Main_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Music Recognition(SHAZAM) with Concrete ML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import librosa\n",
    "import os\n",
    "from numpy.linalg import norm\n",
    "from scipy.ndimage import maximum_filter\n",
    "from scipy.ndimage import generate_binary_structure, binary_erosion\n",
    "from concrete import fhe\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from concrete.ml.sklearn import DecisionTreeClassifier as ConcreteDTC\n",
    "from concrete.ml.sklearn import LogisticRegression as ConcreteLR\n",
    "from sklearn.model_selection import train_test_split\n",
    "import time\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CONSTANTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "MUSIC_DB_PATH = '../dataset_download/dataset2/fma_small'\n",
    "TEST_SAMPLE_PATH = '../dataset_download/test_samples'\n",
    "SAMPLE_RATE = 22050\n",
    "MFCC_SAMPLES_PER_SEC = 2\n",
    "NUM_SONGS = 400"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Necessary functions for model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute features function.\n",
    "This function returns a matrix of features. The ith entry in `feats` is the sum of mfcc coefficients in a 500ms window, the entire song is divided into 500ms chunks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_features(y):\n",
    "    feats = []\n",
    "    win_sz = SAMPLE_RATE//MFCC_SAMPLES_PER_SEC\n",
    "    for i in range(len(y)//win_sz):\n",
    "        y_chunk = y[win_sz*i:win_sz*(i+1)]\n",
    "        mfcc_f = np.sum(librosa.feature.mfcc(y=y_chunk, sr=SAMPLE_RATE), axis=1)\n",
    "        feats.append(mfcc_f)\n",
    "    return feats\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add features function.\n",
    "This function retrieves features for a specified song and appends it to a feature_db array along with its id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_db = []\n",
    "def add_features(y, id):\n",
    "    feats = compute_features(y)\n",
    "    for feat in feats:\n",
    "        feature_db.append((feat, id))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load 3 minute samples from each song from the specified directory and build the feature database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id: 0, song_name: 135054.mp3\n",
      "id: 1, song_name: 135336.mp3\n",
      "id: 2, song_name: 135337.mp3\n",
      "id: 3, song_name: 135043.mp3\n",
      "id: 4, song_name: 135091.mp3\n",
      "id: 5, song_name: 135092.mp3\n",
      "id: 6, song_name: 135044.mp3\n",
      "id: 7, song_name: 135989.mp3\n",
      "id: 8, song_name: 135221.mp3\n",
      "id: 9, song_name: 135369.mp3\n",
      "id: 10, song_name: 135341.mp3\n",
      "id: 11, song_name: 135340.mp3\n",
      "id: 12, song_name: 135368.mp3\n",
      "id: 13, song_name: 135220.mp3\n",
      "id: 14, song_name: 135222.mp3\n",
      "id: 15, song_name: 135342.mp3\n",
      "id: 16, song_name: 135223.mp3\n",
      "id: 17, song_name: 135227.mp3\n",
      "id: 18, song_name: 135226.mp3\n",
      "id: 19, song_name: 135224.mp3\n",
      "id: 20, song_name: 135219.mp3\n",
      "id: 21, song_name: 135225.mp3\n",
      "id: 22, song_name: 135228.mp3\n",
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      "id: 25, song_name: 135229.mp3\n",
      "id: 26, song_name: 135363.mp3\n",
      "id: 27, song_name: 135028.mp3\n",
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      "id: 246, song_name: 133452.mp3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[src/libmpg123/parse.c:do_readahead():1099] warning: Cannot read next header, a one-frame stream? Duh...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id: 247, song_name: 133916.mp3\n",
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      "id: 249, song_name: 133453.mp3\n",
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      "id: 331, song_name: 134950.mp3\n",
      "id: 332, song_name: 134979.mp3\n",
      "id: 333, song_name: 134951.mp3\n",
      "id: 334, song_name: 134825.mp3\n",
      "id: 335, song_name: 134077.mp3\n",
      "id: 336, song_name: 134922.mp3\n",
      "id: 337, song_name: 134936.mp3\n",
      "id: 338, song_name: 134937.mp3\n",
      "id: 339, song_name: 134923.mp3\n",
      "id: 340, song_name: 134935.mp3\n",
      "id: 341, song_name: 134510.mp3\n",
      "id: 342, song_name: 134934.mp3\n",
      "id: 343, song_name: 134449.mp3\n",
      "id: 344, song_name: 134930.mp3\n",
      "id: 345, song_name: 134918.mp3\n",
      "id: 346, song_name: 134919.mp3\n",
      "id: 347, song_name: 134931.mp3\n",
      "id: 348, song_name: 134448.mp3\n",
      "id: 349, song_name: 134933.mp3\n",
      "id: 350, song_name: 134932.mp3\n",
      "id: 351, song_name: 134073.mp3\n",
      "id: 352, song_name: 134452.mp3\n",
      "id: 353, song_name: 134446.mp3\n",
      "id: 354, song_name: 134094.mp3\n",
      "id: 355, song_name: 134447.mp3\n",
      "id: 356, song_name: 134453.mp3\n",
      "id: 357, song_name: 134445.mp3\n",
      "id: 358, song_name: 134486.mp3\n",
      "id: 359, song_name: 134928.mp3\n",
      "id: 360, song_name: 134929.mp3\n",
      "id: 361, song_name: 134054.mp3\n",
      "id: 362, song_name: 134444.mp3\n",
      "id: 363, song_name: 134454.mp3\n",
      "id: 364, song_name: 134939.mp3\n",
      "id: 365, song_name: 134509.mp3\n",
      "id: 366, song_name: 134938.mp3\n",
      "id: 367, song_name: 134643.mp3\n",
      "id: 368, song_name: 134455.mp3\n",
      "id: 369, song_name: 134443.mp3\n",
      "id: 370, song_name: 134053.mp3\n",
      "id: 371, song_name: 134084.mp3\n",
      "id: 372, song_name: 134052.mp3\n",
      "id: 373, song_name: 134790.mp3\n",
      "id: 374, song_name: 134948.mp3\n",
      "id: 375, song_name: 134586.mp3\n",
      "id: 376, song_name: 134949.mp3\n",
      "id: 377, song_name: 134791.mp3\n",
      "id: 378, song_name: 134034.mp3\n",
      "id: 379, song_name: 134801.mp3\n",
      "id: 380, song_name: 134793.mp3\n",
      "id: 381, song_name: 134962.mp3\n",
      "id: 382, song_name: 134792.mp3\n",
      "id: 383, song_name: 134631.mp3\n",
      "id: 384, song_name: 134796.mp3\n",
      "id: 385, song_name: 134594.mp3\n",
      "id: 386, song_name: 134580.mp3\n",
      "id: 387, song_name: 134797.mp3\n",
      "id: 388, song_name: 134385.mp3\n",
      "id: 389, song_name: 134959.mp3\n",
      "id: 390, song_name: 134795.mp3\n",
      "id: 391, song_name: 134794.mp3\n",
      "id: 392, song_name: 134384.mp3\n",
      "id: 393, song_name: 151404.mp3\n",
      "id: 394, song_name: 024367.mp3\n",
      "id: 395, song_name: 024429.mp3\n",
      "id: 396, song_name: 024428.mp3\n",
      "id: 397, song_name: 024366.mp3\n",
      "id: 398, song_name: 024364.mp3\n",
      "id: 399, song_name: 024370.mp3\n",
      "Total number of features to train on: 23770\n"
     ]
    }
   ],
   "source": [
    "idx = []\n",
    "\n",
    "for dirpath, dnames, fnames in os.walk(MUSIC_DB_PATH):\n",
    "    if len(idx) >= NUM_SONGS:\n",
    "        break\n",
    "    for f in fnames:\n",
    "        try:\n",
    "            song_name = f\n",
    "            song_path = os.path.join(dirpath, f)\n",
    "            y, sr = librosa.load(song_path, sr = SAMPLE_RATE, duration = 180)\n",
    "            if song_name not in idx:\n",
    "                idx.append(song_name)\n",
    "                print(f\"id: {idx.index(song_name)}, song_name: {song_name}\")\n",
    "            add_features(y, idx.index(song_name))\n",
    "            if len(idx) >= NUM_SONGS:\n",
    "                break\n",
    "        except:\n",
    "            pass\n",
    "print(f\"Total number of features to train on: {len(feature_db)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initial bincount of features with respect to class ids: [60 59 60 60 59 59 59 60 59 59 59 60 59 59 59 59 59 60 60 60 59 59 60 60\n",
      " 60 59 60 59 59 59 60 59 59 60 59 60 59 59 59 59 60 59 59 59 60 60 60 59\n",
      " 59 59 59 59 59 59 59 60 60 59 59 60 60 59 59 59 59 59 59 59 59 59 59 59\n",
      " 60 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59\n",
      " 59 60 59 59 59 60 60 60 59 59 60 60 60 59 59 60 59 59 60 59 60 60 59 59\n",
      " 59 59 60 59 60 60 60 60 59 59 59 60 59 59 59 59 59 60 60 59 59 59 60 60\n",
      " 59 60 60 59 59 60 60 59 60 60 59 59 59 60 59 60 59 59 59 60 59 59 60 59\n",
      " 59 60 60 60 60 59 59 60 60 60 60 60 59 60 59 60 59 60 59 59 59 60 60 59\n",
      " 60 59 59 59 59 59 59 60 59 60 60 60 59 59 60 59 59 60 60 60 60 59 60 59\n",
      " 60 60 60 60 59 59 60 59 60 60 59 59 59 59 60 59 59 59 59 59 59 60 60 60\n",
      " 59 60 60 59 60 59 60 60 59 60 59 59 60 60 60 60 60 59 59 60 60 60 59 60\n",
      " 59 60 59 59 59 60 59 59 59 60 59 59 60 59 60 60 60 59 59 59 59 60 59 59\n",
      " 60 60 59 60 60 59 60 59 59 59 60 60 59 59 60 60 59 59 60 59 60 60 60 60\n",
      " 59 59 60 60 60 60 59 59 60 59 59 60 60 59 59 59 60 60 59 60 60 59 59 59\n",
      " 59 59 59 59 60 59 60 60 60 60 60 60 59 59 60 59 59 60 60 59 60 59 60 60\n",
      " 59 59 59 59 59 60 59 60 60 60 59 60 59 59 60 59 60 60 59 59 60 59 60 59\n",
      " 59 59 60 59 59 60 60 60 60 60 59 59 60 60 59 59]\n"
     ]
    }
   ],
   "source": [
    "features = np.array([fp[0] for fp in feature_db])\n",
    "ids = np.array([fp[1] for fp in feature_db])\n",
    "print(\"initial bincount of features with respect to class ids:\",np.bincount(ids))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation function\n",
    "- Model: the model to be evaluated\n",
    "- Model Config: Model config parameters such as name, n_bits, max_depth\n",
    "- truth_label: A dictionary which maps the test sample file name to corresponding true id\n",
    "- predict_in_fhe: If true the prediction is run in fhe mode instead of simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, model_config, truth_label, samples_path, predict_in_fhe = False):\n",
    "    evaluation = {**model_config}\n",
    "    evaluation[\"Inference Time\"] = 0\n",
    "    evaluation[\"Accuracy\"] = 0\n",
    "\n",
    "    if predict_in_fhe:\n",
    "        evaluation[\"Inference Time (FHE)\"] = 0\n",
    "        evaluation[\"Accuracy (FHE)\"] = 0\n",
    "\n",
    "    for dirpath, dnames, fnames in os.walk(samples_path):\n",
    "        for f in fnames:\n",
    "            if f not in truth_label:\n",
    "                continue\n",
    "            \n",
    "            print(f\"processing {f}\")\n",
    "            song_path = os.path.join(dirpath, f)\n",
    "            y, sr = librosa.load(song_path, sr = SAMPLE_RATE, duration = 15)\n",
    "            features = compute_features(y)\n",
    "\n",
    "            start = time.time()\n",
    "            preds = model.predict(features)\n",
    "            end = time.time()\n",
    "\n",
    "            evaluation[\"Inference Time\"] += end-start\n",
    "\n",
    "            class_prediction = np.argmax(np.bincount(preds))\n",
    "            if np.bincount(preds)[class_prediction] < 5:\n",
    "                class_prediction = -1 # Unknown Class\n",
    "            \n",
    "            \n",
    "            \n",
    "            evaluation[\"Accuracy\"] += (class_prediction == truth_label[f])\n",
    "    \n",
    "            if predict_in_fhe:\n",
    "                start = time.time()\n",
    "                preds = model.predict(features, fhe=\"execute\")\n",
    "                end = time.time()\n",
    "\n",
    "                evaluation[\"Inference Time (FHE)\"] += end-start\n",
    "\n",
    "                class_prediction = np.argmax(np.bincount(preds))\n",
    "                if np.bincount(preds)[class_prediction] < 5:\n",
    "                    class_prediction = -1 \n",
    "                \n",
    "                \n",
    "                \n",
    "                evaluation[\"Accuracy (FHE)\"] += (class_prediction == truth_label[f])\n",
    "    evaluation[\"Accuracy\"] /= len(truth_label)\n",
    "    evaluation[\"Inference Time\"] /= len(truth_label)\n",
    "\n",
    "    if predict_in_fhe:\n",
    "        evaluation[\"Inference Time (FHE)\"] /= len(truth_label)\n",
    "        evaluation[\"Accuracy (FHE)\"] /= len(truth_label)\n",
    "    return evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "concrete_configs = [ {'n_bits': 8}]\n",
    "concrete_models = [ConcreteLR(**concrete_configs[0]) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "for model in concrete_models:\n",
    "    model.fit(features, ids)\n",
    "    model.compile(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we evaluate the models on a small hand curated recording sample set. This was created by playing the music on the phone and recording it through the laptops microphone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'133916.mp3': 247, '135054.mp3': 0, '024366.mp3': 397}\n"
     ]
    }
   ],
   "source": [
    "#truth_label = {\"sample1.mp3\": idx.index(\"Alena Smirnova - Lyric song.mp3\"),\n",
    "#               \"sample2.mp3\": idx.index(\"Edoy - Topaz.mp3\"),\n",
    "#               \"sample3_recorded.m4a\": idx.index(\"Pierce Murphy - Devil In A Falling Sky.mp3\")}\n",
    "truth_label = {\n",
    "               \"133916.mp3\": idx.index(\"133916.mp3\"),\n",
    "               \"135054.mp3\": idx.index(\"135054.mp3\"),\n",
    "               \"024366.mp3\": idx.index(\"024366.mp3\"),\n",
    "              }\n",
    "\n",
    "print(truth_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing 135054.mp3\n",
      "processing 024366.mp3\n",
      "processing 133916.mp3\n"
     ]
    }
   ],
   "source": [
    "evaluations = {}\n",
    "model_names = [ \"ConcreteLR\"]\n",
    "for i, model in enumerate(concrete_models):\n",
    "    evaluations[model_names[i]] = evaluate(model, concrete_configs[i], truth_label, TEST_SAMPLE_PATH, predict_in_fhe=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "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>ConcreteLR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy (FHE)</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inference Time</th>\n",
       "      <td>0.001352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inference Time (FHE)</th>\n",
       "      <td>0.326448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_bits</th>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ConcreteLR\n",
       "Accuracy                1.000000\n",
       "Accuracy (FHE)          1.000000\n",
       "Inference Time          0.001352\n",
       "Inference Time (FHE)    0.326448\n",
       "n_bits                  8.000000"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(evaluations)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "truth_label_train = {}\n",
    "\n",
    "for i, id in enumerate(idx):\n",
    "    truth_label_train[id] = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing 135054.mp3\n",
      "processing 135336.mp3\n",
      "processing 135337.mp3\n",
      "processing 135043.mp3\n",
      "processing 135091.mp3\n",
      "processing 135092.mp3\n",
      "processing 135044.mp3\n",
      "processing 135989.mp3\n",
      "processing 135221.mp3\n",
      "processing 135369.mp3\n",
      "processing 135341.mp3\n",
      "processing 135340.mp3\n",
      "processing 135368.mp3\n",
      "processing 135220.mp3\n",
      "processing 135222.mp3\n",
      "processing 135342.mp3\n",
      "processing 135223.mp3\n",
      "processing 135227.mp3\n",
      "processing 135226.mp3\n",
      "processing 135224.mp3\n",
      "processing 135219.mp3\n",
      "processing 135225.mp3\n",
      "processing 135228.mp3\n",
      "processing 135374.mp3\n",
      "processing 135375.mp3\n",
      "processing 135229.mp3\n",
      "processing 135363.mp3\n",
      "processing 135028.mp3\n",
      "processing 135010.mp3\n",
      "processing 135986.mp3\n",
      "processing 135372.mp3\n",
      "processing 135373.mp3\n",
      "processing 135039.mp3\n",
      "processing 135365.mp3\n",
      "processing 135371.mp3\n",
      "processing 135370.mp3\n",
      "processing 135364.mp3\n",
      "processing 135990.mp3\n",
      "processing 135089.mp3\n",
      "processing 135339.mp3\n",
      "processing 135338.mp3\n",
      "processing 132117.mp3\n",
      "processing 132675.mp3\n",
      "processing 132310.mp3\n",
      "processing 132272.mp3\n",
      "processing 132448.mp3\n",
      "processing 132138.mp3\n",
      "processing 132677.mp3\n",
      "processing 132139.mp3\n",
      "processing 132449.mp3\n",
      "processing 132566.mp3\n",
      "processing 132567.mp3\n",
      "processing 132773.mp3\n",
      "processing 132954.mp3\n",
      "processing 132789.mp3\n",
      "processing 132171.mp3\n",
      "processing 132561.mp3\n",
      "processing 132788.mp3\n",
      "processing 132775.mp3\n",
      "processing 132589.mp3\n",
      "processing 132774.mp3\n",
      "processing 132779.mp3\n",
      "processing 132786.mp3\n",
      "processing 132792.mp3\n",
      "processing 132962.mp3\n",
      "processing 132142.mp3\n",
      "processing 132963.mp3\n",
      "processing 132793.mp3\n",
      "processing 132787.mp3\n",
      "processing 132778.mp3\n",
      "processing 132791.mp3\n",
      "processing 132961.mp3\n",
      "processing 132424.mp3\n",
      "processing 132140.mp3\n",
      "processing 132141.mp3\n",
      "processing 132784.mp3\n",
      "processing 132790.mp3\n",
      "processing 132019.mp3\n",
      "processing 132964.mp3\n",
      "processing 132794.mp3\n",
      "processing 132434.mp3\n",
      "processing 132420.mp3\n",
      "processing 132568.mp3\n",
      "processing 132781.mp3\n",
      "processing 132795.mp3\n",
      "processing 132965.mp3\n",
      "processing 132436.mp3\n",
      "processing 132040.mp3\n",
      "processing 132134.mp3\n",
      "processing 132914.mp3\n",
      "processing 132041.mp3\n",
      "processing 132453.mp3\n",
      "processing 132679.mp3\n",
      "processing 132678.mp3\n",
      "processing 132122.mp3\n",
      "processing 132042.mp3\n",
      "processing 132456.mp3\n",
      "processing 132045.mp3\n",
      "processing 132455.mp3\n",
      "processing 132118.mp3\n",
      "processing 132454.mp3\n",
      "processing 104276.mp3\n",
      "processing 104277.mp3\n",
      "processing 104063.mp3\n",
      "processing 104062.mp3\n",
      "processing 104066.mp3\n",
      "processing 104067.mp3\n",
      "processing 104065.mp3\n",
      "processing 104064.mp3\n",
      "processing 104010.mp3\n",
      "processing 104625.mp3\n",
      "processing 104779.mp3\n",
      "processing 104009.mp3\n",
      "processing 104357.mp3\n",
      "processing 104356.mp3\n",
      "processing 104008.mp3\n",
      "processing 104434.mp3\n",
      "processing 104780.mp3\n",
      "processing 104227.mp3\n",
      "processing 104068.mp3\n",
      "processing 104283.mp3\n",
      "processing 104724.mp3\n",
      "processing 104725.mp3\n",
      "processing 104282.mp3\n",
      "processing 104279.mp3\n",
      "processing 104668.mp3\n",
      "processing 104278.mp3\n",
      "processing 104086.mp3\n",
      "processing 104284.mp3\n",
      "processing 103519.mp3\n",
      "processing 103518.mp3\n",
      "processing 103523.mp3\n",
      "processing 103522.mp3\n",
      "processing 103520.mp3\n",
      "processing 103521.mp3\n",
      "processing 103800.mp3\n",
      "processing 103808.mp3\n",
      "processing 103600.mp3\n",
      "processing 150018.mp3\n",
      "processing 150268.mp3\n",
      "processing 150080.mp3\n",
      "processing 150078.mp3\n",
      "processing 150079.mp3\n",
      "processing 150287.mp3\n",
      "processing 150063.mp3\n",
      "processing 150062.mp3\n",
      "processing 150288.mp3\n",
      "processing 150267.mp3\n",
      "processing 150064.mp3\n",
      "processing 150266.mp3\n",
      "processing 150073.mp3\n",
      "processing 150265.mp3\n",
      "processing 150015.mp3\n",
      "processing 150017.mp3\n",
      "processing 102114.mp3\n",
      "processing 102112.mp3\n",
      "processing 102092.mp3\n",
      "processing 102195.mp3\n",
      "processing 105141.mp3\n",
      "processing 105196.mp3\n",
      "processing 105022.mp3\n",
      "processing 105142.mp3\n",
      "processing 105145.mp3\n",
      "processing 105408.mp3\n",
      "processing 105144.mp3\n",
      "processing 105916.mp3\n",
      "processing 105719.mp3\n",
      "processing 105718.mp3\n",
      "processing 105917.mp3\n",
      "processing 105685.mp3\n",
      "processing 105450.mp3\n",
      "processing 105915.mp3\n",
      "processing 105914.mp3\n",
      "processing 105451.mp3\n",
      "processing 105119.mp3\n",
      "processing 105722.mp3\n",
      "processing 105683.mp3\n",
      "processing 105720.mp3\n",
      "processing 105912.mp3\n",
      "processing 105443.mp3\n",
      "processing 105682.mp3\n",
      "processing 105670.mp3\n",
      "processing 105664.mp3\n",
      "processing 105671.mp3\n",
      "processing 105673.mp3\n",
      "processing 105920.mp3\n",
      "processing 105713.mp3\n",
      "processing 105712.mp3\n",
      "processing 105672.mp3\n",
      "processing 105662.mp3\n",
      "processing 105919.mp3\n",
      "processing 105716.mp3\n",
      "processing 105717.mp3\n",
      "processing 105918.mp3\n",
      "processing 105887.mp3\n",
      "processing 105715.mp3\n",
      "processing 105714.mp3\n",
      "processing 105890.mp3\n",
      "processing 105674.mp3\n",
      "processing 105411.mp3\n",
      "processing 105412.mp3\n",
      "processing 105413.mp3\n",
      "processing 105826.mp3\n",
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     ]
    }
   ],
   "source": [
    "evaluations = {}\n",
    "model_names = [ \"ConcreteLR\"]\n",
    "for i, model in enumerate(concrete_models):\n",
    "    evaluations[model_names[i]] = evaluate(model, concrete_configs[i], truth_label_train, MUSIC_DB_PATH, predict_in_fhe=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "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>ConcreteLR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.972500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy (FHE)</th>\n",
       "      <td>0.972500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inference Time</th>\n",
       "      <td>0.000586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inference Time (FHE)</th>\n",
       "      <td>0.318599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_bits</th>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ConcreteLR\n",
       "Accuracy                0.972500\n",
       "Accuracy (FHE)          0.972500\n",
       "Inference Time          0.000586\n",
       "Inference Time (FHE)    0.318599\n",
       "n_bits                  8.000000"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(evaluations)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparision with SKlearn LR model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "sklearn_lr = LogisticRegression().fit(features, ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "evaluations[\"sklearn_lr\"] = evaluate(sklearn_lr, {}, truth_label_train, MUSIC_DB_PATH, predict_in_fhe=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>ConcreteLR</th>\n",
       "      <th>sklearn_lr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n_bits</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inference Time</th>\n",
       "      <td>0.000586</td>\n",
       "      <td>0.000174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.977500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inference Time (FHE)</th>\n",
       "      <td>0.318599</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy (FHE)</th>\n",
       "      <td>0.972500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ConcreteLR  sklearn_lr\n",
       "n_bits                  8.000000         NaN\n",
       "Inference Time          0.000586    0.000174\n",
       "Accuracy                0.972500    0.977500\n",
       "Inference Time (FHE)    0.318599         NaN\n",
       "Accuracy (FHE)          0.972500         NaN"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(evaluations)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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