bin' # path to WEM model\n",
    "charter_path = home + 'misc_data/charters_2015.pkl' # path to charter school data file\n",
    "dict_path = home + 'text_analysis/dictionary_methods/dicts/' # path to dictionary files (may not be used here)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# rows and cols:  (10965, 2)\n",
      "# duplicates by NCESSCH: 0\n",
      "\n",
      "Columns and # missing cases (if any): \n",
      "NCESSCH\n",
      "WEBTEXT\n"
     ]
    }
   ],
   "source": [
    "# For counting term frequencies, load text corpus:\n",
    "df = load_filtered_df(charter_path, [\"WEBTEXT\", \"NCESSCH\"])\n",
    "df['WEBTEXT']=df['WEBTEXT'].fillna('') # turn nan to empty iterable for future convenience"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def dict_cohere(thisdict, wem_model):\n",
    "    '''Computes the average cosine similarity score of terms within one dictionary with all other terms in that same dictionary,\n",
    "    effectively measuring the coherence of the dictionary.\n",
    "    ...question for development: does it make sense to compare the average cosine similarity score between all terms \n",
    "    in thisdict and the average cosine similarity among the total model vocabulary? (Could that be, by definition, 0?)\n",
    "    \n",
    "    NOTE: For an unknown reason, calling this function deletes terms from thisdict.\n",
    "    \n",
    "    Inputs: List of key terms, word2vec model.\n",
    "    Output: Average cosine similarity score of each word with all other words in the list of key terms.'''\n",
    "    \n",
    "    # Initialize average distance variables:\n",
    "    word_avg_dist = 0\n",
    "    word_avg_dists = 0\n",
    "    dict_avg_sim = 0\n",
    "    all_avg_dists = 0\n",
    "    model_avg_dists = 0\n",
    "    \n",
    "    # Compute average cosine similarity score of each word with other dict words:\n",
    "    for word in thisdict:\n",
    "        word_avg_dist = (wem_model.distances(word, other_words=thisdict).sum())/len(thisdict) # Total diffs of word with all other words, take average\n",
    "        word_avg_dists += word_avg_dist # Add up each average distance, incrementally\n",
    "    dict_avg_sim = 1 - word_avg_dists/len(thisdict) # Find average cosine similarity score by subtracting avg. distance from 1\n",
    "\n",
    "    # For comparison, compute average cosine similarity score of each word with ALL other words in the model vocabulary:\n",
    "    #for word in thisdict:\n",
    "    #    all_avg_dist = (wem_model.distances(word).sum())/len(model.vocab) # Default is to compare each word with all words\n",
    "    #    all_avg_dists += all_avg_dist\n",
    "    #model_avg_dist = 1 - all_avg_dists/len(model.vocab) # Find average cosine similarity score by subtracting avg. distance from 1\n",
    "\n",
    "    #print(\"Average cosine similarities by word for this dictionary:       \\t\" + str(dict_avg_dist))\n",
    "    #print(\"Compare to avg. cosine similarities by dict words to ALL words:\\t\" + str(model_avg_dist))\n",
    "    \n",
    "    return dict_avg_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def expand_dict(thisdict, coredict, maxlen, wem_model):\n",
    "    '''TO DO: Create function to implement random additions to core dictionary, \n",
    "    in contrast to removing least similar word vectors--as focus_dict() does.'''\n",
    "    \n",
    "    #code here\n",
    "    \n",
    "    thisdict = list(set(thisdict)) # Remove any duplicates\n",
    "    \n",
    "    if len(thisdict) != maxlen: # Quality check\n",
    "        print(\"WARNING: Function produced a dictionary of length \" + str(len(thisdict)) + \\\n",
    "              \", which is not the specified maximum dict length of \" + str(maxlen))\n",
    "    \n",
    "    return thisdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def focus_dict(thisdict, coredict, maxlen, wem_model):\n",
    "    '''Focus thisdict by removing least similar word vectors until reaching maxlen.\n",
    "    If any words from coredict get removed, compensate for fact that they will get added back in.\n",
    "        \n",
    "    Input: A list of terms, core terms not to remove, desired length, and word2vec model.\n",
    "    Output: The input list focused down to desired length, and still containing all the core terms.'''\n",
    "\n",
    "    core_count = 0 # Counts number of coredict terms that were removed\n",
    "    extend_count = 0 # Counts number of terms removed to offset the coming boost of core terms (that were removed and will be added back in)\n",
    "\n",
    "    while len(thisdict) > maxlen: # Narrow thisdict down to maxlen\n",
    "        badvar = model.doesnt_match(thisdict) # Find least matching term\n",
    "        thisdict.remove(badvar) # Remove that least focal term, to focus dict\n",
    "        if badvar in coredict: # Keep track of number of core terms removed\n",
    "            core_count += 1\n",
    "\n",
    "    while extend_count < core_count: # Remove terms until length = maxlen - number of core terms removed (to offset those core terms that will be added back in later in this script)\n",
    "        badvar = model.doesnt_match(thisdict) # Find least matching term\n",
    "        thisdict.remove(badvar) # Remove that least focal term, to focus dict\n",
    "        extend_count += 1 # Keep track of # non-core terms added\n",
    "        if badvar in coredict: # Keep track of number of core terms removed\n",
    "            core_count += 1\n",
    "            \n",
    "    for term in coredict: # Add back in any missing core terms\n",
    "        if term not in thisdict and term in list(model.vocab):\n",
    "            thisdict.append(term)\n",
    "            \n",
    "    thisdict = list(set(thisdict)) # Remove any duplicates\n",
    "    \n",
    "    if len(thisdict) != maxlen: # Quality check\n",
    "        print(\"WARNING: Function produced a dictionary of length \" + str(len(thisdict)) + \\\n",
    "              \", which is not the specified maximum dict length of \" + str(maxlen))\n",
    "    \n",
    "    return thisdict "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_closestwords(wem_model, word, plotnumber):\n",
    "    '''Use tSNE to display a spatial map (i.e., scatterplot) of words vectors close to given word in Word2Vec model.\n",
    "    Projects each word to a 2D space (a reduction from model size) and plots the position of each word with a label.\n",
    "    Based on this blog: \n",
    "    https://medium.com/@aneesha/using-tsne-to-plot-a-subset-of-similar-words-from-word2vec-bb8eeaea6229)\n",
    "    \n",
    "    TO DO: Incorporate bold seed terms, OPTION for getting most similar words\n",
    "    \n",
    "    Args:\n",
    "        Word2Vec model\n",
    "        Input word\n",
    "        Number of words to plot\n",
    "    Returns:\n",
    "        Visualization of relationships between given word and its closest word vectors\n",
    "    '''\n",
    "    \n",
    "    dimensions = 300\n",
    "    \n",
    "    arr = np.empty((0,dimensions), dtype='f')\n",
    "    word_labels = [word]\n",
    "\n",
    "    # get close words\n",
    "    close_words = wem_model.similar_by_word(word, plotnumber)\n",
    "    \n",
    "    # add the vector for each of the closest words to the array\n",
    "    arr = np.append(arr, np.array([model[word]]), axis=0)\n",
    "    for wrd_score in close_words:\n",
    "        wrd_vector = model[wrd_score[0]]\n",
    "        word_labels.append(wrd_score[0])\n",
    "        arr = np.append(arr, np.array([wrd_vector]), axis=0)\n",
    "        \n",
    "    # find tsne coords for 2 dimensions\n",
    "    tsne = TSNE(n_components=2, random_state=0, metric=distance.cosine)\n",
    "    np.set_printoptions(suppress=True)\n",
    "    Y = tsne.fit_transform(arr)\n",
    "\n",
    "    x_coords = Y[:, 0]\n",
    "    y_coords = Y[:, 1]\n",
    "    \n",
    "    sns.set(style='whitegrid')\n",
    "    \n",
    "    # display scatter plot\n",
    "    fig, ax = plt.subplots(figsize=(20,20))\n",
    "    ax.scatter(x_coords, y_coords, alpha=1, color='b')\n",
    "\n",
    "    for label, x, y in zip(word_labels, x_coords, y_coords):\n",
    "        plt.annotate(label, xy=(x, y), xytext=(0, 0), textcoords='offset points').set_fontsize(20)\n",
    "    plt.xlim(x_coords.min()+10, x_coords.max()+10)\n",
    "    plt.ylim(y_coords.min()+10, y_coords.max()+10)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load word embedding model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model = gensim.models.KeyedVectors.load_word2vec_format(wem_path, binary=True) # Load word2vec model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "481295\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['a',\n",
       " 'about',\n",
       " 'academic',\n",
       " 'academics',\n",
       " 'academy',\n",
       " 'activities',\n",
       " 'admissions',\n",
       " 'after',\n",
       " 'all',\n",
       " 'also',\n",
       " 'am',\n",
       " 'an',\n",
       " 'and',\n",
       " 'any',\n",
       " 'application',\n",
       " 'apply',\n",
       " 'april',\n",
       " 'are',\n",
       " 'arts',\n",
       " 'as',\n",
       " 'at',\n",
       " 'athletics',\n",
       " 'back',\n",
       " 'be',\n",
       " 'board',\n",
       " 'board_of_directors',\n",
       " 'by',\n",
       " 'calendar',\n",
       " 'campus',\n",
       " 'can',\n",
       " 'careers',\n",
       " 'center',\n",
       " 'charter',\n",
       " 'child',\n",
       " 'children',\n",
       " 'class',\n",
       " 'classroom',\n",
       " 'click',\n",
       " 'college',\n",
       " 'community',\n",
       " 'contact',\n",
       " 'contact_us',\n",
       " 'content',\n",
       " 'courses',\n",
       " 'curriculum',\n",
       " 'data',\n",
       " 'day',\n",
       " 'district',\n",
       " 'do',\n",
       " 'donate',\n",
       " 'dyslexia',\n",
       " 'each',\n",
       " 'education',\n",
       " 'educational',\n",
       " 'elementary',\n",
       " 'email',\n",
       " 'employment',\n",
       " 'english',\n",
       " 'enroll',\n",
       " 'enrollment',\n",
       " 'events',\n",
       " 'facebook',\n",
       " 'faculty',\n",
       " 'families',\n",
       " 'family',\n",
       " 'fax',\n",
       " 'first',\n",
       " 'for',\n",
       " 'form',\n",
       " 'forms',\n",
       " 'from',\n",
       " 'funding',\n",
       " 'grade',\n",
       " 'grades',\n",
       " 'handbook',\n",
       " 'has',\n",
       " 'have',\n",
       " 'help',\n",
       " 'her',\n",
       " 'here',\n",
       " 'high',\n",
       " 'history',\n",
       " 'home',\n",
       " 'homeschool',\n",
       " 'how',\n",
       " 'i',\n",
       " 'idea',\n",
       " 'if',\n",
       " 'in',\n",
       " 'info',\n",
       " 'information',\n",
       " 'instruction',\n",
       " 'is',\n",
       " 'it',\n",
       " 'january',\n",
       " 'june',\n",
       " 'kindergarten',\n",
       " 'leadership',\n",
       " 'learn',\n",
       " 'learning',\n",
       " 'library',\n",
       " 'life',\n",
       " 'links',\n",
       " 'login',\n",
       " 'lunch',\n",
       " 'march',\n",
       " 'math',\n",
       " 'may',\n",
       " 'me',\n",
       " 'menu',\n",
       " 'method',\n",
       " 'middle',\n",
       " 'minutes',\n",
       " 'mission',\n",
       " 'more',\n",
       " 'my',\n",
       " 'new',\n",
       " 'news',\n",
       " 'no',\n",
       " 'not',\n",
       " 'now',\n",
       " 'of',\n",
       " 'office',\n",
       " 'on',\n",
       " 'one',\n",
       " 'online',\n",
       " 'or',\n",
       " 'other',\n",
       " 'our',\n",
       " 'out',\n",
       " 'page',\n",
       " 'parent',\n",
       " 'parents',\n",
       " 'phone',\n",
       " 'photo_gallery',\n",
       " 'plan',\n",
       " 'please',\n",
       " 'pm',\n",
       " 'policies',\n",
       " 'policy',\n",
       " 'principal',\n",
       " 'problems',\n",
       " 'program',\n",
       " 'programs',\n",
       " 'public',\n",
       " 'read',\n",
       " 'reading',\n",
       " 'resources',\n",
       " 's',\n",
       " 'schedule',\n",
       " 'school',\n",
       " 'schools',\n",
       " 'science',\n",
       " 'search',\n",
       " 'service',\n",
       " 'services',\n",
       " 'she',\n",
       " 'spanish',\n",
       " 'special_education',\n",
       " 'staff',\n",
       " 'staff_directory',\n",
       " 'state',\n",
       " 'stem',\n",
       " 'student',\n",
       " 'students',\n",
       " 'success',\n",
       " 'summer',\n",
       " 'support',\n",
       " 'teacher',\n",
       " 'teachers',\n",
       " 'team',\n",
       " 'technology',\n",
       " 'that',\n",
       " 'the',\n",
       " 'their',\n",
       " 'they',\n",
       " 'this',\n",
       " 'through',\n",
       " 'time',\n",
       " 'to',\n",
       " 'up',\n",
       " 'us',\n",
       " 'useful_links',\n",
       " 'view',\n",
       " 'volunteer',\n",
       " 'was',\n",
       " 'we',\n",
       " 'website',\n",
       " 'welcome',\n",
       " 'what',\n",
       " 'when',\n",
       " 'which',\n",
       " 'who',\n",
       " 'why',\n",
       " 'will',\n",
       " 'with',\n",
       " 'work',\n",
       " 'year',\n",
       " 'you',\n",
       " 'your']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dictionary of words in model (may not work for old gensim)\n",
    "print(len(model.vocab))\n",
    "sorted(list(model.vocab)[:200])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inductive search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect similar vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Repeat inductive process of exploration for a list of seed terms like:\n",
    "inqseed = ['inquiry-based', 'problem-based', 'discovery-based', 'experiential', 'constructivist']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('integrated_interdisciplinary', 0.8495174646377563),\n",
       " ('technology-infused', 0.8478963375091553),\n",
       " ('hands-on_and_minds-on', 0.845363974571228),\n",
       " ('hands-on/minds-on', 0.8449170589447021),\n",
       " ('project-centered', 0.8422079086303711),\n",
       " ('discussion-based', 0.8401801586151123),\n",
       " ('problem-based_learning', 0.8382363319396973),\n",
       " ('experiential_approach', 0.8377059698104858),\n",
       " ('hands-on_minds-on', 0.8366389274597168),\n",
       " ('life-based', 0.8346015214920044),\n",
       " ('science-driven', 0.8336309194564819),\n",
       " ('language-rich', 0.8326779007911682),\n",
       " ('simulations_role-playing', 0.8310806155204773),\n",
       " ('cognitively_demanding', 0.8298742771148682),\n",
       " ('literacy-based', 0.8274999856948853),\n",
       " ('developmentally-based', 0.8263034820556641),\n",
       " ('sensory-rich', 0.8242142796516418),\n",
       " ('inquiry-driven', 0.8225501775741577),\n",
       " ('student-driven_exploration', 0.8221181035041809),\n",
       " ('hand-on', 0.8210930824279785)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar(inqseed, topn = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('failure_and_academically-gifted', 0.6515583992004395),\n",
       " ('construction_management/general', 0.6038428544998169),\n",
       " ('academically_low-achieving', 0.6014186143875122),\n",
       " ('percentages_of_economically', 0.5981494188308716),\n",
       " ('disenfranchised_youth', 0.5930942296981812),\n",
       " ('at-risk_and_disadvantaged', 0.5930824279785156),\n",
       " ('high-ability_students', 0.5911412239074707),\n",
       " ('underachieving', 0.589192271232605),\n",
       " ('justice-involved', 0.5887687802314758),\n",
       " ('under-represented', 0.5879157185554504),\n",
       " ('low-income_and_minority', 0.5879122018814087),\n",
       " ('african-american_and_latino', 0.585237979888916),\n",
       " ('example_the_disenfranchised', 0.5846658945083618),\n",
       " ('out-of-school_youth', 0.5842435956001282),\n",
       " ('high-risk', 0.5792522430419922),\n",
       " ('high-needs', 0.5791978240013123),\n",
       " ('low-achieving', 0.578443169593811),\n",
       " ('better_my_life.', 0.5783981680870056),\n",
       " ('under-resourced', 0.5779532194137573),\n",
       " ('living_in_poverty', 0.5778278708457947),\n",
       " ('evidence-based_approaches', 0.5777392387390137),\n",
       " ('adjudicated_youth', 0.5761195421218872),\n",
       " ('pregnant/parenting', 0.5755578279495239),\n",
       " ('at-risk_adolescents', 0.5749838948249817),\n",
       " ('intellectually_gifted', 0.5731552839279175),\n",
       " ('low_achieving', 0.5727622509002686),\n",
       " ('considered_at-risk', 0.5725160837173462),\n",
       " ('youth_yaş', 0.5718940496444702),\n",
       " ('aimed_at_achieving', 0.5704052448272705),\n",
       " ('low\\xadincome', 0.5703068375587463)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the 30 words closest to test word:\n",
    "test_word = \"at-risk\"\n",
    "candidate_sims = model.most_similar(test_word, topn=30)\n",
    "candidates_list = [pair[0] for pair in candidate_sims] # Convert to list for frequency search below\n",
    "candidate_sims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('zero-tolerance_policy', 0.6990004181861877),\n",
       " ('zero-tolerance', 0.6568768620491028),\n",
       " ('feels_s/he', 0.6339747905731201),\n",
       " ('hate-motivated', 0.6278372406959534),\n",
       " ('gang_policy-the', 0.6266128420829773),\n",
       " ('molestation_prevention', 0.6207634210586548),\n",
       " ('bulling', 0.6071662306785583),\n",
       " ('sexual/racial/religion', 0.6040709614753723),\n",
       " ('cyberbullying_harassment', 0.5921908617019653),\n",
       " ('offensive_or_provocative', 0.5916315317153931)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar(\"zero_tolerance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "riskseed = [\"high-need\", \"low-income\", \"high-poverty\", \"at-risk\", \"high-risk\"]\n",
    "risk20 = [\"high-need\", \"high-needs\", \n",
    "            \"low-income\", \"lower-income\", \"high-poverty\", \"impoverished\", \"economically_challenged\", \n",
    "            \"underserved\", \"under-served\", \"disproportionately\", \n",
    "            \"at-risk\", \"high-risk\", \"under-resourced\", \"under-represented\", \"under-performing\", \n",
    "            \"inner-city\", \"inner_cities\", \n",
    "            \"marginalized\", \"disconnected\", \"disenfranchised\"] #\"justice\", \"afflicting\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('impoverished_areas', 0.8521490693092346),\n",
       " ('inner-city_neighborhoods', 0.8487109541893005),\n",
       " ('high-need_communities', 0.8366204500198364),\n",
       " ('predominantly_low-income', 0.8357141017913818),\n",
       " ('communities—and', 0.834736704826355),\n",
       " ('disconnected_youth', 0.8341152667999268),\n",
       " ('concentrations_of_low-income', 0.8339563608169556),\n",
       " ('historically_marginalized', 0.8304299116134644),\n",
       " ('upper-income', 0.8298649191856384),\n",
       " ('under-served_populations', 0.8291403651237488),\n",
       " ('youth-serving_agencies', 0.8260529041290283),\n",
       " ('tuition-based_private', 0.8223265409469604),\n",
       " ('african-american_and_latino', 0.8217678666114807),\n",
       " ('military-connected', 0.8214602470397949),\n",
       " ('justice-involved', 0.8207274675369263),\n",
       " ('traditionally_under-served', 0.8206344842910767),\n",
       " ('afflicting', 0.819398045539856),\n",
       " ('disenfranchised_youth', 0.8192448616027832),\n",
       " ('socio-economically_diverse', 0.8176484107971191),\n",
       " ('non-montessori', 0.815646767616272)]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar(risk20, topn = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/gensim/models/keyedvectors.py:877: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
      "  vectors = vstack(self.word_vec(word, use_norm=True) for word in used_words).astype(REAL)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'inner-city'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.doesnt_match(risk20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overview of search process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By using the model to look at similar words across terms, create a list of candidate terms for a bigger conceptual dictionary. Manually search all these candidate terms for those that are tightly conceptually related to the seed dictionary. This process blends data-driven search from the model with hand-driven search across the candidate terms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "T0P 15 WORDS CLOSEST TO inquiry-based\n",
      "('problem-based', 0.7188608646392822)\n",
      "('inquiry_based', 0.7165826559066772)\n",
      "('integrated_interdisciplinary', 0.7108476161956787)\n",
      "('literacy-based', 0.7067997455596924)\n",
      "('problem-based_learning', 0.705278754234314)\n",
      "('experiential_approach', 0.6990994215011597)\n",
      "('technology-infused', 0.6932752132415771)\n",
      "('student-driven_exploration', 0.6918015480041504)\n",
      "('prbl', 0.6899794340133667)\n",
      "('showcase_of_self-driven', 0.6882768273353577)\n",
      "('hands-on_and_minds-on', 0.687936007976532)\n",
      "('qcss_employs', 0.6855128407478333)\n",
      "('simulations_role-playing', 0.6846071481704712)\n",
      "('project-centered', 0.6826268434524536)\n",
      "('hands-on/minds-on', 0.6808892488479614)\n",
      "\n",
      "T0P 15 WORDS CLOSEST TO problem-based\n",
      "('problem-based_learning', 0.7890996932983398)\n",
      "('integrated_interdisciplinary', 0.7848905324935913)\n",
      "('content-specific_grade-by-grade', 0.7842892408370972)\n",
      "('laboratory-based', 0.7822384834289551)\n",
      "('hands-on_minds-on', 0.7795132398605347)\n",
      "('discussion-based', 0.7788670063018799)\n",
      "('design-based', 0.7777847051620483)\n",
      "('hands-on_and_minds-on', 0.777582049369812)\n",
      "('project-centered', 0.7771323919296265)\n",
      "('technology-infused', 0.7769192457199097)\n",
      "('life-based', 0.7765124440193176)\n",
      "('hands-on/minds-on', 0.7760508060455322)\n",
      "('socratic', 0.7760000228881836)\n",
      "('simulations_role-playing', 0.7742791175842285)\n",
      "('inquiry_arcs', 0.7734700441360474)\n",
      "\n",
      "T0P 15 WORDS CLOSEST TO discovery-based\n",
      "('learn-by-doing', 0.8363640308380127)\n",
      "('seminar-based', 0.8189511895179749)\n",
      "('language-rich', 0.8160936236381531)\n",
      "('unschooling_is_a_less', 0.8155924081802368)\n",
      "('developmentally-based', 0.8151591420173645)\n",
      "('nuturing', 0.8126906156539917)\n",
      "('values-centered', 0.8097946047782898)\n",
      "('developmentallyappropriate', 0.8090020418167114)\n",
      "('sensory-rich', 0.80750572681427)\n",
      "('project-centered', 0.8046090602874756)\n",
      "('explorative', 0.8015897274017334)\n",
      "('cognitively_demanding', 0.8008060455322266)\n",
      "('andinnovative', 0.8005708456039429)\n",
      "('literature-rich', 0.7991681694984436)\n",
      "('low-stress', 0.798554539680481)\n",
      "\n",
      "T0P 15 WORDS CLOSEST TO experiential\n",
      "('tomorrow_in_rockville', 0.6366349458694458)\n",
      "('integrative', 0.6330317258834839)\n",
      "('journey_for_tma', 0.6237869262695312)\n",
      "('sensory-rich', 0.6203253269195557)\n",
      "('nature-based', 0.6179972887039185)\n",
      "('non-arts_objectives', 0.6178889870643616)\n",
      "('inquiry-driven', 0.612930178642273)\n",
      "('teacher-guided', 0.6102246046066284)\n",
      "('fieldwork_experts', 0.6101946830749512)\n",
      "('hands-on/minds-on', 0.6096264123916626)\n",
      "('service-based', 0.6085801124572754)\n",
      "('literacy-based', 0.6079000234603882)\n",
      "('problem-based_learning', 0.6077450513839722)\n",
      "('project-centered', 0.6064910292625427)\n",
      "('curriculum_that_enhances', 0.6063675284385681)\n",
      "\n",
      "T0P 15 WORDS CLOSEST TO constructivist\n",
      "('constructivism', 0.7486873865127563)\n",
      "('technology-infused', 0.7210007309913635)\n",
      "('whole-brain', 0.7190081477165222)\n",
      "('child-led', 0.71506667137146)\n",
      "('technology-enriched', 0.7139584422111511)\n",
      "('didactic_materials', 0.7123608589172363)\n",
      "('discussion-based', 0.7111490368843079)\n",
      "('instructional_best-practices', 0.7071788907051086)\n",
      "('integrated_interdisciplinary', 0.707124650478363)\n",
      "('language-rich', 0.7067819833755493)\n",
      "('constructivist_approach', 0.7062184810638428)\n",
      "('brain-based', 0.7042311429977417)\n",
      "('hands-on_minds-on', 0.7038856744766235)\n",
      "('student-centered_inquiry-based', 0.7034156322479248)\n",
      "('developmentally-based', 0.7027567625045776)\n"
     ]
    }
   ],
   "source": [
    "# Find the 15 words closest to each term in seed dictionary:\n",
    "for term in inqseed:\n",
    "    print(\"\\nT0P 15 WORDS CLOSEST TO \" + term)\n",
    "    for line in list(model.most_similar(term, topn=15)):\n",
    "        print(line)\n",
    "\n",
    "#model.most_similar(\"problem-based\", topn=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['integrated_interdisciplinary',\n",
       " 'technology-infused',\n",
       " 'hands-on_and_minds-on',\n",
       " 'hands-on/minds-on',\n",
       " 'project-centered',\n",
       " 'discussion-based',\n",
       " 'problem-based_learning',\n",
       " 'experiential_approach',\n",
       " 'hands-on_minds-on',\n",
       " 'life-based',\n",
       " 'science-driven',\n",
       " 'language-rich',\n",
       " 'simulations_role-playing',\n",
       " 'cognitively_demanding',\n",
       " 'literacy-based',\n",
       " 'developmentally-based',\n",
       " 'sensory-rich',\n",
       " 'inquiry-driven',\n",
       " 'student-driven_exploration',\n",
       " 'hand-on',\n",
       " 'whole-brain',\n",
       " 'hands‑on',\n",
       " 'active_inquiry',\n",
       " 'concrete_manipulative',\n",
       " 'content-specific_grade-by-grade',\n",
       " 'design-based',\n",
       " 'interdisciplinary_project-based',\n",
       " 'andinnovative',\n",
       " 'explorative',\n",
       " 'out-the-door',\n",
       " 'student-centered_inquiry-based',\n",
       " 'exploration_and_experimentation',\n",
       " 'thematic_integrated',\n",
       " 'experiential_project-based',\n",
       " 'seminar-based',\n",
       " 'socratic',\n",
       " 'experientially',\n",
       " 'literature-rich',\n",
       " 'interdisciplinary_and_multi-dimensional',\n",
       " 'interdisciplinary_explorations',\n",
       " 'inter-disciplinary',\n",
       " 'rigorous_skills-based',\n",
       " 'cross-disciplinary',\n",
       " 'socratic_dialogue',\n",
       " 'life-enhancing_skills',\n",
       " 'discovery-based_learning',\n",
       " 'movement-based',\n",
       " 'technology-enriched',\n",
       " 'siop_sheltered',\n",
       " 'child-centered_individualized']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can also look for most similar terms across seed dictionary as a whole, and use it to make new dictionary:\n",
    "inq50new = []\n",
    "for line in list(model.most_similar(inqseed, topn=50)):\n",
    "    #print(line[0]) # only print words\n",
    "    inq50new.append(line[0])\n",
    "inq50new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('andinnovative', 0.9082848429679871),\n",
       " ('student-driven_exploration', 0.905834436416626),\n",
       " ('hands-on_and_minds-on', 0.9000217318534851),\n",
       " ('student-centered_inquiry-based', 0.8993304967880249),\n",
       " ('learn-by-doing', 0.8984565734863281),\n",
       " ('cognitively_demanding', 0.896692156791687),\n",
       " ('life-based', 0.8961136937141418),\n",
       " ('experiential_approach', 0.8954914808273315),\n",
       " ('whole-brain', 0.8943194150924683),\n",
       " ('content-specific_grade-by-grade', 0.8941212296485901),\n",
       " ('exploration_and_experimentation', 0.8932093381881714),\n",
       " ('language-rich', 0.891950786113739),\n",
       " ('proven_methodology', 0.891946017742157),\n",
       " ('rigorous_skills-based', 0.891916036605835),\n",
       " ('discussion-based', 0.8918140530586243),\n",
       " ('goal-driven', 0.8915873169898987),\n",
       " ('experiential_project-based', 0.8912684917449951),\n",
       " ('design-based', 0.8896303176879883),\n",
       " ('concrete_manipulative', 0.8890612125396729),\n",
       " ('science-driven', 0.888805627822876),\n",
       " ('iqwst', 0.887614369392395),\n",
       " ('life-enhancing_skills', 0.8874599933624268),\n",
       " ('multi-modality', 0.8868114948272705),\n",
       " ('hands-on/minds-on', 0.8863054513931274),\n",
       " ('sensory-rich', 0.886135458946228),\n",
       " ('skills.the', 0.8840631246566772),\n",
       " ('literature-rich', 0.8838809728622437),\n",
       " ('concept-driven', 0.883530855178833),\n",
       " ('common-core_aligned', 0.8827246427536011),\n",
       " ('cross-curricular_approach', 0.8825916647911072),\n",
       " ('seminar-based', 0.8806145191192627),\n",
       " ('movement-based', 0.8798118829727173),\n",
       " ('literacyinstruction', 0.87842857837677),\n",
       " ('technology-infused', 0.8780218958854675),\n",
       " ('inventive_thinking', 0.8773922920227051),\n",
       " ('activity-oriented', 0.8766010999679565),\n",
       " ('arts-integrated_approach', 0.8763177990913391),\n",
       " ('constructivist-based', 0.8757773637771606),\n",
       " ('inwhich', 0.8755730390548706),\n",
       " ('integrated_interdisciplinary', 0.8755093812942505),\n",
       " ('outside-the-box', 0.8748199343681335),\n",
       " ('constructivists', 0.874788224697113),\n",
       " ('nuturing', 0.8743723630905151),\n",
       " ('developmentally-based', 0.8741036653518677),\n",
       " ('discovery-based', 0.873662531375885),\n",
       " ('inquiry_arcs', 0.8714935779571533),\n",
       " ('fieldwork_and_experts', 0.8713229298591614),\n",
       " ('issues-based', 0.8712214231491089),\n",
       " ('constructivist_pedagogy', 0.8711371421813965),\n",
       " ('fostering_curiosity', 0.8707929849624634)]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By searching through the above candidate terms/phrases, expand from the seed terms into a list of synonyms like this: \n",
    "inq20 = ['active_inquiry', 'activity-based', 'activity/project', 'constructivism', \n",
    "         'experience-based', 'experientially', \n",
    "         'explorative', 'inquiry/research', 'inquiry-driven', \n",
    "         'problem-centered', 'problem-solving', \n",
    "         'project-based', 'project-centered', 'project-focused', 'project-oriented']\n",
    "\n",
    "# Could add more terms, too:\n",
    "model.most_similar(inq20, topn=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('life-based', 0.9069126844406128),\n",
       " ('hands-on_and_minds-on', 0.9033913016319275),\n",
       " ('andinnovative', 0.9029760360717773),\n",
       " ('hands-on/minds-on', 0.9022852182388306),\n",
       " ('discussion-based', 0.899781346321106),\n",
       " ('student-driven_exploration', 0.8974231481552124),\n",
       " ('cognitively_demanding', 0.8939936757087708),\n",
       " ('sensory-rich', 0.8939144611358643),\n",
       " ('exploration_and_experimentation', 0.8926268815994263),\n",
       " ('goal-driven', 0.8916996717453003),\n",
       " ('language-rich', 0.8915148973464966),\n",
       " ('learn-by-doing', 0.8907164931297302),\n",
       " ('whole-brain', 0.890299916267395),\n",
       " ('student-centered_inquiry-based', 0.8873357772827148),\n",
       " ('experiential_approach', 0.8859681487083435),\n",
       " ('outside-the-box', 0.8851909637451172),\n",
       " ('technology-infused', 0.8847143650054932),\n",
       " ('multi-modality', 0.8842328190803528),\n",
       " ('inventive_thinking', 0.8838145136833191),\n",
       " ('design-based', 0.8828551769256592),\n",
       " ('skills.the', 0.8823269605636597),\n",
       " ('science-driven', 0.8811147212982178),\n",
       " ('movement-based', 0.8788178563117981),\n",
       " ('learningand', 0.8782038688659668),\n",
       " ('proven_methodology', 0.8779994249343872),\n",
       " ('experiential_project-based', 0.8778121471405029),\n",
       " ('content-specific_grade-by-grade', 0.8771874904632568),\n",
       " ('simulations_role-playing', 0.8770174384117126),\n",
       " ('rigorous_skills-based', 0.8768175840377808),\n",
       " ('seminar-based', 0.8750816583633423),\n",
       " ('integrated_interdisciplinary', 0.8746935129165649),\n",
       " ('discovery-based_learning', 0.8741582036018372),\n",
       " ('life-enhancing_skills', 0.8736122250556946),\n",
       " (\"'real-world\", 0.8733874559402466),\n",
       " ('interdisciplinary_units', 0.8730072975158691),\n",
       " ('concrete_manipulative', 0.8723492622375488),\n",
       " ('fieldwork_and_experts', 0.872256875038147),\n",
       " ('developmentally-based', 0.8719576597213745),\n",
       " ('interdisciplinary_explorations', 0.8719199895858765),\n",
       " ('literature-rich', 0.8713552951812744),\n",
       " ('hands‑on', 0.8707032799720764),\n",
       " ('cross-curricular_approach', 0.8704647421836853),\n",
       " ('socratic_dialogue', 0.8703606128692627),\n",
       " ('technology-rich_environment', 0.8697389364242554),\n",
       " ('common-core_aligned', 0.8694744110107422),\n",
       " ('inquiry_arcs', 0.8691107630729675),\n",
       " ('laboratory-based', 0.8689903616905212),\n",
       " ('iqwst', 0.868649959564209),\n",
       " ('technology-enriched', 0.8680252432823181),\n",
       " ('low-stress', 0.8677506446838379)]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Expand further into a conceptually tight list of terms like this: \n",
    "inq50 = ['inquiry-based', 'problem-based', 'discovery-based', 'experiential', 'constructivist', \n",
    "         'hands-on', 'problem-solving', 'critical_thinking', 'real-world', 'project-based', 'real-life', \n",
    "         'interdisciplinary', 'student-centered', 'critical_thinkers', 'expeditions', \n",
    "         'child-centered', 'experimentation', 'student-based', 'immersive', 'activity-based', 'student-driven', \n",
    "         'intrinsically', 'reality-based', 'multi-disciplinary', 'learner-centered', 'interest-based', 'minds-on', \n",
    "         'metacognitive', 'integrative', 'experience-based', 'multi-dimensional', 'constructivism', 'student-directed', \n",
    "         'choice-based', 'project-oriented', 'intellectually_stimulating', 'inquiry/research', 'field-based', \n",
    "         'project-focused', 'stimulate_critical', 'student-centric', 'active_inquiry', 'inquiry-driven', \n",
    "         'child-directed', 'child-initiated', 'activity/project', 'experientially', 'socratic', \n",
    "         'problem-centered', 'project-centered']\n",
    "\n",
    "# Could add more terms, too:\n",
    "model.most_similar(inq50, topn=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/gensim/models/keyedvectors.py:877: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
      "  vectors = vstack(self.word_vec(word, use_norm=True) for word in used_words).astype(REAL)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'investigate'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can also search for terms that don't match:\n",
    "model.doesnt_match(inq50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['abilities',\n",
       " 'abilities_will_be_maximized',\n",
       " 'ability-grouped',\n",
       " 'acquire_requisite',\n",
       " 'active_inquiry',\n",
       " 'activities',\n",
       " 'activities/lessons',\n",
       " 'activity/project',\n",
       " 'adept_at_facilitating',\n",
       " 'adherents',\n",
       " 'adjusted_and_thrived',\n",
       " 'admittedly_fallible',\n",
       " 'agile',\n",
       " 'aligned_to_the_ngss',\n",
       " 'allegiances',\n",
       " 'altered/added',\n",
       " 'amazed_by_the_generosity',\n",
       " 'anecdotes',\n",
       " 'appealing_and_relevant',\n",
       " 'approaches_such_as_sheltered',\n",
       " 'appropriately_allocated',\n",
       " 'architecture',\n",
       " 'arguments_and_informative/explanatory',\n",
       " 'arithmetic_reasoning',\n",
       " 'art-filled',\n",
       " 'artistic',\n",
       " 'assemble_the_pieces',\n",
       " 'atomic_nucleus',\n",
       " 'awaken_the_imagination',\n",
       " 'bearing_on_the_viability',\n",
       " 'benefits_of_co-op',\n",
       " 'bi-literacy',\n",
       " 'brain-compatible',\n",
       " 'brain-friendly',\n",
       " 'break/gross_motor',\n",
       " 'brighter_tomorrow',\n",
       " 'brilliantly_infused',\n",
       " 'brochure_to_solidify',\n",
       " 'c++_programming',\n",
       " 'capitalism_and_socialism',\n",
       " 'career-focused_and_technical',\n",
       " 'carefully_weigh',\n",
       " 'case-based',\n",
       " 'championing_issues',\n",
       " 'channels_their_energies',\n",
       " 'character-based_strengths',\n",
       " 'checking-for-understanding',\n",
       " 'child-centered',\n",
       " 'child-centered_individualized',\n",
       " 'child-sized_furniture',\n",
       " 'circles_graphed',\n",
       " 'circulatory_and_nervous',\n",
       " 'city-planning',\n",
       " 'civically_active',\n",
       " 'classical/stem_based',\n",
       " 'climate_improvement/improvement',\n",
       " 'coaches/leaders',\n",
       " 'cogent',\n",
       " 'cognitively_demanding',\n",
       " 'coherent_sequenced',\n",
       " 'cohesiveness',\n",
       " 'collaborate_on_a_large-scale',\n",
       " 'collaborative_student-community',\n",
       " 'college-level_options',\n",
       " 'colorism',\n",
       " 'comma_rules',\n",
       " 'commensurate_with_their_abilities',\n",
       " 'committing_offenses',\n",
       " 'commonly_confused',\n",
       " 'communication/collaboration',\n",
       " 'community-wide',\n",
       " 'comparing_the_inputs',\n",
       " 'compelling_paragraphs',\n",
       " 'competency_and_mastery',\n",
       " 'complex_adult-world',\n",
       " 'complexity',\n",
       " 'comprehensibility',\n",
       " 'comprehensive_16-hour',\n",
       " 'conceiving',\n",
       " 'concept/skill',\n",
       " 'concurrently_with_the_choreography',\n",
       " 'considering_waldorf',\n",
       " 'constructivism',\n",
       " 'constructivist',\n",
       " 'constructivist-based',\n",
       " 'consultation/collaboration',\n",
       " 'content-rich_nonfiction',\n",
       " 'content-specific',\n",
       " 'context',\n",
       " 'continous',\n",
       " 'continuity_and_commitment',\n",
       " 'core-content_courses',\n",
       " 'craftsmanship',\n",
       " 'create_spectacles',\n",
       " 'creative',\n",
       " 'creativity',\n",
       " 'critical-thinkers',\n",
       " 'critical-thinking',\n",
       " 'cross-content',\n",
       " 'crystallization',\n",
       " 'cultural_diversities',\n",
       " 'cultural_heritages',\n",
       " 'curiosity',\n",
       " 'curriculum/standards',\n",
       " 'data-rich',\n",
       " 'decision-making',\n",
       " 'decisionmaking_skills',\n",
       " 'deepen',\n",
       " 'deeper',\n",
       " 'definiteness',\n",
       " 'demanding_college-preparatory',\n",
       " 'derivations',\n",
       " 'describe_distributions',\n",
       " 'design2fab_lab',\n",
       " 'develop/implement',\n",
       " 'developing',\n",
       " 'dialoguing',\n",
       " 'diaries_and_journals',\n",
       " 'different/',\n",
       " 'differentiated/personalized',\n",
       " 'differing_structure',\n",
       " 'discerning_listeners',\n",
       " 'discover',\n",
       " 'discovery-based',\n",
       " 'disempower',\n",
       " 'disparate_personalities',\n",
       " 'dissection_techniques',\n",
       " 'dissipate',\n",
       " 'distraction-free',\n",
       " 'drafting_and_revising',\n",
       " 'drove_the_expedition',\n",
       " 'dynamic_collaboration',\n",
       " 'dynamically_and_immediately',\n",
       " 'edifying',\n",
       " 'editing_and_proofing',\n",
       " 'effectiveness_of_metacognitive',\n",
       " 'effects_of_repetition',\n",
       " 'efficacy_by_encouraging',\n",
       " 'eliminating_unnecessary',\n",
       " 'elitism',\n",
       " 'ellipsis_points',\n",
       " 'embraced_and_supported',\n",
       " 'emojis',\n",
       " 'encourage',\n",
       " 'encouraging',\n",
       " 'enduring_conceptual',\n",
       " 'enduring_relationships',\n",
       " 'engaged_citizen',\n",
       " 'engaging',\n",
       " 'enlargement',\n",
       " 'ennoble',\n",
       " 'ennobling_effect',\n",
       " 'environment',\n",
       " 'equipping_educators',\n",
       " 'esl/family',\n",
       " 'evaluate_the_motives',\n",
       " 'evidence_in_a_nuanced',\n",
       " 'examples_evoke',\n",
       " 'expeditions',\n",
       " 'experience-based',\n",
       " 'experiental_learning',\n",
       " 'experiential',\n",
       " 'experiential_approach',\n",
       " 'experientially',\n",
       " 'experimental_labs',\n",
       " 'experimentation',\n",
       " 'exploration',\n",
       " 'exploration_and_experimentation',\n",
       " 'explorations',\n",
       " 'explorative',\n",
       " 'expose_them_to_a_wider',\n",
       " 'exposed_to_a_coherent',\n",
       " 'facilitation_techniques',\n",
       " 'faint_of_heart',\n",
       " 'feedback',\n",
       " 'field-based',\n",
       " 'fieldwork',\n",
       " 'filming',\n",
       " 'fiscal_mismanagement',\n",
       " 'force_to_be_reckoned',\n",
       " 'fostered',\n",
       " 'fostering',\n",
       " 'fuels_my_desire',\n",
       " 'fully-vetted',\n",
       " 'functional_weakness',\n",
       " 'gender-nonconforming',\n",
       " 'generalizations_and_predictions',\n",
       " 'gets_maximized',\n",
       " 'goal-driven',\n",
       " 'gods_of_asgard',\n",
       " 'grade-by-grade_year-by-year',\n",
       " 'gradual_manner',\n",
       " 'grander_scale',\n",
       " 'grapple_with_difficult',\n",
       " 'greek_philosophers',\n",
       " 'grit_and_zest',\n",
       " 'growth',\n",
       " 'hand-on',\n",
       " 'hands-on',\n",
       " 'hands-on/minds-on',\n",
       " 'hands-on_and_minds-on',\n",
       " 'hands-on_learning',\n",
       " 'hands-on_minds-on',\n",
       " 'hands‑on',\n",
       " 'harmonize',\n",
       " 'headmaster_letters',\n",
       " 'helps',\n",
       " 'higher-level_cognitive',\n",
       " 'highly_scripted',\n",
       " 'home-away-from-home',\n",
       " 'homeschool-friendly',\n",
       " 'hope',\n",
       " 'humanism',\n",
       " 'humanities_major',\n",
       " 'identification_of_learning/behavioral',\n",
       " 'immersive',\n",
       " 'implant_into_the_memories',\n",
       " 'in-demand_transportable',\n",
       " 'incorporating_the_principles',\n",
       " 'increased_connectedness',\n",
       " 'independence',\n",
       " 'indispensable',\n",
       " 'individual/personalized_manner',\n",
       " 'individualize_and_differentiate',\n",
       " 'inflected',\n",
       " 'innovativeness',\n",
       " 'inquiry-based',\n",
       " 'inquiry-driven',\n",
       " 'inquiry/research',\n",
       " 'inspire',\n",
       " 'inspiring',\n",
       " 'instruction_cgi',\n",
       " 'integrated',\n",
       " 'intelligent_and_intentional',\n",
       " 'inter-dependence',\n",
       " 'interdependence',\n",
       " 'interdisciplinary',\n",
       " 'interests',\n",
       " 'interior_monologue',\n",
       " 'internship/interest',\n",
       " 'interpersonal',\n",
       " 'investigate',\n",
       " 'issues-based',\n",
       " 'item/category_to_better',\n",
       " 'jasmine_flower',\n",
       " 'journaling',\n",
       " 'joy-filled',\n",
       " 'kinaesthetic',\n",
       " 'laboratory-based',\n",
       " 'laborious_process',\n",
       " 'language-rich',\n",
       " 'leader_and_a_follower',\n",
       " 'learn',\n",
       " 'learn-by-doing',\n",
       " 'learner-centered',\n",
       " 'learning',\n",
       " 'learning-to-learn',\n",
       " 'lens',\n",
       " 'life-based',\n",
       " 'life-long',\n",
       " 'life-skill',\n",
       " 'lifelong',\n",
       " 'lightness',\n",
       " 'literature-rich',\n",
       " 'literature_has_tended',\n",
       " 'loving_and_attentive',\n",
       " 'low-ability',\n",
       " 'lowest_in_achievement',\n",
       " 'loyalty',\n",
       " 'math/engineering',\n",
       " 'maturationally_ready',\n",
       " 'meaningful',\n",
       " 'mentorship',\n",
       " 'mentorships',\n",
       " 'metacognitive',\n",
       " 'micro-economic_ventures',\n",
       " 'minds',\n",
       " 'minds-on',\n",
       " 'mirror_real-life',\n",
       " 'mixed-ability',\n",
       " 'modality',\n",
       " 'motivate_their_attempts',\n",
       " 'mud_hut',\n",
       " 'multi-dimensional',\n",
       " 'multi-disciplinary',\n",
       " 'multi-faceted',\n",
       " 'multiple-paragraph',\n",
       " 'must-reads',\n",
       " 'mutual',\n",
       " 'myon_reader',\n",
       " 'mythological_creature',\n",
       " 'natural',\n",
       " 'nature',\n",
       " 'nature-based_perspective',\n",
       " 'navigable',\n",
       " 'near-daily_basis',\n",
       " 'need-to-know_facts',\n",
       " 'needing_improvement',\n",
       " 'neuro-anatomy',\n",
       " 'next_generation',\n",
       " 'non-arts',\n",
       " 'non-coercive',\n",
       " 'non-dominant',\n",
       " 'non-instrumental',\n",
       " 'non-montessori',\n",
       " 'noninfectious',\n",
       " 'nonpolitical',\n",
       " 'novel-based',\n",
       " 'nurtures_mutual',\n",
       " 'nurturing',\n",
       " 'observation',\n",
       " 'obsolescence',\n",
       " 'one-segment',\n",
       " 'one2one_digital',\n",
       " 'open-mindedness',\n",
       " 'orientating',\n",
       " 'originated_in_cuba',\n",
       " 'outcome-focused',\n",
       " 'pace_most_suited',\n",
       " 'parabolas_graphed',\n",
       " 'parent/student/staff_compact',\n",
       " 'participating_in_acting/music/arts',\n",
       " 'particularly_vulnerable',\n",
       " 'pedagogical_delivery',\n",
       " 'pep-rally',\n",
       " 'personalized',\n",
       " 'perspective',\n",
       " 'philosophical',\n",
       " 'philosophies',\n",
       " 'photographing',\n",
       " 'pinata',\n",
       " 'point-of-need',\n",
       " 'polite_manners',\n",
       " 'post-production_editing',\n",
       " 'post-school_life',\n",
       " 'power_of_student-faculty',\n",
       " 'powers_of_abstraction',\n",
       " 'practicality',\n",
       " 'prbl',\n",
       " 'pre-teach',\n",
       " 'precise_claim',\n",
       " 'predicated_on_the_realization',\n",
       " 'presumption_and_belief',\n",
       " 'problem-based',\n",
       " 'problem‑solving',\n",
       " 'process-based_writing',\n",
       " 'processes_recursively',\n",
       " 'product/outcome',\n",
       " 'prohibitively_expensive',\n",
       " 'project-based',\n",
       " 'project-centered',\n",
       " 'project-focused',\n",
       " 'providing',\n",
       " 'proxy_servers',\n",
       " 'purveyors',\n",
       " 'qualify_or_justify',\n",
       " 'quality-learning',\n",
       " 'quantitatively_as_well',\n",
       " 'quiet',\n",
       " 'ramp-up_to_readiness',\n",
       " 're-forming',\n",
       " 'real-life',\n",
       " 'real-world',\n",
       " 'reason_mathematically',\n",
       " 'rectangular_array',\n",
       " 'reduces_distractions',\n",
       " 'refine_gross',\n",
       " 'reinforce_previously',\n",
       " 'reinterpret',\n",
       " 'reinvention',\n",
       " 'relatable_characters',\n",
       " 'relational_capacity',\n",
       " 'relationship-oriented_classroom',\n",
       " 'relationships_between_pollinators',\n",
       " 'relevant_to_the_viability',\n",
       " 'reliability',\n",
       " 'repeated_occurrence',\n",
       " 'research-supported',\n",
       " 'respective_histories',\n",
       " 'review_of_progress-monitoring',\n",
       " 'reﬂective',\n",
       " 'richness_and_diversity',\n",
       " 'richness_and_strength',\n",
       " 'robot_to_maneuver',\n",
       " 'rule-making',\n",
       " 'scaled_scores',\n",
       " 'school-life',\n",
       " 'sculptural_forms',\n",
       " 'sculptural_techniques',\n",
       " 'self-appreciation',\n",
       " 'self-awareness',\n",
       " 'self-efficacy',\n",
       " 'self-expression',\n",
       " 'self-motivated',\n",
       " 'sense_of_reverence',\n",
       " 'sensory-motor',\n",
       " 'sensory-rich',\n",
       " 'sentence_workshopping',\n",
       " 'settled_tribe',\n",
       " 'shapes_and_their_attributes',\n",
       " 'shared',\n",
       " 'sherry_alam',\n",
       " 'shows_that_metacognitive',\n",
       " 'simulations_role-playing',\n",
       " 'skills/family',\n",
       " 'small-group',\n",
       " 'small-group_discussion',\n",
       " 'social/soft',\n",
       " 'socialresponsibility',\n",
       " 'socratic',\n",
       " 'socratic_dialogue',\n",
       " 'socratic_method',\n",
       " 'standards-focused',\n",
       " 'staunch_supporter',\n",
       " 'stepstone_360°',\n",
       " 'stewards',\n",
       " 'stopped_and_looked',\n",
       " \"story's_lesson\",\n",
       " 'stress-management',\n",
       " 'strict/warm',\n",
       " 'structurally_similar',\n",
       " 'structured_and_predictable',\n",
       " 'struggles_and_questioning',\n",
       " 'stuck_with_them_affecting',\n",
       " 'student-centered',\n",
       " 'student-centered_inquiry-based',\n",
       " 'student-directed',\n",
       " 'student-driven',\n",
       " 'student-driven_exploration',\n",
       " 'study/teaching',\n",
       " 'styles_and_modalities',\n",
       " 'subject-area_integration',\n",
       " 'succesful',\n",
       " 'suit_their_needs',\n",
       " 'superbowl_party',\n",
       " 'supplemental_curricula',\n",
       " 'sustainability',\n",
       " 'sustaining_of_a_genuine',\n",
       " 'symbolic_representations',\n",
       " 'synthetic_phonics',\n",
       " 'systematic_and_sequential',\n",
       " 'tacitly',\n",
       " 'target/objective',\n",
       " 'teacher-to-teacher',\n",
       " 'team-based_organizational',\n",
       " 'techniques_namely',\n",
       " 'technological_modalities',\n",
       " 'technologies_and_our_science-based',\n",
       " 'technology-assisted',\n",
       " 'technology-infused',\n",
       " 'technology–',\n",
       " 'tedxyouth',\n",
       " 'temper_tantrum',\n",
       " 'text-analysis',\n",
       " 'text_capitalcitypcs',\n",
       " 'textbooks_affording',\n",
       " 'thematic',\n",
       " 'thematically',\n",
       " 'theme-based',\n",
       " 'themes',\n",
       " 'theses',\n",
       " 'thorough_and_accurate',\n",
       " 'three-part_pattern',\n",
       " 'thrives',\n",
       " 'tight-knit',\n",
       " 'time-management',\n",
       " 'timeless_but_indispensable',\n",
       " 'touchpoints',\n",
       " 'trained_appraisers',\n",
       " 'transgender',\n",
       " 'tricycle',\n",
       " 'trustee_elections',\n",
       " 'tunnels_and_snowshed',\n",
       " 'twinery',\n",
       " 'unbidden_encounter',\n",
       " 'understand',\n",
       " 'understands',\n",
       " 'unfamiliar_contexts',\n",
       " 'unschooling',\n",
       " 'uphold_and_enforce',\n",
       " 'valued',\n",
       " 'values-centered',\n",
       " 'virtues',\n",
       " 'virtuous',\n",
       " 'visible_men',\n",
       " 'vitals',\n",
       " 'voluntarism',\n",
       " 'wampanoag',\n",
       " 'warm/strict',\n",
       " 'ways_of_knowing/learning',\n",
       " 'well-articulated_standards-based',\n",
       " 'well-developed',\n",
       " 'whole-group_explicit',\n",
       " 'willow_seedling',\n",
       " 'wishful',\n",
       " 'world-view',\n",
       " 'would_be_off-putting',\n",
       " 'writing-centered',\n",
       " 'writing—decoding',\n",
       " 'yet-to-be-defined_future']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Searching for additional terms similar to this list, you can expand even further!\n",
    "inquiry_fin = [elem.strip('\\n') for elem in load_list('data/inquiry500.txt')] # Load completed dict of 500 terms\n",
    "inquiry_fin = list(set(inquiry_fin)) # Remove duplicates\n",
    "\n",
    "sorted(inquiry_fin) # Show long dictionary resulting from exploring (and hand-cleaning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed stuck_with_them_affecting from core dictionary.\n",
      "Removed styles_and_modalities from core dictionary.\n"
     ]
    }
   ],
   "source": [
    "# Remove any terms from full dict NOT in current model:\n",
    "for word in inquiry_fin:\n",
    "    if word not in list(model.vocab):\n",
    "        inquiry_fin.remove(word)\n",
    "        print(\"Removed \" + str(word) + \" from core dictionary.\")\n",
    "# Repeat for quality:\n",
    "for word in inquiry_fin:\n",
    "    if word not in list(model.vocab):\n",
    "        inquiry_fin.remove(word)\n",
    "        print(\"Removed \" + str(word) + \" from core dictionary.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SEED DICTIONARY: 0.7010571432113648\n",
      "NARROW DICTIONARY: 0.7201398701137967\n",
      "HAND-CHECKED FULL DICTIONARY: 0.6173502521514893\n",
      "DATA-DRIVEN FULL DICTIONARY: 0.7891655696868897\n",
      "LONG DICTIONARY: 0.598329613622959\n"
     ]
    }
   ],
   "source": [
    "# Compare how well these candidate dictionaries hold together:\n",
    "print(\"SEED DICTIONARY:\", dict_cohere(inqseed, model))\n",
    "print(\"NARROW DICTIONARY:\", dict_cohere(inq20, model))\n",
    "print(\"HAND-CHECKED FULL DICTIONARY:\", dict_cohere(inq50, model))\n",
    "print(\"DATA-DRIVEN FULL DICTIONARY:\", dict_cohere(inq50new, model))\n",
    "print(\"LONG DICTIONARY:\", dict_cohere(inquiry_fin, model))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize vector relationships"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most similar words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display 30 closest words to test word\n",
    "display_closestwords(model, test_word, 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manually defined words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TO DO: Adapt function above to be able to perform this visualization\n",
    "# Set terms for visualization:\n",
    "core_words = inqseed\n",
    "close_words = inq50 \n",
    "\n",
    "# Define model parameters\n",
    "dimensions = 300\n",
    "arr = np.empty((0,dimensions), dtype='f')\n",
    "word_labels = []\n",
    "\n",
    "# add the vector for each of the closest words to the array\n",
    "for wrd_label in close_words:\n",
    "    wrd_vector = model[wrd_label]\n",
    "    word_labels.append(wrd_label)\n",
    "    arr = np.append(arr, np.array([wrd_vector]), axis=0)\n",
    "        \n",
    "# find tsne coords for 2 dimensions, using cosine distances as metric\n",
    "tsne = TSNE(n_components=2, random_state=0, metric=distance.cosine)\n",
    "np.set_printoptions(suppress=True)\n",
    "Y = tsne.fit_transform(arr)\n",
    "\n",
    "x_coords = Y[:, 0]\n",
    "y_coords = Y[:, 1]\n",
    "\n",
    "# display scatter plot\n",
    "fig, ax = plt.subplots(figsize=(20,20))\n",
    "ax.scatter(x_coords, y_coords, alpha=1, color='b')\n",
    "\n",
    "sns.set(style='white')\n",
    "\n",
    "for label, x, y in zip(word_labels, x_coords, y_coords):\n",
    "    if label in core_words:\n",
    "        ax.annotate(label, xy=(x, y), xytext=(0, 0), textcoords='offset points', fontweight='bold').set_fontsize(20)\n",
    "    else:\n",
    "        ax.annotate(label, xy=(x, y), xytext=(0, 0), textcoords='offset points').set_fontsize(20)\n",
    "    \n",
    "ax.set_xlim(x_coords.min()-5, x_coords.max()+50)\n",
    "ax.set_ylim(y_coords.min()-5, y_coords.max()+5)\n",
    "ax.set_xlabel('t-SNE dimension 1')\n",
    "ax.set_ylabel('t-SNE dimension 2')\n",
    "ax.xaxis.label.set_fontsize(24)\n",
    "ax.yaxis.label.set_fontsize(24)\n",
    "for item in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(\"data/IBL_50_102419.png\", facecolor=\"white\", transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check word frequencies and ranks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Count frequency of candidate words\n",
    "\n",
    "Required arguments for `count_master()` function:\n",
    "- df: DataFrame with text data, each of which is a list of full-text pages (not necessarily preprocessed)\n",
    "- dict_path: file path to folder containing dictionaries\n",
    "- dict_names: names of dictionaries on file (list or list of lists)\n",
    "- file_ext: file extension for dictionary files (probably .txt)   \n",
    "- local_dicts: list of local dictionaries formatted as last of lists of terms--or if singular, just a list of terms\n",
    "- local_names: names of local dictionaries (list or list of lists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|▏         | 9/367 [00:00<00:05, 71.26it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counting dictionaries per term for corpus...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 367/367 [00:15<00:00, 24.11it/s]\n",
      "100%|██████████| 367/367 [00:00<00:00, 167298.07it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multiprocessing complete. Collecting counts per term per dictionary...\n",
      "Finished. Returning results.\n",
      "TERM COUNTS FOR CANDIDATES DICTIONARY:\n",
      "\n",
      "                               TERM  FREQUENCY\n",
      "29                       low­income       4608\n",
      "14                        high-risk        341\n",
      "24            intellectually_gifted        202\n",
      "15                       high-needs        107\n",
      "25                    low_achieving        105\n",
      "16                    low-achieving        105\n",
      "18                  under-resourced         71\n",
      "9                 under-represented         62\n",
      "7                    underachieving         60\n",
      "21                adjudicated_youth         40\n",
      "8                  justice-involved         15\n",
      "22               pregnant/parenting         13\n",
      "4             disenfranchised_youth          6\n",
      "27                        youth_yaş          4\n",
      "20        evidence-based_approaches          0\n",
      "28               aimed_at_achieving          0\n",
      "26               considered_at-risk          0\n",
      "2        academically_low-achieving          0\n",
      "23              at-risk_adolescents          0\n",
      "17                  better_my_life.          0\n",
      "19                living_in_poverty          0\n",
      "5         at-risk_and_disadvantaged          0\n",
      "1   construction_management/general          0\n",
      "3       percentages_of_economically          0\n",
      "13              out-of-school_youth          0\n",
      "12      example_the_disenfranchised          0\n",
      "11      african-american_and_latino          0\n",
      "10          low-income_and_minority          0\n",
      "6             high-ability_students          0\n",
      "0   failure_and_academically-gifted          0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "countsdfs = count_master(df, dict_path = dict_path, dict_names = [], file_ext = '.txt', \n",
    "                         local_dicts = [candidates_list], local_names = [\"candidates\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relative ranks of dictionary terms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Word2vec also has built-in method to compare relative ranks of terms, which go from most frequent (highest number) to least frequent. <br/> **Note** that they are NOT real counts/frequencies (see above for that)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Counter object: a set containing doubles, where each double is ('term': rank)\n",
    "counts = Counter({word: vocab.count for (word, vocab) in model.wv.vocab.items()})\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TO DO: 1. Sort entries by rank before displaying; 2. show histogram of term counts for highest 20 and lowest 20 words for each dict\n",
    "\n",
    "print(\"RANK FOR SEED DICTIONARY:\")\n",
    "for term in sorted(inqseed):\n",
    "    print(str(term) + \": \" + str(counts[term]))\n",
    "print()\n",
    "print(\"RANK FOR CORE DICTIONARY:\")\n",
    "for term in inq30:\n",
    "    print(str(term) + \": \" + str(counts[term]))\n",
    "print()\n",
    "print(\"RANK FOR FULL DICTIONARY:\")\n",
    "for term in inquiry_fin:\n",
    "    print(str(term) + \": \" + str(counts[term]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 50 most frequent words (according to word2vec tracker):\n",
    "model.wv.index2entity[:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validate dictionaries: Check coherence of terms across sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'inq50new' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-887cf4da0c77>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mdictionary\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0minqseed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minq20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minq50new\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minquiry_fin\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      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict_cohere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdictionary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\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[0;31mNameError\u001b[0m: name 'inq50new' is not defined"
     ]
    }
   ],
   "source": [
    "for dictionary in [inqseed, inq20, inq50new, inquiry_fin]:\n",
    "    print(str(dict_cohere(dictionary, model)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'inq30' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-34c584ef9fb7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Can also check for words that belong LEAST to a given dictionary:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmaindict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minq30\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;31m# initialize iterator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mlength\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaindict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# set length for the for loop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'inq30' is not defined"
     ]
    }
   ],
   "source": [
    "# Can also check for words that belong LEAST to a given dictionary:\n",
    "maindict = inq30\n",
    "\n",
    "i = 0 # initialize iterator\n",
    "length = len(maindict) # set length for the for loop\n",
    "inqtemp = []; inqtemp = maindict # initialize temp list of dict words\n",
    "similar_words = [] # initialize list of dict words sorted by similarity (least similar words most recently added -> at bottom)\n",
    "\n",
    "while i < length:\n",
    "    least_similar_word = model.doesnt_match(inqtemp)\n",
    "    similar_words.append(least_similar_word)\n",
    "    inqtemp.remove(least_similar_word)\n",
    "    i += 1 # add to iterator\n",
    "\n",
    "print(\"List of words from least to most similar:\")\n",
    "for term in similar_words:\n",
    "    print(term)\n",
    "    \n",
    "# Need to re-define dictionary again at this point:\n",
    "inq30 = ['discovery-based', 'student-driven_exploration', 'exploration_and_experimentation', 'laboratory-based', \n",
    "         'problem-based', 'prbl', 'learn-by-doing', \n",
    "         'project-based', 'project-centered', \n",
    "         'experiential', 'experiential_approach', 'experientially',\n",
    "         'inquiry-based', 'inquiry-driven', 'student-centered_inquiry-based', 'active_inquiry', \n",
    "         'constructivist', 'constructivism', \n",
    "         'hands-on', 'hand-on', 'hands-on_learning', 'hands-on_and_minds-on', 'hands-on_minds-on', 'hands-on/minds-on', \n",
    "         'socratic', 'socratic_method', 'socratic_dialogue',\n",
    "         'child-centered', 'learner-centered', 'student-centered']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check integrity of IBL dictionaries of varying lengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 2 out-of-vocabulary terms from full dictionary.\n"
     ]
    }
   ],
   "source": [
    "# Remove any terms from core dict NOT in current model (these will have to be replaced):\n",
    "for word in inq30:\n",
    "    if word not in list(model.vocab):\n",
    "        inq30.remove(word)\n",
    "        print(\"NOT FOUND IN CORE DICT, REMOVING:\\t\" + word)\n",
    "\n",
    "count = 0\n",
    "for word in inquiry_fin:\n",
    "    if word not in list(model.vocab):\n",
    "        inquiry_fin.remove(word)\n",
    "        count += 1\n",
    "        #print(\"NOT FOUND in FULL DICT, REMOVING:\\t\" + word)\n",
    "\n",
    "# Loop twice to catch any remaining OOV terms:\n",
    "for word in inquiry_fin:\n",
    "    if word not in list(model.vocab):\n",
    "        inquiry_fin.remove(word)\n",
    "        count += 1\n",
    "        \n",
    "print(\"Removed \" + str(count) + \" out-of-vocabulary terms from full dictionary.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of original dict:  30\n",
      "Coherence of original dict:  0.694298374917772\n",
      "Length of extended dict:  498\n",
      "Coherence of extended dict:  0.5983296162685932\n"
     ]
    }
   ],
   "source": [
    "print(\"Length of original dict: \", len(inq30))\n",
    "print(\"Coherence of original dict: \", str(dict_cohere(inq30, model)))\n",
    "\n",
    "print(\"Length of extended dict: \", len(inquiry_fin))\n",
    "print(\"Coherence of extended dict: \", str(dict_cohere(inquiry_fin, model)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TESTING DIFFERENT LENGTHS USING RANDOM ADDITIONS:\n",
      "\n",
      "30-term dict has this much coherence: 0.694298374917772\n",
      "\n",
      "40-term dict has this much coherence: 0.6336066746711732\n",
      "\n",
      "50-term dict has this much coherence: 0.6267515384674072\n",
      "\n",
      "75-term dict has this much coherence: 0.6087374311659071\n",
      "\n",
      "100-term dict has this much coherence: 0.6085788740158081\n",
      "\n",
      "200-term dict has this much coherence: 0.6090105971336366\n",
      "\n",
      "300-term dict has this much coherence: 0.6010488780551487\n",
      "\n",
      "400-term dict has this much coherence: 0.6033310662269595\n",
      "\n",
      "500-term dict has this much coherence: 0.5983296162685932\n"
     ]
    }
   ],
   "source": [
    "# Use random adds to create a few dicts of different lengths from max length of 500:\n",
    "\n",
    "print(\"TESTING DIFFERENT LENGTHS USING RANDOM ADDITIONS:\\n\")\n",
    "# TO DO: Write function expand_dict() above to automate this\n",
    "\n",
    "inqlist = []; inqlist = inq30 # initialize placeholder for each growing dict\n",
    "print(str(len(inqlist)) + \"-term dict has this much coherence:\", str(dict_cohere(inq30, model)))\n",
    "print()\n",
    "\n",
    "# 40 terms:\n",
    "inqlist.extend(np.random.choice([term for term in filter(lambda x: x not in inq30, inquiry_fin) if term in list(model.vocab)], size=10, replace=False))\n",
    "inq40 = []; inq40 = inqlist\n",
    "print(str(len(inqlist)) + \"-term dict has this much coherence:\", str(dict_cohere(inq40, model)))\n",
    "print()\n",
    "\n",
    "# 50 terms:\n",
    "inqlist.extend(np.random.choice([term for term in filter(lambda x: x not in inq40, inquiry_fin) if term in list(model.vocab)], size=10, replace=False))\n",
    "inq50 = inqlist\n",
    "#print(\"50 terms: \\n\", [term for term in inq50])\n",
    "print(str(len(inqlist)) + \"-term dict has this much coherence:\", str(dict_cohere(inq50, model)))\n",
    "print()\n",
    "\n",
    "# 75 terms:\n",
    "inqlist.extend(np.random.choice([term for term in filter(lambda x: x not in inq50, inquiry_fin) if term in list(model.vocab)], size=25, replace=False))\n",
    "inq75 = inqlist\n",
    "#print(\"75 terms: \\n\", [term for term in inq75])\n",
    "print(str(len(inqlist)) + \"-term dict has this much coherence:\", str(dict_cohere(inq75, model)))\n",
    "print()\n",
    "\n",
    "# 100 terms:\n",
    "inqlist.extend(np.random.choice([term for term in filter(lambda x: x not in inq75, inquiry_fin) if term in list(model.vocab)], size=25, replace=False))\n",
    "inq100 = inqlist\n",
    "#print(\"100 terms: \\n\", [term for term in inq100])\n",
    "print(str(len(inqlist)) + \"-term dict has this much coherence:\", str(dict_cohere(inq100, model)))\n",
    "print()\n",
    "\n",
    "# 200 terms:\n",
    "inqlist.extend(np.random.choice([term for term in filter(lambda x: x not in inq100, inquiry_fin) if term in list(model.vocab)], size=100, replace=False))\n",
    "inq200 = inqlist\n",
    "#print(\"200 terms: \\n\", [term for term in inq200])\n",
    "print(str(len(inqlist)) + \"-term dict has this much coherence:\", str(dict_cohere(inq200, model)))\n",
    "print()\n",
    "\n",
    "# 300 terms:\n",
    "inqlist.extend(np.random.choice([term for term in filter(lambda x: x not in inq200, inquiry_fin) if term in list(model.vocab)], size=100, replace=False))\n",
    "inq300 = inqlist\n",
    "print(str(len(inqlist)) + \"-term dict has this much coherence:\", str(dict_cohere(inq300, model)))\n",
    "print()\n",
    "\n",
    "# 400 terms:\n",
    "inqlist.extend(np.random.choice([term for term in filter(lambda x: x not in inq300, inquiry_fin) if term in list(model.vocab)], size=100, replace=False))\n",
    "inq400 = inqlist\n",
    "print(str(len(inqlist)) + \"-term dict has this much coherence:\", str(dict_cohere(inq400, model)))\n",
    "print()\n",
    "\n",
    "print(\"500-term dict has this much coherence:\", str(dict_cohere(inquiry_fin, model)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Removed 2 out-of-vocabulary terms from full dictionary.\n",
      "TESTING DICT LENGTHS BY REMOVING LEAST SIMILAR WORD VECTORS:\n",
      "\n",
      "WARNING: Function produced a dictionary of length 498, which is not the specified maximum dict length of 500\n",
      "500-term dicts have this much coherence: 0.5983296162685932\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/gensim/models/keyedvectors.py:876: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
      "  vectors = vstack(self.word_vec(word, use_norm=True) for word in used_words).astype(REAL)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400-term dicts have this much coherence: 0.6033310662269596\n",
      "\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "cannot select a word from an empty list",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-133-55aca80ee596>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[0minqdict\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m     \u001b[0minqdict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfocus_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minquiry_fin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minq30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdictlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\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     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdictlen\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"-term dicts have this much coherence:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict_cohere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minqdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\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[0;32m<ipython-input-21-df565e6c2a5b>\u001b[0m in \u001b[0;36mfocus_dict\u001b[0;34m(thisdict, coredict, maxlen, wem_model)\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0mextend_count\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mcore_count\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# Remove terms until length = maxlen - number of core terms removed (to offset those core terms that will be added back in later in this script)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m         \u001b[0mbadvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdoesnt_match\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthisdict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Find least matching term\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m         \u001b[0mthisdict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbadvar\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Remove that least focal term, to focus dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0mextend_count\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;31m# Keep track of # non-core terms added\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/gensim/models/keyedvectors.py\u001b[0m in \u001b[0;36mdoesnt_match\u001b[0;34m(self, words)\u001b[0m\n\u001b[1;32m    873\u001b[0m             \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"vectors for words %s are not present in the model, ignoring these words\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignored_words\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    874\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mused_words\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 875\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cannot select a word from an empty list\"\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    876\u001b[0m         \u001b[0mvectors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mword_vec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_norm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mused_words\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mREAL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    877\u001b[0m         \u001b[0mmean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmatutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munitvec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvectors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mREAL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: cannot select a word from an empty list"
     ]
    }
   ],
   "source": [
    "# TO DO: Fix this\n",
    "\n",
    "# Use removal of least similar word vectors to create a few dicts of different lengths from max length of 500:\n",
    "lens_list = [500, 400, 300, 200, 100, 75, 60, 50, 40, 30] # Define desired dict lengths\n",
    "\n",
    "# Load full 500-term dicts:\n",
    "inquiry_fin = [elem.strip('\\n') for elem in load_list('data/inquiry500.txt')]\n",
    "\n",
    "# Remove any terms from core dict NOT in current model (these will have to be replaced):\n",
    "for word in inq30:\n",
    "    if word not in list(model.vocab):\n",
    "        inq30.remove(word)\n",
    "        print(\"NOT FOUND IN CORE DICT, REMOVING:\\t\" + word)\n",
    "print()\n",
    "\n",
    "count = 0\n",
    "for word in inquiry_fin:\n",
    "    if word not in list(model.vocab):\n",
    "        inquiry_fin.remove(word)\n",
    "        count += 1\n",
    "        #print(\"NOT FOUND in FULL DICT, REMOVING:\\t\" + word)\n",
    "        \n",
    "for word in inquiry_fin:\n",
    "    if word not in list(model.vocab):\n",
    "        inquiry_fin.remove(word)\n",
    "        count += 1\n",
    "    \n",
    "print(\"Removed \" + str(count) + \" out-of-vocabulary terms from full dictionary.\")\n",
    "\n",
    "print('TESTING DICT LENGTHS BY REMOVING LEAST SIMILAR WORD VECTORS:\\n')\n",
    "\n",
    "for dictlen in lens_list: \n",
    "    inqdict = [],\n",
    "    \n",
    "    inqdict = focus_dict(inquiry_fin, inq30, dictlen, model)\n",
    "    \n",
    "    print(str(dictlen) + \"-term dicts have this much coherence:\", str(dict_cohere(inqdict, model)))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Surprising trends in analyzing how dictionary length affects inter-dictionary opposition and within-dictionary coherence (from this and other analyses):\n",
    "### Greater lengths make for more within-dictionary coherence, but less across-dictionary opposition.\n",
    "### Trimming word vectors randomly yields dictionaries that are less coherent and more opposing than does trimming those word vectors least similar to the others in the dictionary.\n",
    "#### Caveat: The above patterns assume (perhaps consequentially, perhaps not) that as we trim dictionaries, we are keeping a small, core list of concepts (here, 30) intact within each dictionary. The size of this core may influence the above dynamics, as may the relative size of the total dictionary compared to the core list."
   ]
  }
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