embeddings.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute embeddings for terms and audio of the ce game dataset\n",
    "\n",
    "\n",
    "in this notebook we extract the 150 ce game terms used in the pile sorting experiment and compute embeddings for them based on:\n",
    "- CLAP language model\n",
    "- ADA embedding model\n",
    "- EWE embedding model\n",
    "- GTE embedding model\n",
    "- BGE embedding model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib.cm as cm\n",
    "                    \n",
    "import pandas as pd\n",
    "import scipy\n",
    "from collections import defaultdict\n",
    "import copy\n",
    "\n",
    "# sklearn\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# helpers\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(group1_fn, group2_fn):\n",
    "    \"\"\"\n",
    "    Load data of the pile sorting experiments\n",
    "    \"\"\"\n",
    "    group1 = pd.read_csv(group1_fn)\n",
    "    group2 = pd.read_csv(group2_fn)\n",
    "\n",
    "    # Rename both \"Gould\" piles\n",
    "    g1_idx = np.where(group1['Pile'] == 'Gould')[0]\n",
    "    group1['Pile'][g1_idx] = 'Gould1'\n",
    "\n",
    "    g2_idx = np.where(group2['Pile'] == 'Gould')[0]\n",
    "    group2['Pile'][g2_idx] = 'Gould2'\n",
    "\n",
    "    # Sort results by terms alphabetically\n",
    "    group1.sort_values(by='Term', inplace=True)\n",
    "    group2.sort_values(by='Term', inplace=True)\n",
    "\n",
    "    all_terms = group1['Term']\n",
    "\n",
    "    pile_names1 = np.unique(group1.Pile)\n",
    "    pile_names2 = np.unique(group2.Pile)\n",
    "\n",
    "    pile_names = list(pile_names1) + list(pile_names2)\n",
    "    pile_names.sort()\n",
    "\n",
    "    # Create dictionaries of terms per pile\n",
    "    piles1 = defaultdict(list)\n",
    "    piles2 = defaultdict(list)\n",
    "\n",
    "    for term, pile in group1.values:\n",
    "        piles1[pile].append(term)\n",
    "\n",
    "    for term, pile, metapile in group2.values:\n",
    "        piles2[pile].append(term)\n",
    "\n",
    "    return (group1, group2, all_terms, pile_names,\n",
    "            pile_names1, pile_names2, piles1, piles2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### load con espressione game answers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of answers:  1462\n",
      "Number of words in the answers:  3331\n",
      "Number of unique words in the answers:  1585\n"
     ]
    }
   ],
   "source": [
    "# load data\n",
    "path = \"data/con_espressione_game_answers.csv\"\n",
    "df = pd.read_csv(path)\n",
    "list_of_w0 = \", \".join(df.answer).split(\", \")\n",
    "list_of_w = [w.strip().lower().replace(\",\",\"\") for w in list_of_w0 ]\n",
    "vocab = set(list_of_w)\n",
    "print(\"Number of answers: \", len(df))\n",
    "print(\"Number of words in the answers: \", len(list_of_w))\n",
    "print(\"Number of unique words in the answers: \", len(vocab))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# retrieve only relevant columns\n",
    "df_short = df[[\"music_id\",\"answer\",\"piece_name\",\"performer\"]]\n",
    "# df_short.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>answer</th>\n",
       "      <th>performer</th>\n",
       "      <th>piece_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>music_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>floating, brisk, romantic, rebellious, fast, m...</td>\n",
       "      <td>Gieseking</td>\n",
       "      <td>bach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mechanic, dancing, simplistic, staccato, mecha...</td>\n",
       "      <td>Gould</td>\n",
       "      <td>bach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>shy, magical, deep, delicate, sweet, longing, ...</td>\n",
       "      <td>Grimaud</td>\n",
       "      <td>bach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>hasty, nimble, unanimous, fast, mechanical, bo...</td>\n",
       "      <td>Kempff</td>\n",
       "      <td>bach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>searching, exploring, traditional, classical, ...</td>\n",
       "      <td>Richter</td>\n",
       "      <td>bach</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     answer  performer  \\\n",
       "music_id                                                                 \n",
       "1         floating, brisk, romantic, rebellious, fast, m...  Gieseking   \n",
       "2         mechanic, dancing, simplistic, staccato, mecha...      Gould   \n",
       "3         shy, magical, deep, delicate, sweet, longing, ...    Grimaud   \n",
       "5         hasty, nimble, unanimous, fast, mechanical, bo...     Kempff   \n",
       "6         searching, exploring, traditional, classical, ...    Richter   \n",
       "\n",
       "         piece_name  \n",
       "music_id             \n",
       "1              bach  \n",
       "2              bach  \n",
       "3              bach  \n",
       "5              bach  \n",
       "6              bach  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# aggregate by piece\n",
    "aggregation_functions = {'answer': ', '.join, 'performer': max, 'piece_name': max}\n",
    "df_new = df.groupby(df_short['music_id']).aggregate(aggregation_functions)\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pieces_list = list(df_new[\"piece_name\"])\n",
    "pieces_music_id_list = list(df_new.index)\n",
    "performer_list = list(df_new[\"performer\"])\n",
    "piece_performer_list = [pieces_list[i]+\"_\"+performer_list[i] for i in range(len(pieces_list))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load pile information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load results from pile sorting experiment\n",
    "\n",
    "group1_fn = 'data/pile_sorting_group1.csv'\n",
    "group2_fn = 'data/pile_sorting_group2.csv'\n",
    "\n",
    "(group1, group2, all_terms, \n",
    " pile_names, _, \n",
    " _, piles1, piles2) = load_data(group1_fn, group2_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute pile idx per term\n",
    "pile_idx_per_term = np.zeros((150, 2), dtype=int)\n",
    "for i, ((t1, p1), (t2, p2, m2)) in enumerate(zip(group1.values, group2.values)):\n",
    "    p1i = pile_names.index(p1)\n",
    "    p2i = pile_names.index(p2)\n",
    "    pile_idx_per_term[i] = (p1i, p2i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use the terms from the pile sorting experiment to constrain the corpus\n",
    "corpus1 = list(df_new.answer)\n",
    "#tf_matrix = CountVectorizer( vocabulary=list(df_pile.Term) )\n",
    "tf_matrix = CountVectorizer( vocabulary=list(all_terms.values))\n",
    "# transform it to\n",
    "dtm = tf_matrix.fit_transform(corpus1)\n",
    "# document term with 45 performances and 150 terms\n",
    "term_freq = dtm.todense()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### create some helper variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "perf_ids, term_ids, term_frequency = scipy.sparse.find(dtm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# performances for every term\n",
    "perf_per_term = defaultdict(list)\n",
    "# terms for every performance\n",
    "term_per_perf = defaultdict(list)\n",
    "terms = list(all_terms.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for every term, store a list of musicid / performance names where the term was used\n",
    "\n",
    "for term_id, perf_id in zip(term_ids, perf_ids):\n",
    "    current_term = terms[term_id]\n",
    "    current_perf = piece_performer_list[perf_id]\n",
    "    current_music_id = pieces_music_id_list[perf_id]\n",
    "    \n",
    "    #perf_per_term[current_term].append([current_music_id, current_perf])\n",
    "    perf_per_term[current_term].append(current_music_id)\n",
    "    #term_per_perf[current_perf].append([term_id, current_term])\n",
    "    term_per_perf[current_perf].append(term_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# neighbors in pile 1 per term\n",
    "neighbors1 = defaultdict(list)\n",
    "# neighbors in pile 2 per term\n",
    "neighbors2 = defaultdict(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# .. and term and term idx per pile\n",
    "term_per_pile = defaultdict(list)\n",
    "\n",
    "for pilename in piles1.keys():\n",
    "    for term in piles1[pilename]:\n",
    "        term_idx = terms.index(term)\n",
    "        #term_per_pile[pilename].append([term_idx, term])\n",
    "        term_per_pile[pilename].append(term_idx)\n",
    "        \n",
    "for pilename in piles2.keys():\n",
    "    for term in piles2[pilename]:\n",
    "        term_idx = terms.index(term)\n",
    "        #term_per_pile[pilename].append([term_idx, term])\n",
    "        term_per_pile[pilename].append(term_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import pickle\n",
    "#with open(\"data/terms.list\", \"wb\") as fp:   \n",
    "#    pickle.dump(terms, fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What we have so far\n",
    "\n",
    "loaded all c.e. game answers and pile sorting piles into convenient data structures.\n",
    "\n",
    "- term_freq: (45, 150) the occurence of each of the 150 terms in the 45 performances\n",
    "- dtm = term_freq, but sparse\n",
    "- piles1: dict of pile 1 (strings, 25), storing terms each (strings)\n",
    "- piles2: dict of pile 2 (strings, 19), storing terms each (strings)\n",
    "- piece_performer_list: list of strings of the 45 performances (piece + performer, unique)\n",
    "- pieces_list: list of strings of the piece names (45, repeats)\n",
    "- performer_list: list of strings of the performers names (45, repeats)\n",
    "- pieces_music_id_list: list of music IDs of the 45 performances (note, these are not array idx!)\n",
    "- terms: list of terms, sorted alphabetically\n",
    "- pile_names: list of string names of the piles\n",
    "- term_per_pile: dict of piles (strings, 44, both pile groups), storing term idx each (to index \"terms\")\n",
    "- term_per_perf: dict of performance names (strings), storing term idx each (to index \"terms\")\n",
    "- pile_idx_per_term: dict of tuples\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute a pile and performance based similarity matrix\n",
    "\n",
    "based on:\n",
    "- piles: terms used in the same pile increments similarity\n",
    "- performances: terms used for the same performance increments similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "term_similarity = np.zeros((len(group1), len(group1))) # 150 x 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute similarity based on piles\n",
    "\n",
    "for p, terms1 in piles1.items():\n",
    "    for t in terms1:\n",
    "        i = np.where(all_terms == t)[0]\n",
    "        for tt in terms1:\n",
    "            j = np.where(all_terms == tt)[0]\n",
    "            term_similarity[i, j] += 1\n",
    "            \n",
    "            # abuse this loop to store all the pile neighbors per term\n",
    "            if tt != t:\n",
    "                neighbors1[t].append(j[0])\n",
    "            \n",
    "for p, terms2 in piles2.items():\n",
    "    for t in terms2:\n",
    "        i = np.where(all_terms == t)[0]\n",
    "        for tt in terms2:\n",
    "            j = np.where(all_terms == tt)[0]\n",
    "            term_similarity[i, j] += 1 \n",
    "            \n",
    "            # abuse this loop to store all the pile neighbors per term\n",
    "            if tt != t:\n",
    "                neighbors2[t].append(j[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abrupt [0]\n",
      "performances where this was used [13 20 44]\n",
      "terms that were co-used 63\n",
      "UNIQUE term_ids that were co-used 49\n",
      "accentuated [1]\n",
      "performances where this was used [18 27 30 44]\n",
      "terms that were co-used 83\n",
      "UNIQUE term_ids that were co-used 58\n",
      "aggressive [2]\n",
      "performances where this was used [ 6  9 19 22 26 30 33]\n",
      "terms that were co-used 133\n",
      "UNIQUE term_ids that were co-used 70\n",
      "agitated [3]\n",
      "performances where this was used [15 20 22 28 31 44]\n",
      "terms that were co-used 117\n",
      "UNIQUE term_ids that were co-used 68\n",
      "airy [4]\n",
      "performances where this was used [ 2 24 28 36 43]\n",
      "terms that were co-used 100\n",
      "UNIQUE term_ids that were co-used 61\n",
      "amateur [5]\n",
      "performances where this was used [ 0 22]\n",
      "terms that were co-used 49\n",
      "UNIQUE term_ids that were co-used 43\n",
      "anxious [6]\n",
      "performances where this was used [ 6 11 19 34]\n",
      "terms that were co-used 71\n",
      "UNIQUE term_ids that were co-used 52\n",
      "authentic [7]\n",
      "performances where this was used []\n",
      "terms that were co-used 0\n",
      "UNIQUE term_ids that were co-used 0\n",
      "bland [8]\n",
      "performances where this was used [ 4 33 36]\n",
      "terms that were co-used 53\n",
      "UNIQUE term_ids that were co-used 43\n",
      "blurry [9]\n",
      "performances where this was used [ 5 11]\n",
      "terms that were co-used 44\n",
      "UNIQUE term_ids that were co-used 38\n",
      "bold [10]\n",
      "performances where this was used [13]\n",
      "terms that were co-used 21\n",
      "UNIQUE term_ids that were co-used 21\n",
      "boring [11]\n",
      "performances where this was used [ 0  1  2  3  4  6 10 12 13 16 22 24 26 27 36 37 39 40]\n",
      "terms that were co-used 349\n",
      "UNIQUE term_ids that were co-used 128\n",
      "breathing [12]\n",
      "performances where this was used [10 35]\n",
      "terms that were co-used 41\n",
      "UNIQUE term_ids that were co-used 33\n",
      "bright [13]\n",
      "performances where this was used [ 9 14 15 16 22 28 38 41 42]\n",
      "terms that were co-used 195\n",
      "UNIQUE term_ids that were co-used 90\n",
      "brisk [14]\n",
      "performances where this was used [ 0  5 33 38 44]\n",
      "terms that were co-used 111\n",
      "UNIQUE term_ids that were co-used 69\n",
      "bumpy [15]\n",
      "performances where this was used [1 3]\n",
      "terms that were co-used 39\n",
      "UNIQUE term_ids that were co-used 32\n",
      "calm [16]\n",
      "performances where this was used [ 4  7  8 18 25 27 29 39 41 42]\n",
      "terms that were co-used 217\n",
      "UNIQUE term_ids that were co-used 84\n",
      "cantabile [17]\n",
      "performances where this was used [28 39 42]\n",
      "terms that were co-used 64\n",
      "UNIQUE term_ids that were co-used 47\n",
      "careful [18]\n",
      "performances where this was used [ 4  7 17 23 34]\n",
      "terms that were co-used 96\n",
      "UNIQUE term_ids that were co-used 56\n",
      "cerebral [19]\n",
      "performances where this was used [ 1 14 31]\n",
      "terms that were co-used 60\n",
      "UNIQUE term_ids that were co-used 45\n",
      "chaotic [20]\n",
      "performances where this was used [ 9 22 32 34]\n",
      "terms that were co-used 88\n",
      "UNIQUE term_ids that were co-used 56\n",
      "childlike [21]\n",
      "performances where this was used [ 0 25]\n",
      "terms that were co-used 51\n",
      "UNIQUE term_ids that were co-used 43\n",
      "chopped [22]\n",
      "performances where this was used [ 0  1  3  6  9 21 26 34]\n",
      "terms that were co-used 157\n",
      "UNIQUE term_ids that were co-used 85\n",
      "clanking [23]\n",
      "performances where this was used [19 27]\n",
      "terms that were co-used 38\n",
      "UNIQUE term_ids that were co-used 36\n",
      "clean [24]\n",
      "performances where this was used [ 3  5 10 14 16 19 30 31 35 36 39 41 42 44]\n",
      "terms that were co-used 285\n",
      "UNIQUE term_ids that were co-used 102\n",
      "clear [25]\n",
      "performances where this was used [ 1  5 10 12 14 16 18 20 21 24 31 33 36 41 42]\n",
      "terms that were co-used 301\n",
      "UNIQUE term_ids that were co-used 112\n",
      "clumsy [26]\n",
      "performances where this was used [20 22 23 34]\n",
      "terms that were co-used 83\n",
      "UNIQUE term_ids that were co-used 61\n",
      "cold [27]\n",
      "performances where this was used [ 1  2  3  6  8 11 14 19 34 39 44]\n",
      "terms that were co-used 220\n",
      "UNIQUE term_ids that were co-used 101\n",
      "colorful [28]\n",
      "performances where this was used [ 0 21 37]\n",
      "terms that were co-used 54\n",
      "UNIQUE term_ids that were co-used 42\n",
      "complex [29]\n",
      "performances where this was used [9]\n",
      "terms that were co-used 23\n",
      "UNIQUE term_ids that were co-used 23\n",
      "conservative [30]\n",
      "performances where this was used [15]\n",
      "terms that were co-used 19\n",
      "UNIQUE term_ids that were co-used 19\n",
      "controlled [31]\n",
      "performances where this was used [13 16 30 31 41]\n",
      "terms that were co-used 101\n",
      "UNIQUE term_ids that were co-used 62\n",
      "dancing [32]\n",
      "performances where this was used [ 1 21]\n",
      "terms that were co-used 36\n",
      "UNIQUE term_ids that were co-used 30\n",
      "dark [33]\n",
      "performances where this was used [ 5  7 11 17 24 29 31 32 34 35 38 39]\n",
      "terms that were co-used 236\n",
      "UNIQUE term_ids that were co-used 101\n",
      "deep [34]\n",
      "performances where this was used [2 5]\n",
      "terms that were co-used 55\n",
      "UNIQUE term_ids that were co-used 45\n",
      "deliberate [35]\n",
      "performances where this was used [ 0  7 12 25]\n",
      "terms that were co-used 86\n",
      "UNIQUE term_ids that were co-used 59\n",
      "delicate [36]\n",
      "performances where this was used [ 2  5  7  8 10 12 17 24 35 39 41 42]\n",
      "terms that were co-used 259\n",
      "UNIQUE term_ids that were co-used 89\n",
      "determined [37]\n",
      "performances where this was used [ 9 16 31 32]\n",
      "terms that were co-used 78\n",
      "UNIQUE term_ids that were co-used 53\n",
      "differentiated [38]\n",
      "performances where this was used [ 7 15 20 25 39 42]\n",
      "terms that were co-used 131\n",
      "UNIQUE term_ids that were co-used 72\n",
      "dirty [39]\n",
      "performances where this was used [24 26 33]\n",
      "terms that were co-used 59\n",
      "UNIQUE term_ids that were co-used 47\n",
      "distant [40]\n",
      "performances where this was used [ 0  8 13 15 18 28 29 36 37 38 43]\n",
      "terms that were co-used 205\n",
      "UNIQUE term_ids that were co-used 88\n",
      "dramatic [41]\n",
      "performances where this was used [ 2  9 10 11 13 14 15 16 17 18 20 27 28 30 32]\n",
      "terms that were co-used 300\n",
      "UNIQUE term_ids that were co-used 103\n",
      "dreamy [42]\n",
      "performances where this was used [ 2  4  5  8  9 13 20 23 24 27 35 38 41 42 43]\n",
      "terms that were co-used 332\n",
      "UNIQUE term_ids that were co-used 112\n",
      "dry [43]\n",
      "performances where this was used [ 4  5  6 14 16 19 21 22 25 30 32 34 44]\n",
      "terms that were co-used 271\n",
      "UNIQUE term_ids that were co-used 113\n",
      "dull [44]\n",
      "performances where this was used [ 0  7 26]\n",
      "terms that were co-used 64\n",
      "UNIQUE term_ids that were co-used 54\n",
      "eager [45]\n",
      "performances where this was used [25 29]\n",
      "terms that were co-used 44\n",
      "UNIQUE term_ids that were co-used 34\n",
      "easy [46]\n",
      "performances where this was used [24 25 43]\n",
      "terms that were co-used 66\n",
      "UNIQUE term_ids that were co-used 49\n",
      "elegant [47]\n",
      "performances where this was used [ 4  5 25 42]\n",
      "terms that were co-used 103\n",
      "UNIQUE term_ids that were co-used 68\n",
      "emotional [48]\n",
      "performances where this was used [ 2  6  8  9 17 23 28 35 36 38 39 40 41 42 43]\n",
      "terms that were co-used 282\n",
      "UNIQUE term_ids that were co-used 99\n",
      "emphatic [49]\n",
      "performances where this was used [19 32 35]\n",
      "terms that were co-used 56\n",
      "UNIQUE term_ids that were co-used 41\n",
      "energetic [50]\n",
      "performances where this was used [ 0  9 16 18 22 25 30 44]\n",
      "terms that were co-used 179\n",
      "UNIQUE term_ids that were co-used 89\n",
      "erratic [51]\n",
      "performances where this was used [ 9 19 22 26 37]\n",
      "terms that were co-used 94\n",
      "UNIQUE term_ids that were co-used 62\n",
      "etude-like [52]\n",
      "performances where this was used []\n",
      "terms that were co-used 0\n",
      "UNIQUE term_ids that were co-used 0\n",
      "expressive [53]\n",
      "performances where this was used [ 0  2  4  5  7  8  9 10 12 13 14 15 17 18 20 22 25 27 28 29 30 31 32 35\n",
      " 36 37 41 42 43]\n",
      "terms that were co-used 594\n",
      "UNIQUE term_ids that were co-used 134\n",
      "fake [54]\n",
      "performances where this was used [ 1 40]\n",
      "terms that were co-used 32\n",
      "UNIQUE term_ids that were co-used 27\n",
      "fantastic [55]\n",
      "performances where this was used [14 20]\n",
      "terms that were co-used 42\n",
      "UNIQUE term_ids that were co-used 36\n",
      "fast [56]\n",
      "performances where this was used [ 0  3  5  8  9 11 13 14 15 20 21 22 24 25 28 29 30 31 33 35 38 44]\n",
      "terms that were co-used 449\n",
      "UNIQUE term_ids that were co-used 127\n",
      "floating [57]\n",
      "performances where this was used [ 0  2  5 11 17 42]\n",
      "terms that were co-used 138\n",
      "UNIQUE term_ids that were co-used 79\n",
      "flowing [58]\n",
      "performances where this was used [ 0  2  4  5  7  8 11 14 15 17 24 28 32 35 38 41]\n",
      "terms that were co-used 339\n",
      "UNIQUE term_ids that were co-used 109\n",
      "fragile [59]\n",
      "performances where this was used [ 7 42]\n",
      "terms that were co-used 49\n",
      "UNIQUE term_ids that were co-used 41\n",
      "free [60]\n",
      "performances where this was used [16 24 25]\n",
      "terms that were co-used 71\n",
      "UNIQUE term_ids that were co-used 52\n",
      "frenetic [61]\n",
      "performances where this was used [20 24 44]\n",
      "terms that were co-used 67\n",
      "UNIQUE term_ids that were co-used 52\n",
      "full [62]\n",
      "performances where this was used [ 5 13 19 28 35 36]\n",
      "terms that were co-used 118\n",
      "UNIQUE term_ids that were co-used 72\n",
      "fuzzy [63]\n",
      "performances where this was used [22 34]\n",
      "terms that were co-used 46\n",
      "UNIQUE term_ids that were co-used 38\n",
      "galloping [64]\n",
      "performances where this was used [ 1 22 33 44]\n",
      "terms that were co-used 85\n",
      "UNIQUE term_ids that were co-used 57\n",
      "gentle [65]\n",
      "performances where this was used [ 2  4  5  8 17 23 29 34 37 38 42 43]\n",
      "terms that were co-used 247\n",
      "UNIQUE term_ids that were co-used 93\n",
      "happy [66]\n",
      "performances where this was used [21 22 24 25 36 37 41 42]\n",
      "terms that were co-used 166\n",
      "UNIQUE term_ids that were co-used 86\n",
      "harsh [67]\n",
      "performances where this was used [ 0  6 16 35]\n",
      "terms that were co-used 84\n",
      "UNIQUE term_ids that were co-used 63\n",
      "hasty [68]\n",
      "performances where this was used [ 0  3  9 14 15 19 20 29 38 44]\n",
      "terms that were co-used 201\n",
      "UNIQUE term_ids that were co-used 88\n",
      "heavy [69]\n",
      "performances where this was used [ 2 10 16 27 28 29 31]\n",
      "terms that were co-used 142\n",
      "UNIQUE term_ids that were co-used 74\n",
      "hectic [70]\n",
      "performances where this was used [ 0  3 19 29 33 44]\n",
      "terms that were co-used 116\n",
      "UNIQUE term_ids that were co-used 65\n",
      "holding_back [71]\n",
      "performances where this was used [18 32]\n",
      "terms that were co-used 37\n",
      "UNIQUE term_ids that were co-used 28\n",
      "idiosyncratic [72]\n",
      "performances where this was used [ 1 44]\n",
      "terms that were co-used 45\n",
      "UNIQUE term_ids that were co-used 38\n",
      "impatient [73]\n",
      "performances where this was used [11 16 24 33]\n",
      "terms that were co-used 79\n",
      "UNIQUE term_ids that were co-used 56\n",
      "impetuous [74]\n",
      "performances where this was used [30]\n",
      "terms that were co-used 21\n",
      "UNIQUE term_ids that were co-used 21\n",
      "inappropriate [75]\n",
      "performances where this was used [9]\n",
      "terms that were co-used 23\n",
      "UNIQUE term_ids that were co-used 23\n",
      "innocent [76]\n",
      "performances where this was used [1 2]\n",
      "terms that were co-used 50\n",
      "UNIQUE term_ids that were co-used 47\n",
      "intense [77]\n",
      "performances where this was used [ 1 15 20 30]\n",
      "terms that were co-used 83\n",
      "UNIQUE term_ids that were co-used 60\n",
      "ironic [78]\n",
      "performances where this was used []\n",
      "terms that were co-used 0\n",
      "UNIQUE term_ids that were co-used 0\n",
      "joyful [79]\n",
      "performances where this was used [16 23 24 41 42]\n",
      "terms that were co-used 114\n",
      "UNIQUE term_ids that were co-used 66\n",
      "jumpy [80]\n",
      "performances where this was used [ 1 14]\n",
      "terms that were co-used 45\n",
      "UNIQUE term_ids that were co-used 37\n",
      "kitschy [81]\n",
      "performances where this was used [5]\n",
      "terms that were co-used 28\n",
      "UNIQUE term_ids that were co-used 28\n",
      "legato [82]\n",
      "performances where this was used [ 1 18 43]\n",
      "terms that were co-used 57\n",
      "UNIQUE term_ids that were co-used 47\n",
      "light [83]\n",
      "performances where this was used [ 1  3  4  6  7  8 11 14 15 17 21 23 24 25 30 36 37 38 41 44]\n",
      "terms that were co-used 382\n",
      "UNIQUE term_ids that were co-used 116\n",
      "lively [84]\n",
      "performances where this was used [ 3  9 11 14 15 24 25 27 28 38 41 44]\n",
      "terms that were co-used 249\n",
      "UNIQUE term_ids that were co-used 99\n",
      "loud [85]\n",
      "performances where this was used [ 2  7  9 11 13 14 18 19 22 25 28 29 30 32 34 35 40 41]\n",
      "terms that were co-used 365\n",
      "UNIQUE term_ids that were co-used 122\n",
      "marcato [86]\n",
      "performances where this was used [43]\n",
      "terms that were co-used 16\n",
      "UNIQUE term_ids that were co-used 16\n",
      "marching [87]\n",
      "performances where this was used [26 31]\n",
      "terms that were co-used 32\n",
      "UNIQUE term_ids that were co-used 29\n",
      "mechanical [88]\n",
      "performances where this was used [ 0  1  3  5  6 12 14 16 18 22 26 29 31 32 33 34 36 37 40 44]\n",
      "terms that were co-used 377\n",
      "UNIQUE term_ids that were co-used 120\n",
      "mellow [89]\n",
      "performances where this was used [ 2  4  5  8 10 14 15 23 25 28 29 42 43]\n",
      "terms that were co-used 282\n",
      "UNIQUE term_ids that were co-used 100\n",
      "melodious [90]\n",
      "performances where this was used [ 8 18 25 35 41]\n",
      "terms that were co-used 108\n",
      "UNIQUE term_ids that were co-used 63\n",
      "mesmerizing [91]\n",
      "performances where this was used [2]\n",
      "terms that were co-used 27\n",
      "UNIQUE term_ids that were co-used 27\n",
      "metallic [92]\n",
      "performances where this was used [ 6 26 29]\n",
      "terms that were co-used 52\n",
      "UNIQUE term_ids that were co-used 42\n",
      "monotonous [93]\n",
      "performances where this was used [ 1  6  7 14 22 23 26 39 40]\n",
      "terms that were co-used 166\n",
      "UNIQUE term_ids that were co-used 84\n",
      "mysterious [94]\n",
      "performances where this was used [ 4  8 12 13 17 18 20 30 34]\n",
      "terms that were co-used 173\n",
      "UNIQUE term_ids that were co-used 80\n",
      "nervous [95]\n",
      "performances where this was used [ 0  3 19 25 31 33 38 44]\n",
      "terms that were co-used 155\n",
      "UNIQUE term_ids that were co-used 81\n",
      "noble [96]\n",
      "performances where this was used [ 4 27]\n",
      "terms that were co-used 44\n",
      "UNIQUE term_ids that were co-used 36\n",
      "nostalgic [97]\n",
      "performances where this was used [39 42]\n",
      "terms that were co-used 46\n",
      "UNIQUE term_ids that were co-used 35\n",
      "oscillating [98]\n",
      "performances where this was used [21]\n",
      "terms that were co-used 13\n",
      "UNIQUE term_ids that were co-used 13\n",
      "passionate [99]\n",
      "performances where this was used [ 2  9 10 11 12 13 15 19 20 23 25 27 29 30 32 35 37]\n",
      "terms that were co-used 335\n",
      "UNIQUE term_ids that were co-used 112\n",
      "peaceful [100]\n",
      "performances where this was used [ 1  4  7 27 43]\n",
      "terms that were co-used 104\n",
      "UNIQUE term_ids that were co-used 70\n",
      "pensive [101]\n",
      "performances where this was used [ 4 10 18 23 24 32 35 38 39 42]\n",
      "terms that were co-used 206\n",
      "UNIQUE term_ids that were co-used 88\n",
      "playful [102]\n",
      "performances where this was used [ 0  1  3 16 20 21 24 25 29 31 41 43 44]\n",
      "terms that were co-used 264\n",
      "UNIQUE term_ids that were co-used 103\n",
      "ponderous [103]\n",
      "performances where this was used [10 12 23 27 34 42]\n",
      "terms that were co-used 124\n",
      "UNIQUE term_ids that were co-used 69\n",
      "professional [104]\n",
      "performances where this was used [ 2  8 13 15 37]\n",
      "terms that were co-used 103\n",
      "UNIQUE term_ids that were co-used 58\n",
      "pushing_forward [105]\n",
      "performances where this was used [36]\n",
      "terms that were co-used 14\n",
      "UNIQUE term_ids that were co-used 14\n",
      "quiet [106]\n",
      "performances where this was used [ 4  5  8 27 34 41 42 43]\n",
      "terms that were co-used 183\n",
      "UNIQUE term_ids that were co-used 88\n",
      "restrained [107]\n",
      "performances where this was used [ 7 10 15 25 39]\n",
      "terms that were co-used 103\n",
      "UNIQUE term_ids that were co-used 61\n",
      "reverberant [108]\n",
      "performances where this was used [ 0  5 11 35]\n",
      "terms that were co-used 91\n",
      "UNIQUE term_ids that were co-used 62\n",
      "robotic [109]\n",
      "performances where this was used [ 6 26 40]\n",
      "terms that were co-used 42\n",
      "UNIQUE term_ids that were co-used 28\n",
      "romantic [110]\n",
      "performances where this was used [ 0  2  9 23 27 30 35 42]\n",
      "terms that were co-used 185\n",
      "UNIQUE term_ids that were co-used 98\n",
      "round [111]\n",
      "performances where this was used [20 28 30 32 41]\n",
      "terms that were co-used 101\n",
      "UNIQUE term_ids that were co-used 62\n",
      "rubato [112]\n",
      "performances where this was used [25 37]\n",
      "terms that were co-used 40\n",
      "UNIQUE term_ids that were co-used 34\n",
      "rushed [113]\n",
      "performances where this was used [ 0  3  9 11 15 16 20 24 25 29 30 33 34 44]\n",
      "terms that were co-used 293\n",
      "UNIQUE term_ids that were co-used 108\n",
      "sad [114]\n",
      "performances where this was used [ 2  5  7  8 12 18 27 32 39 42]\n",
      "terms that were co-used 216\n",
      "UNIQUE term_ids that were co-used 86\n",
      "searching [115]\n",
      "performances where this was used [4]\n",
      "terms that were co-used 22\n",
      "UNIQUE term_ids that were co-used 22\n",
      "serene [116]\n",
      "performances where this was used [ 2 27 38 41]\n",
      "terms that were co-used 90\n",
      "UNIQUE term_ids that were co-used 59\n",
      "shallow [117]\n",
      "performances where this was used [ 0 27 28]\n",
      "terms that were co-used 66\n",
      "UNIQUE term_ids that were co-used 54\n",
      "sharp [118]\n",
      "performances where this was used [ 5 10 16 19]\n",
      "terms that were co-used 85\n",
      "UNIQUE term_ids that were co-used 62\n",
      "shy [119]\n",
      "performances where this was used [ 2  8 13]\n",
      "terms that were co-used 69\n",
      "UNIQUE term_ids that were co-used 43\n",
      "simple [120]\n",
      "performances where this was used [ 1  5 23]\n",
      "terms that were co-used 68\n",
      "UNIQUE term_ids that were co-used 58\n",
      "sloppy [121]\n",
      "performances where this was used [ 3 22 32 33]\n",
      "terms that were co-used 75\n",
      "UNIQUE term_ids that were co-used 54\n",
      "slow [122]\n",
      "performances where this was used [ 2  7 10 12 13 15 18 21 23 27 32 34 37 39 41 42 43]\n",
      "terms that were co-used 334\n",
      "UNIQUE term_ids that were co-used 108\n",
      "soaring [123]\n",
      "performances where this was used [ 2 10 11 13 17]\n",
      "terms that were co-used 97\n",
      "UNIQUE term_ids that were co-used 56\n",
      "soft [124]\n",
      "performances where this was used [ 2  3  4  5  7  8  9 10 13 14 16 17 18 19 24 25 28 29 30 32 33 34 35 36\n",
      " 37 38 39 41 42 43]\n",
      "terms that were co-used 609\n",
      "UNIQUE term_ids that were co-used 135\n",
      "solemn [125]\n",
      "performances where this was used [ 7 13 30]\n",
      "terms that were co-used 63\n",
      "UNIQUE term_ids that were co-used 47\n",
      "staccato [126]\n",
      "performances where this was used [ 1  6 14 16 21 22 26 34 44]\n",
      "terms that were co-used 180\n",
      "UNIQUE term_ids that were co-used 87\n",
      "static [127]\n",
      "performances where this was used [ 1 12 26 32 33]\n",
      "terms that were co-used 90\n",
      "UNIQUE term_ids that were co-used 65\n",
      "sterile [128]\n",
      "performances where this was used [ 1  5  6 40 42]\n",
      "terms that were co-used 104\n",
      "UNIQUE term_ids that were co-used 70\n",
      "story-telling [129]\n",
      "performances where this was used []\n",
      "terms that were co-used 0\n",
      "UNIQUE term_ids that were co-used 0\n",
      "straightforward [130]\n",
      "performances where this was used [ 0 41]\n",
      "terms that were co-used 49\n",
      "UNIQUE term_ids that were co-used 45\n",
      "strict [131]\n",
      "performances where this was used [ 8 22 40]\n",
      "terms that were co-used 53\n",
      "UNIQUE term_ids that were co-used 44\n",
      "stuttering [132]\n",
      "performances where this was used [ 9 26 34]\n",
      "terms that were co-used 63\n",
      "UNIQUE term_ids that were co-used 48\n",
      "technical [133]\n",
      "performances where this was used [20 33]\n",
      "terms that were co-used 37\n",
      "UNIQUE term_ids that were co-used 33\n",
      "tender [134]\n",
      "performances where this was used [ 4  7  8 10 14 23 24 27 35 39 43]\n",
      "terms that were co-used 225\n",
      "UNIQUE term_ids that were co-used 89\n",
      "tense [135]\n",
      "performances where this was used [10 12 16 18 21 27]\n",
      "terms that were co-used 108\n",
      "UNIQUE term_ids that were co-used 62\n",
      "thin [136]\n",
      "performances where this was used [ 2 29]\n",
      "terms that were co-used 46\n",
      "UNIQUE term_ids that were co-used 38\n",
      "thoughtful [137]\n",
      "performances where this was used [12 23 24 35 38 42]\n",
      "terms that were co-used 123\n",
      "UNIQUE term_ids that were co-used 68\n",
      "too_much_pedal [138]\n",
      "performances where this was used [ 5 37]\n",
      "terms that were co-used 43\n",
      "UNIQUE term_ids that were co-used 38\n",
      "tragic [139]\n",
      "performances where this was used [10 34]\n",
      "terms that were co-used 43\n",
      "UNIQUE term_ids that were co-used 39\n",
      "unchanging_dynamics [140]\n",
      "performances where this was used []\n",
      "terms that were co-used 0\n",
      "UNIQUE term_ids that were co-used 0\n",
      "uneven [141]\n",
      "performances where this was used [22 26 34]\n",
      "terms that were co-used 63\n",
      "UNIQUE term_ids that were co-used 45\n",
      "uniform [142]\n",
      "performances where this was used [ 4 26 31 33 34 38 44]\n",
      "terms that were co-used 134\n",
      "UNIQUE term_ids that were co-used 76\n",
      "uninspired [143]\n",
      "performances where this was used [ 6 24 27]\n",
      "terms that were co-used 63\n",
      "UNIQUE term_ids that were co-used 55\n",
      "unpredictable [144]\n",
      "performances where this was used [ 9 20 26 30]\n",
      "terms that were co-used 81\n",
      "UNIQUE term_ids that were co-used 54\n",
      "unrhythmic [145]\n",
      "performances where this was used [22]\n",
      "terms that were co-used 23\n",
      "UNIQUE term_ids that were co-used 23\n",
      "vast [146]\n",
      "performances where this was used []\n",
      "terms that were co-used 0\n",
      "UNIQUE term_ids that were co-used 0\n",
      "warm [147]\n",
      "performances where this was used [ 0  4  5  7 13 39]\n",
      "terms that were co-used 136\n",
      "UNIQUE term_ids that were co-used 77\n",
      "weightless [148]\n",
      "performances where this was used [24]\n",
      "terms that were co-used 25\n",
      "UNIQUE term_ids that were co-used 25\n",
      "wistful [149]\n",
      "performances where this was used [13 42]\n",
      "terms that were co-used 49\n",
      "UNIQUE term_ids that were co-used 44\n"
     ]
    }
   ],
   "source": [
    "# update similarity by terms used for same performance\n",
    "COOCCURENCE_WEIGHT = 0.15\n",
    "\n",
    "for t in terms:\n",
    "    \n",
    "    # index of this term\n",
    "    i = np.where(all_terms == t)[0]\n",
    "    print(t, i)\n",
    "    # index of this term_id in the dtm (= all the positions this term was used)\n",
    "    # perf_ids, term_ids, term_frequency\n",
    "    j = np.where(term_ids == i)[0]\n",
    "    \n",
    "    # all indices of perf_ids that correspond to the performance where this term was used\n",
    "    print(\"performances where this was used\", perf_ids[j])\n",
    "    #k = (perf_ids[j] == perf_ids)\n",
    "    k = np.any([(l == perf_ids) for l in perf_ids[j]], axis=0)\n",
    "    print(\"terms that were co-used\", k.sum())\n",
    "    #print(\"term_ids that were co-used\", term_ids[k])\n",
    "    local_term_ids = np.unique(term_ids[k])\n",
    "    print(\"UNIQUE term_ids that were co-used\", len(local_term_ids))\n",
    "    # loop over all these found idx\n",
    "    for cooccurence_id in local_term_ids:     \n",
    "        term_similarity[i[0], cooccurence_id] += COOCCURENCE_WEIGHT \n",
    "\n",
    "# normalize\n",
    "term_similarity /= term_similarity.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(term_similarity)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "term_similarity_only_piles = np.zeros((150,150)) # 150 x 150\n",
    "# compute similarity based on piles\n",
    "\n",
    "for p, terms1 in piles1.items():\n",
    "    for t in terms1:\n",
    "        i = np.where(all_terms == t)[0]\n",
    "        for tt in terms1:\n",
    "            j = np.where(all_terms == tt)[0]\n",
    "            term_similarity_only_piles[i, j] += 1\n",
    "\n",
    "for p, terms2 in piles2.items():\n",
    "    for t in terms2:\n",
    "        i = np.where(all_terms == t)[0]\n",
    "        for tt in terms2:\n",
    "            j = np.where(all_terms == tt)[0]\n",
    "            term_similarity_only_piles[i, j] += 1 \n",
    "            \n",
    "term_similarity_only_piles /= term_similarity_only_piles.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "term_similarity_only_pile2 = np.zeros((150,150)) # 150 x 150\n",
    "# compute similarity based on pile 2\n",
    "\n",
    "for p, terms2 in piles2.items():\n",
    "    for t in terms2:\n",
    "        i = np.where(all_terms == t)[0]\n",
    "        for tt in terms2:\n",
    "            j = np.where(all_terms == tt)[0]\n",
    "            term_similarity_only_pile2[i, j] += 1 \n",
    "            \n",
    "term_similarity_only_pile2 /= term_similarity_only_pile2.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "term_similarity_only_pile1 = np.zeros((150,150)) # 150 x 150\n",
    "# compute similarity based on pile 1\n",
    "\n",
    "for p, terms1 in piles1.items():\n",
    "    for t in terms1:\n",
    "        i = np.where(all_terms == t)[0]\n",
    "        for tt in terms1:\n",
    "            j = np.where(all_terms == tt)[0]\n",
    "            term_similarity_only_pile1[i, j] += 1\n",
    "            \n",
    "term_similarity_only_pile1 /= term_similarity_only_pile1.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "term_similarity_only_performances = np.zeros((150,150)) # 150 x 150\n",
    "# compute similarity based on performances only\n",
    "\n",
    "for t in terms:\n",
    "    # index of this term\n",
    "    i = np.where(all_terms == t)[0]\n",
    "    # index of this term_id in the dtm (= all the positions this term was used)\n",
    "    # perf_ids, term_ids, term_frequency\n",
    "    j = np.where(term_ids == i)[0]\n",
    "    # all indices of perf_ids that correspond to the performance where this term was used\n",
    "    k = np.any([(l == perf_ids) for l in perf_ids[j]], axis=0)\n",
    "    local_term_ids = np.unique(term_ids[k])\n",
    "    # loop over all these found idx\n",
    "    for cooccurence_id in local_term_ids:     \n",
    "        term_similarity_only_performances[i[0], cooccurence_id] += 0.15 \n",
    "            \n",
    "term_similarity_only_performances /= term_similarity_only_performances.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "PERF_WEIGHT = 1\n",
    "PILE1_WEIGHT = 1\n",
    "PILE2_WEIGHT = 1\n",
    "term_similarity_weighted = PERF_WEIGHT * term_similarity_only_performances + \\\n",
    "                            PILE1_WEIGHT * term_similarity_only_pile1 + \\\n",
    "                            PILE2_WEIGHT * term_similarity_only_pile2\n",
    "term_similarity_weighted /= term_similarity_weighted.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get embeddings from openAI\n",
    "\n",
    "we get embeddings for all terms from openai's \"text-embedding-ada-002\" model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#kk = \"USE_YOUR_OWN_KEY\"\n",
    "#import openai\n",
    "#openai.api_key = kk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#response = openai.Embedding.create(\n",
    "#        input=terms,\n",
    "#        model=\"text-embedding-ada-002\")\n",
    "#embeddings = response['data'][0]['embedding']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#term_embeddings_ada = list()\n",
    "#for resp in response[\"data\"]:\n",
    "#    term_embeddings_ada.append(resp['embedding'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#term_embeddings_ada_npy = np.array(term_embeddings_ada)\n",
    "#np.save(\"term_embeddings_ada_npy.npy\", term_embeddings_ada_npy, allow_pickle=True, fix_imports=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "term_embeddings_ada_npy = np.load(\"data/term_embeddings_ada_npy.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# these embeddings are normalized, so cosine similarity is given by the dot product\n",
    "similarity_ada = np.dot(term_embeddings_ada_npy, term_embeddings_ada_npy.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(similarity_ada)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get embeddings including context from ADA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "term_embeddings_ada_context_npy = np.load(\"data/term_embeddings_ada_context_npy.npy\")\n",
    "similarity_ada_context = np.dot(term_embeddings_ada_context_npy, term_embeddings_ada_context_npy.T)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get embeddings with and without context from BGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "term_embeddings_bge_npy = np.load(\"data/term_embeddings_bge_npy.npy\")\n",
    "similarity_bge = np.dot(term_embeddings_bge_npy, term_embeddings_bge_npy.T)\n",
    "term_embeddings_bge_context_npy = np.load(\"data/term_embeddings_bge_context_npy.npy\")\n",
    "similarity_bge_context = np.dot(term_embeddings_bge_context_npy, term_embeddings_bge_context_npy.T)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get embeddings with and without context from GTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "term_embeddings_gte_npy = np.load(\"data/term_embeddings_gte_npy.npy\")\n",
    "similarity_gte = np.dot(term_embeddings_gte_npy, term_embeddings_gte_npy.T)\n",
    "term_embeddings_gte_context_npy = np.load(\"data/term_embeddings_gte_context_npy.npy\")\n",
    "similarity_gte_context = np.dot(term_embeddings_gte_context_npy, term_embeddings_gte_context_npy.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.99999994, 0.9573743 , 0.94951224, ..., 0.94309723, 0.9326481 ,\n",
       "        0.94532853],\n",
       "       [0.9573743 , 0.99999994, 0.960732  , ..., 0.9573236 , 0.9345112 ,\n",
       "        0.9551582 ],\n",
       "       [0.94951224, 0.960732  , 0.99999994, ..., 0.940041  , 0.92099106,\n",
       "        0.92967075],\n",
       "       ...,\n",
       "       [0.94309723, 0.9573236 , 0.940041  , ..., 1.        , 0.93420416,\n",
       "        0.95421433],\n",
       "       [0.9326481 , 0.9345112 , 0.92099106, ..., 0.93420416, 1.        ,\n",
       "        0.9264462 ],\n",
       "       [0.94532853, 0.9551582 , 0.92967075, ..., 0.95421433, 0.9264462 ,\n",
       "        1.0000001 ]], dtype=float32)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity_gte_context"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get embeddings from CLAP\n",
    "\n",
    "we get embeddings for all terms from microsoft's CLAP model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this code was executed in separate environment using the weights provided by the CLAP developers.\n",
    "\n",
    "# from CLAPWrapper import CLAPWrapper \n",
    "\n",
    "# clap_model = CLAPWrapper(r\"PATH_TO_CLAP_weights_2022.pth\")\n",
    "\n",
    "# with open(\"terms.list\", \"rb\") as fp:   \n",
    "#     terms = pickle.load(fp)\n",
    "\n",
    "# text_embeddings = clap_model.get_text_embeddings(terms).numpy()\n",
    "\n",
    "# np.save(\"term_embeddings_clap_npy.npy\", text_embeddings, allow_pickle=True, fix_imports=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "term_embeddings_clap_npy = np.load(\"data/term_embeddings_clap_npy.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# these embeddings are normalized, so cosine similarity is given by the dot product\n",
    "similarity_clap = np.dot(term_embeddings_clap_npy, term_embeddings_clap_npy.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(similarity_clap)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get embeddings including context from CLAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# original task\n",
    "# \"Please think of words (if possible, adjectives) that best describe the character of each performance to you\"\n",
    "# prompt = \"I listen to a solo piano performance of a classical piece of music and I'd describe the character of the performance as \"\n",
    "# terms_with_context = [prompt + term for term in terms]\n",
    "\n",
    "# text_embeddings = clap_model.get_text_embeddings(terms_with_context).numpy()\n",
    "# np.save(\"term_embeddings_clap_context_npy.npy\", text_embeddings, allow_pickle=True, fix_imports=True)\n",
    "\n",
    "term_embeddings_clap_context_npy = np.load(\"data/term_embeddings_clap_context_npy.npy\")\n",
    "similarity_clap_context = np.dot(term_embeddings_clap_context_npy, term_embeddings_clap_context_npy.T)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get audio embeddings from CLAP\n",
    "\n",
    "audio files are not part of the published dataset due to copyright."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_audio = np.load(\"data/audio_embeddings_clap_npy.npz\", allow_pickle=True)\n",
    "# audio CLAP data\n",
    "audio_embeddings = data_audio[\"audio_ebeddings\"]\n",
    "file_paths_audio = data_audio[\"file_paths\"]\n",
    "music_ids_audio = data_audio[\"music_ids\"]\n",
    "\n",
    "similarity_audio = np.dot(audio_embeddings, audio_embeddings.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get embeddings from EWE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "term_embeddings_ewe_npy = np.load(\"data/term_embeddings_ewe.npy\")\n",
    "similarity_ewe = np.dot(term_embeddings_ewe_npy, term_embeddings_ewe_npy.T)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### dump it all in a nice numpy pickle\n",
    "\n",
    "extra variables:\n",
    "- term_similarity (150,150), similarity based on cooccurence in piles and performances\n",
    "- term_similarity_weighted\n",
    "- term_similarity_only_performances\n",
    "- term_similarity_only_piles\n",
    "- term_similarity_only_pile1\n",
    "- term_similarity_only_pile2\n",
    "- term_embeddings_ada_npy (150, 1536), embeddings from openai's ada model\n",
    "- term_embeddings_ada_context_npy (150, 1024), embeddings from openai's ada model using context prompts\n",
    "- term_embeddings_clap_npy (150, 1024), embeddings from microsoft's clap model\n",
    "- term_embeddings_clap_context_npy (150, 1024), embeddings from microsoft's clap model using context prompts\n",
    "- term_embeddings_bge_npy (150, 1024), embeddings from baai's bge model\n",
    "- term_embeddings_bge_context_npy (150, 1024), embeddings from baai's bge model using context prompts\n",
    "- term_embeddings_gte_npy (150, 1024), embeddings from damo's gte model\n",
    "- term_embeddings_gte_context_npy (150, 1024), embeddings from damo's gte model using context prompts\n",
    "- term_embeddings_ewe_npy (150, 1024), embeddings from the EWE model\n",
    "- audio_embeddings (45, 1024), audio embeddings from microsoft's clap model\n",
    "- file_paths_audio, list of file paths corresponding to the audio embeddings\n",
    "- music_ids_audio, list of music ids corresponding to the audio embeddings\n",
    "- similarity_ada (150,150), cosine similarity of ada embeddings\n",
    "- similarity_ada_context (150,150), cosine similarity of ada embeddings with context prompts\n",
    "- similarity_clap (150,150), cosine similarity of clap embeddings\n",
    "- similarity_clap_context (150,150), cosine similarity of clap embeddings with context prompts\n",
    "- similarity_audio (150,150), cosine similarity of clap audio embeddings\n",
    "- similarity_ewe (150,150), cosine similarity of ewe embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez(\"data/preprocessed_embedding_data_complete.npz\", \n",
    "             # loaded pile sorting and con espressione game data\n",
    "             term_freq=term_freq,\n",
    "             dtm=dtm,\n",
    "             piles1=piles1,\n",
    "             piles2=piles2,\n",
    "             # helpers\n",
    "             piece_performer_list=piece_performer_list, \n",
    "             pieces_list=pieces_list,\n",
    "             performer_list=performer_list,\n",
    "             pieces_music_id_list=pieces_music_id_list,\n",
    "             terms=terms,\n",
    "             pile_names=pile_names,\n",
    "             term_per_pile=term_per_pile,\n",
    "             term_per_perf = term_per_perf,\n",
    "             pile_idx_per_term=pile_idx_per_term,\n",
    "             # pile similarities\n",
    "             term_similarity=term_similarity,\n",
    "             term_similarity_weighted=term_similarity_weighted,\n",
    "             term_similarity_only_performances=term_similarity_only_performances,\n",
    "             term_similarity_only_piles=term_similarity_only_piles,\n",
    "             term_similarity_only_pile1=term_similarity_only_pile1,\n",
    "             term_similarity_only_pile2=term_similarity_only_pile2,\n",
    "             # embeddings\n",
    "             term_embeddings_ada_npy=term_embeddings_ada_npy,\n",
    "             term_embeddings_ada_context_npy=term_embeddings_ada_context_npy,\n",
    "             term_embeddings_bge_npy=term_embeddings_bge_npy,\n",
    "             term_embeddings_bge_context_npy=term_embeddings_bge_context_npy,\n",
    "             term_embeddings_gte_npy=term_embeddings_gte_npy,\n",
    "             term_embeddings_gte_context_npy=term_embeddings_gte_context_npy,\n",
    "             term_embeddings_clap_npy=term_embeddings_clap_npy,\n",
    "             term_embeddings_clap_context_npy=term_embeddings_clap_context_npy,\n",
    "             term_embeddings_ewe_npy=term_embeddings_ewe_npy,\n",
    "             audio_embeddings=audio_embeddings,\n",
    "             # embedding helpers\n",
    "             file_paths_audio=file_paths_audio,\n",
    "             music_ids_audio=music_ids_audio,\n",
    "             # similarity matrices\n",
    "             similarity_ada=similarity_ada,\n",
    "             similarity_ada_context=similarity_ada_context,\n",
    "             similarity_gte=similarity_gte,\n",
    "             similarity_gte_context=similarity_gte_context,\n",
    "             similarity_bge=similarity_bge,\n",
    "             similarity_bge_context=similarity_bge_context,\n",
    "             similarity_clap=similarity_clap,\n",
    "             similarity_clap_context=similarity_clap_context,\n",
    "             similarity_audio=similarity_audio,\n",
    "             similarity_ewe=similarity_ewe\n",
    "             )"
   ]
  }
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