classification_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This notebook was used for the development of classification models to fit dancer data into dances to specific songs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"parser_output/extracted_features_split_3_28_upsample.csv\")\n",
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>LEFT_EYE_OUTER_POS_x__kurtosis</th>\n",
       "      <th>LEFT_EYE_OUTER_POS_x__standard_deviation</th>\n",
       "      <th>LEFT_EYE_OUTER_POS_x__autocorrelation__lag_10</th>\n",
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       "      <th>LEFT_EYE_OUTER_POS_y__kurtosis</th>\n",
       "      <th>LEFT_EYE_OUTER_POS_y__standard_deviation</th>\n",
       "      <th>...</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
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       "      <td>0.112000</td>\n",
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       "      <td>0.019418</td>\n",
       "      <td>8.488430e+06</td>\n",
       "      <td>1.356007</td>\n",
       "      <td>-1.310822</td>\n",
       "      <td>37.907508</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.860000</td>\n",
       "      <td>134.066000</td>\n",
       "      <td>4.000000</td>\n",
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       "      <td>0.017700</td>\n",
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       "      <td>-0.672379</td>\n",
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       "      <td>0.551654</td>\n",
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       "      <td>2.911378</td>\n",
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       "      <td>-2.717820</td>\n",
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       "      <td>37.603216</td>\n",
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       "      <td>0.146745</td>\n",
       "      <td>0.107539</td>\n",
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       "      <td>0.293654</td>\n",
       "      <td>0.715673</td>\n",
       "      <td>120.029463</td>\n",
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       "      <th>2735</th>\n",
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       "      <td>...</td>\n",
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       "      <td>0.102394</td>\n",
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       "<p>2502 rows × 350 columns</p>\n",
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      ],
      "text/plain": [
       "      Unnamed: 0.1  Unnamed: 0  LEFT_EYE_OUTER_POS_x__kurtosis  \\\n",
       "0                0    1.000000                       -0.500597   \n",
       "1                1    1.000000                       -0.483415   \n",
       "2                2    1.000000                       -1.564327   \n",
       "3                3    1.000000                        5.505027   \n",
       "4                4    1.000000                       -1.651266   \n",
       "...            ...         ...                             ...   \n",
       "2731          2731    1.009253                       -0.429160   \n",
       "2732          2732    1.007549                       -0.295804   \n",
       "2733          2733    1.007922                       -0.453641   \n",
       "2734          2734    1.002436                       -1.371323   \n",
       "2735          2735    1.002979                        0.073124   \n",
       "\n",
       "      LEFT_EYE_OUTER_POS_x__standard_deviation  \\\n",
       "0                                    30.432529   \n",
       "1                                    27.503200   \n",
       "2                                    19.279543   \n",
       "3                                    13.406352   \n",
       "4                                    29.739429   \n",
       "...                                        ...   \n",
       "2731                                 23.699830   \n",
       "2732                                 26.620043   \n",
       "2733                                 38.225051   \n",
       "2734                                 37.603216   \n",
       "2735                                 31.811209   \n",
       "\n",
       "      LEFT_EYE_OUTER_POS_x__autocorrelation__lag_10  \\\n",
       "0                                          0.473995   \n",
       "1                                          0.059756   \n",
       "2                                          0.591867   \n",
       "3                                          0.227827   \n",
       "4                                          0.529723   \n",
       "...                                             ...   \n",
       "2731                                       0.071023   \n",
       "2732                                       0.135026   \n",
       "2733                                       0.551654   \n",
       "2734                                       0.803757   \n",
       "2735                                       0.157222   \n",
       "\n",
       "      LEFT_EYE_OUTER_POS_x__approximate_entropy__m_20__r_0.05  \\\n",
       "0                                              0.019048         \n",
       "1                                              0.019418         \n",
       "2                                              0.019418         \n",
       "3                                              0.017700         \n",
       "4                                              0.017700         \n",
       "...                                                 ...         \n",
       "2731                                           0.024342         \n",
       "2732                                           0.023765         \n",
       "2733                                           0.021456         \n",
       "2734                                           0.024938         \n",
       "2735                                           0.017156         \n",
       "\n",
       "      LEFT_EYE_OUTER_POS_x__c3__lag_10  \\\n",
       "0                         1.156625e+07   \n",
       "1                         1.235949e+07   \n",
       "2                         8.488430e+06   \n",
       "3                         1.841696e+07   \n",
       "4                         9.677430e+06   \n",
       "...                                ...   \n",
       "2731                      2.881071e+07   \n",
       "2732                      2.151611e+07   \n",
       "2733                      1.693204e+07   \n",
       "2734                      2.479525e+07   \n",
       "2735                      3.446335e+07   \n",
       "\n",
       "      LEFT_EYE_OUTER_POS_x__cid_ce__normalize_True  \\\n",
       "0                                         1.356488   \n",
       "1                                         2.453771   \n",
       "2                                         1.356007   \n",
       "3                                         4.185149   \n",
       "4                                         1.264870   \n",
       "...                                            ...   \n",
       "2731                                      3.200530   \n",
       "2732                                      2.511810   \n",
       "2733                                      1.299832   \n",
       "2734                                      1.333436   \n",
       "2735                                      2.112359   \n",
       "\n",
       "      LEFT_EYE_OUTER_POS_y__kurtosis  \\\n",
       "0                           0.947163   \n",
       "1                           5.098091   \n",
       "2                          -1.310822   \n",
       "3                          -0.679397   \n",
       "4                          -0.672379   \n",
       "...                              ...   \n",
       "2731                        4.065613   \n",
       "2732                       -0.992072   \n",
       "2733                        2.911378   \n",
       "2734                       -0.302217   \n",
       "2735                       -0.279968   \n",
       "\n",
       "      LEFT_EYE_OUTER_POS_y__standard_deviation  ...       key   loudness  \\\n",
       "0                                    32.875727  ...  1.000000 -11.683000   \n",
       "1                                    42.493897  ...  1.000000 -11.683000   \n",
       "2                                    37.907508  ...  1.000000 -11.683000   \n",
       "3                                    14.547386  ...  1.000000  -4.961000   \n",
       "4                                    22.561584  ...  1.000000  -4.961000   \n",
       "...                                        ...  ...       ...        ...   \n",
       "2731                                 26.700536  ...  1.001468  -8.044790   \n",
       "2732                                 14.717245  ...  1.009341  -8.044112   \n",
       "2733                                 22.203969  ...  2.005843  -2.717820   \n",
       "2734                                 19.361277  ...  2.006492  -2.708844   \n",
       "2735                                 14.610880  ...  2.004956  -2.716299   \n",
       "\n",
       "          mode  speechiness  acousticness  instrumentalness  liveness  \\\n",
       "0     1.000000     0.471000      0.034600          0.069800  0.112000   \n",
       "1     1.000000     0.471000      0.034600          0.069800  0.112000   \n",
       "2     1.000000     0.471000      0.034600          0.069800  0.112000   \n",
       "3     1.000000     0.040600      0.013000          0.000000  0.094400   \n",
       "4     1.000000     0.040600      0.013000          0.000000  0.094400   \n",
       "...        ...          ...           ...               ...       ...   \n",
       "2731  0.009961     0.064800      0.437464          0.001176  0.119714   \n",
       "2732  0.009960     0.063448      0.440401          0.000517  0.114702   \n",
       "2733  0.007675     0.149796      0.102007          0.001369  0.295883   \n",
       "2734  0.002764     0.146745      0.107539          0.002919  0.293654   \n",
       "2735  0.002322     0.151568      0.102394          0.005549  0.291190   \n",
       "\n",
       "       valence       tempo  time_signature  \n",
       "0     0.394000  120.461000        4.000000  \n",
       "1     0.394000  120.461000        4.000000  \n",
       "2     0.394000  120.461000        4.000000  \n",
       "3     0.860000  134.066000        4.000000  \n",
       "4     0.860000  134.066000        4.000000  \n",
       "...        ...         ...             ...  \n",
       "2731  0.321766  110.022306        4.006691  \n",
       "2732  0.324953  110.021905        4.003841  \n",
       "2733  0.714539  120.035184        4.004975  \n",
       "2734  0.715673  120.029463        4.006945  \n",
       "2735  0.721275  120.035093        4.003433  \n",
       "\n",
       "[2502 rows x 350 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data[['danceability', 'energy', 'loudness', 'speechiness', 'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo']]\n",
    "x = data[data.columns[2:len(data.columns)-12]]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      LEFT_EYE_OUTER_POS_x__kurtosis  \\\n",
      "1688                       -0.203295   \n",
      "1486                       -1.249592   \n",
      "1568                       -0.251412   \n",
      "2010                       -1.246803   \n",
      "751                        -1.153561   \n",
      "...                              ...   \n",
      "1561                       -0.837632   \n",
      "2477                       -0.574216   \n",
      "673                        -0.566503   \n",
      "2505                       -1.117242   \n",
      "108                         0.745708   \n",
      "\n",
      "      LEFT_EYE_OUTER_POS_x__standard_deviation  \\\n",
      "1688                                  5.171542   \n",
      "1486                                 14.797502   \n",
      "1568                                  9.699508   \n",
      "2010                                  9.442287   \n",
      "751                                  14.533151   \n",
      "...                                        ...   \n",
      "1561                                 16.401361   \n",
      "2477                                 22.359905   \n",
      "673                                  29.681002   \n",
      "2505                                 14.886159   \n",
      "108                                  14.836438   \n",
      "\n",
      "      LEFT_EYE_OUTER_POS_x__autocorrelation__lag_10  \\\n",
      "1688                                      -0.041312   \n",
      "1486                                       0.210219   \n",
      "1568                                       0.355538   \n",
      "2010                                       0.243034   \n",
      "751                                        0.518564   \n",
      "...                                             ...   \n",
      "1561                                       0.291210   \n",
      "2477                                       0.451010   \n",
      "673                                        0.056867   \n",
      "2505                                       0.452137   \n",
      "108                                       -0.314754   \n",
      "\n",
      "      LEFT_EYE_OUTER_POS_x__approximate_entropy__m_20__r_0.05  \\\n",
      "1688                                           0.032955         \n",
      "1486                                           0.035491         \n",
      "1568                                           0.027194         \n",
      "2010                                           0.032643         \n",
      "751                                            0.038582         \n",
      "...                                                 ...         \n",
      "1561                                           0.034575         \n",
      "2477                                           0.034417         \n",
      "673                                            0.044524         \n",
      "2505                                           0.037402         \n",
      "108                                            0.031749         \n",
      "\n",
      "      LEFT_EYE_OUTER_POS_x__c3__lag_10  \\\n",
      "1688                      2.432078e+07   \n",
      "1486                      1.127964e+07   \n",
      "1568                      7.964410e+06   \n",
      "2010                      6.959210e+06   \n",
      "751                       2.285970e+07   \n",
      "...                                ...   \n",
      "1561                      1.084006e+07   \n",
      "2477                      8.599648e+06   \n",
      "673                       1.172459e+07   \n",
      "2505                      8.018229e+06   \n",
      "108                       1.194791e+07   \n",
      "\n",
      "      LEFT_EYE_OUTER_POS_x__cid_ce__normalize_True  \\\n",
      "1688                                      4.134517   \n",
      "1486                                      2.121645   \n",
      "1568                                      2.012621   \n",
      "2010                                      2.164681   \n",
      "751                                       2.274757   \n",
      "...                                            ...   \n",
      "1561                                      1.206549   \n",
      "2477                                      0.758802   \n",
      "673                                       1.271883   \n",
      "2505                                      1.654504   \n",
      "108                                       2.781710   \n",
      "\n",
      "      LEFT_EYE_OUTER_POS_y__kurtosis  \\\n",
      "1688                        2.775366   \n",
      "1486                       -0.455646   \n",
      "1568                       -0.301942   \n",
      "2010                       -0.772155   \n",
      "751                        -0.767206   \n",
      "...                              ...   \n",
      "1561                       -0.924375   \n",
      "2477                        0.126849   \n",
      "673                        -0.655249   \n",
      "2505                       -0.350986   \n",
      "108                        -0.672073   \n",
      "\n",
      "      LEFT_EYE_OUTER_POS_y__standard_deviation  \\\n",
      "1688                                  6.891929   \n",
      "1486                                 13.197179   \n",
      "1568                                  7.671759   \n",
      "2010                                 12.142177   \n",
      "751                                  11.084412   \n",
      "...                                        ...   \n",
      "1561                                  5.929522   \n",
      "2477                                  7.981305   \n",
      "673                                   5.383658   \n",
      "2505                                  3.210769   \n",
      "108                                  13.728077   \n",
      "\n",
      "      LEFT_EYE_OUTER_POS_y__autocorrelation__lag_10  \\\n",
      "1688                                      -0.102034   \n",
      "1486                                       0.004796   \n",
      "1568                                       0.118143   \n",
      "2010                                      -0.017082   \n",
      "751                                        0.072220   \n",
      "...                                             ...   \n",
      "1561                                      -0.114410   \n",
      "2477                                      -0.025850   \n",
      "673                                       -0.220129   \n",
      "2505                                       0.059447   \n",
      "108                                       -0.273551   \n",
      "\n",
      "      LEFT_EYE_OUTER_POS_y__approximate_entropy__m_20__r_0.05  ...  \\\n",
      "1688                                           0.035225        ...   \n",
      "1486                                           0.034759        ...   \n",
      "1568                                           0.025069        ...   \n",
      "2010                                           0.030774        ...   \n",
      "751                                            0.037183        ...   \n",
      "...                                                 ...        ...   \n",
      "1561                                           0.033095        ...   \n",
      "2477                                           0.036878        ...   \n",
      "673                                            0.042954        ...   \n",
      "2505                                           0.041680        ...   \n",
      "108                                            0.031749        ...   \n",
      "\n",
      "      RIGHT_ANKLE_DIST_x__autocorrelation__lag_10  \\\n",
      "1688                                    -0.054824   \n",
      "1486                                    -0.021911   \n",
      "1568                                    -0.032664   \n",
      "2010                                    -0.031419   \n",
      "751                                     -0.051680   \n",
      "...                                           ...   \n",
      "1561                                     0.084329   \n",
      "2477                                    -0.136055   \n",
      "673                                     -0.114707   \n",
      "2505                                     0.001437   \n",
      "108                                      0.084170   \n",
      "\n",
      "      RIGHT_ANKLE_DIST_x__approximate_entropy__m_20__r_0.05  \\\n",
      "1688                                           0.037249       \n",
      "1486                                           0.030392       \n",
      "1568                                           0.029612       \n",
      "2010                                           0.036597       \n",
      "751                                            0.038685       \n",
      "...                                                 ...       \n",
      "1561                                           0.035606       \n",
      "2477                                           0.034936       \n",
      "673                                            0.039673       \n",
      "2505                                           0.041507       \n",
      "108                                            0.031749       \n",
      "\n",
      "      RIGHT_ANKLE_DIST_x__c3__lag_10  \\\n",
      "1688                        0.321423   \n",
      "1486                     -262.734605   \n",
      "1568                       -0.849937   \n",
      "2010                        0.095733   \n",
      "751                        -8.289008   \n",
      "...                              ...   \n",
      "1561                       -0.105335   \n",
      "2477                        0.544415   \n",
      "673                         7.015589   \n",
      "2505                        0.062197   \n",
      "108                      -842.122759   \n",
      "\n",
      "      RIGHT_ANKLE_DIST_x__cid_ce__normalize_True  \\\n",
      "1688                                    7.666817   \n",
      "1486                                    8.927249   \n",
      "1568                                   10.624584   \n",
      "2010                                    7.777770   \n",
      "751                                     7.729742   \n",
      "...                                          ...   \n",
      "1561                                    8.169779   \n",
      "2477                                    7.208624   \n",
      "673                                     8.894013   \n",
      "2505                                    7.649177   \n",
      "108                                     7.014257   \n",
      "\n",
      "      RIGHT_ANKLE_DIST_y__kurtosis  RIGHT_ANKLE_DIST_y__standard_deviation  \\\n",
      "1688                     13.026605                                7.657044   \n",
      "1486                      5.510260                               13.378661   \n",
      "1568                     14.071072                                3.647095   \n",
      "2010                      8.096566                                3.325005   \n",
      "751                       7.097848                               11.434352   \n",
      "...                            ...                                     ...   \n",
      "1561                      5.460769                                2.066073   \n",
      "2477                      6.672207                                3.645163   \n",
      "673                       5.914846                                2.991227   \n",
      "2505                      7.102030                                0.714855   \n",
      "108                       6.215553                               16.422997   \n",
      "\n",
      "      RIGHT_ANKLE_DIST_y__autocorrelation__lag_10  \\\n",
      "1688                                    -0.005507   \n",
      "1486                                    -0.105320   \n",
      "1568                                    -0.038979   \n",
      "2010                                     0.016209   \n",
      "751                                     -0.083579   \n",
      "...                                           ...   \n",
      "1561                                    -0.233696   \n",
      "2477                                    -0.149869   \n",
      "673                                     -0.105794   \n",
      "2505                                    -0.047121   \n",
      "108                                      0.046565   \n",
      "\n",
      "      RIGHT_ANKLE_DIST_y__approximate_entropy__m_20__r_0.05  \\\n",
      "1688                                           0.036082       \n",
      "1486                                           0.036768       \n",
      "1568                                           0.029255       \n",
      "2010                                           0.031136       \n",
      "751                                            0.033130       \n",
      "...                                                 ...       \n",
      "1561                                           0.029734       \n",
      "2477                                           0.031172       \n",
      "673                                            0.038099       \n",
      "2505                                           0.042535       \n",
      "108                                            0.031749       \n",
      "\n",
      "      RIGHT_ANKLE_DIST_y__c3__lag_10  \\\n",
      "1688                        0.422126   \n",
      "1486                       52.078704   \n",
      "1568                       -0.518075   \n",
      "2010                       -0.118861   \n",
      "751                         2.417337   \n",
      "...                              ...   \n",
      "1561                       -0.267402   \n",
      "2477                        1.279492   \n",
      "673                         0.434818   \n",
      "2505                       -0.003953   \n",
      "108                       178.425726   \n",
      "\n",
      "      RIGHT_ANKLE_DIST_y__cid_ce__normalize_True  \n",
      "1688                                    7.749833  \n",
      "1486                                   10.301188  \n",
      "1568                                    9.489136  \n",
      "2010                                    8.207715  \n",
      "751                                     8.197087  \n",
      "...                                          ...  \n",
      "1561                                    9.069647  \n",
      "2477                                    8.669369  \n",
      "673                                     9.126559  \n",
      "2505                                    8.681311  \n",
      "108                                     9.734122  \n",
      "\n",
      "[1501 rows x 336 columns]\n",
      "      danceability    energy  loudness  speechiness  acousticness  \\\n",
      "1688      0.851091  0.427860 -9.576399     0.326127      0.070285   \n",
      "1486      0.618324  0.673836 -6.862920     0.270242      0.252838   \n",
      "1568      0.764761  0.844523 -2.717811     0.143210      0.109085   \n",
      "2010      0.563431  0.667163 -5.034748     0.161327      0.339386   \n",
      "751       0.973579  0.601524 -6.879493     0.113143      0.028362   \n",
      "...            ...       ...       ...          ...           ...   \n",
      "1561      0.709604  0.831458 -3.779639     0.060966      0.009530   \n",
      "2477      0.875981  0.528669 -8.047796     0.063344      0.436836   \n",
      "673       0.911819  0.626666 -6.630699     0.108847      0.007085   \n",
      "2505      0.678766  0.696066 -6.178709     0.122151      0.322105   \n",
      "108       0.824000  0.764000 -4.175000     0.085400      0.088800   \n",
      "\n",
      "      instrumentalness  liveness   valence       tempo  \n",
      "1688          0.007054  0.091872  0.161365  145.887462  \n",
      "1486          0.001659  0.081182  0.514221   96.178002  \n",
      "1568          0.002995  0.297835  0.719180  120.034712  \n",
      "2010          0.002291  0.086293  0.689954  166.928839  \n",
      "751           0.007371  0.042054  0.630631  128.602687  \n",
      "...                ...       ...       ...         ...  \n",
      "1561          0.000840  0.072916  0.924558  102.985865  \n",
      "2477          0.000993  0.119315  0.323894  110.024378  \n",
      "673           0.003145  0.100146  0.430367  150.013313  \n",
      "2505          0.007578  0.423727  0.466499   90.035199  \n",
      "108           0.002940  0.117000  0.881000  107.998000  \n",
      "\n",
      "[1501 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X_train)\n",
    "print(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating audio features-to-song dictionary\n",
    "#### Our goal is to phrase a (normally) continuous prediction problem in a classification way...\n",
    "#### This requires associating all samples with the proper class (a song)\n",
    "To do this, we take the music features of the samples and find the songs they correspond to.\n",
    "The songs are then used directly as labels in the classification model.\n",
    "This, unfortunately, gets rid of the utility of knowing the audio features up front."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: re-incorporate the audio features... either a multifactor classification model that basically creates bins \n",
    "# for each audio feature?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import spotipy\n",
    "from spotipy.oauth2 import SpotifyClientCredentials\n",
    "from spotipy.oauth2 import SpotifyOAuth\n",
    "\n",
    "# Instantiate Spotify Client\n",
    "logger = logging.getLogger()\n",
    "logging.basicConfig()\n",
    "CLIENT_ID=\"9793440f0a5047c59c70bcfcf91ad589\"\n",
    "CLIENT_SECRET= \"b66dc3a5f9f34207bebee32a25745368\"\n",
    "REDIRECT_URL=\"http://localhost/\"\n",
    "client_credentials_manager = SpotifyClientCredentials(client_id = CLIENT_ID, client_secret = CLIENT_SECRET)\n",
    "oAuth = SpotifyOAuth(client_id = CLIENT_ID, client_secret = CLIENT_SECRET, redirect_uri = REDIRECT_URL, scope = 'user-modify-playback-state,playlist-modify-public')\n",
    "sp = spotipy.Spotify(auth_manager =oAuth)\n",
    "\n",
    "song_name_list = ['love_girl', 'party_rock_anthem', 'lite_spots', 'girlfriends_best_friend', 'scaredy_cat', \n",
    "                  'girl_anachronism', 'lil_darlin', 'el_invento', 'test_drive', 'weight_in_gold', 'easy', 'comethru', \n",
    "                  'classic', 'tongue_tied', 'motley', 'knife_talk', 'feed_the_fire', 'lose_it', 'thats_what_i_like', \n",
    "                  'humble', 'skeletons', 'stairway_to_heaven', 'slow_dancing_in_the_dark', 'peaches', 'kilby_girl', \n",
    "                  'jupiter_love', 'woman', 'get_up', 'throwin_elbows', 'power', 'ymca', 'all_i_wanna_do', 'easily',\n",
    "                  'feed_the_fire', 'fool_around','good_4_u', 'knife_talk', 'levitating', 'sad_girlz_luv_money',\n",
    "                  'tik_tok', 'a_thousand_miles']\n",
    "\n",
    "artist_name_list = ['cnblue', 'lmfao', 'kaytranada', 'walkabout', 'dpr_ian', 'the_dresden_dolls', 'count_basie', \n",
    "                    'jose_gonzalez_calvaire', 'ariana_grande', 'gallant', 'wheein', 'jeremy_zucker', 'mkto', \n",
    "                    'grouplove', 'post_malone', 'drake', 'lucky_daye', 'swmrs', 'bruno_mars', 'kendrick_lamar', \n",
    "                    'keshi', 'led_zeppelin', 'joji', 'justin_bieber', 'backseat_lovers', 'trey_songz', 'doja_cat', \n",
    "                    'ciara', 'excision', 'little mix', 'village_people', 'jay_park', 'bruno_major', 'lucky_daye',\n",
    "                    'yas', 'olivia_rodrigo', 'drake', 'dua_lipa', 'amaarae', 'kesha', 'vanessa_carlton']\n",
    "\n",
    "def get_song(name_song, name_artist):\n",
    "    results = sp.search(q=name_song + ' '+name_artist, type='track')\n",
    "    items = results['tracks']['items']\n",
    "    if len(items) > 0:\n",
    "        return items[0]\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Spotify Song Features\n",
    "def get_audio_features(song_name, artist_name):\n",
    "\n",
    "    song = get_song(song_name, artist_name)\n",
    "\n",
    "    #if get request didnt get anything\n",
    "    if(song is None):\n",
    "        return None\n",
    "\n",
    "    song_features = sp.audio_features([song['uri']])\n",
    "\n",
    "    audio_feature_list = ['danceability', 'energy', 'loudness', 'speechiness', 'acousticness', \n",
    "                          'instrumentalness', 'liveness', 'valence', 'tempo']\n",
    "\n",
    "    audio_feat = pd.DataFrame(columns = audio_feature_list, index = range(1, 2))\n",
    "\n",
    "    audio_feat['danceability'] = song_features[0].get('danceability')\n",
    "    audio_feat['energy'] = song_features[0].get('energy')\n",
    "    audio_feat['loudness'] = song_features[0].get('loudness')\n",
    "    audio_feat['speechiness'] = song_features[0].get('speechiness')\n",
    "    audio_feat['acousticness'] = song_features[0].get('acousticness')\n",
    "    audio_feat['instrumentalness'] = song_features[0].get('instrumentalness')\n",
    "    audio_feat['liveness'] = song_features[0].get('liveness')\n",
    "    audio_feat['valence'] = song_features[0].get('valence')\n",
    "    audio_feat['tempo'] = song_features[0].get('tempo')\n",
    "\n",
    "    return audio_feat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "extracted_features = pd.DataFrame()\n",
    "featuresToSong = {}\n",
    "featuresList = []\n",
    "\n",
    "for index in range(len(song_name_list)):\n",
    "\n",
    "    song_name = song_name_list[index]\n",
    "    artist_name = artist_name_list[index]\n",
    "    audio_feat = pd.DataFrame({'A' : []})\n",
    "\n",
    "    try:\n",
    "        audio_feat = get_audio_features(song_name, artist_name)\n",
    "    except:\n",
    "        oAuth = SpotifyOAuth(client_id = CLIENT_ID, client_secret = CLIENT_SECRET, redirect_uri = REDIRECT_URL, scope = 'user-modify-playback-state')\n",
    "        sp = spotipy.Spotify(auth_manager =oAuth)\n",
    "        audio_feat = get_audio_features(song_name, artist_name)\n",
    "\n",
    "        \n",
    "    if audio_feat is not None:\n",
    "        if not audio_feat.empty:\n",
    "    \n",
    "            audio_feat = audio_feat.to_numpy()\n",
    "            audio_feat = tuple(map(tuple, audio_feat))[0]\n",
    "            identifier = (song_name, artist_name)\n",
    "\n",
    "            featuresToSong[audio_feat] = (song_name, artist_name)\n",
    "            featuresList.append((audio_feat, identifier))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "arrs = [[], [], [], [], [], [], [], [], []]\n",
    "\n",
    "for i in range(len(featuresList)):\n",
    "    for j in range(len(featuresList[i][0])):\n",
    "        arrs[j].append(featuresList[i][0][j])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_arr = X_train.to_numpy()\n",
    "y_train_arr = y_train.to_numpy()\n",
    "x_test_arr = X_test.to_numpy()\n",
    "y_test_arr = y_test.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def closest_value(input_lists, vals):\n",
    "    arrs = [np.asarray(row) for row in input_lists]\n",
    "    closests = [(np.abs(arrs[i] - vals[i])).argmin() for i in range(len(vals))]\n",
    "\n",
    "    closestIndex = max(set(closests), key=closests.count)\n",
    "    return closestIndex\n",
    "\n",
    "trainLabels = []\n",
    "for row in y_train_arr:\n",
    "    rowVals = tuple(row)\n",
    "    val = closest_value(arrs, rowVals)\n",
    "    trainLabels.append(featuresList[val][1])\n",
    "    \n",
    "testLabels = []\n",
    "for row in y_test_arr:\n",
    "    rowVals = tuple(row)\n",
    "    val = closest_value(arrs, rowVals)\n",
    "    testLabels.append(featuresList[val][1])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "song_artist_pairs = {\n",
    "    '1':('thats_what_i_like','bruno_mars'),\n",
    "    '2':('humble','kendrick_lamar'),\n",
    "    '3':('skeletons','keshi'),\n",
    "    '4':('slow_dancing_in_the_dark','joji'),\n",
    "    '5':('lite_spots','kaytranada'),\n",
    "    '6':('woman','doja_cat'),\n",
    "    '7':('get_up','ciara'),\n",
    "    '8':('throwin_elbows','excision'),\n",
    "    '9':('power','little mix'),\n",
    "    '10':('peaches','justin_bieber'),\n",
    "    '11':('knife_talk','drake'),\n",
    "    '12':('fool_around','yas'),\n",
    "    '13':('levitating','dua_lipa'),\n",
    "    '14':('feed_the_fire','lucky_daye'),\n",
    "    '15':('easily','bruno_major'),\n",
    "    '16':('good_4_u','olivia_rodrigo'),\n",
    "    '17':('all_i_wanna_do','jay_park'),\n",
    "    '18':('sad_girlz_luv_money','amaarae'),\n",
    "    '19':('tik_tok','kesha'),\n",
    "    '20':('ymca','village_people'),\n",
    "    '21':('intuition_interlude','jamie_foxx'),\n",
    "    '22':('kilby_girl','the_backseat_lovers'),\n",
    "    '23':('a_thousand_miles','vanessa_carlton'),\n",
    "    '24':('jupiter_love', 'trey_songz'),\n",
    "    '25':('kilby_girl', 'backseat_lovers')\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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     ]
    }
   ],
   "source": [
    "# Creating labels for the test and train datasets. Labels are songs\n",
    "\n",
    "inv_map = {v: k for k, v in song_artist_pairs.items()}\n",
    "trainLabelNums = [inv_map[label] for label in trainLabels]\n",
    "testLabelNums = [inv_map[label] for label in testLabels]\n",
    "\n",
    "print(trainLabelNums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=15000)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = SVC(C=15000)\n",
    "clf.fit(X_train, trainLabelNums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred_train = clf.predict(X_train)\n",
    "ypred_test = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LINEAR REGRESSION\n",
      "Train Score: \n",
      "0.9999354132518109\n",
      "Test Score: \n",
      "0.9994966350227271\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/paul/opt/miniconda3/lib/python3.9/site-packages/sklearn/metrics/_regression.py:95: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  y_true = check_array(y_true, ensure_2d=False, dtype=dtype)\n",
      "/Users/paul/opt/miniconda3/lib/python3.9/site-packages/sklearn/metrics/_regression.py:96: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  y_pred = check_array(y_pred, ensure_2d=False, dtype=dtype)\n",
      "/Users/paul/opt/miniconda3/lib/python3.9/site-packages/sklearn/metrics/_regression.py:95: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  y_true = check_array(y_true, ensure_2d=False, dtype=dtype)\n",
      "/Users/paul/opt/miniconda3/lib/python3.9/site-packages/sklearn/metrics/_regression.py:96: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  y_pred = check_array(y_pred, ensure_2d=False, dtype=dtype)\n"
     ]
    }
   ],
   "source": [
    "print(\"CLASSIFICATION ACCURACY\")\n",
    "print(\"Train Score: \")\n",
    "print(r2_score(trainLabelNums, ypred_train))\n",
    "print(\"Test Score: \")\n",
    "print(r2_score(testLabelNums, ypred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# save the model to disk\n",
    "filename = 'classification_model.sav'\n",
    "pickle.dump(clf, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.995004995004995\n"
     ]
    }
   ],
   "source": [
    "# load the model from disk\n",
    "loaded_model = pickle.load(open(filename, 'rb'))\n",
    "result = loaded_model.score(X_test, testLabelNums)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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