main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Implementing your own Shazam\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np      \n",
    "import matplotlib.pyplot as plt \n",
    "import scipy.io.wavfile \n",
    "import subprocess\n",
    "import librosa\n",
    "import librosa.display\n",
    "import IPython.display as ipd\n",
    "\n",
    "from pathlib import Path, PurePath   \n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_mp3_to_wav(audio:str) -> str:  \n",
    "    \"\"\"Convert an input MP3 audio track into a WAV file.\n",
    "\n",
    "    Args:\n",
    "        audio (str): An input audio track.\n",
    "\n",
    "    Returns:\n",
    "        [str]: WAV filename.\n",
    "    \"\"\"\n",
    "    if audio[-3:] == \"mp3\":\n",
    "        wav_audio = audio[:-3] + \"wav\"\n",
    "        if not Path(wav_audio).exists():\n",
    "                subprocess.check_output(f\"ffmpeg -i {audio} {wav_audio}\", shell=True)\n",
    "        return wav_audio\n",
    "    \n",
    "    return audio\n",
    "\n",
    "def plot_spectrogram_and_peaks(track:np.ndarray, sr:int, peaks:np.ndarray, onset_env:np.ndarray) -> None:\n",
    "    \"\"\"Plots the spectrogram and peaks \n",
    "\n",
    "    Args:\n",
    "        track (np.ndarray): A track.\n",
    "        sr (int): Aampling rate.\n",
    "        peaks (np.ndarray): Indices of peaks in the track.\n",
    "        onset_env (np.ndarray): Vector containing the onset strength envelope.\n",
    "    \"\"\"\n",
    "    times = librosa.frames_to_time(np.arange(len(onset_env)),\n",
    "                            sr=sr, hop_length=HOP_SIZE)\n",
    "\n",
    "    plt.figure()\n",
    "    ax = plt.subplot(2, 1, 2)\n",
    "    D = librosa.stft(track)\n",
    "    librosa.display.specshow(librosa.amplitude_to_db(np.abs(D), ref=np.max),\n",
    "                            y_axis='log', x_axis='time')\n",
    "    plt.subplot(2, 1, 1, sharex=ax)\n",
    "    plt.plot(times, onset_env, alpha=0.8, label='Onset strength')\n",
    "    plt.vlines(times[peaks], 0,\n",
    "            onset_env.max(), color='r', alpha=0.8,\n",
    "            label='Selected peaks')\n",
    "    plt.legend(frameon=True, framealpha=0.8)\n",
    "    plt.axis('tight')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "def load_audio_peaks(audio, offset, duration, hop_size):\n",
    "    \"\"\"Load the tracks and peaks of an audio.\n",
    "\n",
    "    Args:\n",
    "        audio (string, int, pathlib.Path or file-like object): [description]\n",
    "        offset (float): start reading after this time (in seconds)\n",
    "        duration (float): only load up to this much audio (in seconds)\n",
    "        hop_size (int): the hop_length\n",
    "\n",
    "    Returns:\n",
    "        tuple: Returns the audio time series (track) and sampling rate (sr), a vector containing the onset strength envelope\n",
    "        (onset_env), and the indices of peaks in track (peaks).\n",
    "    \"\"\"\n",
    "    try:\n",
    "        track, sr = librosa.load(audio, offset=offset, duration=duration)\n",
    "        onset_env = librosa.onset.onset_strength(track, sr=sr, hop_length=hop_size)\n",
    "        peaks = librosa.util.peak_pick(onset_env, 10, 10, 10, 10, 0.5, 0.5)\n",
    "    except Error as e:\n",
    "        print('An error occurred processing ', str(audio))\n",
    "        print(e)\n",
    "\n",
    "    return track, sr, onset_env, peaks\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "N_TRACKS = 1413\n",
    "HOP_SIZE = 512\n",
    "OFFSET = 1.0\n",
    "DURATION = 30\n",
    "THRESHOLD = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_folder = Path(\"data/mp3s-32k/\")\n",
    "mp3_tracks = data_folder.glob(\"*/*/*.mp3\")\n",
    "tracks = data_folder.glob(\"*/*/*.wav\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for track in tqdm(mp3_tracks, total=N_TRACKS):\n",
    "    convert_mp3_to_wav(str(track))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Audio signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for idx, audio in enumerate(tracks):\n",
    "    if idx >= 2:\n",
    "        break\n",
    "    track, sr, onset_env, peaks = load_audio_peaks(audio, OFFSET, DURATION, HOP_SIZE)\n",
    "    plot_spectrogram_and_peaks(track, sr, peaks, onset_env)\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Fingerprint hashing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_coefficient(num, upper_bound):\n",
    "    coeff = []\n",
    "    for i in range(0, num):\n",
    "        rand_value = random.randint(0, upper_bound) \n",
    "        coeff.append(rand_value)\n",
    "    return coeff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hashing function\n",
    "def hash(a, b, x, w):\n",
    "    return (a * x + b) % w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "UPPERBOUND = 2**32-1\n",
    "TOTAL = 8\n",
    "#first prime after upperbound\n",
    "PRIME = 4294967311\n",
    "\n",
    "#generate coeff lists A,B\n",
    "coeff_A = gen_coefficient(TOTAL, UPPERBOUND)\n",
    "coeff_B = gen_coefficient(TOTAL, UPPERBOUND)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#generate audio signature\n",
    "def gen_audio_signature(audio):\n",
    "    signature = []\n",
    "    track, sr, onset_env, peaks = load_audio_peaks(audio, OFFSET, DURATION, HOP_SIZE)\n",
    "\n",
    "    for i in range(0, TOTAL) :\n",
    "        hash_list = []\n",
    "        for x in peaks:\n",
    "            hash_value = hash(x, coeff_A[i], coeff_B[i], PRIME)\n",
    "            hash_list.append(hash_value)\n",
    "\n",
    "        #print(hash_list)        \n",
    "        signature.append(min(hash_list))\n",
    "\n",
    "    return signature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TRACK SIGNATURE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f80da93264524ec8855283c661beaf73",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1413 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from joblib import Parallel, delayed\n",
    "\n",
    "tracks_signature = []\n",
    "data_folder = Path(\"data/mp3s-32k/\")\n",
    "tracks = data_folder.glob(\"*/*/*.wav\")\n",
    "\n",
    "def signature(audio, idx):\n",
    "    audio_signature = gen_audio_signature(audio)\n",
    "    return (idx, audio, audio_signature)\n",
    "\n",
    "tracks_signature = Parallel(n_jobs=-1)(delayed(signature)(audio, idx) for idx, audio in tqdm(enumerate(tracks), total=N_TRACKS))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save track signature to csv file\n",
    "tracks_signature_df = pd.DataFrame(tracks_signature, columns=['idx', 'path', 'signature'])\n",
    "tracks_signature_df.to_csv('tracks_signature.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load track signature\n",
    "tracks_signature_ = pd.read_csv('tracks_signature.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUERY SIGNATURE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get query files path\n",
    "query_tracks_ = Path(\"query/\").glob(\"*.wav\")\n",
    "\n",
    "query_signature = []\n",
    "\n",
    "for idx, audio in enumerate(query_tracks_):\n",
    "    audio_signature = gen_audio_signature(audio)\n",
    "    query_signature.append((idx, audio, audio_signature))\n",
    "#print(query_signature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save query signature to csv file\n",
    "query_signature_df = pd.DataFrame(query_signature, columns=['idx', 'path', 'signature'])\n",
    "query_signature_df.to_csv('query_signature.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load track signature\n",
    "query_signature_ = pd.read_csv('query_signature.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MATCHING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Jaccard Similarity function\n",
    "def jaccard(A, B):\n",
    "    return len(A.intersection(B))/len(A.union(B))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hash_sign = []\n",
    "\n",
    "def audio_match(threshold):\n",
    "    for i, row_q in query_signature_.iterrows():\n",
    "        for j, row_t in tracks_signature_.iterrows():\n",
    "\n",
    "            track_hash_sign = list(map(int, row_t['signature'].strip('][').split(', ')))\n",
    "            query_hash_sign = list(map(int, row_q['signature'].strip('][').split(', ')))\n",
    "\n",
    "            idx = int(row_t.idx)\n",
    "            track_path = row_t.path\n",
    "            track_artist = track_path.split('/')[2].replace('_', ' ')\n",
    "            track_album = track_path.split('/')[3].replace('_', ' ')\n",
    "            track_title = track_path.split('/')[-1].split('-')[-1].replace('.wav', '').replace('_', ' ')\n",
    "\n",
    "            query_path = row_q.path\n",
    "            query_title = query_path.split('/')[-1]\n",
    "\n",
    "\n",
    "            jaccard_sim = jaccard(set(query_hash_sign), set(track_hash_sign) )\n",
    "\n",
    "            if jaccard_sim >= threshold:\n",
    "                print(\"Query:\", str(query_title))\n",
    "                print(\"Matched with:\", track_path)\n",
    "                print(\"Title:\", track_title)\n",
    "                print(\"Album:\", track_album)\n",
    "                print(\"Artist:\", track_artist)\n",
    "                print(\"Similarity score:\", jaccard_sim)\n",
    "                print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/green_day/American_Idiot/01-American_Idiot.wav\n",
      "Title: American Idiot\n",
      "Album: American Idiot\n",
      "Artist: green day\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track9.wav\n",
      "Matched with: data/mp3s-32k/depeche_mode/Some_Great_Reward/06-Somebody.wav\n",
      "Title: Somebody\n",
      "Album: Some Great Reward\n",
      "Artist: depeche mode\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/steely_dan/Katy_Lied/01-Black_Friday.wav\n",
      "Title: Black Friday\n",
      "Album: Katy Lied\n",
      "Artist: steely dan\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/queen/The_Works/06-I_Want_To_Break_Free.wav\n",
      "Title: I Want To Break Free\n",
      "Album: The Works\n",
      "Artist: queen\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track3.wav\n",
      "Matched with: data/mp3s-32k/u2/October/07-October.wav\n",
      "Title: October\n",
      "Album: October\n",
      "Artist: u2\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/aerosmith/Aerosmith/03-Dream_On.wav\n",
      "Title: Dream On\n",
      "Album: Aerosmith\n",
      "Artist: aerosmith\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/beatles/The_White_Album_Disc_1/04-Ob-La-Di_Ob-La-Da.wav\n",
      "Title: Da\n",
      "Album: The White Album Disc 1\n",
      "Artist: beatles\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/radiohead/OK_Computer/06-Karma_Police.wav\n",
      "Title: Karma Police\n",
      "Album: OK Computer\n",
      "Artist: radiohead\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/fleetwood_mac/Rumours/05-Go_Your_Own_Way.wav\n",
      "Title: Go Your Own Way\n",
      "Album: Rumours\n",
      "Artist: fleetwood mac\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track6.wav\n",
      "Matched with: data/mp3s-32k/led_zeppelin/Led_Zeppelin_II/05-Heartbreaker.wav\n",
      "Title: Heartbreaker\n",
      "Album: Led Zeppelin II\n",
      "Artist: led zeppelin\n",
      "Similarity score: 1.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "audio_match(1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/led_zeppelin/Led_Zeppelin_II/07-Ramble_On.wav\n",
      "Title: Ramble On\n",
      "Album: Led Zeppelin II\n",
      "Artist: led zeppelin\n",
      "Similarity score: 0.23076923076923078\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/green_day/American_Idiot/01-American_Idiot.wav\n",
      "Title: American Idiot\n",
      "Album: American Idiot\n",
      "Artist: green day\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track9.wav\n",
      "Matched with: data/mp3s-32k/depeche_mode/Some_Great_Reward/06-Somebody.wav\n",
      "Title: Somebody\n",
      "Album: Some Great Reward\n",
      "Artist: depeche mode\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/metallica/Metallica/07-Through_the_Never.wav\n",
      "Title: Through the Never\n",
      "Album: Metallica\n",
      "Artist: metallica\n",
      "Similarity score: 0.23076923076923078\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/steely_dan/Katy_Lied/01-Black_Friday.wav\n",
      "Title: Black Friday\n",
      "Album: Katy Lied\n",
      "Artist: steely dan\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/cure/Wild_Mood_Swings/13-Treasure.wav\n",
      "Title: Treasure\n",
      "Album: Wild Mood Swings\n",
      "Artist: cure\n",
      "Similarity score: 0.23076923076923078\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/cure/Kiss_Me_Kiss_Me_Kiss_Me/05-Why_Can_t_I_Be_You_.wav\n",
      "Title: Why Can t I Be You \n",
      "Album: Kiss Me Kiss Me Kiss Me\n",
      "Artist: cure\n",
      "Similarity score: 0.23076923076923078\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/queen/The_Works/06-I_Want_To_Break_Free.wav\n",
      "Title: I Want To Break Free\n",
      "Album: The Works\n",
      "Artist: queen\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track3.wav\n",
      "Matched with: data/mp3s-32k/u2/October/07-October.wav\n",
      "Title: October\n",
      "Album: October\n",
      "Artist: u2\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/aerosmith/Aerosmith/03-Dream_On.wav\n",
      "Title: Dream On\n",
      "Album: Aerosmith\n",
      "Artist: aerosmith\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/radiohead/Hail_to_the_Theif/09-There_There.wav\n",
      "Title: There There\n",
      "Album: Hail to the Theif\n",
      "Artist: radiohead\n",
      "Similarity score: 0.23076923076923078\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/radiohead/Pablo_Honey/13-Creep_Radio_Edit_.wav\n",
      "Title: Creep Radio Edit \n",
      "Album: Pablo Honey\n",
      "Artist: radiohead\n",
      "Similarity score: 0.23076923076923078\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/depeche_mode/Speak_and_Spell/03-Puppets.wav\n",
      "Title: Puppets\n",
      "Album: Speak and Spell\n",
      "Artist: depeche mode\n",
      "Similarity score: 0.23076923076923078\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/beatles/The_White_Album_Disc_1/04-Ob-La-Di_Ob-La-Da.wav\n",
      "Title: Da\n",
      "Album: The White Album Disc 1\n",
      "Artist: beatles\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/green_day/Insomniac/14-Walking_Contradiction.wav\n",
      "Title: Walking Contradiction\n",
      "Album: Insomniac\n",
      "Artist: green day\n",
      "Similarity score: 0.23076923076923078\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/radiohead/OK_Computer/06-Karma_Police.wav\n",
      "Title: Karma Police\n",
      "Album: OK Computer\n",
      "Artist: radiohead\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/dave_matthews_band/Crash/09-Lie_In_Our_Graves.wav\n",
      "Title: Lie In Our Graves\n",
      "Album: Crash\n",
      "Artist: dave matthews band\n",
      "Similarity score: 0.23076923076923078\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/led_zeppelin/Led_Zeppelin_III/01-Immigrant_Song.wav\n",
      "Title: Immigrant Song\n",
      "Album: Led Zeppelin III\n",
      "Artist: led zeppelin\n",
      "Similarity score: 0.23076923076923078\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/fleetwood_mac/Rumours/05-Go_Your_Own_Way.wav\n",
      "Title: Go Your Own Way\n",
      "Album: Rumours\n",
      "Artist: fleetwood mac\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track6.wav\n",
      "Matched with: data/mp3s-32k/led_zeppelin/Led_Zeppelin_II/05-Heartbreaker.wav\n",
      "Title: Heartbreaker\n",
      "Album: Led Zeppelin II\n",
      "Artist: led zeppelin\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track6.wav\n",
      "Matched with: data/mp3s-32k/tori_amos/Scarlet_s_Walk/05-Carbon.wav\n",
      "Title: Carbon\n",
      "Album: Scarlet s Walk\n",
      "Artist: tori amos\n",
      "Similarity score: 0.23076923076923078\n",
      "\n"
     ]
    }
   ],
   "source": [
    "audio_match(0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/led_zeppelin/Led_Zeppelin_II/07-Ramble_On.wav\n",
      "Title: Ramble On\n",
      "Album: Led Zeppelin II\n",
      "Artist: led zeppelin\n",
      "Similarity score: 0.23076923076923078\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/roxette/Look_Sharp_/10-View_From_A_Hill.wav\n",
      "Title: View From A Hill\n",
      "Album: Look Sharp \n",
      "Artist: roxette\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/roxette/Pearls_of_Passion_1997_Remaster_/13-Pearls_Of_Passion.wav\n",
      "Title: Pearls Of Passion\n",
      "Album: Pearls of Passion 1997 Remaster \n",
      "Artist: roxette\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/steely_dan/Katy_Lied/10-Throw_Back_The_Little_Ones.wav\n",
      "Title: Throw Back The Little Ones\n",
      "Album: Katy Lied\n",
      "Artist: steely dan\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/steely_dan/The_Royal_Scam/09-The_Royal_Scam.wav\n",
      "Title: The Royal Scam\n",
      "Album: The Royal Scam\n",
      "Artist: steely dan\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/beatles/Abbey_Road/09-You_Never_Give_Me_Your_Money.wav\n",
      "Title: You Never Give Me Your Money\n",
      "Album: Abbey Road\n",
      "Artist: beatles\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/beatles/Revolver/14-Tomorrow_Never_Knows.wav\n",
      "Title: Tomorrow Never Knows\n",
      "Album: Revolver\n",
      "Artist: beatles\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/beatles/The_White_Album_Disc_1/04-Ob-La-Di_Ob-La-Da.wav\n",
      "Title: Da\n",
      "Album: The White Album Disc 1\n",
      "Artist: beatles\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/beatles/Beatles_For_Sale/12-I_Don_t_Want_To_Spoil_The_Party.wav\n",
      "Title: I Don t Want To Spoil The Party\n",
      "Album: Beatles For Sale\n",
      "Artist: beatles\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/fleetwood_mac/Tusk/01-Over_Over.wav\n",
      "Title: Over Over\n",
      "Album: Tusk\n",
      "Artist: fleetwood mac\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/fleetwood_mac/Rumours/04-Don_t_Stop.wav\n",
      "Title: Don t Stop\n",
      "Album: Rumours\n",
      "Artist: fleetwood mac\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/aerosmith/Draw_the_Line/03-Critical_Mass.wav\n",
      "Title: Critical Mass\n",
      "Album: Draw the Line\n",
      "Artist: aerosmith\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/aerosmith/Pump/08-Don_t_Get_Mad_Get_Even.wav\n",
      "Title: Don t Get Mad Get Even\n",
      "Album: Pump\n",
      "Artist: aerosmith\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/prince/Parade/06-Life_Can_be_so_Nice.wav\n",
      "Title: Life Can be so Nice\n",
      "Album: Parade\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/prince/The_Love_Symbol_Album/08-I_Wanna_Melt_With_U.wav\n",
      "Title: I Wanna Melt With U\n",
      "Album: The Love Symbol Album\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/creedence_clearwater_revival/Cosmo_s_Factory/04-Ooby_Dooby.wav\n",
      "Title: Ooby Dooby\n",
      "Album: Cosmo s Factory\n",
      "Artist: creedence clearwater revival\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/madonna/Bedtime_Stories/06-Human_Nature.wav\n",
      "Title: Human Nature\n",
      "Album: Bedtime Stories\n",
      "Artist: madonna\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/madonna/Like_A_Prayer/09-Keep_It_Together.wav\n",
      "Title: Keep It Together\n",
      "Album: Like A Prayer\n",
      "Artist: madonna\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/madonna/Erotica/14-Secret_Garden.wav\n",
      "Title: Secret Garden\n",
      "Album: Erotica\n",
      "Artist: madonna\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/madonna/Erotica/02-Fever.wav\n",
      "Title: Fever\n",
      "Album: Erotica\n",
      "Artist: madonna\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/madonna/Ray_of_Light/03-Ray_Of_Light.wav\n",
      "Title: Ray Of Light\n",
      "Album: Ray of Light\n",
      "Artist: madonna\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/suzanne_vega/Nine_Objects_Of_Desire/10-Honeymoon_Suite.wav\n",
      "Title: Honeymoon Suite\n",
      "Album: Nine Objects Of Desire\n",
      "Artist: suzanne vega\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/radiohead/OK_Computer/09-Climbing_Up_The_Walls.wav\n",
      "Title: Climbing Up The Walls\n",
      "Album: OK Computer\n",
      "Artist: radiohead\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/radiohead/Pablo_Honey/08-Vegetable.wav\n",
      "Title: Vegetable\n",
      "Album: Pablo Honey\n",
      "Artist: radiohead\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/green_day/Insomniac/08-Panic_Song.wav\n",
      "Title: Panic Song\n",
      "Album: Insomniac\n",
      "Artist: green day\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/green_day/Nimrod/01-Nice_Guys_Finish_Last.wav\n",
      "Title: Nice Guys Finish Last\n",
      "Album: Nimrod\n",
      "Artist: green day\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/green_day/American_Idiot/01-American_Idiot.wav\n",
      "Title: American Idiot\n",
      "Album: American Idiot\n",
      "Artist: green day\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/dave_matthews_band/Stand_Up/09-Hello_Again.wav\n",
      "Title: Hello Again\n",
      "Album: Stand Up\n",
      "Artist: dave matthews band\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/depeche_mode/Black_Celebration/02-Fly_on_the_windscreen.wav\n",
      "Title: Fly on the windscreen\n",
      "Album: Black Celebration\n",
      "Artist: depeche mode\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/depeche_mode/Black_Celebration/14-Black_day.wav\n",
      "Title: Black day\n",
      "Album: Black Celebration\n",
      "Artist: depeche mode\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/garth_brooks/No_Fences_The_Limited_Series_/10-Mr_Blue.wav\n",
      "Title: Mr Blue\n",
      "Album: No Fences The Limited Series \n",
      "Artist: garth brooks\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/garth_brooks/No_Fences_The_Limited_Series_/09-Same_Old_Story.wav\n",
      "Title: Same Old Story\n",
      "Album: No Fences The Limited Series \n",
      "Artist: garth brooks\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/queen/News_Of_The_World/02-We_Are_The_Champions.wav\n",
      "Title: We Are The Champions\n",
      "Album: News Of The World\n",
      "Artist: queen\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track8.wav\n",
      "Matched with: data/mp3s-32k/queen/A_Night_At_the_Opera/10-Good_Company.wav\n",
      "Title: Good Company\n",
      "Album: A Night At the Opera\n",
      "Artist: queen\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track9.wav\n",
      "Matched with: data/mp3s-32k/metallica/Master_of_Puppets/03-Thing_That_Should_Not_Be_The.wav\n",
      "Title: Thing That Should Not Be The\n",
      "Album: Master of Puppets\n",
      "Artist: metallica\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track9.wav\n",
      "Matched with: data/mp3s-32k/metallica/Load/14-The_Outlaw_Torn.wav\n",
      "Title: The Outlaw Torn\n",
      "Album: Load\n",
      "Artist: metallica\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track9.wav\n",
      "Matched with: data/mp3s-32k/steely_dan/Katy_Lied/04-Daddy_Don_t_Live_In_That_New_York_City_No_More.wav\n",
      "Title: Daddy Don t Live In That New York City No More\n",
      "Album: Katy Lied\n",
      "Artist: steely dan\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track9.wav\n",
      "Matched with: data/mp3s-32k/beatles/A_Hard_Day_s_Night/10-Things_We_Said_Today.wav\n",
      "Title: Things We Said Today\n",
      "Album: A Hard Day s Night\n",
      "Artist: beatles\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track9.wav\n",
      "Matched with: data/mp3s-32k/tori_amos/Under_the_Pink/06-The_Wrong_Band.wav\n",
      "Title: The Wrong Band\n",
      "Album: Under the Pink\n",
      "Artist: tori amos\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track9.wav\n",
      "Matched with: data/mp3s-32k/prince/Sign_O_The_Times_Disc_1_/02-Play_In_The_Sunshine.wav\n",
      "Title: Play In The Sunshine\n",
      "Album: Sign O The Times Disc 1 \n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track9.wav\n",
      "Matched with: data/mp3s-32k/prince/1999/04-Let_s_Pretend_We_re_Married.wav\n",
      "Title: Let s Pretend We re Married\n",
      "Album: 1999\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track9.wav\n",
      "Matched with: data/mp3s-32k/prince/1999/06-Automatic.wav\n",
      "Title: Automatic\n",
      "Album: 1999\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track9.wav\n",
      "Matched with: data/mp3s-32k/green_day/Nimrod/12-Haushinka.wav\n",
      "Title: Haushinka\n",
      "Album: Nimrod\n",
      "Artist: green day\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track9.wav\n",
      "Matched with: data/mp3s-32k/green_day/Warning/10-Waiting.wav\n",
      "Title: Waiting\n",
      "Album: Warning\n",
      "Artist: green day\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track9.wav\n",
      "Matched with: data/mp3s-32k/depeche_mode/Some_Great_Reward/06-Somebody.wav\n",
      "Title: Somebody\n",
      "Album: Some Great Reward\n",
      "Artist: depeche mode\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track9.wav\n",
      "Matched with: data/mp3s-32k/depeche_mode/Speak_and_Spell/05-Nodisco.wav\n",
      "Title: Nodisco\n",
      "Album: Speak and Spell\n",
      "Artist: depeche mode\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track9.wav\n",
      "Matched with: data/mp3s-32k/queen/News_Of_The_World/03-Sheer_Heart_Attack.wav\n",
      "Title: Sheer Heart Attack\n",
      "Album: News Of The World\n",
      "Artist: queen\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/metallica/Metallica/07-Through_the_Never.wav\n",
      "Title: Through the Never\n",
      "Album: Metallica\n",
      "Artist: metallica\n",
      "Similarity score: 0.23076923076923078\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/metallica/Metallica/03-Holier_than_Thou.wav\n",
      "Title: Holier than Thou\n",
      "Album: Metallica\n",
      "Artist: metallica\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/metallica/Reload/06-Slither.wav\n",
      "Title: Slither\n",
      "Album: Reload\n",
      "Artist: metallica\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/metallica/Load/07-Bleeding_Me.wav\n",
      "Title: Bleeding Me\n",
      "Album: Load\n",
      "Artist: metallica\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/metallica/Load/11-Mama_Said.wav\n",
      "Title: Mama Said\n",
      "Album: Load\n",
      "Artist: metallica\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/metallica/Kill_Em_All/10-Metal_Militia.wav\n",
      "Title: Metal Militia\n",
      "Album: Kill Em All\n",
      "Artist: metallica\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/led_zeppelin/Led_Zeppelin_I/01-Good_Times_Bad_Times.wav\n",
      "Title: Good Times Bad Times\n",
      "Album: Led Zeppelin I\n",
      "Artist: led zeppelin\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/led_zeppelin/Led_Zeppelin_III/10-Hats_Off_To_Roy_Harper.wav\n",
      "Title: Hats Off To Roy Harper\n",
      "Album: Led Zeppelin III\n",
      "Artist: led zeppelin\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/led_zeppelin/Led_Zeppelin_III/09-Bron-Y-Aur_Stomp.wav\n",
      "Title: Aur Stomp\n",
      "Album: Led Zeppelin III\n",
      "Artist: led zeppelin\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/roxette/Look_Sharp_/08-Dangerous.wav\n",
      "Title: Dangerous\n",
      "Album: Look Sharp \n",
      "Artist: roxette\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/roxette/Pearls_of_Passion_1997_Remaster_/10-From_One_Heart_To_Another.wav\n",
      "Title: From One Heart To Another\n",
      "Album: Pearls of Passion 1997 Remaster \n",
      "Artist: roxette\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/steely_dan/Aja/02-Aja.wav\n",
      "Title: Aja\n",
      "Album: Aja\n",
      "Artist: steely dan\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/steely_dan/Katy_Lied/01-Black_Friday.wav\n",
      "Title: Black Friday\n",
      "Album: Katy Lied\n",
      "Artist: steely dan\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/steely_dan/Gaucho/07-Third_World_Man.wav\n",
      "Title: Third World Man\n",
      "Album: Gaucho\n",
      "Artist: steely dan\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/steely_dan/The_Royal_Scam/02-The_Caves_of_Altamira.wav\n",
      "Title: The Caves of Altamira\n",
      "Album: The Royal Scam\n",
      "Artist: steely dan\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/steely_dan/Countdown_To_Ecstasy/06-My_Old_School.wav\n",
      "Title: My Old School\n",
      "Album: Countdown To Ecstasy\n",
      "Artist: steely dan\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/tori_amos/The_Beekeeper/02-Sweet_the_Sting.wav\n",
      "Title: Sweet the Sting\n",
      "Album: The Beekeeper\n",
      "Artist: tori amos\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/tori_amos/Scarlet_s_Walk/01-Amber_Waves.wav\n",
      "Title: Amber Waves\n",
      "Album: Scarlet s Walk\n",
      "Artist: tori amos\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/tori_amos/Scarlet_s_Walk/02-A_Sorta_Fairytale.wav\n",
      "Title: A Sorta Fairytale\n",
      "Album: Scarlet s Walk\n",
      "Artist: tori amos\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/fleetwood_mac/Tusk/10-Sisters_of_the_Moon.wav\n",
      "Title: Sisters of the Moon\n",
      "Album: Tusk\n",
      "Artist: fleetwood mac\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/aerosmith/Pump/03-Going_Down-Love_In_An_Elevator.wav\n",
      "Title: Love In An Elevator\n",
      "Album: Pump\n",
      "Artist: aerosmith\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/aerosmith/Rocks/07-Get_The_Lead_Out.wav\n",
      "Title: Get The Lead Out\n",
      "Album: Rocks\n",
      "Artist: aerosmith\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/aerosmith/Rocks/08-Lick_and_a_Promise.wav\n",
      "Title: Lick and a Promise\n",
      "Album: Rocks\n",
      "Artist: aerosmith\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/prince/Parade/01-Christopher_Tracy_s_Parade.wav\n",
      "Title: Christopher Tracy s Parade\n",
      "Album: Parade\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/prince/Lovesexy/07-When_2_R_In_Love.wav\n",
      "Title: When 2 R In Love\n",
      "Album: Lovesexy\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/prince/Sign_O_The_Times_Disc_1_/06-Starfish_And_Coffee.wav\n",
      "Title: Starfish And Coffee\n",
      "Album: Sign O The Times Disc 1 \n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/prince/Sign_O_The_Times_Disc_1_/04-The_Ballad_Of_Dorothy_Parker.wav\n",
      "Title: The Ballad Of Dorothy Parker\n",
      "Album: Sign O The Times Disc 1 \n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/prince/The_Love_Symbol_Album/08-I_Wanna_Melt_With_U.wav\n",
      "Title: I Wanna Melt With U\n",
      "Album: The Love Symbol Album\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/prince/Purple_Rain/03-The_beautiful_ones.wav\n",
      "Title: The beautiful ones\n",
      "Album: Purple Rain\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/cure/Wild_Mood_Swings/13-Treasure.wav\n",
      "Title: Treasure\n",
      "Album: Wild Mood Swings\n",
      "Artist: cure\n",
      "Similarity score: 0.23076923076923078\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/u2/October/08-With_a_Shout.wav\n",
      "Title: With a Shout\n",
      "Album: October\n",
      "Artist: u2\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/u2/Achtung_Baby/06-So_Cruel.wav\n",
      "Title: So Cruel\n",
      "Album: Achtung Baby\n",
      "Artist: u2\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/radiohead/The_Bends/08-My_Iron_Lung.wav\n",
      "Title: My Iron Lung\n",
      "Album: The Bends\n",
      "Artist: radiohead\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/dave_matthews_band/Crash/08-Let_You_Down.wav\n",
      "Title: Let You Down\n",
      "Album: Crash\n",
      "Artist: dave matthews band\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/depeche_mode/Music_for_the_Masses/10-Pimpf.wav\n",
      "Title: Pimpf\n",
      "Album: Music for the Masses\n",
      "Artist: depeche mode\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/depeche_mode/Construction_Time_Again/06-Shame.wav\n",
      "Title: Shame\n",
      "Album: Construction Time Again\n",
      "Artist: depeche mode\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/depeche_mode/Speak_and_Spell/06-What_s_Your_Name-.wav\n",
      "Title: \n",
      "Album: Speak and Spell\n",
      "Artist: depeche mode\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/garth_brooks/Garth_Brooks_The_Limited_Series_/09-Nobody_Gets_Off_In_This_Town.wav\n",
      "Title: Nobody Gets Off In This Town\n",
      "Album: Garth Brooks The Limited Series \n",
      "Artist: garth brooks\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/garth_brooks/No_Fences_The_Limited_Series_/11-Wolves.wav\n",
      "Title: Wolves\n",
      "Album: No Fences The Limited Series \n",
      "Artist: garth brooks\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/queen/Sheer_Heart_Attack/12-She_Makes_Me_Stormtrooper_In_Stilettos_.wav\n",
      "Title: She Makes Me Stormtrooper In Stilettos \n",
      "Album: Sheer Heart Attack\n",
      "Artist: queen\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track10.wav\n",
      "Matched with: data/mp3s-32k/queen/A_Night_At_the_Opera/13-I_m_In_Love_With_My_Car.wav\n",
      "Title: I m In Love With My Car\n",
      "Album: A Night At the Opera\n",
      "Artist: queen\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/metallica/Metallica/09-Of_Wolf_and_Man.wav\n",
      "Title: Of Wolf and Man\n",
      "Album: Metallica\n",
      "Artist: metallica\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/metallica/Ride_The_Lightning/07-Creeping_Death.wav\n",
      "Title: Creeping Death\n",
      "Album: Ride The Lightning\n",
      "Artist: metallica\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/metallica/Load/03-The_House_Jack_Built.wav\n",
      "Title: The House Jack Built\n",
      "Album: Load\n",
      "Artist: metallica\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/roxette/Pearls_of_Passion_1997_Remaster_/09-Joy_Of_A_Toy.wav\n",
      "Title: Joy Of A Toy\n",
      "Album: Pearls of Passion 1997 Remaster \n",
      "Artist: roxette\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/tori_amos/To_Venus_and_Back-Orbiting/08-Riot_Poof.wav\n",
      "Title: Riot Poof\n",
      "Album: To Venus and Back-Orbiting\n",
      "Artist: tori amos\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/fleetwood_mac/Mirage/03-That_s_Alright.wav\n",
      "Title: That s Alright\n",
      "Album: Mirage\n",
      "Artist: fleetwood mac\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/aerosmith/Rocks/01-Back_In_The_Saddle_Again.wav\n",
      "Title: Back In The Saddle Again\n",
      "Album: Rocks\n",
      "Artist: aerosmith\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/prince/Parade/04-Under_The_Cherry_Moon.wav\n",
      "Title: Under The Cherry Moon\n",
      "Album: Parade\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/cure/Kiss_Me_Kiss_Me_Kiss_Me/05-Why_Can_t_I_Be_You_.wav\n",
      "Title: Why Can t I Be You \n",
      "Album: Kiss Me Kiss Me Kiss Me\n",
      "Artist: cure\n",
      "Similarity score: 0.23076923076923078\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/cure/Wish/12-End.wav\n",
      "Title: End\n",
      "Album: Wish\n",
      "Artist: cure\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/cure/Wish/05-Wendy_Time.wav\n",
      "Title: Wendy Time\n",
      "Album: Wish\n",
      "Artist: cure\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/cure/The_Top/04-Give_Me_It.wav\n",
      "Title: Give Me It\n",
      "Album: The Top\n",
      "Artist: cure\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/cure/Wild_Mood_Swings/12-Trap.wav\n",
      "Title: Trap\n",
      "Album: Wild Mood Swings\n",
      "Artist: cure\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/u2/October/11-Is_That_All_.wav\n",
      "Title: Is That All \n",
      "Album: October\n",
      "Artist: u2\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/u2/Achtung_Baby/08-Mysterious_Ways.wav\n",
      "Title: Mysterious Ways\n",
      "Album: Achtung Baby\n",
      "Artist: u2\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/radiohead/Hail_to_the_Theif/09-There_There.wav\n",
      "Title: There There\n",
      "Album: Hail to the Theif\n",
      "Artist: radiohead\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/radiohead/The_Bends/12-Street_Spirit_Fade_Out_.wav\n",
      "Title: Street Spirit Fade Out \n",
      "Album: The Bends\n",
      "Artist: radiohead\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/radiohead/Pablo_Honey/08-Vegetable.wav\n",
      "Title: Vegetable\n",
      "Album: Pablo Honey\n",
      "Artist: radiohead\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/green_day/American_Idiot/10-Letterbomb.wav\n",
      "Title: Letterbomb\n",
      "Album: American Idiot\n",
      "Artist: green day\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/green_day/American_Idiot/05-Are_We_the_Waiting.wav\n",
      "Title: Are We the Waiting\n",
      "Album: American Idiot\n",
      "Artist: green day\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/dave_matthews_band/Stand_Up/02-Old_Dirt_Hill_Bring_That_Beat_Back_.wav\n",
      "Title: Old Dirt Hill Bring That Beat Back \n",
      "Album: Stand Up\n",
      "Artist: dave matthews band\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/dave_matthews_band/Before_These_Crowded_Streets/01-Pantala_Naga_Pampa.wav\n",
      "Title: Pantala Naga Pampa\n",
      "Album: Before These Crowded Streets\n",
      "Artist: dave matthews band\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/dave_matthews_band/Crash/03-Crash_Into_Me.wav\n",
      "Title: Crash Into Me\n",
      "Album: Crash\n",
      "Artist: dave matthews band\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/depeche_mode/Speak_and_Spell/02-I_Sometimes_Wish_I_Was_Dead.wav\n",
      "Title: I Sometimes Wish I Was Dead\n",
      "Album: Speak and Spell\n",
      "Artist: depeche mode\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/depeche_mode/A_Broken_Frame/03-Monument.wav\n",
      "Title: Monument\n",
      "Album: A Broken Frame\n",
      "Artist: depeche mode\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track2.wav\n",
      "Matched with: data/mp3s-32k/queen/The_Works/06-I_Want_To_Break_Free.wav\n",
      "Title: I Want To Break Free\n",
      "Album: The Works\n",
      "Artist: queen\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track3.wav\n",
      "Matched with: data/mp3s-32k/metallica/Metallica/01-Enter_Sandman.wav\n",
      "Title: Enter Sandman\n",
      "Album: Metallica\n",
      "Artist: metallica\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track3.wav\n",
      "Matched with: data/mp3s-32k/u2/Zooropa/01-Zooropa.wav\n",
      "Title: Zooropa\n",
      "Album: Zooropa\n",
      "Artist: u2\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track3.wav\n",
      "Matched with: data/mp3s-32k/u2/October/07-October.wav\n",
      "Title: October\n",
      "Album: October\n",
      "Artist: u2\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track3.wav\n",
      "Matched with: data/mp3s-32k/madonna/Erotica/06-Bad_Girl.wav\n",
      "Title: Bad Girl\n",
      "Album: Erotica\n",
      "Artist: madonna\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track3.wav\n",
      "Matched with: data/mp3s-32k/garth_brooks/The_Chase/07-Learning_To_Live_Again.wav\n",
      "Title: Learning To Live Again\n",
      "Album: The Chase\n",
      "Artist: garth brooks\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track3.wav\n",
      "Matched with: data/mp3s-32k/queen/Sheer_Heart_Attack/07-In_The_Lap_Of_The_Gods.wav\n",
      "Title: In The Lap Of The Gods\n",
      "Album: Sheer Heart Attack\n",
      "Artist: queen\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/metallica/Load/14-The_Outlaw_Torn.wav\n",
      "Title: The Outlaw Torn\n",
      "Album: Load\n",
      "Artist: metallica\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/metallica/Load/13-onnie.wav\n",
      "Title: onnie\n",
      "Album: Load\n",
      "Artist: metallica\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/led_zeppelin/Led_Zeppelin_III/05-Out_On_The_Tiles.wav\n",
      "Title: Out On The Tiles\n",
      "Album: Led Zeppelin III\n",
      "Artist: led zeppelin\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/roxette/Room_Service/09-Try_Just_A_Little_Bit_Harder_.wav\n",
      "Title: Try Just A Little Bit Harder \n",
      "Album: Room Service\n",
      "Artist: roxette\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/roxette/Have_a_Nice_Day/05-Anyone.wav\n",
      "Title: Anyone\n",
      "Album: Have a Nice Day\n",
      "Artist: roxette\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/roxette/Have_a_Nice_Day/06-It_Will_Take_A_Long_Long_Time.wav\n",
      "Title: It Will Take A Long Long Time\n",
      "Album: Have a Nice Day\n",
      "Artist: roxette\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/beatles/Magical_Mystery_Tour/04-Blue_Jay_Way.wav\n",
      "Title: Blue Jay Way\n",
      "Album: Magical Mystery Tour\n",
      "Artist: beatles\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/beatles/Magical_Mystery_Tour/01-Magical_Mystery_Tour.wav\n",
      "Title: Magical Mystery Tour\n",
      "Album: Magical Mystery Tour\n",
      "Artist: beatles\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/beatles/Beatles_For_Sale/10-Honey_Don_t.wav\n",
      "Title: Honey Don t\n",
      "Album: Beatles For Sale\n",
      "Artist: beatles\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/tori_amos/The_Beekeeper/11-Witness.wav\n",
      "Title: Witness\n",
      "Album: The Beekeeper\n",
      "Artist: tori amos\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/tori_amos/From_the_Choirgirl_Hotel/10-Hotel.wav\n",
      "Title: Hotel\n",
      "Album: From the Choirgirl Hotel\n",
      "Artist: tori amos\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/fleetwood_mac/Tusk/19-Tusk.wav\n",
      "Title: Tusk\n",
      "Album: Tusk\n",
      "Artist: fleetwood mac\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/aerosmith/Aerosmith/03-Dream_On.wav\n",
      "Title: Dream On\n",
      "Album: Aerosmith\n",
      "Artist: aerosmith\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/aerosmith/Pump/03-Going_Down-Love_In_An_Elevator.wav\n",
      "Title: Love In An Elevator\n",
      "Album: Pump\n",
      "Artist: aerosmith\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/aerosmith/Toys_In_The_Attic/01-Toys_In_The_Attic.wav\n",
      "Title: Toys In The Attic\n",
      "Album: Toys In The Attic\n",
      "Artist: aerosmith\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/prince/Parade/06-Life_Can_be_so_Nice.wav\n",
      "Title: Life Can be so Nice\n",
      "Album: Parade\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/prince/Parade/05-Girls_Boys.wav\n",
      "Title: Girls Boys\n",
      "Album: Parade\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/prince/1999/11-International_Lover.wav\n",
      "Title: International Lover\n",
      "Album: 1999\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/prince/Purple_Rain/08-Baby_I_m_a_star.wav\n",
      "Title: Baby I m a star\n",
      "Album: Purple Rain\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/creedence_clearwater_revival/Pendulum/01-Pagan_Baby.wav\n",
      "Title: Pagan Baby\n",
      "Album: Pendulum\n",
      "Artist: creedence clearwater revival\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/cure/Disintegration/08-Prayers_For_Rain.wav\n",
      "Title: Prayers For Rain\n",
      "Album: Disintegration\n",
      "Artist: cure\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/u2/Zooropa/09-Dirty_Day.wav\n",
      "Title: Dirty Day\n",
      "Album: Zooropa\n",
      "Artist: u2\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/u2/Achtung_Baby/10-Ultra_Violet_Light_My_Way_.wav\n",
      "Title: Ultra Violet Light My Way \n",
      "Album: Achtung Baby\n",
      "Artist: u2\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/madonna/Ray_of_Light/04-Candy_Perfume_Girl.wav\n",
      "Title: Candy Perfume Girl\n",
      "Album: Ray of Light\n",
      "Artist: madonna\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/suzanne_vega/Songs_in_Red_and_Gray/08-Priscilla.wav\n",
      "Title: Priscilla\n",
      "Album: Songs in Red and Gray\n",
      "Artist: suzanne vega\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/suzanne_vega/99_9_F_/07-_If_You_Were_In_My_Movie.wav\n",
      "Title:  If You Were In My Movie\n",
      "Album: 99 9 F \n",
      "Artist: suzanne vega\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/radiohead/Kid_A/06-Optimistic.wav\n",
      "Title: Optimistic\n",
      "Album: Kid A\n",
      "Artist: radiohead\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/radiohead/Hail_to_the_Theif/09-There_There.wav\n",
      "Title: There There\n",
      "Album: Hail to the Theif\n",
      "Artist: radiohead\n",
      "Similarity score: 0.23076923076923078\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/radiohead/Pablo_Honey/13-Creep_Radio_Edit_.wav\n",
      "Title: Creep Radio Edit \n",
      "Album: Pablo Honey\n",
      "Artist: radiohead\n",
      "Similarity score: 0.23076923076923078\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/green_day/Warning/11-Minority.wav\n",
      "Title: Minority\n",
      "Album: Warning\n",
      "Artist: green day\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/green_day/Dookie/06-Pulling_Teeth.wav\n",
      "Title: Pulling Teeth\n",
      "Album: Dookie\n",
      "Artist: green day\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/dave_matthews_band/Stand_Up/06-Smooth_Rider.wav\n",
      "Title: Smooth Rider\n",
      "Album: Stand Up\n",
      "Artist: dave matthews band\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/dave_matthews_band/Crash/09-Lie_In_Our_Graves.wav\n",
      "Title: Lie In Our Graves\n",
      "Album: Crash\n",
      "Artist: dave matthews band\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/dave_matthews_band/Crash/01-So_Much_To_Say.wav\n",
      "Title: So Much To Say\n",
      "Album: Crash\n",
      "Artist: dave matthews band\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/dave_matthews_band/Busted_Stuff/09-Digging_a_Ditch.wav\n",
      "Title: Digging a Ditch\n",
      "Album: Busted Stuff\n",
      "Artist: dave matthews band\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/dave_matthews_band/Under_The_Table_And_Dreaming/09-Jimi_Thing.wav\n",
      "Title: Jimi Thing\n",
      "Album: Under The Table And Dreaming\n",
      "Artist: dave matthews band\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/depeche_mode/Black_Celebration/11-New_dress.wav\n",
      "Title: New dress\n",
      "Album: Black Celebration\n",
      "Artist: depeche mode\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/depeche_mode/Speak_and_Spell/03-Puppets.wav\n",
      "Title: Puppets\n",
      "Album: Speak and Spell\n",
      "Artist: depeche mode\n",
      "Similarity score: 0.23076923076923078\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/depeche_mode/Speak_and_Spell/12-Dreaming_Of_Me.wav\n",
      "Title: Dreaming Of Me\n",
      "Album: Speak and Spell\n",
      "Artist: depeche mode\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/garth_brooks/Garth_Brooks_The_Limited_Series_/06-Alabama_Clay.wav\n",
      "Title: Alabama Clay\n",
      "Album: Garth Brooks The Limited Series \n",
      "Artist: garth brooks\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/queen/News_Of_The_World/09-Who_Needs_You.wav\n",
      "Title: Who Needs You\n",
      "Album: News Of The World\n",
      "Artist: queen\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/queen/Sheer_Heart_Attack/09-Dear_Friends.wav\n",
      "Title: Dear Friends\n",
      "Album: Sheer Heart Attack\n",
      "Artist: queen\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/queen/The_Works/05-Machines_Back_To_Humans_.wav\n",
      "Title: Machines Back To Humans \n",
      "Album: The Works\n",
      "Artist: queen\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track1.wav\n",
      "Matched with: data/mp3s-32k/queen/The_Works/11-Radio_Ga_Ga_Extended_Version_.wav\n",
      "Title: Radio Ga Ga Extended Version \n",
      "Album: The Works\n",
      "Artist: queen\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/led_zeppelin/Led_Zeppelin_III/08-That_s_The_Way.wav\n",
      "Title: That s The Way\n",
      "Album: Led Zeppelin III\n",
      "Artist: led zeppelin\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/roxette/Room_Service/09-Try_Just_A_Little_Bit_Harder_.wav\n",
      "Title: Try Just A Little Bit Harder \n",
      "Album: Room Service\n",
      "Artist: roxette\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/roxette/Pearls_of_Passion_1997_Remaster_/13-Pearls_Of_Passion.wav\n",
      "Title: Pearls Of Passion\n",
      "Album: Pearls of Passion 1997 Remaster \n",
      "Artist: roxette\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/roxette/Have_a_Nice_Day/13-Staring_At_The_Ground.wav\n",
      "Title: Staring At The Ground\n",
      "Album: Have a Nice Day\n",
      "Artist: roxette\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/steely_dan/The_Royal_Scam/09-The_Royal_Scam.wav\n",
      "Title: The Royal Scam\n",
      "Album: The Royal Scam\n",
      "Artist: steely dan\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/beatles/Abbey_Road/04-Oh_Darling.wav\n",
      "Title: Oh Darling\n",
      "Album: Abbey Road\n",
      "Artist: beatles\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/beatles/A_Hard_Day_s_Night/06-Tell_Me_Why.wav\n",
      "Title: Tell Me Why\n",
      "Album: A Hard Day s Night\n",
      "Artist: beatles\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/beatles/The_White_Album_Disc_1/04-Ob-La-Di_Ob-La-Da.wav\n",
      "Title: Da\n",
      "Album: The White Album Disc 1\n",
      "Artist: beatles\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/fleetwood_mac/Tusk/06-What_Makes_You_Think_You_re_the_One.wav\n",
      "Title: What Makes You Think You re the One\n",
      "Album: Tusk\n",
      "Artist: fleetwood mac\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/aerosmith/Aerosmith/01-Make_It.wav\n",
      "Title: Make It\n",
      "Album: Aerosmith\n",
      "Artist: aerosmith\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/prince/Purple_Rain/02-Take_me_with_u.wav\n",
      "Title: Take me with u\n",
      "Album: Purple Rain\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/creedence_clearwater_revival/Pendulum/08-It_s_Just_A_Thought.wav\n",
      "Title: It s Just A Thought\n",
      "Album: Pendulum\n",
      "Artist: creedence clearwater revival\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/madonna/Ray_of_Light/03-Ray_Of_Light.wav\n",
      "Title: Ray Of Light\n",
      "Album: Ray of Light\n",
      "Artist: madonna\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/suzanne_vega/Songs_in_Red_and_Gray/03-_I_ll_Never_Be_Your_Maggie_May.wav\n",
      "Title:  I ll Never Be Your Maggie May\n",
      "Album: Songs in Red and Gray\n",
      "Artist: suzanne vega\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/suzanne_vega/Days_of_Open_Hand/03-Rusted_Pipe.wav\n",
      "Title: Rusted Pipe\n",
      "Album: Days of Open Hand\n",
      "Artist: suzanne vega\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/radiohead/OK_Computer/12-The_Tourist.wav\n",
      "Title: The Tourist\n",
      "Album: OK Computer\n",
      "Artist: radiohead\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/green_day/Insomniac/14-Walking_Contradiction.wav\n",
      "Title: Walking Contradiction\n",
      "Album: Insomniac\n",
      "Artist: green day\n",
      "Similarity score: 0.23076923076923078\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/green_day/Insomniac/08-Panic_Song.wav\n",
      "Title: Panic Song\n",
      "Album: Insomniac\n",
      "Artist: green day\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/green_day/American_Idiot/01-American_Idiot.wav\n",
      "Title: American Idiot\n",
      "Album: American Idiot\n",
      "Artist: green day\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/green_day/American_Idiot/13-Whatsername.wav\n",
      "Title: Whatsername\n",
      "Album: American Idiot\n",
      "Artist: green day\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/depeche_mode/Black_Celebration/14-Black_day.wav\n",
      "Title: Black day\n",
      "Album: Black Celebration\n",
      "Artist: depeche mode\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/depeche_mode/Speak_and_Spell/11-Just_Can_t_Get_Enough.wav\n",
      "Title: Just Can t Get Enough\n",
      "Album: Speak and Spell\n",
      "Artist: depeche mode\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/garth_brooks/Fresh_Horses_2000/09-It_s_Midnight_Cinderella.wav\n",
      "Title: It s Midnight Cinderella\n",
      "Album: Fresh Horses 2000\n",
      "Artist: garth brooks\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track4.wav\n",
      "Matched with: data/mp3s-32k/queen/Jazz/15-Bicycle_Race_1991_Remix_.wav\n",
      "Title: Bicycle Race 1991 Remix \n",
      "Album: Jazz\n",
      "Artist: queen\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/metallica/Metallica/02-Sad_but_True.wav\n",
      "Title: Sad but True\n",
      "Album: Metallica\n",
      "Artist: metallica\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/led_zeppelin/Houses_of_the_Holy/05-Dancing_Days.wav\n",
      "Title: Dancing Days\n",
      "Album: Houses of the Holy\n",
      "Artist: led zeppelin\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/led_zeppelin/Led_Zeppelin_II/07-Ramble_On.wav\n",
      "Title: Ramble On\n",
      "Album: Led Zeppelin II\n",
      "Artist: led zeppelin\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/roxette/Room_Service/10-Fool.wav\n",
      "Title: Fool\n",
      "Album: Room Service\n",
      "Artist: roxette\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/roxette/Room_Service/12-My_World_My_Love_My_Life.wav\n",
      "Title: My World My Love My Life\n",
      "Album: Room Service\n",
      "Artist: roxette\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/roxette/Joyride/14-perfect_day.wav\n",
      "Title: perfect day\n",
      "Album: Joyride\n",
      "Artist: roxette\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/roxette/Pearls_of_Passion_1997_Remaster_/07-Neverending_Love.wav\n",
      "Title: Neverending Love\n",
      "Album: Pearls of Passion 1997 Remaster \n",
      "Artist: roxette\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/steely_dan/Katy_Lied/09-Any_World.wav\n",
      "Title: Any World\n",
      "Album: Katy Lied\n",
      "Artist: steely dan\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/beatles/Beatles_For_Sale/10-Honey_Don_t.wav\n",
      "Title: Honey Don t\n",
      "Album: Beatles For Sale\n",
      "Artist: beatles\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/tori_amos/Scarlet_s_Walk/06-Crazy.wav\n",
      "Title: Crazy\n",
      "Album: Scarlet s Walk\n",
      "Artist: tori amos\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/fleetwood_mac/Tusk/05-Sarah_Edit_.wav\n",
      "Title: Sarah Edit \n",
      "Album: Tusk\n",
      "Artist: fleetwood mac\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/fleetwood_mac/Behind_the_Mask/11-Freedom.wav\n",
      "Title: Freedom\n",
      "Album: Behind the Mask\n",
      "Artist: fleetwood mac\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/aerosmith/Toys_In_The_Attic/03-Adam_s_Apple.wav\n",
      "Title: Adam s Apple\n",
      "Album: Toys In The Attic\n",
      "Artist: aerosmith\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/prince/Parade/05-Girls_Boys.wav\n",
      "Title: Girls Boys\n",
      "Album: Parade\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/prince/1999/04-Let_s_Pretend_We_re_Married.wav\n",
      "Title: Let s Pretend We re Married\n",
      "Album: 1999\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/prince/1999/08-Free.wav\n",
      "Title: Free\n",
      "Album: 1999\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/creedence_clearwater_revival/Cosmo_s_Factory/10-I_Heard_It_Through_The_Grapevine.wav\n",
      "Title: I Heard It Through The Grapevine\n",
      "Album: Cosmo s Factory\n",
      "Artist: creedence clearwater revival\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/cure/Disintegration/11-Homesick.wav\n",
      "Title: Homesick\n",
      "Album: Disintegration\n",
      "Artist: cure\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/u2/Zooropa/08-The_First_Time.wav\n",
      "Title: The First Time\n",
      "Album: Zooropa\n",
      "Artist: u2\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/madonna/Bedtime_Stories/09-Sanctuary.wav\n",
      "Title: Sanctuary\n",
      "Album: Bedtime Stories\n",
      "Artist: madonna\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/suzanne_vega/Songs_in_Red_and_Gray/08-Priscilla.wav\n",
      "Title: Priscilla\n",
      "Album: Songs in Red and Gray\n",
      "Artist: suzanne vega\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/radiohead/OK_Computer/06-Karma_Police.wav\n",
      "Title: Karma Police\n",
      "Album: OK Computer\n",
      "Artist: radiohead\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/radiohead/Pablo_Honey/04-Stop_Whispering.wav\n",
      "Title: Stop Whispering\n",
      "Album: Pablo Honey\n",
      "Artist: radiohead\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/radiohead/Amnesiac/05-I_Might_Be_Wrong.wav\n",
      "Title: I Might Be Wrong\n",
      "Album: Amnesiac\n",
      "Artist: radiohead\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/green_day/Nimrod/08-Platypus_I_Hate_You_.wav\n",
      "Title: Platypus I Hate You \n",
      "Album: Nimrod\n",
      "Artist: green day\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/green_day/Dookie/06-Pulling_Teeth.wav\n",
      "Title: Pulling Teeth\n",
      "Album: Dookie\n",
      "Artist: green day\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/green_day/Dookie/02-Having_a_Blast.wav\n",
      "Title: Having a Blast\n",
      "Album: Dookie\n",
      "Artist: green day\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/dave_matthews_band/Crash/09-Lie_In_Our_Graves.wav\n",
      "Title: Lie In Our Graves\n",
      "Album: Crash\n",
      "Artist: dave matthews band\n",
      "Similarity score: 0.23076923076923078\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/dave_matthews_band/Busted_Stuff/10-Big_Eyed_Fish.wav\n",
      "Title: Big Eyed Fish\n",
      "Album: Busted Stuff\n",
      "Artist: dave matthews band\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/depeche_mode/Construction_Time_Again/05-Two_Minute_Warning.wav\n",
      "Title: Two Minute Warning\n",
      "Album: Construction Time Again\n",
      "Artist: depeche mode\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/queen/News_Of_The_World/06-Fight_From_The_Inside.wav\n",
      "Title: Fight From The Inside\n",
      "Album: News Of The World\n",
      "Artist: queen\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track5.wav\n",
      "Matched with: data/mp3s-32k/queen/Sheer_Heart_Attack/10-Misfire.wav\n",
      "Title: Misfire\n",
      "Album: Sheer Heart Attack\n",
      "Artist: queen\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/metallica/Metallica/12-The_Struggle_Within.wav\n",
      "Title: The Struggle Within\n",
      "Album: Metallica\n",
      "Artist: metallica\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/led_zeppelin/Physical_Graffiti_Disc_2_/08-Black_Country_Woman.wav\n",
      "Title: Black Country Woman\n",
      "Album: Physical Graffiti Disc 2 \n",
      "Artist: led zeppelin\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/led_zeppelin/Physical_Graffiti_Disc_2_/02-Bron-Yr-Aur.wav\n",
      "Title: Aur\n",
      "Album: Physical Graffiti Disc 2 \n",
      "Artist: led zeppelin\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/led_zeppelin/Led_Zeppelin_I/08-I_Can_t_Quit_You_Baby.wav\n",
      "Title: I Can t Quit You Baby\n",
      "Album: Led Zeppelin I\n",
      "Artist: led zeppelin\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/led_zeppelin/Led_Zeppelin_III/04-Since_I_ve_Been_Loving_You.wav\n",
      "Title: Since I ve Been Loving You\n",
      "Album: Led Zeppelin III\n",
      "Artist: led zeppelin\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/led_zeppelin/Led_Zeppelin_III/01-Immigrant_Song.wav\n",
      "Title: Immigrant Song\n",
      "Album: Led Zeppelin III\n",
      "Artist: led zeppelin\n",
      "Similarity score: 0.23076923076923078\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/roxette/Joyride/03-fading_like_a_flower_everytime_you_leave_.wav\n",
      "Title: fading like a flower everytime you leave \n",
      "Album: Joyride\n",
      "Artist: roxette\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/beatles/Abbey_Road/16-The_End.wav\n",
      "Title: The End\n",
      "Album: Abbey Road\n",
      "Artist: beatles\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/beatles/Revolver/08-Good_Day_Sunshine.wav\n",
      "Title: Good Day Sunshine\n",
      "Album: Revolver\n",
      "Artist: beatles\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/beatles/The_White_Album_Disc_1/17-Julia.wav\n",
      "Title: Julia\n",
      "Album: The White Album Disc 1\n",
      "Artist: beatles\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/tori_amos/To_Venus_and_Back-Orbiting/01-Bliss.wav\n",
      "Title: Bliss\n",
      "Album: To Venus and Back-Orbiting\n",
      "Artist: tori amos\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/fleetwood_mac/Tusk/05-Sarah_Edit_.wav\n",
      "Title: Sarah Edit \n",
      "Album: Tusk\n",
      "Artist: fleetwood mac\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/fleetwood_mac/Rumours/05-Go_Your_Own_Way.wav\n",
      "Title: Go Your Own Way\n",
      "Album: Rumours\n",
      "Artist: fleetwood mac\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/aerosmith/Get_Your_Wings/02-Lord_Of_The_Thighs.wav\n",
      "Title: Lord Of The Thighs\n",
      "Album: Get Your Wings\n",
      "Artist: aerosmith\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/aerosmith/Aerosmith/02-Somebody.wav\n",
      "Title: Somebody\n",
      "Album: Aerosmith\n",
      "Artist: aerosmith\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/aerosmith/Pump/10-What_It_Takes.wav\n",
      "Title: What It Takes\n",
      "Album: Pump\n",
      "Artist: aerosmith\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/prince/Parade/11-Anotherloverholenyohead.wav\n",
      "Title: Anotherloverholenyohead\n",
      "Album: Parade\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/prince/The_Love_Symbol_Album/12-Arrogance.wav\n",
      "Title: Arrogance\n",
      "Album: The Love Symbol Album\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/prince/The_Love_Symbol_Album/01-My_Name_Is_Prince.wav\n",
      "Title: My Name Is Prince\n",
      "Album: The Love Symbol Album\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/prince/1999/01-1999.wav\n",
      "Title: 1999\n",
      "Album: 1999\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/prince/1999/08-Free.wav\n",
      "Title: Free\n",
      "Album: 1999\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/prince/Purple_Rain/06-When_doves_cry.wav\n",
      "Title: When doves cry\n",
      "Album: Purple Rain\n",
      "Artist: prince\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/creedence_clearwater_revival/Pendulum/07-Hey_Tonight.wav\n",
      "Title: Hey Tonight\n",
      "Album: Pendulum\n",
      "Artist: creedence clearwater revival\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/cure/Kiss_Me_Kiss_Me_Kiss_Me/15-A_Thousand_Hours.wav\n",
      "Title: A Thousand Hours\n",
      "Album: Kiss Me Kiss Me Kiss Me\n",
      "Artist: cure\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/cure/The_Top/03-Wailing_Wall.wav\n",
      "Title: Wailing Wall\n",
      "Album: The Top\n",
      "Artist: cure\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/u2/Achtung_Baby/11-Acrobat.wav\n",
      "Title: Acrobat\n",
      "Album: Achtung Baby\n",
      "Artist: u2\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/u2/The_Joshua_Tree/05-Running_to_Stand_Still.wav\n",
      "Title: Running to Stand Still\n",
      "Album: The Joshua Tree\n",
      "Artist: u2\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/suzanne_vega/Days_of_Open_Hand/01-Tired_Of_Sleeping.wav\n",
      "Title: Tired Of Sleeping\n",
      "Album: Days of Open Hand\n",
      "Artist: suzanne vega\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/suzanne_vega/Suzanne_Vega/07-Some_Journey.wav\n",
      "Title: Some Journey\n",
      "Album: Suzanne Vega\n",
      "Artist: suzanne vega\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/radiohead/OK_Computer/03-Subterranean_Homesick_Alien.wav\n",
      "Title: Subterranean Homesick Alien\n",
      "Album: OK Computer\n",
      "Artist: radiohead\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/radiohead/Pablo_Honey/10-I_Can_t.wav\n",
      "Title: I Can t\n",
      "Album: Pablo Honey\n",
      "Artist: radiohead\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/dave_matthews_band/Stand_Up/01-Dreamgirl.wav\n",
      "Title: Dreamgirl\n",
      "Album: Stand Up\n",
      "Artist: dave matthews band\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/dave_matthews_band/Crash/10-Cry_Freedom.wav\n",
      "Title: Cry Freedom\n",
      "Album: Crash\n",
      "Artist: dave matthews band\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/dave_matthews_band/Everyday/07-What_You_Are.wav\n",
      "Title: What You Are\n",
      "Album: Everyday\n",
      "Artist: dave matthews band\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/depeche_mode/Music_for_the_Masses/12-Never_let_me_down_again_Aggro_mix_.wav\n",
      "Title: Never let me down again Aggro mix \n",
      "Album: Music for the Masses\n",
      "Artist: depeche mode\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/garth_brooks/In_Pieces/01-Standing_Outside_The_Fire.wav\n",
      "Title: Standing Outside The Fire\n",
      "Album: In Pieces\n",
      "Artist: garth brooks\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track7.wav\n",
      "Matched with: data/mp3s-32k/garth_brooks/The_Chase/03-Mr_Right.wav\n",
      "Title: Mr Right\n",
      "Album: The Chase\n",
      "Artist: garth brooks\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track6.wav\n",
      "Matched with: data/mp3s-32k/metallica/Metallica/10-The_God_that_Failed.wav\n",
      "Title: The God that Failed\n",
      "Album: Metallica\n",
      "Artist: metallica\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track6.wav\n",
      "Matched with: data/mp3s-32k/led_zeppelin/Led_Zeppelin_I/05-Your_Time_Is_Gonna_Come.wav\n",
      "Title: Your Time Is Gonna Come\n",
      "Album: Led Zeppelin I\n",
      "Artist: led zeppelin\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track6.wav\n",
      "Matched with: data/mp3s-32k/led_zeppelin/Led_Zeppelin_II/05-Heartbreaker.wav\n",
      "Title: Heartbreaker\n",
      "Album: Led Zeppelin II\n",
      "Artist: led zeppelin\n",
      "Similarity score: 1.0\n",
      "\n",
      "Query: track6.wav\n",
      "Matched with: data/mp3s-32k/steely_dan/The_Royal_Scam/09-The_Royal_Scam.wav\n",
      "Title: The Royal Scam\n",
      "Album: The Royal Scam\n",
      "Artist: steely dan\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track6.wav\n",
      "Matched with: data/mp3s-32k/tori_amos/Scarlet_s_Walk/05-Carbon.wav\n",
      "Title: Carbon\n",
      "Album: Scarlet s Walk\n",
      "Artist: tori amos\n",
      "Similarity score: 0.23076923076923078\n",
      "\n",
      "Query: track6.wav\n",
      "Matched with: data/mp3s-32k/fleetwood_mac/Tango_In_The_Night/05-Tango_In_The_Night.wav\n",
      "Title: Tango In The Night\n",
      "Album: Tango In The Night\n",
      "Artist: fleetwood mac\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track6.wav\n",
      "Matched with: data/mp3s-32k/fleetwood_mac/Rumours/01-Second_Hand_News.wav\n",
      "Title: Second Hand News\n",
      "Album: Rumours\n",
      "Artist: fleetwood mac\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track6.wav\n",
      "Matched with: data/mp3s-32k/aerosmith/Draw_the_Line/05-Bright_Light_Fright.wav\n",
      "Title: Bright Light Fright\n",
      "Album: Draw the Line\n",
      "Artist: aerosmith\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track6.wav\n",
      "Matched with: data/mp3s-32k/aerosmith/Pump/07-My_Girl.wav\n",
      "Title: My Girl\n",
      "Album: Pump\n",
      "Artist: aerosmith\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track6.wav\n",
      "Matched with: data/mp3s-32k/aerosmith/Toys_In_The_Attic/06-Sweet_Emotion.wav\n",
      "Title: Sweet Emotion\n",
      "Album: Toys In The Attic\n",
      "Artist: aerosmith\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track6.wav\n",
      "Matched with: data/mp3s-32k/cure/Kiss_Me_Kiss_Me_Kiss_Me/05-Why_Can_t_I_Be_You_.wav\n",
      "Title: Why Can t I Be You \n",
      "Album: Kiss Me Kiss Me Kiss Me\n",
      "Artist: cure\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track6.wav\n",
      "Matched with: data/mp3s-32k/cure/The_Head_on_the_Door/02-Kyoto_Song.wav\n",
      "Title: Kyoto Song\n",
      "Album: The Head on the Door\n",
      "Artist: cure\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track6.wav\n",
      "Matched with: data/mp3s-32k/green_day/Dookie/05-Welcome_to_Paradise.wav\n",
      "Title: Welcome to Paradise\n",
      "Album: Dookie\n",
      "Artist: green day\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track6.wav\n",
      "Matched with: data/mp3s-32k/dave_matthews_band/Before_These_Crowded_Streets/13-Hidden_Track_2.wav\n",
      "Title: Hidden Track 2\n",
      "Album: Before These Crowded Streets\n",
      "Artist: dave matthews band\n",
      "Similarity score: 0.14285714285714285\n",
      "\n",
      "Query: track6.wav\n",
      "Matched with: data/mp3s-32k/queen/A_Night_At_the_Opera/07-Seaside_Rendezvous.wav\n",
      "Title: Seaside Rendezvous\n",
      "Album: A Night At the Opera\n",
      "Artist: queen\n",
      "Similarity score: 0.14285714285714285\n",
      "\n"
     ]
    }
   ],
   "source": [
    "audio_match(0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Grouping songs together!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from tqdm.notebook import tqdm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "import numpy as np\n",
    "import kmeans\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "from yellowbrick.cluster import KElbowVisualizer\n",
    "from sklearn.datasets import make_blobs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Getting your data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "echonest = pd.read_csv('csv/echonest.csv')\n",
    "features = pd.read_csv('csv/features.csv')\n",
    "tracks = pd.read_csv('csv/tracks.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13129 entries, 0 to 13128\n",
      "Columns: 250 entries, track_id to temporal_features_223\n",
      "dtypes: float64(244), int64(1), object(5)\n",
      "memory usage: 25.0+ MB\n"
     ]
    }
   ],
   "source": [
    "echonest.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 106574 entries, 0 to 106573\n",
      "Columns: 519 entries, track_id to zcr_std_01\n",
      "dtypes: float64(518), int64(1)\n",
      "memory usage: 422.0 MB\n"
     ]
    }
   ],
   "source": [
    "features.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 106574 entries, 0 to 106573\n",
      "Data columns (total 53 columns):\n",
      " #   Column                    Non-Null Count   Dtype  \n",
      "---  ------                    --------------   -----  \n",
      " 0   track_id                  106574 non-null  int64  \n",
      " 1   album_comments            106574 non-null  int64  \n",
      " 2   album_date_created        103045 non-null  object \n",
      " 3   album_date_released       70294 non-null   object \n",
      " 4   album_engineer            15295 non-null   object \n",
      " 5   album_favorites           106574 non-null  int64  \n",
      " 6   album_id                  106574 non-null  int64  \n",
      " 7   album_information         83149 non-null   object \n",
      " 8   album_listens             106574 non-null  int64  \n",
      " 9   album_producer            18060 non-null   object \n",
      " 10  album_tags                106574 non-null  object \n",
      " 11  album_title               105549 non-null  object \n",
      " 12  album_tracks              106574 non-null  int64  \n",
      " 13  album_type                100066 non-null  object \n",
      " 14  artist_active_year_begin  22711 non-null   object \n",
      " 15  artist_active_year_end    5375 non-null    object \n",
      " 16  artist_associated_labels  14271 non-null   object \n",
      " 17  artist_bio                71156 non-null   object \n",
      " 18  artist_comments           106574 non-null  int64  \n",
      " 19  artist_date_created       105718 non-null  object \n",
      " 20  artist_favorites          106574 non-null  int64  \n",
      " 21  artist_id                 106574 non-null  int64  \n",
      " 22  artist_latitude           44544 non-null   float64\n",
      " 23  artist_location           70210 non-null   object \n",
      " 24  artist_longitude          44544 non-null   float64\n",
      " 25  artist_members            46849 non-null   object \n",
      " 26  artist_name               106574 non-null  object \n",
      " 27  artist_related_projects   13152 non-null   object \n",
      " 28  artist_tags               106574 non-null  object \n",
      " 29  artist_website            79256 non-null   object \n",
      " 30  artist_wikipedia_page     5581 non-null    object \n",
      " 31  set_split                 106574 non-null  object \n",
      " 32  set_subset                106574 non-null  object \n",
      " 33  track_bit_rate            106574 non-null  int64  \n",
      " 34  track_comments            106574 non-null  int64  \n",
      " 35  track_composer            3670 non-null    object \n",
      " 36  track_date_created        106574 non-null  object \n",
      " 37  track_date_recorded       6159 non-null    object \n",
      " 38  track_duration            106574 non-null  int64  \n",
      " 39  track_favorites           106574 non-null  int64  \n",
      " 40  track_genre_top           49598 non-null   object \n",
      " 41  track_genres              106574 non-null  object \n",
      " 42  track_genres_all          106574 non-null  object \n",
      " 43  track_information         2349 non-null    object \n",
      " 44  track_interest            106574 non-null  int64  \n",
      " 45  track_language_code       15024 non-null   object \n",
      " 46  track_license             106487 non-null  object \n",
      " 47  track_listens             106574 non-null  int64  \n",
      " 48  track_lyricist            311 non-null     object \n",
      " 49  track_number              106574 non-null  int64  \n",
      " 50  track_publisher           1263 non-null    object \n",
      " 51  track_tags                106574 non-null  object \n",
      " 52  track_title               106573 non-null  object \n",
      "dtypes: float64(2), int64(16), object(35)\n",
      "memory usage: 43.1+ MB\n"
     ]
    }
   ],
   "source": [
    "tracks.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "set_echonest = set(echonest.track_id.to_list())\n",
    "set_features = set(features.track_id.to_list())\n",
    "set_tracks = set(tracks.track_id.to_list())\n",
    "echo_feat = set_echonest.issubset(set_features)\n",
    "echo_trac = set_echonest.issubset(set_tracks)\n",
    "print(echo_feat)\n",
    "print(echo_trac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge\n",
    "new_df = pd.merge(echonest, features, on='track_id', how='inner')\n",
    "main_df = pd.merge(new_df, tracks, on='track_id', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>This World</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>0.951670</td>\n",
       "      <td>0.658179</td>\n",
       "      <td>0.924525</td>\n",
       "      <td>0.965427</td>\n",
       "      <td>0.115474</td>\n",
       "      <td>0.032985</td>\n",
       "      <td>111.562</td>\n",
       "      <td>0.963590</td>\n",
       "      <td>2008-03-11</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54881</td>\n",
       "      <td>en</td>\n",
       "      <td>Attribution-NonCommercial-NoDerivatives (aka M...</td>\n",
       "      <td>50135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>Freeway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>134</td>\n",
       "      <td>0.452217</td>\n",
       "      <td>0.513238</td>\n",
       "      <td>0.560410</td>\n",
       "      <td>0.019443</td>\n",
       "      <td>0.096567</td>\n",
       "      <td>0.525519</td>\n",
       "      <td>114.290</td>\n",
       "      <td>0.894072</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1126</td>\n",
       "      <td>en</td>\n",
       "      <td>Attribution-NonCommercial-ShareAlike 3.0 Inter...</td>\n",
       "      <td>943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>Street Music</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 820 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   track_id  audio_features_acousticness  audio_features_danceability  \\\n",
       "0         2                     0.416675                     0.675894   \n",
       "1         3                     0.374408                     0.528643   \n",
       "2         5                     0.043567                     0.745566   \n",
       "3        10                     0.951670                     0.658179   \n",
       "4       134                     0.452217                     0.513238   \n",
       "\n",
       "   audio_features_energy  audio_features_instrumentalness  \\\n",
       "0               0.634476                         0.010628   \n",
       "1               0.817461                         0.001851   \n",
       "2               0.701470                         0.000697   \n",
       "3               0.924525                         0.965427   \n",
       "4               0.560410                         0.019443   \n",
       "\n",
       "   audio_features_liveness  audio_features_speechiness  audio_features_tempo  \\\n",
       "0                 0.177647                    0.159310               165.922   \n",
       "1                 0.105880                    0.461818               126.957   \n",
       "2                 0.373143                    0.124595               100.260   \n",
       "3                 0.115474                    0.032985               111.562   \n",
       "4                 0.096567                    0.525519               114.290   \n",
       "\n",
       "   audio_features_valence metadata_album_date  ... track_information  \\\n",
       "0                0.576661                 NaN  ...               NaN   \n",
       "1                0.269240                 NaN  ...               NaN   \n",
       "2                0.621661                 NaN  ...               NaN   \n",
       "3                0.963590          2008-03-11  ...               NaN   \n",
       "4                0.894072                 NaN  ...               NaN   \n",
       "\n",
       "   track_interest track_language_code  \\\n",
       "0            4656                  en   \n",
       "1            1470                  en   \n",
       "2            1933                  en   \n",
       "3           54881                  en   \n",
       "4            1126                  en   \n",
       "\n",
       "                                       track_license track_listens  \\\n",
       "0  Attribution-NonCommercial-ShareAlike 3.0 Inter...          1293   \n",
       "1  Attribution-NonCommercial-ShareAlike 3.0 Inter...           514   \n",
       "2  Attribution-NonCommercial-ShareAlike 3.0 Inter...          1151   \n",
       "3  Attribution-NonCommercial-NoDerivatives (aka M...         50135   \n",
       "4  Attribution-NonCommercial-ShareAlike 3.0 Inter...           943   \n",
       "\n",
       "  track_lyricist  track_number  track_publisher  track_tags   track_title  \n",
       "0            NaN             3              NaN          []          Food  \n",
       "1            NaN             4              NaN          []  Electric Ave  \n",
       "2            NaN             6              NaN          []    This World  \n",
       "3            NaN             1              NaN          []       Freeway  \n",
       "4            NaN             5              NaN          []  Street Music  \n",
       "\n",
       "[5 rows x 820 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#519 + 250 + 53 - 2 (track_id duplicates) = 820 columns\n",
    "main_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# main_df.to_csv('csv/main_df.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13129 entries, 0 to 13128\n",
      "Columns: 820 entries, track_id to track_title\n",
      "dtypes: float64(764), int64(16), object(40)\n",
      "memory usage: 82.2+ MB\n"
     ]
    }
   ],
   "source": [
    "main_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Choose your features (variables)!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "echo_cp = echonest.copy()\n",
    "feat_cp = features.copy()\n",
    "tracks_cp = tracks.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### PCA on Echonest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# we want to use the PCA only on the temporal columns, so we need to take them\n",
    "col_temp = [x for x in echo_cp if x.startswith('temporal')]\n",
    "other_col = [x for x in echo_cp.columns.to_list() if x not in col_temp]\n",
    "echo_PCA = echo_cp.loc[:, col_temp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Standardize the Data\n",
    "scaler = StandardScaler()\n",
    "echo_PCA = scaler.fit_transform(echo_PCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Apply PCA\n",
    "pca = PCA(0.90)\n",
    "pc_echo = pca.fit_transform(echo_PCA)\n",
    "pca_num = len(pca.components_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Final Echonest dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "       track_id  audio_features_acousticness  audio_features_danceability  \\\n0             2                     0.416675                     0.675894   \n1             3                     0.374408                     0.528643   \n2             5                     0.043567                     0.745566   \n3            10                     0.951670                     0.658179   \n4           134                     0.452217                     0.513238   \n...         ...                          ...                          ...   \n13124    124857                     0.007592                     0.790364   \n13125    124862                     0.041498                     0.843077   \n13126    124863                     0.000124                     0.609686   \n13127    124864                     0.327576                     0.574426   \n13128    124911                     0.993606                     0.499339   \n\n       audio_features_energy  audio_features_instrumentalness  \\\n0                   0.634476                         0.010628   \n1                   0.817461                         0.001851   \n2                   0.701470                         0.000697   \n3                   0.924525                         0.965427   \n4                   0.560410                         0.019443   \n...                      ...                              ...   \n13124               0.719288                         0.853114   \n13125               0.536496                         0.865151   \n13126               0.895136                         0.846624   \n13127               0.548327                         0.452867   \n13128               0.050622                         0.945677   \n\n       audio_features_liveness  audio_features_speechiness  \\\n0                     0.177647                    0.159310   \n1                     0.105880                    0.461818   \n2                     0.373143                    0.124595   \n3                     0.115474                    0.032985   \n4                     0.096567                    0.525519   \n...                        ...                         ...   \n13124                 0.720715                    0.082550   \n13125                 0.547949                    0.074001   \n13126                 0.632903                    0.051517   \n13127                 0.075928                    0.033388   \n13128                 0.095965                    0.065189   \n\n       audio_features_tempo  audio_features_valence metadata_album_date  ...  \\\n0                   165.922                0.576661                 NaN  ...   \n1                   126.957                0.269240                 NaN  ...   \n2                   100.260                0.621661                 NaN  ...   \n3                   111.562                0.963590          2008-03-11  ...   \n4                   114.290                0.894072                 NaN  ...   \n...                     ...                     ...                 ...  ...   \n13124               141.332                0.890461                 NaN  ...   \n13125               101.975                0.476845                 NaN  ...   \n13126               129.996                0.496667                 NaN  ...   \n13127               142.009                0.569274                 NaN  ...   \n13128               119.965                0.204652          2009-10-23  ...   \n\n      pc_echo_61  pc_echo_62 pc_echo_63  pc_echo_64 pc_echo_65 pc_echo_66  \\\n0       0.355263    0.027481  -0.157763   -0.402039   0.616848   0.214498   \n1       0.646931   -0.710005   0.117975    0.629340   0.497755   0.480609   \n2       1.335908   -0.759942  -1.022644    0.702416  -0.007906  -0.073801   \n3      -0.612253    0.669184  -0.822872    0.816882  -2.254254   0.742618   \n4       0.632782   -0.020439  -0.015756    0.016399   0.546659  -0.994958   \n...          ...         ...        ...         ...        ...        ...   \n13124  -0.050183   -0.170786   0.525707   -0.209289   0.796353  -0.220514   \n13125   1.044994   -0.029680   0.990371    0.127782   0.057740   0.865349   \n13126   0.975914   -0.710217   0.703303   -0.660872  -0.240822  -0.325763   \n13127  -0.236248   -0.152645  -0.093776    0.536602  -0.039933  -0.331019   \n13128  -1.353259   -0.317855   0.638832   -0.704776   0.303844  -0.541205   \n\n       pc_echo_67  pc_echo_68  pc_echo_69  pc_echo_70  \n0       -0.590703   -0.054720   -0.578055   -0.663876  \n1       -0.119040   -1.234916   -1.123355    0.611513  \n2        0.047319   -0.523209   -0.601322    0.187780  \n3        0.557756   -0.347822    1.052869    0.056952  \n4       -0.266806    0.271617   -0.214411   -0.335870  \n...           ...         ...         ...         ...  \n13124    0.137357   -0.066496    0.041413   -0.323454  \n13125    0.514955   -0.238700    0.156817    1.179561  \n13126   -0.718858    0.518075   -0.084971    0.104979  \n13127    0.247020   -0.356199    0.515033    0.661125  \n13128    0.785733   -1.112386    3.207147   -0.362227  \n\n[13129 rows x 97 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\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>track_id</th>\n      <th>audio_features_acousticness</th>\n      <th>audio_features_danceability</th>\n      <th>audio_features_energy</th>\n      <th>audio_features_instrumentalness</th>\n      <th>audio_features_liveness</th>\n      <th>audio_features_speechiness</th>\n      <th>audio_features_tempo</th>\n      <th>audio_features_valence</th>\n      <th>metadata_album_date</th>\n      <th>...</th>\n      <th>pc_echo_61</th>\n      <th>pc_echo_62</th>\n      <th>pc_echo_63</th>\n      <th>pc_echo_64</th>\n      <th>pc_echo_65</th>\n      <th>pc_echo_66</th>\n      <th>pc_echo_67</th>\n      <th>pc_echo_68</th>\n      <th>pc_echo_69</th>\n      <th>pc_echo_70</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2</td>\n      <td>0.416675</td>\n      <td>0.675894</td>\n      <td>0.634476</td>\n      <td>0.010628</td>\n      <td>0.177647</td>\n      <td>0.159310</td>\n      <td>165.922</td>\n      <td>0.576661</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>0.355263</td>\n      <td>0.027481</td>\n      <td>-0.157763</td>\n      <td>-0.402039</td>\n      <td>0.616848</td>\n      <td>0.214498</td>\n      <td>-0.590703</td>\n      <td>-0.054720</td>\n      <td>-0.578055</td>\n      <td>-0.663876</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3</td>\n      <td>0.374408</td>\n      <td>0.528643</td>\n      <td>0.817461</td>\n      <td>0.001851</td>\n      <td>0.105880</td>\n      <td>0.461818</td>\n      <td>126.957</td>\n      <td>0.269240</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>0.646931</td>\n      <td>-0.710005</td>\n      <td>0.117975</td>\n      <td>0.629340</td>\n      <td>0.497755</td>\n      <td>0.480609</td>\n      <td>-0.119040</td>\n      <td>-1.234916</td>\n      <td>-1.123355</td>\n      <td>0.611513</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5</td>\n      <td>0.043567</td>\n      <td>0.745566</td>\n      <td>0.701470</td>\n      <td>0.000697</td>\n      <td>0.373143</td>\n      <td>0.124595</td>\n      <td>100.260</td>\n      <td>0.621661</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>1.335908</td>\n      <td>-0.759942</td>\n      <td>-1.022644</td>\n      <td>0.702416</td>\n      <td>-0.007906</td>\n      <td>-0.073801</td>\n      <td>0.047319</td>\n      <td>-0.523209</td>\n      <td>-0.601322</td>\n      <td>0.187780</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10</td>\n      <td>0.951670</td>\n      <td>0.658179</td>\n      <td>0.924525</td>\n      <td>0.965427</td>\n      <td>0.115474</td>\n      <td>0.032985</td>\n      <td>111.562</td>\n      <td>0.963590</td>\n      <td>2008-03-11</td>\n      <td>...</td>\n      <td>-0.612253</td>\n      <td>0.669184</td>\n      <td>-0.822872</td>\n      <td>0.816882</td>\n      <td>-2.254254</td>\n      <td>0.742618</td>\n      <td>0.557756</td>\n      <td>-0.347822</td>\n      <td>1.052869</td>\n      <td>0.056952</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>134</td>\n      <td>0.452217</td>\n      <td>0.513238</td>\n      <td>0.560410</td>\n      <td>0.019443</td>\n      <td>0.096567</td>\n      <td>0.525519</td>\n      <td>114.290</td>\n      <td>0.894072</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>0.632782</td>\n      <td>-0.020439</td>\n      <td>-0.015756</td>\n      <td>0.016399</td>\n      <td>0.546659</td>\n      <td>-0.994958</td>\n      <td>-0.266806</td>\n      <td>0.271617</td>\n      <td>-0.214411</td>\n      <td>-0.335870</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>13124</th>\n      <td>124857</td>\n      <td>0.007592</td>\n      <td>0.790364</td>\n      <td>0.719288</td>\n      <td>0.853114</td>\n      <td>0.720715</td>\n      <td>0.082550</td>\n      <td>141.332</td>\n      <td>0.890461</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>-0.050183</td>\n      <td>-0.170786</td>\n      <td>0.525707</td>\n      <td>-0.209289</td>\n      <td>0.796353</td>\n      <td>-0.220514</td>\n      <td>0.137357</td>\n      <td>-0.066496</td>\n      <td>0.041413</td>\n      <td>-0.323454</td>\n    </tr>\n    <tr>\n      <th>13125</th>\n      <td>124862</td>\n      <td>0.041498</td>\n      <td>0.843077</td>\n      <td>0.536496</td>\n      <td>0.865151</td>\n      <td>0.547949</td>\n      <td>0.074001</td>\n      <td>101.975</td>\n      <td>0.476845</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>1.044994</td>\n      <td>-0.029680</td>\n      <td>0.990371</td>\n      <td>0.127782</td>\n      <td>0.057740</td>\n      <td>0.865349</td>\n      <td>0.514955</td>\n      <td>-0.238700</td>\n      <td>0.156817</td>\n      <td>1.179561</td>\n    </tr>\n    <tr>\n      <th>13126</th>\n      <td>124863</td>\n      <td>0.000124</td>\n      <td>0.609686</td>\n      <td>0.895136</td>\n      <td>0.846624</td>\n      <td>0.632903</td>\n      <td>0.051517</td>\n      <td>129.996</td>\n      <td>0.496667</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>0.975914</td>\n      <td>-0.710217</td>\n      <td>0.703303</td>\n      <td>-0.660872</td>\n      <td>-0.240822</td>\n      <td>-0.325763</td>\n      <td>-0.718858</td>\n      <td>0.518075</td>\n      <td>-0.084971</td>\n      <td>0.104979</td>\n    </tr>\n    <tr>\n      <th>13127</th>\n      <td>124864</td>\n      <td>0.327576</td>\n      <td>0.574426</td>\n      <td>0.548327</td>\n      <td>0.452867</td>\n      <td>0.075928</td>\n      <td>0.033388</td>\n      <td>142.009</td>\n      <td>0.569274</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>-0.236248</td>\n      <td>-0.152645</td>\n      <td>-0.093776</td>\n      <td>0.536602</td>\n      <td>-0.039933</td>\n      <td>-0.331019</td>\n      <td>0.247020</td>\n      <td>-0.356199</td>\n      <td>0.515033</td>\n      <td>0.661125</td>\n    </tr>\n    <tr>\n      <th>13128</th>\n      <td>124911</td>\n      <td>0.993606</td>\n      <td>0.499339</td>\n      <td>0.050622</td>\n      <td>0.945677</td>\n      <td>0.095965</td>\n      <td>0.065189</td>\n      <td>119.965</td>\n      <td>0.204652</td>\n      <td>2009-10-23</td>\n      <td>...</td>\n      <td>-1.353259</td>\n      <td>-0.317855</td>\n      <td>0.638832</td>\n      <td>-0.704776</td>\n      <td>0.303844</td>\n      <td>-0.541205</td>\n      <td>0.785733</td>\n      <td>-1.112386</td>\n      <td>3.207147</td>\n      <td>-0.362227</td>\n    </tr>\n  </tbody>\n</table>\n<p>13129 rows × 97 columns</p>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pc_echo_df = pd.DataFrame(data=pc_echo, columns=['pc_echo_'+str(i) for i in range(pca_num)])\n",
    "echo_final = pd.concat([echo_cp[other_col], pc_echo_df], axis=1)\n",
    "echo_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With the PCA, we had a reduction of the temporal columns of echonest dataset from 224 to 71\n"
     ]
    }
   ],
   "source": [
    "print(f\"With the PCA, we had a reduction of the temporal columns of echonest dataset from {len(col_temp)} to {pca_num}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13129 entries, 0 to 13128\n",
      "Data columns (total 97 columns):\n",
      " #   Column                              Non-Null Count  Dtype  \n",
      "---  ------                              --------------  -----  \n",
      " 0   track_id                            13129 non-null  int64  \n",
      " 1   audio_features_acousticness         13129 non-null  float64\n",
      " 2   audio_features_danceability         13129 non-null  float64\n",
      " 3   audio_features_energy               13129 non-null  float64\n",
      " 4   audio_features_instrumentalness     13129 non-null  float64\n",
      " 5   audio_features_liveness             13129 non-null  float64\n",
      " 6   audio_features_speechiness          13129 non-null  float64\n",
      " 7   audio_features_tempo                13129 non-null  float64\n",
      " 8   audio_features_valence              13129 non-null  float64\n",
      " 9   metadata_album_date                 2717 non-null   object \n",
      " 10  metadata_album_name                 2872 non-null   object \n",
      " 11  metadata_artist_latitude            9770 non-null   float64\n",
      " 12  metadata_artist_location            9770 non-null   object \n",
      " 13  metadata_artist_longitude           9770 non-null   float64\n",
      " 14  metadata_artist_name                13129 non-null  object \n",
      " 15  metadata_release                    13129 non-null  object \n",
      " 16  ranks_artist_discovery_rank         2825 non-null   float64\n",
      " 17  ranks_artist_familiarity_rank       2825 non-null   float64\n",
      " 18  ranks_artist_hotttnesss_rank        2824 non-null   float64\n",
      " 19  ranks_song_currency_rank            2033 non-null   float64\n",
      " 20  ranks_song_hotttnesss_rank          2206 non-null   float64\n",
      " 21  social_features_artist_discovery    13129 non-null  float64\n",
      " 22  social_features_artist_familiarity  13129 non-null  float64\n",
      " 23  social_features_artist_hotttnesss   13129 non-null  float64\n",
      " 24  social_features_song_currency       13129 non-null  float64\n",
      " 25  social_features_song_hotttnesss     13129 non-null  float64\n",
      " 26  pc_echo_0                           13129 non-null  float64\n",
      " 27  pc_echo_1                           13129 non-null  float64\n",
      " 28  pc_echo_2                           13129 non-null  float64\n",
      " 29  pc_echo_3                           13129 non-null  float64\n",
      " 30  pc_echo_4                           13129 non-null  float64\n",
      " 31  pc_echo_5                           13129 non-null  float64\n",
      " 32  pc_echo_6                           13129 non-null  float64\n",
      " 33  pc_echo_7                           13129 non-null  float64\n",
      " 34  pc_echo_8                           13129 non-null  float64\n",
      " 35  pc_echo_9                           13129 non-null  float64\n",
      " 36  pc_echo_10                          13129 non-null  float64\n",
      " 37  pc_echo_11                          13129 non-null  float64\n",
      " 38  pc_echo_12                          13129 non-null  float64\n",
      " 39  pc_echo_13                          13129 non-null  float64\n",
      " 40  pc_echo_14                          13129 non-null  float64\n",
      " 41  pc_echo_15                          13129 non-null  float64\n",
      " 42  pc_echo_16                          13129 non-null  float64\n",
      " 43  pc_echo_17                          13129 non-null  float64\n",
      " 44  pc_echo_18                          13129 non-null  float64\n",
      " 45  pc_echo_19                          13129 non-null  float64\n",
      " 46  pc_echo_20                          13129 non-null  float64\n",
      " 47  pc_echo_21                          13129 non-null  float64\n",
      " 48  pc_echo_22                          13129 non-null  float64\n",
      " 49  pc_echo_23                          13129 non-null  float64\n",
      " 50  pc_echo_24                          13129 non-null  float64\n",
      " 51  pc_echo_25                          13129 non-null  float64\n",
      " 52  pc_echo_26                          13129 non-null  float64\n",
      " 53  pc_echo_27                          13129 non-null  float64\n",
      " 54  pc_echo_28                          13129 non-null  float64\n",
      " 55  pc_echo_29                          13129 non-null  float64\n",
      " 56  pc_echo_30                          13129 non-null  float64\n",
      " 57  pc_echo_31                          13129 non-null  float64\n",
      " 58  pc_echo_32                          13129 non-null  float64\n",
      " 59  pc_echo_33                          13129 non-null  float64\n",
      " 60  pc_echo_34                          13129 non-null  float64\n",
      " 61  pc_echo_35                          13129 non-null  float64\n",
      " 62  pc_echo_36                          13129 non-null  float64\n",
      " 63  pc_echo_37                          13129 non-null  float64\n",
      " 64  pc_echo_38                          13129 non-null  float64\n",
      " 65  pc_echo_39                          13129 non-null  float64\n",
      " 66  pc_echo_40                          13129 non-null  float64\n",
      " 67  pc_echo_41                          13129 non-null  float64\n",
      " 68  pc_echo_42                          13129 non-null  float64\n",
      " 69  pc_echo_43                          13129 non-null  float64\n",
      " 70  pc_echo_44                          13129 non-null  float64\n",
      " 71  pc_echo_45                          13129 non-null  float64\n",
      " 72  pc_echo_46                          13129 non-null  float64\n",
      " 73  pc_echo_47                          13129 non-null  float64\n",
      " 74  pc_echo_48                          13129 non-null  float64\n",
      " 75  pc_echo_49                          13129 non-null  float64\n",
      " 76  pc_echo_50                          13129 non-null  float64\n",
      " 77  pc_echo_51                          13129 non-null  float64\n",
      " 78  pc_echo_52                          13129 non-null  float64\n",
      " 79  pc_echo_53                          13129 non-null  float64\n",
      " 80  pc_echo_54                          13129 non-null  float64\n",
      " 81  pc_echo_55                          13129 non-null  float64\n",
      " 82  pc_echo_56                          13129 non-null  float64\n",
      " 83  pc_echo_57                          13129 non-null  float64\n",
      " 84  pc_echo_58                          13129 non-null  float64\n",
      " 85  pc_echo_59                          13129 non-null  float64\n",
      " 86  pc_echo_60                          13129 non-null  float64\n",
      " 87  pc_echo_61                          13129 non-null  float64\n",
      " 88  pc_echo_62                          13129 non-null  float64\n",
      " 89  pc_echo_63                          13129 non-null  float64\n",
      " 90  pc_echo_64                          13129 non-null  float64\n",
      " 91  pc_echo_65                          13129 non-null  float64\n",
      " 92  pc_echo_66                          13129 non-null  float64\n",
      " 93  pc_echo_67                          13129 non-null  float64\n",
      " 94  pc_echo_68                          13129 non-null  float64\n",
      " 95  pc_echo_69                          13129 non-null  float64\n",
      " 96  pc_echo_70                          13129 non-null  float64\n",
      "dtypes: float64(91), int64(1), object(5)\n",
      "memory usage: 9.7+ MB\n"
     ]
    }
   ],
   "source": [
    "echo_final.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### PCA on Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The features dataset has so many columns, and since some of them have a string on their name in common, we are going to apply the PCA separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "col_temp_chroma = [x for x in feat_cp if x.startswith('chroma')]\n",
    "col_temp_mfcc = [x for x in feat_cp if x.startswith('mfcc')]\n",
    "col_temp_rmse = [x for x in feat_cp if x.startswith('rmse')]\n",
    "col_temp_spectral = [x for x in feat_cp if x.startswith('spectral')]\n",
    "col_temp_tonnetz = [x for x in feat_cp if x.startswith('tonnetz')]\n",
    "col_temp_zcr = [x for x in feat_cp if x.startswith('zcr')]\n",
    "col_temp = col_temp_chroma + col_temp_mfcc + col_temp_rmse + col_temp_spectral + col_temp_tonnetz + col_temp_zcr\n",
    "other_col = [x for x in feat_cp.columns.to_list() if x not in col_temp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# chroma column\n",
    "feat_PCA_chroma = feat_cp.loc[:, col_temp_chroma]\n",
    "scaler1 = StandardScaler()\n",
    "feat_PCA_chroma = scaler1.fit_transform(feat_PCA_chroma)\n",
    "\n",
    "# mfcc column\n",
    "feat_PCA_mfcc = feat_cp.loc[:, col_temp_mfcc]\n",
    "scaler2 = StandardScaler()\n",
    "feat_PCA_mfcc = scaler2.fit_transform(feat_PCA_mfcc)\n",
    "\n",
    "# rmse column\n",
    "feat_PCA_rmse = feat_cp.loc[:, col_temp_rmse]\n",
    "scaler3 = StandardScaler()\n",
    "feat_PCA_rmse = scaler3.fit_transform(feat_PCA_rmse)\n",
    "\n",
    "# spectral column\n",
    "feat_PCA_spectral = feat_cp.loc[:, col_temp_spectral]\n",
    "scaler4 = StandardScaler()\n",
    "feat_PCA_spectral = scaler4.fit_transform(feat_PCA_spectral)\n",
    "\n",
    "# tonnetz column\n",
    "feat_PCA_tonnetz = feat_cp.loc[:, col_temp_tonnetz]\n",
    "scaler5 = StandardScaler()\n",
    "feat_PCA_tonnetz = scaler5.fit_transform(feat_PCA_tonnetz)\n",
    "\n",
    "# zcr column\n",
    "feat_PCA_zcr = feat_cp.loc[:, col_temp_zcr]\n",
    "scaler6 = StandardScaler()\n",
    "feat_PCA_zcr = scaler6.fit_transform(feat_PCA_zcr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# chroma column\n",
    "pca1 = PCA(0.90)\n",
    "pc_feat_chroma = pca1.fit_transform(feat_PCA_chroma)\n",
    "pca_num_chroma = len(pca1.components_)\n",
    "\n",
    "# mfcc column\n",
    "pca2 = PCA(0.90)\n",
    "pc_feat_mfcc = pca2.fit_transform(feat_PCA_mfcc)\n",
    "pca_num_mfcc = len(pca2.components_)\n",
    "\n",
    "# rmse column\n",
    "pca3 = PCA(0.90)\n",
    "pc_feat_rmse = pca3.fit_transform(feat_PCA_rmse)\n",
    "pca_num_rmse = len(pca3.components_)\n",
    "\n",
    "# spectral column\n",
    "pca4 = PCA(0.90)\n",
    "pc_feat_spectral = pca4.fit_transform(feat_PCA_spectral)\n",
    "pca_num_spectral = len(pca4.components_)\n",
    "\n",
    "# tonnetz column\n",
    "pca5 = PCA(0.90)\n",
    "pc_feat_tonnetz = pca5.fit_transform(feat_PCA_tonnetz)\n",
    "pca_num_tonnetz = len(pca5.components_)\n",
    "\n",
    "# zcr column\n",
    "pca6 = PCA(0.90)\n",
    "pc_feat_zcr = pca6.fit_transform(feat_PCA_zcr)\n",
    "pca_num_zcr = len(pca6.components_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# chroma column\n",
    "pc_feat_chroma_df = pd.DataFrame(data=pc_feat_chroma, columns=['pc_feat_chroma_'+str(i) for i in range(pca_num_chroma)])\n",
    "\n",
    "# mfcc column\n",
    "pc_feat_mfcc_df = pd.DataFrame(data=pc_feat_mfcc, columns=['pc_feat_mfcc_'+str(i) for i in range(pca_num_mfcc)])\n",
    "\n",
    "# rmse column\n",
    "pc_feat_rmse_df = pd.DataFrame(data=pc_feat_rmse, columns=['pc_feat_rmse_'+str(i) for i in range(pca_num_rmse)])\n",
    "\n",
    "# spectral column\n",
    "pc_feat_spectral_df = pd.DataFrame(data=pc_feat_spectral, columns=['pc_feat_spectral_'+str(i) for i in range(pca_num_spectral)])\n",
    "\n",
    "# tonnetz column\n",
    "pc_feat_tonnetz_df = pd.DataFrame(data=pc_feat_tonnetz, columns=['pc_feat_tonnetz_'+str(i) for i in range(pca_num_tonnetz)])\n",
    "\n",
    "# zcr column\n",
    "pc_feat_zcr_df = pd.DataFrame(data=pc_feat_zcr, columns=['pc_feat_zcr_'+str(i) for i in range(pca_num_zcr)])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Final Features dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "        track_id  pc_feat_chroma_0  pc_feat_chroma_1  pc_feat_chroma_2  \\\n0              2          0.775576          0.363594          1.361543   \n1              3          2.396017         -2.216482         -5.514670   \n2              5          0.238587         -0.583145         -3.281558   \n3             10          6.340997          1.307150         -4.068519   \n4             20         -1.902813         -1.745450         -2.876881   \n...          ...               ...               ...               ...   \n106569    155316          1.563061         -1.342380          6.758747   \n106570    155317          0.976325         -4.937334          2.160015   \n106571    155318          3.719993         -4.143312          2.385685   \n106572    155319          4.565423         -3.903901          0.804858   \n106573    155320          3.451314         -2.755545         -0.698787   \n\n        pc_feat_chroma_3  pc_feat_chroma_4  pc_feat_chroma_5  \\\n0              -3.699529         -6.147773         -5.360282   \n1              -0.510122          1.642152         -3.034927   \n2              -1.874630         -1.639829         -0.529630   \n3              -2.896113          1.793527         -2.828162   \n4               2.710228         -0.255238          0.354165   \n...                  ...               ...               ...   \n106569         -1.639735          4.023439          2.421908   \n106570          2.554928         -2.190888          6.302024   \n106571         -1.277097          1.464155          1.783208   \n106572          0.306852          1.538969         -1.027913   \n106573          2.824309          0.947577          2.939001   \n\n        pc_feat_chroma_6  pc_feat_chroma_7  pc_feat_chroma_8  ...  \\\n0              -1.897546          2.495594         -0.448821  ...   \n1              -3.248663          2.734878         -0.931649  ...   \n2              -2.849928          1.959087         -0.719294  ...   \n3              -2.905909          1.605703         -0.014508  ...   \n4              -0.259853         -0.680845          1.649494  ...   \n...                  ...               ...               ...  ...   \n106569          6.661562         -0.091210         -1.808362  ...   \n106570         -0.158702          1.094481         -0.163301  ...   \n106571          3.163242          1.525225         -0.570905  ...   \n106572          0.382135          0.735833          0.115947  ...   \n106573          2.222361         -0.642788          0.764591  ...   \n\n        pc_feat_tonnetz_13  pc_feat_tonnetz_14  pc_feat_tonnetz_15  \\\n0                 1.247071           -1.481287           -0.974557   \n1                -1.893135           -0.590861            0.539187   \n2                -1.393036           -3.392923            0.067738   \n3                -0.723196           -0.966325           -1.228191   \n4                -0.820243           -0.194318           -0.568280   \n...                    ...                 ...                 ...   \n106569           -0.259396            0.601070            0.542979   \n106570            0.833649            1.390238           -0.092980   \n106571            0.379637            0.132028           -0.427939   \n106572            0.305969           -0.487434            0.108620   \n106573            0.237984           -0.550480            0.556238   \n\n        pc_feat_tonnetz_16  pc_feat_tonnetz_17  pc_feat_tonnetz_18  \\\n0                 1.478836           -0.802692           -1.134505   \n1                 0.068886           -0.248310           -0.548751   \n2                 2.889566           -2.384271           -1.169644   \n3                -2.836012            0.106454            0.514897   \n4                -1.378036            0.150246           -0.751232   \n...                    ...                 ...                 ...   \n106569            0.643495           -0.633165            0.268631   \n106570           -0.301943            0.478629           -0.241331   \n106571           -0.718780           -0.895336           -0.217903   \n106572           -0.486189           -0.727463            0.095465   \n106573            0.003327           -0.579678           -0.208322   \n\n        pc_feat_zcr_0  pc_feat_zcr_1  pc_feat_zcr_2  pc_feat_zcr_3  \n0           -1.545477       0.216026      -0.536895       0.496658  \n1           -1.577371       0.246390      -0.750121       0.305139  \n2           -0.114501      -0.275687      -0.554619       0.115641  \n3           -0.904230       0.286333      -0.133111       0.686239  \n4            0.209431       0.075854      -0.298410      -0.085887  \n...               ...            ...            ...            ...  \n106569       1.002904      -1.588391       0.458332      -0.144659  \n106570       0.918410      -1.372796       0.139755      -0.050931  \n106571       0.555178      -1.416186       0.351057      -0.013998  \n106572       0.557101      -1.318067       0.685069      -0.064413  \n106573      -2.366025       2.875471      -1.516451      -1.632513  \n\n[106574 rows x 168 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\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>track_id</th>\n      <th>pc_feat_chroma_0</th>\n      <th>pc_feat_chroma_1</th>\n      <th>pc_feat_chroma_2</th>\n      <th>pc_feat_chroma_3</th>\n      <th>pc_feat_chroma_4</th>\n      <th>pc_feat_chroma_5</th>\n      <th>pc_feat_chroma_6</th>\n      <th>pc_feat_chroma_7</th>\n      <th>pc_feat_chroma_8</th>\n      <th>...</th>\n      <th>pc_feat_tonnetz_13</th>\n      <th>pc_feat_tonnetz_14</th>\n      <th>pc_feat_tonnetz_15</th>\n      <th>pc_feat_tonnetz_16</th>\n      <th>pc_feat_tonnetz_17</th>\n      <th>pc_feat_tonnetz_18</th>\n      <th>pc_feat_zcr_0</th>\n      <th>pc_feat_zcr_1</th>\n      <th>pc_feat_zcr_2</th>\n      <th>pc_feat_zcr_3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2</td>\n      <td>0.775576</td>\n      <td>0.363594</td>\n      <td>1.361543</td>\n      <td>-3.699529</td>\n      <td>-6.147773</td>\n      <td>-5.360282</td>\n      <td>-1.897546</td>\n      <td>2.495594</td>\n      <td>-0.448821</td>\n      <td>...</td>\n      <td>1.247071</td>\n      <td>-1.481287</td>\n      <td>-0.974557</td>\n      <td>1.478836</td>\n      <td>-0.802692</td>\n      <td>-1.134505</td>\n      <td>-1.545477</td>\n      <td>0.216026</td>\n      <td>-0.536895</td>\n      <td>0.496658</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3</td>\n      <td>2.396017</td>\n      <td>-2.216482</td>\n      <td>-5.514670</td>\n      <td>-0.510122</td>\n      <td>1.642152</td>\n      <td>-3.034927</td>\n      <td>-3.248663</td>\n      <td>2.734878</td>\n      <td>-0.931649</td>\n      <td>...</td>\n      <td>-1.893135</td>\n      <td>-0.590861</td>\n      <td>0.539187</td>\n      <td>0.068886</td>\n      <td>-0.248310</td>\n      <td>-0.548751</td>\n      <td>-1.577371</td>\n      <td>0.246390</td>\n      <td>-0.750121</td>\n      <td>0.305139</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5</td>\n      <td>0.238587</td>\n      <td>-0.583145</td>\n      <td>-3.281558</td>\n      <td>-1.874630</td>\n      <td>-1.639829</td>\n      <td>-0.529630</td>\n      <td>-2.849928</td>\n      <td>1.959087</td>\n      <td>-0.719294</td>\n      <td>...</td>\n      <td>-1.393036</td>\n      <td>-3.392923</td>\n      <td>0.067738</td>\n      <td>2.889566</td>\n      <td>-2.384271</td>\n      <td>-1.169644</td>\n      <td>-0.114501</td>\n      <td>-0.275687</td>\n      <td>-0.554619</td>\n      <td>0.115641</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10</td>\n      <td>6.340997</td>\n      <td>1.307150</td>\n      <td>-4.068519</td>\n      <td>-2.896113</td>\n      <td>1.793527</td>\n      <td>-2.828162</td>\n      <td>-2.905909</td>\n      <td>1.605703</td>\n      <td>-0.014508</td>\n      <td>...</td>\n      <td>-0.723196</td>\n      <td>-0.966325</td>\n      <td>-1.228191</td>\n      <td>-2.836012</td>\n      <td>0.106454</td>\n      <td>0.514897</td>\n      <td>-0.904230</td>\n      <td>0.286333</td>\n      <td>-0.133111</td>\n      <td>0.686239</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>20</td>\n      <td>-1.902813</td>\n      <td>-1.745450</td>\n      <td>-2.876881</td>\n      <td>2.710228</td>\n      <td>-0.255238</td>\n      <td>0.354165</td>\n      <td>-0.259853</td>\n      <td>-0.680845</td>\n      <td>1.649494</td>\n      <td>...</td>\n      <td>-0.820243</td>\n      <td>-0.194318</td>\n      <td>-0.568280</td>\n      <td>-1.378036</td>\n      <td>0.150246</td>\n      <td>-0.751232</td>\n      <td>0.209431</td>\n      <td>0.075854</td>\n      <td>-0.298410</td>\n      <td>-0.085887</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>106569</th>\n      <td>155316</td>\n      <td>1.563061</td>\n      <td>-1.342380</td>\n      <td>6.758747</td>\n      <td>-1.639735</td>\n      <td>4.023439</td>\n      <td>2.421908</td>\n      <td>6.661562</td>\n      <td>-0.091210</td>\n      <td>-1.808362</td>\n      <td>...</td>\n      <td>-0.259396</td>\n      <td>0.601070</td>\n      <td>0.542979</td>\n      <td>0.643495</td>\n      <td>-0.633165</td>\n      <td>0.268631</td>\n      <td>1.002904</td>\n      <td>-1.588391</td>\n      <td>0.458332</td>\n      <td>-0.144659</td>\n    </tr>\n    <tr>\n      <th>106570</th>\n      <td>155317</td>\n      <td>0.976325</td>\n      <td>-4.937334</td>\n      <td>2.160015</td>\n      <td>2.554928</td>\n      <td>-2.190888</td>\n      <td>6.302024</td>\n      <td>-0.158702</td>\n      <td>1.094481</td>\n      <td>-0.163301</td>\n      <td>...</td>\n      <td>0.833649</td>\n      <td>1.390238</td>\n      <td>-0.092980</td>\n      <td>-0.301943</td>\n      <td>0.478629</td>\n      <td>-0.241331</td>\n      <td>0.918410</td>\n      <td>-1.372796</td>\n      <td>0.139755</td>\n      <td>-0.050931</td>\n    </tr>\n    <tr>\n      <th>106571</th>\n      <td>155318</td>\n      <td>3.719993</td>\n      <td>-4.143312</td>\n      <td>2.385685</td>\n      <td>-1.277097</td>\n      <td>1.464155</td>\n      <td>1.783208</td>\n      <td>3.163242</td>\n      <td>1.525225</td>\n      <td>-0.570905</td>\n      <td>...</td>\n      <td>0.379637</td>\n      <td>0.132028</td>\n      <td>-0.427939</td>\n      <td>-0.718780</td>\n      <td>-0.895336</td>\n      <td>-0.217903</td>\n      <td>0.555178</td>\n      <td>-1.416186</td>\n      <td>0.351057</td>\n      <td>-0.013998</td>\n    </tr>\n    <tr>\n      <th>106572</th>\n      <td>155319</td>\n      <td>4.565423</td>\n      <td>-3.903901</td>\n      <td>0.804858</td>\n      <td>0.306852</td>\n      <td>1.538969</td>\n      <td>-1.027913</td>\n      <td>0.382135</td>\n      <td>0.735833</td>\n      <td>0.115947</td>\n      <td>...</td>\n      <td>0.305969</td>\n      <td>-0.487434</td>\n      <td>0.108620</td>\n      <td>-0.486189</td>\n      <td>-0.727463</td>\n      <td>0.095465</td>\n      <td>0.557101</td>\n      <td>-1.318067</td>\n      <td>0.685069</td>\n      <td>-0.064413</td>\n    </tr>\n    <tr>\n      <th>106573</th>\n      <td>155320</td>\n      <td>3.451314</td>\n      <td>-2.755545</td>\n      <td>-0.698787</td>\n      <td>2.824309</td>\n      <td>0.947577</td>\n      <td>2.939001</td>\n      <td>2.222361</td>\n      <td>-0.642788</td>\n      <td>0.764591</td>\n      <td>...</td>\n      <td>0.237984</td>\n      <td>-0.550480</td>\n      <td>0.556238</td>\n      <td>0.003327</td>\n      <td>-0.579678</td>\n      <td>-0.208322</td>\n      <td>-2.366025</td>\n      <td>2.875471</td>\n      <td>-1.516451</td>\n      <td>-1.632513</td>\n    </tr>\n  </tbody>\n</table>\n<p>106574 rows × 168 columns</p>\n</div>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_final = pd.concat([feat_cp[other_col], pc_feat_chroma_df, pc_feat_mfcc_df,\n",
    "                        pc_feat_rmse_df, pc_feat_spectral_df, pc_feat_tonnetz_df,\n",
    "                        pc_feat_zcr_df], axis=1)\n",
    "feat_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With the PCA, we had a reduction of the chroma columns of features dataset from 252 to 69\n",
      "With the PCA, we had a reduction of the mfcc columns of features dataset from 140 to 46\n",
      "With the PCA, we had a reduction of the rmse columns of features dataset from 7 to 4\n",
      "With the PCA, we had a reduction of the spectral columns of features dataset from 70 to 25\n",
      "With the PCA, we had a reduction of the tonnetz columns of features dataset from 42 to 19\n",
      "With the PCA, we had a reduction of the zcr columns of features dataset from 7 to 4\n"
     ]
    }
   ],
   "source": [
    "print(f\"With the PCA, we had a reduction of the chroma columns of features dataset from {len(col_temp_chroma)} to {pca_num_chroma}\")\n",
    "print(f\"With the PCA, we had a reduction of the mfcc columns of features dataset from {len(col_temp_mfcc)} to {pca_num_mfcc}\")\n",
    "print(f\"With the PCA, we had a reduction of the rmse columns of features dataset from {len(col_temp_rmse)} to {pca_num_rmse}\")\n",
    "print(f\"With the PCA, we had a reduction of the spectral columns of features dataset from {len(col_temp_spectral)} to {pca_num_spectral}\")\n",
    "print(f\"With the PCA, we had a reduction of the tonnetz columns of features dataset from {len(col_temp_tonnetz)} to {pca_num_tonnetz}\")\n",
    "print(f\"With the PCA, we had a reduction of the zcr columns of features dataset from {len(col_temp_zcr)} to {pca_num_zcr}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 106574 entries, 0 to 106573\n",
      "Columns: 168 entries, track_id to pc_feat_zcr_3\n",
      "dtypes: float64(167), int64(1)\n",
      "memory usage: 136.6 MB\n"
     ]
    }
   ],
   "source": [
    "feat_final.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Tracks dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this dataset, applying a PCA is useless, so we selected only the useful columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "track_col = ['track_id', 'album_id', 'album_listens', 'album_title', 'artist_id', 'artist_location', 'artist_name', 'track_bit_rate',\n",
    "             'track_duration', 'track_favorites', 'track_genre_top', 'track_genres', 'track_genres_all', 'track_language_code', 'track_title']\n",
    "\n",
    "tracks_final = tracks_cp.loc[:, track_col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "        track_id  album_id  album_listens                    album_title  \\\n0              2         1           6073           AWOL - A Way Of Life   \n1              3         1           6073           AWOL - A Way Of Life   \n2              5         1           6073           AWOL - A Way Of Life   \n3             10         6          47632              Constant Hitmaker   \n4             20         4           2710                          Niris   \n...          ...       ...            ...                            ...   \n106569    155316     22940           1506  Live at Monty Hall, 2/17/2017   \n106570    155317     22940           1506  Live at Monty Hall, 2/17/2017   \n106571    155318     22940           1506  Live at Monty Hall, 2/17/2017   \n106572    155319     22940           1506  Live at Monty Hall, 2/17/2017   \n106573    155320     22906           7481      What I Tell Myself Vol. 2   \n\n        artist_id        artist_location       artist_name  track_bit_rate  \\\n0               1             New Jersey              AWOL          256000   \n1               1             New Jersey              AWOL          256000   \n2               1             New Jersey              AWOL          256000   \n3               6                    NaN         Kurt Vile          192000   \n4               4     Colchester England        Nicky Cook          256000   \n...           ...                    ...               ...             ...   \n106569      24357             New Jersey           Spowder          320000   \n106570      24357             New Jersey           Spowder          320000   \n106571      24357             New Jersey           Spowder          320000   \n106572      24357             New Jersey           Spowder          320000   \n106573      21615  Jersey City, NJ 07302  Forget the Whale          320000   \n\n        track_duration  track_favorites track_genre_top   track_genres  \\\n0                  168                2         Hip-Hop           [21]   \n1                  237                1         Hip-Hop           [21]   \n2                  206                6         Hip-Hop           [21]   \n3                  161              178             Pop           [10]   \n4                  311                0             NaN      [76, 103]   \n...                ...              ...             ...            ...   \n106569             162                1            Rock           [25]   \n106570             217                1            Rock           [25]   \n106571             404                2            Rock           [25]   \n106572             146                0            Rock           [25]   \n106573             198                1             NaN  [10, 12, 169]   \n\n         track_genres_all track_language_code  \\\n0                    [21]                  en   \n1                    [21]                  en   \n2                    [21]                  en   \n3                    [10]                  en   \n4       [17, 10, 76, 103]                  en   \n...                   ...                 ...   \n106569           [25, 12]                 NaN   \n106570           [25, 12]                 NaN   \n106571           [25, 12]                 NaN   \n106572           [25, 12]                 NaN   \n106573   [169, 10, 12, 9]                 NaN   \n\n                                      track_title  \n0                                            Food  \n1                                    Electric Ave  \n2                                      This World  \n3                                         Freeway  \n4                                 Spiritual Level  \n...                                           ...  \n106569                                  The Auger  \n106570                            Let's Skin Ruby  \n106571         My House Smells Like Kim Deal/Pulp  \n106572                    The Man With Two Mouths  \n106573  Another Trick Up My Sleeve (Instrumental)  \n\n[106574 rows x 15 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\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>track_id</th>\n      <th>album_id</th>\n      <th>album_listens</th>\n      <th>album_title</th>\n      <th>artist_id</th>\n      <th>artist_location</th>\n      <th>artist_name</th>\n      <th>track_bit_rate</th>\n      <th>track_duration</th>\n      <th>track_favorites</th>\n      <th>track_genre_top</th>\n      <th>track_genres</th>\n      <th>track_genres_all</th>\n      <th>track_language_code</th>\n      <th>track_title</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2</td>\n      <td>1</td>\n      <td>6073</td>\n      <td>AWOL - A Way Of Life</td>\n      <td>1</td>\n      <td>New Jersey</td>\n      <td>AWOL</td>\n      <td>256000</td>\n      <td>168</td>\n      <td>2</td>\n      <td>Hip-Hop</td>\n      <td>[21]</td>\n      <td>[21]</td>\n      <td>en</td>\n      <td>Food</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3</td>\n      <td>1</td>\n      <td>6073</td>\n      <td>AWOL - A Way Of Life</td>\n      <td>1</td>\n      <td>New Jersey</td>\n      <td>AWOL</td>\n      <td>256000</td>\n      <td>237</td>\n      <td>1</td>\n      <td>Hip-Hop</td>\n      <td>[21]</td>\n      <td>[21]</td>\n      <td>en</td>\n      <td>Electric Ave</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5</td>\n      <td>1</td>\n      <td>6073</td>\n      <td>AWOL - A Way Of Life</td>\n      <td>1</td>\n      <td>New Jersey</td>\n      <td>AWOL</td>\n      <td>256000</td>\n      <td>206</td>\n      <td>6</td>\n      <td>Hip-Hop</td>\n      <td>[21]</td>\n      <td>[21]</td>\n      <td>en</td>\n      <td>This World</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10</td>\n      <td>6</td>\n      <td>47632</td>\n      <td>Constant Hitmaker</td>\n      <td>6</td>\n      <td>NaN</td>\n      <td>Kurt Vile</td>\n      <td>192000</td>\n      <td>161</td>\n      <td>178</td>\n      <td>Pop</td>\n      <td>[10]</td>\n      <td>[10]</td>\n      <td>en</td>\n      <td>Freeway</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>20</td>\n      <td>4</td>\n      <td>2710</td>\n      <td>Niris</td>\n      <td>4</td>\n      <td>Colchester England</td>\n      <td>Nicky Cook</td>\n      <td>256000</td>\n      <td>311</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>[76, 103]</td>\n      <td>[17, 10, 76, 103]</td>\n      <td>en</td>\n      <td>Spiritual Level</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>106569</th>\n      <td>155316</td>\n      <td>22940</td>\n      <td>1506</td>\n      <td>Live at Monty Hall, 2/17/2017</td>\n      <td>24357</td>\n      <td>New Jersey</td>\n      <td>Spowder</td>\n      <td>320000</td>\n      <td>162</td>\n      <td>1</td>\n      <td>Rock</td>\n      <td>[25]</td>\n      <td>[25, 12]</td>\n      <td>NaN</td>\n      <td>The Auger</td>\n    </tr>\n    <tr>\n      <th>106570</th>\n      <td>155317</td>\n      <td>22940</td>\n      <td>1506</td>\n      <td>Live at Monty Hall, 2/17/2017</td>\n      <td>24357</td>\n      <td>New Jersey</td>\n      <td>Spowder</td>\n      <td>320000</td>\n      <td>217</td>\n      <td>1</td>\n      <td>Rock</td>\n      <td>[25]</td>\n      <td>[25, 12]</td>\n      <td>NaN</td>\n      <td>Let's Skin Ruby</td>\n    </tr>\n    <tr>\n      <th>106571</th>\n      <td>155318</td>\n      <td>22940</td>\n      <td>1506</td>\n      <td>Live at Monty Hall, 2/17/2017</td>\n      <td>24357</td>\n      <td>New Jersey</td>\n      <td>Spowder</td>\n      <td>320000</td>\n      <td>404</td>\n      <td>2</td>\n      <td>Rock</td>\n      <td>[25]</td>\n      <td>[25, 12]</td>\n      <td>NaN</td>\n      <td>My House Smells Like Kim Deal/Pulp</td>\n    </tr>\n    <tr>\n      <th>106572</th>\n      <td>155319</td>\n      <td>22940</td>\n      <td>1506</td>\n      <td>Live at Monty Hall, 2/17/2017</td>\n      <td>24357</td>\n      <td>New Jersey</td>\n      <td>Spowder</td>\n      <td>320000</td>\n      <td>146</td>\n      <td>0</td>\n      <td>Rock</td>\n      <td>[25]</td>\n      <td>[25, 12]</td>\n      <td>NaN</td>\n      <td>The Man With Two Mouths</td>\n    </tr>\n    <tr>\n      <th>106573</th>\n      <td>155320</td>\n      <td>22906</td>\n      <td>7481</td>\n      <td>What I Tell Myself Vol. 2</td>\n      <td>21615</td>\n      <td>Jersey City, NJ 07302</td>\n      <td>Forget the Whale</td>\n      <td>320000</td>\n      <td>198</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>[10, 12, 169]</td>\n      <td>[169, 10, 12, 9]</td>\n      <td>NaN</td>\n      <td>Another Trick Up My Sleeve (Instrumental)</td>\n    </tr>\n  </tbody>\n</table>\n<p>106574 rows × 15 columns</p>\n</div>"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracks_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 106574 entries, 0 to 106573\n",
      "Data columns (total 15 columns):\n",
      " #   Column               Non-Null Count   Dtype \n",
      "---  ------               --------------   ----- \n",
      " 0   track_id             106574 non-null  int64 \n",
      " 1   album_id             106574 non-null  int64 \n",
      " 2   album_listens        106574 non-null  int64 \n",
      " 3   album_title          105549 non-null  object\n",
      " 4   artist_id            106574 non-null  int64 \n",
      " 5   artist_location      70210 non-null   object\n",
      " 6   artist_name          106574 non-null  object\n",
      " 7   track_bit_rate       106574 non-null  int64 \n",
      " 8   track_duration       106574 non-null  int64 \n",
      " 9   track_favorites      106574 non-null  int64 \n",
      " 10  track_genre_top      49598 non-null   object\n",
      " 11  track_genres         106574 non-null  object\n",
      " 12  track_genres_all     106574 non-null  object\n",
      " 13  track_language_code  15024 non-null   object\n",
      " 14  track_title          106573 non-null  object\n",
      "dtypes: int64(7), object(8)\n",
      "memory usage: 12.2+ MB\n"
     ]
    }
   ],
   "source": [
    "tracks_final.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "new_df_red = pd.merge(tracks_final, feat_final, on='track_id', how='inner')\n",
    "main_df_red = pd.merge(new_df_red, echo_final, on='track_id', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "      <th></th>\n",
       "      <th>track_id</th>\n",
       "      <th>album_id</th>\n",
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       "      <td>AWOL - A Way Of Life</td>\n",
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       "      <td>-0.759942</td>\n",
       "      <td>-1.022644</td>\n",
       "      <td>0.702416</td>\n",
       "      <td>-0.007906</td>\n",
       "      <td>-0.073801</td>\n",
       "      <td>0.047319</td>\n",
       "      <td>-0.523209</td>\n",
       "      <td>-0.601322</td>\n",
       "      <td>0.187780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>47632</td>\n",
       "      <td>Constant Hitmaker</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kurt Vile</td>\n",
       "      <td>192000</td>\n",
       "      <td>161</td>\n",
       "      <td>178</td>\n",
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       "      <td>0.816882</td>\n",
       "      <td>-2.254254</td>\n",
       "      <td>0.742618</td>\n",
       "      <td>0.557756</td>\n",
       "      <td>-0.347822</td>\n",
       "      <td>1.052869</td>\n",
       "      <td>0.056952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>134</td>\n",
       "      <td>1</td>\n",
       "      <td>6073</td>\n",
       "      <td>AWOL - A Way Of Life</td>\n",
       "      <td>1</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>AWOL</td>\n",
       "      <td>256000</td>\n",
       "      <td>207</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
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       "      <td>-0.020439</td>\n",
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       "      <td>0.016399</td>\n",
       "      <td>0.546659</td>\n",
       "      <td>-0.994958</td>\n",
       "      <td>-0.266806</td>\n",
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       "      <td>-0.214411</td>\n",
       "      <td>-0.335870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 278 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   track_id  album_id  album_listens           album_title  artist_id  \\\n",
       "0         2         1           6073  AWOL - A Way Of Life          1   \n",
       "1         3         1           6073  AWOL - A Way Of Life          1   \n",
       "2         5         1           6073  AWOL - A Way Of Life          1   \n",
       "3        10         6          47632     Constant Hitmaker          6   \n",
       "4       134         1           6073  AWOL - A Way Of Life          1   \n",
       "\n",
       "  artist_location artist_name  track_bit_rate  track_duration  \\\n",
       "0      New Jersey        AWOL          256000             168   \n",
       "1      New Jersey        AWOL          256000             237   \n",
       "2      New Jersey        AWOL          256000             206   \n",
       "3             NaN   Kurt Vile          192000             161   \n",
       "4      New Jersey        AWOL          256000             207   \n",
       "\n",
       "   track_favorites  ... pc_echo_61 pc_echo_62 pc_echo_63 pc_echo_64  \\\n",
       "0                2  ...   0.355263   0.027481  -0.157763  -0.402039   \n",
       "1                1  ...   0.646931  -0.710005   0.117975   0.629340   \n",
       "2                6  ...   1.335908  -0.759942  -1.022644   0.702416   \n",
       "3              178  ...  -0.612253   0.669184  -0.822872   0.816882   \n",
       "4                3  ...   0.632782  -0.020439  -0.015756   0.016399   \n",
       "\n",
       "  pc_echo_65  pc_echo_66  pc_echo_67  pc_echo_68  pc_echo_69  pc_echo_70  \n",
       "0   0.616848    0.214498   -0.590703   -0.054720   -0.578055   -0.663876  \n",
       "1   0.497755    0.480609   -0.119040   -1.234916   -1.123355    0.611513  \n",
       "2  -0.007906   -0.073801    0.047319   -0.523209   -0.601322    0.187780  \n",
       "3  -2.254254    0.742618    0.557756   -0.347822    1.052869    0.056952  \n",
       "4   0.546659   -0.994958   -0.266806    0.271617   -0.214411   -0.335870  \n",
       "\n",
       "[5 rows x 278 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df_red.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13129 entries, 0 to 13128\n",
      "Columns: 278 entries, track_id to pc_echo_70\n",
      "dtypes: float64(258), int64(7), object(13)\n",
      "memory usage: 27.9+ MB\n"
     ]
    }
   ],
   "source": [
    "main_df_red.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Clustering!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before checking how our own implementation of the KMeans algorithm performs, we should prepare the dataframe we obtained earlier by dimensionality reduction through PCA.\n",
    "\n",
    "First, we select out of all columns only those with (meaningful) numeric values (hence excluding columns like `track_id`, `album_id`, and so on)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "clust_df = main_df_red.copy().select_dtypes(include = np.number).iloc[:,7:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we can standardize each of those column so they have zero mean and unity standard deviation, and replacing all NaN with zeros (the mean)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "std_scaler = StandardScaler()\n",
    "clust_df = pd.DataFrame(std_scaler.fit_transform(clust_df.values), columns = clust_df.columns).fillna(value = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to find the optimal number $ K $ of clusters, we can use the elbow method and compute the inertia (or SSE, Sum of Squared Errors) – defined as the sum of the squared distances between each point and its assigned cluster – for each of the models we obtained with a different choice of $ K $.\n",
    "\n",
    "We expect this value to decrease as the number of clusters increases, so we want to find the right balance between having as few clusters as possible (to avoid overfitting the data) but also low inertia (to have points of the same cluster well grouped together and not too spread apart).\n",
    "\n",
    "We can do this by looking at the curve traced by plotting the inertia *vs* $ K $ and identifying the value of $ K $ at which the curve starts showing a more *constant-like* behaviour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting data with K = 10 clusters\r"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inertia = kmeans.elbow(clust_df, range(1, 11), \n",
    "                       plot = True, progress = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By fitting the model with $ K=1,\\dots,10 $ clusters we can see how $ K = 4 $ may be an adequate choice.\n",
    "\n",
    "Let's focus on $ K = 4 $ and explore the model we get."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = kmeans.KMeans(K = 4)\n",
    "model.fit(clust_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our KMeans implementation with the default threshold of 0.005 took"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.n_iter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "iterations to converge.\n",
    "\n",
    "We can check the clustering we got by plotting the two main components obtained by PCA on the `echonest` dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.display(clust_df.iloc[:,-71], clust_df.iloc[:,-70], show_centroids = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can immediately see how all points – regardless of the label they were given – are grouped together and overlay each other; this tells us that a 2D scatter plot is definitely not sufficient to appreciate how spread out the clusters actually are (and to confirm whether they are in fact well separated or not).\n",
    "\n",
    "Same story if we look at the two main components out of the `features` dataset, although the separation is clearer here and the positions of the centroids actually reflect the regions of the different clusters, meaning that points of different clusters are somewhat more spread out than in the previous plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.display(clust_df.iloc[:, 0], clust_df.iloc[:,1], show_centroids = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's perform a Silhouette analysis and see if we get any numeric confirmation of what these plots tell us.\n",
    "\n",
    "The Silhouette score of a point ranges between -1 and 1, and is a measure of how well the point is \"represented\" by the cluster it was assigned to: a score of 1 indicates the point is very close to the center of the cluster it belongs in, and very far to all the other centers, with a score of -1 representing the exact opposite (implying the point was badly labelled); on the other hand, a score of 0 indicates the point sits on the boundary of two or more clusters, meaning that it could have been assigned equally well – or badly – to any of those.\n",
    "\n",
    "By comparing each score to the overall average, we can assess how well the clusters were formed, and consequently how optimal was in fact the $ K $ we picked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13128\r"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "silh = model.silhouette(clust_df, plot = True, progress = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot describes what we preliminarly saw earlier: the Silhouette score is in fact very close to 0 for most points, meaning that there is no clear separation of the dataset in clusters.\n",
    "\n",
    "This means that – even though the \"orange\" cluster looks to be much less representative of the points it's made of than the other three – many of the points of each cluster could have been assigned to any of the other clusters just by a different initialization of the centroids, still producing in the end an average score close to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Silhouette score: 0.012\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Average Silhouette score: {np.mean(silh):.3f}\")\n",
    "\n",
    "sns.histplot(silh)\n",
    "plt.xlabel(\"Silhouette score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In conclusion, we believe $K = 4$ is in fact a good choice, mainly due to the fact that no \"bad\" cluster was formed. \n",
    "\n",
    "The cluster overlay could have been mitigated by a different preprocessing / dimensionality-reduction method, but we are confident it is more than anything due to the intrinsically diverse and complex nature of the data.\n",
    "\n",
    "Now that have we have a better grasp of the model obtained with our algorithm, let's see how it actually stacks up against scikit's implementation of `k-means++`, performance-wise and results-wise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converged in 34 iterations\n"
     ]
    }
   ],
   "source": [
    "model_sk = KMeans(n_clusters = 4, init = \"k-means++\")\n",
    "model_sk.fit(clust_df)\n",
    "print(f\"Converged in {model_sk.n_iter_} iterations\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The difference in execution times is definitely very impressive: on a mid-range 2016 laptop, scikit's `k-means++` converged in just below 2 seconds, whereas our implementation took about 20.\n",
    "\n",
    "Since the initialization of the centroids in our KMeans was random, the execution time will vary from run to run, but the difference was substantial in every test.\n",
    "\n",
    "On the other hand, the results are definitely comparable.\n",
    "\n",
    "The Silhouette score – which as expected was computed **much** faster – is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.010760290880559771"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_score(clust_df, model_sk.fit_predict(clust_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "very close to the value our algorithm produced:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.012250674740932367"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(silh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which suggests very similar clusters were formed.\n",
    "\n",
    "Let's actually count how many out of the ~13000 total points were labelled differently.\n",
    "\n",
    "First, we should make sure the labels are actually comparable; in order to do so, we can define a `new_labels` vector obtained by transforming every label in \"scikit's\" vector into its respective value in \"our\" vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our labels       = [1 1 1 ... 3 0 0]\n",
      "scikit's labels  = [2 2 2 ... 1 0 0]\n",
      "New scikit's     = [1 1 1 ... 3 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(f\"Our labels       = {model.labels}\")\n",
    "print(f\"scikit's labels  = {model_sk.labels_}\")\n",
    "\n",
    "new_labels = []\n",
    "\n",
    "for lab in model_sk.labels_:\n",
    "    if lab == 0:\n",
    "        new_labels.append(0)\n",
    "    elif lab == 1:\n",
    "        new_labels.append(3)\n",
    "    elif lab == 2:\n",
    "        new_labels.append(1)\n",
    "    elif lab == 3:\n",
    "        new_labels.append(2)\n",
    "        \n",
    "new_labels = np.array(new_labels)\n",
    "\n",
    "print(f\"New scikit's     = {new_labels}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we get is that the vast majority of points were clustered the same way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12526 / 13129 share the same label\n"
     ]
    }
   ],
   "source": [
    "same_label_count = np.sum(model.labels == new_labels)\n",
    "print(f\"{same_label_count} / {len(clust_df.values)} share the same label\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just by taking a look again at the two main components of the `echonest` dataset and of the `features` dataset we do in fact get pretty much the same clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# two main components of 'echonest'\n",
    "x = clust_df.iloc[:,-71]\n",
    "y = clust_df.iloc[:,-70]\n",
    "\n",
    "# our model\n",
    "plt.figure(figsize = (5,5))\n",
    "plt.title(\"Our KMeans\")\n",
    "model.display(x, y)\n",
    "plt.show()\n",
    "\n",
    "# sci-kit's model\n",
    "plt.figure(figsize = (5,5))\n",
    "plt.title(\"scikit k-means++\")\n",
    "plt.scatter(x, y, \n",
    "            edgecolors = \"white\", \n",
    "            alpha = 0.8, \n",
    "            color = [sns.color_palette(\"muted\")[label]\n",
    "                             for label in new_labels])\n",
    "plt.xlabel(x.name)\n",
    "plt.ylabel(y.name)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# two of the main components of 'features'\n",
    "x = clust_df.iloc[:,0]\n",
    "y = clust_df.iloc[:,1]\n",
    "\n",
    "# our model\n",
    "plt.figure(figsize = (5,5))\n",
    "plt.title(\"Our KMeans\")\n",
    "model.display(x, y)\n",
    "plt.show()\n",
    "\n",
    "# sci-kit's model\n",
    "plt.figure(figsize = (5,5))\n",
    "plt.title(\"scikit k-means++\")\n",
    "plt.scatter(x, y, \n",
    "            edgecolors = \"white\", \n",
    "            alpha = 0.8, \n",
    "            color = [sns.color_palette(\"muted\")[label]\n",
    "                             for label in new_labels])\n",
    "plt.xlabel(x.name)\n",
    "plt.ylabel(y.name)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 576x396 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<AxesSubplot:title={'center':'Calinski Harabasz Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='calinski harabasz score'>"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualizer = KElbowVisualizer(model_sk, k=(2,10), metric=\"calinski_harabasz\", timings=False)\n",
    "visualizer.fit(clust_df)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4 Analysing your results!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>track_id</th>\n",
       "      <th>cluster</th>\n",
       "      <th>audio_features_acousticness</th>\n",
       "      <th>audio_features_danceability</th>\n",
       "      <th>audio_features_energy</th>\n",
       "      <th>audio_features_instrumentalness</th>\n",
       "      <th>audio_features_liveness</th>\n",
       "      <th>audio_features_speechiness</th>\n",
       "      <th>audio_features_tempo</th>\n",
       "      <th>audio_features_valence</th>\n",
       "      <th>...</th>\n",
       "      <th>track_information</th>\n",
       "      <th>track_interest</th>\n",
       "      <th>track_language_code</th>\n",
       "      <th>track_license</th>\n",
       "      <th>track_listens</th>\n",
       "      <th>track_lyricist</th>\n",
       "      <th>track_number</th>\n",
       "      <th>track_publisher</th>\n",
       "      <th>track_tags</th>\n",
       "      <th>track_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.416675</td>\n",
       "      <td>0.675894</td>\n",
       "      <td>0.634476</td>\n",
       "      <td>0.010628</td>\n",
       "      <td>0.177647</td>\n",
       "      <td>0.159310</td>\n",
       "      <td>165.922</td>\n",
       "      <td>0.576661</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4656</td>\n",
       "      <td>en</td>\n",
       "      <td>Attribution-NonCommercial-ShareAlike 3.0 Inter...</td>\n",
       "      <td>1293</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>Food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.374408</td>\n",
       "      <td>0.528643</td>\n",
       "      <td>0.817461</td>\n",
       "      <td>0.001851</td>\n",
       "      <td>0.105880</td>\n",
       "      <td>0.461818</td>\n",
       "      <td>126.957</td>\n",
       "      <td>0.269240</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1470</td>\n",
       "      <td>en</td>\n",
       "      <td>Attribution-NonCommercial-ShareAlike 3.0 Inter...</td>\n",
       "      <td>514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>Electric Ave</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.043567</td>\n",
       "      <td>0.745566</td>\n",
       "      <td>0.701470</td>\n",
       "      <td>0.000697</td>\n",
       "      <td>0.373143</td>\n",
       "      <td>0.124595</td>\n",
       "      <td>100.260</td>\n",
       "      <td>0.621661</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1933</td>\n",
       "      <td>en</td>\n",
       "      <td>Attribution-NonCommercial-ShareAlike 3.0 Inter...</td>\n",
       "      <td>1151</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>This World</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>0.951670</td>\n",
       "      <td>0.658179</td>\n",
       "      <td>0.924525</td>\n",
       "      <td>0.965427</td>\n",
       "      <td>0.115474</td>\n",
       "      <td>0.032985</td>\n",
       "      <td>111.562</td>\n",
       "      <td>0.963590</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54881</td>\n",
       "      <td>en</td>\n",
       "      <td>Attribution-NonCommercial-NoDerivatives (aka M...</td>\n",
       "      <td>50135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>Freeway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>134</td>\n",
       "      <td>1</td>\n",
       "      <td>0.452217</td>\n",
       "      <td>0.513238</td>\n",
       "      <td>0.560410</td>\n",
       "      <td>0.019443</td>\n",
       "      <td>0.096567</td>\n",
       "      <td>0.525519</td>\n",
       "      <td>114.290</td>\n",
       "      <td>0.894072</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1126</td>\n",
       "      <td>en</td>\n",
       "      <td>Attribution-NonCommercial-ShareAlike 3.0 Inter...</td>\n",
       "      <td>943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>Street Music</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 821 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   track_id  cluster  audio_features_acousticness  \\\n",
       "0         2        1                     0.416675   \n",
       "1         3        1                     0.374408   \n",
       "2         5        1                     0.043567   \n",
       "3        10        3                     0.951670   \n",
       "4       134        1                     0.452217   \n",
       "\n",
       "   audio_features_danceability  audio_features_energy  \\\n",
       "0                     0.675894               0.634476   \n",
       "1                     0.528643               0.817461   \n",
       "2                     0.745566               0.701470   \n",
       "3                     0.658179               0.924525   \n",
       "4                     0.513238               0.560410   \n",
       "\n",
       "   audio_features_instrumentalness  audio_features_liveness  \\\n",
       "0                         0.010628                 0.177647   \n",
       "1                         0.001851                 0.105880   \n",
       "2                         0.000697                 0.373143   \n",
       "3                         0.965427                 0.115474   \n",
       "4                         0.019443                 0.096567   \n",
       "\n",
       "   audio_features_speechiness  audio_features_tempo  audio_features_valence  \\\n",
       "0                    0.159310               165.922                0.576661   \n",
       "1                    0.461818               126.957                0.269240   \n",
       "2                    0.124595               100.260                0.621661   \n",
       "3                    0.032985               111.562                0.963590   \n",
       "4                    0.525519               114.290                0.894072   \n",
       "\n",
       "   ... track_information track_interest  track_language_code  \\\n",
       "0  ...               NaN           4656                   en   \n",
       "1  ...               NaN           1470                   en   \n",
       "2  ...               NaN           1933                   en   \n",
       "3  ...               NaN          54881                   en   \n",
       "4  ...               NaN           1126                   en   \n",
       "\n",
       "                                       track_license  track_listens  \\\n",
       "0  Attribution-NonCommercial-ShareAlike 3.0 Inter...           1293   \n",
       "1  Attribution-NonCommercial-ShareAlike 3.0 Inter...            514   \n",
       "2  Attribution-NonCommercial-ShareAlike 3.0 Inter...           1151   \n",
       "3  Attribution-NonCommercial-NoDerivatives (aka M...          50135   \n",
       "4  Attribution-NonCommercial-ShareAlike 3.0 Inter...            943   \n",
       "\n",
       "  track_lyricist track_number  track_publisher  track_tags   track_title  \n",
       "0            NaN            3              NaN          []          Food  \n",
       "1            NaN            4              NaN          []  Electric Ave  \n",
       "2            NaN            6              NaN          []    This World  \n",
       "3            NaN            1              NaN          []       Freeway  \n",
       "4            NaN            5              NaN          []  Street Music  \n",
       "\n",
       "[5 rows x 821 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters = model.labels\n",
    "main_df_cp = main_df.copy()\n",
    "main_df_cp.insert(1, \"cluster\", clusters)\n",
    "main_df_cp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "In this part, it is required to select 5-10 variables, in order to build pivot tables and to identify the genre of a song.\n",
    "So, we selected the following varables:\n",
    "- *audio_features_acousticness*\n",
    "- *audio_features_danceability*\n",
    "- *audio_features_energy*\n",
    "- *track_duration*\n",
    "- *track_listens*\n",
    "- *track_language_code*\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Since there are some numeric variables, it is appropriate to categorize them in 4 classes, which will be $ 0\\%-25\\% $, $ 25\\%-50\\% $, $ 50\\%-75\\% $ and $ 75\\%-100\\% $, compare to their values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def category(elem, class_):\n",
    "    if elem <= class_[0]:\n",
    "        return 'Category 1'\n",
    "    elif class_[0] < elem <= class_[1]:\n",
    "        return 'Category 2'\n",
    "    elif class_[1] < elem <= class_[2]:\n",
    "        return 'Category 3'\n",
    "    else:\n",
    "        return 'Category 4'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "audio_acoust_des = list(main_df_cp.audio_features_acousticness.describe()[4:7])\n",
    "audio_dance_des = list(main_df_cp.audio_features_danceability.describe()[4:7])\n",
    "audio_ener_des = list(main_df_cp.audio_features_energy.describe()[4:7])\n",
    "track_dur_des = list(main_df_cp.track_duration.describe()[4:7])\n",
    "track_list_des = list(main_df_cp.track_listens.describe()[4:7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "audio_acoust_clus = main_df_cp.audio_features_acousticness.apply(lambda row: category(row, audio_acoust_des))\n",
    "audio_dance_clus = main_df_cp.audio_features_danceability.apply(lambda row: category(row, audio_dance_des))\n",
    "audio_ener_clus = main_df_cp.audio_features_energy.apply(lambda row: category(row, audio_ener_des))\n",
    "track_dur_clus = main_df_cp.track_duration.apply(lambda row: category(row, track_dur_des))\n",
    "track_list_clus = main_df_cp.track_listens.apply(lambda row: category(row, track_list_des))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pivot table of audio_features_acousticness\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cluster 1</th>\n",
       "      <th>Cluster 2</th>\n",
       "      <th>Cluster 3</th>\n",
       "      <th>Cluster 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Category 1</th>\n",
       "      <td>4.81</td>\n",
       "      <td>38.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>32.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 2</th>\n",
       "      <td>15.66</td>\n",
       "      <td>36.36</td>\n",
       "      <td>0.71</td>\n",
       "      <td>25.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 3</th>\n",
       "      <td>33.23</td>\n",
       "      <td>19.58</td>\n",
       "      <td>2.55</td>\n",
       "      <td>26.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 4</th>\n",
       "      <td>46.30</td>\n",
       "      <td>5.37</td>\n",
       "      <td>96.75</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.01</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cluster 1  Cluster 2  Cluster 3  Cluster 4\n",
       "Category 1       4.81      38.69       0.00      32.77\n",
       "Category 2      15.66      36.36       0.71      25.99\n",
       "Category 3      33.23      19.58       2.55      26.73\n",
       "Category 4      46.30       5.37      96.75      14.50\n",
       "Total          100.00     100.00     100.01      99.99"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Pivot table of audio_features_acousticness')\n",
    "pivot_1 = pd.crosstab(index = audio_acoust_clus, columns = clusters,\n",
    "            colnames = ['clusters']).apply(lambda row: round(row/row.sum()*100,2), axis = 0)\n",
    "total_1 = []\n",
    "for i in range(4):\n",
    "    total_1.append(sum(pivot_1[i]))\n",
    "\n",
    "pivot_1.columns = ['Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4']\n",
    "pivot_1.index = ['Category 1', 'Category 2', 'Category 3', 'Category 4']\n",
    "pivot_1.loc['Total'] = total_1\n",
    "pivot_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The pivot table of `audio_features_acousticness` seems not to be well balanced, and it is noticeable for the Cluster 3, where the \"Category 1\" has 0% and \"Category 4\" ~ 96%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pivot table of audio_features_danceability\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cluster 1</th>\n",
       "      <th>Cluster 2</th>\n",
       "      <th>Cluster 3</th>\n",
       "      <th>Cluster 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Category 1</th>\n",
       "      <td>27.18</td>\n",
       "      <td>3.83</td>\n",
       "      <td>36.78</td>\n",
       "      <td>40.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 2</th>\n",
       "      <td>29.75</td>\n",
       "      <td>12.06</td>\n",
       "      <td>36.92</td>\n",
       "      <td>31.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 3</th>\n",
       "      <td>28.23</td>\n",
       "      <td>26.63</td>\n",
       "      <td>19.52</td>\n",
       "      <td>21.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 4</th>\n",
       "      <td>14.85</td>\n",
       "      <td>57.48</td>\n",
       "      <td>6.79</td>\n",
       "      <td>6.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>100.01</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.01</td>\n",
       "      <td>100.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cluster 1  Cluster 2  Cluster 3  Cluster 4\n",
       "Category 1      27.18       3.83      36.78      40.67\n",
       "Category 2      29.75      12.06      36.92      31.07\n",
       "Category 3      28.23      26.63      19.52      21.75\n",
       "Category 4      14.85      57.48       6.79       6.52\n",
       "Total          100.01     100.00     100.01     100.01"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Pivot table of audio_features_danceability')\n",
    "pivot_2 = pd.crosstab(index = audio_dance_clus, columns = clusters,\n",
    "                      colnames = ['clusters']).apply(lambda row: round(row/row.sum()*100,2), axis = 0)\n",
    "total_2 = []\n",
    "for i in range(4):\n",
    "    total_2.append(sum(pivot_2[i]))\n",
    "\n",
    "pivot_2.columns = ['Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4']\n",
    "pivot_2.index = ['Category 1', 'Category 2', 'Category 3', 'Category 4']\n",
    "pivot_2.loc['Total'] = total_2\n",
    "pivot_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Also for this table, which contains the danceability of a song, the values are not well balanced, as we can see, some categories have values lower than 10%, and \"Category 4\" reaches in the second cluster, the value of ~ 57%, which is the highest in the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pivot table of audio_features_energy\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cluster 1</th>\n",
       "      <th>Cluster 2</th>\n",
       "      <th>Cluster 3</th>\n",
       "      <th>Cluster 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Category 1</th>\n",
       "      <td>57.41</td>\n",
       "      <td>10.31</td>\n",
       "      <td>88.54</td>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 2</th>\n",
       "      <td>33.94</td>\n",
       "      <td>29.71</td>\n",
       "      <td>10.33</td>\n",
       "      <td>15.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 3</th>\n",
       "      <td>7.79</td>\n",
       "      <td>34.24</td>\n",
       "      <td>1.13</td>\n",
       "      <td>34.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 4</th>\n",
       "      <td>0.87</td>\n",
       "      <td>25.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>47.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>100.01</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cluster 1  Cluster 2  Cluster 3  Cluster 4\n",
       "Category 1      57.41      10.31      88.54       2.32\n",
       "Category 2      33.94      29.71      10.33      15.75\n",
       "Category 3       7.79      34.24       1.13      34.22\n",
       "Category 4       0.87      25.74       0.00      47.71\n",
       "Total          100.01     100.00     100.00     100.00"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Pivot table of audio_features_energy')\n",
    "pivot_3 = pd.crosstab(index = audio_ener_clus, columns = clusters,\n",
    "                      colnames = ['clusters']).apply(lambda row: round(row/row.sum()*100,2), axis = 0)\n",
    "total_3 = []\n",
    "for i in range(4):\n",
    "    total_3.append(sum(pivot_3[i]))\n",
    "\n",
    "pivot_3.columns = ['Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4']\n",
    "pivot_3.index = ['Category 1', 'Category 2', 'Category 3', 'Category 4']\n",
    "pivot_3.loc['Total'] = total_3\n",
    "pivot_3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "It is noticeable in this table, that \"Category 4\" is not balanced at all, since for the first and third cluster its values do not reach even the 1%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pivot table of track_duration\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cluster 1</th>\n",
       "      <th>Cluster 2</th>\n",
       "      <th>Cluster 3</th>\n",
       "      <th>Cluster 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Category 1</th>\n",
       "      <td>18.09</td>\n",
       "      <td>24.92</td>\n",
       "      <td>33.38</td>\n",
       "      <td>29.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 2</th>\n",
       "      <td>23.01</td>\n",
       "      <td>23.48</td>\n",
       "      <td>38.76</td>\n",
       "      <td>26.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 3</th>\n",
       "      <td>25.26</td>\n",
       "      <td>27.43</td>\n",
       "      <td>16.12</td>\n",
       "      <td>23.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 4</th>\n",
       "      <td>33.64</td>\n",
       "      <td>24.18</td>\n",
       "      <td>11.74</td>\n",
       "      <td>20.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>100.00</td>\n",
       "      <td>100.01</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cluster 1  Cluster 2  Cluster 3  Cluster 4\n",
       "Category 1      18.09      24.92      33.38      29.69\n",
       "Category 2      23.01      23.48      38.76      26.67\n",
       "Category 3      25.26      27.43      16.12      23.26\n",
       "Category 4      33.64      24.18      11.74      20.39\n",
       "Total          100.00     100.01     100.00     100.01"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Pivot table of track_duration')\n",
    "pivot_4 = pd.crosstab(index = track_dur_clus, columns = clusters,\n",
    "                      colnames = ['clusters']).apply(lambda row: round(row/row.sum()*100,2), axis = 0)\n",
    "total_4 = []\n",
    "for i in range(4):\n",
    "    total_4.append(sum(pivot_4[i]))\n",
    "\n",
    "pivot_4.columns = ['Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4']\n",
    "pivot_4.index = ['Category 1', 'Category 2', 'Category 3', 'Category 4']\n",
    "pivot_4.loc['Total'] = total_4\n",
    "pivot_4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The `track_duration` table seems to be the most balanced at this point: for cluster 2 and 4 the values are well balance, because are in a range between 20% and 30%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pivot table of track_listens\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cluster 1</th>\n",
       "      <th>Cluster 2</th>\n",
       "      <th>Cluster 3</th>\n",
       "      <th>Cluster 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Category 1</th>\n",
       "      <td>23.17</td>\n",
       "      <td>23.55</td>\n",
       "      <td>4.67</td>\n",
       "      <td>31.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 2</th>\n",
       "      <td>24.82</td>\n",
       "      <td>24.90</td>\n",
       "      <td>13.30</td>\n",
       "      <td>27.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 3</th>\n",
       "      <td>23.72</td>\n",
       "      <td>26.92</td>\n",
       "      <td>29.00</td>\n",
       "      <td>23.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 4</th>\n",
       "      <td>28.29</td>\n",
       "      <td>24.63</td>\n",
       "      <td>53.04</td>\n",
       "      <td>18.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.01</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cluster 1  Cluster 2  Cluster 3  Cluster 4\n",
       "Category 1      23.17      23.55       4.67      31.11\n",
       "Category 2      24.82      24.90      13.30      27.04\n",
       "Category 3      23.72      26.92      29.00      23.63\n",
       "Category 4      28.29      24.63      53.04      18.22\n",
       "Total          100.00     100.00     100.01     100.00"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Pivot table of track_listens')\n",
    "pivot_5 = pd.crosstab(index = track_list_clus, columns = clusters,\n",
    "                      colnames = ['clusters']).apply(lambda row: round(row/row.sum()*100,2), axis = 0)\n",
    "total_5 = []\n",
    "for i in range(4):\n",
    "    total_5.append(sum(pivot_5[i]))\n",
    "\n",
    "pivot_5.columns = ['Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4']\n",
    "pivot_5.index = ['Category 1', 'Category 2', 'Category 3', 'Category 4']\n",
    "pivot_5.loc['Total'] = total_5\n",
    "pivot_5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "For this table, the first, the second and the fourth cluster are well balanced, but the third for \"Category 1\" has around 4% and for \"Category 4\" around 53%, this means the values are not balanced enough."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pivot table of track_language_code\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cluster 1</th>\n",
       "      <th>Cluster 2</th>\n",
       "      <th>Cluster 3</th>\n",
       "      <th>Cluster 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ar</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bm</th>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cs</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>de</th>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>el</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>en</th>\n",
       "      <td>96.88</td>\n",
       "      <td>93.50</td>\n",
       "      <td>99.75</td>\n",
       "      <td>96.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>es</th>\n",
       "      <td>0.82</td>\n",
       "      <td>3.22</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fi</th>\n",
       "      <td>0.12</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fr</th>\n",
       "      <td>0.18</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>it</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ja</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ko</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pt</th>\n",
       "      <td>0.24</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ru</th>\n",
       "      <td>0.47</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sr</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sw</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tr</th>\n",
       "      <td>0.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zh</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>100.01</td>\n",
       "      <td>99.99</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Cluster 1  Cluster 2  Cluster 3  Cluster 4\n",
       "ar          0.24       0.06       0.00       0.00\n",
       "bm          0.06       0.00       0.00       0.00\n",
       "cs          0.00       0.00       0.00       0.09\n",
       "de          0.06       0.00       0.00       0.14\n",
       "el          0.00       0.00       0.00       0.23\n",
       "en         96.88      93.50      99.75      96.02\n",
       "es          0.82       3.22       0.25       2.20\n",
       "fi          0.12       0.06       0.00       0.00\n",
       "fr          0.18       1.01       0.00       0.09\n",
       "it          0.29       0.44       0.00       0.05\n",
       "ja          0.00       0.19       0.00       0.00\n",
       "ko          0.00       0.00       0.00       0.05\n",
       "pt          0.24       1.14       0.00       0.19\n",
       "ru          0.47       0.06       0.00       0.09\n",
       "sr          0.00       0.25       0.00       0.09\n",
       "sw          0.24       0.00       0.00       0.05\n",
       "tr          0.41       0.00       0.00       0.66\n",
       "zh          0.00       0.06       0.00       0.05\n",
       "Total     100.01      99.99     100.00     100.00"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Pivot table of track_language_code')\n",
    "pivot_6 = pd.crosstab(index = main_df_cp.track_language_code, columns = clusters,\n",
    "                      colnames = ['clusters']).apply(lambda row: round(row/row.sum()*100,2), axis = 0)\n",
    "total_6 = []\n",
    "for i in range(4):\n",
    "    total_6.append(sum(pivot_6[i]))\n",
    "\n",
    "pivot_6.columns = ['Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4']\n",
    "pivot_6.index = list(pivot_6.index)\n",
    "pivot_6.loc['Total'] = total_6\n",
    "pivot_6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "This table highlights a common trend in music, which is the prevalence of songs written in english, which dominates each cluster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "track_genre_top\n",
      "track_genres\n",
      "track_genres_all\n"
     ]
    }
   ],
   "source": [
    "for col in main_df_cp.columns:\n",
    "    if \"genre\" in str(col):\n",
    "        print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pivot table of track_genre_top\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cluster 1</th>\n",
       "      <th>Cluster 2</th>\n",
       "      <th>Cluster 3</th>\n",
       "      <th>Cluster 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Blues</th>\n",
       "      <td>0.18</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Classical</th>\n",
       "      <td>0.62</td>\n",
       "      <td>10.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronic</th>\n",
       "      <td>23.74</td>\n",
       "      <td>11.00</td>\n",
       "      <td>10.93</td>\n",
       "      <td>49.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Experimental</th>\n",
       "      <td>0.31</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Folk</th>\n",
       "      <td>6.15</td>\n",
       "      <td>25.42</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hip-Hop</th>\n",
       "      <td>3.51</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2.89</td>\n",
       "      <td>30.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Instrumental</th>\n",
       "      <td>1.60</td>\n",
       "      <td>1.58</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>International</th>\n",
       "      <td>0.92</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1.43</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jazz</th>\n",
       "      <td>2.15</td>\n",
       "      <td>6.63</td>\n",
       "      <td>1.29</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Old-Time / Historic</th>\n",
       "      <td>0.31</td>\n",
       "      <td>14.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pop</th>\n",
       "      <td>3.26</td>\n",
       "      <td>4.07</td>\n",
       "      <td>3.19</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rock</th>\n",
       "      <td>57.26</td>\n",
       "      <td>20.20</td>\n",
       "      <td>75.04</td>\n",
       "      <td>11.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>100.01</td>\n",
       "      <td>100.02</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Cluster 1  Cluster 2  Cluster 3  Cluster 4\n",
       "Blues                     0.18       1.63       0.54       0.37\n",
       "Classical                 0.62      10.91       0.00       0.00\n",
       "Electronic               23.74      11.00      10.93      49.24\n",
       "Experimental              0.31       0.30       0.07       0.12\n",
       "Folk                      6.15      25.42       4.28       2.21\n",
       "Hip-Hop                   3.51       0.90       2.89      30.53\n",
       "Instrumental              1.60       1.58       0.34       0.45\n",
       "International             0.92       2.57       1.43       0.65\n",
       "Jazz                      2.15       6.63       1.29       0.53\n",
       "Old-Time / Historic       0.31      14.81       0.00       0.25\n",
       "Pop                       3.26       4.07       3.19       4.25\n",
       "Rock                     57.26      20.20      75.04      11.40\n",
       "Total                   100.01     100.02     100.00     100.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Pivot table of track_genre_top')\n",
    "pivot_6 = pd.crosstab(index = main_df_cp.track_genre_top, columns = clusters,\n",
    "                      colnames = ['clusters']).apply(lambda row: round(row/row.sum()*100,2), axis = 0)\n",
    "total_6 = []\n",
    "for i in range(4):\n",
    "    total_6.append(sum(pivot_6[i]))\n",
    "\n",
    "pivot_6.columns = ['Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4']\n",
    "pivot_6.index = list(pivot_6.index)\n",
    "pivot_6.loc['Total'] = total_6\n",
    "pivot_6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most representative genre for each cluster are:\n",
    "* Cluster 1: Rock\n",
    "* Cluster 2: Folk\n",
    "* Cluster 3: Rock\n",
    "* Cluster 4: Electronic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.4.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reduced using PCA\n",
    "df_reduced = new_df_red.copy().select_dtypes(include = np.number).iloc[:,7:]\n",
    "# standardize the data\n",
    "cluster_df_reduced = pd.DataFrame(StandardScaler().fit_transform(df_reduced.values), columns = df_reduced.columns).fillna(value = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate optimal number of clusters (K=5)\n",
    "inertia_reduced = kmeans.elbow(cluster_df_reduced, range(1, 11), plot = True, progress = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converged in 27 iterations\n"
     ]
    }
   ],
   "source": [
    "#Kmenas++\n",
    "model_sk_reduced = KMeans(n_clusters = 5, init = \"k-means++\")\n",
    "model_sk_reduced.fit(cluster_df_reduced)\n",
    "print(f\"Converged in {model_sk_reduced.n_iter_} iterations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.013076151510911377"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "silhouette_score(cluster_df_reduced, model_sk_reduced.fit_predict(cluster_df_reduced))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>track_id</th>\n",
       "      <th>cluster</th>\n",
       "      <th>chroma_cens_kurtosis_01</th>\n",
       "      <th>chroma_cens_kurtosis_02</th>\n",
       "      <th>chroma_cens_kurtosis_03</th>\n",
       "      <th>chroma_cens_kurtosis_04</th>\n",
       "      <th>chroma_cens_kurtosis_05</th>\n",
       "      <th>chroma_cens_kurtosis_06</th>\n",
       "      <th>chroma_cens_kurtosis_07</th>\n",
       "      <th>chroma_cens_kurtosis_08</th>\n",
       "      <th>...</th>\n",
       "      <th>track_information</th>\n",
       "      <th>track_interest</th>\n",
       "      <th>track_language_code</th>\n",
       "      <th>track_license</th>\n",
       "      <th>track_listens</th>\n",
       "      <th>track_lyricist</th>\n",
       "      <th>track_number</th>\n",
       "      <th>track_publisher</th>\n",
       "      <th>track_tags</th>\n",
       "      <th>track_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7.180653</td>\n",
       "      <td>5.230309</td>\n",
       "      <td>0.249321</td>\n",
       "      <td>1.347620</td>\n",
       "      <td>1.482478</td>\n",
       "      <td>0.531371</td>\n",
       "      <td>1.481593</td>\n",
       "      <td>2.691455</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4656</td>\n",
       "      <td>en</td>\n",
       "      <td>Attribution-NonCommercial-ShareAlike 3.0 Inter...</td>\n",
       "      <td>1293</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>Food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1.888963</td>\n",
       "      <td>0.760539</td>\n",
       "      <td>0.345297</td>\n",
       "      <td>2.295201</td>\n",
       "      <td>1.654031</td>\n",
       "      <td>0.067592</td>\n",
       "      <td>1.366848</td>\n",
       "      <td>1.054094</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1470</td>\n",
       "      <td>en</td>\n",
       "      <td>Attribution-NonCommercial-ShareAlike 3.0 Inter...</td>\n",
       "      <td>514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>Electric Ave</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.527563</td>\n",
       "      <td>-0.077654</td>\n",
       "      <td>-0.279610</td>\n",
       "      <td>0.685883</td>\n",
       "      <td>1.937570</td>\n",
       "      <td>0.880839</td>\n",
       "      <td>-0.923192</td>\n",
       "      <td>-0.927232</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1933</td>\n",
       "      <td>en</td>\n",
       "      <td>Attribution-NonCommercial-ShareAlike 3.0 Inter...</td>\n",
       "      <td>1151</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>This World</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3.702245</td>\n",
       "      <td>-0.291193</td>\n",
       "      <td>2.196742</td>\n",
       "      <td>-0.234449</td>\n",
       "      <td>1.367364</td>\n",
       "      <td>0.998411</td>\n",
       "      <td>1.770694</td>\n",
       "      <td>1.604566</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54881</td>\n",
       "      <td>en</td>\n",
       "      <td>Attribution-NonCommercial-NoDerivatives (aka M...</td>\n",
       "      <td>50135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>Freeway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.193837</td>\n",
       "      <td>-0.198527</td>\n",
       "      <td>0.201546</td>\n",
       "      <td>0.258556</td>\n",
       "      <td>0.775204</td>\n",
       "      <td>0.084794</td>\n",
       "      <td>-0.289294</td>\n",
       "      <td>-0.816410</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>978</td>\n",
       "      <td>en</td>\n",
       "      <td>Attribution-NonCommercial-NoDerivatives (aka M...</td>\n",
       "      <td>361</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>Spiritual Level</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 572 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   track_id  cluster  chroma_cens_kurtosis_01  chroma_cens_kurtosis_02  \\\n",
       "0         2        2                 7.180653                 5.230309   \n",
       "1         3        2                 1.888963                 0.760539   \n",
       "2         5        2                 0.527563                -0.077654   \n",
       "3        10        1                 3.702245                -0.291193   \n",
       "4        20        2                -0.193837                -0.198527   \n",
       "\n",
       "   chroma_cens_kurtosis_03  chroma_cens_kurtosis_04  chroma_cens_kurtosis_05  \\\n",
       "0                 0.249321                 1.347620                 1.482478   \n",
       "1                 0.345297                 2.295201                 1.654031   \n",
       "2                -0.279610                 0.685883                 1.937570   \n",
       "3                 2.196742                -0.234449                 1.367364   \n",
       "4                 0.201546                 0.258556                 0.775204   \n",
       "\n",
       "   chroma_cens_kurtosis_06  chroma_cens_kurtosis_07  chroma_cens_kurtosis_08  \\\n",
       "0                 0.531371                 1.481593                 2.691455   \n",
       "1                 0.067592                 1.366848                 1.054094   \n",
       "2                 0.880839                -0.923192                -0.927232   \n",
       "3                 0.998411                 1.770694                 1.604566   \n",
       "4                 0.084794                -0.289294                -0.816410   \n",
       "\n",
       "   ...  track_information  track_interest  track_language_code  \\\n",
       "0  ...                NaN            4656                   en   \n",
       "1  ...                NaN            1470                   en   \n",
       "2  ...                NaN            1933                   en   \n",
       "3  ...                NaN           54881                   en   \n",
       "4  ...                NaN             978                   en   \n",
       "\n",
       "                                       track_license  track_listens  \\\n",
       "0  Attribution-NonCommercial-ShareAlike 3.0 Inter...           1293   \n",
       "1  Attribution-NonCommercial-ShareAlike 3.0 Inter...            514   \n",
       "2  Attribution-NonCommercial-ShareAlike 3.0 Inter...           1151   \n",
       "3  Attribution-NonCommercial-NoDerivatives (aka M...          50135   \n",
       "4  Attribution-NonCommercial-NoDerivatives (aka M...            361   \n",
       "\n",
       "   track_lyricist  track_number  track_publisher  track_tags      track_title  \n",
       "0             NaN             3              NaN          []             Food  \n",
       "1             NaN             4              NaN          []     Electric Ave  \n",
       "2             NaN             6              NaN          []       This World  \n",
       "3             NaN             1              NaN          []          Freeway  \n",
       "4             NaN             3              NaN          []  Spiritual Level  \n",
       "\n",
       "[5 rows x 572 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters_reduced = model_sk_reduced.labels_\n",
    "main_df_cp_reduced = pd.merge(features, tracks, on='track_id', how='inner')\n",
    "main_df_cp_reduced.insert(1, \"cluster\", clusters)\n",
    "main_df_cp_reduced.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "track_interest_1 = list(main_df_cp_reduced.track_interest.describe()[4:7])\n",
    "track_comments_1 = list(main_df_cp_reduced.track_comments.describe()[4:7])\n",
    "track_favorites_1 = list(main_df_cp_reduced.track_favorites.describe()[4:7])\n",
    "track_dur_des_1 = list(main_df_cp_reduced.track_duration.describe()[4:7])\n",
    "track_list_des_1 = list(main_df_cp_reduced.track_listens.describe()[4:7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "track_interest_clus_1 = main_df_cp_reduced.track_interest.apply(lambda row: category(row, track_interest_1))\n",
    "track_comments_clus_1 = main_df_cp_reduced.track_comments.apply(lambda row: category(row, track_comments_1))\n",
    "track_favorites_clus_1 = main_df_cp_reduced.track_favorites.apply(lambda row: category(row, track_favorites_1))\n",
    "track_dur_clus_1 = main_df_cp_reduced.track_duration.apply(lambda row: category(row, track_dur_des_1))\n",
    "track_list_clus_1 = main_df_cp_reduced.track_listens.apply(lambda row: category(row, track_list_des_1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pivot table of track_interest (k-means++)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cluster 1</th>\n",
       "      <th>Cluster 2</th>\n",
       "      <th>Cluster 3</th>\n",
       "      <th>Cluster 4</th>\n",
       "      <th>Cluster 5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Category 1</th>\n",
       "      <td>23.50</td>\n",
       "      <td>19.64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.15</td>\n",
       "      <td>25.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 2</th>\n",
       "      <td>25.06</td>\n",
       "      <td>21.78</td>\n",
       "      <td>75.0</td>\n",
       "      <td>27.27</td>\n",
       "      <td>25.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 3</th>\n",
       "      <td>25.62</td>\n",
       "      <td>25.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.87</td>\n",
       "      <td>25.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 4</th>\n",
       "      <td>25.82</td>\n",
       "      <td>33.12</td>\n",
       "      <td>25.0</td>\n",
       "      <td>17.71</td>\n",
       "      <td>23.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cluster 1  Cluster 2  Cluster 3  Cluster 4  Cluster 5\n",
       "Category 1      23.50      19.64        0.0      31.15      25.21\n",
       "Category 2      25.06      21.78       75.0      27.27      25.95\n",
       "Category 3      25.62      25.46        0.0      23.87      25.09\n",
       "Category 4      25.82      33.12       25.0      17.71      23.75\n",
       "Total          100.00     100.00      100.0     100.00     100.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Pivot table of track_interest (k-means++)')\n",
    "pivot_1_ = pd.crosstab(index = track_interest_clus_1, columns = clusters_reduced,\n",
    "                      colnames = ['clusters']).apply(lambda row: round(row/row.sum()*100,2), axis = 0)\n",
    "total_1_ = []\n",
    "for i in range(5):\n",
    "    total_1_.append(sum(pivot_1_[i]))\n",
    "\n",
    "pivot_1_.columns = ['Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4', 'Cluster 5']\n",
    "pivot_1_.index = ['Category 1', 'Category 2', 'Category 3', 'Category 4']\n",
    "pivot_1_.loc['Total'] = total_1_\n",
    "pivot_1_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pivot table of track_comments (k-means++)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cluster 1</th>\n",
       "      <th>Cluster 2</th>\n",
       "      <th>Cluster 3</th>\n",
       "      <th>Cluster 4</th>\n",
       "      <th>Cluster 5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Category 1</th>\n",
       "      <td>97.86</td>\n",
       "      <td>97.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>98.27</td>\n",
       "      <td>98.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 2</th>\n",
       "      <td>2.14</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>100.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cluster 1  Cluster 2  Cluster 3  Cluster 4  Cluster 5\n",
       "Category 1      97.86       97.5      100.0      98.27      98.39\n",
       "Category 2       2.14        2.5        0.0       1.73       1.61\n",
       "Total          100.00      100.0      100.0     100.00     100.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Pivot table of track_comments (k-means++)')\n",
    "pivot_2_ = pd.crosstab(index = track_comments_clus_1, columns = clusters_reduced,\n",
    "                      colnames = ['clusters']).apply(lambda row: round(row/row.sum()*100,2), axis = 0)\n",
    "total_2_ = []\n",
    "for i in range(5):\n",
    "    total_2_.append(sum(pivot_2_[i]))\n",
    "\n",
    "pivot_2_.columns = ['Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4', 'Cluster 5']\n",
    "pivot_2_.index = ['Category 1', 'Category 2']\n",
    "pivot_2_.loc['Total'] = total_2_\n",
    "pivot_2_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pivot table of track_favorites (k-means++)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cluster 1</th>\n",
       "      <th>Cluster 2</th>\n",
       "      <th>Cluster 3</th>\n",
       "      <th>Cluster 4</th>\n",
       "      <th>Cluster 5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Category 1</th>\n",
       "      <td>38.38</td>\n",
       "      <td>33.27</td>\n",
       "      <td>75.0</td>\n",
       "      <td>44.80</td>\n",
       "      <td>41.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 2</th>\n",
       "      <td>22.92</td>\n",
       "      <td>20.42</td>\n",
       "      <td>25.0</td>\n",
       "      <td>22.84</td>\n",
       "      <td>22.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 3</th>\n",
       "      <td>18.95</td>\n",
       "      <td>19.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.63</td>\n",
       "      <td>18.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 4</th>\n",
       "      <td>19.75</td>\n",
       "      <td>26.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.73</td>\n",
       "      <td>17.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cluster 1  Cluster 2  Cluster 3  Cluster 4  Cluster 5\n",
       "Category 1      38.38      33.27       75.0      44.80      41.03\n",
       "Category 2      22.92      20.42       25.0      22.84      22.64\n",
       "Category 3      18.95      19.35        0.0      17.63      18.49\n",
       "Category 4      19.75      26.96        0.0      14.73      17.84\n",
       "Total          100.00     100.00      100.0     100.00     100.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Pivot table of track_favorites (k-means++)')\n",
    "pivot_3_ = pd.crosstab(index = track_favorites_clus_1, columns = clusters_reduced,\n",
    "                      colnames = ['clusters']).apply(lambda row: round(row/row.sum()*100,2), axis = 0)\n",
    "total_3_ = []\n",
    "for i in range(5):\n",
    "    total_3_.append(sum(pivot_3_[i]))\n",
    "\n",
    "pivot_3_.columns = ['Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4', 'Cluster 5']\n",
    "pivot_3_.index = ['Category 1', 'Category 2', 'Category 3', 'Category 4']\n",
    "pivot_3_.loc['Total'] = total_3_\n",
    "pivot_3_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pivot table of track_duration (k-means++)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cluster 1</th>\n",
       "      <th>Cluster 2</th>\n",
       "      <th>Cluster 3</th>\n",
       "      <th>Cluster 4</th>\n",
       "      <th>Cluster 5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Category 1</th>\n",
       "      <td>15.57</td>\n",
       "      <td>23.82</td>\n",
       "      <td>50.0</td>\n",
       "      <td>31.84</td>\n",
       "      <td>30.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 2</th>\n",
       "      <td>23.68</td>\n",
       "      <td>24.73</td>\n",
       "      <td>25.0</td>\n",
       "      <td>28.28</td>\n",
       "      <td>22.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 3</th>\n",
       "      <td>29.69</td>\n",
       "      <td>24.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.25</td>\n",
       "      <td>22.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 4</th>\n",
       "      <td>31.05</td>\n",
       "      <td>26.88</td>\n",
       "      <td>25.0</td>\n",
       "      <td>17.64</td>\n",
       "      <td>24.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>99.99</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.01</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cluster 1  Cluster 2  Cluster 3  Cluster 4  Cluster 5\n",
       "Category 1      15.57      23.82       50.0      31.84      30.39\n",
       "Category 2      23.68      24.73       25.0      28.28      22.13\n",
       "Category 3      29.69      24.57        0.0      22.25      22.73\n",
       "Category 4      31.05      26.88       25.0      17.64      24.74\n",
       "Total           99.99     100.00      100.0     100.01      99.99"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Pivot table of track_duration (k-means++)')\n",
    "pivot_4_ = pd.crosstab(index = track_dur_clus_1, columns = clusters_reduced,\n",
    "                      colnames = ['clusters']).apply(lambda row: round(row/row.sum()*100,2), axis = 0)\n",
    "total_4_ = []\n",
    "for i in range(5):\n",
    "    total_4_.append(sum(pivot_4_[i]))\n",
    "\n",
    "pivot_4_.columns = ['Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4', 'Cluster 5']\n",
    "pivot_4_.index = ['Category 1', 'Category 2', 'Category 3', 'Category 4']\n",
    "pivot_4_.loc['Total'] = total_4_\n",
    "pivot_4_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pivot table of track_listens (k-means++)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cluster 1</th>\n",
       "      <th>Cluster 2</th>\n",
       "      <th>Cluster 3</th>\n",
       "      <th>Cluster 4</th>\n",
       "      <th>Cluster 5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Category 1</th>\n",
       "      <td>24.41</td>\n",
       "      <td>19.38</td>\n",
       "      <td>50.0</td>\n",
       "      <td>30.10</td>\n",
       "      <td>26.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 2</th>\n",
       "      <td>24.76</td>\n",
       "      <td>21.67</td>\n",
       "      <td>25.0</td>\n",
       "      <td>28.02</td>\n",
       "      <td>25.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 3</th>\n",
       "      <td>25.75</td>\n",
       "      <td>25.54</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.01</td>\n",
       "      <td>24.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category 4</th>\n",
       "      <td>25.08</td>\n",
       "      <td>33.42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.87</td>\n",
       "      <td>24.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>100.00</td>\n",
       "      <td>100.01</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cluster 1  Cluster 2  Cluster 3  Cluster 4  Cluster 5\n",
       "Category 1      24.41      19.38       50.0      30.10      26.19\n",
       "Category 2      24.76      21.67       25.0      28.02      25.10\n",
       "Category 3      25.75      25.54       25.0      24.01      24.51\n",
       "Category 4      25.08      33.42        0.0      17.87      24.20\n",
       "Total          100.00     100.01      100.0     100.00     100.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Pivot table of track_listens (k-means++)')\n",
    "pivot_5_ = pd.crosstab(index = track_list_clus_1, columns = clusters_reduced,\n",
    "                      colnames = ['clusters']).apply(lambda row: round(row/row.sum()*100,2), axis = 0)\n",
    "total_5_ = []\n",
    "for i in range(5):\n",
    "    total_5_.append(sum(pivot_5_[i]))\n",
    "\n",
    "pivot_5_.columns = ['Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4', 'Cluster 5']\n",
    "pivot_5_.index = ['Category 1', 'Category 2', 'Category 3', 'Category 4']\n",
    "pivot_5_.loc['Total'] = total_5_\n",
    "pivot_5_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Algorithmic questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You are given a list of integers, *A*, and another integer *s*. Write an algorithm that outputs all the pairs in *A* that equal *x*.\n",
    "\n",
    "For example, if\n",
    "```\n",
    "A = [7, -2, 8, 2, 6, 4, -7, 2, 1, 3, -3] and s = 4\n",
    "```\n",
    "the algorithm should output: `(7, -3), (-2, 6), (2, 2), (3, 1)`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To solve this question, we have written 3 different algorithms, and we have compared them on the level of time complexity. Also, for this task, we thought to remove duplicates elements on the input list, which has a linear cost $ O(n) $."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Brute Force\n",
    "The time complexity of the algorithm below is $ O(n^{2}) $, because there are two `for` loops (nested `for`), which basically read all the elements of the list two times (the complexity of a `for` for a list is $ O(n) $, with $ n $ being the size of the input list)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def find_pairs_n_2(A, s):\n",
    "    tup_list = []\n",
    "    new_a = []\n",
    "    for num in A:\n",
    "        if num not in new_a: new_a.append(num)\n",
    "    for i in range(len(new_a)-1):\n",
    "        for j in range(i+1, len(new_a)):\n",
    "            if new_a[i] + new_a[j] == s:\n",
    "                tup = tuple((new_a[i], new_a[j]))\n",
    "                tup_list.append(tup)\n",
    "    return tup_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sorting\n",
    "The first algorithm is the worst for the complexity, so we need to find another one, and for this case, we wrote the one in the cell below, with time complexity equal to $ O(n \\cdot log(n)) $, which sorts in ascending order the input list and uses two indices to search on the list the wanted numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def find_pairs_n_logn(A, s):\n",
    "    tup_list = []\n",
    "    new_a = []\n",
    "    for num in A:\n",
    "        if num not in new_a: new_a.append(num)\n",
    "    new_a.sort()\n",
    "    start = 0\n",
    "    end = len(new_a)-1\n",
    "    while start < end:\n",
    "        if new_a[start] + new_a[end] == s:\n",
    "            tup_list.append((new_a[start], new_a[end]))\n",
    "            start += 1\n",
    "        elif new_a[start] + new_a[end] > s:\n",
    "            end -= 1\n",
    "        else:\n",
    "            start += 1\n",
    "\n",
    "    return tup_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hashing\n",
    "Even though the sorting algorithm is way better than the Brute-Force one, we thought that we could do better, so we wrote an algorithm using a hash table. So we used a dictionary to insert each element of the list in it, because the `in` checking has constant complexity ($ O(1) $), resulting in the final complexity of the algorithm as $ O(n) $, which at the end is the best so far."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def find_pairs_n(A, s):\n",
    "\n",
    "    d = {}\n",
    "    tup_list = []\n",
    "    new_a = []\n",
    "    for num in A:\n",
    "        if num not in new_a: new_a.append(num)\n",
    "    for i, e in enumerate(new_a):\n",
    "        if s - e in d:\n",
    "            tup_list.append((new_a[d.get(s - e)], new_a[i]))\n",
    "\n",
    "        d[e] = i\n",
    "\n",
    "    return tup_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "###################################\n",
      "Lenght of the list: 10\n",
      "A = [7, 4, -3, 3, 4, -1, 3, 3, 7, -7]\n",
      "s = 8\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "1.81 µs ± 93.4 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "1.01 µs ± 22.4 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "1.02 µs ± 24.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "###################################\n",
      "Lenght of the list: 20\n",
      "A = [-5, -4, -7, 7, -5, 15, -13, -13, -7, -5, 1, -6, -8, -14, -9, -9, -12, -7, 14, -3]\n",
      "s = 16\n",
      "[(15, 1)]\n",
      "[(1, 15)]\n",
      "[(15, 1)]\n",
      "6.17 µs ± 119 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "2.46 µs ± 23 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "2.49 µs ± 72.4 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "###################################\n",
      "Lenght of the list: 30\n",
      "A = [9, -13, -7, 3, -10, -21, -18, 5, 11, -4, -9, 4, -1, 6, -2, 17, -19, 11, 10, 18, -21, 14, 19, -1, -8, 10, -1, -21, -15, -11]\n",
      "s = 22\n",
      "[(3, 19), (5, 17), (4, 18)]\n",
      "[(3, 19), (4, 18), (5, 17)]\n",
      "[(5, 17), (4, 18), (3, 19)]\n",
      "15.3 µs ± 187 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "5.04 µs ± 139 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "4.99 µs ± 124 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "###################################\n",
      "Lenght of the list: 40\n",
      "A = [-25, 26, -3, 23, -27, 22, -11, -19, -14, -24, 16, 9, 12, 28, 27, -19, 21, 8, -10, -3, -26, 22, -5, 21, -23, 20, -9, -23, 17, -28, -8, 0, -9, 5, 8, 14, -2, 5, 26, -14]\n",
      "s = 24\n",
      "[(26, -2), (-3, 27), (16, 8)]\n",
      "[(-3, 27), (-2, 26), (8, 16)]\n",
      "[(-3, 27), (16, 8), (26, -2)]\n",
      "23.2 µs ± 612 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "7.25 µs ± 106 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "6.8 µs ± 46.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "###################################\n",
      "Lenght of the list: 50\n",
      "A = [-1, 28, -28, 18, 21, 10, -10, -29, -15, 3, -28, 35, 21, -30, 8, -31, 18, 28, -18, 12, -10, -35, 13, -21, 32, 15, 26, 27, 1, -12, 26, -11, -11, 2, -35, 39, 26, 1, 25, -1, 10, -34, -18, -8, -2, 10, -14, -30, -22, 20]\n",
      "s = 18\n",
      "[(28, -10), (10, 8), (3, 15), (-21, 39), (32, -14), (26, -8), (-2, 20)]\n",
      "[(-21, 39), (-14, 32), (-10, 28), (-8, 26), (-2, 20), (3, 15), (8, 10)]\n",
      "[(28, -10), (10, 8), (3, 15), (-21, 39), (26, -8), (32, -14), (-2, 20)]\n",
      "30.2 µs ± 547 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "9.13 µs ± 135 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "8.72 µs ± 108 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "###################################\n"
     ]
    }
   ],
   "source": [
    "print('###################################')\n",
    "\n",
    "for i in range(1, 6):\n",
    "    n = i*10\n",
    "    A = np.random.randint(-(n-i*3), n-i*2, n).tolist()\n",
    "    s = n-2**i\n",
    "    print(\"Lenght of the list:\", n)\n",
    "    print(\"A =\", A)\n",
    "    print(\"s =\", s)\n",
    "\n",
    "    pairs_n_2 = find_pairs_n_2(A, s)\n",
    "    print(pairs_n_2)\n",
    "    pairs_n_logn = find_pairs_n_logn(A, s)\n",
    "    print(pairs_n_logn)\n",
    "    pairs_n = find_pairs_n(A, s)\n",
    "    print(pairs_n)\n",
    "    %timeit find_pairs_n_2(A, s)\n",
    "    %timeit find_pairs_n_logn(A, s)\n",
    "    %timeit find_pairs_n(A, s)\n",
    "    print('###################################')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the results, the final time for the Hashing algorithm is the lowest for most of the configuration, as expected."
   ]
  }
 ],
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