Final GTZAN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4c24ec93",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06950e3c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6b12f53a",
   "metadata": {},
   "outputs": [],
   "source": [
    "Xy_test_aa = pd.read_csv(\"Xy_test.csv\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "744de260",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = Xy_test_aa.iloc[:,:-1]\n",
    "y_test = Xy_test_aa.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4418159e",
   "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>chroma_stft_mean</th>\n",
       "      <th>chroma_stft_var</th>\n",
       "      <th>rms_mean</th>\n",
       "      <th>rms_var</th>\n",
       "      <th>spectral_centroid_mean</th>\n",
       "      <th>spectral_centroid_var</th>\n",
       "      <th>spectral_bandwidth_mean</th>\n",
       "      <th>spectral_bandwidth_var</th>\n",
       "      <th>rolloff_mean</th>\n",
       "      <th>rolloff_var</th>\n",
       "      <th>...</th>\n",
       "      <th>mfcc16_mean</th>\n",
       "      <th>mfcc16_var</th>\n",
       "      <th>mfcc17_mean</th>\n",
       "      <th>mfcc17_var</th>\n",
       "      <th>mfcc18_mean</th>\n",
       "      <th>mfcc18_var</th>\n",
       "      <th>mfcc19_mean</th>\n",
       "      <th>mfcc19_var</th>\n",
       "      <th>mfcc20_mean</th>\n",
       "      <th>mfcc20_var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.190921</td>\n",
       "      <td>0.497513</td>\n",
       "      <td>0.856328</td>\n",
       "      <td>-0.307767</td>\n",
       "      <td>1.578576</td>\n",
       "      <td>0.322970</td>\n",
       "      <td>1.896166</td>\n",
       "      <td>0.066536</td>\n",
       "      <td>1.877364</td>\n",
       "      <td>-0.004520</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.729045</td>\n",
       "      <td>0.448346</td>\n",
       "      <td>-0.301005</td>\n",
       "      <td>0.088114</td>\n",
       "      <td>0.488690</td>\n",
       "      <td>0.067097</td>\n",
       "      <td>0.769328</td>\n",
       "      <td>0.698413</td>\n",
       "      <td>0.116609</td>\n",
       "      <td>-0.278356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.705910</td>\n",
       "      <td>1.276524</td>\n",
       "      <td>-0.553290</td>\n",
       "      <td>-0.439650</td>\n",
       "      <td>2.476527</td>\n",
       "      <td>0.024962</td>\n",
       "      <td>2.334708</td>\n",
       "      <td>-0.710806</td>\n",
       "      <td>2.364000</td>\n",
       "      <td>-0.764069</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.582751</td>\n",
       "      <td>-0.590022</td>\n",
       "      <td>0.577039</td>\n",
       "      <td>-0.520509</td>\n",
       "      <td>0.843710</td>\n",
       "      <td>-0.050255</td>\n",
       "      <td>1.704421</td>\n",
       "      <td>-0.075965</td>\n",
       "      <td>1.339031</td>\n",
       "      <td>-0.283492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.200638</td>\n",
       "      <td>0.947305</td>\n",
       "      <td>-0.752767</td>\n",
       "      <td>-0.433216</td>\n",
       "      <td>0.419776</td>\n",
       "      <td>1.655229</td>\n",
       "      <td>1.121596</td>\n",
       "      <td>2.281659</td>\n",
       "      <td>0.666121</td>\n",
       "      <td>2.836340</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.404243</td>\n",
       "      <td>0.105645</td>\n",
       "      <td>-0.676465</td>\n",
       "      <td>0.307438</td>\n",
       "      <td>-0.177594</td>\n",
       "      <td>-0.166756</td>\n",
       "      <td>1.362335</td>\n",
       "      <td>1.683163</td>\n",
       "      <td>0.640323</td>\n",
       "      <td>0.505634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.457995</td>\n",
       "      <td>0.235012</td>\n",
       "      <td>0.647645</td>\n",
       "      <td>0.654434</td>\n",
       "      <td>0.320163</td>\n",
       "      <td>-0.404765</td>\n",
       "      <td>0.081464</td>\n",
       "      <td>-0.537576</td>\n",
       "      <td>0.280313</td>\n",
       "      <td>-0.604025</td>\n",
       "      <td>...</td>\n",
       "      <td>0.875043</td>\n",
       "      <td>-0.456332</td>\n",
       "      <td>-0.606393</td>\n",
       "      <td>-0.614830</td>\n",
       "      <td>-0.119796</td>\n",
       "      <td>-0.093524</td>\n",
       "      <td>0.664381</td>\n",
       "      <td>-0.449732</td>\n",
       "      <td>1.122482</td>\n",
       "      <td>0.083922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.403115</td>\n",
       "      <td>0.527785</td>\n",
       "      <td>-0.033084</td>\n",
       "      <td>-0.122074</td>\n",
       "      <td>0.218807</td>\n",
       "      <td>-0.054397</td>\n",
       "      <td>0.289764</td>\n",
       "      <td>-0.391046</td>\n",
       "      <td>0.425376</td>\n",
       "      <td>-0.456622</td>\n",
       "      <td>...</td>\n",
       "      <td>0.744751</td>\n",
       "      <td>-0.037636</td>\n",
       "      <td>-0.232036</td>\n",
       "      <td>0.606131</td>\n",
       "      <td>0.034778</td>\n",
       "      <td>-0.638722</td>\n",
       "      <td>-0.887771</td>\n",
       "      <td>0.362257</td>\n",
       "      <td>0.603048</td>\n",
       "      <td>0.031929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>0.361102</td>\n",
       "      <td>-0.339933</td>\n",
       "      <td>0.138413</td>\n",
       "      <td>-0.029374</td>\n",
       "      <td>-0.095071</td>\n",
       "      <td>0.521655</td>\n",
       "      <td>0.084859</td>\n",
       "      <td>-0.281272</td>\n",
       "      <td>0.111091</td>\n",
       "      <td>0.072867</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.290236</td>\n",
       "      <td>-0.610116</td>\n",
       "      <td>-0.514593</td>\n",
       "      <td>-0.541216</td>\n",
       "      <td>-0.068948</td>\n",
       "      <td>-0.569445</td>\n",
       "      <td>-1.344699</td>\n",
       "      <td>0.397351</td>\n",
       "      <td>0.831254</td>\n",
       "      <td>-0.529894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>-0.354484</td>\n",
       "      <td>-0.486155</td>\n",
       "      <td>1.060989</td>\n",
       "      <td>0.078498</td>\n",
       "      <td>0.276789</td>\n",
       "      <td>-0.649913</td>\n",
       "      <td>0.186171</td>\n",
       "      <td>-0.943665</td>\n",
       "      <td>0.408731</td>\n",
       "      <td>-0.822668</td>\n",
       "      <td>...</td>\n",
       "      <td>1.363349</td>\n",
       "      <td>0.262615</td>\n",
       "      <td>0.139082</td>\n",
       "      <td>-0.238903</td>\n",
       "      <td>1.821206</td>\n",
       "      <td>-0.428615</td>\n",
       "      <td>0.462848</td>\n",
       "      <td>0.839443</td>\n",
       "      <td>-0.123797</td>\n",
       "      <td>0.099759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>-1.115917</td>\n",
       "      <td>0.670937</td>\n",
       "      <td>-1.233578</td>\n",
       "      <td>-0.550960</td>\n",
       "      <td>-1.518207</td>\n",
       "      <td>-0.808895</td>\n",
       "      <td>-1.116982</td>\n",
       "      <td>-0.354635</td>\n",
       "      <td>-1.407527</td>\n",
       "      <td>-0.585042</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.414376</td>\n",
       "      <td>-0.695091</td>\n",
       "      <td>-0.083050</td>\n",
       "      <td>-0.572266</td>\n",
       "      <td>0.666949</td>\n",
       "      <td>0.527153</td>\n",
       "      <td>2.844366</td>\n",
       "      <td>1.006903</td>\n",
       "      <td>1.063897</td>\n",
       "      <td>0.547844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>-0.327370</td>\n",
       "      <td>1.031480</td>\n",
       "      <td>0.507046</td>\n",
       "      <td>0.438850</td>\n",
       "      <td>-0.494185</td>\n",
       "      <td>1.244002</td>\n",
       "      <td>-0.623448</td>\n",
       "      <td>1.011344</td>\n",
       "      <td>-0.541641</td>\n",
       "      <td>0.612821</td>\n",
       "      <td>...</td>\n",
       "      <td>0.519017</td>\n",
       "      <td>2.192150</td>\n",
       "      <td>-0.375639</td>\n",
       "      <td>-0.290807</td>\n",
       "      <td>-0.791234</td>\n",
       "      <td>-0.610295</td>\n",
       "      <td>-0.284236</td>\n",
       "      <td>0.078789</td>\n",
       "      <td>1.063246</td>\n",
       "      <td>0.458261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>-1.501822</td>\n",
       "      <td>-0.424972</td>\n",
       "      <td>-1.444802</td>\n",
       "      <td>-0.536938</td>\n",
       "      <td>-0.902436</td>\n",
       "      <td>-0.603219</td>\n",
       "      <td>-1.285952</td>\n",
       "      <td>-0.344753</td>\n",
       "      <td>-1.039827</td>\n",
       "      <td>-0.681098</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063649</td>\n",
       "      <td>1.537312</td>\n",
       "      <td>0.453367</td>\n",
       "      <td>1.016004</td>\n",
       "      <td>-0.637947</td>\n",
       "      <td>0.198084</td>\n",
       "      <td>0.179352</td>\n",
       "      <td>0.717183</td>\n",
       "      <td>-0.326505</td>\n",
       "      <td>0.124471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>999 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     chroma_stft_mean  chroma_stft_var  rms_mean   rms_var  \\\n",
       "0           -0.190921         0.497513  0.856328 -0.307767   \n",
       "1           -0.705910         1.276524 -0.553290 -0.439650   \n",
       "2            0.200638         0.947305 -0.752767 -0.433216   \n",
       "3            0.457995         0.235012  0.647645  0.654434   \n",
       "4            0.403115         0.527785 -0.033084 -0.122074   \n",
       "..                ...              ...       ...       ...   \n",
       "994          0.361102        -0.339933  0.138413 -0.029374   \n",
       "995         -0.354484        -0.486155  1.060989  0.078498   \n",
       "996         -1.115917         0.670937 -1.233578 -0.550960   \n",
       "997         -0.327370         1.031480  0.507046  0.438850   \n",
       "998         -1.501822        -0.424972 -1.444802 -0.536938   \n",
       "\n",
       "     spectral_centroid_mean  spectral_centroid_var  spectral_bandwidth_mean  \\\n",
       "0                  1.578576               0.322970                 1.896166   \n",
       "1                  2.476527               0.024962                 2.334708   \n",
       "2                  0.419776               1.655229                 1.121596   \n",
       "3                  0.320163              -0.404765                 0.081464   \n",
       "4                  0.218807              -0.054397                 0.289764   \n",
       "..                      ...                    ...                      ...   \n",
       "994               -0.095071               0.521655                 0.084859   \n",
       "995                0.276789              -0.649913                 0.186171   \n",
       "996               -1.518207              -0.808895                -1.116982   \n",
       "997               -0.494185               1.244002                -0.623448   \n",
       "998               -0.902436              -0.603219                -1.285952   \n",
       "\n",
       "     spectral_bandwidth_var  rolloff_mean  rolloff_var  ...  mfcc16_mean  \\\n",
       "0                  0.066536      1.877364    -0.004520  ...    -0.729045   \n",
       "1                 -0.710806      2.364000    -0.764069  ...    -1.582751   \n",
       "2                  2.281659      0.666121     2.836340  ...    -0.404243   \n",
       "3                 -0.537576      0.280313    -0.604025  ...     0.875043   \n",
       "4                 -0.391046      0.425376    -0.456622  ...     0.744751   \n",
       "..                      ...           ...          ...  ...          ...   \n",
       "994               -0.281272      0.111091     0.072867  ...    -0.290236   \n",
       "995               -0.943665      0.408731    -0.822668  ...     1.363349   \n",
       "996               -0.354635     -1.407527    -0.585042  ...    -0.414376   \n",
       "997                1.011344     -0.541641     0.612821  ...     0.519017   \n",
       "998               -0.344753     -1.039827    -0.681098  ...     0.063649   \n",
       "\n",
       "     mfcc16_var  mfcc17_mean  mfcc17_var  mfcc18_mean  mfcc18_var  \\\n",
       "0      0.448346    -0.301005    0.088114     0.488690    0.067097   \n",
       "1     -0.590022     0.577039   -0.520509     0.843710   -0.050255   \n",
       "2      0.105645    -0.676465    0.307438    -0.177594   -0.166756   \n",
       "3     -0.456332    -0.606393   -0.614830    -0.119796   -0.093524   \n",
       "4     -0.037636    -0.232036    0.606131     0.034778   -0.638722   \n",
       "..          ...          ...         ...          ...         ...   \n",
       "994   -0.610116    -0.514593   -0.541216    -0.068948   -0.569445   \n",
       "995    0.262615     0.139082   -0.238903     1.821206   -0.428615   \n",
       "996   -0.695091    -0.083050   -0.572266     0.666949    0.527153   \n",
       "997    2.192150    -0.375639   -0.290807    -0.791234   -0.610295   \n",
       "998    1.537312     0.453367    1.016004    -0.637947    0.198084   \n",
       "\n",
       "     mfcc19_mean  mfcc19_var  mfcc20_mean  mfcc20_var  \n",
       "0       0.769328    0.698413     0.116609   -0.278356  \n",
       "1       1.704421   -0.075965     1.339031   -0.283492  \n",
       "2       1.362335    1.683163     0.640323    0.505634  \n",
       "3       0.664381   -0.449732     1.122482    0.083922  \n",
       "4      -0.887771    0.362257     0.603048    0.031929  \n",
       "..           ...         ...          ...         ...  \n",
       "994    -1.344699    0.397351     0.831254   -0.529894  \n",
       "995     0.462848    0.839443    -0.123797    0.099759  \n",
       "996     2.844366    1.006903     1.063897    0.547844  \n",
       "997    -0.284236    0.078789     1.063246    0.458261  \n",
       "998     0.179352    0.717183    -0.326505    0.124471  \n",
       "\n",
       "[999 rows x 57 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a3616752",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      7\n",
       "1      3\n",
       "2      8\n",
       "3      0\n",
       "4      9\n",
       "      ..\n",
       "994    9\n",
       "995    2\n",
       "996    0\n",
       "997    2\n",
       "998    1\n",
       "Name: label, Length: 999, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8656077f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0179d134",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f74fd8d7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d506109c",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('model_cat.pkl', 'rb') as f:\n",
    "    cat = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d236eb89",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('model_rforest.pkl', 'rb') as f:\n",
    "    rforest = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "074103c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('model_ada.pkl', 'rb') as f:\n",
    "    ada = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bd4b911b",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('model_sgd.pkl', 'rb') as f:\n",
    "    sgd = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "74661eaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('model_gaussian.pkl', 'rb') as f:\n",
    "    g_NaiveB = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "badcbb51",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b877e54b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = cat.predict(X_test)\n",
    "precision_cat = precision_score(y_test, y_pred,average='macro')\n",
    "accuracy_cat = accuracy_score(y_test, y_pred)\n",
    "f1_cat = f1_score(y_test, y_pred,average='macro')\n",
    "recall_cat = recall_score(y_test, y_pred,average='macro')\n",
    "print(precision_cat)\n",
    "print(accuracy_cat)\n",
    "print(f1_cat)\n",
    "print(recall_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a5c2749",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rforest_normal.predict(X_test)\n",
    "precision_r = precision_score(y_test, y_pred,average='macro')\n",
    "accuracy_r = accuracy_score(y_test, y_pred)\n",
    "f1_r = f1_score(y_test, y_pred,average='macro')\n",
    "recall_r = recall_score(y_test, y_pred,average='macro')\n",
    "print(precision_r)\n",
    "print(accuracy_r)\n",
    "print(f1_r)\n",
    "print(recall_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d7edf64",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = ada1.predict(X_test)\n",
    "precision_ada = precision_score(y_test, y_pred,average='macro')\n",
    "accuracy_ada = accuracy_score(y_test, y_pred)\n",
    "f1_ada = f1_score(y_test, y_pred,average='macro')\n",
    "recall_ada = recall_score(y_test, y_pred,average='macro')\n",
    "print(precision_ada)\n",
    "print(accuracy_ada)\n",
    "print(f1_ada)\n",
    "print(recall_ada)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a9862fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = sgd_final.predict(X_test)\n",
    "precision_sgd = precision_score(y_test, y_pred,average='macro')\n",
    "accuracy_sgd = accuracy_score(y_test, y_pred)\n",
    "f1_sgd = f1_score(y_test, y_pred,average='macro')\n",
    "recall_sgd = recall_score(y_test, y_pred,average='macro')\n",
    "print(precision_sgd)\n",
    "print(accuracy_sgd)\n",
    "print(f1_sgd)\n",
    "print(recall_sgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b99a1777",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = gaussian_NB_final.predict(X_test)\n",
    "precision_gaussian = precision_score(y_test, y_pred,average='macro')\n",
    "accuracy_gaussian = accuracy_score(y_test, y_pred)\n",
    "f1_gaussian = f1_score(y_test, y_pred,average='macro')\n",
    "recall_gaussian = recall_score(y_test, y_pred,average='macro')\n",
    "print(precision_gaussian)\n",
    "print(accuracy_gaussian)\n",
    "print(f1_gaussian)\n",
    "print(recall_gaussian)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e479fc0a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9732c89a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e4b472a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b21464b7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}