listening-analysis/playlist-classifier.ipynb

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 "cells": [
  {
   "source": [
    "# Playlist Classifier\n",
    "\n",
    "Given a list of playlists, can unknown tracks be correctly classified?"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "playlist_names = [\"RAP\", \"EDM\", \"ROCK\", \"METAL\", \"JAZZ\", \"POP\"] # super-genres\n",
    "# playlist_names = [\"DNB\", \"HOUSE\", \"TECHNO\", \"GARAGE\", \"DUBSTEP\", \"BASS\"] # EDM playlists\n",
    "# playlist_names = [\"20s rap\", \"10s rap\", \"00s rap\", \"90s rap\", \"80s rap\"] # rap decades\n",
    "# playlist_names = [\"UK RAP\", \"US RAP\"] # UK/US split\n",
    "# playlist_names = [\"uk rap\", \"grime\", \"drill\", \"afro bash\"] # british rap playlists\n",
    "# playlist_names = [\"20s rap\", \"10s rap\", \"00s rap\", \"90s rap\", \"80s rap\", \"trap\", \"gangsta rap\", \"industrial rap\", \"weird rap\", \"jazz rap\", \"boom bap\", \"trap metal\"] # american rap playlists\n",
    "# playlist_names = [\"rock\", \"indie\", \"punk\", \"pop rock\", \"bluesy rock\", \"hard rock\", \"chilled rock\", \"emo\", \"pop punk\", \"stoner rock/metal\", \"post-hardcore\", \"melodic hardcore\", \"art rock\", \"post-rock\", \"classic pop punk\", \"90s rock & grunge\", \"90s indie & britpop\", \"psych\"] # rock playlists\n",
    "# playlist_names = [\"metal\", \"metalcore\", \"mathcore\", \"hardcore\", \"black metal\", \"death metal\", \"doom metal\", \"sludge metal\", \"classic metal\", \"industrial\", \"nu metal\", \"calm metal\", \"thrash metal\"] # metal playlists\n",
    "\n",
    "# headers = float_headers + [\"duration_ms\", \"mode\", \"loudness\", \"tempo\"]\n",
    "headers = float_headers"
   ]
  },
  {
   "source": [
    "Pull and process playlist information.\n",
    "\n",
    "1. Get live playlist track information from spotify\n",
    "2. Filter listening history for these tracks\n",
    "\n",
    "Filter out tracks without features and drop duplicates before taking only the descriptor parameters"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "playlists = [get_playlist(i, spotnet) for i in playlist_names] # 1)\n",
    "\n",
    "# filter playlists by join with playlist track/artist names\n",
    "filtered_playlists = [pd.merge(track_frame(i.tracks), scrobbles, on=['track', 'artist']) for i in playlists] # 2)\n",
    "\n",
    "filtered_playlists = [i[pd.notnull(i[\"uri\"])] for i in filtered_playlists]\n",
    "# distinct on uri\n",
    "filtered_playlists = [i.drop_duplicates(['uri']) for i in filtered_playlists]\n",
    "# select only descriptor float columns\n",
    "filtered_playlists = [i.loc[:, headers] for i in filtered_playlists]"
   ]
  },
  {
   "source": [
    "Construct the dataset with associated labels before splitting into a train and test set."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.concat(filtered_playlists)\n",
    "labels = [np.full(len(plst), idx) for idx, plst in enumerate(filtered_playlists)]\n",
    "labels = np.concatenate(labels)\n",
    "\n",
    "data_train, data_test, labels_train, labels_test = train_test_split(dataset, labels, test_size=0.33, random_state=70)"
   ]
  },
  {
   "source": [
    "# SVM\n",
    "Support Vector Machine"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "73.47% Accurate\n"
     ]
    },
    {
     "output_type": "display_data",
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     },
     "metadata": {}
    }
   ],
   "source": [
    "### TRAIN ###\n",
    "clf = svm.SVC()\n",
    "clf.fit(data_train, labels_train)\n",
    "\n",
    "### TEST ###\n",
    "pred_test_labels = clf.predict(data_test)\n",
    "\n",
    "### EVALUATE ###\n",
    "correct = [i == pred_test_labels[idx] for idx, i in enumerate(labels_test)]\n",
    "correct = sum(correct) / len(correct)\n",
    "print(f'{correct*100:.2f}% Accurate')\n",
    "\n",
    "plt.pie([correct, 1 - correct], labels=[\"Correct\", \"Incorrect\"], colors=['g', 'r'], startangle=90)\n",
    "plt.title(\"Classifier Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot_confusion_matrix(clf, data_test, labels_test, display_labels=playlist_names, normalize='true')\n",
    "plt.title('SVM Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "From the above it is clear that the Pop playlists was not effective for classifying similar tracks. This is likely because of the overlap with other genres such as Rap and EDM where much of the confusion occured. Also not helping is that one of the sub-playlists is shared across EDM and Pop, electropop. as EDM is already such a larger playlist it is unsurpising that this performance was poor. Additionally the overlap with Rock is understandable as Pop contains an Indie Pop sub-playlist which could have cause some confusion. Quite surprising was the confusion for Jazz as I wouldn't have thought there would be much overlap here.\n",
    "\n",
    "The other major confusion was with Rock and Metal, specifically classing Metal tracks as Rock. This could be expected due to the similarity in tone."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Other Tests\n",
    "\n",
    "Take a handful of other tracks which I don't listen to and aren't in any playlists to see if they can also be classified"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Top Of The World (Five Finger Death Punch) could be ROCK ✓\nAston Martin Music (Rick Ross) could be RAP ✓\nOn The Sunny Side Of The Street (Dizzy Gillespie) could be JAZZ ✓\nVibez (ZAYN) could be EDM ✗\nShot In The Dark (AC/DC) could be ROCK ✓\nTo Hell and Back (Sabaton) could be ROCK ✗\nWithstand The Fall Of Time (Immortal) could be METAL ✓\nAlone Together - Rudy Van Gelder Remaster (Kenny Dorham) could be JAZZ ✓\nFeel No Ways (Drake) could be RAP ✓\nBO$$ (Fifth Harmony) could be EDM ✗\n\n70.00% Accurate\n"
     ]
    }
   ],
   "source": [
    "### PREPARE ###\n",
    "test_uris = [\"spotify:track:53yqxU2EKKzbuQZEUEVtxc\",\n",
    "             \"spotify:track:5W7xC99N2Zzfh69r7I7zWK\",\n",
    "             \"spotify:track:38R2EViAkYOFG8ZkG3GLtW\",\n",
    "             \"spotify:track:6T6D9CIrHkALcHPafDFA6L\",\n",
    "             \"spotify:track:0sfdiwck2xr4PteGOdyOfz\",\n",
    "             \"spotify:track:1BrgjqSg9du0lj3TUMLluL\",\n",
    "             \"spotify:track:5nCnSnLtotQ8eB4E189U91\",\n",
    "             \"spotify:track:3GOZbK2epuHzCt5YvvVFHO\",\n",
    "             \"spotify:track:3cjF2OFRmip8spwZYQRKxP\",\n",
    "             \"spotify:track:1COvXs6jaykXC73h9OSBVM\"]\n",
    "# inferring what playlists these would go in\n",
    "test_labels = [\"ROCK\", \"RAP\", \"JAZZ\", \"POP\", \"ROCK\", \"METAL\", \"METAL\", \"JAZZ\", \"RAP\", \"POP\"] \n",
    "\n",
    "test_tracks = spotnet.tracks(uris=test_uris)\n",
    "spotnet.populate_track_audio_features(tracks=test_tracks)\n",
    "\n",
    "test_features = [   {j: k for j, k in i.audio_features.to_dict().items() \n",
    "                    if j in headers} \n",
    "                for i in test_tracks] # filter down to descriptor columns\n",
    "\n",
    "### PREDICT ###\n",
    "predictable_frame = pd.DataFrame(test_features)\n",
    "\n",
    "predicted_labels = clf.predict(predictable_frame)\n",
    "labels_correct = [i == playlist_names[predicted_labels[idx]] for idx, i in enumerate(test_labels)]\n",
    "\n",
    "### EVALUATE ###\n",
    "for track, label, correct in zip(test_tracks, predicted_labels, labels_correct):\n",
    "    print(f'{track.name} ({track.artists[0].name}) could be {playlist_names[label]} {\"✓\" if correct else \"✗\"}')\n",
    "\n",
    "correct = sum(labels_correct) / len(labels_correct)\n",
    "print(f'\\n{correct*100:.2f}% Accurate')"
   ]
  },
  {
   "source": [
    "# Imports & Setup"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "from google.cloud import bigquery\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi'] = 120\n",
    "\n",
    "from analysis.net import get_spotnet, get_playlist, track_frame\n",
    "from analysis.query import *\n",
    "from analysis import spotify_descriptor_headers, float_headers, days_since\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn import svm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "client = bigquery.Client()\n",
    "spotnet = get_spotnet()\n",
    "cache = 'query.csv'\n",
    "first_day = datetime(year=2017, month=11, day=3)"
   ]
  },
  {
   "source": [
    "## Read Scrobble Frame"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "scrobbles = get_query(cache=cache)"
   ]
  },
  {
   "source": [
    "## Write Scrobble Frame"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "scrobbles.reset_index().to_csv(cache, sep='\\t')"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 6,
   "outputs": []
  }
 ]
}