Hyperparameter search.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import traceback\n",
    "import time\n",
    "import sqlite3\n",
    "from pprint import pprint\n",
    "from itertools import product\n",
    "from collections import defaultdict\n",
    "import numpy as np\n",
    "from abracadabra import settings\n",
    "from abracadabra.storage import setup_db\n",
    "from abracadabra.recognise import register_directory, recognise_song"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters to search\n",
    "\n",
    "SAMPLE_RATE = [11025, 22050]\n",
    "POINT_EFFICIENCY = [0.6, 0.8]\n",
    "TARGET_T = [1.8, 2, 2.2]\n",
    "TARGET_F = [2000, 4000, 6000]\n",
    "FFT_WINDOW_SIZE = [0.1, 0.2, 0.4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_db_path = \"temp_experiment.db\"\n",
    "test_path = \"test/\"\n",
    "\n",
    "def clean_for_experiment():\n",
    "    try:\n",
    "        os.remove(experiment_db_path)\n",
    "    except OSError:\n",
    "        pass\n",
    "\n",
    "def run_experiment(parameter_set):\n",
    "    # configure parameters\n",
    "    settings.configure(**parameter_set)\n",
    "    settings.configure(DB_PATH=experiment_db_path)\n",
    "    \n",
    "    # set up DB\n",
    "    setup_db()\n",
    "    \n",
    "    # register what's in music\n",
    "    register_start_time = time.time()\n",
    "    register_directory(\"/home/cameron/Music\")\n",
    "    register_time = time.time() - register_start_time\n",
    "    \n",
    "    # run test suite\n",
    "    wrong_songs = []\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    \n",
    "    test_start_time = time.time()\n",
    "    for root, _, files in os.walk(test_path):\n",
    "        for f in files:\n",
    "            if f.split('.')[-1] != 'wav':\n",
    "                continue\n",
    "            file_path = os.path.join(root, f)\n",
    "            result = recognise_song(file_path)\n",
    "            result_title = result[2]\n",
    "            if result_title.replace(\" \", \"\").lower() in f:\n",
    "                correct += 1\n",
    "            else:\n",
    "                wrong_songs.append(file_path)\n",
    "            total += 1\n",
    "    test_time = time.time() - test_start_time\n",
    "    \n",
    "    db_size_mb = os.stat(experiment_db_path).st_size / (1024*1024)\n",
    "    \n",
    "    return register_time, test_time, correct/total, wrong_songs, db_size_mb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "sample_parameter_set = {\n",
    "    'SAMPLE_RATE': 22050,\n",
    "    'PEAK_BOX_SIZE': 40,\n",
    "    'POINT_EFFICIENCY': 0.6,\n",
    "    'TARGET_T': 1.6,\n",
    "    'TARGET_F': 2000,\n",
    "    'FFT_WINDOW_SIZE': 0.1\n",
    "}\n",
    "\n",
    "# the below takes 233s to register, and that's a lower bound\n",
    "run_experiment(sample_parameter_set)\n",
    "```\n",
    "\n",
    "Result:\n",
    "\n",
    "```\n",
    "(233.28253030776978,\n",
    " 3.6409788131713867,\n",
    " 0.4666666666666667,\n",
    " ['test/aha/thebluesky3.wav',\n",
    "  ...\n",
    "  'test/chriscornell/wideawake3.wav'],\n",
    " 60.953125)\n",
    "```\n",
    " \n",
    "Testing num workers:\n",
    "\n",
    "```python\n",
    "sample_parameter_set = {\n",
    "    'SAMPLE_RATE': 11025,\n",
    "    'PEAK_BOX_SIZE': 40,\n",
    "    'POINT_EFFICIENCY': 0.6,\n",
    "    'TARGET_T': 1.6,\n",
    "    'TARGET_F': 2000,\n",
    "    'FFT_WINDOW_SIZE': 0.1\n",
    "}\n",
    "\n",
    "clean_for_experiment()\n",
    "settings.configure(NUM_WORKERS=24)\n",
    "run_experiment(sample_parameter_set)\n",
    "\n",
    "# NUM_WORKERS = 16 => 94s register\n",
    "# NUM_WORKERS = 20 => 91s register\n",
    "# NUM_WORKERS = 24 => 90s register\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "hide-input"
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    "    'test/chriscornell/callmeadog1.wav',\n",
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    " ({'SAMPLE_RATE': 44100,\n",
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    "  (1957.8937346935272,\n",
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    "   0.9777777777777777,\n",
    "   ['test/aha/huntinghighandlow3.wav',\n",
    "    'test/aha/huntinghighandlow2.wav',\n",
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    "   1939.36328125))]"
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  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.5777777777777777, Time: 144.55441999435425, DB size 106.15625\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.6592592592592592, Time: 155.0715572834015, DB size 107.93359375\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.6444444444444445, Time: 164.47072005271912, DB size 116.0234375\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 4000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.6518518518518519, Time: 188.36431622505188, DB size 198.13671875\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 4000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.7481481481481481, Time: 182.1498625278473, DB size 195.8046875\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 4000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.7185185185185186, Time: 183.81430172920227, DB size 206.25390625\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.6888888888888889, Time: 203.175945520401, DB size 260.97265625\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.7703703703703704, Time: 202.6064474582672, DB size 255.765625\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.725925925925926, Time: 194.08933401107788, DB size 269.55078125\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.5777777777777777, Time: 145.7257149219513, DB size 117.8828125\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.6592592592592592, Time: 142.40554428100586, DB size 117.37890625\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.6444444444444445, Time: 136.98515963554382, DB size 116.02734375\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 4000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.6518518518518519, Time: 157.1101245880127, DB size 218.98046875\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 4000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.7333333333333333, Time: 153.13887810707092, DB size 213.66796875\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 4000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.7185185185185186, Time: 151.89263105392456, DB size 206.31640625\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.6888888888888889, Time: 169.70318484306335, DB size 287.7265625\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.7555555555555555, Time: 165.28016448020935, DB size 279.05078125\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.725925925925926, Time: 162.49510741233826, DB size 269.4921875\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.5703703703703704, Time: 137.19442653656006, DB size 129.55078125\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.6962962962962963, Time: 134.51132011413574, DB size 126.90625\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.6370370370370371, Time: 134.18439602851868, DB size 136.48046875\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 4000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.6518518518518519, Time: 156.06449604034424, DB size 239.58203125\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 4000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.762962962962963, Time: 152.56839036941528, DB size 231.37890625\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 4000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.725925925925926, Time: 152.66477584838867, DB size 243.63671875\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.6888888888888889, Time: 171.7338469028473, DB size 314.40625\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.7777777777777778, Time: 166.02712059020996, DB size 302.50390625\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.7333333333333333, Time: 167.05440616607666, DB size 318.4765625\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.6592592592592592, Time: 177.0868661403656, DB size 156.3984375\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.7333333333333333, Time: 173.98217606544495, DB size 155.61328125\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.7185185185185186, Time: 172.31087064743042, DB size 171.8203125\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 4000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.7333333333333333, Time: 204.44522500038147, DB size 301.34765625\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
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      " 'TARGET_T': 1.8}\n",
      "Acc: 0.8444444444444444, Time: 199.41517233848572, DB size 294.65625\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
      " 'SAMPLE_RATE': 11025,\n",
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      "Acc: 0.8148148148148148, Time: 199.72034907341003, DB size 316.3203125\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
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      " 'TARGET_T': 1.8}\n",
      "Acc: 0.7777777777777778, Time: 229.439923286438, DB size 410.8203125\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
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      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.8518518518518519, Time: 221.59822177886963, DB size 400.58203125\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.8444444444444444, Time: 223.67864775657654, DB size 426.82421875\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.6592592592592592, Time: 176.68104815483093, DB size 174.6484375\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.725925925925926, Time: 173.14148926734924, DB size 169.109375\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.7185185185185186, Time: 171.49167013168335, DB size 171.83984375\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 4000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.7555555555555555, Time: 206.6891746520996, DB size 334.25\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 4000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.837037037037037, Time: 200.86732387542725, DB size 321.2421875\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 4000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.8148148148148148, Time: 199.37368488311768, DB size 316.57421875\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.7777777777777778, Time: 233.73346257209778, DB size 454.4765625\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
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      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.8518518518518519, Time: 226.5818736553192, DB size 436.734375\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.8444444444444444, Time: 222.42616057395935, DB size 426.6328125\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
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      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 2.2}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: 0.6666666666666666, Time: 177.05954837799072, DB size 192.6171875\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
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      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.7555555555555555, Time: 172.62787437438965, DB size 182.7734375\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
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      " 'TARGET_T': 2.2}\n",
      "Acc: 0.7185185185185186, Time: 172.7547845840454, DB size 201.37890625\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
      " 'SAMPLE_RATE': 11025,\n",
      " 'TARGET_F': 4000,\n",
      " 'TARGET_T': 2.2}\n",
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      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
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      " 'TARGET_F': 4000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.8518518518518519, Time: 203.22838521003723, DB size 347.49609375\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
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      " 'TARGET_T': 2.2}\n",
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      "{'FFT_WINDOW_SIZE': 0.1,\n",
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      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.7851851851851852, Time: 240.00787663459778, DB size 497.30859375\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
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      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.8518518518518519, Time: 230.7825789451599, DB size 472.67578125\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
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      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.8518518518518519, Time: 235.09925746917725, DB size 501.64453125\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
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      "Acc: 0.7333333333333333, Time: 282.6430525779724, DB size 219.98046875\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
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      " 'TARGET_T': 1.8}\n",
      "Acc: 0.8148148148148148, Time: 278.09115076065063, DB size 210.69921875\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
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      " 'TARGET_F': 2000,\n",
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      "Acc: 0.7777777777777778, Time: 279.3247973918915, DB size 235.9609375\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
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      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
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      "Acc: 0.8962962962962963, Time: 317.2185468673706, DB size 419.67578125\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
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      " 'TARGET_T': 1.8}\n",
      "Acc: 0.8592592592592593, Time: 318.4261269569397, DB size 458.53515625\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 22050,\n",
      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.9259259259259259, Time: 359.8842787742615, DB size 633.99609375\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
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      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.9111111111111111, Time: 353.3691337108612, DB size 604.01953125\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 22050,\n",
      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.8814814814814815, Time: 358.32455039024353, DB size 655.921875\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 22050,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.7407407407407407, Time: 283.03252148628235, DB size 246.30078125\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 22050,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.837037037037037, Time: 278.7503514289856, DB size 229.375\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 22050,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.7777777777777778, Time: 278.90173149108887, DB size 235.890625\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 22050,\n",
      " 'TARGET_F': 4000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.8814814814814815, Time: 325.6964542865753, DB size 489.79296875\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 22050,\n",
      " 'TARGET_F': 4000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.9037037037037037, Time: 318.86489176750183, DB size 456.7421875\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 22050,\n",
      " 'TARGET_F': 4000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.8592592592592593, Time: 317.95427942276, DB size 458.58984375\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 22050,\n",
      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.9037037037037037, Time: 368.68577790260315, DB size 702.96484375\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 22050,\n",
      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.9111111111111111, Time: 359.7167363166809, DB size 657.9609375\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
      " 'SAMPLE_RATE': 22050,\n",
      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.8814814814814815, Time: 359.2123930454254, DB size 655.95703125\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
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      "Acc: 0.7481481481481481, Time: 284.59954261779785, DB size 272.125\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
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      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.8444444444444444, Time: 279.5370559692383, DB size 247.30078125\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
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      " 'TARGET_T': 2.2}\n",
      "Acc: 0.7481481481481481, Time: 279.6683967113495, DB size 276.0703125\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
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      " 'TARGET_T': 2.2}\n",
      "Acc: 0.8740740740740741, Time: 329.4978678226471, DB size 537.84765625\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
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      " 'SAMPLE_RATE': 22050,\n",
      " 'TARGET_F': 4000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.9185185185185185, Time: 321.2371380329132, DB size 493.33984375\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
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      " 'TARGET_T': 2.2}\n",
      "Acc: 0.8740740740740741, Time: 325.4377992153168, DB size 537.71875\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
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      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.9111111111111111, Time: 377.1712381839752, DB size 771.0234375\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
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      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.9407407407407408, Time: 364.20033526420593, DB size 711.40234375\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.6,\n",
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      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.9037037037037037, Time: 372.7632553577423, DB size 770.96875\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
      " 'SAMPLE_RATE': 22050,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.7777777777777778, Time: 423.6953339576721, DB size 319.88671875\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
      " 'SAMPLE_RATE': 22050,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.8962962962962963, Time: 419.0145411491394, DB size 293.54296875\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
      " 'SAMPLE_RATE': 22050,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.8444444444444444, Time: 421.295862197876, DB size 335.65234375\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
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      " 'TARGET_F': 4000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.8814814814814815, Time: 479.58902645111084, DB size 662.26171875\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
      " 'SAMPLE_RATE': 22050,\n",
      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 1.8}\n",
      "Acc: 0.8962962962962963, Time: 543.5278236865997, DB size 967.44140625\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
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      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.7703703703703704, Time: 426.34719824790955, DB size 360.56640625\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
      " 'SAMPLE_RATE': 22050,\n",
      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.8888888888888888, Time: 418.9832990169525, DB size 319.0390625\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
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      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.8444444444444444, Time: 418.20398712158203, DB size 335.46484375\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
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      " 'TARGET_F': 4000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.8814814814814815, Time: 478.15187096595764, DB size 662.5078125\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
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      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2}\n",
      "Acc: 0.8962962962962963, Time: 542.3392200469971, DB size 967.140625\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
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      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 2.2}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: 0.8148148148148148, Time: 428.4971299171448, DB size 399.64453125\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
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      " 'TARGET_F': 2000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.8962962962962963, Time: 420.0734758377075, DB size 343.88671875\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
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      " 'TARGET_T': 2.2}\n",
      "Acc: 0.8592592592592593, Time: 419.7284965515137, DB size 391.046875\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
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      " 'TARGET_T': 2.2}\n",
      "Acc: 0.9185185185185185, Time: 497.44073390960693, DB size 798.0703125\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
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      " 'TARGET_T': 2.2}\n",
      "Acc: 0.9333333333333333, Time: 482.87402534484863, DB size 698.234375\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
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      " 'TARGET_T': 2.2}\n",
      "Acc: 0.8888888888888888, Time: 486.77300810813904, DB size 774.7421875\n",
      "{'FFT_WINDOW_SIZE': 0.1,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
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      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.9333333333333333, Time: 574.132798910141, DB size 1163.01953125\n",
      "{'FFT_WINDOW_SIZE': 0.2,\n",
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      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.9481481481481482, Time: 549.5703568458557, DB size 1030.7890625\n",
      "{'FFT_WINDOW_SIZE': 0.4,\n",
      " 'POINT_EFFICIENCY': 0.8,\n",
      " 'SAMPLE_RATE': 22050,\n",
      " 'TARGET_F': 6000,\n",
      " 'TARGET_T': 2.2}\n",
      "Acc: 0.9259259259259259, Time: 560.3336403369904, DB size 1132.26953125\n"
     ]
    }
   ],
   "source": [
    "def result_already_calculated(params):\n",
    "    for result in results:\n",
    "        if params == result[0]:\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "for comb in product(SAMPLE_RATE, POINT_EFFICIENCY, TARGET_T, TARGET_F, FFT_WINDOW_SIZE):\n",
    "    exp_parameters = {\n",
    "        'SAMPLE_RATE': comb[0],\n",
    "        'POINT_EFFICIENCY': comb[1],\n",
    "        'TARGET_T': comb[2],\n",
    "        'TARGET_F': comb[3],\n",
    "        'FFT_WINDOW_SIZE': comb[4]\n",
    "    }\n",
    "    if result_already_calculated(exp_parameters):\n",
    "        continue\n",
    "    pprint(exp_parameters)\n",
    "    completed = False\n",
    "    while not completed:\n",
    "        try:\n",
    "            clean_for_experiment()\n",
    "            result = exp_parameters, run_experiment(exp_parameters)\n",
    "            print(f\"Acc: {result[1][2]}, Time: {result[1][0] + result[1][1]}, DB size {result[1][-1]}\")\n",
    "            results.append(result)\n",
    "            completed = True\n",
    "        except sqlite3.OperationalError as e:\n",
    "            print(\"Failed\")\n",
    "            print(traceback.format_exc())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   'FFT_WINDOW_SIZE': 0.1},\n",
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       "    'test/ab/metalingus1.wav',\n",
       "    'test/chriscornell/ashopeandpromisefade2.wav',\n",
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       "    'test/chriscornell/groundzero3.wav',\n",
       "    'test/chriscornell/groundzero1.wav'],\n",
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       "   'FFT_WINDOW_SIZE': 0.1},\n",
       "  (419.863139629364,\n",
       "   8.633990287780762,\n",
       "   0.8148148148148148,\n",
       "   ['test/aha/takeonme5.wav',\n",
       "    'test/aha/scoundreldays1.wav',\n",
       "    'test/aha/huntinghighandlow3.wav',\n",
       "    'test/aha/takeonme2.wav',\n",
       "    'test/aha/andyoutellme1.wav',\n",
       "    'test/aha/thebluesky1.wav',\n",
       "    'test/aha/scoundreldays2.wav',\n",
       "    'test/aha/scoundreldays3.wav',\n",
       "    'test/aha/theswingofthings2.wav',\n",
       "    'test/aha/october2.wav',\n",
       "    'test/aha/huntinghighandlow2.wav',\n",
       "    'test/aha/huntinghighandlow1.wav',\n",
       "    'test/aha/theswingofthings3.wav',\n",
       "    'test/ab/stillremains3.wav',\n",
       "    'test/ab/stillremains2.wav',\n",
       "    'test/ab/wonderfullife1.wav',\n",
       "    'test/ab/peaceisbroken3.wav',\n",
       "    'test/ab/peaceisbroken2.wav',\n",
       "    'test/ab/makeitright3.wav',\n",
       "    'test/ab/peaceisbroken5.wav',\n",
       "    'test/ab/peaceisbroken4.wav',\n",
       "    'test/ab/theuninvited5.wav',\n",
       "    'test/ab/cryofachilles8.wav',\n",
       "    'test/ab/cryofachilles9.wav',\n",
       "    'test/chriscornell/callmeadog3.wav'],\n",
       "   399.64453125)),\n",
       " ({'SAMPLE_RATE': 22050,\n",
       "   'POINT_EFFICIENCY': 0.8,\n",
       "   'TARGET_T': 2.2,\n",
       "   'TARGET_F': 2000,\n",
       "   'FFT_WINDOW_SIZE': 0.2},\n",
       "  (415.61518144607544,\n",
       "   4.45829439163208,\n",
       "   0.8962962962962963,\n",
       "   ['test/aha/thebluesky3.wav',\n",
       "    'test/aha/scoundreldays1.wav',\n",
       "    'test/aha/huntinghighandlow3.wav',\n",
       "    'test/aha/thebluesky1.wav',\n",
       "    'test/aha/theswingofthings1.wav',\n",
       "    'test/aha/manhattanskyline2.wav',\n",
       "    'test/aha/trainofthought4.wav',\n",
       "    'test/aha/huntinghighandlow1.wav',\n",
       "    'test/aha/theswingofthings3.wav',\n",
       "    'test/aha/softrainsofapril2.wav',\n",
       "    'test/ab/stillremains2.wav',\n",
       "    'test/ab/cryofachilles4.wav',\n",
       "    'test/ab/openyoureyes1.wav',\n",
       "    'test/chriscornell/callmeadog3.wav'],\n",
       "   343.88671875)),\n",
       " ({'SAMPLE_RATE': 22050,\n",
       "   'POINT_EFFICIENCY': 0.8,\n",
       "   'TARGET_T': 2.2,\n",
       "   'TARGET_F': 2000,\n",
       "   'FFT_WINDOW_SIZE': 0.4},\n",
       "  (416.3304092884064,\n",
       "   3.3980872631073,\n",
       "   0.8592592592592593,\n",
       "   ['test/aha/thebluesky3.wav',\n",
       "    'test/aha/takeonme5.wav',\n",
       "    'test/aha/huntinghighandlow3.wav',\n",
       "    'test/aha/takeonme2.wav',\n",
       "    'test/aha/manhattanskyline1.wav',\n",
       "    'test/aha/october1.wav',\n",
       "    'test/aha/takeonme1.wav',\n",
       "    'test/aha/theswingofthings1.wav',\n",
       "    'test/aha/theswingofthings2.wav',\n",
       "    'test/aha/huntinghighandlow2.wav',\n",
       "    'test/aha/huntinghighandlow1.wav',\n",
       "    'test/ab/stillremains3.wav',\n",
       "    'test/ab/allhopeisgone1.wav',\n",
       "    'test/ab/makeitright3.wav',\n",
       "    'test/ab/cryofachilles2.wav',\n",
       "    'test/ab/addictedtopain1.wav',\n",
       "    'test/ab/cryofachilles7.wav',\n",
       "    'test/ab/wonderfullife2.wav',\n",
       "    'test/chriscornell/groundzero2.wav'],\n",
       "   391.046875)),\n",
       " ({'SAMPLE_RATE': 22050,\n",
       "   'POINT_EFFICIENCY': 0.8,\n",
       "   'TARGET_T': 2.2,\n",
       "   'TARGET_F': 4000,\n",
       "   'FFT_WINDOW_SIZE': 0.1},\n",
       "  (482.9752404689789,\n",
       "   14.465493440628052,\n",
       "   0.9185185185185185,\n",
       "   ['test/aha/scoundreldays1.wav',\n",
       "    'test/aha/huntinghighandlow3.wav',\n",
       "    'test/aha/andyoutellme1.wav',\n",
       "    'test/aha/theswingofthings2.wav',\n",
       "    'test/aha/huntinghighandlow2.wav',\n",
       "    'test/aha/theswingofthings3.wav',\n",
       "    'test/ab/stillremains3.wav',\n",
       "    'test/ab/wonderfullife1.wav',\n",
       "    'test/ab/cryofachilles8.wav',\n",
       "    'test/ab/onedayremains2(loud).wav',\n",
       "    'test/ab/cryofachilles9.wav'],\n",
       "   798.0703125)),\n",
       " ({'SAMPLE_RATE': 22050,\n",
       "   'POINT_EFFICIENCY': 0.8,\n",
       "   'TARGET_T': 2.2,\n",
       "   'TARGET_F': 4000,\n",
       "   'FFT_WINDOW_SIZE': 0.2},\n",
       "  (476.85980558395386,\n",
       "   6.014219760894775,\n",
       "   0.9333333333333333,\n",
       "   ['test/aha/thebluesky3.wav',\n",
       "    'test/aha/huntinghighandlow3.wav',\n",
       "    'test/aha/thebluesky1.wav',\n",
       "    'test/aha/theswingofthings1.wav',\n",
       "    'test/aha/theswingofthings2.wav',\n",
       "    'test/aha/huntinghighandlow2.wav',\n",
       "    'test/aha/theswingofthings3.wav',\n",
       "    'test/aha/softrainsofapril2.wav',\n",
       "    'test/ab/openyoureyes1.wav'],\n",
       "   698.234375)),\n",
       " ({'SAMPLE_RATE': 22050,\n",
       "   'POINT_EFFICIENCY': 0.8,\n",
       "   'TARGET_T': 2.2,\n",
       "   'TARGET_F': 4000,\n",
       "   'FFT_WINDOW_SIZE': 0.4},\n",
       "  (482.4067122936249,\n",
       "   4.36629581451416,\n",
       "   0.8888888888888888,\n",
       "   ['test/aha/thebluesky3.wav',\n",
       "    'test/aha/huntinghighandlow3.wav',\n",
       "    'test/aha/takeonme2.wav',\n",
       "    'test/aha/manhattanskyline1.wav',\n",
       "    'test/aha/loveisreason3.wav',\n",
       "    'test/aha/theswingofthings1.wav',\n",
       "    'test/aha/theswingofthings2.wav',\n",
       "    'test/aha/huntinghighandlow2.wav',\n",
       "    'test/aha/trainofthought4.wav',\n",
       "    'test/aha/huntinghighandlow1.wav',\n",
       "    'test/ab/stillremains3.wav',\n",
       "    'test/ab/makeitright3.wav',\n",
       "    'test/ab/bleeditdry1.wav',\n",
       "    'test/ab/addictedtopain1.wav',\n",
       "    'test/ab/cryofachilles7.wav'],\n",
       "   774.7421875)),\n",
       " ({'SAMPLE_RATE': 22050,\n",
       "   'POINT_EFFICIENCY': 0.8,\n",
       "   'TARGET_T': 2.2,\n",
       "   'TARGET_F': 6000,\n",
       "   'FFT_WINDOW_SIZE': 0.1},\n",
       "  (555.341787815094,\n",
       "   18.791011095046997,\n",
       "   0.9333333333333333,\n",
       "   ['test/aha/scoundreldays1.wav',\n",
       "    'test/aha/huntinghighandlow3.wav',\n",
       "    'test/aha/theswingofthings2.wav',\n",
       "    'test/aha/huntinghighandlow2.wav',\n",
       "    'test/aha/theswingofthings3.wav',\n",
       "    'test/ab/stillremains3.wav',\n",
       "    'test/ab/wonderfullife1.wav',\n",
       "    'test/ab/cryofachilles8.wav',\n",
       "    'test/ab/cryofachilles9.wav'],\n",
       "   1163.01953125)),\n",
       " ({'SAMPLE_RATE': 22050,\n",
       "   'POINT_EFFICIENCY': 0.8,\n",
       "   'TARGET_T': 2.2,\n",
       "   'TARGET_F': 6000,\n",
       "   'FFT_WINDOW_SIZE': 0.2},\n",
       "  (541.746274471283,\n",
       "   7.824082374572754,\n",
       "   0.9481481481481482,\n",
       "   ['test/aha/huntinghighandlow3.wav',\n",
       "    'test/aha/thebluesky1.wav',\n",
       "    'test/aha/theswingofthings1.wav',\n",
       "    'test/aha/theswingofthings2.wav',\n",
       "    'test/aha/huntinghighandlow1.wav',\n",
       "    'test/aha/softrainsofapril2.wav',\n",
       "    'test/ab/openyoureyes1.wav'],\n",
       "   1030.7890625)),\n",
       " ({'SAMPLE_RATE': 22050,\n",
       "   'POINT_EFFICIENCY': 0.8,\n",
       "   'TARGET_T': 2.2,\n",
       "   'TARGET_F': 6000,\n",
       "   'FFT_WINDOW_SIZE': 0.4},\n",
       "  (555.6798532009125,\n",
       "   4.653787136077881,\n",
       "   0.9259259259259259,\n",
       "   ['test/aha/huntinghighandlow3.wav',\n",
       "    'test/aha/takeonme2.wav',\n",
       "    'test/aha/manhattanskyline1.wav',\n",
       "    'test/aha/theswingofthings1.wav',\n",
       "    'test/aha/theswingofthings2.wav',\n",
       "    'test/aha/huntinghighandlow2.wav',\n",
       "    'test/aha/huntinghighandlow1.wav',\n",
       "    'test/ab/stillremains3.wav',\n",
       "    'test/ab/makeitright3.wav',\n",
       "    'test/ab/addictedtopain1.wav'],\n",
       "   1132.26953125))]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample rate:\n",
      "22050: T 465.3062574171251 A 0.801911589008363 S 660.5575856854839 N 62\n",
      "44100: T 1880.893725067377 A 0.7296296296296296 S 1654.8203125 N 8\n",
      "11025: T 182.12063121354137 A 0.733058984910837 S 262.22309027777777 N 54\n",
      "Efficiency:\n",
      "0.8: T 558.4471000625241 A 0.8229390681003582 S 648.6294732862904 N 62\n",
      "1: T 754.6775241494179 A 0.30092592592592593 S 1240.16943359375 N 8\n",
      "0.6: T 242.02798979370684 A 0.7724279835390947 S 337.34809027777777 N 54\n",
      "Target T:\n",
      "1.8: T 488.5869929898869 A 0.7496632996632997 S 566.5537997159091 N 44\n",
      "2: T 492.28342108834875 A 0.7498316498316498 S 611.3244850852273 N 44\n",
      "2.2: T 293.6764896247122 A 0.8100823045267491 S 459.07042100694446 N 36\n",
      "Target F:\n",
      "4000: T 476.25101857835597 A 0.7686868686868685 S 566.2109375 N 44\n",
      "6000: T 536.2040799422698 A 0.7882154882154881 S 807.2028586647727 N 44\n",
      "2000: T 255.07298641734653 A 0.739917695473251 S 220.08257378472223 N 36\n",
      "FFT Window:\n",
      "0.1: T 499.5755120082335 A 0.677104377104377 S 652.880859375 N 44\n",
      "0.2: T 483.1778463341973 A 0.8284511784511784 S 552.8489879261364 N 44\n",
      "0.4: T 291.375113301807 A 0.8026748971193416 S 425.0296223958333 N 36\n",
      "NPERSEG:\n",
      "2205.0: T 357.6215671479702 A 0.7359259259259259 S 537.46767578125 N 40\n",
      "4410.0: T 486.6628076000647 A 0.7727272727272727 S 577.6681463068181 N 44\n",
      "8820.0: T 667.438351555304 A 0.8818181818181821 S 757.96484375 N 22\n",
      "1102.5: T 184.94343841075897 A 0.6835390946502058 S 264.5470920138889 N 18\n"
     ]
    }
   ],
   "source": [
    "by_sample_rate = defaultdict(list)\n",
    "by_point_efficiency = defaultdict(list)\n",
    "by_target_t = defaultdict(list)\n",
    "by_target_f = defaultdict(list)\n",
    "by_fft_window_size = defaultdict(list)\n",
    "by_nperseg = defaultdict(list)\n",
    "\n",
    "for result in results:\n",
    "    by_sample_rate[result[0]['SAMPLE_RATE']].append(result[1])\n",
    "    by_point_efficiency[result[0]['POINT_EFFICIENCY']].append(result[1])\n",
    "    by_target_t[result[0]['TARGET_T']].append(result[1])\n",
    "    by_target_f[result[0]['TARGET_F']].append(result[1])\n",
    "    by_fft_window_size[result[0]['FFT_WINDOW_SIZE']].append(result[1])\n",
    "    by_nperseg[result[0]['FFT_WINDOW_SIZE'] * result[0]['SAMPLE_RATE']].append(result[1])\n",
    "\n",
    "def show_stats(d):\n",
    "    for value, results in d.items():\n",
    "        time = np.mean([r[0] + r[1] for r in results])\n",
    "        acc  = np.mean([r[2] for r in results])\n",
    "        size = np.mean([r[-1] for r in results])\n",
    "        print(f\"{value}: T {time} A {acc} S {size} N {len(results)}\")\n",
    "\n",
    "print(\"Sample rate:\")\n",
    "show_stats(by_sample_rate)\n",
    "print(\"Efficiency:\")\n",
    "show_stats(by_point_efficiency)\n",
    "print(\"Target T:\")\n",
    "show_stats(by_target_t)\n",
    "print(\"Target F:\")\n",
    "show_stats(by_target_f)\n",
    "print(\"FFT Window:\")\n",
    "show_stats(by_fft_window_size)\n",
    "print(\"NPERSEG:\")\n",
    "show_stats(by_nperseg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from csv import writer\n",
    "\n",
    "table_results = []\n",
    "for r in results:\n",
    "    table_results.append([r[0]['SAMPLE_RATE'], r[0]['POINT_EFFICIENCY'],\n",
    "                          r[0]['TARGET_T'], r[0]['TARGET_F'], r[0]['FFT_WINDOW_SIZE'],\n",
    "                          r[0]['FFT_WINDOW_SIZE']*r[0]['SAMPLE_RATE'], *r[1]])\n",
    "    \n",
    "with open('results.csv', 'a') as csv_file:\n",
    "    w = writer(csv_file)\n",
    "    w.writerows(table_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'SAMPLE_RATE': 22050, 'PEAK_BOX_SIZE': 25, 'TARGET_T': 1.8, 'TARGET_F': 5000, 'FFT_WINDOW_SIZE': 0.1}\n",
      "Acc: 0.9259259259259259, Time: 561.7235832214355, DB size 1131.98828125\n",
      "{'SAMPLE_RATE': 22050, 'PEAK_BOX_SIZE': 20, 'POINT_EFFICIENCY': 0.8, 'TARGET_T': 1.8, 'TARGET_F': 5000, 'FFT_WINDOW_SIZE': 0.1}\n",
      "Acc: 0.9259259259259259, Time: 573.3089210987091, DB size 1132.11328125\n",
      "{'SAMPLE_RATE': 22050, 'PEAK_BOX_SIZE': 25, 'POINT_EFFICIENCY': 0.6, 'TARGET_T': 1.8, 'TARGET_F': 5000, 'FFT_WINDOW_SIZE': 0.1}\n",
      "Acc: 0.9259259259259259, Time: 571.6769626140594, DB size 1132.25390625\n",
      "{'SAMPLE_RATE': 22050, 'PEAK_BOX_SIZE': 20, 'POINT_EFFICIENCY': 0.6, 'TARGET_T': 1.8, 'TARGET_F': 5000, 'FFT_WINDOW_SIZE': 0.1}\n",
      "Acc: 0.9259259259259259, Time: 573.9296202659607, DB size 1132.109375\n"
     ]
    }
   ],
   "source": [
    "interesting_exp_parameters = [\n",
    "    {\n",
    "        'SAMPLE_RATE': 22050,\n",
    "        'PEAK_BOX_SIZE': 25,\n",
    "        'TARGET_T': 1.8,\n",
    "        'TARGET_F': 5000,\n",
    "        'FFT_WINDOW_SIZE': 0.1\n",
    "    },\n",
    "    {\n",
    "        'SAMPLE_RATE': 22050,\n",
    "        'PEAK_BOX_SIZE': 20,\n",
    "        'POINT_EFFICIENCY': 0.8,\n",
    "        'TARGET_T': 1.8,\n",
    "        'TARGET_F': 5000,\n",
    "        'FFT_WINDOW_SIZE': 0.1\n",
    "    },\n",
    "    {\n",
    "        'SAMPLE_RATE': 22050,\n",
    "        'PEAK_BOX_SIZE': 25,\n",
    "        'POINT_EFFICIENCY': 0.6,\n",
    "        'TARGET_T': 1.8,\n",
    "        'TARGET_F': 5000,\n",
    "        'FFT_WINDOW_SIZE': 0.1\n",
    "    },\n",
    "    {\n",
    "        'SAMPLE_RATE': 22050,\n",
    "        'PEAK_BOX_SIZE': 20,\n",
    "        'POINT_EFFICIENCY': 0.6,\n",
    "        'TARGET_T': 1.8,\n",
    "        'TARGET_F': 5000,\n",
    "        'FFT_WINDOW_SIZE': 0.1\n",
    "    }\n",
    "]\n",
    "\n",
    "interesting_results = []\n",
    "\n",
    "for params in interesting_exp_parameters:\n",
    "    print(params)\n",
    "    clean_for_experiment()\n",
    "    result = exp_parameters, run_experiment(exp_parameters)\n",
    "    print(f\"Acc: {result[1][2]}, Time: {result[1][0] + result[1][1]}, DB size {result[1][-1]}\")\n",
    "    interesting_results.append(result)"
   ]
  }
 ],
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