0. Create train-test-valid datasets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating matrices from dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating a dataset with sequences of length 20 with 2 sequences per song\n",
      "Syllable embedding length : 20\n"
     ]
    }
   ],
   "source": [
    "seqlength = 20\n",
    "num_midi_features = 3\n",
    "num_sequences_per_song = 2\n",
    "training_rate = 0.8\n",
    "validation_rate = 0.1\n",
    "test_rate = 0.1\n",
    "\n",
    "syll_model_path = './enc_models/syllEncoding_20190419.bin'\n",
    "word_model_path = './enc_models/wordLevelEncoder_20190419.bin'\n",
    "songs_path = './data/songs_word_level'\n",
    "\n",
    "print('Creating a dataset with sequences of length', seqlength, \n",
    "      'with', num_sequences_per_song, 'sequences per song')\n",
    "\n",
    "syllModel = Word2Vec.load(syll_model_path)\n",
    "wordModel = Word2Vec.load(word_model_path)\n",
    "syll2Vec = syllModel.wv['Hello']\n",
    "word2Vec = wordModel.wv['world']\n",
    "num_syll_features = len(syll2Vec) + len(word2Vec)\n",
    "print('Syllable embedding length :', num_syll_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of songs :  7497\n"
     ]
    }
   ],
   "source": [
    "files = os.listdir(songs_path)\n",
    "num_songs = len(files)\n",
    "print(\"Total number of songs : \", num_songs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "print(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 3, 20)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_syll_features, num_midi_features, seqlength"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect content of sample file\n",
    "sample_file = files[0]\n",
    "sample_features = np.load(os.path.join(songs_path, sample_file), allow_pickle=True) # load midi files to feature\n",
    "len(sample_features), len(sample_features[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "198\n",
      "[[[0.0, 0.09062500000000284, 659.2551138257398, 124.0]], [[0.2109375, 0.06718750000000284, 659.2551138257398, 124.0]], [[0.17343750000000213, 0.09687500000000071, 659.2551138257398, 124.0]], [[0.3515625, 1.1515625000000007, 659.2551138257398, 124.0]], [[4.515625, 0.3000000000000007, 523.2511306011972, 109.0]], [[0.7484374999999979, 0.6765625000000028, 698.4564628660078, 101.0], [0.6875, 0.4765625, 659.2551138257398, 103.0]], [[0.5015625000000021, 1.0812500000000007, 523.2511306011972, 98.0]], [[4.084374999999998, 0.4921875, 659.2551138257398, 98.0]], [[0.5093749999999986, 0.19843750000000426, 587.3295358348151, 85.0]], [[0.2265625, 0.3343750000000014, 587.3295358348151, 83.0], [0.3734375000000014, 1.3953125000000028, 523.2511306011972, 91.0]], [[1.8765624999999986, 0.3343750000000014, 659.2551138257398, 97.0]], [[0.3515625, 0.19843750000000426, 587.3295358348151, 88.0]], [[0.390625, 0.17031250000000142, 587.3295358348151, 93.0], [0.3890625000000014, 1.5671875000000028, 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114.0]], [[1.6953125, 0.04218750000001137, 587.3295358348151, 104.0]], [[0.18593749999999432, 0.06718750000001705, 587.3295358348151, 114.0]], [[0.171875, 0.046875, 659.2551138257398, 104.0]], [[0.22031250000000568, 0.6359375000000114, 698.4564628660078, 124.0]], [[0.7171874999999943, 0.921875, 783.9908719634985, 118.0]], [[1.6890625000000057, 0.17812499999999432, 783.9908719634985, 94.0], [0.1953125, 0.7249999999999943, 1046.5022612023945, 122.0]], [[0.7578125, 0.26875000000001137, 783.9908719634985, 124.0]], [[2.0, 0.5062499999999943, 783.9908719634985, 122.0]], [[0.59375, 0.46406250000001137, 698.4564628660078, 115.0]], [[0.5296874999999943, 0.504687500000017, 659.2551138257398, 104.0], [0.546875, 0.359375, 587.3295358348151, 104.0]], [[0.3843750000000057, 0.17031249999999432, 659.2551138257398, 94.0]], [[4.717187499999994, 0.53125, 587.3295358348151, 85.0]], [[0.5703125, 0.1875, 523.2511306011972, 94.0]], [[0.4000000000000057, 0.3343749999999943, 659.2551138257398, 95.0]], [[0.5687499999999943, 1.3125, 587.3295358348151, 88.0]], [[27.71875, 0.05156250000001705, 783.9908719634985, 103.0]], [[0.171875, 0.1484375, 783.9908719634985, 105.0]], [[0.2109375, 0.11406250000001705, 880.0, 84.0]], [[0.21250000000000568, 1.0109375000000114, 932.3275230361799, 103.0]], [[1.03125, 0.7687500000000114, 1046.5022612023945, 104.0]], [[1.2093749999999943, 0.09375, 1046.5022612023945, 104.0]], [[0.3843750000000057, 0.6937499999999943, 1396.9129257320155, 103.0], [0.7406249999999943, 0.27031250000001705, 1046.5022612023945, 107.0]], [[0.3843750000000057, 0.7328124999999943, 1046.5022612023945, 103.0]], [[0.75, 0.31562500000001137, 932.3275230361799, 111.0]], [[0.9203124999999943, 0.05156250000001705, 698.4564628660078, 98.0]], [[0.16562500000000568, 0.12812500000001137, 698.4564628660078, 90.0]], [[0.1953125, 0.08437499999999432, 783.9908719634985, 86.0]], [[0.1953125, 0.6750000000000114, 880.0, 98.0]], [[0.7406249999999943, 1.403125000000017, 932.3275230361799, 106.0]], [[1.515625, 0.09843750000001705, 932.3275230361799, 108.0]], [[0.18906250000000568, 0.11250000000001137, 932.3275230361799, 103.0]], [[0.1875, 0.37031250000001137, 880.0, 107.0]], [[0.4046874999999943, 0.31718750000001705, 783.9908719634985, 99.0], [0.3531250000000057, 0.3031249999999943, 698.4564628660078, 88.0]], [[0.375, 0.7562499999999943, 783.9908719634985, 91.0]], [[1.6640625, 0.05000000000001137, 783.9908719634985, 101.0]], [[0.1796875, 0.12343749999999432, 783.9908719634985, 103.0]], [[0.19374999999999432, 0.12968750000001705, 880.0, 90.0]], [[0.20468750000000568, 0.6468749999999943, 932.3275230361799, 108.0]], [[0.7015624999999943, 0.809375000000017, 1046.5022612023945, 98.0]], [[1.7109375, 0.17656250000001705, 1046.5022612023945, 90.0], [0.2265625, 0.778125000000017, 1396.9129257320155, 99.0]], [[0.8062500000000057, 0.2953124999999943, 1046.5022612023945, 116.0]], [[0.40625, 0.6437500000000114, 1046.5022612023945, 113.0]], [[0.6640625, 0.44062500000001137, 932.3275230361799, 109.0]], [[0.7484374999999943, 0.0625, 698.4564628660078, 90.0], [0.1640625, 0.05937500000001705, 698.4564628660078, 77.0]], [[0.1953125, 0.32500000000001705, 698.4564628660078, 94.0]], [[0.390625, 0.3203125, 783.9908719634985, 91.0]], [[0.7437500000000057, 0.24843749999999432, 932.3275230361799, 92.0]], [[1.4984374999999943, 0.05937500000001705, 932.3275230361799, 101.0]], [[0.1953125, 0.09375, 932.3275230361799, 94.0]], [[0.203125, 0.33281250000001705, 880.0, 90.0]], [[0.375, 0.3203125, 783.9908719634985, 97.0]], [[0.3609375000000057, 0.3187499999999943, 698.4564628660078, 72.0]], [[0.3656249999999943, 0.879687500000017, 783.9908719634985, 84.0]], [[1.4546875000000057, 0.6593750000000114, 1046.5022612023945, 114.0]], [[0.7421875, 0.3031249999999943, 1046.5022612023945, 93.0]], [[0.4046874999999943, 0.21562500000001705, 1046.5022612023945, 100.0]], [[0.3843750000000057, 0.3031249999999943, 1046.5022612023945, 103.0]], [[0.375, 0.22499999999999432, 1046.5022612023945, 100.0]], [[1.140625, 0.13124999999999432, 1046.5022612023945, 104.0], [0.1953125, 0.10781249999999432, 1046.5022612023945, 87.0]], [[0.20937499999999432, 0.684375000000017, 1046.5022612023945, 107.0]], [[0.6968750000000057, 0.3421874999999943, 932.3275230361799, 109.0]], [[0.375, 0.6046875000000114, 1046.5022612023945, 101.0]], [[0.640625, 0.38593750000001137, 932.3275230361799, 96.0]], [[0.8265624999999943, 0.12187500000001705, 1396.9129257320155, 116.0]], [[0.203125, 0.10625000000001705, 1396.9129257320155, 104.0], [0.1875, 0.3125, 1396.9129257320155, 114.0]], [[0.3828125, 0.29375000000001705, 1174.6590716696303, 107.0]], [[0.75, 1.3203125, 1174.6590716696303, 118.0], [1.4781250000000057, 0.09218749999999432, 1174.6590716696303, 99.0]], [[0.19374999999999432, 0.07500000000001705, 1174.6590716696303, 91.0], [0.19687500000000568, 0.3421874999999943, 1174.6590716696303, 104.0]], [[0.3968749999999943, 0.34062500000001705, 1046.5022612023945, 101.0]], [[0.3765625000000057, 0.26093750000001137, 880.0, 97.0]], [[0.3515625, 0.18593749999999432, 1046.5022612023945, 104.0]], [[0.1953125, 0.14375000000001137, 880.0, 91.0]], [[0.1875, 0.8859375000000114, 783.9908719634985, 84.0]], [[1.3578124999999943, 0.04375000000001705, 783.9908719634985, 120.0]], [[0.3125, 0.12968750000001705, 880.0, 81.0], [0.22031250000000568, 0.6593750000000114, 932.3275230361799, 124.0]], [[0.7093749999999943, 0.7578125, 1046.5022612023945, 122.0]], [[1.6968750000000057, 0.21406250000001137, 1046.5022612023945, 99.0]], [[0.21718749999999432, 0.793750000000017, 1396.9129257320155, 120.0]], [[0.8203125, 0.2890625, 1046.5022612023945, 126.0], [0.296875, 0.957812500000017, 1244.5079348883237, 122.0]], [[1.890625, 0.5390625, 1046.5022612023945, 105.0]]]\n"
     ]
    }
   ],
   "source": [
    "print(len(sample_features[0][0]))\n",
    "print(sample_features[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "198\n",
      "[[[76.0, 0.25, 0.0]], [[76.0, 0.75, 0.5]], [[76.0, 0.25, 0.0]], [[76.0, 3.0, 1.0]], [[72.0, 1.0, 8.0]], [[77.0, 2.0, 0.0], [76.0, 1.0, 0.0]], [[72.0, 3.0, 0.0]], [[76.0, 1.0, 8.0]], [[74.0, 0.5, 0.0]], [[74.0, 1.0, 0.0], [72.0, 4.0, 0.0]], [[76.0, 1.0, 1.0]], [[74.0, 1.0, 0.0]], [[74.0, 1.0, 0.0], [72.0, 4.0, 0.0]], [[76.0, 2.0, 1.0]], [[74.0, 0.75, 0.0]], [[74.0, 1.0, 0.0], [72.0, 6.0, 0.0]], [[77.0, 1.0, 0.0]], [[74.0, 3.0, 1.0]], [[67.0, 0.75, 2.0]], [[76.0, 1.0, 0.0]], [[74.0, 0.75, 0.0]], [[74.0, 0.5, 0.0]], [[72.0, 3.0, 1.0], [72.0, 0.75, 2.0]], [[76.0, 0.25, 0.0]], [[77.0, 1.0, 1.0]], [[72.0, 3.0, 0.0], [76.0, 0.5, 4.0]], [[74.0, 0.75, 0.0]], [[77.0, 1.0, 0.0]], [[74.0, 3.0, 0.0], [76.0, 2.0, 2.0]], [[72.0, 0.5, 0.0], [74.0, 3.0, 0.0]], [[74.0, 1.0, 4.0]], [[76.0, 0.75, 0.0]], [[72.0, 4.0, 0.0]], [[72.0, 0.75, 2.0]], [[74.0, 1.0, 0.0]], [[72.0, 1.0, 0.0]], [[79.0, 4.0, 0.0]], [[74.0, 2.0, 2.0]], [[74.0, 0.75, 8.0]], [[74.0, 0.75, 0.0]], [[76.0, 0.75, 0.0]], [[77.0, 2.0, 0.0]], [[79.0, 2.0, 0.0]], [[79.0, 0.75, 2.0]], [[84.0, 2.0, 0.0], [79.0, 0.75, 0.0]], [[81.0, 2.0, 0.0]], [[79.0, 0.5, 0.0]], [[77.0, 0.25, 0.0]], [[72.0, 0.75, 2.0]], [[72.0, 1.5, 0.0]], [[74.0, 1.5, 0.0]], [[76.0, 1.0, 0.0]], [[77.0, 2.0, 0.0]], [[77.0, 0.75, 2.0]], [[77.0, 0.75, 0.0]], [[76.0, 1.5, 0.0], [74.0, 1.0, 0.0]], [[72.0, 0.25, 0.0]], [[74.0, 2.0, 1.0]], [[74.0, 0.75, 2.0]], [[74.0, 0.75, 0.0]], [[76.0, 0.75, 0.0]], [[77.0, 2.0, 0.0]], [[79.0, 0.75, 2.0], [79.0, 0.5, 0.0]], [[84.0, 2.0, 0.0]], [[79.0, 1.0, 0.0]], [[79.0, 2.0, 0.0]], [[77.0, 0.5, 0.0], [72.0, 0.75, 2.0]], [[72.0, 0.75, 0.0]], [[72.0, 1.0, 0.0]], [[77.0, 0.75, 2.0]], [[77.0, 0.75, 0.0]], [[76.0, 1.0, 0.0]], [[74.0, 1.0, 0.0]], [[72.0, 1.0, 0.0]], [[76.0, 0.25, 8.0], [76.0, 1.0, 0.0], [74.0, 0.75, 0.0], [74.0, 0.25, 0.0]], [[74.0, 0.75, 0.0], [74.0, 0.75, 0.0], [72.0, 6.0, 0.0]], [[76.0, 2.0, 0.0]], [[74.0, 1.0, 0.0], [72.0, 6.0, 0.0]], [[77.0, 1.0, 0.0]], [[74.0, 4.0, 1.0]], [[67.0, 0.75, 2.0]], [[76.0, 1.0, 0.0]], [[74.0, 0.75, 0.0]], [[74.0, 0.5, 0.0], [72.0, 4.0, 1.0]], [[74.0, 1.0, 0.0]], [[76.0, 1.0, 0.0]], [[77.0, 1.0, 0.0], [72.0, 3.0, 0.0]], [[76.0, 1.0, 4.0]], [[74.0, 0.25, 0.0]], [[77.0, 1.5, 1.0]], [[74.0, 3.0, 0.0]], [[76.0, 1.0, 2.0]], [[72.0, 1.0, 0.0]], [[74.0, 4.0, 0.0]], [[72.0, 0.75, 2.0]], [[76.0, 0.75, 0.0]], [[72.0, 4.0, 0.0]], [[79.0, 4.0, 0.0]], [[72.0, 0.75, 2.0]], [[74.0, 1.0, 0.0]], [[76.0, 0.5, 0.0], [72.0, 4.0, 0.0]], [[74.0, 0.75, 8.0]], [[74.0, 0.75, 0.0]], [[76.0, 0.75, 0.0]], [[77.0, 2.5, 0.0]], [[79.0, 2.0, 0.0]], [[79.0, 0.75, 2.0]], [[84.0, 2.0, 0.0], [79.0, 1.0, 0.0]], [[79.0, 2.0, 0.0]], [[77.0, 0.5, 0.0]], [[72.0, 0.75, 2.0]], [[72.0, 0.75, 0.0]], [[72.0, 1.5, 0.0]], [[74.0, 1.5, 0.0], [76.0, 1.0, 0.0]], [[77.0, 2.0, 0.0]], [[79.0, 0.75, 0.0], [76.0, 1.0, 0.0]], [[74.0, 1.0, 0.0], [72.0, 1.0, 0.0]], [[74.0, 3.0, 0.0]], [[74.0, 0.75, 2.0]], [[74.0, 0.75, 0.0]], [[76.0, 0.75, 0.0]], [[77.0, 2.0, 0.0]], [[79.0, 3.0, 0.0]], [[79.0, 0.5, 0.0], [84.0, 2.0, 0.0]], [[79.0, 1.5, 0.0]], [[79.0, 2.5, 1.0]], [[77.0, 1.0, 0.0]], [[76.0, 2.0, 0.0], [74.0, 1.0, 0.0]], [[76.0, 0.5, 0.0]], [[74.0, 2.0, 0.0]], [[72.0, 1.0, 0.0]], [[76.0, 1.5, 0.0]], [[74.0, 4.0, 0.0]], [[79.0, 0.75, 32.0]], [[79.0, 0.5, 0.0]], [[81.0, 0.75, 0.0]], [[82.0, 3.0, 0.0]], [[84.0, 2.0, 0.0]], [[84.0, 0.25, 1.0]], [[89.0, 2.0, 0.0], [84.0, 1.5, 0.0]], [[84.0, 2.0, 0.0]], [[82.0, 1.0, 0.0]], [[77.0, 0.75, 2.0]], [[77.0, 0.5, 0.0]], [[79.0, 0.75, 0.0]], [[81.0, 2.0, 0.0]], [[82.0, 4.5, 0.0]], [[82.0, 0.75, 0.0]], [[82.0, 0.25, 0.0]], [[81.0, 1.0, 0.0]], [[79.0, 1.0, 0.0], [77.0, 1.0, 0.0]], [[79.0, 2.0, 0.0]], [[79.0, 0.75, 2.0]], [[79.0, 0.25, 0.0]], [[81.0, 0.5, 0.0]], [[82.0, 2.0, 0.0]], [[84.0, 2.0, 0.0]], [[84.0, 0.5, 0.0], [89.0, 2.0, 0.0]], [[84.0, 1.5, 0.0]], [[84.0, 2.0, 0.0]], [[82.0, 1.0, 0.0]], [[77.0, 0.75, 1.0], [77.0, 0.75, 0.0]], [[77.0, 1.0, 0.0]], [[79.0, 1.0, 0.0]], [[82.0, 0.5, 0.0]], [[82.0, 0.75, 4.0]], [[82.0, 0.75, 0.0]], [[81.0, 1.0, 0.0]], [[79.0, 1.0, 0.0]], [[77.0, 1.0, 0.0]], [[79.0, 3.0, 0.0]], [[84.0, 2.0, 2.0]], [[84.0, 1.5, 0.0]], [[84.0, 1.0, 0.0]], [[84.0, 1.0, 0.0]], [[84.0, 1.0, 0.0]], [[84.0, 0.5, 0.0], [84.0, 0.75, 0.0]], [[84.0, 2.0, 0.0]], [[82.0, 1.0, 0.0]], [[84.0, 2.0, 0.0]], [[82.0, 1.0, 0.0]], [[89.0, 0.25, 1.0]], [[89.0, 0.25, 0.0], [89.0, 1.0, 0.0]], [[86.0, 1.0, 0.0]], [[86.0, 4.5, 0.0], [86.0, 0.75, 0.0]], [[86.0, 0.75, 0.0], [86.0, 1.0, 0.0]], [[84.0, 1.0, 0.0]], [[81.0, 1.5, 0.0]], [[84.0, 0.5, 0.0]], [[81.0, 0.5, 0.0]], [[79.0, 3.0, 0.0]], [[79.0, 0.75, 1.0]], [[81.0, 1.0, 0.0], [82.0, 2.0, 0.0]], [[84.0, 2.0, 0.0]], [[84.0, 0.5, 0.0]], [[89.0, 2.0, 0.0]], [[84.0, 1.0, 0.0], [87.0, 3.0, 0.0]], [[84.0, 2.0, 2.0]]]\n"
     ]
    }
   ],
   "source": [
    "print(len(sample_features[0][1]))\n",
    "print(sample_features[0][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "198\n",
      "[['Must'], ['have'], ['been'], ['love'], ['but'], ['over', 'over'], ['now'], ['Lay'], ['a'], ['whisper', 'whisper'], ['on'], ['my'], ['pillow', 'pillow'], ['leave'], ['the'], ['winter', 'winter'], ['on'], ['the'], ['ground'], ['I'], ['wake'], ['up'], ['lonely', 'lonely'], ['air'], ['of'], ['silence', 'silence'], ['in'], ['the'], ['bedroom', 'bedroom'], ['around', 'around'], ['Touch'], ['me'], ['now'], ['I'], ['close'], ['my'], ['eyes'], ['dream'], ['It'], ['must'], ['have'], ['been'], ['love'], ['but'], ['over', 'over'], ['now'], ['It'], ['must'], ['have'], ['been'], ['good'], ['but'], ['I'], ['lost'], ['it'], ['somehow', 'somehow'], ['It'], ['must'], ['have'], ['been'], ['love'], ['but'], ['over', 'over'], ['now'], ['from'], ['the'], ['moment', 'moment'], ['we'], ['touched'], ['the'], ['time'], ['had'], ['run'], ['out'], ['Makebelieving', 'Makebelieving', 'Makebelieving', 'Makebelieving'], ['together', 'together', 'together'], ['that'], ['sheltered', 'sheltered'], ['by'], ['your'], ['But'], ['in'], ['and'], ['outside', 'outside'], ['turned'], ['to'], ['water', 'water'], ['like'], ['a'], ['tear'], ['drop'], ['in'], ['your'], ['palm'], ['And'], ['a'], ['hard'], ['day'], ['I'], ['dream'], ['away', 'away'], ['It'], ['must'], ['have'], ['been'], ['love'], ['but'], ['over', 'over'], ['now'], ['it'], ['was'], ['that'], ['I'], ['wanted', 'wanted'], ['now'], ['living', 'living'], ['without', 'without'], ['It'], ['must'], ['have'], ['been'], ['love'], ['but'], ['over', 'over'], ['now'], ['where'], ['the'], ['water', 'water'], ['flows'], ['where'], ['the'], ['wind'], ['blows'], ['It'], ['must'], ['have'], ['been'], ['love'], ['but'], ['over', 'over'], ['now'], ['It'], ['must'], ['have'], ['been'], ['love'], ['but'], ['I'], ['lost'], ['it'], ['somehow', 'somehow'], ['It'], ['must'], ['have'], ['been'], ['love'], ['but'], ['over', 'over'], ['now'], ['from'], ['the'], ['moment', 'moment'], ['we'], ['loved'], ['the'], ['time'], ['had'], ['run'], ['out'], ['Yeah'], ['it'], ['must'], ['have'], ['been'], ['love'], ['but'], ['over', 'over'], ['now'], ['it'], ['was'], ['that'], ['I'], ['wanted', 'wanted'], ['now'], ['living', 'living'], ['without', 'without'], ['It'], ['must'], ['have'], ['been'], ['love'], ['but'], ['over', 'over'], ['now'], ['where'], ['the'], ['water', 'water'], ['flows']]\n"
     ]
    }
   ],
   "source": [
    "print(len(sample_features[0][2]))\n",
    "print(sample_features[0][2]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "198\n",
      "[['Must'], ['have'], ['been'], ['love'], ['but'], ['o', 'ver'], ['now'], ['Lay'], ['a'], ['whis', 'per'], ['on'], ['my'], ['pil', 'low'], ['leave'], ['the'], ['win', 'ter'], ['on'], ['the'], ['ground'], ['I'], ['wake'], ['up'], ['lone', 'ly'], ['air'], ['of'], ['si', 'lence'], ['in'], ['the'], ['bed', 'room'], ['a', 'round'], ['Touch'], ['me'], ['now'], ['I'], ['close'], ['my'], ['eyes'], ['dream'], ['It'], ['must'], ['have'], ['been'], ['love'], ['but'], ['o', 'ver'], ['now'], ['It'], ['must'], ['have'], ['been'], ['good'], ['but'], ['I'], ['lost'], ['it'], ['some', 'how'], ['It'], ['must'], ['have'], ['been'], ['love'], ['but'], ['o', 'ver'], ['now'], ['from'], ['the'], ['mo', 'ment'], ['we'], ['touched'], ['the'], ['time'], ['had'], ['run'], ['out'], ['Make', 'be', 'liev', 'ing'], ['to', 'geth', 'er'], ['that'], ['shel', 'tered'], ['by'], ['your'], ['But'], ['in'], ['and'], ['out', 'side'], ['turned'], ['to'], ['wa', 'ter'], ['like'], ['a'], ['tear'], ['drop'], ['in'], ['your'], ['palm'], ['And'], ['a'], ['hard'], ['day'], ['I'], ['dream'], ['a', 'way'], ['It'], ['must'], ['have'], ['been'], ['love'], ['but'], ['o', 'ver'], ['now'], ['it'], ['was'], ['that'], ['I'], ['want', 'ed'], ['now'], ['liv', 'ing'], ['with', 'out'], ['It'], ['must'], ['have'], ['been'], ['love'], ['but'], ['o', 'ver'], ['now'], ['where'], ['the'], ['wa', 'ter'], ['flows'], ['where'], ['the'], ['wind'], ['blows'], ['It'], ['must'], ['have'], ['been'], ['love'], ['but'], ['o', 'ver'], ['now'], ['It'], ['must'], ['have'], ['been'], ['love'], ['but'], ['I'], ['lost'], ['it'], ['some', 'how'], ['It'], ['must'], ['have'], ['been'], ['love'], ['but'], ['o', 'ver'], ['now'], ['from'], ['the'], ['mo', 'ment'], ['we'], ['loved'], ['the'], ['time'], ['had'], ['run'], ['out'], ['Yeah'], ['it'], ['must'], ['have'], ['been'], ['love'], ['but'], ['o', 'ver'], ['now'], ['it'], ['was'], ['that'], ['I'], ['want', 'ed'], ['now'], ['liv', 'ing'], ['with', 'out'], ['It'], ['must'], ['have'], ['been'], ['love'], ['but'], ['o', 'ver'], ['now'], ['where'], ['the'], ['wa', 'ter'], ['flows']]\n"
     ]
    }
   ],
   "source": [
    "print(len(sample_features[0][3]))\n",
    "print(sample_features[0][3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# for a file, we will work with features[0][1] & features[0][3] representing \n",
    "# list of midi_list & list of syll_list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14994, 460)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_matrix = np.zeros(shape=(num_sequences_per_song*num_songs, \n",
    "                              (num_syll_features + num_midi_features) * seqlength))\n",
    "data_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_filename_list = [] # to keep track of filename from which a sequence is extracted\n",
    "small_file_cntr = 0 # to keep track of files with less than 20 syllable-note pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sequence  100\n",
      "sequence number  200\n",
      "sequence  300\n",
      "sequence number  400\n",
      "sequence  500\n",
      "sequence  600\n",
      "sequence number  700\n",
      "sequence  800\n",
      "sequence number  900\n",
      "sequence number  1000\n",
      "sequence number  1100\n",
      "sequence number  1200\n",
      "sequence  1300\n",
      "sequence number  1400\n",
      "sequence  1500\n",
      "sequence  1600\n",
      "sequence  1700\n",
      "sequence number  1800\n",
      "sequence number  1900\n",
      "sequence  2000\n",
      "sequence number  2100\n",
      "sequence  2200\n",
      "sequence number  2300\n",
      "sequence number  2400\n",
      "sequence  2500\n",
      "sequence  2600\n",
      "sequence number  2700\n",
      "sequence  2800\n",
      "sequence  2900\n",
      "sequence number  3000\n",
      "sequence  3100\n",
      "sequence number  3200\n",
      "sequence  3300\n",
      "sequence  3400\n",
      "sequence number  3500\n",
      "sequence number  3600\n",
      "sequence  3700\n",
      "sequence number  3800\n",
      "sequence number  3900\n",
      "sequence number  4000\n",
      "sequence number  4100\n",
      "sequence  4200\n",
      "sequence number  4300\n",
      "sequence  4400\n",
      "sequence number  4500\n",
      "sequence  4600\n",
      "sequence  4700\n",
      "sequence  4800\n",
      "sequence number  4900\n",
      "sequence number  5000\n",
      "sequence number  5100\n",
      "sequence  5200\n",
      "sequence number  5300\n",
      "sequence number  5400\n",
      "sequence  5500\n",
      "sequence  5600\n",
      "sequence number  5700\n",
      "sequence  5800\n",
      "sequence  5900\n",
      "sequence  6000\n",
      "sequence number  6100\n",
      "sequence number  6200\n",
      "sequence number  6300\n",
      "sequence  6400\n",
      "sequence number  6500\n",
      "sequence  6600\n",
      "sequence  6700\n",
      "sequence  6800\n",
      "sequence number  6900\n",
      "sequence number  7000\n",
      "sequence number  7100\n",
      "sequence number  7200\n",
      "sequence  7300\n",
      "sequence number  7400\n",
      "sequence  7500\n",
      "sequence  7600\n",
      "sequence  7700\n",
      "sequence  7800\n",
      "sequence number  7900\n",
      "sequence number  8000\n",
      "sequence  8100\n",
      "sequence  8200\n",
      "sequence number  8300\n",
      "sequence number  8400\n",
      "sequence number  8500\n",
      "sequence  8600\n",
      "sequence  8700\n",
      "sequence number  8800\n",
      "sequence  8900\n",
      "sequence  9000\n",
      "sequence number  9100\n",
      "sequence  9200\n",
      "sequence  9300\n",
      "sequence  9400\n",
      "sequence  9500\n",
      "sequence  9600\n",
      "sequence  9700\n",
      "sequence number  9800\n",
      "sequence number  9900\n",
      "sequence  10000\n",
      "sequence  10100\n",
      "sequence  10200\n",
      "sequence  10300\n",
      "sequence number  10400\n",
      "sequence number  10500\n",
      "sequence number  10600\n",
      "sequence  10700\n",
      "sequence  10800\n",
      "sequence number  10900\n",
      "sequence  11000\n",
      "sequence  11100\n",
      "sequence  11200\n",
      "sequence  11300\n",
      "sequence number  11400\n",
      "sequence number  11500\n",
      "sequence  11600\n",
      "sequence  11700\n",
      "sequence  11800\n",
      "sequence number  11900\n",
      "sequence  12000\n",
      "sequence number  12100\n",
      "sequence number  12200\n",
      "sequence  12300\n",
      "sequence number  12400\n",
      "sequence number  12500\n",
      "sequence number  12600\n",
      "sequence number  12700\n",
      "sequence  12800\n",
      "sequence number  12900\n",
      "sequence number  13000\n",
      "sequence number  13100\n",
      "sequence  13200\n",
      "sequence  13300\n",
      "sequence number  13400\n",
      "sequence  13500\n",
      "sequence number  13600\n",
      "sequence number  13700\n",
      "sequence number  13800\n",
      "sequence  13900\n"
     ]
    }
   ],
   "source": [
    "# load all the songs, cut to 20 note-sequence, convert to song embeddinds\n",
    "\n",
    "i = 0\n",
    "j = 0\n",
    "\n",
    "for file in files:\n",
    "    features = np.load(os.path.join(songs_path, file), allow_pickle=True) # load midi files to feature\n",
    "    \n",
    "    if len(features[0][1]) >= seqlength: # seqlength = 20, if length of song > 20 note\n",
    "        j = 0\n",
    "        for midiList, syllList in zip(features[0][1], features[0][3]):\n",
    "            word = ''\n",
    "            for syll in syllList:\n",
    "                #print(syll)\n",
    "                word = word + syll\n",
    "                if word in list(wordModel.wv.vocab):\n",
    "                    word2Vec = wordModel.wv[word]\n",
    "            for midi, syll in zip(midiList, syllList):\n",
    "                if syll in list(syllModel.wv.vocab):\n",
    "                    syll2Vec = syllModel.wv[syll]\n",
    "                    syllWordVec = np.concatenate((syll2Vec,word2Vec)) # joint embedding = syllabus + words\n",
    "                    if j < seqlength:\n",
    "                        data_matrix[i][num_midi_features * j:num_midi_features * j + num_midi_features] = midi # append midi\n",
    "                        data_matrix[i][num_midi_features * seqlength + num_syll_features * j:\n",
    "                                       num_midi_features * seqlength + num_syll_features * (j + 1)] = syllWordVec # append joint embedding\n",
    "                        j += 1\n",
    "                    else:\n",
    "                        break\n",
    "        i += 1\n",
    "        seq_filename_list.append(file)\n",
    "        #print(syllWordVec)\n",
    "        if i%100 == 0:\n",
    "            print(\"sequence \", i)\n",
    "    \n",
    "    else: # seqlength < 20\n",
    "        small_file_cntr += 1\n",
    "        \n",
    "    if len(features[0][1]) >= 2*seqlength: #  if length of song > 40 note\n",
    "        j = 0\n",
    "        for midiList, syllList in zip(features[0][1][seqlength:], features[0][3][seqlength:]): # cut one more from note 21\n",
    "            word = ''\n",
    "            for syll in syllList:\n",
    "                word = word + syll\n",
    "                if word in list(wordModel.wv.vocab):\n",
    "                    word2Vec = wordModel.wv[word]\n",
    "            for midi, syll in zip(midiList, syllList):\n",
    "                if syll in list(syllModel.wv.vocab):\n",
    "                    syll2Vec = syllModel.wv[syll]\n",
    "                    syllWordVec = np.concatenate((syll2Vec,word2Vec))\n",
    "                    if j < seqlength:\n",
    "                        data_matrix[i][num_midi_features * j:num_midi_features * j + num_midi_features] = midi\n",
    "                        data_matrix[i][num_midi_features * seqlength + num_syll_features * j:\n",
    "                                       num_midi_features * seqlength + num_syll_features * (j + 1)] = syllWordVec\n",
    "                        j += 1\n",
    "                    else:\n",
    "                        break\n",
    "        i += 1\n",
    "        seq_filename_list.append(file)\n",
    "        if i%100 == 0:\n",
    "            print(\"sequence number \", i)\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 343 files out of 7497 with less than 20 syllable-note pairs hence not considered.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print('There are {} files out of {} with less than 20 syllable-note pairs hence not considered.'.format(small_file_cntr, len(files)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13937, 460)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_matrix = data_matrix[0:i, :]\n",
    "data_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# dump sequence filename list & data matrix\n",
    "\n",
    "pickle.dump(seq_filename_list, open('./data/dataset_filenames/full_filename_list.pkl', 'wb'))\n",
    "np.save('./data/dataset_matrices/full_data_matrix.npy', data_matrix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# end of the data matrix generation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# creating train, valid & test matrix from data matrix \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13937, 460)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data_matrix.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13937, 11149.6, 2787.4)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "len(data_matrix), 0.80 * len(data_matrix), 0.20 * len(data_matrix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# we split the data into 80% train with 11149 sequences and 10% validation & test with 1394 sequences each.\n",
    "\n",
    "# we want all the sequences extracted from a file to be present exclusively in one set (train/validation/test) only.\n",
    "\n",
    "# this allows us to bind files to specific sets (train/validation/test)\n",
    "\n",
    "# this strategy gives us freedom to reconstruct data matrices with different sequence lengths, lyrics encoders etc.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of 13937 sequence 11824 sequences are unique\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print('Out of {} sequence {} sequences are unique'.format(len(data_matrix), len(np.unique(data_matrix, axis=0))))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((11149, 460),\n",
       " 11149,\n",
       " ['a24475ca7efdfccf9d003abece827b4c.npy',\n",
       "  'a24475ca7efdfccf9d003abece827b4c.npy'])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# assign the first 11149 sequences in the data matrix to the training set\n",
    "\n",
    "train_data_matrix = data_matrix[:11149]\n",
    "train_filename_list = seq_filename_list[:11149]\n",
    "train_data_matrix.shape, len(train_filename_list), train_filename_list[-2:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# remove duplicates and create test & valildation set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2113"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data_matrix_df = pd.DataFrame(data_matrix)\n",
    "is_duplicated_df = data_matrix_df.duplicated()\n",
    "is_duplicated_df.values.sum() # 13937 - 11824\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11824, 460)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "clean_data_matrix_df = data_matrix_df.drop_duplicates() # clean_data_matrix_df contains no duplicate sequences\n",
    "clean_data_matrix_df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11824 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 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13934, 13935, 13936]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "clean_data_matrix_df_index = clean_data_matrix_df.index\n",
    "print(len(clean_data_matrix_df_index), list(clean_data_matrix_df_index))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2102 [11149, 11150, 11151, 11152, 11153, 11154, 11155, 11156, 11157, 11158, 11159, 11160, 11161, 11162, 11163, 11164, 11165, 11166, 11167, 11168, 11169, 11172, 11173, 11174, 11175, 11176, 11177, 11178, 11179, 11182, 11183, 11188, 11189, 11190, 11191, 11192, 11193, 11194, 11195, 11196, 11197, 11198, 11199, 11200, 11203, 11204, 11205, 11206, 11207, 11208, 11209, 11212, 11213, 11214, 11215, 11218, 11219, 11220, 11221, 11224, 11225, 11226, 11227, 11228, 11229, 11230, 11231, 11232, 11233, 11234, 11237, 11238, 11239, 11240, 11244, 11245, 11246, 11247, 11248, 11249, 11250, 11251, 11252, 11253, 11254, 11255, 11256, 11257, 11258, 11259, 11260, 11261, 11264, 11265, 11268, 11269, 11270, 11271, 11272, 11273, 11274, 11275, 11276, 11277, 11279, 11280, 11281, 11282, 11283, 11286, 11287, 11288, 11289, 11290, 11291, 11292, 11293, 11294, 11295, 11296, 11297, 11298, 11299, 11300, 11301, 11302, 11303, 11304, 11309, 11310, 11311, 11312, 11313, 11314, 11315, 11316, 11317, 11318, 11319, 11320, 11321, 11324, 11325, 11326, 11327, 11328, 11329, 11330, 11331, 11332, 11333, 11334, 11335, 11338, 11339, 11344, 11345, 11346, 11347, 11348, 11349, 11350, 11351, 11352, 11353, 11358, 11361, 11362, 11363, 11364, 11365, 11366, 11369, 11370, 11371, 11372, 11373, 11374, 11375, 11376, 11377, 11378, 11379, 11380, 11383, 11384, 11385, 11386, 11390, 11393, 11394, 11395, 11396, 11397, 11398, 11399, 11400, 11401, 11402, 11403, 11404, 11405, 11406, 11407, 11408, 11409, 11412, 11413, 11414, 11415, 11416, 11417, 11418, 11419, 11420, 11421, 11422, 11423, 11426, 11427, 11428, 11429, 11432, 11433, 11434, 11435, 11436, 11437, 11438, 11439, 11440, 11441, 11442, 11443, 11444, 11445, 11448, 11449, 11450, 11451, 11456, 11457, 11461, 11464, 11465, 11466, 11467, 11472, 11473, 11476, 11477, 11480, 11481, 11482, 11484, 11485, 11486, 11487, 11488, 11489, 11490, 11491, 11492, 11493, 11496, 11497, 11498, 11499, 11500, 11501, 11502, 11503, 11504, 11505, 11506, 11507, 11508, 11509, 11510, 11511, 11512, 11513, 11514, 11515, 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13620, 13623, 13624, 13625, 13626, 13629, 13630, 13632, 13633, 13634, 13639, 13640, 13643, 13644, 13645, 13646, 13647, 13648, 13649, 13650, 13651, 13652, 13653, 13654, 13655, 13656, 13658, 13661, 13662, 13663, 13664, 13667, 13668, 13669, 13670, 13671, 13672, 13673, 13674, 13675, 13676, 13677, 13680, 13681, 13686, 13687, 13688, 13689, 13690, 13691, 13692, 13693, 13694, 13695, 13696, 13697, 13698, 13699, 13700, 13701, 13704, 13705, 13706, 13707, 13714, 13715, 13716, 13717, 13718, 13719, 13720, 13721, 13724, 13725, 13726, 13727, 13728, 13729, 13730, 13731, 13733, 13734, 13735, 13736, 13737, 13738, 13739, 13740, 13741, 13744, 13745, 13746, 13747, 13748, 13749, 13755, 13756, 13757, 13758, 13759, 13760, 13761, 13762, 13763, 13764, 13765, 13766, 13767, 13768, 13769, 13770, 13771, 13772, 13773, 13774, 13775, 13776, 13778, 13779, 13780, 13781, 13784, 13785, 13788, 13789, 13790, 13791, 13792, 13793, 13794, 13795, 13796, 13797, 13798, 13799, 13800, 13801, 13804, 13805, 13806, 13807, 13810, 13811, 13812, 13813, 13818, 13819, 13820, 13821, 13822, 13823, 13824, 13825, 13826, 13827, 13828, 13829, 13830, 13831, 13832, 13833, 13834, 13835, 13836, 13837, 13838, 13840, 13841, 13842, 13843, 13844, 13845, 13846, 13847, 13848, 13849, 13850, 13851, 13852, 13853, 13854, 13855, 13858, 13859, 13860, 13863, 13864, 13865, 13866, 13867, 13868, 13869, 13870, 13871, 13872, 13875, 13876, 13879, 13880, 13881, 13882, 13883, 13884, 13885, 13886, 13889, 13890, 13891, 13892, 13893, 13894, 13895, 13896, 13897, 13898, 13899, 13900, 13901, 13903, 13904, 13907, 13908, 13909, 13910, 13911, 13912, 13913, 13914, 13917, 13918, 13919, 13920, 13921, 13922, 13925, 13926, 13928, 13929, 13930, 13931, 13932, 13933, 13934, 13935, 13936]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# find indices from clean_data_matrix_df_index which are not part of training set \n",
    "clean_data_matrix_df_non_train_index = clean_data_matrix_df_index[clean_data_matrix_df_index >= 11149]\n",
    "print(len(clean_data_matrix_df_non_train_index), list(clean_data_matrix_df_non_train_index))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "valid_test_data_matrix = data_matrix[clean_data_matrix_df_non_train_index]\n",
    "valid_test_filename_list = np.asarray(seq_filename_list)[clean_data_matrix_df_non_train_index].tolist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2102, 460), 2102)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "valid_test_data_matrix.shape, len(valid_test_filename_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# out of 2102 sequences present in valid_test_matrix, first 50% i.e. 1051 sequences will form validation matrix \n",
    "# and remaining 1051 sequences will form the test matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1051, 460), 1051, (1051, 460), 1051)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "valid_data_matrix = valid_test_data_matrix[:1051]\n",
    "valid_filename_list = valid_test_filename_list[:1051]\n",
    "\n",
    "test_data_matrix = valid_test_data_matrix[1051:]\n",
    "test_filename_list = valid_test_filename_list[1051:]\n",
    "\n",
    "valid_data_matrix.shape, len(valid_filename_list), test_data_matrix.shape, len(test_filename_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# end of data matrix creation and post processing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# save matrix and file list for different sets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "np.save('./data/dataset_matrices/train_data_matrix.npy', train_data_matrix)\n",
    "np.save('./data/dataset_matrices/valid_data_matrix.npy', valid_data_matrix)\n",
    "np.save('./data/dataset_matrices/test_data_matrix.npy',  test_data_matrix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pickle.dump(train_filename_list, open('./data/dataset_filenames/train_filename_list.pkl', 'wb'))\n",
    "pickle.dump(valid_filename_list, open('./data/dataset_filenames/valid_filename_list.pkl', 'wb'))\n",
    "pickle.dump(test_filename_list,  open('./data/dataset_filenames/test_filename_list.pkl', 'wb'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# end of the notebook\n"
   ]
  }
 ],
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